In-App Purchase Tier Optimization and Price Elasticity Modeler

Role: Principal App Store Monetization Engineer & Quantitative Analyst

Context

• Target App Identifier: {{target_app_id}}
• Historical Transaction Dataset: {{historical_iap_csv}}
• Global Currency FX & Purchasing Power Parity Matrix: {{currency_matrix_json}}
• Prior Elasticity Mean Estimate: {{elasticity_prior_mean}}
• Statistical Confidence Threshold: {{confidence_threshold}}
• Target App Store Platforms: {{target_store_platforms}}

Task

Develop a modular, production-ready Python script that analyzes historical store transactions, estimates price elasticity of demand via empirical Bayesian regression across localized storefronts, and computes revenue-maximizing price point allocations for {{target_app_id}} across {{target_store_platforms}}.

Method

1. Ingest transaction logs from {{historical_iap_csv}} and validate categorical schemas across {{target_store_platforms}}.
2. Normalize multi-currency historical transactions using {{currency_matrix_json}} to decouple currency fluctuation from demand shifts.
3. Implement a log-log demand regression model incorporating {{elasticity_prior_mean}} as a Bayesian prior.
4. Compute price elasticity coefficients (epsilon) per localized storefront with uncertainty intervals at {{confidence_threshold}}.
5. Map continuous optimal price curves to discrete Apple App Store / Google Play tier matrix increments.
6. Simulate net revenue variations factoring in platform commission tiers (15% small business vs 30% standard).
7. Output synthetic validation tests and a structured CLI execution harness with parameter logging.

Constraints

• MUST output runnable Python 3.10+ code using NumPy, SciPy, and Pandas without pseudo-code placeholders.
• MUST NOT hardcode currency conversion rates; all FX mappings must ingest from {{currency_matrix_json}}.
• Elasticity calculations MUST reject sample cohorts below statistical significance at {{confidence_threshold}}.
• Error handling MUST catch zero-variance pricing windows and log mathematical boundary warnings.

Output format

Executable Python script organized as follows:

• Header docstring with mathematical definitions and input expectations
• Imports and configuration dataclasses
• Core analytics classes (DataCleaner, ElasticityModeler, TierOptimizer)
• CLI entrypoint (if __name__ == '__main__':) accepting runtime arguments
• Formatted markdown execution guide appended as a concluding comment block

Self-review

• Ensure Bayesian prior calculations avoid matrix singularity issues during inversion.
• Verify discrete tier mapping strictly adheres to standard App Store pricing brackets.
• Confirm all {{target_store_platforms}} commission logic branches are mathematically sound.