Iterative Bidding System for Optimized Offer Allocation
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Solution Overview
Problem
Retail stores face challenges in effectively distributing limited offers to target customers due to high costs and low redemption rates when offering discounts or promotions to a large customer base, as the likelihood of customers using the offers is often small.
Innovation Solution
A system that allows customers to bid on offers based on score values and current costs, iteratively assigning bids to maximize the aggregate value for all customers, using a memory unit, current cost unit, and bid establishing unit to manage and update offers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If offers are provided to a large number of customers, then the coverage and visibility of promotions are improved, but the cost increases and the likelihood of redemption decreases
Solution Approach 1:
The system applies local quality by differentiating offer distribution across different customer segments. Instead of uniform distribution, it identifies specific target customers with high redemption likelihood and directs offers to them, while limiting or excluding offers to low-probability customers. This segmented approach optimizes resource allocation by concentrating promotional resources where they generate maximum value.
Solution Approach 2:
The system changes the parameter of customer selection from broad inclusion to targeted filtering based on predictive analytics. By adjusting the selection criteria parameters (using score values derived from customer data), the system transforms the offer distribution strategy from quantity-driven to quality-driven, achieving better cost-efficiency through precise parameter optimization.
2Quantity of substance
If offers are provided to a large number of customers, then the coverage and visibility of promotions are improved, but the redemption rate decreases
Solution Approach 1:
The system enhances redemption reliability by applying local quality principles to customer targeting. It identifies and focuses on specific customer segments with locally optimized characteristics (high engagement, relevant purchase history, demonstrated responsiveness to similar offers) rather than applying uniform distribution. This concentrated approach to high-probability customers maintains higher redemption rates.
Solution Approach 2:
The system performs preliminary action by pre-screening and scoring customers before offer distribution. Using predictive analytics and score values calculated in advance, it identifies customers most likely to redeem offers before the actual offer is sent. This preliminary filtering action ensures that offers reach only those with high redemption probability, thereby maintaining high redemption rates.
3Productivity
If the system uses iterative bidding to maximize aggregate value, then the allocation efficiency is improved, but the computational complexity increases
Solution Approach 1:
The system applies dynamics by implementing an iterative bidding mechanism where offer allocations are not static but dynamically adjusted through multiple rounds of bidding. Customers submit bids, allocations are made, and the process repeats with updated information until convergence to optimal allocation. This dynamic iterative process enables efficient resource allocation while managing computational complexity through structured convergence criteria.
Solution Approach 2:
The system incorporates feedback mechanisms in the iterative bidding process. Each bidding round provides feedback on allocation outcomes, which informs subsequent bidding decisions. This feedback loop allows the system to learn from previous allocations and progressively optimize the distribution, achieving high allocation efficiency through information feedback while keeping computational complexity manageable through iterative refinement rather than exhaustive optimization.
Data Source
AI summary
A system can perform certain acts. The acts can include determining score values to identify a plurality of target customers associated with a plurality of potential offers. The acts can include receiving bids from the plurality of target customers for the plurality of potential offers. The acts can include performing an iterative process for each respective target customer of the plurality of target customers to take turns to submit a respective bid for each respective potential offer of the plurality of potential offers associated with the respective target customer. The acts can include determining a respective final bid for each of the plurality of potential offers such that an aggregate value for the plurality of target customers that can be maximized across the plurality of potential offers. The acts can include sending instructions to deliver the plurality of offers to at least a portion of the plurality of target customers.