Conversion Rate Optimization Checklist
A recurring CRO workflow for in-house growth and demand-gen teams to audit landing pages, run A/B tests, and ship winning variants. Covers hypothesis design, GA4 event verification, statistical significance checks, and post-test rollout.
Baseline & Hypothesis
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Pull baseline conversion data from GA4
The growth lead exports the last 28-90 days of sessions, conversion rate, and primary conversion event volume from GA4 for the target page or funnel. Confirm the conversion event is firing on actual submit, not on email-blur or button-click — mis-mapped events are the most common reason CRO tests show false lifts.
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Review heatmaps and session recordings
Pull the last two weeks of Hotjar, FullStory, or Microsoft Clarity recordings for the target URL. Look for rage clicks, dead clicks, scroll drop-off above the fold, and form-field abandonment patterns — these point to the friction the test should address.
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Write the test hypothesis and KPI
Format: "Because we observed [evidence], we believe that [change] will cause [primary KPI] to [direction] for [audience]. We will know we are right when [metric threshold] is reached." The primary KPI must be a single conversion event — not a list. Secondary metrics (bounce, scroll depth, AOV) go in a separate field.
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Calculate sample size and runtime
Use Optimizely's or VWO's sample-size calculator with the baseline rate, the minimum detectable effect (MDE) you care about, and 95% confidence / 80% power. If the math says you need 6+ weeks to reach significance on this page's traffic, the MDE is too small — pick a higher-traffic page or a bolder variant.
Landing Page Variant Build
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Rewrite the headline against the value prop
Copywriter drafts 3-5 headline options that lead with outcome, not feature. Pressure-test against the messaging house and ICP language pulled from sales-call transcripts. The control headline stays unchanged in variant A.
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Redesign the primary CTA placement
Designer mocks the variant in Figma with above-the-fold CTA, mid-content CTA, and sticky-footer CTA on mobile. Keep button copy action-oriented ("Get the demo" not "Submit"). UTM parameters on the destination URL must follow the team's source / medium / campaign convention.
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Trim form fields to essential inputs
Coordinate with RevOps on which fields the lead-routing rules actually need. Company-size and industry can often be enriched downstream by Clearbit or ZoomInfo rather than asked on the form. Inline validation on email and phone reduces submit errors.
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Add social proof above the form
Customer logos, a named testimonial with title and company, or a G2 / Capterra rating badge. Any quantitative testimonial claim ("we tripled pipeline") needs a substantiation file per FTC Endorsement Guides — link the source case study or attribution data.
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Run Core Web Vitals and mobile checks
Run the variant URL through PageSpeed Insights and confirm LCP under 2.5s, INP under 200ms, CLS under 0.1 on mobile. Page weight regressions from added images or video embeds will eat your conversion lift before the test even starts.
QA, Tagging & Launch
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Verify GA4 conversion event in GTM Preview
Marketing ops opens Google Tag Manager Preview, walks the variant flow end to end, and confirms the primary conversion event fires once on actual form submission — not on email-blur, page-scroll, or button mousedown. Check the DebugView in GA4 to confirm the event reaches the property with the correct parameters.
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Confirm consent gating with the CMP
OneTrust, Cookiebot, or Didomi must gate the Meta Pixel, LinkedIn Insight Tag, and any non-essential analytics until the user accepts. Test from an EU IP via VPN — pre-consent firing on EU traffic is a GDPR violation regardless of where the company is based.
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Get legal and brand sign-off
Slack DM approvals don't count as audit trail. Capture timestamped approval from legal (claim substantiation, testimonial usage rights) and brand (visual identity, tone of voice) in the project record before launch.
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Launch the test at 50/50 traffic split
Optimizely, VWO, or Convert handles random assignment. Confirm the goal metric in the test platform matches the GA4 conversion event one-to-one. Note the launch timestamp — significance calculations depend on it.
Mid-Test Monitoring
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Check sample ratio mismatch on day 3
SRM check: actual traffic split should be within ~2% of the configured 50/50. A larger gap usually means a redirect, a caching layer, or a bot-filter is biasing assignment — the test data is unreliable until that's fixed. Run a chi-square SRM test or use the test platform's built-in alert.
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Run weekly readout for stakeholders
Share a snapshot of conversion rate, lift estimate, and confidence interval — but explicitly do not call the test until the pre-registered sample size is hit. Peeking and stopping early inflates false-positive rate; flag this in the readout if anyone asks to call it early.
Analysis & Rollout
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Confirm the test reached significance
Pull final results once the pre-registered sample size is hit. Report the lift, the 95% confidence interval, and the p-value. If the CI crosses zero, the test is inconclusive — don't roll out the variant on the strength of a directional read.
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Ship the winning variant to 100%
Promote the variant from the test platform into the CMS as the new control. Confirm the GA4 event still fires post-rollout and that the test platform's snippet is removed or archived to avoid stale variant code drag.
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Design a follow-up test for inconclusive results
An inconclusive result usually means the MDE was too small for the traffic, or the variant was not bold enough. Revisit the hypothesis with the growth lead and pick a higher-leverage element (offer, pricing display, hero proof) for the next round rather than re-running the same change.
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Log the test in the experiment repository
Record hypothesis, variant screenshots, sample size, runtime, outcome, and learnings in the team's Notion or Airtable experiment log. This is what prevents the team from re-running the same test in 9 months when the original owner has rotated off.
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