User Group Determination for Churn Identification and Content Selection
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Solution Overview
Problem
Existing systems face challenges in accurately identifying customer churn and selecting appropriate content for presentation to users based on their purchasing and activity patterns across different entities, leading to inefficient targeting and engagement.
Innovation Solution
The system analyzes purchase and activity data to determine Recency-Frequency-Monetary (RFM) metrics for users, clusters them into groups based on these metrics, and identifies customer churn by comparing inactive and active groups across entities, allowing for targeted content selection for presentation to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify customer churn and select content, then the system is simpler to operate, but the accuracy of churn identification and content targeting is insufficient
Solution Approach 1:
The patent segments users into distinct groups (active users, inactive users, churned users) based on their purchase behavior and engagement metrics. By dividing the user base into these segments, the system can apply different content strategies to each group, improving churn identification accuracy without overwhelming system complexity
Solution Approach 2:
The patent utilizes multiple parameters (purchase frequency, recency, monetary value, engagement metrics) to identify and segment users. By changing and analyzing multiple parameters simultaneously, the system achieves more accurate churn identification while managing complexity through systematic parameter evaluation
2Adaptability or versatility
If comprehensive purchase and activity data is analyzed to improve content targeting, then content relevance improves, but data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary segmentation of users into active, inactive, and churned groups before content selection. This preliminary action organizes the data structure in advance, making subsequent content targeting more efficient and reducing the computational burden during actual content delivery
Solution Approach 2:
The patent extracts key metrics (purchase frequency, recency, monetary value) from comprehensive purchase and activity data to create simplified user segments. By taking out only the most relevant features for churn identification, the system achieves effective content targeting without processing the entire dataset in real-time
Data Source
AI summary
One or more computing devices, systems, and/or methods are provided. In an example, purchase data associated with users may be determined. The purchase data may be indicative of purchases by users from entities. The purchase data may be analyzed to determine purchase metrics associated with the users. The purchase metrics may be analyzed to determine sets of groups of users associated with the entities. One or more groups of users, of the sets of groups of users, that include the user may be determined. Content may be selected for presentation via a first device associated with the first user based upon the one or more groups of users.