Retail Unit Market Segmentation via Price-Profit Regression
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Solution Overview
Problem
Existing market segmentation studies fail to account for the effect of prices on profitability when assigning retail units to economic markets, leading to ineffective marketing and strategic planning.
Innovation Solution
A method that performs regression analysis on retail unit data to identify variables affecting the relationship between prices and profits, standardizes and weights these variables to group stores based on price sensitivity, allowing for targeted marketing and pricing strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional market segmentation studies are used to identify characteristics associated with profitability, then store characteristics can be identified, but the effect of prices on profitability is not accounted for
Solution Approach 1:
The patent transforms the analysis from examining raw profit values to examining the relationship between prices and profits through regression analysis. By changing the parameter from absolute profit to price-profit elasticity, the method captures how price changes affect profitability, thereby recovering the lost information about price sensitivity while maintaining analytical tractability through standardized variables.
2Measurement precision
If regression analysis is performed on retail unit data to identify variables affecting the relationship between prices and profits, then price sensitivity can be determined, but the complexity of data processing increases
Solution Approach 1:
The patent segments the analysis into distinct components: first identifying store characteristics, then performing regression analysis to determine price-profit relationships, and finally using these relationships to group stores. This segmentation allows precise measurement of price sensitivity through regression coefficients while managing complexity by breaking the problem into manageable analytical stages.
Solution Approach 2:
The patent replaces complex manual analysis with regression analysis and statistical methods. By using mathematical models to automatically identify relationships between prices and profits, the system achieves precise measurement of price sensitivity while reducing the need for manual interpretation and simplifying the overall analytical process.
3Productivity
If stores are grouped by traditional characteristics only, then simple classification is achieved, but ineffective marketing and strategic planning result
Solution Approach 1:
The patent applies local quality by recognizing that different stores have different price sensitivities based on their characteristics. Instead of applying a uniform grouping method, the analysis identifies specific variables that affect price-profit relationships for different store types, allowing tailored marketing strategies for each segment while maintaining overall system coherence through standardized regression analysis.
Data Source
AI summary
A method of grouping retail units of a set of units in a chain uses store and market-specific characteristics, including store profitability, to group stores into like economic markets. The relation between profits and prices defines markets; stores facing the same relation, that is the same profit function, are in the same economic market. These stores can follow similar pricing and promotion strategies. Multiple regression analysis is used to identify those characteristics that affect the relation between prices and profits (not simply variables correlated with profits). Upon suitable standardization and weighting, these variables are subsequently used with a statistical cluster analysis to classify units in two markets. Based on the estimated relationship and homegenity valuations from discriminant analysis, new stores can be more accurately added to the appropriate group.