Customer-Perception Pricing With Dynamic Competitor Price Weighting
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Solution Overview
Problem
Existing competitive pricing techniques 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, capturing left-over and delayed responses through a regression model, to estimate sales units, revenue, and margins, ultimately determining an ideal price that maximizes yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional competitive pricing techniques are used, then pricing decisions can be made based on competitor prices, but customer perception of competitor prices is not accurately considered
Solution Approach 1:
The patent applies dynamics by transitioning from static competitor price data to dynamic weighted competitor prices that evolve over time. The system uses time-decay weights that automatically adjust the importance of historical competitor prices, making the pricing model adaptive to changing customer perceptions and market conditions without requiring manual intervention.
Solution Approach 2:
The patent changes the parameter of competitor price representation from simple historical prices to weighted derived competitor prices. By introducing weight parameters that decay over time and incorporating customer perception factors, the system transforms raw price data into a more accurate representation of what customers actually perceive about competitor pricing.
2Loss of information
If historical competitor prices are used directly, then the pricing model is simple, but it does not capture customer perception including left-over and delayed responses
Solution Approach 1:
The patent applies preliminary action by pre-processing historical competitor price data into weighted derived competitor prices before using them in pricing decisions. This preliminary transformation incorporates time-decay weights and customer perception factors in advance, so that when pricing decisions are made, the data is already optimized to reflect customer perception without requiring complex real-time calculations.
Solution Approach 2:
The patent introduces an intermediary layer between raw competitor prices and pricing decisions. The weighted derived competitor prices act as an intermediary representation that mediates between historical price data and customer perception. This intermediary transformation captures left-over and delayed responses while maintaining a manageable level of complexity through systematic weight application.
3Productivity
If static pricing strategies are used, then implementation is straightforward, but the retailer cannot optimize for maximum yield considering dynamic market conditions
Solution Approach 1:
The patent applies self-service by enabling the pricing system to automatically generate optimal prices without requiring manual intervention. The weighted derived competitor prices and pricing model work together to self-adjust prices based on customer perception and market conditions, allowing the system to optimize revenue and profit automatically while maintaining ease of operation through automated decision-making.
Solution Approach 2:
The patent incorporates feedback by using customer perception of competitor prices to inform pricing decisions. The weighted derived competitor prices provide feedback about how customers view competitor offerings, which feeds back into the pricing model to adjust optimal prices. This feedback loop enables continuous optimization of revenue and profit while the automation maintains ease of operation.
Data Source
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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.