Paid-Social Landing Pages: A Controlled Experiment

For the surrounding account decisions, compare Paid-Social Landing Pages: An Evidence Checklist and use Paid Acquisition Retention: An Experiment Design as the next diagnostic.

In short

This guide owns one decision artifact: the filled, auditable structure below. Reader-supplied thresholds stay explicit; missing evidence stays missing.

Hero-evidence page test

• Cell A: Control page retains the current hero claim and layout.
• Cell B: Variant changes one element only: adds the named substantiation artifact directly beneath the unchanged hero claim.
• Falsifiable expectation: The evidence placement changes matured contribution per eligible session; equality or reversal falsifies the directional expectation.
• Held invariant: Ad, audience, offer, price, inventory, page code outside the evidence block, checkout, traffic allocation, device eligibility, event schema, and attribution.
• Budget allocation: Declare eligible traffic budget B; route B ÷ 2 concurrently to each page version with a stable assignment key.
• Maturity window: Run through the preregistered session window plus purchase, cancellation, and return cutoff.
• One primary metric: Matured contribution per eligible session = (recognized revenue − COGS − fulfilment − shipping − payment fees − refunds/returns − allocated media spend) ÷ (eligible assigned sessions). Source: assignment log, page analytics, spend, and commerce/cost ledger; limitation: unmatched sessions remain classified.
• Stop rule: Stop for claim inconsistency, page failure, cash, inventory, consent, or tracking-integrity risk.
• Inconclusive rule: Inconclusive when assignment balance misses tolerance, the page differs beyond the one evidence block, sample/power rule is unmet, cross-version contamination occurs, or outcomes remain immature.

Interpretation boundary

Use the single hero-evidence page change only for its stated decision. Keep the control intact and add only the named substantiation artifact beneath the unchanged claim in the variant, with stable session assignment. Extra page differences, assignment imbalance, contamination, insufficient power, or immature contribution invalidate the page read. Reader-supplied thresholds remain inputs, not universal standards.

Can software help?

Bach.ai audits your connected Meta account, estimates the revenue impact of what it finds, and proposes specific fixes. It applies a change only after you approve it. Think of it as an automated audit layer that surfaces issues and proposed fixes for your review — not a replacement for your team’s judgment, and creative production is not its core job, though the Pro and Agency plans can generate a limited number of variants.

FAQ

How do you isolate one hero-evidence change on a landing page?

Keep the control intact and add only the named substantiation artifact beneath the unchanged claim in the variant, with stable session assignment.

What invalidates a paid-social landing-page experiment?

Extra page differences, assignment imbalance, contamination, insufficient power, or immature contribution invalidate the page read.

When can the landing-page test support a causal interpretation?

It compares the declared cells in the single hero-evidence page change. A causal interpretation additionally depends on valid assignment, stable invariants, adequate power, and contamination within the preregistered limit.

Method and sources

“Keep the control page intact and add only the named substantiation artifact, so that a single variable separates the two cells.”

Source: Where this guide describes platform behaviour, it follows Meta’s published advertising and Marketing API documentation, which changes without notice — verify anything load-bearing against the current version before you act on it. Every threshold the guide asks you to supply is first-party, drawn from your own account exports and commerce ledger, because no external benchmark can stand in for your own margin structure.