A/B Testing Landing Pages for Meta Ads Traffic

A landing-page test isolates a page change only when eligible visitors are assigned consistently and outcomes are measured under a defined design. Two ads pointing to different pages compare advertising packages as well as pages. Use one unchanged ad brief and destination entry route, then assign the page experience separately.

Implement assignment with a maintained tool

GrowthBook’s JavaScript SDK is one documented option. The following is an implementation plan, not a claim that this site has installed it:

• Install @growthbook/growthbook and configure the SDK connection with apiHost and clientKey.
• Provide attributes.id with the stable eligible visitor identifier. Evaluate a feature flag experiment with A/B page values and the intended equal allocation.
• Record experiment.key and result.key from trackingCallback with the assignment identifier in your permitted analytics store.
• Initialize features before rendering the tested content and explicitly handle a load failure.
• Read the page value with getFeatureValue and render the matching page. For an SPA, update the SDK URL on navigation.
• Use a supported sticky bucket service if your experiment supports it. Define consent, anonymous-to-login identity and storage behavior before enrollment.

The SDK documentation establishes those integration mechanisms. The experiment still needs an outcome join, valid identity handling, and QA; installing an SDK does not make the analysis valid.

Test enrollment, repeat visits and missing data

A filled planning card and budget

Illustrative assumptions: independent randomized visitors, baseline purchase rate 3%, meaningful alternative 3.5%, two-sided α = 0.05, power 80%, equal groups, one purchase indicator per visitor. The normal approximation described in the sample-size guide gives approximately 19,749 visitors per group when z values 1.96 and 0.842 are used.

At an assumed $0.80 per eligible visitor, 39,498 visitors cost $31,598.40 in media. At 2,000 eligible visitors per day, collection takes roughly 20 days; a chosen seven-day purchase follow-up makes the final review roughly day 27 after enrollment begins. These are planning inputs, not delivery promises. If the budget is $5,000, it purchases only about 6,250 eligible visitors at that assumed cost: reduce the decision scope or defer the test rather than promise the same detectable effect.

Read 60/2,000 versus 70/2,000 without forcing a winner

Illustrative result: rates are 3% and 3.5%, a 0.5 percentage-point or about 16.7% relative difference. The approximate 95% difference interval is −0.60 to +1.60 percentage points; the pooled two-sided test gives p about 0.37. This outcome is inconclusive under the planned conventional test. Report the interval, costs and guardrails rather than calling B a reliable winner.

Separate a repair log from an experiment

Use confirmed paid outcomes and return or qualification guardrails. Stop operational failures immediately; use the chosen fixed-horizon or valid sequential rule for inferential stopping. ASA guidance explains why a p-value alone cannot determine business value.

Keep the campaign brief consistent

Prepare the unchanged audience, offer and ad message as a reviewable NovaStorm campaign draft. Page construction, GrowthBook assignment and experiment analysis remain separate work. Draft preparation does not create landing pages or randomize visitors.

Sources: GrowthBook JavaScript SDK; ASA statement on p-values.