CRO Audit Template Checklist
A diagnostic walkthrough of the six surfaces that decide whether a store converts — what to check, what to flag, and how to turn findings into a prioritised test backlog.
CRO Audit Template
A structured checklist that walks every surface of an online store — homepage to thank-you page — to surface conversion leaks before you design tests.
A CRO audit template is the diagnostic pass you run before any experimentation roadmap. It forces a consistent walk-through of the six surfaces that decide whether a visitor converts: homepage, product listing pages (PLP), product detail pages (PDP), cart, checkout, and thank-you. For each surface you note friction, trust gaps, and quantitative signals (drop-off, scroll depth, rage clicks) against a fixed rubric, then translate those notes into hypotheses ranked by leverage.
The value isn't the checklist itself — it's the consistency. Auditing the same way every quarter makes regressions obvious and stops the team from re-discovering the same problems.
Most teams skip the audit and jump straight to testing the headline above the fold. That's how you end up with a backlog of low-leverage variants on a page that wasn't the bottleneck. An audit fixes the sequencing: diagnose first, then decide which surface earns the next experiment.
Run the full pass in one sitting if you can — two hours for a mid-sized Shopify store. Walk the funnel as a buyer would, on mobile first (that's where 70%+ of your sessions live), and capture screenshots. Treat findings as raw input, not conclusions; the prioritisation happens after.
Pair the heuristic walk with data
A purely qualitative audit catches obvious friction but misses where the money actually leaks. Pull GA4 funnel exploration alongside — checkout-step drop-off, PDP exit rate by SKU, and add-to-cart rate by traffic source — so each finding has a quantitative weight attached to it.
The six surfaces, in order
Homepage. Check the above-the-fold value proposition (does it name the product category and the differentiator in under 5 seconds?), the primary CTA's clarity, social proof placement, and the mobile navigation depth. Flag carousels that auto-rotate, hero videos that block the LCP, and any unclear category labels. Homepages convert via clarity, not creativity.
PLP (collection pages). Audit filter density, sort defaults, product card information (price, swatches, stock status, badges), and load behaviour. If a shopper has to click into three PDPs to compare sizing, the PLP is failing. Check that out-of-stock items aren't burning click budget at the top of the grid.
PDP. The highest-leverage surface in most stores. Walk the gallery, variant selector, price clarity, shipping/returns visibility, reviews, size guidance, and add-to-cart placement on mobile. Flag any spec ambiguity — a single unanswered question ("will this fit?", "when will it ship?") is enough to lose the session. Sticky ATC bars usually lift mobile add-to-cart by 8-15%.
Cart, checkout, thank-you. Cart: are upsells relevant, is the shipping threshold visible, can the user edit quantities without a page reload? Checkout: count form fields, audit guest-checkout availability, surface trust signals near payment, check Apple Pay / Shop Pay placement. Thank-you: this page is almost always under-used — it should drive account creation, referrals, and second-order intent, not just say "thanks".
Frequently asked questions
For a store with 50-500 SKUs, budget two focused hours for the heuristic walk and another hour to pull supporting GA4 data. Bigger catalogues don't take much longer because you audit page templates, not every SKU. Set aside a separate half-day to translate findings into a ranked test backlog.
Quarterly is the sweet spot for most stores. Re-audit immediately after a theme update, a checkout migration, or any major redesign — those are the moments regressions sneak in. A light monthly funnel-data review between full audits will catch acute drops earlier.
Mobile first, always. Most online retail traffic is mobile, and most friction (small tap targets, hidden filters, slow LCP, awkward variant pickers) is mobile-specific. Desktop audits often look clean while the mobile experience is leaking 30% of the funnel.
You can run a useful heuristic pass without it, but the prioritisation step needs data. Pair the walk with GA4 funnel exploration, a heatmap tool, and session recordings on the surfaces you flagged. Without numbers, you're ranking gut feelings.
Heavy overlap, different lens. A UX audit asks "is this usable and pleasant?" — a CRO audit asks "is this converting?". They share methods (heuristics, recordings, walkthroughs) but the CRO version always ends in a ranked test list tied to revenue impact, not just usability scores.
Score each finding on three axes: traffic volume on the affected surface, estimated lift from fixing it, and implementation cost. ICE or PIE scoring works fine — the model matters less than applying it consistently. Quick wins (low cost, clear lift, no test needed) ship straight away; the rest become hypotheses.
Yes — that's most agency CRO work. You don't need historical context to spot friction; you need a buyer's eye and a structured rubric. The only thing that takes longer on an unfamiliar store is understanding the catalogue logic and which segments matter most commercially.
PDP and checkout, in that order, for most stores in the €1M-€15M range. PDP because it's where the buying decision actually happens; checkout because friction there destroys sessions you've already paid to acquire. Homepage tests look exciting but usually move smaller numbers.
No. Ship the genuine bugs and the no-brainer fixes immediately (broken links, missing trust badges, obvious copy errors). Anything where the right answer is debatable — layout changes, copy variants, new sections — should go through A/B testing, not gut-call deployment.
The audit is the diagnostic step. After it, you typically use a hypothesis template to write up each test idea, an experiment-brief template to spec the variant, and a results template to document the outcome. The audit feeds the backlog; the other templates run the experiments.
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