Self-optimizing personalized landing pages
Tryozi matches each landing page to the visitor's intent.
Landing pages adapt to campaign context using approved variants, while continuous experimentation shows which experiences deserve more traffic.
The impact
23%
higher intake-start conversion
Self-optimizing personalized landing pages
Case study facts
- Industry
- Telehealth
- Team
- Marketing
- Systems connected
- 4
- Time to live
- 3 weeks
- Engagement
- AI implementation
The impact
Results for the team
- higher intake-start conversion
- 23%
- higher intake-start conversion
- More visitors start intake from landing pages that use approved messaging matched to their campaign context.
- more page variants tested
- 5×
- more page variants tested
- The marketing team tests more page variants within approved claim and offer limits.
- less time preparing variants
- 72%
- less time preparing variants
- Approved page components and messages reduce the work needed to assemble and check each new variant.
Situation
Different intentions. The same page.
One page has to serve visitors arriving with different expectations. Manually creating campaign variants makes learning slow, while unconstrained optimization risks changing claims and offers the business has not approved.
Challenge
More variants needed clearer boundaries.
Personalization had to reflect arrival context while preserving approved claims and offers. The team also needed dependable experiment assignment and measurement before treating a change as an improvement.
What we built
Relevant pages within approved rules.
The optimization agent assembles pages from approved content components using campaign and consented session context. Experiments compare variants against a defined conversion event and guardrail measures.
Results drive traffic allocation and the next variant proposal, while any material claim or offer change returns to human review instead of being published by the optimizer.
- Read arrival context
Use campaign and available session signals, never guessing sensitive traits.
- Select approved content
Draw only from approved messages, layouts and offer boundaries.
- Assemble the page
The agent composes a coherent variant matched to the visitor's intent.
- Validate before launch
Claims, event tracking and usability are checked before any exposure.
- Run the experiment
Variants compete against a control within agreed guardrails.
- Propose the next iteration
Measured outcomes steer the next variant and traffic allocation.
Evals and guardrails
Test generated pages. Keep changes within bounds.
Page-generation evals
Check generated pages against approved claims and layout rules. Repeat tests with missing or conflicting campaign information.
Experiment evals
Test that visitors stay in the same test group and actions are recorded correctly. Check groups stay separate before changing traffic.
Guardrails
Use only approved page elements and claims. Block guesses about sensitive personal traits and require review of new claims or offers.
Results
Page learning carries into the next test.
More visitors start intake from landing pages that use approved messaging matched to their campaign context. The marketing team tests more page variants within approved claim and offer limits.
Approved page components and messages reduce the work needed to assemble and check each new variant.
- higher intake-start conversion
- 23%higher intake-start conversion
- more page variants tested
- 5×more page variants tested
- less time preparing variants
- 72%less time preparing variants
From Tryozi
“We had tried other partners and nobody could unblock it. Revensi came in, understood the problem quickly, and turned a bottleneck into something that just runs. Total game changer.”
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