Individualized Offer Execution System Using ML Segmentation
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Solution Overview
Problem
Existing systems fail to provide truly customized offers to individual customers due to their inability to identify and cater to unique needs, as they typically treat multiple customer IDs within a segment as identical, leading to arbitrary matching of pre-generated offers.
Innovation Solution
A processor-implemented method using machine learning and analytics to ingest member and third-party data, dynamically evaluate individualized offers by selecting attributes, and generate unique offers for each member ID within a target segment, incorporating numerical and logical parts of base offers with real-time processing of transactional data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-generated offers are targeted toward multiple customers with a specific customer profile, then offer creation is simplified and can be applied at scale, but the offers are arbitrary and do not truly meet the unique needs of individual customers
Solution Approach 1:
The patent segments customers into target segments based on multiple attributes (demographics, behavior, preferences) rather than treating all customers in a segment identically. This allows offers to be tailored to sub-groups within segments, balancing scalability with individualization.
Solution Approach 2:
The system applies different offer attributes (discounts, rewards, promotions) to different customers within the same target segment based on their individual characteristics. This enables localized customization while maintaining a unified offer framework that can be applied at scale.
2Adaptability or versatility
If data segmentation is combined with customer profile to create customized offers, then customer experience is enhanced, but the system cannot identify and cater to unique needs of individual customers on large-scale data
Solution Approach 1:
The patent creates a universal offer template that can serve multiple functions - it can be applied to entire target segments or customized for individual customers within those segments. The same offer framework handles both bulk segmentation and individual customization, maintaining efficiency while enabling personalization.
Solution Approach 2:
The system dynamically adjusts offer parameters (discount percentages, reward types, promotion conditions) based on customer attributes and behavior. This allows the same base offer to be customized for different individuals by changing specific parameters rather than creating entirely separate offers.
3Device complexity
If multiple customer IDs within a segment are treated as identical, then system complexity is reduced and processing is simplified, but truly tailored offers cannot be provided to individual customers
Solution Approach 1:
The system dynamically evaluates customer attributes and adjusts offer assignments in real-time rather than using static segment-based rules. This allows the system to recognize individual customers within segments without requiring complex pre-processing, maintaining simplicity while enabling personalization.
Solution Approach 2:
The patent introduces an intermediary layer between customer segments and offers that evaluates individual customer attributes. This intermediary process automatically matches customers to personalized offers without requiring complex system restructuring, bridging the gap between simple segmentation and individual customization.
Data Source
AI summary
A method for executing an individualized offer for each member ID in a plurality of target segment are provided. The method (i) identifying a target segment for each member ID from the plurality of target segments using a machine learning and analytics model, (ii) selecting a base offer from a plurality of base offers for the target segment, (iii) generating a rule for the base offer associated with the target segment from a rules database, (iv) selecting a set of attributes for the target segment, (v) applying the set of static attributes and the set of mathematical attributes on the base offer, (vi) generating individualized segments of each member ID based on product attributes matching using the rule generated for the target segment, (vii) calculating differential offers for each member ID in the target segment based on to automatically executing the individualized offers for each member ID in target segment.