Collaborative Offer Portal for Consumer ROI Optimization
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Solution Overview
Problem
Current promotion optimization methods rely on backward-looking, aggregate historical data, which fails to account for unanticipated events and individual consumer behavior, leading to inefficient promotion strategies and high costs, while also being opaque and ineffective in managing promotion generation and redemption between retailers and manufacturers.
Innovation Solution
A collaborative offer portal system that uses transaction logs to calculate ROI for targeted consumer groups, optimizing offer structures and redemption rates, allowing for forward-looking promotion optimization and democratized redemption across retailers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If backward-looking aggregate historical data is used for promotion optimization, then implementation simplicity is maintained, but promotion effectiveness and accuracy deteriorate
Solution Approach 1:
The patent segments the aggregate historical data into individual consumer-level records, allowing analysis at the consumer segment level rather than relying on aggregated totals. This segmentation enables precise measurement of individual consumer responses to promotions while maintaining analytical rigor.
Solution Approach 2:
The patent transitions from backward-looking aggregate data to forward-looking predictive modeling by adding a temporal dimension. The system uses historical data to train machine learning models that predict future consumer responses, moving from past aggregate outcomes to future individual predictions.
2Device complexity
If traditional promotion optimization methods are used, then operational complexity is minimized, but cost efficiency and return on investment deteriorate
Solution Approach 1:
The system enables automated self-service promotion optimization through machine learning models that automatically analyze consumer data, predict responses, and generate optimized promotion strategies without requiring complex manual operational processes. The automation reduces both operational complexity and costs.
Solution Approach 2:
The patent changes key parameters from aggregate-level promotions to individual consumer-level targeted promotions. By modifying the granularity and targeting parameters, the system achieves higher cost efficiency and ROI while the automated machine learning framework keeps operational complexity manageable.
3Device complexity
If opaque promotion management systems are used, then system simplicity is maintained, but collaboration effectiveness between retailers and manufacturers deteriorates
Solution Approach 1:
The patent introduces a shared digital platform as an intermediary between retailers and manufacturers. This platform provides transparent visibility into promotion performance, consumer responses, and redemption data, enabling effective collaboration while maintaining manageable system complexity through standardized interfaces.
Solution Approach 2:
The system implements real-time feedback mechanisms that provide both retailers and manufacturers with visible data on promotion performance, consumer responses, and redemption rates. This transparency enables collaborative optimization while the automated feedback loops maintain system simplicity.
4Device complexity
If aggregate historical data is used, then data processing simplicity is maintained, but consumer behavior prediction accuracy deteriorates
Solution Approach 1:
The patent segments aggregate historical data into individual consumer records with detailed attributes and purchase histories. This segmentation enables machine learning models to learn from individual consumer behaviors while the automated data processing framework maintains operational simplicity.
Solution Approach 2:
The patent replaces manual data processing methods with automated machine learning algorithms. The machine learning models automatically process and analyze consumer data at scale, achieving high prediction accuracy while the automation maintains data processing simplicity despite the complexity of the algorithms.
Data Source
AI summary
Systems and methods for a collaborative offer portal is provided. A proposed offer is received from a manufacturer, including an offer structure and a number of consumers they wish to target. Transaction logs of a retailer are accessed to determine an audience for the offer by calculating a return on investment (ROI) for the customer base using the retailer's records given the offer type. The consumers are then grouped by their ROI distribution, and the ROI for the deal is calculated based upon the offer size in light of this distribution. From the offer ROI a discount percentage to be paid by the retailer versus the merchant can be created. The retailer may then choose to accept the offer for deployment.