Shopper Valuation System Using Machine Learning for Dynamic Fee Reduction
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Solution Overview
Problem
Current loyalty programs often require merchants to pay incremental fees based on customer profiles, regardless of whether rewards are offered, leading to inefficiencies and potential misallocation of marketing efforts.
Innovation Solution
A shopper valuation system that uses machine learning to determine shopper values based on historical purchase data, including payment types and product categories, and offers digital coupons as incentives to enroll in retailer loyalty programs, allowing for targeted marketing and fee structures aligned with customer value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If merchants pay incremental fees based on customer profile groups in loyalty programs, then merchants can target specific customer segments with rewards and offers, but merchants incur fees regardless of whether rewards are actually offered to customers
Solution Approach 1:
The patent changes the parameter of fee calculation from a fixed profile-group basis to a dynamic basis that considers actual reward offer status. The system calculates shopper values and determines whether to offer rewards based on real-time analysis, transforming the fee structure from static to adaptive, thereby eliminating payments for unused targeting capabilities.
Solution Approach 2:
The system enables automatic determination of shopper values and reward offer decisions through machine learning algorithms and predictive analytics. The merchant system autonomously evaluates whether to offer rewards based on calculated shopper values, eliminating the need for manual review and ensuring fees are only incurred when rewards are actually provided.
2Ease of operation
If loyalty programs use broad customer profile groups for targeting, then merchants can simplify program management, but marketing efforts may be misallocated to low-value customers
Solution Approach 1:
The patent applies local quality by assigning different levels of analytical depth to different shoppers based on their individual characteristics and value potential. High-value shoppers receive more sophisticated predictive modeling and personalized reward strategies, while lower-value shoppers receive standardized approaches, optimizing both precision and operational efficiency across the customer base.
Solution Approach 2:
The system segments customers into dynamically formed groups based on calculated shopper values rather than static demographic profiles. This segmentation allows the system to apply different marketing strategies and fee structures to different value segments, maintaining simplicity through automated grouping while achieving precision through value-based differentiation.
3Reliability
If merchants offer rewards to all customers in a profile group, then customer engagement may increase, but marketing budget is wasted on low-value customers
Solution Approach 1:
The patent implements partial action by offering rewards selectively to only those customers within profile groups who are predicted to have high shopper values and are most likely to respond positively to rewards. Rather than offering rewards to all customers in a segment, the system identifies and targets the subset most likely to generate positive returns, optimizing budget allocation while maintaining engagement effectiveness.
4Ease of manufacture
If merchants use traditional coupon-based promotions, then customers can receive discounts, but the system lacks personalization and targeting precision
Solution Approach 1:
The patent transforms static, one-size-fits-all coupon promotions into dynamic, personalized reward offers. The system continuously calculates shopper values, predicts customer responses, and adjusts reward offerings in real-time based on individual shopper characteristics, purchase history, and predicted future behavior. This dynamic approach maintains the simplicity of automated promotion delivery while achieving high personalization through adaptive algorithms.
Data Source
AI summary
A shopper valuation system may include a user device associated with a given shopper, and a shopper valuation server. The server may obtain historical purchase data associated with shoppers at a given retailer, apply a machine learning algorithm to determine a corresponding shopper value for each of the shoppers, and obtain historical purchase data associated with the given shopper at the given retailer. The server may apply the algorithm to determine a shopper value for the given shopper based upon the historical purchase data for the shoppers and, the corresponding shopper values for the shoppers, and update the machine learning algorithm based upon the shopper value for the given shopper. The server may also communicate a notification to the user device soliciting the given shopper to enroll in a retailer loyalty program for payment to the given shopper of the shopper value for the given shopper.