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Case study · ecommerce · checkout

The shipping-cost surprise: Why shoppers bail at checkout

A fictional, interactive case study: how a plant shop finds a late shipping-cost surprise, scores a fix with PXL, plans an A/B test and rolls out the learning with WXM.

WXM 1 · Wonder (Look)WXM 2 · Architect (Plan)WXM 3 · Validate (Test)WXM 4 · Expand (Grow)
Fictional demonstration

An invented business and wireframes used to teach a method. Any numbers are hypothetical.

By Phil the CRO Guy

Phil the CRO Guy

Mentorele's fictional CRO guide

Phil is the friendly, fictional guide who walks you through every lesson and case study. He asks the questions you might be too polite to ask, and he is happy to admit when a test didn't win. Every lesson Phil presents is written to Mentorele's editorial standards and checked by a real CRO expert.

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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

Filip’s expertise at Wavesy LinkedIn profile

Updated · How we write and review

Slide 1 of 10

Leafy Lane has a leak at shipping

Leafy Lane is a fictional online plant shop. Its product pages get plenty of visits and the add-to-cart rate looks healthy. But many shoppers who start checkout never finish, especially on mobile.

Phil is going to walk through one full WXM cycle: Wonder, Architect, Validate, Expand.

Before we change a single pixel, let's find out what's actually happening.

Slide 2 of 10

WXM 1 · Wonder (Look)

Wonder: where exactly do shoppers leave?

The team first checks that checkout tracking fires correctly on every device. Then they build the funnel and split it by device.

  1. Desktop: most shoppers who reach the shipping step complete it.
  2. Mobile: a far larger share leave at the shipping step than on desktop.
  3. That step is where delivery options and their costs appear for the first time.
Illustrative funnel chart from the fictional shop's analytics Original wireframe, not a screenshot.
  1. The leak. The biggest gap between devices is at the shipping step.

Slide 3 of 10

WXM 1 · Wonder (Look)

Wonder: why do they leave?

Numbers say where. Research says why. The team watches 30 mobile recordings of the shipping step and adds a one-question exit survey.

  1. Recordings: shoppers scroll back up to the cart, then leave.
  2. Exit survey ("What stopped you today?"): many answers mention delivery cost or not knowing when it would arrive.
  3. This matches published research: in Baymard's US survey, extra costs are the most cited reason among abandoners who weren't just browsing. [Source](#src-baymard-checkout)

Recordings show the behaviour. The survey gives us the words. Together they point at one thing: a late surprise.

Slide 4 of 10

WXM 1 · Wonder (Look)

Wonder: what the shopper sees today

On the current product page there is no word about delivery. The first time cost and timing appear is inside checkout.

Control (A)
Fictional product page, current version Original wireframe, not a screenshot.
  1. Price without context. €34 looks like the whole cost. Delivery is a surprise later.
  2. Missing answer. No delivery cost or date anywhere above the fold.

Slide 5 of 10

WXM 2 · Architect (Plan)

Architect: from evidence to a hypothesis

Because recordings and the exit survey show uncertainty about delivery cost and timing at checkout, we believe showing the delivery cost and estimated date on the product page for mobile and desktop visitors will increase completed orders per visitor, because it removes a late surprise that feels like a loss.

Your turn: Which primary metric fits this test best?
Show Phil’s answer

Completed orders per visitor. The change aims at abandonment later in the journey. Add-to-cart might even dip slightly as people see the full cost earlier, yet completed orders could rise. Measure the outcome you care about.

Notice the 'because' at the end. If this loses, we'll know which belief to question.

Slide 6 of 10

WXM 2 · Architect (Plan)

Architect: how does it score on PXL?

The team scores the idea with PXL alongside the rest of the backlog. It lands near the top: noticeable, on high-traffic pages, quick to build, and backed by survey and analytics evidence.

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?No0
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 implementationUp to 8 hours2

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

The variation adds one compact line under the price: the delivery cost (or the free-delivery threshold) and an estimated delivery date. Nothing else changes, so any difference can be attributed to this line.

Control (A)
A: current product page Original wireframe, not a screenshot.
Variation (B)
B: delivery cost and date shown under the price Original wireframe, not a screenshot.
  1. The one change. Cost and date are visible before the decision, so nothing surprises the shopper later.

Slide 8 of 10

WXM 3 · Validate (Test)

Validate: the test plan, written before launch

Everything that decides the result is fixed in advance.

  1. Primary metric: completed orders per visitor.
  2. Guardrails: average order value, refund rate, payment errors.
  3. Sample size: set from the baseline rate and a minimum detectable effect worth shipping. In this fictional example it needs two full weeks of traffic.
  4. QA: mobile and desktop, screen reader announces the delivery line, tracking checked.
  5. Decision rule: ship if completed orders improve and the confidence interval excludes zero with guardrails holding; document and rethink if not.

And no peeking. We read the result when the planned sample is reached, not on the first exciting Tuesday.

Slide 9 of 10

WXM 4 · Expand (Grow)

Expand: whatever the result, the cycle continues

This is a fictional case, so there is no result to report, and we won't invent one. Here's what each outcome would lead to.

  1. Win: roll the delivery line out to every product template and show the same date in the cart and confirmation email.
  2. Inconclusive: check whether shoppers noticed the line (heatmaps, a quick 5-second test); maybe make it more prominent, or the cost itself is the problem.
  3. Loss: revisit the belief: maybe the issue is the delivery price, not the surprise. Next Wonder: survey on acceptable delivery costs.

Every outcome teaches us something. That's why Expand always hands new questions back to Wonder.

Slide 10 of 10

Your turn

Open your own product page on a phone. Can a first-time shopper see the total cost and when it will arrive before adding to cart? If not, you have a candidate hypothesis. Score it in the PXL calculator and read the checkout optimization guide.

What this case teaches

  • Wonder combined device-split funnel data with recordings and a one-question survey to find a specific cause.
  • Architect wrote a hypothesis with a mechanism and scored it with PXL (11/14).
  • Validate fixed the metric, guardrails, sample size and decision rule before launch.
  • Expand planned what to do for every outcome, without pretending to know the result.

Leafy Lane is fictional. All figures are illustrative and do not describe a real company or experiment.

Frequently asked questions

Is Leafy Lane a real company?

No. Leafy Lane and every number in this case study are fictional and illustrative. The case teaches a method; it does not report a real experiment.

Sources

  1. 50 Cart Abandonment Rate Statistics 2026. Baymard Institute. Accessed 4 November 2026.
  2. WXM: the Wave Experimentation Model. Wavesy. Accessed 4 November 2026.
  3. PXL: A Better Way to Prioritize Your A/B Tests. CXL. Accessed 4 November 2026.

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