Personalized Retail Pricing Server Using Customer Purchase History
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Solution Overview
Problem
Existing loyalty programs fail to provide personalized discounts and rewards tailored to individual customer needs, interests, and purchasing habits, offering the same incentives to all customers regardless of their unique preferences.
Innovation Solution
A system that utilizes a server with linked databases to store customer data, including purchase history, to generate and display customized offers based on individual customer profiles, allowing customers to select and receive personalized discounts and rewards, and updates their accounts accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a retailer offers the same discount to all loyalty program customers, then implementation is simple and uniform, but the discount does not align with individual customer needs, interests, and purchasing habits
Solution Approach 1:
The patent segments the customer base into individual profiles with unique purchasing habits, interests, and needs. Each customer receives customized offers based on their specific segment characteristics rather than uniform treatment, enabling personalized marketing while managing complexity through systematic segmentation of customer data
Solution Approach 2:
The system performs preliminary analysis of customer purchase history and preferences before generating offers. By pre-segmenting customers and pre-analyzing their buying patterns, the system prepares personalized offer templates in advance, reducing the complexity of real-time decision-making while maintaining high adaptability to individual customer needs
2Adaptability or versatility
If a retailer implements a loyalty program to monitor and provide discounts, then customer retention improves, but the program fails to deliver tailored incentives that match individual customer preferences
Solution Approach 1:
The system continuously monitors customer purchase behavior and uses this feedback to refine and update individual customer profiles. By analyzing purchase history data and adjusting offers based on customer responses, the system ensures that personalized offers remain aligned with evolving customer preferences, effectively utilizing previously underused preference information
Solution Approach 2:
The patent applies local quality by tailoring the characteristics of discounts and offers to match the specific preferences, purchasing habits, and interests of each individual customer. Rather than applying a uniform discount structure, the system customizes offer attributes (such as discount amount, product categories, and timing) to match the local characteristics of each customer profile
Data Source
AI summary
An individualized discount and reward server includes one or more linked databases storing customer data including at least one retailer account and purchase history associated with the customer. The server including a processor to allocate at least one customized offer to the retailer account using at least the purchase history associated with the customer, the at least one customized offer including a loyalty adjustment changing a general market price point of a retail product in accordance with a competitor price. The processor further displays to the customer, via the interface, a display page including the at least one customized offer and an option for selection of the at least one customized offer. The processor also updates, upon reception of the customer input indicating selection of the at least one customized offer, the retailer account to indicate selection of the at least one customized offer.