App Store Variant Testing for Scalable Conversion Measurement
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Solution Overview
Problem
Existing technologies struggle to efficiently and accurately test multiple variants of software applications in large-scale software application stores, leading to resource-intensive and time-consuming processes for app developers to determine the most effective visual aspects for conversion rate optimization.
Innovation Solution
Implement scalable variant testing systems and methods that utilize statistical regularization, Monte-Carlo simulations, machine learning models, and data engineering to efficiently process conversion data, providing fast Bayesian power analyses and daily user profile aggregation for accurate conversion rate estimation and hypothesis testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional variant testing is performed by individual app developers in a large-scale software application store, then each developer can test their own variants, but the process becomes very resource intensive and time-consuming
Solution Approach 1:
The patent merges individual developer variant testing into a centralized platform-wide testing system. The system aggregates users across multiple applications and combines testing infrastructure, allowing simultaneous testing of numerous variants across the entire app store rather than each developer maintaining separate testing operations. This consolidation resolves the contradiction by improving overall testing efficiency while maintaining measurement precision through unified data collection and analysis.
Solution Approach 2:
The system creates a universal testing platform that serves multiple applications and developers simultaneously. A single infrastructure performs variant testing across diverse app types and user bases, providing multi-functional capability that resolves the resource intensity issue. The platform handles conversion tracking, variant management, and statistical analysis universally, eliminating the need for each developer to build dedicated testing systems.
2Measurement precision
If comprehensive conversion data is collected for all variants across millions of applications, then accurate conversion rate estimation is achieved, but resource consumption and processing time increase significantly
Solution Approach 1:
The patent segments the massive dataset into manageable units organized by application, user, and time period. Rather than processing all conversion data simultaneously, the system divides data into discrete facts tables that can be processed independently and aggregated. This segmentation enables accurate conversion rate estimation while reducing computational resource consumption through parallel processing and optimized data retrieval.
Solution Approach 2:
The system performs preliminary data aggregation and preprocessing before full analysis. Conversion facts are collected and organized in advance into structured formats, with user profiles and application metadata prepared beforehand. This preliminary action reduces the computational burden during actual variant testing by having data ready in optimized structures, thereby reducing processing time and resource consumption while maintaining estimation accuracy.
Data Source
AI summary
This Application sets forth techniques for variant testing at scale. In particular, the embodiments set forth provide systems and methods for testing, on a large-scale software application store, visual aspects of one or more variants of representative data associated with an application available through the software application store. According to some embodiments, a method may include using a subset of conversion data associated with a control object and a subset of the conversion data associated with at least one variant object to compute at least one conversion metric for the control object and at least one conversion metric for the at least one variant object. The method may also include generating a performance measurement by applying at least one statistical hypothesis testing function to the at least one conversion metric for the control object and the at least one conversion metric for the at least one variant object.