Consumer Surplus Factor Pricing Model for Market Share
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Solution Overview
Problem
Current methods for determining market share potential and optimum pricing for consumer brands in competitive markets are inadequate, as they fail to accurately account for consumer willingness to pay and do not effectively handle data spikes, multicollinearity, and promotional activities, often requiring large data sets and not incorporating Consumer Surplus Factor.
Innovation Solution
A system and method using a data processor to calculate Consumer Surplus Factor, which determines Market Share Potential and Optimum Price by smoothing sales data, addressing multicollinearity, and employing convex optimization algorithms to automatically select the best models, considering promotional activities and competitive effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard regression techniques are used to compute market share potential and optimum price, then the computation can be performed with available data, but the results may be incorrect due to multicollinearity and promotional activity effects
Solution Approach 1:
The patent transforms the pricing problem from standard regression to convex optimization by changing the mathematical parameters and objective function. This allows incorporating Consumer Surplus Factor as a key parameter while handling multicollinearity through regularization techniques and promotional effects through time-variant parameter estimation.
Solution Approach 2:
The patent introduces Consumer Surplus Factor as an intermediary variable that mediates between price, volume sales, and consumer perceived value. This intermediary helps isolate the true pricing signal from noise caused by promotions and multicollinearity, enabling more accurate computation of optimum price and market share potential.
2Loss of information
If consumer surveys are used to capture consumer intent, then consumer perspective can be understood, but intent does not translate to actual purchase/sales
Solution Approach 1:
The patent uses actual volume and value sales data as feedback to continuously refine the estimation of Consumer Surplus Factor and Consumer Perceived Value. This feedback loop connects consumer behavior (purchases) back to the pricing model, allowing the system to learn from actual transactions rather than relying solely on survey intent data.
Solution Approach 2:
The patent replaces the mechanical survey-based intent capture system with a data-driven convex optimization system that directly analyzes transactional sales data. This substitution moves from subjective consumer statements to objective purchasing behavior analysis, improving predictive accuracy.
3Measurement precision
If huge amount of data points are required for existing methods to work, then statistical accuracy may be improved, but data availability is generally limited
Solution Approach 1:
The patent applies partial action by using a focused set of key variables (price, volume sales, promotional activities, competitive prices) rather than requiring comprehensive huge datasets. The convex optimization framework with regularization techniques enables accurate estimation even with limited data by concentrating analytical effort on the most critical parameters.
Solution Approach 2:
The patent changes the mathematical parameters and estimation techniques from traditional regression to convex optimization with time-variant parameter estimation. This allows the model to work effectively with smaller datasets by dynamically adjusting parameters based on available data patterns rather than requiring large static datasets.
4Adaptability or versatility
If multiple forms of explanatory variables are considered in pricing models, then model comprehensiveness is improved, but the number of possible models grows exponentially
Solution Approach 1:
The patent introduces dynamics by using time-variant parameter estimation where the form and weight of explanatory variables can change over time based on their predictive performance. This dynamic approach allows the model to adaptively select the most relevant variable forms without exhaustively evaluating all possible combinations, reducing complexity while maintaining comprehensiveness.
Solution Approach 2:
The patent segments the explanatory variables into distinct categories (price variables, promotional variables, competitive variables, control variables) and applies targeted transformations to each segment. This segmentation allows systematic handling of multiple variable forms without creating exponential model complexity, as each segment can be optimized independently.
Data Source
AI summary
The present disclosure presents a system and method for determining market Share Potential for a brand in a competitive market. The disclosed system and methods also teach the determination of optimum price for a brand as well. The present invention uses Consumer Surplus Factor for the same, thereby countering the effect of spikes in data due to promotional activities, effects of multicollinearity among other things. Further, the disclosed system and method automatically update the price of the products of the brand based on the market share potential and optimum price. Beneficially, the present disclosure can help businesses to make informed decisions from consumer's perspective so as to increase their market share and earn better profits and gives an opportunity for business owners to take data driven decisions all along maintaining their competitive advantage over other brands.