Predictive Machine Learning Model for Customer Purchase and Lapse Analysis
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Solution Overview
Problem
Financial services entities face challenges in identifying suitable additional products for existing customers and predicting the likelihood of policy lapses, as conventional methods require labor-intensive needs analysis and are not efficient in navigating the broad product offerings.
Innovation Solution
A processor-based method using predictive machine learning models, specifically gradient boosting and logistic regression, to analyze customer purchase history and profile data, categorizing customers into target and non-target groups for product offerings and predicting lapse likelihood, thereby facilitating personalized marketing and retention strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional needs analysis methods are used to identify suitable products for customers, then product suitability can be determined, but the process requires labor-intensive manual analysis and is time-consuming
Solution Approach 1:
The patent replaces manual needs analysis with automated machine learning models that process customer data and predict product suitability. The system uses trained models to automatically score and rank products for each customer based on their profile and purchase history, eliminating the need for labor-intensive manual analysis while maintaining or improving accuracy.
Solution Approach 2:
The system enables self-service by automatically generating product recommendations without requiring manual intervention. The machine learning models autonomously analyze customer data, determine product suitability, and provide actionable insights to financial advisors, allowing the system to serve itself in the analysis process.
2Adaptability or versatility
If a broad product offering is provided to customers, then more suitable products can be found, but it becomes difficult to navigate and identify appropriate products
Solution Approach 1:
The patent segments the broad product offering into personalized recommendations for each customer. The machine learning models analyze individual customer profiles and generate tailored product lists, breaking down the overwhelming broad offering into manageable, relevant segments specific to each customer's needs and preferences.
Solution Approach 2:
The system applies local quality by providing different product recommendations to different customers based on their specific characteristics. Each customer receives a customized view of the product portfolio with products ranked by suitability, ensuring that the broad product offering is presented in a way that is locally optimized for each individual customer.
3Loss of information
If manual methods are used to determine customer motivations for purchase, then understanding can be gained, but the process is labor-intensive and inefficient
Solution Approach 1:
The patent replaces manual analysis of customer motivations with automated machine learning models that process purchase history and profile data to infer motivations. The models automatically identify patterns and drivers behind customer purchasing behavior, maintaining deep understanding while dramatically improving efficiency and scalability.
4Measurement precision
If conventional methods are used to predict policy lapses and customer value, then insights can be obtained, but the determination is labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual assessment of lapse risk and customer value with automated machine learning models. The system processes customer data through trained models that predict lapse likelihood and calculate customer value metrics automatically, maintaining prediction accuracy while eliminating the time-consuming manual processes.
Data Source
AI summary
A processor-based system and method retrieve customer purchase history information from an internal customer purchase history database for a plurality of customer records representing customers that previously purchased products of an enterprise, and retrieve customer profile information for each customer record. The processor executes a predictive machine learning model to determine a set of product purchase scores for each of the customers by applying a logistic regression model utilizing gradient boosting to the customer purchase history information and the customer profile information. The processor classifies the customers into a target customer group and a non-target customer group by applying a classification criterion to the set of product purchase scores, and generates a report of customers in the target customer group including highest product purchase scores and products recommended for cross-sale. In some embodiments, the predictive machine learning model is configured to forecast likelihood that given customers will lapse in payment.