Right-Hand Side Elasticity Model for Price Optimization
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Solution Overview
Problem
Determining the optimal product price to maximize margins is challenging due to the complexity of fitting elasticity models to sales data, particularly in segmenting and analyzing historical transactional data for margin optimization.
Innovation Solution
The method involves grouping historical transactional data into segments of similar products and customers, transforming the selling price into a normalized metric, and fitting a distribution model to the histogram distribution, with a right-hand side elasticity model being applied to optimize the selected metric.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If companies manually analyze sales data and fit elasticity models to determine optimal price, then they can maximize margins, but the process becomes arduous and resource-intensive
Solution Approach 1:
The patent segments historical transactional data into groups based on product characteristics and customer attributes. This segmentation transforms the complex overall dataset into manageable segments that can be analyzed separately, reducing the computational complexity of fitting elasticity models while maintaining optimization accuracy for each segment
Solution Approach 2:
The system implements automated model selection and fitting procedures that self-determine the appropriate elasticity model for each data segment without manual intervention. The automation includes automatically selecting from multiple elasticity models, fitting parameters, and determining optimal prices, thereby eliminating the arduous manual analysis process while preserving margin optimization accuracy
2Measurement precision
If companies segment historical transactional data for detailed analysis, then margin optimization improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary segmentation of transactional data into product and customer groups before applying elasticity models. This pre-processing step organizes the data structure in advance, making subsequent model fitting more efficient and reducing overall computational time while maintaining the precision benefits of segmented analysis
Solution Approach 2:
The system automatically adjusts model parameters and selection criteria based on the characteristics of each data segment. By dynamically changing parameters such as elasticity model type, segmentation granularity, and fitting methods, the system optimizes the balance between analysis precision and computational efficiency for different product-customer segments
Data Source
AI summary
A system that optimizes price for a product based on an elasticity model fitted to the right hand side of histogram of normalized price. First, historical transactional data is grouped into segments, each segment containing transactions for a set of mutually similar products and mutually similar customers. Each segment is then processed separately. Selling price in the data is transformed to a normalized metric (e.g., margin percentage or discount percentage). Distribution of the normalized metric is represented with its histogram. Segmentation is done so that the histogram in each segment is unimodal and is well represented by a distribution model A distribution model is fit to the histogram distribution. The right-hand side of the distribution model is then selected, and a right hand side (RHS) elasticity model is fit to the distribution. Optimized selected metric is determined from the fitted model.