Product Consumption Data Clustering for Optimal Pricing
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Solution Overview
Problem
Manual calculation of optimal product value for consumable products is time-consuming, prone to errors, and may not accurately maximize margins due to limited historical data assessment.
Innovation Solution
A system for clustering product consumption data into non-overlapping regions using re-scaled cumulative data, allowing for the determination of an optimal unit product value that maximizes margin through machine learning models and user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual calculation of optimal product value is performed, then flexibility and adaptability are maintained, but time consumption and error probability increase significantly
Solution Approach 1:
The patent replaces manual mechanical calculation with an automated computer-based system that processes historical product consumption data. The system uses electronic data processing and algorithmic computation to determine optimal product values, eliminating manual arithmetic operations and significantly reducing time consumption while improving accuracy through systematic data analysis.
Solution Approach 2:
The system enables automatic determination of optimal product values by processing historical data and generating recommendations without requiring manual intervention. The computer-based system serves itself by automatically retrieving data, performing calculations, and producing results, thereby eliminating the time-consuming manual calculation process while maintaining reliability through consistent algorithmic application.
2Loss of information
If manual assessment of historical data is performed, then human judgment and adaptability are utilized, but the scope of data that can be assessed is limited
Solution Approach 1:
The patent implements a multi-functional computer-based system that can process various types of historical product consumption data, handle different data formats, and perform multiple analytical operations including data retrieval, processing, clustering, and optimal value determination. This universal system replaces limited manual assessment capabilities with comprehensive automated analysis that can utilize the complete historical data scope.
Solution Approach 2:
The system segments the complex task of optimal product value determination into distinct computational stages: data retrieval from historical records, data processing and cleaning, clustering analysis using algorithms, and optimal value calculation. This segmentation allows the system to handle large volumes of historical data systematically while managing complexity through modular processing steps.
3Productivity
If manual margin optimization is attempted, then simplicity and ease of understanding are maintained, but the ability to maximize margins is compromised due to limited data assessment
Solution Approach 1:
The patent replaces manual margin optimization processes with automated computer-based calculations that systematically analyze historical consumption data to determine optimal product values. The system performs complex margin maximization computations that would be impractical to execute manually, thereby improving productivity and margin optimization efficiency despite the increased computational complexity.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously analyzing historical product consumption data and using the results to refine optimal product value determinations. The automated system processes complete historical datasets, identifies patterns through clustering, and generates feedback-driven recommendations that continuously improve margin maximization, overcoming the limitations of manual assessment.
Data Source
AI summary
Approaches for product consumption data clustering are described. In an example implementation, cumulative product consumption data of each product of plurality of products, including at least three data points indicative of varying cumulative product consumption quantities with respect to varying unit product values, is obtained and re-scaled by normalizing varying cumulative product consumption quantities and varying unit product values to be within first predefined numerical range and second predefined numerical range, respectively. Re-scaled cumulative product consumption data of each product is clustered, by cluster generation module, into plurality of clusters based on mapping of re-scaled cumulative product consumption data with respect to plurality of non-overlapping regions in a predefined cluster determination plot generated by processor. Each non-overlapping region is confined by subset of values within first predefined numerical range associated with cumulative product consumption quantity and subset of values within second predefined numerical range associated with unit product value.