Two weeks later, she encounters AI again but this time while dealing with an insurance claim. When she asks to speak to a person, she’s redirected to another chatbot. What felt helpful while shopping now feels like a barrier.

Same customer, same technology, opposite experiences. In one moment, AI helps her make a decision. In the other, it stands between her and the help she wants.

Consumers are increasingly comfortable using AI on their own terms. But that comfort doesn’t automatically extend to brands using AI on their behalf. AI customer trust depends on whether customers understand how AI is being used, see value from it, and retain control over the experience.

Our research makes that tension clear. Forsta’s 2026 Retail consumer study found that among shoppers who used AI over the holiday season, 58% bought something because of its recommendation. Asked how far they trust retailers to use AI responsibly, only 22% said a lot or quite a bit.

Consumers are embracing AI faster than they’re granting brands permission to use it on their behalf.  The aforementioned customer’s disappointment has identifiable causes. Our research validates that earning AI customer trust requires giving people enough transparency, value, and control to feel confident about what your brand does with AI.

Consumers have embraced AI but on their own terms

AI has become part of everyday life, and Forsta’s internal research shows how routine: 43% of consumers use it for research and finding information, 34% for personal tasks, 25% for work, and 20% while shopping.

The behavior underneath those figures is consistent. One in three U.S. shoppers used AI over the recent holiday season, and among those who did, 60% came back to it repeatedly rather than trying it once, per Forsta’s Retail Consumer Study. Returning to a tool signals it earned a place in how someone shops.

What people use it for stays narrow. 87% turned to AI primarily for gift ideas rather than price or product comparisons, and fewer than one in ten completed a purchase through an AI platform in any category.

That openness shouldn’t be mistaken for unconditional enthusiasm. Younger consumers may be among AI’s most active adopters, but their skepticism is rising just as quickly. Bentley University-Gallup research found that 47% of adults aged 18 to 29 now believe AI does more harm than good, up 11 percentage points from 2025 and from just 30% in 2023.

That was the largest year-over-year increase of any age group. For brands, the takeaway is that familiarity with AI doesn’t automatically translate into trust. Younger consumers may be comfortable using the technology, but that experience can also make them more discerning about where, when, and how brands deploy it.

Consumer AI adoption is outpacing trust

Forsta’s 2025 State of CX report, drawn from 4,000 consumers across the U.S. and UK, found 48% of U.S. consumers and 45% of UK consumers open to AI-led customer experiences when it means faster service. Only one in five feel very comfortable dealing with AI on its own. 

Consumers don’t automatically trust brands to use AI responsibly, and the disparity between open to and comfortable with is where AI customer service earns a relationship or quietly costs one.

On personalization specifically, Forsta’s internal research finds consumers divided almost evenly in three ways: comfortable, uncomfortable, and undecided. The undecided third is the group worth designing for, because their position is still moving.

Comfort drops further as consequences rise. Close to half remain uncomfortable with AI-generated financial or insurance recommendations, where being wrong costs real money.

The published retail data shows the same caution among people already using the technology. Asked how far they trust retailers to use AI responsibly, 22% of AI users said a lot or quite a bit, and 39% said a little or not at all. The largest group, at 40%, landed on somewhat, which is a hedge rather than a verdict.

Underneath sits the data question. 69% of U.S. consumers will share personal information in exchange for a better experience, while 19% trust brands to handle that information responsibly. The UK figures track closely, at 64% and 17%.

Four questions shaping AI customer trust

Consumers are asking questions brands haven’t fully answered. Four come up repeatedly in the research, and each maps to a decision someone inside the business has already made without telling the customer.

How is my data being used?

Privacy ranked as the top concern among shoppers who used AI, though most of that concern sat in the moderate range rather than the extreme, which makes it addressable. What people want is visibility into how their information feeds the system. Forsta’s State of CX research found 43% of U.S. consumers trust a brand more when it discloses AI use openly, which is a low-cost benefit to give them.

How are decisions being made?

Accuracy and transparency each drew 18% of AI users describing themselves as very or extremely concerned. The issue underneath both is explainability. A recommendation that arrives without reasoning asks the customer to take it on faith, and faith is exactly what’s in short supply. Showing why a product surfaced often does more for confidence than improving the recommendation itself.

Who is accountable?

Customers hold the brand responsible, not the model. When an AI customer support interaction gives someone the wrong answer, nobody files it under vendor error. That exposure grows as AI speaks for the brand more often: nearly eight in ten AI users say they value AI recommendations as much as or more than advice from a retailer. Governance and oversight are what keep that influence from becoming liability.

Can I trust the outcome?

Bias drew the lowest concern scores of the four, which probably understates it, since people can only report bias they noticed. Some did. One respondent described AI that seemed “biased toward certain vendors and products.” Consumers need to believe a recommendation is accurate and serves them rather than the brand paying for placement.

Personalization requires a stronger value exchange

Consumers expect clear benefits before sharing their data, and the State of CX research shows they will trade when the return is legible. 69% of consumers say they will share personal information for a better experience. That willingness is conditional on the exchange being visible, which is where most programs fall down. The data goes in, the benefit stays vague, and the customer concludes the transaction ran one way.

What counts as a meaningful return is unglamorous but ultra-meaningful:

• Support that resolves faster because the agent already has the context
• Recommendations that reflect what someone actually bought instead of what the segment bought
• Loyalty rewards that arrive without being chased
• An experience that picks up where the last interaction left off
• Offers and communications that reflect what someone actually needs right now 

The research shows what happens when AI in customer experience misses that mark. Shoppers praised the technology for speed and convenience, then described missed context, irrelevant results, and generic suggestions as the main friction. Forsta’s own summary of the finding is that AI is winning on utility and lagging on relevance. Generic personalization is worse than none, because it proves the data was collected and reveals it wasn’t used.

Around 30% of consumers in both markets say they would consider switching brands for more personalized experiences, and nearly one in five already have. The value exchange isn’t a philosophical position. It’s a retention number.

AI customer trust and expectations vary by generation

One AI strategy won’t resonate with every customer, and the research splits cleanly enough to plan around. AI use in shopping skews younger, though the retail study notes adoption expanding across age groups rather than concentrating at one end.

The sharper divide is over data. The State of CX research found 49% of Gen Z willing to share personal information against 18% of Boomers, with the Gen Z willingness explicitly conditional on seeing value returned. The same research describes Boomers disengaging from digital-first retail altogether.

The obvious read is that younger customers are the easy audience. The data says otherwise. 71% of Gen Z and 68% of Millennials have walked away from a retail purchase over a poor experience. They adopt faster, share more, and leave sooner. Their openness is a rolling assessment rather than a settled preference, which means a brand earning it has to keep earning it.

Older customers hold the opposite position and are more consistent about it. Emphasis on security, preference for a person on complex or high-stakes issues, and more scepticism toward AI-driven recommendations. That’s a higher bar to clear once and a more durable relationship afterward.

Neither group wants the same AI, and neither is served well by the average of the two.

Human interaction remains essential

AI should enhance human experiences rather than replace them, and consumers are consistent about where the line falls. The State of CX research found that even in highly digitized sectors, many people actively seek human contact when resolving complex or high-stakes issues. Only one in five feel very comfortable dealing with AI on its own.

The pattern in the retail data points the same way. AI use concentrates in high-consideration categories where decisions are complex and confidence matters, and it stops short of the transaction. Among AI users, 5-8% completed an instant purchase through an AI platform in any category. People use AI to think through a decision and then take the decision themselves.

That instinct intensifies as stakes rise. A gift recommendation carries a low cost of being wrong. A mortgage application, a claims decision, a diagnosis, or a complaint that has already failed once carries a high one. Financial services, insurance, healthcare, and customer support are where the demand for a person is loudest, and where routing someone into an AI-only path does the most damage.

The practical version is unremarkable. Automate the volume. Make it easy to reach a human when the stakes are high. Make the handover to a person easy to find rather than buried behind three menu levels.

How brands can build trust in AI customer experiences

Building AI customer trustcomes down to three things: transparency, value, and control. Tell customers when AI is involved and how their data is being used. Make the benefit of sharing that data visible in the experience they receive. And give them meaningful control, including an easy route to a person when they want one.

Sixty-three percent of U.S. consumers will leave after one or two bad experiences, so responsible AI design is a standard applied to every interaction rather than a policy published once.

Consumers have already made AI part of how they make decisions. They use it, they trust its recommendations as much as a brand’s, and will keep doing so on their own terms. What they haven’t decided is whether to extend that confidence to the companies deploying it, and that question is still open in every sector represented in this research.

The organizations that earn customer trust in AI will be the ones that treat transparency, control, and access to a person as design requirements rather than as concessions. Start with disclosure, since it costs the least and moves trust the most.

Speak to an expert about building trust in your customer experience program today.

Now AI is changing expectations again, and something unexpected is happening.

Consumers are embracing AI in their personal lives while becoming increasingly cautious about brands using AI on their behalf. They’re willing to share data but only when they trust the company asking for it and clearly understand what’s in it for them. The brands best positioned for growth over the next decade will be those that earn it.

Loyalty has entered a new era

The old loyalty playbook ran on rewards: repeat purchases, points, retention, satisfaction scores. Deliver a good experience, hand out a perk, book the return visit.

That model still works, but it no longer explains why customers stay.

Today’s loyalty runs on something harder to buy: trust, transparency, consistency, confidence. Edelman’s brand trust report now ranks trust alongside price and quality as a reason people choose a brand at all — a factor that used to sit far below both.

Brand loyalty follows the same logic. Customers keep coming back to the brands they believe will do right by them, not the ones with the slickest points scheme.

The customer trust gap is becoming the loyalty gap

The gap has two sides. On one, customers want everything AI makes possible: personalization and automation, with convenience and speed. On the other, they don’t trust brands to use their data to deliver it.

That space between what customers want and what they’ll trust you to do is the trust gap, and AI is stretching it wider. The more a brand can do with a customer’s data, the more that customer wonders what’s being done with it.

The numbers show how wide it runs. Our own research shows that nearly six in ten consumers are uncomfortable with AI-driven personalization.. Half of U.S. consumers say they’d rather buy from brands that don’t use generative AI in the experiences they see, according to Gartner. People happily use AI themselves, then turn wary when a brand uses it on them. What they’re flagging is a control problem: whose hands the data is in, and to what end.

Left alone, the customer trust gap becomes a loyalty gap. Customers who don’t trust how you’ll use their data share less of it, and less data means thinner personalization and a flatter experience; one they’ve no reason to stay for, and nothing holding them when a competitor makes a sharper offer. The competitive line has moved from who personalizes best to who customers trust enough to let personalize at all.

The loyalty paradox

Line up what customers expect and it reads like a list no brand can satisfy. More personalization, but less data collection. More automation, but more human interaction. More convenience, but more control.

You don’t get to pick a side; they want it all, all at once, and all from you. The modern customer experience has to hold both ends of every pair: tailored yet restrained, automated yet personal, effortless yet transparent. That’s where a brand promise gets tested. Anyone can pledge personalization and privacy in a headline. Delivering both, at once, across every interaction is the promise customers actually judge you on.

Without an underlying foundation of trust, the same demands pull the experience apart: personalization curdles into surveillance, automation reads as a brush-off, and control becomes something the customer has to fight you for.

Why customers stop trusting brands

Every competitor in the category is talking about churn, but few talk about what comes before it. Customer trust rarely collapses in a single moment. It erodes through the experiences a brand never thinks to measure, and customer loyalty erodes with it.

No loyalty program offsets a customer who has stopped believing you’ll do right by them: points can buy a transaction, but not customer retention once trust is gone. Four things wear it down.

Small moments of friction

Trust erodes through friction so minor no one flags it. Repeating your account number to the third agent. A handoff that drops half the context. A personalization engine that greets you by the wrong name. Service that’s sharp on Monday and sloppy on Thursday. Each is forgettable alone. Stacked, they tell a customer the brand isn’t paying attention.

The data bears it out. Ask customers what drives them away and the top answer is a loss of trust (28%), ahead of multiple small inconveniences piling up over time (21%) and a single major failure (19%) (Forsta). Small friction does more quiet damage than the occasional disaster.

Catching it means connecting signals across calls, chats, surveys, reviews, and operational data to uncover the root causes of friction, which is exactly what a customer experience platform is built to do. AI-powered insight helps organizations identify patterns across these interactions, revealing the hidden sources of friction before they become larger loyalty problems.

Even strong customer service only recovers ground that friction keeps giving away. The real opportunity comes from closing the loop; using customer feedback to trigger action, resolve issues quickly and continuously improve the experiences that matter most.

Lack of transparency

Customers now ask three questions before they hand over data:

• Why do you need it?
• How will you use it?
• What do I get back?

When a brand can’t answer plainly, customer trust drops and silence reads as evasion. The brands that hold trust treat every data request as a small, honest exchange, clear on the ask and clear on the return, instead of hoping no one reads the fine print.

AI without explanation

People rarely reject AI on principle. What they reject is AI they can’t follow: a recommendation with no visible logic, or a decision they can’t question. Perhaps a chatbot that loops without ever reaching a person.

Unexplained automation feels like being handled, not helped. Explain how decisions are made, when AI is being used, and when customers can reach a person — why this offer, why now, and how to reach a human — and the same AI that eroded trust starts to build it.

Inconsistent human experiences

One outstanding employee and one indifferent interaction, and customers stop knowing which version of you they’ll get. That unpredictability is its own kind of distrust.

Consistency is a people problem before it’s a technology one: employee experience directly shapes customer experience, and a customer-centric culture is what keeps service steady when no script covers the moment.

Give your support team the context and the authority to act, and every interaction reinforces the same brand identity instead of chipping away at it. That’s where trust is won or lost, one human exchange at a time.

The value exchange brands keep getting wrong

Customers will hand over their data. They’re waiting for a fair trade and most brands are offering a bad one.

Watch how the ask lands. “Share your preferences” gives the customer nothing to weigh: all cost, no visible return. Flip it and name the payoff in the same breath: share this, and you’ll get faster support, fewer repeated questions, recommendations that fit, service shaped around how you buy.

Nothing about the data changed. The framing did, and the framing is the deal.

The wariness is real, and it’s specific. Most consumers are slightly wary about a financial provider using their personal data to personalize the experience. About a third are fine with it when they see clear value in return. Almost a third stay cautious, wanting control over how their data gets used.

People will trade data for something tangible and immediate, and they walk the moment the value goes vague.

Get the exchange right, consistently, and it stops reading as a data grab and starts feeling like service: a brand that remembers you to save effort, not to sell harder. Get it wrong, and the same request reads as extraction, however warm the copy.

Why personalization alone won’t create loyalty

Personalization is a multiplier, and on its own there’s nothing to multiply. The same recommendation reads as surveillance from a brand you don’t trust and as service from one you do — same data, same tactic, opposite feeling. Aim it better without earning trust and you don’t get loyalty; you get a sharper version of the thing already pushing customers away.

Human experience becomes the loyalty multiplier

The framing that pits AI against people gets the relationship backwards. What customers want is both: fast, frictionless digital service for the routine, and a real person the moment things get complicated or personal.

Forsta’s research points the same way: the more complex or emotionally charged the interaction, the more people want a human on the other end. That’s the idea behind Human Experience (HX): customer, employee, and brand experience treated as one connected system instead of three separate channels.

Used well, AI makes those human moments count for more.

When automation absorbs the password resets and order lookups, your people are free for the interactions that actually decide loyalty: the claim filed after an accident, the account that won’t reconcile, the complaint that’s really about trust. Give those moments to someone with the context and authority to fix things, and every one of them becomes a reason to stay.

The new trust equation

Trust reads as a feeling, which is why most brands treat it as one. But that reduces trust to something you hope to earn, not something you can manage. Break it into parts and it turns into a system you can actually build against:

Trust = Transparency + Reliability + Consistency + Humanity

• Transparency means customers understand what you’re doing with their data and why, with no fine print doing the real talking.
• Reliability means the experience works: the app loads, the handoff holds, the promise is kept.
• Consistency means every interaction reinforces the last, so customers always know which version of you they’ll get.
• Humanity means people feel understood, not processed, especially when something goes wrong.

Miss one and the whole thing wobbles. A transparent brand that can’t deliver loses trust as fast as a competent one that feels cold. The value of the equation is that it makes trust diagnosable: when confidence slips, you can name which term broke instead of guessing.

Score yourself honestly on all four, and you know exactly where the next fix goes.

Five questions every CX leader should ask

A framework only earns its keep if it changes what you measure. So point the trust equation back at your own program. Take these five into your next leadership review and watch which ones draw a confident answer and which draw a pause:

1. Can customers explain why they trust us?
2. Would customers willingly share more of their data with us?
3. Are we earning trust faster than expectations are rising?
4. Does every AI interaction strengthen confidence?
5. Can employees deliver experiences AI can’t?

The pauses are the map. Every question you can’t answer cleanly is a place trust is leaking and loyalty is following it out. Pick the weakest answer and fix that first.

That’s where AI-powered insight becomes most valuable, not simply identifying where trust is breaking down but helping organizations prioritize the actions that will have the greatest impact on customers, employees, and the business.

Turning trust into growth

Stop pitching trust as a way to “improve loyalty” and start framing it as a growth engine, because that’s what it becomes the moment it compounds. Here’s the chain. ‘

Trust earns permission to collect data. More data sharpens your insight. Sharper insight builds better personalization. Better personalization creates better experiences. Better experiences deepen loyalty.

And loyalty raises lifetime value, which funds the next round of better experiences and starts the chain over, stronger each time.

The trust-to-growth flywheel

That chain isn’t a funnel, and the difference matters. A funnel is linear and one-way: pour prospects in the top, convert a few, then refill it next quarter from scratch.

A flywheel keeps its momentum. Every turn makes the next one easier, because the trust you earned last quarter is still working for you this one.

The future belongs to trusted brands

For years, a better experience was the edge. Now expectations climb faster than any experience can keep pace with, and AI only accelerates the climb. Customer journeys will keep getting reshaped. Personalization will stop being a differentiator and start being the baseline. Automation will fade into the background until customers barely notice it running.

When all of that is table stakes, the algorithm stops being the thing that sets you apart. The brands customers remember will be the ones they trust enough to share their data, believe their recommendations, and come back to again and again.

Because in the age of AI, trust isn’t the outcome of loyalty. It’s what makes loyalty possible.

Forsta helps organizations measure, understand and improve the experiences that build customer trust. Explore how AI-powered insights and Human Experience (HX) solutions can help you strengthen customer loyalty and drive better business outcomes.

The issue isn’t visibility. The signals are there. The feedback exists in reviews, surveys, contact center conversations, and employee comments. The problem is that the people who can fix it often don’t see it until it’s too late.

The regional manager won’t see it until next month’s report, the store manager won’t hear about it until corporate flags it, and by then the weekend rush has come and gone. So have several hundred customers who walked into the same friction and didn’t bother to write it up.

This is the gap most experience programs are stuck in. Organizations understand more about their customers than ever before, but most still can’t turn it into outcomes in time to matter. Closing that gap is what separates the programs that drive business outcomes from the ones that just report on them.

 AI is changing what’s possible, turning experience signals into recommendations and helping frontline employees respond in real time.

From insight to outcome

Customers don’t wait for the next reporting cycle. Research in our 2025 State of CX report found that 59% of consumers expect brands to respond within 24 hours, and 67% expect some kind of follow-up after an interaction. A complaint posted Friday morning carries an expiration date. So does the goodwill of the customer who posted it.

Meanwhile, the people best positioned to fix the problem rarely have what they need to do it. Frontline employees field competing priorities, work from incomplete context, and often lack the authority to make the call without escalation. By the time guidance reaches them, the customer has moved on, somewhere further along the customer journey where the friction has already done its damage.

The traditional reporting model wasn’t built for this. Feedback gets collected, analyzed, packaged into a slide, presented in a meeting, debated, eventually assigned, and finally acted on. Each step adds days. Some add weeks. The signal arrives at the operational team long after the moment that produced it.

AI allows organizations to move from insight to outcome in time to matter.

Organizations that do this well are connecting customer, employee, and operational signals into a shared understanding of what’s happening and what needs to happen next. That’s the foundation of Human Experience (HX): understanding experiences across people, not just touchpoints.

Modern HX platforms combine listening, real-time analytics, and AI-driven recommendations to help organizations respond while experiences are still unfolding. The shift is from generating insight to delivering outcomes.

Why experience data isn’t enough

Most experience programs can answer three questions well:

• What happened?
• Where did it happen?
• How often did it happen?

The dashboards are good. The reporting is detailed. The data flows.

What they struggle with is the next set: who should act, what they should do, and how fast they can respond. Those questions don’t get answered by another report.

McKinsey research found that companies operationalizing customer experience grow revenue at roughly twice the rate of competitors that don’t. Yet only 15% of companies routinely use customer insight to steer decisions, and just 23% follow up to confirm they’re delivering value. 

