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#A/B Testing

A/B Testing on The Latent Daily: 2 prompts. News, explainers and guides on A/B Testing, each linked to its sources.

2 items

Prompts tagged #A/B Testing

Marketingintermediate

Ad copy variants for A/B testing

You are a performance marketer. Write ad copy variants designed for a clean A/B test. Product or offer: [WHAT YOU SELL AND THE SPECIFIC OFFER] Audience: [WHO WILL SEE THE AD] Platform and placement: [e.g. "search ads", "Instagram feed", "LinkedIn sponsored post"] Character limits: [e.g. "headline 30, description 90"] Proof points I can use: [REAL NUMBERS, REVIEWS, AWARDS, GUARANTEES] Words or claims to avoid: [e.g. "cheap", "best", regulated claims] Landing page promise: [WHAT THE PAGE AFTER THE CLICK SAYS] Create 5 variants. Each must test ONE angle: A. Main benefit B. Pain point or problem C. Social proof (use only my proof points) D. Urgency or a specific offer detail (only if the offer is real) E. Curiosity or a question For each variant give: - Angle and the hypothesis in one sentence ("Readers who ... will click because ...") - Headline and description within the character limits (show character counts) - Call to action Then recommend which two variants to test first and why, and what minimum result would make you call a winner. Keep every variant consistent with the landing page promise.

Works with: ChatGPT, Claude, Gemini, Any LLM

Data analysisadvanced

Interpret A/B test results without fooling yourself

You are an experienced experimentation analyst. Help me interpret this A/B test honestly. Hypothesis: [e.g. "A shorter checkout form will increase completed purchases"] Primary metric: [e.g. "conversion rate = purchases / visitors who reached checkout"] Secondary and guardrail metrics: [e.g. "average order value", "refund rate", "page load time"] Planned design: [e.g. "50/50 split, planned sample of 20,000 per variant, 2 weeks, two-sided test, alpha 0.05"] What actually happened: [e.g. "ran 16 days; we checked results daily"] Results: | Variant | Users | Conversions | [OTHER METRICS] | | Control | [N] | [N] | | | Treatment | [N] | [N] | | Please: 1. Calculate the conversion rate per variant, the absolute and relative difference, and a 95% confidence interval for the difference. Show the formula and the numbers so I can verify them. 2. Check for pitfalls: sample ratio mismatch (is the split close to plan?), peeking or stopping early, multiple metrics or segments tested, novelty effects, too short a run to cover weekly cycles. 3. Look at guardrail metrics for harm. 4. Give a recommendation: ship, do not ship, or run longer, with the main reason. 5. Write a 3-sentence summary for non-technical stakeholders, including uncertainty. Do not claim certainty the data cannot support. If a calculation needs information I did not give, say so.

Works with: ChatGPT, Claude, Gemini