Dynamic Competitor Price Perception for Accurate Retail Pricing
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Solution Overview
Problem
Existing competitive pricing methods fail to accurately consider customer perception of competitor prices, leading to suboptimal pricing strategies that do not provide a business advantage for retailers.
Innovation Solution
A method and system that incorporate customer perception by applying dynamic weights to historical competitor prices to capture the left-over and delayed response effects, using a regression model to determine an ideal price that maximizes sales, revenue, and yield, considering factors like weather and store location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional competitive pricing methods are used, then pricing decisions can be made based on competitor prices, but customer perception of competitor prices is not accurately captured leading to suboptimal pricing strategies
Solution Approach 1:
The patent applies dynamics by introducing time-varying weights (dynamic weights) that change over time to reflect the evolving importance of different competitor prices. The weight for each competitor price is adjusted based on the time elapsed since that price was set, allowing the model to adapt to changing customer perception patterns without requiring a completely new model structure.
Solution Approach 2:
The patent applies preliminary action by pre-calculating and storing the dynamic weights for different time periods in advance. These pre-computed weights are then applied to historical competitor prices to derive the perceived competitor price, reducing the computational complexity during real-time pricing decisions while maintaining accuracy.
2Measurement precision
If dynamic weights are applied to capture customer perception, then pricing accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming the static competitor price into a dynamic perceived competitor price through the application of time-dependent weight parameters. The weight parameter varies based on the time difference between when the competitor price was set and when it is being evaluated, allowing the model to capture the decay of price impact over time while maintaining a relatively simple computational structure.
3Loss of information
If historical competitor prices are considered with time delays, then customer perception is better captured, but data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-computing the dynamic weights for different time periods and storing them for reuse. This allows the system to quickly apply the appropriate weight to historical competitor prices without performing complex calculations during real-time pricing decisions, reducing the time loss while maintaining complete information utilization.
Data Source
AI summary
This disclosure relates generally to competitive pricing and more particularly to a method and system for competitive pricing using customer perception. Conventional methods for competitive pricing consider techniques such as research analysis, strategic decision making, marketing strategy and the like. However, these techniques do not provide accurate competitive pricing. Embodiments of the present disclosure considers customer perception by capturing ideal delayed response and ideal left-over effect of competitor price based on data captured during sales process of an item. Accuracy of estimation of competitor elasticity is maximized by applying specific dynamic weights to capture ideal delayed response and ideal left-over competitor price effect of the item. A customized model is developed for determining sales of the item and further to estimate yield of the item considering other miscellaneous factors. The disclosed method is used to find the ideal price of the item which provides business advantage to the retailer.