The reasons are familiar to anyone running a multi-location experience program:

• Customer and employee feedback live in separate systems, owned by different teams
• Reporting cycles are too slow to catch issues while they’re still small
• Information overload buries the signals that matter under everything else
• No one owns the follow-through, so issues get acknowledged but not resolved
• Frontline teams are already juggling more than they can handle

In turn, experience programs stall in the same place. Insight is plentiful. Execution lags. The gap between what the program sees and what the operation does is where customer loyalty gets lost. As we explored in our article titled Operationalizing CX, the harder question for most organizations isn’t whether they have enough data. The harder question is whether they’ve built systems that turn that data into something the people closest to the customer can actually use.

How AI enables frontline teams to take action

Field enablement isn’t just training. It isn’t workforce management either. Those are pieces of it, but they describe an organizing function, not what frontline employees actually need in the moment.

Modern field enablement is the ability to put four things in an employee’s hands at exactly the moment they can act on them:

• Relevant context. What’s happening, where, and why it matters to this specific employee right now
• Recommended actions. A starting point for what to do, not just a signal that something’s off
• Prioritized issues. A short, ranked list, not an inbox of equal-weight alerts
• Continuous feedback. Whether the action worked and what to adjust

When that system is wired into how teams operate, daily work changes across industries:

Retail

A store manager gets a Friday morning alert that service scores have softened across three weekend shifts running, with a suggested staffing adjustment for the rush ahead.

Healthcare

A unit leader sees emerging patient concerns in feedback signals before they become systemic, with enough context to investigate the cause.

Hospitality

A property manager opens her tablet to automated summaries of recurring guest issues from the past week, sorted by impact.

Financial services

A branch manager identifies service friction in new account opening before it shows up as customer attrition.

In every case, the employee isn’t being asked to comb through hundreds of comments or interpret a dashboard. AI-powered summaries and alerts handle the volume, sorting thousands of signals into the few that need attention now. The person does the judging and the acting. That division of labor is what makes field enablement work at scale.

From dashboards to recommendations

The old model was a pipeline: collect, analyze, report, discuss, act. Each step had its own cycle time. Add them up and the lag was measured in weeks.

The AI-driven model compresses the middle: collect, analyze, recommend, act. The recommendation lands close to the moment, and it lands with the person who can do something about it.

This is a meaningful shift, and it’s the one most experience programs haven’t fully made. More dashboards aren’t the answer. Better decision support is. AI earns its place in an experience program through pattern detection and prioritization at a scale humans can’t match.

Modern text analytics tools, built on machine learning and sentiment analysis, can process thousands of survey verbatims, chat transcripts, reviews, and support notes in the time it would take a team to skim a fraction of them. The output is real-time insights an operator can actually use.

What lands on the operator’s screen looks different too. Instead of another chart, they get a shortlist:

• The emerging themes worth attention
• The anomalies that don’t fit the usual pattern
• The risks ranked by likely impact
• The root causes behind recurring problems
• The next-best actions, with the reasoning attached

That’s the move from reports to recommendations. Done well, it’s also the move from analyst-driven programs to operator-driven ones. The analyst still matters. Judgment, context, and advocacy are still human work. But the bottleneck of manual sorting goes away.

Solutions like Forsta AI are built to handle this layer of the work: scanning thousands of comments across surveys, transcripts, and reviews, surfacing what matters, and routing it to the people who can act. As we wrote in Closing the customer insight-to-action gap with AI, let the technology handle the sorting and keep people on the steering wheel.

Four ways AI turns insight into action

AI-powered experience management helps organizations close the gap between insight and action. The shift from dashboards to recommendations plays out in four practical ways once it’s wired into how experience programs operate.

1. Spotting emerging issues earlier

Predictive analytics applied to real-time data can detect that service complaints are rising at a specific cluster of locations before that cluster shows up in the monthly rollup. It can flag employee burnout signals trending in pulse data, or product issues appearing across reviews, calls, and chat at the same time. Early visibility is what makes early intervention possible. Catching a pattern in week one prevents the problem from spreading to weeks two through six.

2. Prioritizing what actually needs attention

Not every signal is urgent. AI helps separate minor concerns from operational risks, loyalty threats, and compliance issues, so teams can put their attention where impact is greatest. Without that triage, the alert volume becomes its own problem and the urgent gets buried with the routine.

3. Delivering guidance, not just data

There’s a meaningful difference between “customer satisfaction declined this month” and “wait times jumped 18% during peak periods at locations A, B, and C — consider adjusting weekend staffing.” The first is a finding. The second is a starting point. AI-driven guidance reduces decision friction by translating signals into possible moves, with the data the person needs to evaluate them.

4. Closing the loop

Real-time alerts are only half the job. The other half is making sure feedback actually results in measurable improvement. AI can route issues to the right owner — store manager, regional leader, operations team, HR — based on the type of issue, the location, and the severity. Once assigned, the system tracks whether the follow-up happened, what was done, and what the outcome was.

Tools like Forsta’s action management capability are designed to do exactly this: monitor signals across the program, assign cases by configurable rules, send alerts to mobile devices so on-the-go staff can act in the moment, and enforce conditions that must be met before a case can be marked closed.

The goal goes beyond tracking activity. The goal is making sure feedback ends in something the customer can feel — a fix, a response, a change in how the next interaction goes.

These four shifts together change what an experience program is for. It stops being a measurement function reporting on what already happened and starts being an operational function shaping what happens next, connected to the people, the workflows, and the decisions that actually move the business.

How leading organizations operationalize experience insights

The teams pulling this off share a handful of habits that distinguish them from the programs still stuck in quarterly readouts.

They connect experience data across channels

Surveys, reviews, contact center transcripts, employee feedback, and operational data sit in one place and inform each other. A spike in service complaints gets correlated with a drop in employee engagement at the same locations and a staffing change two weeks prior. The full picture lives in one view, not five.

They measure programs by actionability, not activity

Response rates, resolution times, and the experience improvements that follow are the success metrics. Survey completion rates are a means, not an end. Gartner found that 85% of customer service leaders are already exploring or piloting customer-facing generative AI, a clear signal that the operational bar is shifting toward acting on insight, not just collecting it.

They deliver insight where the work happens

The store manager sees what they need on their phone before the weekend rush. The regional director sees portfolio-level patterns in their morning briefing. The contact center supervisor sees real-time alerts as conversations unfold. Insights don’t live in an analyst’s report. They live in the workflows of the people making decisions.

They measure impact in business terms

Faster issue resolution. Higher employee engagement. Stronger user experiences. Customer loyalty and customer success outcomes that compound over time. Revenue retention. The point of an experience program is changing what those numbers look like a year from now, not generating more accurate descriptions of what they look like today.

None of this requires a heroic transformation. It requires building the connective tissue between what the program sees and what the operation does. That’s where the competitive advantage compounds: the teams that learn to act faster also learn faster, and the gap widens.

The last mile matters most

The hard part of experience management was never collecting feedback. Customers, employees, and operations have been telling organizations what they think for years, in surveys, reviews, calls, comments, and conversations. The hard part is the last mile: getting the right insight, with the right context and the right suggested action, in front of the right person at the moment it matters.

That’s the gap AI is finally helping experience programs close. Not by replacing human judgment, but by removing the friction that kept frontline employees from acting on what the program already knew.

The data was always there. What was missing was the system to turn it into something usable in the moment: alerts a store manager can act on before the weekend, summaries a property manager can read between check-ins, recommendations a branch leader can take into a 1:1 with their team.

For experience leaders, the question to take into the year ahead has changed shape. The work is no longer about collecting more feedback. The work is making sure the feedback already being collected reaches the people who can do something about it, fast enough to matter.

Real-time action has stopped being a luxury for organizations operating at scale. It’s becoming the baseline expectation for customers, for employees, and for the business outcomes experience programs are measured against.

See how Forsta helps organizations activate experience data. Speak with one of our experts about closing your real-time experience gap.

Customers can tell. According to Rio SEO’s 2025 Local Search Consumer Behavior Study, 67% of consumers say it’s important for brands to follow up about their experience, and 59% expect a response within 24 hours when they reach out.

The appetite for engagement is there. But most programs aren’t built to meet it at speed.

The organizations that break the pattern pair the platform with the people who know how to operationalize it. Most organizations struggle to act on data because they lack the resources, the right insight layer, or a clear place to start.

Why most Voice of Customer programs fail to drive action

There’s a common assumption that implementing a Voice of Customer (VoC) platform means having a mature CX program. It doesn’t. It means having the capability to build one.

The pattern repeats across enterprise programs:

• Teams get dashboards but lack direction on which signals matter
• Insights are generated but never prioritized against business outcomes
• Data sits accessible but disconnected from operations, strategy, or the daily decisions of frontline teams

A feedback tool is only as valuable as the decisions it informs. Plenty of organizations roll out enterprise-grade platforms expecting customer satisfaction scores to climb on their own, then watch the numbers flatline through quarter after quarter of board reviews. The platform isn’t the problem. The team around it is.

Mature programs treat CX not as a reporting layer but as a thread that runs through corporate strategy. The brand promise made in marketing has to match the experience delivered in operations, and that match doesn’t happen because a dashboard exists — it happens because someone built the workflow that closes the gap.

CX and EX initiatives that stay siloed from each other tend to produce the same disconnect on the inside. A customer-centric culture isn’t declared in a town hall; it’s built when employee experience and customer experience are measured, owned, and improved together, with leadership treating both as the same problem.

CX maturity is measured by decision speed — how quickly the organization moves from a signal to a fix to a result the customer can feel. Most programs stall above the platform layer, not inside it.

What CX consulting does (beyond the platform)

The role of consulting in a mature CX program isn’t to run the platform. It’s to make sure the platform runs the right things. That work breaks down four ways.

1. Direction

A consultant helps the organization decide what matters most: which signals to prioritize, which to ignore, and how to tie CX metrics to business KPIs the C-suite already cares about. Without that filter, every metric feels equally urgent, which means none of them are.

2. Accelerating time to value

Programs that try to mature on their own tend to take twelve to eighteen months to find their rhythm. Consulting compresses that timeline by bringing the patterns, frameworks, and missteps of comparable programs into the room from day one.

3. Cross-functional alignment

CX, EX, digital, and operations usually run on parallel tracks with parallel data. A consultant brings them into the same conversation, connects the data sources, and builds a shared view of experience that survives leadership changes and re-orgs.

4. Embedding CX into the business

This is the hardest one. Moving from reports to workflows, from insights to decisions, from a dashboard people glance at to a feedback loop that shapes operations and strategy. A 24-hour response expectation isn’t met by a quarterly readout; it’s met by an operating rhythm. A consultant designs that integration; the platform makes it run.

How RS Group elevated its VoC program with CX Consulting

RS Group came to Forsta with a familiar challenge. Customer feedback was fragmented across systems. The data existed, but unifying and operationalizing it was the gap between a reporting function and a program that could actually shift the business.

The work paired the Forsta platform with CX consulting expertise to embed targeted microsurveys across the moments that shape buying decisions: search, checkout, product pages. The volume tells one part of the story: over 22,000 pieces of actionable feedback, tied directly to product decisions and customer journey improvements.

The shift in how the program operated tells the rest. RS moved from looking at customer experience after the fact to acting on it close to real time.

The lesson isn’t to run more surveys. It’s better signals, captured at the right moment, turned into action without delay.

What to look for in a CX consulting partner

Not every consulting engagement earns its line item. The good ones share a profile; and recognizing it before the contract is signed saves twelve months of frustration.

They challenge assumptions, not just execute them. A strong partner pushes back on the brief. They’ll tell you when the metric you’re chasing is the wrong one, when your customer journey mapping has a blind spot, or when leadership support for the program is softer than the org chart suggests. Execution-only consultants are easy to find and easy to outgrow. The ones worth keeping are willing to disagree.

They tie CX to business outcomes a CFO would recognize. Customer Lifetime Value, retention rates, share of wallet, cost-to-serve, brand advocacy that converts to revenue. A consultant who can’t draw a line from a CX signal to a number in the financial model isn’t helping the program survive its next budget review.

They help you prioritize, not just analyze. Reporting paralysis is the failure state most programs slide into — endless dashboards, no decisions. A strong partner walks in and asks which three things matter most this quarter, not which forty-seven things the platform can measure. They reduce noise. They flag the CX gap that’s actually moving customers, and they ignore the ones that aren’t.

They build cross-functional teams, not the dependency. The best consultants leave a stronger internal CX function behind them, with sharper judgment, clearer playbooks, better-trained owners across operations, digital, and customer-facing teams. They contribute to your CX foundation and CX vision rather than substituting for them. Strategic planning gets handed back. Day-to-day performance management stays in-house.

The simplest test: a year into the engagement, is your team better at running the program without the consultant than they were when the engagement started? If yes, it’s a partnership. If no, it’s a dependency dressed as one.

When you need CX consulting (and when you don’t)

A few patterns suggest the platform alone isn’t enough:

• The VoC program feels reactive. Feedback comes in, gets logged, gets reported quarterly. Nothing in the operating rhythm forces a decision in between. The program is responding to data instead of using it to set direction.
• Insights aren’t turning into action. Agent workflows haven’t changed in a year. Customer journey mapping sits in a slide deck nobody references. The insights exist; the bridge from insight to operations doesn’t.
• Teams aren’t aligned on priorities. CX has one priority list, EX has another, digital has a third, operations has a fourth. Without leadership support to converge them, every team optimizes locally and the customer feels the seams.
• Data volume is growing but business impact is flat. More surveys, more dashboards, more responses — same retention numbers, same CLV, same complaints. That gap is the clearest signal that the program needs help moving from measurement to outcomes.
• Data quality is degrading. Duplicate records, inconsistent tagging, surveys firing at the wrong moments. Data relevance erodes quietly until one day the dashboards stop being trusted, and once trust is gone, the program is harder to rebuild than it was to launch.

Any one of these on its own is fixable internally. Two or more together is the point at which outside help compounds and the longer the patterns persist, the more expensive the eventual fix.

Why CX consulting and technology drivers faster CX maturity

Platforms provide the capability. CX consulting provides the direction. Together they build the operating system that turns Voice of Customer data into measurable business performance.

The organizations moving fastest right now aren’t the ones with the biggest CX budgets. They’re the ones who paired their platform with the strategic planning, human oversight, and outside perspective needed to operationalize it. They built a CX foundation that scales. They closed the gap between insight and action. And they did it on a timeline their competitors can’t match.

Want to see what that looks like applied to your program? Connect with our experts and explore how consulting + technology can accelerate your CX strategy.

We’re now in the next great leap: shoppers are turning to AI to cut through the noise and help them make shopping decisions.  AI is going beyond improving efficiency; it’s driving measurable results. In fact, 69% of retailers using AI report increased revenue, reinforcing its growing role in shaping customer experience and business performance.

Our recent research backs this up. Fifty-eight percent of shoppers made a purchase based on an AI recommendation, and 91% either purchased or seriously considered a purchase after AI input. But AI’s role is different from past digital channels: it excels in planning, discovery, and comparison, especially where choices and stakes are high. It helps shoppers make decisions with confidence. 

But AI isn’t acting alone. While it’s great at narrowing choices, retail expertise is what reassures shoppers they’re making the right decision. AI provides retailers with deep product knowledge, transparent policies, and trusted reputation. Retail brands who can clearly express that authority will be the ones shoppers choose. 

AI in retail customer experience benefits are a two-way street for both consumers and retailers.  Consumers get faster access to relevant information, while AI helps surface retailers’ expertise, empower employees, and elevate the customer–retailer interaction.  So when retail employees wonder “will AI replace me?”, it’s actually going to unlock more time for them to do meaningful, higher-impact work. 

How shoppers use AI today 

Understanding the impact of AI in retail customer experience helps retailers adapt to meet shopper needs effectively.

Shoppers lean on AI in retail customer experience most in high-consideration categories where the stakes are greater, and the number of choices can feel overwhelming. Forty-eight percent of consumers used AI for price comparisons in electronics, and 36% looked to AI for apparel inspiration. In planning-heavy moments (hosting, décor, special-occasion attire), shoppers used AI for ideas and comparisons far more than instant checkout. Only 5–6% completed purchases directly through AI platforms.  

That aligns with my own recent shopping experience. I used AI to narrow down options for a new wearable health device that needed to be comfortable, stylish, and capable of meeting specific health-tracking needs, without breaking the bank. Not long ago that process would have meant reviewing numerous product pages, social posts, reviews, and prices across multiple sites. Now AI can gather, consolidate, and offer personalized recommendations and pros and cons for each in seconds.  

That’s the pattern that is playing out more broadly. AI is compressing the most time-consuming part of the shopping journey from product discovery, comparison, and early evaluation with fewer touchpoints for the consumer.  

During my time in e-commerce retail, I remember how much effort went into improving product discovery: better search, richer content, clearer filters, and more helpful reviews. I think about how AI in retail customer experience today is using that product data, content, and consumer provided information like ratings & reviews, user-generated insights, and more to pull together those recommendations for shoppers.  

This shift creates a clear inflection point for retailers. As AI takes on more of the planning and evaluation work, the quality, clarity, and consistency of the information retailers provide increasingly determines how and whether they show up in these new customer shopping journey moments. 

How retailers show up in an AI‑influenced world 

As shoppers increasingly rely on AI to plan and compare, it’s imperative that a retailer or brand’s information is set up to signal to AI that it is the one to recommend. AI won’t create a brand’s expertise—it will surface whatever is already there.  If a brand’s product details, policies, and content are thin, inconsistent, or hard to interpret, AI can’t confidently do that.  

What AI needs to confidently represent a retailer:  

• Complete, structured product fundamentals: Clear attributes, use-case context, compatibility, care, and warranty details—supported by structured data (such as product schema markup) that makes this information machine-readable. AI uses these signals the same way a store associate would to understand relevance and suitability.

• Decision-support content that mirrors how shoppers think: Comparisons, size guidance, FAQs, “best for” framing, and clear tradeoffs. These act as the confidence‑building moments that shoppers traditionally get from expert associates or detailed product pages.  

• Consistent service and policy clarity: Availability, fulfillment options, returns, pickup, and service commitments. When AI pulls these details into recommendations, it reduces uncertainty and shapes which brands shoppers trust. 

• Credible brand signals: Reviews, user-generated content (UGC), accessibility and sustainability claims, and consistent expression of brand expertise and values. AI uses these cues to judge credibility when shoppers ask for “trusted,” “reliable,” “durable,” or “ethical” recommendations. 

As AI reshapes discovery, information quality increasingly determines visibility. In many ways, AI-ready content is becoming the next evolution of SEO. It’s not about gaming algorithms but supplying clean, structured information that AI can interpret accurately.  

Retailers earn trust in AI‑supported experiences the same way they’ve always earned trust in stores: by showing their work. That includes: 

• Reliable product attributes and naming conventions 

• A content supply chain that keeps every SKU complete and updated 

• Structured data that AI systems can parse 

• A single, trustworthy source for policies and service information 

• Governance to keep content fresh as assortments change 

This makes it incredibly easy for AI to understand, trust, and advocate for your brand. 

How retailers prove credibility and expertise in an AI-led journey 

Shoppers increasingly see AI as credible. Forty-one percent say AI recommendations are equal in value to retailer advice, and 38% say they value AI even more. But trust isn’t simply given. Concerns about privacy, accuracy, transparency, and bias remain widespread, often moderate rather than extreme, which means shoppers are willing to engage, but they’re watching closely.  

Retailers earn trust by showing their work: 

• Being transparent about where and how AI is used: Clear labeling of AI‑generated suggestions or automated decisions builds confidence rather than confusion. 

• Citing the data behind recommendations: Whether it’s product information, reviews, or policies, customers expect to know why something was suggested. 

• Explaining the logic (“best for X because Y”): This mirrors how the best in‑store associates talk—specific, contextual, and confidence‑building. 

• Auditing for accuracy, bias, and hallucinations: Shoppers notice when AI gets something wrong. Retailers who monitor quality build long‑term trust. 

• Protecting privacy and giving customers control: Clear consent, control over data, and practical privacy choices reassure shoppers that the value exchange is fair. 

Done right, AI in retail customer experience amplifies brand credibility. And this is where human expertise comes back into the picture. AI can inform and guide, but people validate, reassure, and help customers feel confident in their final decision. 

What I’d do as a retailer: Where to invest AI in retail customer experience for real impact 

If I were a retailer, I wouldn’t go in trying to involve AI everywhere, and I don’t think most retailers think that. But they’re certainly likely overwhelmed wondering whether to start internally or externally… I say both. I’d start with where decisions are the hardest and confidence matters most. This supports both the customer and employee (a win-win). 

1. Prioritize AI where decisions are hardest

I’d focus on high intent, high complexity categories where shoppers face choice overload. AI is most effective at doing the heavy pre‑work. It shortens the journey and gets customers to a point where a meaningful conversation can happen, faster. 

2. Make sure your brand is eligible to show up in those moments

Before investing in new AI experiences, I’d make sure my product and brand information is strong enough to be recommended in the first place. Priority categories need to be clearly described, easy to compare, and supported by consistent product and policy information. If complex categories aren’t well defined, retailers simply won’t appear when customers are deciding. 

