User Engagement Prediction Model Using Segmentation and Preliminary Actions
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Solution Overview
Problem
Maintaining user engagement in applications over time is challenging due to the difficulty in understanding user actions and preferences, leading to ineffective reengagement strategies that may deter users from interacting with the application.
Innovation Solution
A machine learning-based engagement model is developed to predict user behavior and generate personalized prompts, categorizing users into groups based on similar features to enhance in-application engagement and reengagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reengagement strategies are used without understanding user features, then implementation is simple, but user engagement is lost and users may be deterred from interacting with the application
Solution Approach 1:
The system segments users into distinct groups based on their features, behavior patterns, and engagement characteristics. By dividing the user base into segments, the system can apply tailored reengagement strategies to each group, improving engagement reliability without overwhelming complexity through targeted rather than universal approaches
Solution Approach 2:
The system changes key parameters by using machine learning models to predict user engagement probability and identify influential features. These parameter changes enable dynamic adjustment of reengagement strategies based on predicted user responses, transforming static reengagement attempts into adaptive, data-driven interventions that improve reliability
2Measurement precision
If machine learning engagement models are implemented to predict user behavior, then user engagement prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by training engagement models in advance using historical user data and features. These pre-trained models are ready to predict user engagement probability before reengagement actions are taken, enabling accurate prediction without adding runtime complexity. The heavy computational work is done beforehand, allowing real-time predictions to be simple and efficient
Solution Approach 2:
The machine learning engagement model acts as an intermediary between raw user data and reengagement decisions. This intermediary layer processes and interprets complex user features, translating them into actionable engagement probability scores and feature importance rankings, thereby simplifying the decision-making process while maintaining high measurement precision
Data Source
AI summary
Aspects of the present disclosure relate to generating an engagement model to predict actions that may have a high probability of maintaining user engagement in-application or causing a user to reengage with the application. To generate the engagement model, an approach has been developed which incorporates features analysis of the application and application users. Users may be grouped based on similar features that are used to generate machine learning engagement models. The output of an engagement model may be a prediction on whether a user will continue to engage with an application. The prediction may be provided to a reengagement model which may output prompts to help increase user engagement with the application. The prompts may be based on an understanding of application users and their preferences.