Automated Product Subcategory Clustering via Pricing Data
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Solution Overview
Problem
In retail environments, customers face difficulty in selecting the appropriate type or brand of a product, as existing merchandising methods rely on manual or arbitrary grouping of products, which do not accurately reflect the implicit subcategories based on pricing or other characteristics, leading to inefficient customer product selection.
Innovation Solution
An automated system that uses a subcategory identification service to analyze pricing and sales data to intelligently group products into subcategories, employing clustering algorithms to define optimal price ranges and present these subcategories to customers, thereby facilitating informed purchasing decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual expert grouping is used to create product displays, then domain expertise can be applied to create meaningful categories, but the process is time-consuming and difficult to maintain
Solution Approach 1:
The system enables automatic self-grouping of products based on pricing data and clustering algorithms. The computer automatically analyzes product prices, identifies pricing clusters, and generates subcategory displays without requiring manual expert intervention, thus eliminating time loss while maintaining accurate groupings through data-driven clustering
Solution Approach 2:
The manual mechanical process of expert categorization is replaced with an automated computational system using clustering algorithms. The system substitutes human expert analysis with machine learning techniques that automatically process pricing data and generate product groupings, significantly reducing time investment while preserving categorization accuracy
2Loss of time
If arbitrary product grouping is used for wide variety of products, then displays can be created quickly, but the groupings do not accurately reflect implicit subcategories based on pricing
Solution Approach 1:
The system changes the parameter basis for grouping from arbitrary categories to data-driven pricing clusters. By analyzing actual pricing parameters and identifying natural clusters in the price distribution, the system generates accurate subcategories that reflect implicit product groupings while maintaining quick automated generation
Solution Approach 2:
The system uses feedback from pricing data analysis to continuously refine product groupings. By monitoring pricing patterns and customer interactions with different price ranges, the clustering algorithms adapt to accurately reflect implicit subcategories, ensuring both speed and accuracy in display creation
3Loss of information
If detailed product listings are shown without subcategory navigation, then all product options are visible, but customers face difficulty in selecting the appropriate type or brand
Solution Approach 1:
The system segments the complete product catalog into meaningful pricing-based subcategories. By dividing products into distinct price range groups with clear navigation, customers can easily navigate to their preferred price range while the system maintains complete product information within each segment, solving both information completeness and selection ease
Solution Approach 2:
The system adds a pricing-based dimensional layer to product navigation. Instead of flat listing, products are organized along a price dimension with clickable price range filters, enabling customers to navigate through subcategories by price while preserving access to all product details, thus improving ease of operation without losing information
Data Source
AI summary
Disclosed are various embodiments for defining subcategories of items to be used in merchandising. The subcategories may be defined on the basis of item data and/or sales data for the items. Based on a distribution of the items in accordance with one or more of the item and/or sales data, implicit groups or subcategories can be identified and selected for merchandising purposes.