3. Equip associates to build on AI research—not repeat it

What’s great about customers arriving having already used AI is that it speeds up the conversation, so associates can pick up where they left off versus starting at the beginning. They should be trained and equipped to ask what the shopper already considered, understand what AI recommended (and why), and identify the missing context AI didn’t capture. Great associates don’t just answer questions. They surface the questions customers didn’t think to ask such as usage, priorities, and what their own customers have shared back with them.  

4. Use AI to support associates in real time 

To make this work at scale, I’d invest in AI tools that put product knowledge, comparisons, inventory visibility, and policy clarity directly in associates’ hands. This will help eliminate time spent searching for information, reinforce consistent brand guidance, and free up mental space for empathy, listening, and personalization. The result is more confident associates and better customer moments. 

5. Redefine success for high-intent and complex categories (your AI pilot categories)

I’d measure success differently in these categories. While improved conversion rates are usually the largest goal, lower return rates and fewer post-purchase issues can also result, along with higher associate productivity, measured by sales per hour and time spent advising vs. searching.  

AI creates efficiency; the retailers who win will reinvest it into higher‑touch service that builds confidence and loyalty. 

The bottom line 

AI in retail customer experience is the largest disrupter in retail’s evolution since online shopping. It’s becoming a planning partner shoppers rely on when decisions are complex and confidence matters. That’s good news for retailers who are willing to do the work: prepare your data, empower your people, and design the handoff between digital guidance and human credibility. 

The brands that win won’t be the ones trying to replace humans with machines. They’ll be the ones who use AI in retail customer experience to clear the path and let their people do what only people can do: connect, reassure, and earn customers for life. 

None of these moments define customer experience on their own but how quickly you respond to them does.

Wait too long, and that gap fills with frustration. Stretch it further, and it turns into churn. Leave it unanswered, and it becomes distrust.

That’s the pressure on CX today.

And that means speed isn’t a nice-to-have. It’s the experience.

The brands pulling ahead aren’t just listening better. They’re listening faster and doing something with it while it still matters.

This is the essence of quick listening. Quick listening captures insight in the moment and puts it in the hands of people who can act. It collapses the distance between signal and response, closing the loop before the customer has moved on to a similar brand. And that changes everything.

Get it right, and feedback becomes a live wire — something you can feel, respond to, and turn into impact across empowered teams throughout the organization.

Why speed now defines the customer experience

In today’s fast-paced environment, quick listening in CX is more crucial than ever for maintaining customer loyalty.

Customer expectations didn’t evolve gradually; they’ve accelerated. Most CX programs (and most customer service models) weren’t built for the pace.

Today, when customers share feedback, they expect a response, and quickly. According to a 2025 Local Search Consumer Behavior study, 20% of U.S. consumers expect a response the same business day, and 39% expect one within 24 hours.

That’s not a stretch goal. It’s baseline customer behavior.

Always-on digital channels, including chat, social, messaging, and the call center, have compressed patience windows. Customers move fluidly between them, carrying the same expectation of speed at every step.

So, when responses lag, the signal is immediate. It doesn’t feel like a delay in the process, but a lack of attention. That’s why speed now defines the experience. Customers judge how present, responsive, and human your brand feels based on how quickly you respond, moment to moment, across every interaction.

A fast response feels human. A slow one feels automated.

The breakdown of traditional listening programs

Most traditional listening models were built for reporting, not responding. They capture customer insights at the end of a journey, long after the moment has passed and any chance to influence customer satisfaction is gone.

Think about the flow:

• A customer has an experience.
• Days or weeks later, they’re asked about it.
• That feedback moves into CX analytics, gets aggregated, analyzed, and eventually surfaced.

By then, it’s too late. The issue has either been forgotten or escalated.

Centralized CX teams add another layer of delay. Insights have to be reviewed, prioritized, and routed before anything happens. What starts as a signal turns into a queue.

Most dashboards don’t fix that. They store insight rather than creating action. So even when patterns are clear, the response isn’t immediate. It’s scheduled, managed, or deferred.

Meanwhile, CX expectations keep moving in the opposite direction — toward immediacy, responsiveness, and real-time engagement.

That’s the gap: The way most organizations listen is fundamentally out of sync with how customers experience.

To move faster, organizations don’t just need better data. They need to listen differently.

What “quick listening” really means

Quick listening isn’t faster surveys. Speed shifts both how you collect customer signals and where you collect them from.

Instead of relying on delayed surveys, capture real-time feedback across the full customer experience from digital touchpoints and service interactions to conversations and everything in between.

That includes:

• Conversations happening in the moment
• Text and voice feedback from real interactions
• Reviews from the time of doorstep delivery
• Operational data and employee input
• Open-ended, unstructured signals that don’t fit neatly into a form

This is where digital transformation shows up in CX: not as more systems, but as faster connections between experience and response.

When you capture feedback in real time, something else changes significantly. It’s not just when you hear it; it’s who can act on it.

Real-time responsiveness depends on getting insight into the hands of the people closest to the moment — your frontline teams, service reps, and in-location staff. Not just analysts reviewing dashboards after the fact.

And that’s where speed to insight becomes speed to action. With real-time analysis, signals don’t sit. They move. They trigger, prompt, and guide.

Quick listening isn’t about collecting more data. It’s about relevance — capturing the right signal, at the right moment, when it still has the power to change the outcome.

Turning insight into action before trust is lost

Insight without action builds CX debt, and every piece of feedback you collect but don’t act on adds to it.

Customers feel that gap immediately. They’re not measuring your data velocity or your speed-to-insight, though. They’re measuring what happens next.

Did anyone respond? Did anything change? Did the experience improve?

They remember the follow-through. A fast acknowledgment signals presence. A fast resolution signals accountability. Together, they shape brand reputation in real time.

This is where speed creates real value. Real-time tracking lets you see what’s happening, but time to value comes from what you do about it; how quickly you close the loop and turn feedback into action.

Wait too long, and the cost compounds:

• Issues escalate instead of resolve
• Frustration turns into negative reviews
• Private moments become public dissatisfaction

And once that happens, you’re not recovering an experience, you’re repairing trust. Chances are, you’ll have to win that customer back from a competitor, too, as CXDive reports that about 3 in 5 consumers report leaving a brand after just one poor experience.

Speed changes the dynamic by turning signals into something you can respond to, resolve, and learn from while it still matters.

Because the faster you act, the more likely you are to keep the customer.

AI’s role in accelerating CX, without replacing human judgment

Speed at this level doesn’t happen on its own… it needs help.

AI-powered solutions make real-time CX possible, not by replacing people but by clearing the path for them to act faster and with more clarity.

Generative AI can surface patterns instantly, pulling meaning from unstructured feedback that would otherwise take days to process. It can flag emerging issues, highlight shifts in sentiment, and prioritize what needs attention now.

It turns noise into something usable. And in AI-supported service environments, speed matters. It means frontline teams aren’t waiting for reports. They’re responding in the moment, with context already in hand.

But speed without judgment becomes risky. Automating action without understanding the situation — especially in emotional, high-stakes, or trust-sensitive moments — can do more harm than good. A fast response that misses the nuance feels just as wrong as a slow one.

That’s where people come in. Human oversight brings context, reads tone, and knows when a situation needs care, not just closure.

AI accelerates the listening, but human judgment guides the response. And that’s where trust is built.

Empowerment is the real speed multiplier

Remember, speed doesn’t come from dashboards; it comes from decisions, and how quickly that can happen depends on who’s allowed to act.

Most CX programs slow down at the same point: insight reaches a central team, and everything waits. Approval, prioritization, escalation. What should be a moment turns into a process.

That’s the bottleneck, so fast organizations remove it.

They democratize access to insights, putting real-time visibility into the hands of the people closest to the customer. Frontline teams don’t have to wait for direction. They have what they need to respond in the moment.

That in turn shifts where action happens: Closer to the experience. Closer to the problem. Closer to the customer.

But speed without structure doesn’t hold. The teams that move fastest operate with:

• Clear guardrails that define what good looks like
• Shared accountability across roles and locations
• Confidence in the data they’re acting on

That combination creates momentum — where people trust the signal and know they’re empowered to act on it, so they don’t hesitate. They respond.

The fastest and most effective CX teams are the ones built and empowered in that way.

Speed reveals what averages hide

Speed exposes what averages hide. Most CX reporting smooths things out by aggregating scores, blending metrics, and delaying trends. It tells you how the customer experience performs overall, but doesn’t tell you where it breaks.

Quick listening does.

When you connect signals across the full customer journey — social media analytics, CRM systems, digital touchpoints, and the voice channel — you start to see where time creates friction, in real-time.

You see that delayed response after a service interaction. A missed follow-up between teams. A gap between online intent and in-location experience.

These are the moments that erode customer loyalty, and with quick listening, they’re no longer invisible until it’s too late.

Speed changes that.

It highlights where intervention matters most, where acting quickly can prevent churn instead of reacting after the fact. It surfaces handoffs between teams as risk zones, where delays compound and accountability blurs.

That’s the advantage of journey-level insight. You’re not just tracking what happened. You’re seeing where it’s slowing down, and where to step in before the experience breaks.

What CX leaders must do now

Speed isn’t a CX metric. It’s a strategic capability, and it needs to be treated that way.

Most programs still measure success by how quickly they can report insight, but reporting doesn’t change experiences. Action does.

So the question shifts from “How fast did we analyze this?” to “How fast did we respond?”

That’s the standard now, and it forces a different kind of clarity:

• How quickly do we actually respond to feedback?
• Who is empowered to act in the moment?
• Where are we still slow by design?

Those answers reveal the truth. They’ll tell your organization where friction lives, and show you where decisions stall. You’ll see clearly where customers are left waiting.

Customers aren’t expecting perfection. They want to see presence, and to feel heard and acknowledged while the moment still matters. Quick listening signals that. It shows respect and care. It demonstrates accountability.

The brands that win in 2026 won’t just understand their customers better. They’ll respond faster, and that gap is what will separate leaders from laggards.

Turn insight into action while it still matters.

Discover how Forsta helps organizations move from delayed feedback to real-time, actionable experience intelligence.

As this rollout includes features like “Screen Unknown Senders,” short message service (SMS)-based outreach, which has long considered one of the most effective channels for customer feedback, new sources of friction will inherently be introduced. And while the immediate impact may appear modest, the implications are much bigger. 

This update may seem small in nature, but it’s a clear signal of where customer experience (CX) is heading next. In this post, we will share what the future of SMS surveys may look like given the new iOS 26 update and how CX leaders can revolutionize their digital feedback strategy to align with shifts in the market. 

What SMS responses look like post iOS update 

When analyzing how the iOS 26 update is impacting survey response rate, early analysis shows a slight but measurable decline in SMS response rates. This doesn’t come as a surprise given the new hurdle customers have to face to receive notifications from a business. 

Filtering unknown senders introduces an additional barrier between brands and customers. This is especially when messages come from numbers that aren’t saved in a user’s contacts, which is often the case. 

This isn’t alarming, given that response rates across digital channels have been declining for years. 

What this represents an acceleration of an existing trend. 

Customers are becoming more selective. More protective of their attention and what they consume. They’re also becoming less tolerant of outreach that feels impersonal or interruptive. It may appear as though Apple is contributing to this imposition, but in reality, customer expectations are shifting simultaneously.   

In other words, this isn’t about Apple. It’s about expectations. 

Why this matters beyond SMS 

It would be easy to frame this as a tactical issue: adjust your outreach mix, optimize your SMS strategy, and move on. But that misses the bigger shift. What’s happening with SMS is part of a broader reality: No single channel is guaranteed anymore. 

What we are seeing is: 

• SMS can be filtered  

• Email can be ignored and unsubscribed 

• Push notifications can be turned off 

• Apps can be muted  

• Surveys can be abandoned  

The reliability of any of the traditional outreach channels is decreasing. That means CX programs built around channel optimization alone are becoming more fragile. The organizations that adapt fastest will be tasked with rethinking how they listen. 

From channel strategy to connected listening 

The real takeaway isn’t SMS weakening. It’s that fragmented listening strategies are no longer sustainable. 

When feedback lives in silos, small disruptions create outsized impact. A dip in one channel suddenly looks like a data problem. But it’s actually a much larger visibility problem. 

Leading organizations and CX leaders are responding by moving toward connected listening, a strategy and practice that brings together: 

• Customer feedback across channels  

• Behavioral and transactional data  

• Operational signals  

• Employee insights  

• Unstructured feedback such as reviews, transcripts, and comments 

The goal is to reduce dependency on any single source of truth. Churn risk, dissatisfaction, and loyalty shifts show up in different channels which means you must listen to each to get the full picture of your CX efforts. 

What high-performing CX teams are doing differently 

Mature CX businesses are leading the charge by focusing on building a resilient strategy. To do so, they’re actively taking the following steps.  

1. Diversifying outreach without breaking measurement 

Email is proving to be a stable complement to SMS, especially as filtering increases. Additionally, email tends to be a reliable channel in terms of open rates, with the average open rate sitting at 35.63%. But switching channels only works if you can maintain consistent measurement. This is where many CX programs struggle. 

Without consistent measurement across channels, changes in outreach strategy can distort trendlines and erode confidence in insights. It’s imperative that you have a connected listening system in place to ensure you’re able to both diversify and compare. 

2. Reducing friction at the source 

Reducing friction at the customer-level is one of the most effective solutions and also one of the simplest to implement such as: 

• Encouraging customers to save contact numbers  

• Using QR codes or in-moment prompts to build recognition  

• Embedding feedback opportunities within existing journeys 

• Continue to build trust with your audience with credible first- and third-party reviews  

• Timing outreach to moments of highest relevance 

• Leveraging omnichannel reinforcement 

Trusted senders get seen. Unknown senders get filtered. Experience and trust now influence deliverability. 

3. Moving from feedback collection to signal intelligence 

Even with optimized outreach, response-based feedback will always be partial. 

The highest-performing organizations are expanding beyond surveys to include: 

• Digital behavior  

• Service interactions  

• Operational data  

• Unstructured feedback (reviews, transcripts, comments)  

This creates a more complete picture, especially for the silent majority who often don’t respond to surveys. 

4. Prioritizing action over volume 

More responses don’t automatically lead to better outcomes. In fact, many CX programs already have more data than they can act on. 

The differentiator isn’t how much feedback you collect but rather how quickly and effectively you act on it. 

That’s where artificial intelligence (AI) is increasingly playing a role by: 

• Identifying emerging risks earlier  

• Prioritizing issues by business impact  

• Triggering workflows that drive response  

• Triggering workflows that drive response 

• Surfacing root causes across fragmented data sources 

• Enabling real-time, closed-loop feedback across the organization 

In this model, CX shifts from listening at scale to acting with the precision and swiftness consumers have come to expect. 

The bigger shift: CX in a post-channel world 

iOS 26 is a great reminder that customer access is no longer guaranteed. Brands don’t control the inbox, nor do they control the device. Increasingly, they don’t control whether a message is even seen. 

What they can control is how well they understand and respond to experience signals. That’s why the future of CX isn’t tied to any one channel. 

It’s defined by connected listening systems and coordinated action.  

Where Forsta fits in 

At Forsta, we see this shift clearly. We understand that organizations need a better way to connect data everywhere it lives. 

By unifying customer, employee, and operational data into a single experience intelligence layer, Forsta helps organizations: 

• Maintain visibility as channels evolve  

• Link experience to revenue, cost, and risk  

• Identify emerging issues before they escalate  

• Turn insight into coordinated, enterprise-wide action  

Forsta’s HX Platform allows you to connect these insights into one comprehensive view so you can act decisively and quickly. This allows you to, in turn, build a CX strategy that holds up no matter how the channels change. 

The future of SMS surveys 

SMS isn’t going away. Email isn’t replacing it. And iOS 26 isn’t the last change we’ll see. 

The path forward is clear. 

Customers are setting the expectations, and technology is reinforcing them. CX programs need to evolve accordingly to keep up with customer demands. iOS 26 tells us CX practitioners that this isn’t a temporary channel issue that we need to temporarily adjust our tactics for. It requires a structural shift that builds a stronger CX strategy, one in which connect, resilient, AI-powered listening and action takes place at scale. 

Forsta can help you get there. 

They’re telling you what’s working, what’s broken, what’s confusing, and what’s costing you trust. They’re saying it in surveys, chats, reviews, support transcripts, emails, and call center notes.

The signal is there. In most CX programs, the problem is not a lack of feedback, but the lag that follows.

Too often, teams collect the voice of the customer (VoC) insights in real time yet act on it far too late. Feedback comes in fast; analysis takes longer. Alignment takes longer still. This delay is what many organizations now recognize as the customer insight-to-action gap.

Customers feel this lag, too. Research shows that 59% of consumers expect brands to respond within 24 hours and 67% expect follow-up after an interaction. When feedback cycles stretch into weeks, brands risk falling behind those expectations.

During our recent webinar, High-tech, high impact: AI efficiency for insights teams, we explored how the volume and speed of customer feedback is changing the way organizations approach VoC programs. More importantly, we discussed how AI is helping CX teams close a growing gap between what customers say and how quickly organizations act on that insight.

Here are a few of the key CX takeaways from the conversation.

The real challenge is speed

Customers won’t sit around and wait for your next quarterly readout. Customer feedback moves faster than many CX programs can process.

They tell you what happened the moment it happens, in a survey response after a branch visit, or perhaps in an online review posted before they leave the parking lot. It may happen in a support conversation that starts with one issue and ends with three more. In chat transcripts, social channels, call notes, and those support tickets your team is still working through.

It should be good news for CX leaders that you have more ways to hear the customer than ever before. You can tap into customer sentiments across the full journey, from digital friction to service recovery to brand perception. You can see what customers say, where they say it, and often how they felt when they said it.

But abundance has its own problem: the pile keeps growing. Customers share their experiences in real time, and increasingly, they expect brands to respond just as quickly.

This creates what many CX leaders now recognize as the insight-to-action gap.

In most organizations, feedback flows through a familiar lifecycle:

• Feedback is collected through surveys, reviews, support interactions, and social channels.
• CX teams analyze that feedback to identify patterns and themes.
• Insights are shared with operational leaders.
• Teams take action to improve the experience.

In theory, that cycle runs continuously. Feedback arrives, insights surface, and improvements follow.

In practice, each step often slows the next. Data waits to be analyzed. Analysts spend time organizing feedback instead of interpreting it. Patterns surface weeks after customers first experienced the issue.

By the time the insight reaches the team who can fix the problem, the moment has already passed. Customers have moved on, and the issue may have spread to more journeys, more interactions, and more frustrated customers.

The challenge isn’t that organizations lack insight.

It’s that they struggle to operationalize insight while it still matters.

Don’t chase automation; capitalize on the momentum

This is why most AI conversations in CX start in the same place: “We need faster survey programming! Faster dashboards! Faster reports!“

Those improvements matter. No one misses the hours spent scripting a survey or rebuilding the same presentation deck every month, and AI-powered tools can remove a lot of that operational friction.

But those wins, by themselves, are not the breakthrough. The real value of AI in CX shows up when the entire insight lifecycle starts to move faster.

Think about how customer feedback typically flows through a CX program:

1. Collect feedback through CSAT surveys, call transcripts, social media coverage, and support interactions.
2. Analyze feedback using feedback tools and reporting systems.
3. Identify patterns that signal emerging issues or opportunities.
4. Communicate insights to operational and executive teams.
5. Take action that improves the experience.

In theory, that cycle should run continuously, in a loop. Feedback comes in. Insights surface. Teams respond.

In practice, though, each step slows the next.

Data sits waiting to be analyzed. Analysts spend time organizing feedback instead of interpreting it. Patterns surface weeks after customers first experienced the problem.

By the time insights reach decision-makers, the issue has already spread across more journeys, more customers, and more support interactions.

Where traditional workflows break momentum, AI changes the pace.

Instead of waiting for periodic analysis cycles, CX teams can work with real-time insights drawn from large volumes of feedback, including signals from call transcripts, CSAT surveys, and social media coverage. When these are analyzed as they appear, patterns emerge earlier, and teams respond sooner.

And this is where AI customer feedback analysis becomes transformative.

It goes far beyond automating tasks, actually compressing the distance between what customers say and what organizations do next. As that distance shrinks, momentum builds. Feedback moves faster through the system. Insights reach decision-makers sooner. Teams can act while the signal is still fresh.

That’s when customer feedback stops being a reporting exercise and starts becoming a driver of real operational change.

Open-text feedback is where the real insight lives

If you look closely at where that lifecycle slows down, the bottleneck often appears in the same place: open text.

Scores move quickly through a CX system. CSAT, NPS, and rating scales can be aggregated and visualized almost instantly. But the richest feedback customers leave behind rarely comes in the form of a number.

It comes in their own words via:

• Survey verbatims
• Chat transcripts from the contact center
• Product reviews
• Email responses to support teams
• Comments from product reviews
• Notes captured during customer service interactions.

