E-commerce UX redesign – improving product discovery

E-commerce UX redesign for discovery

E-commerce UX redesign works when it makes product discovery faster, clearer, and harder to mess up.

If your store has traffic but shoppers still bounce, scroll, and leave like they forgot the oven is on, discovery is usually the issue. This is for you if you have a real catalogue, limited dev time, and the nagging feeling that “it looks fine” is not a strategy.

TOC: what discovery is, scope, PLPs, filters, search, product data, mobile, metrics, a practical plan.

What ‘product discovery’ really is

Product discovery is the system that turns “I might buy” into “I found it.” It’s not just categories. It’s navigation, on-site search, filters, sorting, product data, and the way your pages help people narrow choices without getting lost.

Here’s the part teams forget: shoppers do not experience your store as pages. They experience it as decisions.

• “Show me the right category.”
• “Let me narrow it.”
• “Let me compare it.”
• “Let me trust what I’m seeing.”

If any step breaks, they don’t write a bug report. They leave.

Takeaway: Discovery is a decision system, not a UI checklist.

E-commerce UX redesign scope

A redesign scope should describe decision flows, not a pile of screens. Start by mapping the two most common journeys:

1. “I know what I want, help me find it.”
2. “I’m browsing, help me choose.”

Now list the moments where users need clarity: category choice, filtering, sorting, comparing, and confirming they are still in the right place.

If you need the bigger picture, start with store redesign basics

Also, define what success means in plain numbers: fewer dead-end searches, higher filter usage, more PDP (product detail page) visits from PLPs (product listing pages), and fewer pogo-sticks back and forth.

Takeaway: Scope the decisions, then design the screens.

Category pages that guide

Your PLP (product listing page), category page, collection page, whatever you call it, is where most stores quietly lose people. Not because it’s ugly, but because it’s vague.

A good PLP answers these questions without making users work for it:

• What am I looking at?
• How do I narrow this?
• What’s “different” between these items?
• What happens if I click?

Practical patterns that earn their rent:

Show meaningful differences in the grid (size range, compatibility, material, key feature).

Make sorting human, not internal logic (“Newest” is not a decision, it’s a shrug).
Use visual hierarchy so the product name doesn’t fight price, badges, and shipping.

First-party experience (what we see a lot): stores copy-paste PDP details into PLPs, then wonder why nothing stands out. The fix is not “more info.” It’s the right info.

Takeaway: Make scanning easier than thinking.

Filters people can trust

Filtering is where intent becomes specific. It’s also where stores get weirdly passive-aggressive: “Sure you can filter, but we won’t show what you did, good luck.”
Baymard found that 32% of top e-commerce sites don’t show an applied filters overview, which creates disorientation and slows users down (Source: Baymard Institute, 2020).
That’s not a small UX nit. That’s users losing trust in the list.

This is why Baymard’s applied filter research is still painfully relevant:

Filtering rules that usually win:

• Always show applied filters, visibly, removable, and close to the list.
• Make filter labels match how people speak (“Skin type” beats “Dermal profile”).
• Keep count labels honest (don’t show “0” results after selecting, block it or warn).
• Don’t drown users in filters that rarely matter. Less, but meaningful.

Pitfall: adding filters because stakeholders asked, not because users need them. Filters are not a museum of product attributes.

Takeaway: Visible context beats clever controls.

Search that behaves

On-site search is not a text box. It’s a promise. If it breaks, users don’t “browse instead,” they assume you don’t stock what they want.

Define what search must handle:

• synonyms (sofa, couch)
• typos
• partial queries
• brand + model combos
• intent queries (“gift for runner”) if your catalogue supports it

Also define what search results should do:

• show relevance, not randomness
• support narrowing with filters
• highlight why a result matches (without turning it into a novel)

Quick check: open your analytics, look at top internal search terms. If you see repeated terms with low engagement, search is failing silently.

Takeaway: Search UX is a product, not a box.

Product data that sells

Discovery breaks when your product data is vague, inconsistent, or missing. That’s not “content work.” That’s the foundation of filtering, sorting, and relevance.

Typical issues:

• attribute values don’t match (Blue, Navy, Dark blue, Midnight)
• sizes are text blobs instead of structured variants
• compatibility is buried in descriptions
• key differentiators are missing entirely

If you want faceted filtering (filters that combine attributes like size, price, and brand), you need structured attributes. Otherwise filters turn into a placebo UI.

Simple but brutal rule: if an attribute can’t be filtered, sorted, or compared, it’s probably not structured enough.

Takeaway: Weak attributes create weak discovery.

