Promotional Uplift Coefficient Calculation via Multivariable Regression
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Solution Overview
Problem
Current demand forecasting systems for retail operations, such as Teradata Demand Chain Management, face challenges in accurately calculating promotional uplift coefficients, especially when dealing with multiple event types and price discounts, leading to inconsistencies and reduced reliability in demand forecasting during promotional activities.
Innovation Solution
The implementation of a multivariable regression model that calculates promotional uplifts using a combination of additive and multiplicative coefficients, transformed into a single multiplicative uplift coefficient, to improve demand forecasting accuracy by modeling causal relationships between product demand and attributes of past promotional activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a simple promotional uplift calculation method is used, then the calculation process is simple and quick, but the accuracy and reliability of demand forecasting during promotional activities deteriorates
Solution Approach 1:
The patent transforms the promotional uplift calculation from a simple ratio-based approach to a multivariable regression model that incorporates multiple parameters including additive and multiplicative coefficients. This parameter expansion allows the model to capture complex promotional effects while maintaining computational feasibility through structured coefficient transformation.
Solution Approach 2:
The patent introduces an intermediary transformation process that converts multiple regression coefficients into a single multiplicative uplift coefficient. This intermediary step bridges the gap between complex multivariable analysis and simple forecast application, allowing accurate promotional forecasting without requiring complex calculations at the point of use.
2Adaptability or versatility
If multiple promotional factors are incorporated into the forecasting model, then the comprehensiveness of promotional analysis is improved, but the complexity of the forecasting system increases
Solution Approach 1:
The patent segments the promotional analysis into distinct components: additive coefficients for baseline promotional effects and multiplicative coefficients for interaction effects. This segmentation allows multiple promotional factors to be systematically incorporated while maintaining model structure and reducing computational complexity through organized coefficient handling.
Solution Approach 2:
The patent creates a universal forecasting framework that handles multiple promotional factors through a unified multivariable regression model. The model structure can accommodate various promotional types and factors while maintaining a consistent calculation approach, making the system versatile without proportionally increasing complexity.
3Reliability
If historical promotional data is extensively analyzed, then the reliability of uplift coefficients is improved, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary analysis by pre-calculating additive and multiplicative coefficients from historical promotional data through regression analysis. These pre-computed coefficients are stored and can be quickly applied to new promotional scenarios without repeating the full analysis, thus improving reliability while reducing real-time computational burden.
Solution Approach 2:
The patent creates a simplified copy of the complex promotional effects through the transformed multiplicative uplift coefficient. This single coefficient captures the essence of multiple promotional factors and historical analysis, allowing quick application to forecasts without requiring access to or processing of the extensive historical data each time.
Data Source
AI summary
An improved method for forecasting and modeling product demand for a product during promotional periods. The forecasting methodology employs a multivariable regression model to model the causal relationship between product demand and the attributes of past promotional activities. The model is utilized to calculate the promotional uplift from the coefficients of the regression equation. The methodology utilizes a mathematical formulation that transforms regression coefficients, a combination of additive and multiplicative coefficients, into a single promotional uplift coefficient that can be used directly in promotional demand forecasting calculations.