This is where customers explain what actually happened. They describe the moment a process broke, or why something felt frustrating or confusing. They reveal the details behind falling customer satisfaction, rising customer churn, or declining loyalty.

For CX teams, this kind of feedback is incredibly valuable. It provides the context that structured metrics alone cannot.

But historically, it has also been the slowest part of the analysis process.

Traditional text analytics requires building complex rule systems. Analysts create taxonomies to categorize feedback, define keyword rules, map synonyms, and adapt models to handle spelling variations and multiple languages. Over time, those models require constant maintenance as customers describe experiences in new ways.

The work is detailed and often highly specialized.

Because of that complexity, analysis rarely happens in real time. Teams may review only small samples of feedback, or they wait for periodic updates to text models before new insights appear.

By the time those insights surface, the underlying issue may have already spread across multiple customer journeys.

Ironically, the most actionable insight in VoC programs often sits inside open text.

But traditional methods make it difficult to operationalize that insight at the speed modern CX requires.

AI is helping close the insight-to-action gap

This is where AI is beginning to change the pace of CX analytics. And consumers are increasingly open to AI when it improves the experience. Nearly half say they’re comfortable with AI-led CX if it delivers faster service.

On the other hand, 85% of customer service leaders are already exploring or piloting customer-facing generative AI solutions.

Modern AI tools can process far larger volumes of feedback than traditional approaches ever allowed. Instead of sampling small portions of survey responses or support transcripts, organizations can analyze 100 percent of their feedback data across channels.

AI models can automatically identify themes in open-ended comments, detect sentiment patterns, and surface emerging trends across surveys, call transcripts, and social conversations.

Signals that once required weeks of manual analysis can appear almost immediately.

When those signals appear sooner, organizations can respond sooner.

Instead of spending hours preparing data, CX teams can focus on the work that actually improves the experience. They can investigate root causes behind recurring customer issues, prioritize improvements that affect retention or loyalty, and bring clearer insight into conversations with operational leaders.

Importantly, AI doesn’t replace human expertise.

AI excels at scanning large volumes of data and identifying patterns. Human CX leaders still play the critical role of interpreting those signals, deciding what matters most, and guiding the organization toward the right actions.

When used effectively, AI becomes a research assistant for CX teams, accelerating the work machines do best so people can focus on strategy and improvement.

Turning feedback into forward momentum

During the webinar, we also discussed how AI-powered analytics tools like Narrative HX are helping organizations accelerate this process.

Narrative HX uses generative AI to transform open-ended feedback into structured insight at scale. Instead of building and maintaining complex rule-based text analytics models, teams can generate tailored models in minutes and analyze feedback across surveys, contact center transcripts, social conversations, and other sources.

Because the models rely on large language models rather than rigid keyword rules, they can interpret context across different languages and phrasing variations without constant manual maintenance.

The insights don’t sit in a separate system, either. Narrative HX feeds results directly into existing Forsta dashboards, so teams can see themes and sentiment developing across their VoC analytics environment without learning a new tool.

The result is a much faster path from feedback to action:

• Feedback enters the system.
• Patterns emerge across customer data.
• Teams focus on what needs to change.

And when that happens, Voice of the Customer programs stop chasing insight after the fact. They start driving forward motion across the customer experience.

Let AI do the sorting, and keep people on the steering wheel

AI brings with it legit concerns for teams. If machines can analyze feedback faster than humans, what happens to the people whose job it is to interpret it?

The reality is more practical than dramatic. AI excels at the parts of VoC analytics that involve scale.

Natural language processing allows systems to scan thousands of comments across surveys, support transcripts, and social media channels, detecting patterns that would take analysts far longer to find. AI can categorize feedback, summarize themes, and surface signals across large volumes of CX data. It can highlight emerging issues across the customer journey and feed those insights into real-time dashboards where teams can review them quickly.

In other words, AI is very good at sorting the signal.

What it cannot do, at least not reliably, is decide what that signal means for the business.

Context still matters. Understanding the operational realities behind a customer complaint requires knowledge of the organization, its processes, and its priorities. Strategic decisions require judgment. CX leaders must decide which issues matter most, which ones can wait, and how to balance competing priorities across the business.

Human expertise also plays a critical role in governance and ethics. Customer data carries responsibility, and organizations must decide how it is used, who has access to it, and how AI-generated recommendations are validated.

And perhaps most importantly, insight still needs a human advocate.

VoC programs succeed when someone can translate Voice of the Customer data into a story that resonates with executives and operational teams. That requires influence, communication, and the ability to connect insight to action.

AI customer feedback analysis works best in this environment when it acts as a research assistant rather than a decision-maker.

It accelerates the work that machines do well — pattern detection, categorization, and large-scale analysis — so that humans can focus on what they do best: interpreting context, shaping strategy, and guiding improvements across the customer experience.

Let the technology handle the sorting, and keep people on the steering wheel.

The teams that move faster will learn faster

The conversation around AI in customer experience often focuses on capabilities.

• What can the technology do?
• How accurate are the models?
• Which tools should we invest in?

But the real differentiator may turn out to be something simpler: learning speed.

Organizations that integrate AI into their Voice of the Customer programs earlier gain more opportunities to experiment, test ideas, and refine their understanding of the customer journey.

They can explore patterns in feedback faster, validate assumptions more quickly, and build stronger instincts about what drives customer satisfaction and loyalty.

Over time, those learning cycles compound.

Within a few years, AI capabilities will likely be embedded across most CX platforms. The technology itself will no longer be the differentiator.

How organizations use it will be.

And the teams that start now will already know how to translate customer feedback into faster operational decisions.

Watch the full webinar

The insights above only scratch the surface of the discussion.

In the full webinar, we explore how evolving shopper expectations are reshaping retail CX, how brands can identify emerging signals earlier, and how AI-driven analytics is helping organizations close the gap between customer feedback and meaningful action.

Watch the full webinar replay to see these ideas in action and learn how leading brands are adapting their CX strategies.

People move fast, skip surveys, and share feedback on their own terms — and they often do it in places brands don’t own like public reviews, social platforms, and peer communities that shape perception long before a customer ever engages directly.

At the same time, AI-driven search is reshaping how customers discover, evaluate, and talk about businesses. Search is no longer just a list of links. It’s an interpretation layer that pulls from reviews, sentiment, and third-party signals to form opinions at scale.

It’s the perfect reputation management storm for enterprise brands, most of which simply aren’t built to keep up. Data lives in silos. Feedback loops lag behind. And while organizations are collecting more signals than ever, fewer of them lead to meaningful action.

This is the new challenge for CX leaders: how to listen deeply across a growing mix of channels, journeys, and behaviors — and make that insight useful before it goes stale.

To meet that challenge, leading organizations are expanding their CX programs into something broader: Human Experience (HX). HX brings together customer input, employee perspective, and brand perception into a connected system of insight.

Let’s take a look at 8 of the most pressing CX listening challenges for multi-location brands right now, and how to solve each one with an HX-centric approach.

Challenge #1: Survey fatigue and declining response rates

Why it’s happening

Long-form, end-of-journey surveys are fading fast. Customers move too quickly now and expect brands to do the same. Even when they’re open to sharing feedback, they want it to feel relevant, immediate, and worth their time.

That’s the disconnect. Most customer experience teams still rely heavily on post-interaction surveys, often delivered hours or days after the moment that mattered. It’s too late. And it feels like a chore.

The good news is that people still want to be heard. In fact, 67% of consumers say it’s important that brands follow up about their experience. They just don’t want to answer ten questions to do it. They’re looking for smarter ways to engage.

How to solve it

From an HX perspective, listening needs to happen in the moment, not after the fact.

Instead of waiting until the end of the journey, brands need to capture feedback as it happens, through microsurveys, event-based triggers, and behavioral signals baked into the digital user experience.

Small moments reveal big truths. A single question on a checkout page. A thumb rating in a help article. A review prompt timed to an in-store visit. These micro-interactions offer real-time customer insights without overwhelming the customer, and they create a continuous stream of Voice of Customer (VoC) data that’s far more reflective of what’s actually happening.

Here’s what that looks like in action. RS Group partnered with Forsta to embed targeted microsurveys across key digital touchpoints like search, checkout, and product pages. The result? Over 22,000 pieces of actionable feedback, tied directly to product decisions and customer journey improvements.

The path forward isn’t more surveys. It’s better signals, captured at the right time, and turned into action without delay.

Challenge #2: Surveys alone don’t tell the whole story

Why it’s happening

Customers don’t just speak through surveys. They leave signals across customer touchpoints including reviews, chats, social posts, and call center transcripts. And they often do it without addressing the brand directly.

And while customer service representatives may catch issues in conversation, those insights rarely make it back to the core CX program.

If you’re only listening through structured surveys, you’re missing the bigger picture: emerging risks, unmet needs, and the everyday friction customers won’t take the time to explain in a survey.

How to solve it

Brands need a modern Voice of Customer program to connect the dots. That means integrating VoE (employee feedback), brand perception insights, and operational data alongside traditional VoC signals. With a unified view, brands can catch what surveys miss, and act before problems scale.

This is where machine learning and AI-powered text analytics come in. Forsta ingests and structures large volumes of unstructured data, surfacing real-time themes, sentiment shifts, and root causes across every channel. You see what customers are actually saying, whether or not they fill out a form.

The payoff is faster issue detection, more personalized interactions, and insights that go beyond lagging indicators. Listening at scale isn’t about adding more surveys. It’s about understanding the full story behind every signal.

Challenge #3: Fragmented journeys and siloed data

Why it’s happening

Customers don’t experience departments. They move fluidly across channels, researching online, chatting with support, and hopefully visiting a store. It’s all part of one journey in their eyes.

Most organizations still manage CX through siloed systems and teams, each optimizing its own slice without visibility into the full journey.

Inevitably, this creates blind spots, journey friction, and missed opportunities to drive customer satisfaction at the moments that matter.

How to solve it

HX starts by mapping the journey from the outside in. Align systems and insights around how customers actually behave, not how teams are organized.

Forsta’s centralized data hub pulls in customer listening data from every source, from interactions and reviews to employee feedback and brand signals, for a single, connected view. This creates the reliable source of truth needed to cut across silos and highlights where friction is costing you trust and revenue.

Want to build a customer-centric culture? Give every team access to the same journey map, and the power to improve it. With unified CX intelligence, you don’t just react to problems. You orchestrate experiences that work better everywhere.

Challenge #4: Trust erosion and the rise of AI-influenced customer decisions

Why it’s happening

There are many benefits to how AI is shaping how people discover brands, compare options, and decide where to buy. From generative search to automated recommendations, machines now influence more CX decisions than ever.

That comes with new risks, as AI draws heavily from public signals, like reviews, social sentiment, and third-party data. If your brand’s online presence is inconsistent or outdated, trust erodes before a human ever gets involved.

Worse, poorly deployed AI (think clunky chatbots, rigid automation, or inaccessible contact center flows) can push people away when they’re looking for real help.

How to solve it

AI should strengthen human-powered customer service, not replace it. Use CX insights to ensure automation enhances, rather than undermines, your most trusted touchpoints.

Monitor and analyze your review ecosystem, social commentary, and AI search visibility to understand how your brand is being interpreted — because that perception drives conversion.

Balance AI speed with human nuance. Use your CRM system to flag high-stakes interactions for personal outreach. Keep humans in the loop where it counts. That’s how you build the kind of trust that drives customer retention, not just efficiency.

Challenge #5: Overwhelming data and underwhelming action

Why it’s happening

Enterprise CX programs generate massive volumes of data, but insight alone doesn’t move the needle. When analysis lives with a centralized team, it slows everything down. By the time insights reach customer service or operations, the moment to act has passed.

Business decisions are happening in real time, and traditional CX reporting cycles can’t keep up.

How to solve it

Break the bottleneck. HX democratizes your CX data so every team — from care and digital, to retail and frontline customer service — can access the signals that matter most to them.

Use AI-powered summaries and automation to surface themes instantly, without waiting for manual analysis.

Then, build rapid feedback loops so local teams can act independently, without waiting for quarterly reports or HQ approvals. This is how modern CX leaders shift from insight to impact faster, and at scale.

Challenge #6: Slow response and lack of real-time recovery

Why it’s happening

Today’s customer journey doesn’t pause when there’s a complaint; it keeps right on moving. When brands delay, they miss a moment of CX recovery. And worse, they lose trust.

Response time has become its own trust signal. Rio SEO’s latest local consumer behavior survey shows that 59% of consumers expect a response within 24 hours, and many expect it much sooner. If your team isn’t ready to meet that window, you’re not just behind… you’re invisible.

How to solve it

HX programs codify real-time care. Set closed-loop service standards that prioritize speed: respond, resolve, and follow up within hours, not days.

Use smart triage automation to route high-risk or high-value cases to the right team instantly. And give frontline staff the tools they need — quick-reference guides, AI summaries, and a live knowledge base — to act without waiting on escalation.

Most importantly, connect response speed to revenue. When teams understand how fast recovery protects loyalty and lifts retention, they act with urgency.

Challenge #7: Emerging channels are changing how people share experiences

Why it’s happening

The customer experience no longer lives solely in surveys or service calls. It plays out on TikTok, in AI-generated search results, and across social commerce platforms where Gen Z shops, shares, and shapes brand perception.

AI search is now a core discovery channel, and the data backs it up. Sixty percent of consumers say they click on AI-generated overviews in search. These summaries often replace your website, your listing, and even your voice.

How to solve it

Start listening where the volume’s rising. Monitor high-impact channels like TikTok, Reddit, and AI search overviews — especially for industries like retail, tech, and consumer services where the purchase journey is increasingly influenced by algorithmic interpretation.

Track how brand trust is earned (or eroded) across these spaces, and use those insights to sharpen messaging, improve digital experiences, and respond to the expectations customers are broadcasting, whether they tag you or not.

Experience is still being shared. Just not always with you.

If you want to dig in here, watch our on-demand webinar: Challenging the CX status quo: How a mature listening program drives business-wide transformation. You’ll learn how top financial services brands are evolving beyond dashboards and into action using advanced tools like AI Summarize and Text Analytics to democratize insights, break silos, and prove ROI across the enterprise.

Challenge #8: Misalignment on AI’s role in CX

Why it’s happening

In boardrooms, AI often gets sold as a silver bullet. But those looking to cut costs, fix CX, and scale instantly by automating everything will be sorely disappointed.

When automation replaces nuance, customer frustration rises, and that’s especially true for older or less tech-savvy users. Overconfident rollouts (think: unhelpful chatbots, clunky self-service flows) don’t just miss the mark; they create loyalty gaps that are hard to close.

The risk isn’t using AI. It’s assuming AI is the strategy.

How to solve it

HX positions AI as an augmentation layer, not a shortcut. The best CX programs scale empathy through intelligent routing, faster insights, and more relevant experiences created by skilled, knowledgeable people empowered by AI.

Put governance in place: validation, oversight, and ethical guardrails that ensure automation supports your brand promise.

And use Human Experience (HX) frameworks to align the C-suite. Show how trust, effort, and AI efficiency can coexist when experience stays in the driver’s seat.

AI can accelerate CX impact. But only if it listens before it acts.

Building a future-ready CX listening strategy

Most CX programs weren’t built for this pace or level of complexity. Customers move fast. Feedback comes from everywhere. And the old way of waiting for quarterly reports or post-journey surveys doesn’t cut it anymore.

To lead in 2026, listening needs to be continuous, connected, and built for action. That means capturing signals across channels, analyzing them in real time, and putting insights into the hands of people who can use them in as near to real-time as possible.

Human Experience (HX) makes this possible by connecting customer feedback with employee insight and brand perception. It’s your key to a clearer view of what’s happening, why it matters, and where to act.

AI can help scale that effort, but it needs structure. Guardrails. Human judgment. The smartest brands are using automation to speed up analysis, not replace it, and keeping trust at the center of every interaction.

Forsta brings together active listening, connected data, and real-time insight so your teams can move faster, stay aligned, and turn feedback into real outcomes.

Turning listening into competitive advantage

Customer experience isn’t a function to pass off to a team. It’s a growth engine. The brands that win will be the ones that listen better, as in faster, deeper, and more completely.

Modernizing your CX listening approach doesn’t mean collecting more data. It’s about connecting the right signals, acting on them in real time, and proving impact across the business so the data you have is put to use.

That’s how you build trust.

That’s how you drive loyalty.

And that’s how you outperform.

Ready to see how Forsta can help you get there? Schedule a demo and see what enterprise-grade listening looks like in action.

Consider that more than half of all digital interactions happen on mobile devices, and 80% of consumers say they’re more likely to buy when brands personalize their experience. These aren’t trends, but the new default.

Going mobile-first doesn’t just mean “meeting customers where they are.” You’re walking beside them. Capturing their voice in the moment, not after the fact. You get richer, real-time insights to drive your digital strategies while your audience gets a feedback experience that feels effortless. That’s the mobile advantage: more context, more candor, and more connection.

What it means for customer experience (CX) professionals:

• Stronger digital marketing powered by real behavior, not guesswork.
• Smarter user experience design, shaped by live feedback.
• Higher customer engagement because asking for input feels natural, not intrusive.
• A digital feedback strategy that runs at the speed of your audience.

Mobile isn’t a nice-to-have; it’s the frontline of your brand. Let’s look at how mobile-first feedback is reshaping the customer experience, and how Forsta helps you stay one step ahead.

The shift to mobile-first feedback

Remember when mobile became a part of the customer journey? Well, now it is the journey.

From tapping through mobile apps to email marketing preferences to scrolling social media, people interact with brands on their own terms, in their own time. And they expect customer engagement methods to follow suit.

Why mobile-first feedback works

Mobile-first feedback is a must for several reasons, as we outline below:

• It’s in the moment. Real-time responses mean fewer gaps, fresher insights, and a clearer view of customer experience as it happens.
• It gets answered. Mobile-first surveys consistently outperform email and desktop, because they show up where people are already paying attention and willing to share user experience insights.
• It’s rich with context. Every tap tells a story. Mobile lets you capture not just what customers say, but where they are, what device they’re using, and what they’re doing.

The tech has shifted, and that means brands that want to truly tune into customer experience need to follow suit with digital strategies that work. A digital customer experience strategy that leads with mobile creates faster feedback loops, deeper understanding, and smarter next steps.

Catch preferences while they’re still preferences

Customers won’t always tell you when their expectations change, but their behavior will.

Mobile feedback surfaces those shifts while they’re still fresh, from subtle mood swings to major pain points, so you can tune your digital strategies while it still matters.

That could mean tracking how preferences evolve from tap to tap, and being able to spot patterns across moments, not months. It also means being able to respond in time to prevent customers from drifting away.

Forget waiting on lagging indicators. You’re now working with live inputs. That’s the power of feedback designed to move with your users.

Digital feedback channels that win on mobile

Great mobile feedback doesn’t interrupt the user experience; it fits right into it. These channels are collecting data, then building stronger customer connections, right in the flow of real life. Here are a few ways to make it happen:

In-app surveys

Tap. Respond. Move on. In-app surveys are a powerful tool that gives you a window into the customer’s mindset while they’re still in the experience. Ask a quick question after a completed order or service task. Trigger a micro-survey at the right moment in the app flow. No delays or memory gaps, just insights that feel natural and effortless.

SMS & mobile web links

Text is still king when it comes to speed. A simple SMS or mobile web link lets you reach customers wherever they are, whether that’s at the bus stop, in the waiting room, in a digital marketing interaction, or walking out of your store. These messages cut through the noise and make giving feedback as easy as tapping a link.

QR codes

A poster. A menu. A receipt. QR codes make static spaces interactive, turning everyday surfaces into digital marketing touchpoints. They work especially well in environments like retail, hospitality, and healthcare, where customers are on the move and unlikely to type a web address. With one scan, they’re instantly where you want them: in your feedback flow.

Kiosk mode

Sometimes the best feedback happens right before they walk out the door. Kiosk mode puts mobile-optimized feedback tools on shared devices like tablets at checkout or exits. It’s quick, visual, and easy to navigate, making this feedback format ideal for high-traffic environments where you need fast, usable responses without slowing anyone down.

Forsta’s HX platform seamlessly powers all these mobile-first methods. You and your team don’t need to navigate around bolted-on tools or half-baked workarounds. Instead, you get smooth digital experiences across all tough points that boost customer engagement, improve user experience, and sharpen your brand communication strategy with every touchpoint.

Build feedback into your culture, not just your toolkit

Mobile-first doesn’t just change how you collect feedback. It changes who uses it, and how often.

When feedback is quick to give and easy to act on, it becomes part of the everyday: fuel for smarter service, sharper messaging, and better decisions across the board. That’s what it means to build a real feedback culture.

• Ops teams fix issues before they spread
• Product teams make changes customers actually want
• Marketing doesn’t guess; they know what lands

This isn’t a side quest. It’s how brands build customer loyalty in a world where expectations shift fast and patience runs thin.