Mobile discovery realities

Mobile discovery is where good intentions go to die quietly. Not because users are dumb, but because the interface is cramped and attention is rented by the millisecond.

Make these choices deliberately:

• filter access (persistent button, bottom sheet)
• applied filter visibility (chips, stacked list)
• sort clarity (plain language)
• pagination vs infinite scroll (infinite is fine until users need orientation)

Best for:

• Small catalogues (under ~200 SKUs): curated collections, fewer filters, stronger editorial guidance.
• Large catalogues (500+ SKUs): filters and search must be excellent, navigation alone won’t carry it.

Timeframe reality: mobile fixes can ship in 2–6 weeks if data is ready, 6–12+ if product attributes need rework.

Takeaway: Thumbs are honest, they leave fast.

What to measure weekly

Conversion rate is a lagging metric. Product discovery needs leading indicators, the stuff that tells you “finding is working” before revenue shifts.

Track:

• % of sessions using filters
• % of sessions using on-site search
• search exit rate (search performed, then left)
• PDP visits per PLP session
• “zero results” searches and top terms
• add-to-cart rate from search results vs category browsing

A basic, useful calculation:

If you have 50,000 monthly sessions and 2% conversion, that’s 1,000 orders. If better discovery lifts conversion to 2.2%, that’s 1,100 orders. The redesign didn’t “feel nicer,” it created 100 extra decisions that ended in purchase. Now multiply by AOV (average order value), and you get a number your finance brain can respect.

Takeaway: Track narrowing and finding, not just buying.

A sane redesign plan

This is where teams either ship value or stage a redesign as performance art.

Phase 1 (1–2 weeks): diagnose

• map decision flows
• audit filters/search/data
• pick top 2 journeys
• define weekly metrics

Phase 2 (2–6 weeks): fix the discovery spine

• PLP layout and information hierarchy
• applied filter visibility
• search results UX
• product attributes cleanup (the unglamorous part that pays rent)

Phase 3 (ongoing): iterate without chaos

• weekly review of discovery metrics
• one test at a time
• document learnings so you don’t re-argue basics every sprint

Align build decisions with your ecommerce development approach.

Treat checkout as part of discovery, especially when you’re picking payment gateway choices.

Takeaway: Ship in slices, validate each slice.

If you want a second set of eyes on your discovery flow, we can review your PLPs, filters, and internal search and tell you what’s actually blocking decisions. No theatre, just a shortlist. Book a quick 30-min video call, we will show you exactly what to fix.

Monitoring note (monthly)

Check these monthly in search results and AI answers for this topic:

• Whether AI summaries recommend “UI refresh” over discovery systems, that’s usually wrong.
• Whether “filters” guidance includes applied filter visibility, if not, it’s missing the point.
• Changes in UX standards and tooling (search providers, platform updates, analytics defaults).
• If your platform updates change how filters, variants, or indexing works, re-test discovery flows.

E-commerce UX redesign succeeds when product discovery is treated as a system: navigation + search + filters + product data working together, so shoppers can narrow choices without losing context. Baymard found 32% of top e-commerce sites still fail to show an applied filters overview, which slows users and causes disorientation (Source: Baymard Institute, 2020). Studio Ubique helps you choose within 2–6 weeks for a focused discovery sprint.

FAQs

Q: What is product discovery in e-commerce UX?

Product discovery is how users find, narrow, and choose products using categories, search, filters, and sorting. It includes the product data underneath, because filters and relevance only work when attributes are structured. If discovery fails, users blame your catalogue, not your UI, and leave.

Q: Should we redesign category pages or on-site search first?

Start where intent is highest. If internal search exists and people use it, fix search UX first because it’s a direct ‘I want this’ signal. If most users browse categories, prioritise PLPs and filtering. Use analytics: high search exits usually mean search is failing.

Q: How many filters should an online store have?

As many as users need to make real decisions, and no more. Too many filters creates noise and reduces confidence. Keep filters tied to meaningful differences (size, price, compatibility, material), and remove vanity attributes that don’t help users narrow the list.

Q: What metrics show product discovery is working?

Look at leading indicators: filter usage, internal search usage, PDP visits per PLP session, zero-result searches, and search exit rate. Conversion rate matters, but it’s late. Discovery metrics tell you earlier whether users are finding what they came for.

Q: Can a redesign fix product discovery without changing product data?

Sometimes, but usually only for small catalogues. For large stores, product attributes and variants drive filtering and relevance. If your data is inconsistent, the UI can’t rescue it. You’ll get prettier screens and the same confusion, just in higher resolution.