Data-Driven Product Grouping via Dimensionality Reduction
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Solution Overview
Problem
Conventional product grouping methods based on utility do not effectively account for varying purchase patterns, leading to inefficient product distribution and marketing strategies.
Innovation Solution
A data-driven approach that converts categorical and numeric data into numeric data, reduces dimensions, and generates clusters to assign unique product identifiers, allowing for predictive modeling of purchase likelihoods based on entity characteristics such as lifestyle and life-stage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If products are grouped based on utility, then product categorization is simple and straightforward, but purchase patterns for products in the same group can vary significantly leading to minimal or no information gain
Solution Approach 1:
The patent transforms categorical product data into numeric representations through dimensionality reduction techniques, changing the parameter type from categorical to numeric to enable clustering based on purchase patterns while preserving the underlying product characteristics
Solution Approach 2:
The patent replaces the conventional manual utility-based grouping mechanism with an automated data-driven clustering system that uses machine learning algorithms to objectively group products based on actual purchase behavior patterns
2Ease of operation
If conventional utility-based grouping is used, then product classification is easy to implement, but it does not account for varying purchase patterns resulting in inefficient product distribution
Solution Approach 1:
The patent performs preliminary dimensionality reduction and clustering analysis on historical purchase data before product distribution decisions are made, creating pre-computed product groups that can be directly applied to improve distribution efficiency without complex real-time calculations
Solution Approach 2:
The system enables products to be automatically grouped based on their own purchase pattern characteristics without requiring manual intervention, with the clustering algorithm autonomously identifying and grouping products with similar purchase behaviors
Data Source
AI summary
Data for a plurality of entities that can be offered a plurality of products can be obtained. The data can include categorical data and numeric data. Based on business constraints, some of all of the data can be selected. The selected data can be converted to another set of numeric data, wherein the categorical values are converted to numeric values. Dimensions of the converted data can be reduced to generate another set of data. Based on this another set of data, clusters of entities can be formed. The products can be grouped by assigning a unique product identifier of each product to a corresponding cluster. This grouping of products can be used by a predictive model to predict a likelihood of an entity to purchase a particular product in a future time period. Related methods, apparatus, systems, techniques and articles are also described.