Loyalty System Customer Segmentation and Targeted Incentives
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Solution Overview
Problem
Existing loyalty programs lack flexibility in rewarding customers based on changing merchant objectives and customer demographics, leading to inefficient benefit accrual and reduced commercial benefits for merchants and card issuers.
Innovation Solution
A system and method that processes transaction data to classify customers into groups based on attributes, generating targeted incentives and rewards, and presenting them in electronic financial card statements, allowing merchants to tailor benefits according to specific customer segments and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional loyalty programs use uniform benefit structures for all customers, then implementation is simple and cost-effective, but customer engagement and commercial benefits are reduced due to lack of personalization
Solution Approach 1:
The patent segments the customer base into distinct groups based on transaction data analysis, allowing different benefit structures to be applied to different segments. This enables personalized loyalty programs while maintaining manageable complexity through systematic classification of customers into manageable groups.
Solution Approach 2:
The system dynamically adjusts benefit accrual rates and structures based on real-time analysis of transaction data, customer behavior patterns, and merchant objectives. This allows the loyalty program to adapt and personalize benefits without requiring complex manual configuration for each customer scenario.
2Loss of information
If loyalty programs track detailed transaction data for each customer, then targeted incentives can be generated, but data processing requirements and system complexity increase
Solution Approach 1:
The patent introduces an intermediary system that automatically analyzes transaction data, identifies customer patterns, and generates targeted incentive recommendations. This intermediary layer handles the complex data processing tasks, allowing merchants to benefit from detailed data utilization without directly managing the computational complexity.
Solution Approach 2:
The system performs self-service data analysis by automatically processing transaction data, identifying customer segments, and generating incentive proposals without requiring manual intervention. This reduces the burden on merchants while still utilizing detailed transaction information for personalized loyalty programs.
3Productivity
If merchants provide significant benefits to acquire new customers, then customer acquisition succeeds, but acquisition costs increase
Solution Approach 1:
The system applies partial action by providing differentiated benefit levels to different customer segments rather than uniform high benefits to all potential customers. This allows merchants to focus resources on customer segments that provide the best return on acquisition investment, reducing overall acquisition costs while maintaining effectiveness.
Solution Approach 2:
The patent implements feedback mechanisms that track the effectiveness of loyalty benefits in driving customer acquisition and retention. This feedback allows merchants to optimize benefit structures based on actual performance data, ensuring that acquisition spending delivers maximum value and can be adjusted based on measured results.
Data Source
AI summary
Systems, methods, devices and computer-readable media for generating incentives for a loyalty system are described. A method includes: receiving, at at least one processor, data reflective of transactions conducted by a plurality of customers; processing, at the at least one processor, the data to determine a set of attributes for each of the plurality of customers; classifying, at the at least one processor, each of the plurality of customers according to at least one of a plurality of customer groups based on the determined set of attributes; and generating, at the at least one processor, an incentive targeting customers in at least one of the plurality of customer groups.