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Case study · ecommerce · conversion

The filters nobody found: Helping mobile shoppers narrow 400 products

An interactive case study: a running shop hides mobile filters behind an unlabelled icon. The team tests visible filter chips and measures orders.

WXM 1 · Wonder (Look)WXM 2 · Architect (Plan)WXM 3 · Validate (Test)WXM 4 · Expand (Grow)
Teaching example
By Phil the CRO Guy

Phil the CRO Guy

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Expert-reviewed by Filip Doneski

Filip Doneski

CRO Expert and Strategist · Partner and Lead CRO Strategist at Wavesy

10+ years in conversion optimization and experimentation for DTC, retail and SaaS. Reviews every Mentorele lesson for accuracy before it’s published.

A/B testingConversion rate optimizationDigital psychology and persuasion

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Updated

Slide 1 of 10

Stride Lab: 400 shoes and a tiny icon

Stride Lab sells running shoes. Each category page lists hundreds of models. On desktop, filters sit in a sidebar. On mobile, they hide behind a small funnel icon in the corner.

Mobile shoppers view lots of category pages but few product pages.

400 shoes is a great range or an impossible choice. The difference is how easy it is to narrow down.

Slide 2 of 10

WXM 1 · Wonder (Look)

Wonder: who uses filters?

The team tracks filter usage as events (they weren't tracked before), then compares devices after two weeks.

  1. Desktop: a large share of category visitors apply at least one filter.
  2. Mobile: very few do.
  3. Shoppers who filter view more products and are more likely to order. (Correlation: keen shoppers both filter and buy.)
Filter usage by device (illustrative) Original wireframe, not a screenshot.
  1. Same shoppers, different behaviour. Mobile shoppers hardly ever filter. Is it a choice, or can they not find it?

Slide 3 of 10

WXM 1 · Wonder (Look)

Wonder: can people find the filters?

Five usability sessions on phones: 'Find road running shoes in size 42 under €120 that you'd consider buying.' Small tests like this find most usability problems quickly. [Source](#src-nng-five-users)

  1. 4 of 5 participants didn't notice the filter icon and scrolled the full list.
  2. Two gave up after a few screens: "There's too many, I'd go to a shop."
  3. The one who found the icon said: "Oh, that's filters? I thought it was sorting."

When four out of five people miss something, it's not hidden by accident anymore. It's hidden by design.

Slide 4 of 10

WXM 1 · Wonder (Look)

Wonder: the category page today

412 products, and the only way to narrow them is an unlabelled icon.

Control (A)
Mobile category page, current version Original wireframe, not a screenshot.
  1. Mystery icon. No label. Most shoppers don't recognise it as filters.

Slide 5 of 10

WXM 2 · Architect (Plan)

Architect: from evidence to a hypothesis

Because mobile shoppers rarely filter and 4 of 5 usability participants couldn't find the filter icon, we believe showing labelled filter chips for size, running surface and price above the product grid for mobile visitors will increase orders per category visitor, because shoppers can narrow a huge range to a manageable shortlist.

Your turn: Which metric would be misleading as the primary metric?
Show Phil’s answer

Number of filter taps. Filter taps will obviously rise when filters are visible. That tells you people saw them, not that they bought more. Use it as a check, not the decision.

Slide 6 of 10

WXM 2 · Architect (Plan)

Architect: how does it score on PXL?

Above the fold, very noticeable, adds an element, supported by user testing and analytics, on high-traffic pages. Moderate build effort.

PXL score: 11 / 14

Is the change above the fold?Yes1
Is it noticeable within 5 seconds?Yes2
Does it add or remove an element?Yes2
Is it designed to increase user motivation?Yes1
Does it run on high-traffic page(s)?Yes1
Does it address an issue found in user testing?Yes1
Does it address an issue found in qualitative feedback (surveys, polls, interviews)?Yes1
Does it address insights found in digital analytics?Yes1
Is it supported by mouse-tracking heatmaps or eye tracking?No0
Ease of implementationUnder 2 days1

PXL by CXL. A high score ranks an idea; it does not predict a win.

Slide 7 of 10

WXM 3 · Validate (Test)

Validate: the variation

Three labelled chips sit above the grid: Size, Surface, Price. Each opens a short sheet with result counts. A 'More filters' chip opens the full list.

Control (A)
A: unlabelled icon Original wireframe, not a screenshot.
Variation (B)
B: labelled filter chips Original wireframe, not a screenshot.
  1. Visible choices. The filters shoppers use most, labelled, right where they start scrolling.

Slide 8 of 10

WXM 3 · Validate (Test)

Validate: the test plan, written before launch

Fixed in advance:

  1. Primary metric: orders per mobile category-page visitor.
  2. Secondary: filter use, product page views, zero-result filter combinations.
  3. Guardrails: returns (wrong size), page speed.
  4. QA: chips work with screen readers and keyboards, applied filters are announced and easy to clear.
  5. Decision rule: ship if orders improve with returns and speed holding.

Slide 9 of 10

WXM 4 · Expand (Grow)

Expand: whatever the result, the cycle continues

This is a teaching case, so there's no result to report. Here's what each outcome would lead to.

  1. Win: roll out to all categories; research which filters each category needs first.
  2. Inconclusive: maybe shoppers filter but find nothing they like. Check filter combinations with few results.
  3. Loss: the chips may push products below the fold. Try a slimmer design.

Slide 10 of 10

Your turn

Give someone your phone, open your biggest category, and ask them to find one specific product. Don't help. If they don't use the filters, you have a hypothesis. Read choice overload for why narrowing matters.

What this case teaches

  • Wonder added missing tracking first, then used a small usability test to explain the numbers.
  • Architect focused on helping shoppers choose, not on cutting the range.
  • Validate refused filter taps as the decision metric.
  • Expand prepared for results that point somewhere unexpected.

Stride Lab is a made-up shop used for teaching.

Frequently asked questions

How should filters work on mobile category pages?

Make them visible and labelled, start with the filters that match how shoppers decide (size, use, price), show how many results each choice leaves, and make applied filters easy to see and remove.

Is Stride Lab a real company?

No. It's a made-up shop used to teach the method, so there's no real result to report.

Sources (4)

  1. Choice overload: A conceptual review and meta-analysis. Chernev, Böckenholt and Goodman, Journal of Consumer Psychology 25(2), 2015. Accessed 12 November 2026.
  2. Why You Only Need to Test with 5 Users. Nielsen Norman Group. Accessed 14 November 2026.
  3. WXM: the Wave Experimentation Model. Wavesy. Accessed 4 November 2026.
  4. PXL: A Better Way to Prioritize Your A/B Tests. CXL. Accessed 4 November 2026.

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