Checkout abandonment isn't a single problem — it's an aggregate of separate leaks that look identical in the dashboard. Instrument every checkout step with GA4 custom events, watch real sessions, and you'll find three distinct problems where the business saw one. Fix each independently and the checkout doesn't need rebuilding: we cut abandonment from 73.1% to 53.9% (–26% relative) in two sprints with zero architecture changes.
The story starts with a number the team already knew: 73.1% of checkouts were abandoned. The number nobody knew was why — because no checkout step below "added to cart" had ever been instrumented.
Checkout abandonment rate is the share of users who start checkout but never complete a purchase. In this teardown, that's the metric we moved from 73.1% to 53.9%.
What the data actually showed
Three months of GA4 data existed at Sierra Living Concepts. Nobody had configured custom events for the checkout flow, so the team was optimising on aggregate numbers and anecdotal support tickets — trying to fix a leak they couldn't see.
Two weeks of proper instrumentation changed everything:
- GA4 custom events at every checkout step — cart view, shipping information, payment information, order review, purchase.
- Microsoft Clarity session recordings — 50+ full checkout sessions across desktop and mobile.
- Cross-referenced rage-click and drop-off data — three distinct clusters emerged.
What looked like "high cart abandonment" was actually three separate problems happening to different users at different points:
- Shipping estimate abandonment — users reached the shipping step and saw the full cost for the first time.
- Promo code rage clicks — a non-functional promo code field with no validation feedback.
- Payment error exits — a generic "something went wrong" message with no recovery path.
Treating them as one problem would have fixed nothing. Each required an independent fix.
The three fixes we shipped
Fix 1 — Shipping cost on the cart page. Show the complete total (including shipping) before checkout entry, so the shipping step never surprises anyone.
Fix 2 — Promo code validation. Inline error messaging that says exactly what went wrong — wrong format, expired code, or ineligible category — instead of silently failing.
Fix 3 — Payment error recovery. Specific error copy referencing card type, expiry, or network decline, plus a visible retry path.
All three shipped in two sprints. Each was validated independently. The checkout template stayed identical.
What changed
| Metric | Before | After | | --- | --- | --- | | Cart abandonment | 73.1% | 53.9% | | Mobile conversion rate | — | +47% | | Checkout completion | — | +49% | | Sessions analysed | 480,000 | (GA4 + Clarity) |
The fixes outperformed Wayfair's checkout on mobile conversion within 30 days of the final sprint — a benchmark nobody had thought to measure against.
What I'd do differently
- Instrument before you diagnose. The three problems looked like one because the data only showed the aggregate. Step-level instrumentation was the unlock.
- Turn on session recordings earlier. We had 480K sessions of data we couldn't use. Two weeks of recordings gave more actionable insight than three months of aggregate dashboards.
- Map external benchmarks to your own implementation. Baymard's 8 critical checkout violations had been sitting in a folder for months — useful only once mapped to our specific flow.
This is the anchor case study behind my CRO work — the full breakdown lives at the cart & checkout case study.