Smarter, faster insights with mobile plus AI

As you well know by now, collecting mobile feedback is only half the job. The real value comes from turning that raw input into something you can quickly activate. That’s where Forsta’s AI-powered tools step in.

AI Summarize

No more wasted time wading through endless comment threads. AI Summarize distills high-volume mobile feedback into clear, concise summaries, so your team gets the full picture in seconds, not hours.

Text Analytics

What are customers really saying? Forsta’s Text Analytics tells you exactly what they’re thinking. It’s a powerful tool that pulls out patterns, flags sentiment shifts, and highlights key themes across mobile responses. It’s built to handle scale without sacrificing nuance.

AI Open Assist

Strong prompts lead to stronger data. AI Open Assist helps you write better open-ended questions for mobile users, making responses from existing and potential customers richer, sharper, and easier to analyze.

The payoff

This isn’t about shaving minutes off your analysis time. We’re talking about transforming how your team works.

When AI handles the heavy lifting of sorting, summarizing, and spotting trends, your frontline staff and CX team can focus on what actually moves the needle. They’re not buried in spreadsheets or jumping between tools. They’re making decisions, solving problems, and delivering better experiences, faster.

• Product teams respond to bugs or blockers the same day, not the next quarter.
• CX teams spot friction before it becomes colossal chaos.
• Marketing gets real-time reads on campaigns and can course-correct on the fly.
• Executives walk into meetings with tangible insight, not guesswork.

Mobile in tandem with AI gives you speed, scale, and smarts all at once. That’s how feedback becomes fuel for action.

Designing mobile-optimized feedback experiences

The best mobile surveys don’t feel like surveys. They feel like conversations. They’re fast and clear, and they feel as personal to the customer as their relationship with your brand. But it takes more than shrinking a desktop form to fit a smaller screen; it means rethinking the whole experience from the ground up.

Here’s how to get it right.

Keep it short, visual, and intuitive

When you’re on a phone, every extra second feels like a chore. So make the experience feel effortless.

Strip your survey down to what really matters, then strip it again. Think of their customer journey through this process, and aim for less text, more interaction. Sliders over radio buttons. Emojis over essays. Progress bars that keep people moving.

You’re not writing an exam. You’re inviting a quick gut-check. A tap. A swipe.

Use logic branching to personalize

One-size-fits-all feels lazy, and on mobile, that’s a deal-breaker. Personalization isn’t just polite; it’s expected.

Logic branching helps you ask smarter questions based on the customer insights you already know. Someone’s location? Type of service? First-time buyer? Great… use that to guide the flow. Hide what doesn’t apply. Ask more when it’s worth it. Drop the rest.

You end up with a smoother experience for them and cleaner data for you.

Optimize for speed and touch

If your survey lags or pinches, people bounce. Plain and simple.

Design with thumbs in mind. Big targets. Clear spacing. No tiny checkboxes or zoom-in zones. Use components that load fast (even on a spotty signal) and keep the cognitive load low. Every second saved is a second closer to a completed response.

Treat feedback as conversation, not collection

People don’t just want to speak, they want to know someone’s listening.

When you close the loop on feedback, you’re not just solving problems. You’re strengthening your brand communication every time you respond.

• “We heard you” beats “thanks for your input”
• A one-line follow-up builds more trust than a slick ad campaign
• A small change, visibly made, has a big impact

When feedback flows both ways, customers stick around. They stop being silent users and start becoming advocates.

Add voice-to-text

People on the move don’t want to peck out a paragraph of customer feedback, but they might speak it.

Adding voice-to-text unlocks a richer layer of feedback, allowing you to capture insights that are more detailed and emotional. It’s just more human. It also lowers barriers for users with accessibility needs or limited dexterity, enabling you to tap into more of your customer base.

It’s the difference between “fine” and knowing exactly what went wrong.

Make it accessible for everyone

Designing for mobile means designing for everybody. Not just the tech-savvy or the able-bodied.

That means:

• Large, legible fonts
• Strong contrast ratios
• Keyboard navigation and screen reader support
• Touch-friendly layouts that work one-handed

Recommended reading: The Definitive Guide to Designing Mobile Surveys

The moment someone can’t respond, their voice disappears… and so does your chance to understand them.

Designing for mobile isn’t just about aesthetics, and accessibility isn’t a compliance checkbox to tick off. These are integral parts of the customer experience and demonstrate the deep respect you have for your customers’ time, needs, and reality. This is how you build brand loyalty… when you get it right, it builds trust — click by click, tap by tap.

Real-world results: A mobile-first feedback success story

When you serve millions of customers across multiple brands, keeping the experience consistent isn’t easy. For Nuuday, Denmark’s largest telecom provider, it was becoming impossible without a smarter, faster way to collect and act on feedback.

Their customer experience (CX) program was strong. But as they scaled, complexity followed. Feedback poured in from all angles — apps, emails, service calls — and they were capturing it. They just couldn’t move fast enough to use it.

What they needed was a digital strategy that worked across brands and fit into everyday moments with customers.

The shift

With mobile-first surveys tailored to each brand journey, Nuuday started collecting feedback where it mattered most: in the moment.

A service call ends? Survey triggered. A plan gets upgraded? Quick NPS with a follow-up to find out why. Every interaction became an opportunity to learn, then act.

Behind the scenes, everything flowed into a single hub connected to their data warehouse. Teams could see what was happening, spot the “why” behind customer sentiment, and quickly adjust.

The results

• Faster feedback loops. Insights came in quicker, and so did coaching for frontline teams.
• Higher response rates. Short, intuitive mobile surveys kept customers engaged.
• Stronger customer experience. Root causes didn’t get lost in the noise; they were flagged and fixed.
• Real-time reporting. Execs didn’t have to wait for monthly decks; they had live visibility across every brand.
• Better NPS. More relevant feedback led to better service and higher scores.

Nuuday didn’t just digitize their feedback strategy. They transformed it into a system that adapts, listens, and delivers at every touchpoint.

The bottom line: Don’t just go digital; go mobile-first

Digital isn’t enough. Not if it’s still stuck behind a desktop login or buried in an email inbox.

To truly understand your customers, you need to meet them in the moment. On their phones. In their flow. Mobile-first feedback strategies do exactly that—giving you real-time, real-world insight that’s more honest, more immediate, and more useful.

Flexible collection methods. AI-powered analysis. Smarter design. It all adds up to sharper decisions and better customer experiences.

What to do next:

• Audit your feedback channels. Are your surveys easy to use on mobile? Do they load fast, look great, and respect your users’ time?
• Explore modern feedback tools. Look for platforms that flex across apps, SMS, web, and shared devices—without breaking the experience.
• Talk to someone who gets it. Our team can show you how to go mobile-first with confidence and get results.

Turn feedback into action. Put customers in control. And bring your CX strategy up to speed, one tap at a time.

Ready to go mobile-first? Request a demo or talk to a Forsta expert to do it with confidence.

What happens after the sale defines how customers remember your brand. It’s the difference between a transaction and a relationship. Post-purchase engagement is where true loyalty takes shape.

It’s where customer experience (CX) becomes human experience (HX): reassuring, responsive, and personal. Skip that, and you not only miss a follow-up sale but lose trust, too.

Budgets pour into acquisition with splashy campaigns, polished journeys, and perfect first impressions. But after the sale? Crickets. Support lags, communication drops, and feedback disappears into a void.

The result? Customers feel forgotten, and they drift away. The brands that stand out stay present. They follow through.

The good news? That gap — the post-purchase gap — is one of the biggest opportunities to create real connection and long-term advocacy.

The post-purchase gap: where many brands fall short

Here’s where things start to slip.

Traditional CX stops at the sale. It’s built to get customers in, not keep them close. Once the “thank you” page loads, the attention vanishes. The drop-off is real, and it’s costly.

Customers feel the disjointed onboarding and robotic emails. They grow frustrated with vague order tracking and the siloed support teams who ask for the same info twice. And when things go wrong, that’s what they remember. Not how smooth the checkout was, but how invisible they felt afterward.

This is the post-purchase gap. And it’s bigger than most brands think.

In fact, research shows most customer complaints and churn happen after a purchase — not before. Further, a Radial/eMarketer study found that 79% of consumers say they may not buy again from a brand after a poor post‑purchase experience, and 83% think this phase could be improved.

Post-purchase engagement is when expectations are highest. And when service falls short, trust takes the hit.

You’ll spot the signs in the data: rising return rates, support tickets that spike post-sale, feedback that never makes it back to the teams who need it. But the real damage is emotional, because loyalty isn’t just logical; it’s felt.

The human value of post-purchase engagement

What happens after the sale tells customers everything about your brand. That’s because post-purchase experience isn’t a line item. It’s a signal of how much a brand values its relationship, not just its revenue.

Post-purchase engagement isn’t about automated “we miss you” emails or boilerplate tracking updates. It’s about making people feel seen, heard, and valued. Not as transactions, but as humans. In fact, our 2025 State of CX report found that 58% of U.S. consumers still prefer speaking to a real person, even in a digital-first world

When you get it right, they create memory-making moments, not just service recoveries. Empathy, responsiveness, and recognition don’t just resolve issues, they build loyalty. A quick reply from support can defuse frustration. A tailored recommendation can feel like a brand knows you. Even a thoughtful “thank you” page can land as a moment of genuine appreciation.

These aren’t minor touches; they’re memory-makers.

Like a retailer who turns a delayed delivery into a loyalty moment by owning the mistake early, complete with updates, a personal message, and a goodwill offer.

Or the brand that takes a single line of customer feedback, fixes a product feature, and loops back to say, “You asked. We listened.” That’s takes what could feel like transactional customer services and turns it into a partnership.

This is where CX becomes human.

Human experience (HX) transforms frustration into connection and data into empathy.

From confirmation pages and shipping notifications to order tracking emails and delivery experiences, every touchpoint is a chance to show customers they matter. Feedback doesn’t just get logged, it gets acted on. Communication isn’t scripted — it’s personal. And the relationship doesn’t end on the receipt.

It’s just getting started.

The role of data, AI, and feedback loops

The best post-purchase strategies start with a mindset shift: stop guessing, start listening.

Many brands only collect feedback at two points: when a customer buys, or when they cancel. That leaves a lot of silence in between.

Forsta fills the gaps with always-on listening across surveys, reviews, support tickets, and social channels. Every piece of feedback is a signal, and when connected, it tells a richer, faster story.

AI sharpens the view. It flags emerging sentiment patterns, friction points in the delivery experience, and moments of frustration buried in post-interaction notes. But the magic happens when humans step in to read between the lines, prioritize what matters, and act with empathy.

This is you transform and evolve from measurement to movement.

With Forsta’s Gather–Analyze–Visualize–Act framework, brands can turn raw feedback into real change. Think faster resolutions, more relevant support, smoother handoffs — all powered by insight, not instinct.

Think branded tracking pages that answer questions before they’re asked. Loyalty programs that reward meaningful moments, not just transactions. Proactive service tools that spot risk before it turns into churn. These aren’t perks., they’re proof that customers are being heard.

When you connect data to action, and AI to people, you don’t just improve satisfaction scores. You build something bigger: a community of customers who feel known, valued, and ready to stick around.

Turning feedback into action: From insight to intervention

Insight without action is just noise. The real power lies in what you do next.

This is where Forsta shines: at turning streams of feedback into sharp, human-centered interventions. Our tools don’t just listen. They learn, adapt, and act fast, because your customers expect nothing less.

Speed matters. According to Rio SEO’s 2025 study, customers expect acknowledgment and resolution within hours when an issue arises— not days. Wait too long, and what started as a fixable issue becomes a loyalty killer.

Real-time insights give brands the edge. You can spot at-risk customers before the exit. Pinpoint pain points as they’re happening, not after the damage is done. And respond with empathy that feels personal, not programmed.

That’s what HX looks like in practice:

• Sentiment analysis that decodes emotion behind the words.
• Satisfaction surveys that feed smarter support scripts.
• Review incentives that rebuild trust in the moment.
• Hyper-relevant content that shows you’re paying attention.
• Consistent communication across every channel, every time.

It’s not just about closing the loop. It’s about showing customers they were never just a ticket number to begin with.

Building trust through connection

At its core, Forsta’s philosophy is simple: the ultimate differentiator isn’t speed, price, or even product.

It’s human connection at scale.

That’s what HX delivers.

Where traditional CX sees post-purchase engagement as damage control, HX reframes it as relationship deepening. It’s not about fixing problems, it’s about strengthening bonds.

Every piece of hyper-relevant content, every personalized offer, every thoughtful follow-up shows customers they’re not just heard but fully and completely understood.

This is empathy and data working together. Technology listens. People interpret. Brands evolve.

Trust doesn’t appear in a dashboard. It shows up in the moments that matter: when a customer sees their feedback reflected in a product change… when support reaches out before an issue escalates… when post-purchase behavior isn’t just tracked, but appreciated.

That’s how your most passionate brand advocates are born. It doesn’t happen through points or perks, but through consistent proof that someone’s paying attention.

At the end of the day, HX isn’t a metric. It’s a mindset. One that values what people feel as much as what they do.

A practical framework: How brands can excel at post-purchase engagement

Turning feedback into connection takes more than good intentions it takes a HX playbook.

At Forsta, we’ve built a framework that transforms post-purchase engagement from a checkbox into a competitive advantage. It’s not about adding more noise, but creating meaningful, measurable moments that deepen connection.

Here’s how it works:

• Listen continuously — Capture feedback across every touchpoint: surveys, chats, social, and reviews. Don’t wait for complaints; seek signals.
• Analyze Intelligently — Use AI to interpret emotion, detect friction, and spot early churn risks.
• Visualize Clearly — Empower every team with insights that matter — from marketing to support.
• Act with Empathy — Personalize outreach, close the loop fast, and show that feedback drives change.
• Measure Meaningfully — Look beyond NPS. Track emotional resonance, response speed, and the moments that spark advocacy.

This isn’t a linear process. It’s a living cycle designed to evolve as your customers do. When you connect insight to empathy, you don’t just retain buyers. You earn believers.

Redefining loyalty through post-purchase engagement

The sale isn’t the end of your customer’s journey. That’s just the starting line.

Loyalty doesn’t begin with a discount or end with a receipt. It begins with what happens after the buy. In the delivery updates, the support chats, the outreach that shows customers they matter long after checkout.

When brands close the post-purchase gap, they turn data into empathy and transactions into trust. That’s what Human Experience (HX) is all about: technology that listens, people who act, and connections that last.

Our HX Platform brings every piece of post-purchase feedback together — from surveys and social sentiment to support interactions — and turns it into action. With AI-powered insights and intuitive dashboards, your teams don’t just measure satisfaction; they respond with empathy, in real time.Ready to bridge your post-purchase gap?

Discover how Forsta helps you listen, learn, and act at every stage of the journey.

Artificial Intelligence (AI) steps in to help. AI customer feedback analysis can scan thousands of voices in seconds, and spot patterns people might miss. It sorts the noise, pulls out themes, and flags unusual activity, but it doesn’t always get the story right. Context, empathy, and even sarcasm and humor are often caught by humans but can be easily missed by a machine.

That’s why balance and proper integration matters. At Press Ganey, we view AI as the engine that drives rapid progress. But the steering wheel belongs to people; the ones who know when a comment is more than a complaint, or when a small frustration signals something bigger. That mix of machine speed and human touch captures the heart of Human Experience (HX): technology to capture every voice, and human empathy to act on what really matters.

The scale of the feedback challenge

Surveys come in by the hundreds, reviews keep building on Google and Yelp, and social posts never slow down. Support tickets keep Customer Support teams stretched. For large brands, the sheer amount of feedback is impossible to process manually.

The numbers show prove it: 84% of consumers search online for local businesses every day, 77% within the past week. And for restaurants, three out of four customers act within 48 hours of searching. The window to understand and respond is tight.

Old approaches such as manual coding, endless spreadsheets, or a small team slogging through comments just can’t keep up. By the time patterns surface, the moment to act is gone. And hat delay affects more than operations. Marketing campaigns lose relevance, social media posts miss the mark, and quality standards slip in ways customers notice.

This is where AI earns its place. Generative AI, machine learning, and sentiment analysis can scan thousands of comments, group them into themes, and feed insights directly into marketing analytics. Instead of drowning in data, teams get signals they can use right away.

And still, AI customer feedback analysis can’t run on its own. AI-human collaboration keeps those insights on track—machines surface what matters, and people shape the response, so it builds trust.

What AI does best in feedback analysis

There are parts of feedback work where machines have a clear edge. A review here or there is easy for people to read. But when you’re looking at tens of thousands of survey comments or support transcripts, the scale breaks human limits. AI tools excel at scale and speed.

AI tools don’t skim; they devour. In seconds, they can sort thousands of comments, connect similar themes, and flag when sentiment takes a sudden turn. It’s the kind of early warning system that helps brands step in before a problem grows.

Speed is another strength. Instead of waiting days for someone to code and tally comments, real-time summaries give teams something they can act on right away. A marketing campaign can be adjusted while it’s still running. Customer support can shift resources before wait times spiral. Even everyday content creation—social media posts, follow-up emails—benefits from knowing what customers are feeling in the moment.

AI also brings a certain fairness. People sometimes downplay issues that don’t feel urgent to them or give extra weight to the loudest voices. Machines don’t. They treat every piece of customer data the same, which gives a steadier base for data-driven decisions.

Still, the output needs a human touch. What machines surface still needs human judgment to turn into the right response—one that fits the moment and stays true to the brand voice. That’s the heart of AI-human collaboration.

Where human oversight is irreplaceable

We’ve seen what happens when AI customer feedback analysis runs unchecked. AI can crunch numbers all day, but it struggles with tone. A sarcastic ‘great job’ or a culture reference often lands flat unless a person can interpret it. That’s why the human touch matters. People bring the context that keeps customer feedback analysis from drifting into mistakes that erode trust.

There’s also the question of priorities. Machine learning can point to rising themes in customer data, but only people can judge which one’s match business goals, which one’s risk ethical concerns, and which ones need immediate attention. A sudden spike in complaints buried in support tickets feels very different from a slow pattern in survey responses. And with 59% of customers expecting a reply within 24 hours—and 67% wanting follow-ups—those decisions have to be made quickly.

Then comes the harder part: action. Dashboards and charts can highlight the issue, but they don’t fix a broken process or calm an angry customer. Humans decide whether a finding calls for retraining a team, adjusting marketing campaigns, or reworking content creation so it fits the brand voice. That translation step is where data-driven decisions become something customers can actually feel in their interactions with a brand.

Human oversight isn’t just quality checking or guarding against safety concerns. It’s what keeps customer interactions consistent, makes sure insights respect brand consistency, and ensures feedback aligns with lived experience. AI-human collaboration works best when machines surface the patterns and people choose how to respond.

The risks of AI customer feedback analysis imbalance

When AI customer feedback analysis happens doesn’t have human oversight, the results can be disastrous. Consider the following fictitious (yet highly probable) example. A national retailer trusted an AI tool to manage customer service emails. Within weeks, customers were getting canned replies that read like AI-generated content; they were technically accurate but cold and repetitive. Complaints about delivery delays were met with the same “thank you for your patience” line. It didn’t take long before social media lit up with screenshots. What started as a push for efficiency became a hit to brand perception.

Accuracy is another trap. Consider if a healthcare network used automation to update its clinic hours across listings and one error slipped through. Patients would show up to locked doors and, in turn, the calls that would follow wouldn’t just be angry; they would question the integrity of the whole organization.

That’s the human cost behind the data point we know: 53% of consumers say they won’t visit a business if its information is wrong. When quality standards slip, credibility takes a hit that’s hard to win back.

The opposite extreme, humans without AI customer feedback analysis, can be just as damaging. A customer service team that prides itself on reading every support ticket by hand means well, but with thousands of messages coming in each week, they’re constantly behind. Urgent complaints about billing are buried under general feedback. Marketing campaigns stall because customer insights arrived weeks too late. In the meantime, competitors moved faster, adjusting content creation and campaigns in real time.

There are also safety concerns that don’t always make headlines. Without human oversight, AI tools can mishandle data privacy, pulling personal details into places they don’t belong. Without machine support, humans lean on gut instinct and sometimes misread sentiment analysis, letting bias guide decisions. Either way, imbalance erodes confidence.

The bigger risk is erosion of confidence. Customers don’t care whether a misstep came from a machine or a person. They just know the brand didn’t listen. And once customer engagement feels transactional, business growth slows. That’s why balance matters.

AI-powered tools should speed up the work, but people need to shape the response, making sure it reflects the brand voice and respects the customer.

Building a human-AI feedback framework

At Forsta, we describe the balance between technology and people through our feedback flywheel: Gather, Analyze, Visualize, Act. It’s a framework designed to bring AI and the human touch together in customer feedback analysis. Each stage has a role for both machines and people.

Gather

The first challenge is scale. Feedback arrives from surveys, reviews, support tickets, social media posts, and more. AI tools powered by deep learning capture and categorize these inputs instantly, creating a foundation for predictive capabilities. But humans still guide what’s gathered, ensuring that sources align with brand strategy and quality standards.

