Improve website navigation using feedback
Customers tell you a site is hard to navigate, but the analytics look healthy — clicks happen, pages load, nothing is broken. NEXT reads the feedback customers leave in tickets, surveys, reviews, and chat, and groups the complaints about finding and browsing things. You get a short brief showing where people get lost, the exact words they used, how many are affected, and what it costs in conversion.
Heatmaps show you where people clicked. They can't tell you that shoppers expected "Sale" to list every discounted item, or that nobody could find a way to browse all the jackets without filtering one at a time.
What the navigation alert looks like
Example output based on grouped findability complaints from reviews, support chat, and post-purchase surveys.
Navigation area
Category browse and the "Sale" landing page
Where customers get lost
Shoppers expect "Sale" to show every discounted product; instead it opens three sub-categories. Apparel browse forces filter-by-filter selection with no full-list view.
What customers say
"I clicked Sale expecting everything that's discounted and got three boxes to choose from. Where's the rest of it?"
"I can search for one product fine, but I can't just look through all the winter coats. I gave up and went to a competitor."
Affected shoppers
About 1,400 sessions raised navigation or findability complaints in the last two weeks, concentrated on mobile.
Commercial exposure
The affected browse paths carry roughly €120K in monthly checkout value, and conversion on those paths runs well below the site average.
Signal strength
Strong and consistent on the "Sale" expectation mismatch; mixed on apparel browse, where some shoppers prefer guided filtering.
The brief names the structural fix — flatten the Sale page, add a full-list browse view — in the customer's own words, and routes it to the digital experience team.
How NEXT does this
NEXT reads where shoppers actually describe the problem: support chat, reviews, post-purchase surveys, and service notes. It keeps a continuously updated record of what customers say about finding and browsing products, so a one-off gripe and a repeating pattern look different. When complaints about the same navigation area cluster past a threshold, NEXT groups them, attaches the verbatim quotes and the affected sessions, and writes a short recommendation — usually a structural or labeling change — in plain language. It lands where the digital experience team already plans work. NEXT brings the pattern and the proof; the team decides whether and how to restructure.
Why navigation problems surface late today
The number on the dashboard moves, but it doesn't tell you why. Bounce on a category page climbs and you're left guessing whether it's price, stock, layout, or labeling. Open an AI assistant and ask, and you get the loudest recent thread — one angry review — not the pattern across the quarter. Neither comes looking for you; you have to remember to go looking for them.
Meanwhile the real explanation is sitting in plain sight, scattered. A shopper tells support "I couldn't find the size guide." That gets logged as a closed ticket. A reviewer says the menu is confusing, and that sits on a review site nobody reads internally. By the time anything reaches the digital team, the specific wording is gone and all that's left is a vague "navigation needs work."
A heatmap shows where people clicked. It can't tell you what they expected to find and didn't. NEXT works from what customers said, not just where they tapped.
How this compares to the tools you already know
Approach | Where the evidence lives | What the digital team does at decision time |
|---|---|---|
Heatmaps / clickstream | Aggregated click and scroll data | Infers intent from behavior and guesses at the cause |
Onsite survey tools | Raw responses in a separate dashboard | Reads and tags responses by hand to find themes |
AI assistant | Wherever you think to ask | Asks a question and gets the loudest recent answer |
NEXT | Attached to the routed fix, in the team's planning flow | Reviews a clustered pattern with quotes and exposure, then decides the fix |
What changes for the digital experience team
Today you open a redesign request that says "improve navigation" and spend an afternoon reconstructing why. You pull survey exports, skim reviews, ask CS what they hear. Half the original wording is paraphrased into something generic before it reaches you.
With NEXT, the request arrives with the pattern already attached. You can see that 1,400 shoppers hit the same "Sale" mismatch, read their exact words, and see that the affected paths convert below average. The "Sale" page looked like a minor labeling tweak until the monthly checkout exposure was attached next to it. You scope the change from clearer demand instead of a hunch, and you can tell the apparel-browse signal is mixed — worth a test, not a rebuild.
