Churn Prediction System Using Client Data Segmentation
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Solution Overview
Problem
Human capital management (HCM) systems face challenges in identifying potential client churn related to tax services, as issues often go undiscovered until clients terminate services, leading to difficulties in recruitment and retention.
Innovation Solution
A computer-implemented method and system that aggregates client profile information and tax services data to model at-risk clients by comparing current clients to former clients who have terminated services, identifying dissimilarities to predict potential churn and display at-risk clients on a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional HCM systems are used for tax services, then service delivery is maintained, but client churn is not detected until termination occurs
Solution Approach 1:
The system performs preliminary actions by continuously monitoring client interactions and analyzing sentiment data before churn occurs. It proactively identifies at-risk clients through real-time analysis of support tickets, emails, and communication patterns, enabling early intervention to prevent churn rather than detecting it after termination
Solution Approach 2:
The system implements continuous feedback loops by analyzing client sentiment from various communication channels and feeding this information back to retention teams. The sentiment analysis system monitors changes in client attitudes and provides real-time alerts when negative sentiment patterns emerge, allowing proactive response to potential churn risks
2Measurement precision
If comprehensive client data is collected for churn prediction, then identification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments client data into distinct categories including support ticket data, email communication data, survey response data, and demographic data. Each data type is processed and analyzed separately through specialized algorithms, with results aggregated to form a comprehensive churn risk profile. This modular approach maintains high prediction accuracy while managing system complexity through organized data handling
Solution Approach 2:
The system introduces sentiment analysis algorithms as intermediary components that transform raw, unstructured communication data into structured sentiment scores. These intermediary sentiment metrics serve as bridges between diverse data sources and the final churn prediction model, simplifying the integration of multiple data types while maintaining prediction precision
Data Source
AI summary
An illustrative embodiment provides a computer-implemented method, computer system, and computer program product for identifying potential client chum. Client profile information is aggregated for a set of clients. Tax services data related to providing tax services for the set of clients is aggregated. A number of former clients who have terminated the tax services is modeled according to the client profile information and the tax services data. The client profile information and the tax services data for a number of current clients who have not terminated the tax services is compared to the modeled number of former clients. A number of at-risk clients is identified from among the number of current clients based on dissimilarities between the current clients and the former clients. The number of at-risk clients are displayed on a graphical user interface.