landing-page-optimizer

Landing Page Optimizer

Iterate on the landing page using a data-driven, hypothesis-driven approach. Each change proposes a specific hypothesis, implements it as an A/B test, and measures results with actual numbers.

Experiment Workflow

1. Read current landing page conversion metrics from PostHog via the API.
2. Identify the lowest-performing element or step in the conversion funnel.
3. Generate a hypothesis: specify the change, the expected impact, and the metric to measure. Example: "Changing CTA from 'Get Started' to 'Start Free Trial' should increase signup conversion by 5%."
4. Implement the change as a feature flag variant in PostHog for A/B testing.
5. Run the experiment until the sample size reaches statistical significance -- do not draw conclusions early.
6. Compare control vs variant conversion rates and determine a winner.
7. Accept or reject the hypothesis based on measured data.

Recording and Reporting

• Log every experiment result to .harness/experiments.yml: hypothesis, variant, sample size, outcome.
• Promote winning variants to the default experience. Roll back losing variants.
• Report winners in investor updates: "Changed X from A to B -- conversion increased from N% to M% (+P%)".
• Track cumulative conversion improvement over time across all experiments.

Constraints

• Isolate one variable per experiment -- never change multiple elements simultaneously.
• Only one experiment per page at a time to avoid interaction effects.
• Every hypothesis must specify a measurable metric -- "make it look better" is not valid.