ML Scoring for User Action Sequences and Retention
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Solution Overview
Problem
Users, especially new ones, face difficulties in discovering and accessing new features in computer-based systems, leading to frustration and abandonment due to inadequate user guidance, which is often static and impersonal.
Innovation Solution
A machine learning-based method that generates a sequential database of historical user actions, retrieves retention data, and uses a machine learning model to score potential actions by predicting the likelihood of user engagement and system retention, providing personalized recommendations to improve user guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If static user guidance (tutorials and FAQs) is provided, then users can be educated about new features, but users still have difficulty discovering and accessing features, leading to system abandonment
Solution Approach 1:
The system implements feedback by analyzing user action sequences and providing dynamic recommendations based on observed behavior patterns. The machine learning model continuously learns from user interactions and adjusts guidance recommendations accordingly, creating a closed-loop system that adapts to individual user needs rather than providing static information.
Solution Approach 2:
The system enables self-service by allowing users to receive personalized guidance automatically based on their own action patterns. The machine learning model identifies users who need assistance and provides contextual recommendations without requiring users to actively search for help, making the guidance system serve itself through behavioral analysis.
2Adaptability or versatility
If personalized recommendations are implemented using machine learning, then user guidance effectiveness improves, but system complexity increases
Solution Approach 1:
The machine learning model serves multiple functions: it analyzes user behavior patterns, predicts future actions, identifies users needing assistance, and generates personalized recommendations. This multi-functional approach consolidates what could be separate complex systems into a single versatile model, reducing overall system complexity while maintaining adaptability.
Solution Approach 2:
The machine learning model acts as an intermediary between raw user action data and guidance recommendations. It processes complex behavioral patterns and transforms them into actionable insights, simplifying the connection between user behavior and personalized guidance without requiring direct complex rule-based systems.
3Measurement precision
If machine learning models analyze user behavior patterns, then recommendation accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system applies partial action by focusing computational resources only on users who exhibit patterns indicating they need assistance, rather than analyzing all user behavior uniformly. The machine learning model identifies and prioritizes specific user segments, reducing overall computational energy while maintaining high prediction accuracy for target users.
Solution Approach 2:
The system performs preliminary action by pre-processing and storing user action sequences in a structured format before analysis. This preparation work is done in advance, allowing the machine learning model to work with pre-organized data during runtime, reducing real-time computational energy requirements while maintaining analysis accuracy.
Data Source
AI summary
Systems and methods for scoring potential actions are disclosed. An example method may be performed by one or more processors of a system and include training a machine learning model based at least in part on a sequential database and retention data, identifying an action subsequence executed by a user, generating, for each of a plurality of potential actions, using the machine learning model, a first value indicating a probability that the user will execute the potential action immediately after executing the action subsequence, a second value indicating a probability that the user will continue to use the system if the user executes the potential action immediately after executing the action subsequence, and a confidence score indicating a likelihood that recommending the potential action to the user will result in the user continuing to use the system, the confidence score generated based on the first value and the second value.