The Hypothesis

Does enforcing message coherence between an ad and its landing page measurably improve conversion — and can it be automated?

The Concept

When an ad says "See how teams save 5 hours a week" but the landing page leads with a generic product pitch, users bounce. Message match — ensuring the ad's promise is reflected in the landing page headline, hero, and CTA — is a known best practice, but it's almost always manual. This experiment automates it: when the system generates ad creative and a landing page for the same campaign, it enforces coherence. And when either one evolves, the other adapts automatically.

Ad-to-landing-page message match

The hypothesis

Does enforcing message coherence between an ad and its landing page measurably improve conversion — and can it be automated?

The concept

When an ad says “See how teams save 5 hours a week” but the landing page leads with a generic product pitch, users bounce. Message match — ensuring the ad’s promise is reflected in the landing page headline, hero, and CTA — is a known best practice, but it’s almost always manual. This experiment automates it: when the system generates ad creative and a landing page for the same campaign, it enforces coherence. And when either one evolves, the other adapts automatically.

How it works

1. Campaign brief — single brief generates both ad creative and landing page
2. Extract core promise — the key value proposition from the ad copy
3. Enforce on landing page — headline, hero section, and primary CTA reflect the ad’s promise
4. Score match quality — semantic similarity score between ad and page
5. Co-evolve — landing page shifts value prop → ad copy adapts (and vice versa)

Ad and landing page are treated as one unit, not two disconnected assets.

What it explores

• How much does message match actually impact conversion rate?
• Can semantic similarity scoring reliably detect match vs. mismatch?
• Does automated co-evolution maintain coherence as both assets optimise?
• What happens when the optimal ad message and optimal landing page message diverge?
• Is there a minimum match score below which conversion drops sharply?

What we found

• Message-matched campaigns converted 23% higher than mismatched ones
• Semantic similarity score above 0.8 correlated strongly with higher conversion
• Below 0.6, conversion dropped sharply regardless of individual ad or page quality
• Co-evolution maintained coherence for copy-led changes but broke down when visual direction shifted

Learnings

• Semantic similarity scoring works as a deployment gate — a hard threshold eliminates the subjectivity of manual review.

• The biggest match failures come from separate teams owning ads and landing pages — unified generation eliminates the organisational gap entirely.

• Co-evolution needs a shared “core promise” anchor — without it, ad and page optimise in different directions over time.

• Visual coherence matters almost as much as copy coherence but is harder to automate — this is the current blind spot.

Where it goes next

The 0.6 threshold is worth hardening into a deployment gate. We’re also exploring whether visual coherence can be scored the same way — right now it’s the blind spot. This pairs directly with the ad creative generator experiment.