Analyze

This is where artificial intelligence excels. Machine learning and predictive analytics can cluster themes, run sentiment analysis, and flag anomalies across vast datasets. But analysis without judgment risks blind spots. Humans add the context—recognizing motivation and intent, cultural nuance, or emerging ethical concerns—and decide which signals connect to business goals and which can wait.

Visualize

Insights only matter if people across the organization can actually use them. AI-powered tools generate dashboards and reporting, but humans shape how those findings are communicated. Translating data into marketing content, brand voice, or content creation for campaigns takes interpretation. This step ensures that visualizations don’t just show trends but tell a story that inspires customer engagement across the organization.

Act

Action is where AI-human collaboration becomes visible to customers. Predictive analytics can kick off automated workflows—speeding up replies in customer service or flagging needed changes in a campaign. People still oversee the response, adding empathy and keeping it consistent with the brand. They ensure AI-generated content feels authentic, not robotic, and that decisions align with safety concerns, data privacy, and the lived customer experience.

The Feedback Flywheel keeps turning. Gather, Analyze, Visualize, Act—then repeat. Each cycle sharpens both machine intelligence and human judgment, so data-driven decisions get faster, smarter, and more connected to the people behind the feedback.

Why balance builds HX

AI customer feedback analysis brings the reach. Humans bring the understanding. Together they create the trust that sits at the center of every Human Experience. Efficiency matters, but so does empathy. Customers expect both.

Forsta’s view is straightforward. HX isn’t about choosing sides, we don’t see this as AI vs. people. It takes AI to capture and analyze the constant flow of feedback, and people to interpret, prioritize, and act on what really matters. That mix is what turns feedback into insight and insight into action.

Our AI tools were built with this balance in mind. They’re designed to support human oversight, not replace it. They help teams listen at scale, find patterns quickly, and respond faster, while keeping empathy and accountability in human hands.

If you want to see how this approach can shape your brand strategy and AI customer feedback analysis, explore the Forsta HX Platform. Together, we can turn every voice into a better experience.

“Why can’t I use my promo code on this item?” 

“I’m looking for boots with a heel no higher than 2 inches, camel brown, real leather, comfortable, and under $150.” 

“My order’s delayed. Can I cancel it and get something else that’ll arrive on time?” 

These are just a few examples of how consumers engage with AI in their shopping experiences today. Whether it is personalized product discovery, chatting with AI assistants for support, or enjoying seamless omnichannel experiences powered by real-time inventory sync and dynamic pricing, – AI is everywhere. And it’s not going anywhere.  

When used responsibly, AI can streamline and elevate the shopping experience. But it also requires consumers to share a significant amount of personal information. That’s why the real question in this early era of retail AI isn’t if it’s used, but how it’s used. This holiday season will magnify both the benefits and the risks of AI in customer interactions—and the stakes have never been higher. 

How can retailers harness AI to enhance customer experience without compromising trust? In this post, we’ll discuss exactly that, to help your brand leverage AI in meaningful ways that enhance the retail customer experience at every stage. 

It’s go time: How AI powers retail’s holiday rush

The fourth quarter is retail’s most critical season, where speed, scale, and service can make or break customer relationships. AI offers powerful support in managing volume and accelerating response times, but retailers must balance automation with empathy, especially during a season that can be both exciting and stressful for shoppers.  

As digital tools become more sophisticated, so do consumer expectations. According to Forsta’s recent study on trust in the digital age, 59% of consumers expect a brand to respond within 24 hours, 67% expect a follow-up, and over 60% will walk away after just one or two poor experiences. Speed is essential—but quality and resolution matter just as much when it comes to earning and keeping consumer trust. 

The busiest time of the year is also the best opportunity to impress. Every chat, call, and interaction is an opportunity to deliver standout service. Shoppers share their experiences, and great promotions only go so far if the service doesn’t match. AI can help manage the surge, but it must do more than respond—it must understand, resolve, and support the conversation. 

The new front door: AI as the first touchpoint

Before customers even land on a retailer’s site, they’re increasingly turning to AI-powered assistants like ChatGPT.  

In fact, according to Modern Retail, ChatGPT has quickly become one of the most influential tools for product discovery, now driving significant referral traffic to major retailers like Walmart, Target, Etsy, and eBay.

As consumers increasingly click through AI-generated shopping recommendations, ChatGPT is emerging as a new gateway for how people find products and services online. With the recent launch of its own browser, Atlas, OpenAI is doubling down on making ChatGPT a central hub for search, shopping, and discovery—potentially reshaping the digital shopping journey once dominated by Google and Amazon.  

This shift has major implications for retailers. Traditional SEO strategies that focus on keywords and ranking are no longer enough. Retailers who ignore this shift risk falling behind.  To stay competitive, they must rethink traditional SEO strategies and embrace GEO (Generative Engine Optimization).  This means understanding customer intent not through keywords, but through context, scenarios, and natural language.  

Strong product data, rich service descriptions, conversational content like FAQs, guides, and blog posts, plus encouraging ratings and reviews; all of these matter. But it’s not just about volume. Quality, authenticity, and relevancy are what resonate with consumers. It’s one thing to show up in search results. It’s another to show up when it counts. 

Where AI shines and where it stumbles 

I’ve heard a paradox of phrases when it comes to AI from “this is the worst AI will ever” to “that is SO AI” to “is that AI?!”. It’s all evolving; AI is learning, we’re learning, and even the AI detectors are learning. One thing is for certain; consumers care that it works.  

AI excels at being predictable, helps with repetitive tasks but struggles with nuance, emotion, and complex problem-solving. It misses the mark on human touch and understanding. For example, AI could be profoundly useful in retail this holiday season for tasks like order tracking (“where is my order?”), product comparisons, return policy questions, and personalized product discovery. In fact, 48% of U.S. and 45% of UK consumers welcome AI-led customer experience for faster service according to the same report.   

But not everything is straightforward. Product issues, exchanges, or emotionally charged scenarios can overwhelm an AI assistant. Which is why today only 1 in 5 are “very comfortable” with AI alone.  

I recently experienced this firsthand. I contacted a major home improvement retailer about a product with next-day delivery clearly promised throughout the shopping journey—from product page to checkout to confirmation email. Yet the next day, I received a notice that delivery would take five business days. When I reached out via chat, the AI failed to grasp my concern, simply repeating the standard shipping window and ignoring the discrepancy. This is where AI fell short—not in speed, but in comprehension and resolution. 

The double-edged sword of AI: Power and responsibility 

Having led both store operations and ecommerce, I’ve seen how AI offers retailers immense potential to elevate customer service, craft high-converting promotions, personalized messaging for distinct customer segments, and ultimately drive sales and loyalty. But with great power comes great responsibility. Without thoughtful implementation, AI can erode trust and damage customer relationships. 

Every day, consumers make conscious choices about whether to share personal data with brands. Consider how often you’re asked to enter your birthdate—usually with the hope of receiving a birthday reward. Or the moment you input your phone number at checkout to access your loyalty account. These interactions reflect a broader trend: 69% of U.S. consumers and 64% of U.K. consumers are still willing to share personal data in exchange for better experiences. Data has become the new currency. But customers will only “spend” if the exchange feels secure, respectful, and worthwhile. 

That value exchange might come in the form of birthday perks, personalized loyalty programs, exclusive offers, subscription services that learn your preferences, or tailored product recommendations. Personalization drives action—30% of U.S. and 28% of U.K. consumers would consider switching brands for a more personalized experience. This is especially critical during the holiday season, when retailers must cut through the noise to win attention, drive conversions, and capture new customer data that fuels engagement into the new year. 

Yet despite their willingness to share, trust remains fragile. Only 22% of consumers trust retailers to use their data responsibly, and that number drops even further when it comes to generative AI tools like ChatGPT. That’s why transparency is non-negotiable. Retailers must clearly communicate when AI is being used, what data is being collected, and how it will be stored and applied. Building trust isn’t just good ethics—it’s good business. And that pays off: 71% of U.S. and 66% of UK consumers would choose a brand they trust with their data, even if it costs more. 

Human-AI teams shaping the future of retail

AI is already streamlining operations, automating routine tasks, optimizing inventory management, assisting with customer inquiries, and uncovering service improvement opportunities—delivering fast, tangible wins. While many of these wins happen behind the scenes, the next chapter of AI will increasingly blend human and machine interactions, creating seamless, often invisible experiences. The most successful retailers will harness AI’s efficiency while preserving human empathy and oversight. 

Striking the right balance between human touch and AI tools unlocks real advantages. Employee feedback and frontline actions can surface valuable insights, while AI can amplify these by identifying patterns and scaling solutions. When used responsibly and consistently across channels, AI can enhance customer benefits and loyalty. Importantly, 62% of consumers are willing to share their data—fueling these AI systems—when they receive meaningful value and improved experiences in return. Trust and transparency will be key to sustaining this exchange.  

5 Rules of engagement for responsible retail AI use

1. Use AI thoughtfully and transparently Deploy AI with clear intent and communicate openly when and how it’s being used — whether in customer service, personalization, or decision-making. 
2. Be clear about data practices Inform customers what data is being collected, why it’s needed, and how it will be used. Respect privacy and comply with all relevant regulations. 
3. Highlight the value to customers Explain how AI enhances the customer experience — faster service, smarter recommendations, or more personalized interactions and even rewards. 
4. Maintain human oversight Continuously monitor AI outputs and customer feedback. Ensure human intervention is available to resolve issues with empathy and care. 
5. Act quickly and communicate honestly If something goes wrong, respond swiftly and transparently. Own the issue, communicate clearly, and prioritize restoring trust. 

Winning the holiday season starts with earning trust

AI is here, and its adoption is accelerating across retail. When used thoughtfully—with the customer at the center—it can be a powerful tool for delivering faster, more personalized, and more efficient experiences. But trust isn’t automatically earned. Retailers who prioritize transparency, responsible use, and human-AI collaboration won’t just win the holiday season, they’ll build lasting customer loyalty. 

As with any emerging technology, the smartest path forward is to pilot, measure, and learn. Gather feedback from both customers and employees to understand the full experience and use that insight to refine and scale. AI can be a game-changer this holiday season, and its impact will only grow. Those who move quickly, but responsibly, will gain more than sales and efficiency; they’ll earn trust. 

Because the future of retail isn’t just automated. 

It’s trusted. 

Every day, customers leave a trail of unfiltered thoughts across the digital landscape: reviews, social posts, chat transcripts, and open-text survey responses. This unstructured feedback reveals more than a score ever could, as it shows how people really feel at each step of the customer journey.

For CX teams, this type of feedback is gold. Passive listening reveals honest, in-the-moment perspectives that scripted feedback often misses. And with the help of sentiment analysis, those raw words transform into powerful signals about customer satisfaction, friction points, and unmet needs.

Unstructured data may look messy from the outside, but in the right hands it becomes a clear, continuous feed of insight, fueling decisions that make customer experience stronger at every turn. To see how, let’s first define what passive listening really means and why it matters for CX leaders.

What is passive listening in CX?

Passive listening is hearing what customers say without prompting them to respond. Instead of asking for structured survey answers, CX teams tune into the organic conversations already happening

Think about the everyday places where people share their experiences:

• Review sites like Google, Yelp, and Trustpilot
• Social media chatter that spreads fast and wide
• Chat logs and customer support tickets
• Open-text survey responses that go beyond rating scales
• Contact Center transcripts filled with raw emotion and urgency

These are all unstructured streams of customer data. They capture the real Voice of the Customer; that is, the things people say when they’re not filling out a feedback form but simply trying to be heard.

That’s the key difference between active and passive listening in CX. Active listening comes from structured surveys, focus groups, or interviews. Valuable, yes—but it’s responses framed by the questions you choose to ask. Passive listening pulls directly from organic conversations, revealing new patterns in customer engagement, unmet customer needs, and hidden drivers of customer satisfaction.

When combined with tools like generative AI and advanced analytics, this unsolicited customer feedback stops being noise. It turns into clear guidance for customer service teams, product leaders, and anyone tasked with improving the customer experience.

Why passive listening matters more than ever

Customers don’t wait for a survey to share what’s on their mind. They post reviews before they’ve even left the parking lot. vent on social media mid-experience or share frustrations with call center agents they’d never write in a feedback form.

Unsolicited comments carry an authenticity that’s hard to manufacture. They show the moments that spark joy, the frustrations that lead to customer churn, and the small service failures that chip away at customer loyalty. And because these signals come in fast and at scale, they give brands a running view of customer satisfaction long before quarterly reports roll in.

The advantage is clear: companies that know how to decode unstructured data see blind spots before they become breaking points. They spot sentiment shifts early, improve customer service in real time, and design experiences that strengthen customer loyalty.

For CX teams serious about elevating customer experience, passive listening has gone from nice-to-have to mission-critical.

The challenges of unstructured CX data

Listening passively may sound simple, but unstructured data quickly tests the limits of most organizations.

Volume

Thousands of review site comments, support chats, and call transcripts pile up every week across platforms. Add in social chatter and open-ended survey responses, and CX leaders are suddenly inundated with information they can’t easily organize.

Complexity

Language is nuanced Sarcasm, frustration, joy, and tone don’t translate neatly into numbers. A customer might give you a high Net Promoter Score but leave a review that tells a different story. Without the right analysis, those contradictions slip through the cracks.

Silos

Reviews live on public platforms, support conversations sit in ticketing systems, and call transcripts stay locked inside the Contact Center. When CX teams can’t connect those dots, they miss the bigger picture of the customer journey.

The challenge isn’t that customers aren’t talking, but that most brands don’t yet have the systems to listen well enough. Traditional dashboards weren’t built for this kind of work. Spreadsheets and dashboards can summarize scores, but they can’t interpret free text at scale.

That’s where AI technology comes in, to parse massive amounts of raw feedback, highlight themes, and even generate action plans that give leaders a clear path forward.

Turning unstructured feedback into structured insight

Unstructured data feels messy, but with the right approach, it becomes actionable and gives CX leaders clarity to act on. The process is straightforward: centralize, analyze, and act.

Step 1: Centralize

Pull reviews, support tickets, call transcripts, and feedback forms into a single hub. When customer listening happens in one place, patterns across the customer journey become visible. CX automation ensures nothing gets lost, whether it’s a frustrated post on social media or a quiet comment buried in a Contact Center transcript.

Step 2: Analyze

With everything in one view, feedback analysis tools powered by AI do the heavy lifting.

• Text Analytics detects sentiment, recurring themes, urgency, and even subtle emotion and intent. A fast-casual Mexican restaurant, for example, uncovered service bottlenecks hiding in its review site comments, insights it would have missed with surveys alone.
• AI Open Assist extracts key ideas from qualitative input, surfacing the customer needs behind the words.
• AI Summarize condenses pages of support chats or call logs into crisp takeaways, accelerating decision-making for busy teams.
• Generative AI also plays a role, classifying complex feedback and blending it with behavioral data for deeper context. Real-time analytics let CX programs move from passive reporting to real-time listening.

Step 3: Act

Insight without action is just noise. CX leaders use these findings to design better customer support flows, strengthen service training, or launch improvements that directly close the loop. Action plans shaped by AI tools ensure improvements stick and satisfaction grows.

CX leaders are moving from quarterly sentiment analysis to real-time analytics. Instead of waiting weeks, organizations now identify churn risk in minutes and intervene before loyalty is lost.

When you get it right, feedback doesn’t just describe what happened; it becomes your playbook for shaping what happens next.

Forsta’s approach to passive listening

At Forsta, passive listening turns scattered customer feedback into a clear path for growth. Our HX platform was built to help CX leaders cut through complexity and act with confidence.

• An AI-powered Text Analytics engine digs into nuance, detecting tone, intent, and shifts in sentiment that drive customer experience forward.
• Integrations with major review platforms and social listening tools pull in the full Voice of the Customer, from Google Reviews to social media streams.
• Visualization tools make it easy to map themes over time, so CX teams see how customer engagement evolves with every touchpoint.
• We see everything through an HX lens that ties passive data back to real people, linking unstructured words to the human context behind them.

With Generative AI and other advanced AI tools, Forsta’s platform goes beyond dashboards. It classifies feedback, extracts meaning, and accelerates action planning to help customer service teams, product managers, and marketers align on the improvements that matter most.

Early adopters like GNC are already seeing the impact. Using Forsta’s AI Summarize tool, GNC reduced open-ended analysis time by 90%, giving teams faster visibility into customer behaviors, preferences, and pain points. That speed means decisions are made with sharper insight, and customer-centric strategies get implemented before issues turn into churn.

From listening to action: Practical steps for CX leaders

This is your moment to turn passive listening into purposeful action. The following framework will help you to build a CX program that’s less about dashboards and more about meaningful impact.

1. Audit your current sources of unstructured feedback

Begin with a reality check. What is your CX program already catching—and what’s slipping through the cracks? Examine reviews, social listening streams, support tickets, Contact Center transcripts, open-text survey responses, even chatter in internal feedback forms. That’s your raw customer feedback.

Then ask: Who owns each channel? What tools do they use? How fast can you act on what comes in? This audit reveals gaps in insight—and the places where cross-functional alignment can begin.

2. Blend passive and active listening strategies

Customer sentiment doesn’t only surface in unsolicited review site comments or social media posts; it also lives in structured feedback from surveys. Blend the two. Use active feedback to probe deeper on themes spotted through passive listening and let passive signals reveal blind spots active listening might miss.

That kind of harmony across your customer journey map gives you a more complete, authentic, and timely picture of what’s really moving the needle in customer satisfaction.

3. Champion cross-functional collaboration

Don’t let Voice of the Customer stop with CX teams. Pull in marketing, product, operations, and even frontline Customer Service staff. Shared ownership of feedback turns insight into action. Imagine support tickets triggering a product tweak. Or social listening spotting a campaign issue before it becomes a PR problem.

Teams that own feedback together deliver smoother journeys and build stronger customer loyalty.

4. Invest in tools that go beyond dashboards to real insight

Your CX program needs more than charts and colors on a screen. Look for AI-powered platforms that offer real-time listening—not just historical dashboards. A platform like Forsta’s with Generative AI features like AI Summarize, AI Recommend, and AI Compose help you surface key themes, generate actions, and respond with empathy and speed.

That’s how you unlock operational CX, not just reporting. Forsta’s AI enhancements, built into the HX Platform, accelerate insight-to-action and empower teams to anticipate customer needs, even before dissatisfaction rises.

5. Make listening part of how you operate, not just what you report

Passive listening only makes impact when it’s woven into your rhythm—stand‑ups, planning cadences, and frontline coaching. Send alerts when sentiment turns negative. Equip CX Leaders and Customer Support with real-time insight dashboards tied to action workflows.

We’ve said it before when talking about operationalizing CX, “Insight without action is inertia” and CX must transform from measurement to momentum to drive revenue growth. Embed outcomes, not just metrics, into how decisions get made.

6. Lean into customer-centric strategies powered by AI

Human-centered tools still need horsepower. Use emotion and intent analysis (powered by AI technology) to decode tone and urgency across behavior and sentiment signals. That gives you next-level context.

For example, identify churn risk from subtle shifts in contact center transcripts, with speed that lets you intervene before customer loyalty kicks the door open. Real-time analytics and behavioral data combined help make listening active and deeply actionable.

Your next step in CX

Customers never stop talking. The question is whether organizations are ready to listen and act with speed and empathy. Every review, transcript, and social post holds clues to what drives loyalty and what pushes customers away. Passive listening, powered by AI, turns that constant stream of unstructured feedback into a competitive advantage.

For CX leaders, the opportunity is clear: shift from reporting to real-time action, from fragmented data to a connected view of the human experience. Those who embrace this shift will see stronger loyalty, reduced churn, and measurable business impact. Those who don’t will be left guessing.

Forsta helps you make the shift. By transforming unstructured feedback into clarity and clarity into action, the HX Platform empowers organizations to design experiences that grow relationships and revenue. The future belongs to brands who listen—not just loudly, but wisely.

Discover how Forsta can help you turn feedback into impact—explore our unstructured data solution or book a demo today.

As retailers eagerly race to embrace digital innovation, many are unintentionally abandoning the very thing that drives their bottom line: trust. And without trust, retailers won’t gain the competitive edge necessary to leave a lasting mark in a highly crowded industry.

In a hyper-connected world, customer experience in retail is under the microscope. Every checkout page, online search, app experience, and store visit adds up, leaving minimal room for error across myriad channels. When even one customer experience falls short, even just a little bit, consumers don’t just notice and give you a pass. They leave your retail brand behind in favor of a competitor.

Building trust in retail isn’t a passing trend. It’s a strategic inflection point. Retailers are being pulled in two divergent directions driven by generational expectations. The days of only viewing customer experience (CX) as a transaction are long gone. A human-centered model reaps the greatest rewards, helping retailers gain both loyalty and profitability.

