Retail Price Elasticity Using Dynamic Time Windows
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Solution Overview
Problem
Conventional methods for calculating price elasticity of demand fail to account for the variable time lag before demand stabilizes in response to price changes, leading to inaccurate elasticity calculations due to considering irrelevant price and demand pairs.
Innovation Solution
A dynamic time window-based method that adjusts the 'leave' and 'take' values to form windows around price change points, using Fourier transformations to identify seasonality and calculate point elasticity, with error correction to ensure accurate demand shifts are captured.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the entire duration between two price change dates is considered for elasticity calculation, then more data points are included, but the accuracy of elasticity calculation deteriorates due to inclusion of irrelevant price-demand pairs
Solution Approach 1:
The patent divides the time period between price changes into multiple segments using dynamic time windows. Instead of considering the entire duration, it segments the data into relevant pre-change and post-change periods based on demand stabilization patterns, thereby excluding irrelevant data points while maintaining sufficient sample size for accurate elasticity calculation.
Solution Approach 2:
The patent employs dynamic time windows that adjust their boundaries based on the actual demand response patterns. The window size and position are not fixed but dynamically determined by monitoring when demand stabilizes after a price change, allowing the system to adaptively include only the most relevant data points for each calculation.
2Ease of operation
If a fixed time window is used for elasticity calculation, then the calculation process is simplified, but the accuracy deteriorates due to inability to account for variable time lags of different products
Solution Approach 1:
The patent replaces fixed time windows with dynamic time windows that automatically adjust their parameters based on product-specific demand patterns. The system monitors demand stabilization and adjusts window boundaries accordingly, maintaining calculation simplicity while achieving product-specific accuracy through adaptive parameter adjustment.
Solution Approach 2:
The patent changes the parameters of the time window (size, position, boundaries) based on observed demand patterns and product characteristics. Rather than using static parameters, the system dynamically modifies window parameters to match the variable time lags inherent in different product categories, thereby maintaining both simplicity and accuracy.
3Quantity of substance
If price change points with insignificant demand shifts are included in elasticity calculation, then more data is utilized, but the reliability of demand forecasting deteriorates
Solution Approach 1:
The patent performs preliminary filtering of price change points before conducting elasticity calculations. It pre-identifies and excludes price changes that do not result in significant demand shifts, ensuring that only meaningful data points are included in the analysis. This preliminary action prevents contamination of the dataset with irrelevant observations.
Solution Approach 2:
The patent incorporates feedback mechanisms that evaluate the actual demand response to each price change. Based on this feedback regarding demand shift significance, the system adjusts which price change points are included in elasticity calculations, thereby maintaining high reliability by continuously validating the relevance of included data points.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a more precise calculation of price elasticity by considering varying time gaps and seasonality, resulting in improved accuracy and reliability of demand forecasting.
Implementation Method 1
estimating a hidden periodicity in the weekly demand data points using a Fourier transformation of a weekly demand curve and selecting one or more predefined Fourier coefficients to identify a period of seasonality
Data Source
AI summary
The embodiments of the present disclosure herein address unresolved problems of elasticity calculation through a dynamic multi window approach. Embodiments herein provide a method and system for a dynamic time window-based point elasticity calculation for a retail merchandise. The system is configured for forming different time windows and an average demand of each window is used to calculate point elasticities. It is assumed that the market has an inherent delay in responding to price changes. But time to respond is unknown and that also varies from product to product, market to market and season to season basis. So, a few demand points are left before and after the price change. Again, the size of the window is also set to different values, to get the effect of price change in different timescales, so that at the final stage, only the sustained and prominent shifts in average demand prevail.