t1k:marketing:iap:pricing-experiments
| Field | Value |
|---|---|
| Module | iap |
| Version | 0.2.5 |
| Effort | medium |
| Tools | — |
How to invoke
Section titled “How to invoke”/t1k:marketing:iap:pricing-experimentsIAP Pricing Experiments
Section titled “IAP Pricing Experiments”This skill designs and interprets A/B experiments on IAP price points — including sample size requirements, experiment duration, sequential testing pitfalls, and geographically split alternatives when platform A/B tooling is unavailable. Monetization PMs and product analysts use this skill when validating a new SKU price before full rollout, when investigating whether a price change will increase or decrease net revenue, and when interpreting inconclusive price test results. It does NOT cover general experiment design (see your analytics team’s experimentation framework) or pricing strategy decisions (see price-tiering and pricing-psychology).
When to Use
Section titled “When to Use”- Considering a price change on an existing SKU and needing statistical validation before full rollout
- Current entry SKU price has not been tested since launch; conversion data is purely observational
- Platform (iOS/Android) A/B tooling is unavailable and a geo-split methodology is needed
Purpose
Section titled “Purpose”IAP price tests are high-stakes because the variable being tested (price) directly affects both conversion rate and average order value, and these two effects pull in opposite directions. A lower price increases CVR but decreases AOV; the correct test measures net revenue per exposed player (ARPPU × CVR), not just one metric in isolation. Sample size requirements are large — detecting a 10% net revenue difference at 80% power requires 50,000–100,000 exposed players per variant at typical mobile CVR rates (1–5%). Sequential testing (peeking before the sample size is reached) inflates false positive rates to 40–60%, making it one of the most common sources of misleading price test conclusions.
Status
Section titled “Status”Stub — deepen iteratively via /t1k:sync-back as real usage surfaces priority. Priority sources for when deepening: Mobile Dev Memo experimentation methodology, GameAnalytics A/B testing guide.
References
Section titled “References”- See brainstorm: theonekit-core/plans/reports/sales-modules-brainstorm-260527-1625.md