At Press Ganey Forsta, we believe the answer to impactful and meaningful CX lies in Human Experience (HX). This approach to CX fosters a smarter, more inclusive CX: one that listens, adapts, and delivers with intention and human interests in mind.

In this post we’ll reveal key retail findings from our 2025 CX trust deficit report and what it takes to drive customer loyalty in the retail realm in 2025.

Cracks in the loyalty curve

Our own original research in partnership with Watermelon paints a clear picture: loyalty today is conditional. And the stakes are highest in the retail sector. We found gaps in generational expectations, which highlights the need for a holistic CX, that considers varying age groups that may be shopping for your products.

• Gen Z consumers are leading the charge for change. Digital by default, they switch brands fast. In fact, our research shows 63% of US and 62% of UK Gen Z shoppers say they jump ship after just one or two poor experiences. The takeaways are clear: Gen Z shoppers are less forgiving and expect seamless, tailored interactions. When any semblance of friction, confusion, or generic engagement shows up, they’re gone.
• Older generations, on the other hand, are often more loyal, but they’re being left behind. These customers prefer traditional touchpoints and human interaction. Many are disengaging because they feel excluded by digital-first strategies that don’t reflect their needs or preferences.

This widening disconnect is costly. As evidenced by our research, fragmented journeys are draining the bottom line:

• UK retailers lose an estimated £3.1 billion annually to poor CX
• In the US, that figure climbs even higher to $191 billion

Retailers must ask: What’s the cost of not listening? And what’s the payoff when we do?

Delivering personalization that feels personal

Retail shoppers today expect personalization. But they don’t want gimmicks—they want relevance. Our research shows:

• Shoppers will share data, but only if they see value in return.
• Retail consumers crave experiences that anticipate needs, respect boundaries, and eliminate unnecessary steps.

For many retailers, the challenge isn’t access to data but rather how to use it meaningfully. That means designing experiences around actual human behavior, not marketing assumptions.

At Press Ganey Forsta, we help brands move beyond personalization as a checkbox. Our HX platform uses behavioral data, multichannel feedback, and AI-enhanced insights to power real-time journey refinement. It’s personalization with a purpose—where every touchpoint is smarter, smoother, and more aligned with customer intent.

It also means respecting privacy. Our approach supports:

• Ethical data collection
• Consent-driven feedback loops
• AI that enhances, not replaces, the human experience

When personalization works, it feels effortless. We help ensure it always lands on the side of trust.

Small frictions, big fallout

Loyalty doesn’t unravel all at once. It erodes in the small moments. Consider the following scenarios and step into a retail customers shoes:

• Using a retailer’s mobile app that crashes mid-checkout
• Checking out at a self-service terminal that doesn’t scan properly
• Trying to use a promo that works online but doesn’t ring up in-store

These seemingly minor issues are loyalty killers. Our data shows:

• 38% of UK shoppers and 32% of US shoppers have abandoned purchases due to app malfunctions
• 36% of UK and 33% of US customers switched brands after a bad self-checkout experience
• Nearly 40% of shoppers would walk out the store if faced with long checkout lines

Yet most of these pain points go unreported. Why? Because traditional CX programs aren’t built to capture unstructured feedback at scale.

Press Ganey Forsta changes that. Our platform synthesizes data from both structured and unstructured data such as reviews, chat, surveys, and social media to surface patterns, pinpoint issues, and eliminate friction. We turn noise into signals and signals into action.

The result? Fewer lost sales. Smarter operations. And a loyalty curve that trends upward.

Trust and transparency in the AI era

Retailers are leaning heavily into AI—from chatbots to dynamic pricing to product recommendations. But while the tech is impressive, trust is fragile.

• 40% of UK consumers and 38% of US consumers say they’d lose trust—or stop shopping entirely—if they discovered AI was used without clear disclosure.

Consumers don’t reject AI outright. They just want to know when it’s in play. And more importantly, they want to see that it serves them, not just the brand’s efficiency goals.

Press Ganey Forsta helps retailers take an ethical, transparent approach to AI adoption:

• We support clear labeling and disclosures
• We embed ethical principles into AI governance
• We focus on enhancing, not replacing, the human touch

When AI is deployed with care and context, it becomes a value-add. When it’s hidden, it becomes a trust risk.

The power of HX: Reconnecting with every customer

To rebuild trust and retain loyalty, retailers need to go beyond CX. They need to adopt HX: Human Experience at every touchpoint.

HX is about seeing customers not as transactions or personas, but as people. It means:

• Listening across every touchpoint
• Understanding not just what customers do, but why
• Integrating emotional, behavioral, and contextual data

Press Ganey Forsta enables this holistic view. We help retailers map journeys that reflect real human behavior—from the first click to the final receipt.

HX also helps brands:

• Resolve the generational gap
• Balance automation with empathy
• Foster brand relationships built on relevance and respect

When a brand truly listens, it doesn’t just improve experiences. It creates loyal advocates.

See what customers are really telling you

Want to know how your brand measures up? Ready to bridge the generational gap and re-earn trust?

Download the 2025 CX trust deficit report and discover what it takes to deliver human-centered retail experiences that last.

As financial services continue to rapidly accelerate toward digital transformation, a new challenge has emerged: earning (and keeping) trust across a multigenerational customer base. While digital banking promises speed, convenience, and scale, it also introduces friction.

Younger consumers expect hyper-personalized, tech-powered experiences. However, older generations want clarity, security, and access to human support. The result? Different needs, rising expectations, and a widening trust gap.

At PG Forsta, we believe closing that gap requires more than technology. It demands a commitment to Human Experience (HX)—powered by deep listening, ethical AI, and meaningful action.

Financial institutions that elevate HX can transform customer relationships, foster loyalty, and navigate the digital future with confidence, as evidenced by our 2025 CX trust deficit report, developed in partnership with Watermelon.

A loyalty shift in motion

Banks continue to hold a trust advantage. In both the UK and the US, consumers still think of banks as the most reliable custodians of their personal data. In fact, our research shows 81% of UK consumers and 69% of US consumers trust banks to safeguard their information. But the landscape is shifting. A generational divide is creating new complexities in customer experience (CX).

Younger generations, digital natives aged 18 to 24, are at the forefront of change. Raised on digital-first interactions, they expect tailored, intelligent experiences.

• 57% of younger US consumers and more than 75% of their UK counterparts say they would switch providers for better personalization.
• But here’s the twist: only 59% of these same consumers trust their current financial services providers with personal data.

This paradox, demanding personalization while questioning data usage, highlights the clearly fragile nature of trust. Banks must prove not only that they can deliver value, but also that they can do so transparently and ethically.

Meanwhile, customers over 65 present a different challenge. This group is largely loyal and trusting, 81% of them express high levels of confidence in their banks. However, many feel excluded by the shift toward digital-first services. They prefer face-to-face conversations, clear language, and direct channels of communication. The sense of disconnection they feel can be subtle but deeply impactful. It threatens both customer satisfaction and retention.

This growing disconnect highlights a key insight: CX strategies can no longer take a one-size-fits-all approach. Financial institutions must learn to listen and respond in ways that align with each generation’s needs, values, and comfort levels.

Turning listening into loyalty

Trust in banking requires more than just gathering feedback. It demands being proactive and making strides toward addressing CX issues. Consumers today don’t want passive acknowledgment but rather real, demonstrable change. They want to feel understood and heard.

That means closing the loop on feedback, addressing concerns quickly, and most importantly, showing customers how their input shapes outcomes. This responsiveness builds a powerful foundation for trust.

Our research shows that this is especially true in the digital era. While consumers may embrace digital tools, they also seek reassurance that someone is listening behind the screen.

• In the US, more than 55% of consumers say they have recently sought out human support in banking.
• In the UK, that number stands at 40%, revealing a widespread desire for human contact, even in a tech-driven environment.

Press Ganey Forsta’s HX platform helps financial services brands meet this need head-on. By capturing feedback in real time and translating it into actionable insights, banks can resolve issues faster, personalize services more effectively, and demonstrate a tangible commitment to customer care.

The result? Greater satisfaction, stronger retention, and a brand that customers know they can trust.

Trustworthy AI starts with transparency

Artificial Intelligence (AI) is transforming financial services. From automated customer support to AI-powered recommendations, the technology is becoming integral to everyday banking. But trust in banking and AI doesn’t come easy.

Consumers want to know when and how AI is used. They want clarity around the role it plays in decision-making. And above all, they want assurances that AI is being used ethically and responsibly.

• 80% of consumers in both the UK and the US say banks should clearly disclose when AI is involved in financial decisions.
• Nearly 40% say their trust would decrease, or they would stop doing business altogether, if AI were used without their knowledge.

This isn’t just a technology issue. It’s a human one. Customers aren’t rejecting AI outright. They’re asking for transparency and respect.

With the right approach, AI can become a trust-builder, not a trust-breaker.

Bridging the generational trust divide with HX

The road to trust in banking doesn’t start with technology. It starts with empathy.

Understanding what matters to customers, such as their fears, motivations, and preferences, is key to creating experiences that feel relevant and reassuring.

This is where HX becomes a strategic advantage for financial services brands. By moving beyond basic demographics and tapping into behavioral and contextual data, financial institutions can deliver journeys that feel intuitive, personal, and respectful, irrespective of the customer’s age.

With PG Forsta’s solutions, banks can:

• Listen at scale. Collect feedback across digital, voice, and in-person channels.
• Segment by behavior. Tailor journeys based on needs, habits, and emotional drivers.
• Act with precision. Use insights to inform product development, support, and communication strategies.

These capabilities are essential for bridging generational gaps. A 25-year-old and a 70-year-old may use the same mobile app, but they navigate it with different expectations and goals. A human-centered approach helps banks recognize these differences and meet each customer appropriately.

The role of ethical personalization

Personalization isn’t optional, it’s a baseline expectation. But it must be earned through ethical data practices.

Consumers are willing to share personal information if they believe it will be used to create better experiences. But they need to understand the value exchange. They want to fully understand what they are giving up and what they are getting in return.

Transparency is the linchpin. When banks clearly communicate how data is used—and deliver on the promise of better, safer, more relevant service—they can turn personalization into a trust multiplier.

Key principles of ethical personalization include:

• Consent and control. Give users the ability to opt in, opt out, and update preferences easily.
• Clear value propositions. Explain how data sharing benefits the user.
• Privacy-first design. Embed security and compliance into every interaction.

Redefining success: Going beyond metrics

In an industry defined by numbers, it’s easy to focus on KPIs and benchmarks, but trust in banking can’t be measured in isolation. It lives in how customers feel and how they perceive a brand’s intent.

This is where HX turns insights into impact. By layering quantitative data with qualitative feedback, financial institutions can move beyond satisfaction scores to understand the “why” behind behavior.

• Why did a customer abandon their application?
• Why do older users avoid a mobile feature?
• Why do younger consumers hesitate to recommend their provider?

These answers aren’t always in the data. They can be found in the stories, sentiments, and experiences that technology like ours helps bring to the surface.

We help our financial services partners reframe success around trust, relevance, and emotional resonance. Because when a customer trusts you, they’ll not only stay but advocate for you.

Start building the future of trust in banking

Generational divides are real, but they’re not insurmountable. With the right tools and mindset, financial institutions can deliver experiences that resonate across age groups, drive long-term loyalty, and strengthen their place in an evolving marketplace.

It all starts with listening, acting on insight and seeing every customer as a person first.

Explore the insights driving trust in banking and financial services brands in 2025.

Download the 2025 CX trust deficit report now.

Collecting feedback doesn’t improve the customer experience. Acting on it does. That’s where too many programs stall. Not because feedback is missing, but because insights aren’t put to work in meaningful, human-centric ways that drive real process improvement.

In fact, according to Forsta’s original research, only 1 in 5 consumers feel that brands actually act on the feedback they collect.

This isn’t a beginner’s guide. It’s a gut check for CX leaders ready to build a Voice of Customer (VoC) program that works across the customer journey, across the business. In this post, we’ll explore three common VoC mistakes to avoid ensuring your customer experience program is impactful and successful.

Mistake #1: Treating VoC as a data collection initiative instead of a strategic operating system

The trap: Starting with surveys, sentiment analysis, or social media listening before agreeing on why you’re listening in the first place.

A lot of Voice of Customer programs kick off with good intentions and a stack of NPS surveys. The marketing team runs one, customer service launches another, maybe operations spins up a dashboard. Without shared goals, those surveys become siloed snapshots. Data piles up. Direction vanishes.

Why it fails: Collecting customer data without purpose leads to disjointed views, duplicated efforts, and what we call initiative fatigue. Teams focus on higher scores instead of solving problems. The CX Center becomes a reporting hub, not a driver of change. Seeking out customer feedback is one thing—but applying it? That’s what earns favor from 77% of customers, according to Microsoft.

A better way forward: Flip the script. Don’t start with tools; start with intent.

Anchor your VoC program in experience goals tied directly to business outcomes: customer lifetime value, retention, revenue, reputation. Align teams around the customer journey, not internal org charts. Make it clear how every piece of feedback—whether from a call center, chatbot, or social feed—connects to real improvement.

VoC isn’t a listening tool. It’s your experience operating system; the core of how you act, improve, and grow. It’s the engine that turns listening into action. Insight into impact. And scattered touchpoints into a consistent, human experience that actually moves the needle on customer satisfaction.

Real-world results:

Just look at Cognita Schools, a global network spanning 100+ schools across 16 countries. They didn’t start with surveys; they started with strategy. Embedding VoC into daily decision-making using Forsta’s HX platform helped them turn feedback into foresight.

With real-time dashboards and a retention risk analysis tool in place, they improved parent satisfaction scores by 30% over two years. That’s VoC as an operating system to drive action, not just answers.

Mistake #2: Designing for departments, not people

The trap: Once VoC is treated as strategic, the next challenge is organizational design, especially avoiding siloed ownership. Building your VoC program around the org chart, where marketing owns surveys, ops owns support data, and the CX Center tries to stitch it all together.

That structure may look tidy on paper, but customers don’t care who owns which dashboard. They just want a consistent, human experience. One that feels seamless no matter the channel, the challenge, or the touchpoint.

Why it fails: When departments act in isolation, you get competing priorities, inconsistent data points, and a journey that feels more like a maze than a map. Standard operating procedures take precedence over customer-centric strategies. And valuable feedback from customer support interactions often gets buried or outright ignored.

A better way forward: Start with the people, not the process.

Use the journey mapping process to connect feedback to real moments that matter. Map emotions, expectations, and friction points across the user experience map, not just departmental handoffs. Then go deeper with persona-driven listening, so you’re not just capturing what customers say, but understanding why they say it. What’s urgent, what’s emotional, and what’s driving real customer engagement.

It also means thinking beyond channels. In a multichannel world, a single experience can span chat, phone, email, and in-person touchpoints—all in the same day. Your survey design and listening approach need to reflect that.

This is where Human Experience (HX) matters most.

Because real transformation takes cross-functional accountability, not isolated action plans. And increasingly, it takes smart tech, like Artificial Intelligence (AI), to connect the dots at scale. From trend detection to emotion analysis, AI can elevate listening from transactional to transformational.

Design for people. Align across teams. And let the voice of your customer lead the way.

Global proof point:

DHL Global Forwarding tackled the silo problem head-on. Using Forsta Visualizations, they unified feedback from NPS programs and deep-dive surveys across 68 countries and 1,500+ colleagues. As a result, they got a single, shared view of the customer journey that supports faster issue resolution and smarter decisions. No more feedback silos. Just one voice of the customer, heard loud and clear across the business.

Mistake #3: Confusing real-time noise for actionable insight

The trap: Speed gets mistaken for value. Dashboards light up with real-time customer feedback, but without context, they create more confusion than clarity.

Teams end up scrolling through streams of customer interaction data, unsure what to prioritize. Customer service flags every issue. Customer Success doesn’t know where to step in. The content strategy team sees a spike in sentiment but no direction on what to fix.

Why it fails: When insight isn’t organized or operationalized, it doesn’t scale. You end up with a reactive culture. Lots of alerts, very little action. And over time, that noise erodes customer satisfaction, not improves it.

A better way forward: Slow down to speed up.

Use AI-enhanced signal detection to cut through the clutter. With tools like AI Summarize, dashboard filtering, and trend analysis, you can surface the most urgent patterns without losing the big picture.

Then get specific: What are the root causes? Where’s the friction in the customer journey map? Which survey questions need refining to better capture what matters?

This isn’t just about metrics. It’s about creating momentum.

With intelligent alerting, predictive tagging, and root cause clustering, teams can move from passive observers to active problem-solvers. becomes the catalyst for smarter decisions and tangible improvements., not just a red flag.

And because every customer interaction ties back to the larger Voice of Customer strategy, you’re not just reacting to moments. You’re shaping the full end-to-end experience map. From first click to follow-up call, every touchpoint contributes to a better customer experience—and stronger customer engagement.

Results that matter:

eir Large Business didn’t flood dashboards. They focused. Partnering with W5, they built a Voice of Customer program on Forsta’s platform to get closer to what really drives customer relationships. Within a year, they cut churn by 1.5% for voice services. Saved €624k in annual revenue. And lifted NPS by 24 points. That’s what happens when insight gets used and not just stored away.

From listening to leading

A successful VoC program isn’t defined by how many tools you deploy. It’s measured by the transformation that follows across customer service, across the customer journey, across your entire business.

Collecting customer feedback is just the start. What sets leaders apart is how they use it. From online reviews to open-text responses, every signal—when understood through Natural Language Processing and mapped to the customer journey map—can drive real change. Higher customer satisfaction. Sharper decisions. Smarter action.

So, where are you in your journey? Are there any VoC mistakes you can avoid?

Take a moment. Look at your current program. Is it built to listen? Or built to lead?

Explore how Forsta helps teams turn feedback into momentum and bring Human Experience (HX) to life across the entire customer experience.

Imagine this, you’ve just had an accident. You reach for your phone, only to get stuck in a maze of outdated forms across digital channels, long hold times, and robotic scripts. Stress turns into frustration.

That’s the old way. And policyholders aren’t having it anymore.

They expect insurance to work like everything else in their lives: fast, intuitive, and human. They want service that remembers who they are, not just what they’ve bought.

Leading insurers are listening. Not just to complaints, but to context. Not just once, but always.

They’re trading clunky transactions for real conversations, powered by active listening and smart tools like Insurance HX.

The CX shift in insurance: From frustration to empowerment

Most insurers say they listen. But real listening means more than skimming survey scores or checking off compliance boxes.

Active listening is different. It’s real-time, razor-focused feedback that fits the moment. It’s short, specific, and personal. Not the 15-minute post-call survey. Not the blanket form sent days after the journey ends.

Here’s why that shift matters now:

• Traditional methods are failing. Response rates are falling fast. Customers are skipping long forms, closing tabs, and moving on.
• Regulators are watching. Transparency, fairness, and timely service aren’t just nice, they’re mandated.
• Fintechs are circling. Digital-first challengers are winning hearts with speed, empathy, and simplicity.
• Expectations are sky-high. Consumer behavior is changing. People want to feel heard right now, not remembered after the fact.

Active listening builds something deeper: trust.

And in insurance, trust isn’t a bonus but the whole brand promise. It’s what keeps policyholders around for years, not months. It’s how you show empathy when they’re at their most vulnerable.

It’s also a smart way to stay ahead. Insurers that listen at every step—onboarding, updates, claims—solve issues faster, personalize better, and boost customer satisfaction.

Because when you hear what people actually need, you can give them a seamless experience that feels designed just for them.

What is active listening and why does it matter now?

Insurance isn’t a one-time transaction. It’s a journey; one where every step holds a chance to listen, learn, and lead.

Forget waiting until the end to ask how things went. Leading insurers are weaving listening into the digital experience itself:

• Micro-moment check-ins during simple actions, like uploading documents or tracking a claim, ask quick, relevant questions without slowing the customer down.
• Targeted surveys after major moments, like policy adjustments or a claim approval, capture emotion while it’s still fresh.
• Streamlined end-of-journey prompts gather overall impressions with minimal effort.

You end up with an always-on feedback loop that’s invisible when it needs to be, and invaluable when it counts.

This approach respects your customer’s time and context. It meets them in the moment—on their screen, in their flow—not days later in their inbox.

It’s what turns disconnected digital interactions into strong, human-centered customer relationships and drives transformational insights into customer behaviors.

Want more smart ways to gather feedback? Explore these 11 creative ways to gather customer feedback.

How AI turns feedback into faster action

Gathering feedback is just the start. Turning it into action is what separates digital insurance leaders from the rest.

Today’s insurers face nonstop pressure to deliver a smoother customer experience, across every channel. That means using AI not just to listen, but to learn and respond.