NEXT already supports digital and product teams at retailers like Action and Rituals in connecting customer feedback from reviews, tickets, and surveys to experience decisions.
The recommendation stays a recommendation. NEXT supplies the pattern and the proof; what you restructure, when, and how you balance it against the rest of the roadmap stays your call.
Downstream effects
Fewer redesigns built on assumption. Changes ship against language shoppers actually used, so the team is less likely to flatten a menu that some segments relied on.
Earlier visibility on conversion drag. A findability problem shows up as a cluster of complaints before it has fully worked through the conversion numbers, so the team can scope it before the next campaign drives traffic into it.
A shared record across teams. Merchandising, CX, and digital see the same grouped feedback, so the "Sale" debate starts from demand context instead of three competing opinions.
Where the human stays in control
You set the threshold for how many related complaints make a cluster worth routing, and which sources count. You can have NEXT hold matches for the team to review before anything is written into the planning flow, so a noisy week of seasonal gripes doesn't generate work on its own. This is configuration — coverage, thresholds, routing — not sign-off on every comment. NEXT never restructures the site; it surfaces the pattern and waits for the team to decide.
What to configure first
Start with source coverage. The brief is only as good as what it reads, so connect the channels where shoppers actually describe navigation trouble — support chat, reviews, post-purchase surveys — before trusting the clusters. Set the clustering threshold to match your traffic: too low and every stray complaint routes a fix; too high and a real pattern waits too long. Decide which team owns the routed recommendation and where it should land in their planning. And calibrate seasonality — return windows and post-sale periods spike feedback in ways that can distort what looks structural.
Where this breaks down
Thin or one-sided sources
If most feedback comes from one channel — say, only the angriest reviews — the cluster skews toward complaints and misses shoppers who quietly left. Broader source coverage makes the pattern more representative.
Preference, not a defect
Some navigation problems are split preferences. Part of your audience wants guided filtering; part wants a full list. NEXT marks that signal as mixed; treating it as a clear defect leads to a redesign that annoys the other half.
Structural versus content fixes
A clustered complaint about not finding a product can be an information-architecture problem or a stock and merchandising one. The recommendation points at structure; someone still has to check whether the item simply wasn't there.
Seasonal noise read as a trend
A sale week generates a burst of navigation complaints that fades. Without seasonal calibration, a temporary spike can look like a permanent structural failure.
FAQ
How is this different from heatmaps and session replay?
A heatmap shows where people clicked and how far they scrolled. It can't tell you what they were looking for and failed to find. NEXT works from what shoppers wrote in tickets, reviews, and surveys, groups the findability complaints, attaches their exact words and the affected sessions, and points at the structural fix. Behavior shows the symptom; the feedback explains the cause.
Does NEXT redesign the navigation for us?
No. NEXT clusters the complaints, attaches the proof, and recommends a structural or labeling change in the customer's language. The digital experience team decides whether to make it, how to scope the change, and how to weigh it against other work. The recommendation is an input, not an instruction.
What sources does it read?
The channels where shoppers describe finding and browsing problems: support chat and tickets, product and site reviews, post-purchase and onsite surveys, and service notes. Coverage matters — if a channel is missing, the pattern can skew. You choose which sources count toward a cluster.
How does it avoid routing every minor complaint?
You set a threshold for how many related complaints make a pattern worth routing, and you can hold matches for review before anything is written into planning. Weak, one-off gripes are less likely to clutter the team's queue, though no threshold is perfect — calibration is part of setup.
Can it tell a real structural problem from a passing spike?
It's better at it than a raw count, because it keeps a running record and you can calibrate for seasonality. A sale-week burst that fades looks different from a complaint that repeats every week. Still, judgment stays with the team — NEXT surfaces the pattern; you decide if it's structural.
Does this work for B2B sites, not just retail?
Yes. The same approach applies anywhere customers describe trouble finding things — documentation, a portal, a configurator. The sources and language differ, but the workflow is the same: read the feedback, cluster the findability complaints, attach the demand context, route the fix.