Automated User Journey Management for Application Retention
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Solution Overview
Problem
Human analysts face challenges in effectively and reproducibly identifying changes in user retention metrics for specific user segments across application activations, leading to suboptimal user experience personalization.
Innovation Solution
An automated system that detects user interactions and events across multiple applications, trains classifiers based on these interactions and events, and generates personalized offers to renew application activations, optimizing user retention and satisfaction without human input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human analysts manually analyze data attributes to identify user retention changes, then user experience personalization is achieved, but the process is not reproducible and analysts cannot effectively identify changes in metrics corresponding to particular user segment combinations
Solution Approach 1:
The patent replaces the mechanical system of manual analyst review with an automated machine learning classification system. The classifier automatically processes user interaction data, segments users, and identifies retention metric changes without human intervention, thereby improving measurement precision while eliminating the limitations of manual analysis.
Solution Approach 2:
The system enables self-service by allowing the machine learning classifier to autonomously perform the entire analysis process - from data collection and user segmentation to metric evaluation and offer generation. This automated self-service approach resolves the contradiction by making the process both precise and fully automated.
2Productivity
If analysts manually review user data on a daily basis, then some user retention insights are obtained, but the process is time-consuming and not reproducible across different user segments
Solution Approach 1:
The patent substitutes manual analyst processes with an automated machine learning system that processes user data at scale without time constraints. The classifier can evaluate multiple user segments simultaneously, dramatically increasing productivity while eliminating the time loss associated with sequential manual review.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and preprocessing user interaction data in real-time, so that when analysis is needed, the data is already prepared and segmented. This eliminates the time-consuming data preparation step that analysts would otherwise need to perform manually for each review cycle.
3Adaptability or versatility
If human analysts perform repetitive analysis tasks, then user experience optimization is achieved, but the process lacks reproducibility and scalability to specific user segment combinations
Solution Approach 1:
The patent applies segmentation by dividing the user base into distinct segments based on their interaction patterns with the application. The machine learning classifier creates reproducible user segments that can be consistently analyzed across different time periods and applied to specific combinations of user characteristics, thereby improving adaptability while maintaining full automation.
Solution Approach 2:
The system achieves adaptability through parameter changes by dynamically adjusting classification thresholds and offer parameters based on the specific user segment being analyzed. The machine learning model can modify its behavior to suit different user segments while remaining fully automated, resolving the contradiction between versatility and automation.
Data Source
AI summary
An application activation method includes enabling an activation of one or more applications, including an activation of a first application, on a computing device. A first plurality of interactions of a user with the one or more applications on the computing device are detected. A first offer to renew the activation of the first application is generated based on the first plurality of interactions of the user. The first offer is provided to the user via the computing device. An acceptance of the first offer is received from the user, and the activation of the first application is renewed responsive to receiving the acceptance of the first offer.