Here’s how it works in practice:

• Prompt better feedback: AI-guided prompts encourage customers to explain issues in their own words, so you get richer, more relevant insights and not just box-ticked answers.
• Adapt surveys in real time: If someone’s struggling during a claim or policy update, the questions shift instantly—keeping the interaction intuitive and human.
• Understand open comments at scale: Forsta’s HX platform uses natural language processing to analyze feedback from every digital interaction, spotting trends, pain points, and shifts in sentiment, all without manual tagging.
• Detect risk before it spreads: From churn signals to customer vulnerability, AI surfaces what matters most and routes it where it needs to go (and fast).
• Automate the next step: Smart workflows built into Insurance HX move insights into action: alerts to service teams, recommendations to customer service agents, or dashboards to leadership.

Forsta’s connected platform makes this all possible.

How active listening redefines the insurance customer experience

Conversational CX isn’t just a feel-good upgrade; it’s a performance engine. When insurers listen actively and respond in the moment, the ripple effects touch every part of the business.

Start at the front lines. Claims processing evolves from a basic transaction and becomes a moment of trust. When feedback is captured during key tasks, service teams gain the clarity to deliver faster resolution and more efficient service. No more guesswork. No more repeat calls. Just quick resolution through human interaction, powered by intuitive interfaces and mobile experiences customers actually want to use.

From frontline insight to enterprise impact

Zoom out, and the impact scales. Active listening fuels advanced data analytics that uncover what’s working, what’s failing, and where customers are losing confidence. CX leaders get a real-time read on friction points across the customer journey. Operations teams gain tools to tailor services, detect fraud faster, and optimize claims workflows. Compliance and risk teams get early signals, before small issues become regulatory headaches.

It’s a shift from disconnected moments to a connected insurance customer experience. From generic service to personalized services. From reactive fixes to proactive customer experience optimization.

With platforms like Forsta’s Insurance HX, this isn’t theoretical. It’s happening now.

Leaders are using real-time feedback and customer data analysis to reduce cost to serve, increase policyholder satisfaction, and build cohesive customer journeys that hold up under pressure and scale with ease.

Because when your service feels personal, customer retention grows. When your insights are sharp, your strategy is stronger. And when your tools are built for action, the entire business performs better.

One insurer making this shift real is Erie Insurance. After nearly a century in business, Erie Insurance launched an online Voice of the Customer program with Forsta to transform how it gathered and acted on claims feedback. By automating surveys tied to specific claim types, ERIE gained real-time insight into customer needs and expectations.

Barbara Lincoln, Director of Customer Integration & Satisfaction, shares how this helped Erie Insurance evolve from a service-centric to a truly customer-centric culture—with support from leadership and impact felt across the business. Watch her story here.

Insurance HX: Built for insurance. Built for change.

Insurers don’t have time to build CX systems from scratch. They need tools that are purpose-built, proven, and ready to flex fast.

That’s where Insurance HX delivers.

It’s not a generic platform dressed up for insurance. It’s a pre-built, customizable solution designed specifically for the complexity and urgency of insurance customer experiences. From onboarding to claims to policy changes, it helps brands listen actively and act instantly.

Every touchpoint is covered. With a rich library of ready-to-use surveys, teams can gather feedback without slowing the journey. Dashboards are tailored by role so agents, managers, and executives each get what they need, no filtering required.

Behind the scenes, pre-trained AI models decode open-ended feedback, flag risk, and highlight moments that matter. Whether it’s churn signals or service breakdowns, you’ll see it before it snowballs.

And the engine behind it all? Forsta’s SmartHub. It connects customer feedback with operational data, building a full view of each customer—and giving you the power to personalize, optimize, and scale.

Insurance HX isn’t just fast to deploy. It’s built to grow with you. Because today’s expectations won’t wait. And neither should your CX.

Want to see what this looks like in the real world? Watch this short video to hear how

How PHLY turned feedback into faster action

Philadelphia Insurance Companies (PHLY) wanted to go beyond collecting feedback—they wanted to act on it in real time. With Forsta’s Insurance HX, they launched case alerts for low NPS scores, empowering teams to follow up fast. Dashboards tailored to departments made CX insights more actionable across the business.

PHLY also rolled out Text Analytics to decode sentiment and spot themes at scale. One major result? A brand-new billing experience built entirely from customer feedback.

The result? A more responsive, proactive, and customer-focused experience—backed by a platform that evolves with their needs.

Ready to lead the CX evolution?

Insurance customer experience has entered a new era. What used to be reactive and rule-bound is now becoming proactive and people-focused.

The shift is clear. Leading insurers aren’t just responding to issues. They’re building relationships with policyholders, using insight to guide every decision. Insurance customer experience isn’t a cost center—it’s a strategic advantage.

This starts with a mindset shift: every interaction is a chance to connect, not just complete a task. When feedback flows through the journey, service becomes smarter. Trust grows. Customers stay.

Want to turn customer feedback into business growth?

Download the eBook to discover how Insurance HX helps you gather richer feedback, act faster, and deliver standout experiences at scale.

Well-designed dashboards are essential to moving a CX program from passive insight collection to real, measurable action. When done right, they don’t just report—they motivate. They help teams see what’s working, what’s not working and where to focus. To be truly effective, dashboards should be easy to use, action-oriented, and designed with the end user in mind. They should guide the viewer from observation to action in a clear, concise flow—removing ambiguity and delivering value at a glance. 

This guide outlines seven core principles for creating CX dashboards that are impactful, intuitive, and relevant to stakeholder needs and objectives. 

1) Start with clear business and user goals 

It is vital that dashboards are relevant and actionable to end-users. Data should be presented in a way that is tied to corporate, team and individual objectives such as using KPI’s (key performance indicators), employees should be enabled to not only make more data-driven decisions but also enhance productivity and target actions towards improving customer experience.  

Every dashboard should begin with a clear purpose. Ask: 

• What outcomes are we trying to drive for the business and our customers? 
• What do we want end-users to do with this information? 

 Knowing the purpose and outcome you want will help determine the inputs and outputs to achieve them. With this perspective, priorities will be clear, and information will be geared toward decision making vs. reporting for reporting’s sake. 

2) Align the dashboard with customer journeys 

Before delving into the ideation phase of dashboard design, it is critical to understand the end-to-end customer journey framework. This is a crucial step that requires strategic thinking to identify journeys that are important for both customers and the organisation. Essentially, we want to identify which journeys will deliver the most value, which causes the most pain for your target customers, which causes pain for your organization, and which have enough complexity to warrant journey mapping.  

Once we have this information, we can align the dashboard structure with the relevant customer journey(s) and interactions. This marks an evolution of your CX management practice from merely managing CX across touchpoints to effectively managing CX across journeys. 

An additional benefit is that this approach enables you to connect dashboards with internal business counterparts.  

Example of key journeys, and their business counterparts: 

• Purchase journey and acquisition team 
• Usage journey and retention team 
• Help and support journey and customer service team 

When each team sees the part of the journey they influence, they’re better equipped to drive improvement. 

3) Choose metrics that connect journeys to outcomes 

Once you’ve identified the key customer journeys, the next step is determining the metrics to populate your dashboard. A robust CX dashboard should not only report on VoC metrics but encompass these three sets of metrics: 

• VoC metrics: Capturing customer sentiments and perceptions. 
• Financial or business metrics: Evaluating the impact on the business’s bottom line. 
• Operational metrics: Assessing the efficiency and effectiveness of internal processes. 

These metrics should also be intricately linked to the specific interactions and customer journeys that have been identified and make sense in the context of your business.  

For example:  

 Support Journey Buy Journey Search Bar Interaction VOC Metrics CSAT (Customer Satisfaction) or CES (Customer Effort Score) Website CSAT, Website Accomplishment % Yes Meet Needs Financial / Business Metrics Cost per ticket, Customer Retention Cost Conversion Rate, Basket Value, Average Order Value Conversation Rate for Search Users, Average Order Value Increase Operational Metrics FCR (First Contact Resolution), Ticket Volume, Time to Close Average Page Load Time, Checkout Error Messages, Order Fulfilment Times Search Load Speed, % No Results, Number Clicks  

To select the metrics most appropriate for your dashboard, ask yourself those two questions:  

• Which one of the identified metrics represents the best, the goal the customer is trying to achieve? 
• What is your corresponding business goal? What’s the most appropriate metric to measure it? 

By aligning metrics with specific journeys, your CX dashboards become a powerful tool for understanding, managing, and optimizing the customer experience 

4) Get stakeholder buy-in 

Ultimately, CX is an organizational effort, not confined to a single team or person. It is necessary to understand key stakeholder requirements for CX dashboards to ensure that the dashboard operates in a way which provides value to end-users.

Engage stakeholders in each relevant department and facilitate workshops to design these dashboards. Additionally, early involvement with stakeholders will encourage them to take ownership and accountability for ensuring CX success, therefore increasing cultural adoption of becoming a truly customer centric organization.  

It is important for a central governance team to maintain oversight and control over dashboard design to ensure that companywide frameworks are being adhered to and that the dashboards will make sense within the larger CX program.  

5) Create role-based dashboards

Many organizations with comprehensive CX programs find they are trying to provide value to several business areas. Within those business areas, there will be a variety of stakeholders with different roles, responsibilities and objectives when accessing results through dashboards. Therefore, not only should CX dashboards be aligned with the customer journey, but they should also align with the roles of the end-users. Role-based dashboards provide information that is specifically relevant to the responsibilities and priorities of each role.  

For example: 

• A customer service manager might track FCR, satisfaction, and ticket volume. 
• A product manager might focus on feature usage, NPS, and verbatim feedback. 
• A regional leader may want an at-a-glance view of their location’s performance trends. 

Ensure that each dashboard provides enough information for end-users to explore the data to understand pain-points, root causes and impact.  

Role-based CX dashboards ensure that the most pertinent data is shown which enables faster and more informed decision-making, as users are not overwhelmed with extraneous information. 

Users save time as they do not need to sift through irrelevant data to find the insights they need. This allows them to focus more on their core tasks and responsibilities. 

An additional benefit is that employees feel more empowered when they have access to data that directly impacts their role. Role-based dashboards can also highlight individual and team achievements, fostering a culture of recognition and appreciation. When employees can see the direct impact of their actions on the customer experience, it builds a stronger sense of ownership and purpose in their daily work. 

6) Design for usability and storytelling 

Each dashboard should tell a story that end-users can act on to improve the customer experience. Therefore, dashboard functionality and how data is displayed is very important. Make sure the content you include is easy to read and understand. Keep the dashboard design simple, overcomplicating it will impede your ability to identify and act on insights.  

Make sure your CX dashboards include room for customer stories. These can be simple, like customer quotes that show real emotion. Or they can be more complex, like sharing a video of a customer telling their story.  

Connecting with the real emotion of human stories will do more to get leaders and others to appreciate the importance of the customer experience. Stories and emotions help foster a sense of understanding and connection with the customer, therefore inciting empathy with stakeholders and drive action. 

Here are some practical guidelines for dashboard design: 

• Prioritize simplicity so content is easy to read and understand. 
• Keep font choices and colors to a minimum.  
• Keep design choices consistent across dashboards. 
• Enable features for employees to quickly share or download data. 
• Offer historical and current view of business data to enable employees to quickly identify trends. 
• Offer filtering options that are meaningful to the end-user and are directly connected to business goals (e.g. filter for persona types). 
• Add instructions and information on the dashboard itself, to ensure end-users know how to use the dashboard and what information they can extract. This helps to future proof the dashboard in case new stakeholders come on board.  
• Use technology to pre-package insights in a meaningful way (e.g. Text Analytics, AI Summary widgets). 

7) Review and improve  

Dashboards require ongoing evaluation and refinement rather than a one-time setup. Maintaining close engagement with stakeholders is essential to ensure they continue to meet evolving business needs. A structured approach could involve regular review meetings scheduled every 6 to 12 months, depending on the pace of business changes. 

For a more agile feedback loop, consider embedding lightweight surveys directly within the dashboard. Prompt users with questions like, “What would make this dashboard more helpful?” or “What additional views or metrics would you find valuable?” This real-time input enables continuous refinement, keeping dashboards user-focused and high-impact. 

It’s also important to stay on top of new dashboard product features. At Forsta, account teams discuss CX dashboards as part of their regular QBR’s with clients ensuring that clients get the most out of our latest innovations.  

Ready to build CX dashboards that drive results?

A dashboard is more than a reporting tool; it’s a lever for transformation. When built with purpose, designed with users in mind, and aligned to key journeys and outcomes, it becomes a catalyst for better decisions, stronger accountability, and a more customer-centered culture. 

Ready to turn your CX dashboards into drivers of action? 

At Forsta, we help organizations transform their dashboards into intuitive, insight-led experiences that empower teams and accelerate change. Whether you’re just starting to build or looking to enhance what you already have, our HX Platform and expert services can help you design CX dashboards that not only inform but inspire. Talk to us about your dashboard strategy today. 

There’s more data than ever: NPS charts, CSAT dashboards, comment tags, real-time alerts, and more.

And yet real change is still moving at a glacial pace.

We’ve built machines to measure the customer. We just haven’t figured out how to move for them. The challenge now isn’t gathering feedback but using it to spark organizational change.

Insight without action is inertia

Too often, CX efforts focus on measurement over momentum. The report gets shared. The score moves. But meaningful change? That’s harder to track.

When the number drops, concern rises. When it goes up, it’s back to business as usual. But real CX isn’t just about tracking performance; it’s about improving it.

Here’s where many teams get stuck:

• Reporting without resolving
• Tracking without transforming
• Collecting without connecting

We’re not short on data. We’re short on activation.

Most teams are doing the right things: gathering feedback, tracking key metrics, sharing insights across the org. The next step is where the opportunity lies, turning those insights into outcomes.

Because the most impactful CX programs don’t just measure the “what.” They influence the “so what.” They close the loop between listening and leading. And that’s where real transformation begins.

The stakes are high. Companies that build CX into how they operate (not just how they report) grow revenue at twice the rate of their competitors, according to McKinsey. But very few actually do it. Only 15% routinely use customer insight to steer decisions. Just 23% check in with customers to confirm they’re delivering real value.

That gap between listening and acting isn’t a failure but rather an opportunity. A chance to do more with the insights we already have. The answer isn’t another dashboard. It’s a new way of working; one that turns feedback into forward motion and makes CX part of how the business operates, not just how it reports. So, what does that actually look like in practice?

What operational CX really means

Let’s reset the definition. Operational CX isn’t a software feature or a dashboard toggle. It’s a system. A habit. A culture shift that drives sustained growth.

It’s what happens when insight gets embedded into real decisions, in real time, across real teams.

It starts by asking: what’s the outcome we’re solving for? Not “what do the numbers say?” but “what should we do with them?”

Feedback should lead to outcomes: revenue, retention, reputation. If it’s not moving the business forward, it’s just noise.

To do that, CX has to break out of its bubble. In too many organizations, marketing hears one version of the story, ops hear another, and product a third. CX is the thread that can bring those narratives together, ensuring every decision is guided by the full picture.

That takes more than tools. It takes clarity. Clear ownership. Shared priorities. Because when CX is truly a team sport, everyone knows their role; and the customer feels the difference.

And then comes focus. Not all metrics matter equally. Great programs zero in on a few key drivers—the ones that explain the most and change the fastest. They avoid drowning in data by chasing clarity, not volume.

Finally, operational CX shows up in the work itself. It’s not a report shared at quarter’s end; it’s guidance built into sprints, stand-ups, store huddles, and frontline coaching. It’s baked into how work gets done.

When that shift happens, CX becomes a performance engine, not just a pulse check.

That’s why AI matters, not as a trend, but as a traction engine. Done right, it makes feedback feel immediate, actionable, and human.

What AI can do (and what it can’t)

Artificial intelligence (AI) isn’t a magic fix for broken CX. It won’t align your teams. It won’t redefine your culture. It won’t decide what really matters.

But what it will do is remove friction and streamline action management.

It takes on the heavy lifting of analyzing thousands of open-text responses, spotting emotion and urgency, surfacing patterns that matter now. It speeds up analysis, sharpens focus, and closes the loop faster than any human team could.

When it’s wired into the right process, AI becomes a multiplier. A traction engine. A strategic sidekick.

But AI without action is just automation, insight without outcome, and another tech tool with no teeth.

Recommended reading: Overcoming data quality challenges with AI

What operationalizing CX looks like

To operationalize CX, you don’t need more tools. You need more traction, and here’s where to start.

• Link feedback to outcomes. Retention. Revenue. Reputation. If it’s not moving the business forward, it might be time to rethink what you’re measuring.
• Kill the silos. Marketing, ops, support—everyone hears different parts of the story. CX connects the dots and steers the response.
• Focus where it counts. Not every metric matters. Find the three that do and build your response system around them.
• Embed CX where decisions happen. Don’t wait for the monthly report. Bring insight into the sprint. Into the store. Into the moment.
• Track the shift. If you can’t measure the change your CX program is making, you don’t have a CX program. You have a report.

This is what separates the “we’ve got a dashboard” crowd from the teams actually changing experiences.

From signal to system: Turning insight into action plans

Once you’ve got the feedback, then what?

Most teams can capture customer sentiment. They can even build a dashboard to show where things hurt. But very few have the operational framework to turn those pain points into progress. That’s where things stall.

They see the warning lights, but no one’s holding the wheel.

Operational CX builds that muscle. It’s not just about collecting feedback. It’s about creating a repeatable response system. One that links every data point to a decision. And every decision to a business goal.

That’s what Forsta’s AI makes possible.

It turns unstructured comments into actionable insights, not just summaries, but strategic signals, enabling CX professionals to answer:

• What needs attention now?
• Who needs to act?
• How urgent is the issue?
• What’s the downstream impact on customer satisfaction, customer lifetime value, or revenue growth?

It doesn’t just track how people feel. It shows why it matters, and what to do next.

Because what good is knowing that a customer was frustrated, if you can’t fix the moment that caused it?

In our eBook Scaling customer experience in the age of AI, we explore how high-performing teams:

• Bake feedback into product design cycles and sprint planning
• Build cross-functional triggers into their experience design
• Create frontline playbooks to resolve issues without red tape

The result? A feedback loop that actually loops. A system that scales. And customer journeys that don’t just feel heard but continuously improve.

This is where AI shifts from tool to teammate, from insight support to operational scaffolding.

Some teams build this muscle. Others build momentum. And a few go further, embedding customer experience strategy into the foundation of how they operate.

What elite CX teams do differently

Plenty of companies talk about being “customer obsessed”. But building a truly customer-centric culture takes more than words. It’s a practice that’s strengthened by habits, supported by systems, and powered by purpose-driven data.

The teams that do it best? They don’t just respond to feedback. They design for it. They plan with the customer in mind from day one, using insight to guide decisions—not just validate them.

Because being truly customer-led means making hard tradeoffs. It means challenging pet projects, reshuffling priorities, and changing the way you plan, launch, and lead.

That’s what separates the good from the great. The reactive from the relentless.

The elite CX teams? They embed the customer at every stage of the business strategy. They don’t wait for feedback. They anticipate it. They design for it.

They take action, and:

• Turn feedback into a north star for product design
• Use emotional intelligence to decode not just what customers say—but how they feel
• Align company culture around shared business goals, not just vanity metrics
• Empower frontline teams to act in the moment, not wait on approvals
• Measure success in exceptional customer experiences, not just NPS points

The teams that do this well don’t just improve satisfaction. They accelerate sustained growth. Because when you build with the customer, they stick with you longer, spend more, and shout louder.

That’s the promise of real operational CX. Not just listening, but leading. And building a business that scales relevance, speed, and trust—without losing your human edge.

CX isn’t support. It’s strategy.

The most successful teams treat customer experience as a growth driver, not just a support function.

Because service happens after the experience. Strategy shapes it from the start.

When CX leads, teams stop reacting and start prioritizing. Tools like a prioritization matrix help focus on what matters most, based on urgency, impact, and alignment with business goals.

It’s not about doing more. It’s about doing the right things, at the right time, for the right reasons.

And when strategy is shared, not siloed, CX becomes the force that moves the whole business forward.

Collaboration across teams—product, operations, support, marketing—is where great experience design begins. Shared context. Shared insight. Shared accountability. Real organizations aren’t waiting on someone else to lead the charge. They’re integrating CX into how they plan, launch, and learn.

That’s what Forsta powers: AI that translates feedback into clear priorities, routes them to the right teams, and closes the loop faster.

CX teams that make this shift don’t just collect insight, they activate it. They prioritize with it. Shape roadmaps around it. They move beyond scorekeeping to strategic storytelling and bring the customer into conversations, not just into reports.

This isn’t about responding faster. It’s about rethinking how the business runs and who gets to shape it.

Want the playbook?

We wrote one.

Scaling customer experience in the age of AI cuts through the noise and shows how to scale relevance without losing your human edge.

Inside, you’ll find:

• The real reason legacy CX systems break at scale
• What automation should (and shouldn’t) touch
• How smart brands beat survey fatigue
• Why real-time isn’t a luxury—it’s table stakes
• How to connect the dots between data, teams, and change

Ask yourself, are we capturing stories or changing outcomes?

Download the full Scaling customer experience in the age of AI eBook to see how leading brands connect feedback to real results—then book a demo to uncover your own CX opportunities.