Graph-Based Churn Prediction Using Clickstream Data
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Solution Overview
Problem
Existing systems face challenges in predicting and preventing user churn from software applications, as they lack effective methods to identify users who are likely to stop using the application and fail to take proactive measures to retain them.
Innovation Solution
A method and system utilizing clickstream data to generate graphs, where nodes and edges represent user interactions, allowing for the identification of churn likelihood through active and churn user counts, enabling updates to the user interface or messaging to reduce churn by targeting high-risk users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional churn prediction methods are used, then the system lacks the capability to identify at-risk users, but implementing graph-based analysis increases system complexity
Solution Approach 1:
The patent transitions from traditional single-dimensional user analysis to multi-dimensional graph-based analysis by representing users, events, and interactions as nodes and edges in a graph structure. This dimensional transformation enables capturing complex user behavior patterns across multiple dimensions (user attributes, event sequences, interaction frequencies) that cannot be analyzed using conventional methods, thereby improving churn prediction accuracy while the graph abstraction manages the complexity through structured relationships.
2Measurement precision
If graph-based analysis is implemented to improve churn prediction, then identification of at-risk users improves, but data processing requirements increase
Solution Approach 1:
The patent segments the overall churn prediction task into distinct graph processing operations: graph construction from clickstream data, graph traversal to identify patterns, metric calculation for each user-node, and threshold-based classification. This segmentation allows processing large volumes of clickstream data in manageable stages, reducing memory requirements and enabling incremental computation, thereby handling increased data processing requirements through structured breakdown of the analysis pipeline.
Solution Approach 2:
The patent performs preliminary actions by pre-processing clickstream data into graph structures, pre-calculating user behavior metrics, and pre-identifying potential churn patterns before final prediction. This preliminary processing organizes raw data into meaningful graph representations with pre-computed attributes, reducing the computational burden during actual prediction phases and enabling faster real-time churn assessment when needed.
3Reliability
If proactive churn prevention measures are implemented, then user retention improves, but intervention costs increase
Solution Approach 1:
The patent applies local quality by targeting churn prevention interventions specifically to identified at-risk users rather than applying uniform treatments to all users. The graph-based analysis identifies specific users with high churn probability based on their unique behavior patterns, allowing customized interventions (such as targeted notifications, personalized offers, or specific feature recommendations) to be applied only where needed, thereby improving retention effectiveness while minimizing unnecessary intervention costs for low-risk users.
Data Source
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AI summary
A method implements churn prevention using graphs. The method includes receiving clickstream data, which includes an event, of a user session with an application. The method further includes identifying the event as corresponding to a churn user account and mapping the event to a pair of nodes of a graph. The method further includes updating a churn user count of the pair of nodes in response to identifying the event as corresponding to the churn user account. The method further includes identifying an edge of the graph, corresponding to the pair of nodes. The method further includes updating a value of the edge using an active user count and the churn user count presenting an update responsive to the value.