Dynamic Base Price Optimization via Segmented Test Promotions
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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 cannot validate the impact of promotional events on sales volume, leading to inefficient resource allocation and potential long-term damage to brand equity.
Innovation Solution
Implement a forward-looking promotion optimization system that administers test promotions on purposefully segmented subpopulations to gather actual revealed preferences, allowing for iterative testing and validation of promotional variables to maximize sales without exceeding budget, and dynamically adjust discounts based on real-time price fluctuations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If backward-looking aggregate historical data is used for promotion optimization, then data collection is simple, but the ability to account for unanticipated events and validate promotional impact is poor
Solution Approach 1:
The patent segments the population into multiple subpopulations and administers test promotions to each segment separately. This allows for isolated validation of promotional variables on specific demographic or behavioral groups, enabling precise measurement of promotional impact while controlling for external factors that affect aggregate data.
Solution Approach 2:
The patent performs preliminary testing with test promotions on segmented subpopulations before implementing full-scale promotions. This advance testing validates the effectiveness of promotional variables and allows optimization of promotion strategies based on actual revealed preferences from test results, rather than relying on backward-looking aggregate data.
2Measurement precision
If test promotions on segmented subpopulations are administered, then promotional variable validation accuracy improves, but resource allocation complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where results from test promotions on segmented subpopulations are analyzed and used to optimize resource allocation for full-scale promotions. The system learns from test results and adjusts promotion strategies, budget distribution, and target segments based on actual performance data and revealed preferences.
Solution Approach 2:
The patent changes key parameters dynamically based on test results, including which subpopulations receive which promotions, the timing and magnitude of discounts, and the allocation of promotional budgets. This parameter optimization resolves the contradiction by using data-driven decisions to simplify resource allocation despite the complexity of segmented testing.
3Measurement precision
If dynamic base prices are used with predictive modeling, then promotion strategy accuracy improves, but computational requirements increase
Solution Approach 1:
The patent performs predictive modeling and base price optimization in advance before promotions are executed. By pre-calculating optimal base prices and promotion strategies using historical data and predictive algorithms, the system reduces the need for complex real-time computations during actual promotions, thereby lowering computational energy requirements while maintaining high accuracy.
Data Source
AI summary
Methods and apparatus for generating intelligent offers with base prices are provided. In one embodiment, a promotion generator receives a current product base price, and also receives or calculates a remaining promotional program budget, a remaining promotional program duration, and a minimum discounted price for the product using the current product base price and any available previous base price data for the promoted product, creating or updating a predictive model of future product base prices.