Customer Lifetime Value Prediction via Segmented Churn Analysis
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Solution Overview
Problem
Existing methods for predicting Customer Lifetime Value (CLV) are inaccurate due to reliance on incomplete parameters, failure to consider customer lifetime and discount rate, and inefficiency in handling large volumes of data, leading to inappropriate predictions across diverse customer segments.
Innovation Solution
A Data Analysis System (DAS) that segments customers based on weighted scores from transaction data, computes churn values, and predicts CLV by analyzing multiple parameters and large volumes of data, including purchasing behavior, to provide accurate and customized predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods use limited parameters for CLV prediction, then the prediction process is simple, but the accuracy of CLV prediction deteriorates
Solution Approach 1:
The patent segments customers into different groups based on their characteristics and behavior patterns. By dividing the customer base into segments, the system can apply different prediction models and parameters to each segment, improving overall prediction accuracy while managing complexity through modular processing of customer groups rather than treating all customers uniformly
Solution Approach 2:
The patent introduces multiple additional parameters beyond traditional CLV calculations, including discount rate, customer lifetime, and segment-specific parameters. This expansion of parameters from simple to comprehensive allows the system to capture more nuances in customer value while using structured approaches to manage the increased complexity
2Productivity
If existing methods process customer data, then some predictions are generated, but the efficiency in handling large volumes of data deteriorates
Solution Approach 1:
By segmenting customers into groups with similar characteristics, the system can process data in manageable batches rather than handling all customer data individually. This segmentation enables efficient parallel processing and reduces the computational burden while maintaining prediction accuracy through segment-specific models
Solution Approach 2:
The patent transforms raw customer data into standardized segments with aggregated characteristics. This parameter transformation from individual customer attributes to segment-level statistics improves processing efficiency while preserving the essential information needed for accurate CLV prediction
3Adaptability or versatility
If existing methods use uniform prediction approach, then the process is simple to implement, but the adaptability to different customer segments deteriorates
Solution Approach 1:
The patent divides the customer base into distinct segments based on behavior, demographics, and other relevant factors. Each segment receives customized prediction models and parameters tailored to its specific characteristics, enabling the system to adapt to diverse customer groups while managing complexity through standardized segmentation criteria and modular model structures
Data Source
AI summary
Method(s) and System(s) for predicting Customer Lifetime Value (CLV) based on segment level churn includes segmenting the customers into multiple segments based on weighted RFM scores associated with data within a dataset. The data is representative of purchasing behavior of customers over a predefined time period. The segmenting is performed such that customers with similar and close weighted RFM scores are placed in one segment. Further, the method includes computing a churn value for each of the customer segments based on the buying behavior of the customers within each segment. The churn value is associated with transaction characteristics associated with customers corresponding to the data in each segment. Expected lifetime period in years for the customers is then predicted from the calculated segment level chum values. Thereafter, CLV, that indicates profitability associated with customers, is predicted for each customer based on their expected lifetime value in years.