Rules
Part of Conversion rate optimization: parts worth your attention
Conversion rate optimization checklist: one experiment
A conversion rate optimization checklist for one experiment: hypothesis, baseline, plan, variant checks, analysis and the rollout decision.
What to take away
- A checked box permits a bounded test. It does not guarantee a lift or justify a claim.
- Fix the hypothesis, the stopping rule and the decision rule before any traffic is assigned.
- Stop when measurement, accessibility, informed choice, security, operations, or rollback is not under control.
- Give every item an owner, evidence link, date, status, exception, and review trigger.
A test runs in a fixed order: hypothesis, baseline, instrumentation, experiment plan, quality checks, analysis, then the rollout decision. The conversion rate optimization work around it matters, but this order keeps one experiment honest from first note to final call.
Write the hypothesis first
A test starts from evidence about a barrier, not from a favorite idea. Review funnel exits, form errors, support, search terms, objections, complaints, cancellations, and returns, and observe suitable customers attempting the task.
Then write the audience, the change, the outcome, the minimum useful effect, the mechanism, and the disconfirming evidence. Prioritize by evidence, value, feasibility, reversibility, accessibility, capacity, and risk.
Lock the baseline and the events
Baseline definition checklist
- Define eligible population
- Define state change
- Define event and value
- Define window and counting rule
- List exclusions
- Separate primary outcome from diagnostics
- Maintain an event dictionary with triggers, parameters, identity, consent, owner, and latency.
- Reconcile analytics with CRM, payment, fulfillment, or another final system of record.
For cross-channel campaigns, keep channel and device context beside the baseline so outcomes can be read across the customer journey. Preserve any change to the definition, denominator, identity rule, or reporting delay.
Worked example (illustrative). Item: reconcile analytics with the CRM. Owner: analytics lead. Evidence: reconciliation sheet saved with the test plan. Date: two weeks before test start. Status: passed, no exception. Review trigger: any change to the CRM event mapping.
Fix the plan before traffic arrives
- Record objective, assignment unit, control, variants, audience, allocation, metrics, and guardrails.
- Choose the statistical method in advance (a fixed-horizon test, a Bayesian approach or a sequential design), along with the sample approach, duration, stopping rule, exclusions, and decision rule.
- Set pause rules for broken transactions, customer harm, sample mismatch, missing data, and capacity overload.
NIST's engineering handbook says that planning an experiment begins with carefully considering what the objectives are. Record execution checks, analysis, interpretation, and the decision route, and obtain qualified review when the method or stakes require it.
Check the variant before launch
Test control and variant journeys across channels, devices, browsers, consent states, and error paths. Check targeting, persistence, mutual exclusion, performance, security, localization, consent, and rollback. Then look for missing events, duplicates, assignment balance, bots, staff traffic, and reporting delays.
Test keyboard use, focus, screen-reader output, reflow, zoom, touch, and interruption recovery against the W3C Web Content Accessibility Guidelines (WCAG) 2.2. Show price, renewal, delivery, data use, eligibility, limits, and cancellation before commitment.
Reject hidden information, preselected extras, false urgency, disguised advertisements, and obstruction, the dark patterns the Federal Trade Commission describes in its 2022 staff report, Bringing Dark Patterns to Light. Where an input error is detected and a correction is known, the form should suggest it.
Analyze and decide
- Report counts, rates, absolute and relative differences, uncertainty, runtime, exclusions, and missingness.
- Review the primary outcome, guardrails, mechanism, segments planned in advance, and operational effects.
- Match cohorts to acceptance, payment, delivery, activation, support, returns, retention, contribution, and fraud.
- Approve rollout, revision, retest, or stop; name the owner, monitor persistence, and retain rollback.
- Archive research, plan, screenshots, code, incidents, analysis, limits, decision, and later results.
Read the result against the pause and stop rules
| What the test shows | Check before acting | Likely decision |
|---|---|---|
| Primary outcome and guardrails hold | Owner, monitoring, and rollback are in place | Approve rollout |
| Result is unclear or the sample does not match | Missing data, exclusions, assignment balance | Revise or retest |
| Transactions break or customers are harmed | Pause rules named in the plan | Stop |
| Downstream quality is unknown | Cohorts matched to payment, delivery, returns | Hold the rollout |
Record privacy and security before launch
For California consumers, assess whether the California Consumer Privacy Act (CCPA), as amended by the California Privacy Rights Act (CPRA), applies to the test, and identify other applicable federal and state requirements before launch. Keep the evidence beside the test plan.
For any third-party testing tool, add security questions. CISA's software acquisition guide addresses software assurance and cybersecurity transparency in the acquisition process. It was written for government buyers, so treat it as a source of questions, not as approval.
Common questions
Who owns the CRO checklist?
One accountable experiment owner should coordinate measurement, research, design, accessibility, engineering, privacy, operations, and commercial approvals according to risk.
Does every checklist item apply to every test?
The control areas apply, but the depth should match the affected decision, data, population, implementation, and possible harm.
When should the checklist be repeated?
Repeat it for every test and again before permanent rollout, especially after changes to code, targeting, metrics, price, terms, audience, or operations.
