Promotion Pricing Parameter Optimization via Predictive Revenue Modeling
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Solution Overview
Problem
Current methods for determining promotion pricing parameters are inefficient and prone to manual errors, leading to potential losses for both merchants and promotion and marketing services due to inadequate return on investment and imbalance in profitability.
Innovation Solution
A method and apparatus that utilize predictive models, such as demand and margin models, derived from historical promotion performance data to determine optimal promotion pricing parameters, ensuring a positive return on investment for merchants while maintaining a favorable margin for the promotion and marketing service, by generating a revenue equation and selecting input sets of promotion pricing parameters to maximize estimated revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to determine promotion pricing parameters, then the process is simple to implement, but it is prone to errors and leads to inadequate return on investment
Solution Approach 1:
The patent replaces manual mechanical determination of pricing parameters with automated computational models. Regression analysis algorithms and predictive models automatically calculate optimal promotion pricing parameters, eliminating human error and subjectivity while maintaining systematic control through software-based solutions.
Solution Approach 2:
The system enables self-service through automated predictive models that independently determine optimal pricing parameters without requiring manual intervention. The regression analysis and revenue equations automatically process historical data and generate pricing recommendations, allowing the system to serve itself in determining promotion parameters.
2Productivity
If automated predictive models are used to determine promotion pricing parameters, then the return on investment is improved, but the system complexity increases
Solution Approach 1:
The system performs preliminary action by pre-processing historical promotion performance data through regression analysis to establish predictive models before actual promotion pricing is needed. The revenue equations and demand models are prepared in advance, allowing rapid determination of optimal pricing parameters when promotions are created without performing complex calculations in real-time.
Solution Approach 2:
The patent utilizes parameter changes by transforming historical data into predictive parameters through regression analysis. The system converts raw historical promotion performance data into meaningful parameters such as demand elasticity, revenue functions, and optimal pricing thresholds that can be applied to determine promotion pricing parameters efficiently.
3Reliability
If promotion pricing parameters are optimized for merchant ROI, then merchant profitability is improved, but the margin for the promotion and marketing service may be reduced
Solution Approach 1:
The system employs parameter changes by dynamically adjusting multiple pricing parameters simultaneously - promotion accepted value, promotional value, residual value, and margin - to achieve an optimal balance between merchant ROI and service margin. The regression analysis identifies the specific parameter combinations that maximize overall revenue while ensuring merchant profitability thresholds are met.
Solution Approach 2:
The patent introduces another dimension by adding the service margin parameter to the traditional merchant ROI calculation. The multi-dimensional revenue equation simultaneously optimizes for merchant profitability and service margin by considering factors such as promotion redemption rate, promotion size, and refund rate across multiple dimensions rather than single-parameter optimization.
4Measurement precision
If historical promotion performance data is used to generate predictive models, then the accuracy of pricing parameters is improved, but the time and resources required for data processing increase
Solution Approach 1:
The system applies preliminary action by pre-processing and analyzing historical promotion performance data in advance to build predictive models before they are needed for actual promotion pricing. The regression analysis is performed on historical data sets beforehand, creating ready-to-use revenue equations and demand models that can quickly determine pricing parameters without re-processing raw data each time.
Solution Approach 2:
The patent uses copying by creating simplified predictive models that replicate the relationships found in historical data. Instead of re-analyzing all historical data each time a promotion is priced, the system copies the essential patterns and relationships into pre-built regression models and revenue equations that can be applied rapidly to new promotion scenarios.
Data Source
AI summary
A method, apparatus, and computer program product are disclosed to improve selection of promotion pricing parameters. The method may determine one or more promotion pricing parameters for a promotion that is offered by a promotion and marketing service. The method includes generating one or more predictive models based on historical promotion performance data and generating a revenue equation using the one or more predictive models. The revenue equation provides an estimate of a revenue received by the promotion and marketing service based on the one or more predictive models. The method further includes determining an estimated revenue using the revenue equation based on one or more input sets of promotion pricing parameters provided as input to the revenue equation, and selecting at least one of the input sets of promotion pricing parameters for the promotion based on the estimated revenue. A corresponding apparatus and computer program product are also provided.