Community-Based Catalog Customization via Business Type Segmentation
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Solution Overview
Problem
Existing systems for providing customized information and services fail to effectively cater to specific business communities by not utilizing business type information to tailor catalogs and services accurately, leading to irrelevant product offerings.
Innovation Solution
Establishing a buying community based on discerned business types, using company name information to group similar businesses and tailor catalogs and services, including customized product recommendations and search results, to meet the specific needs of community members.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If customized catalogs are provided using general demographic and purchasing information, then customer personalization is improved, but business community specificity deteriorates
Solution Approach 1:
The patent segments customers into distinct buying communities based on business type information extracted from company names. This segmentation allows the system to create customized catalogs for each community while preserving business type information, resolving the contradiction between personalization and information loss.
Solution Approach 2:
The system performs preliminary action by extracting and storing business type information from company names before catalog generation. This advance preparation ensures business type information is preserved and can be used to create community-specific customized catalogs without loss.
2Device complexity
If general product recommendations are provided to all customers, then system simplicity is maintained, but product relevance to specific business communities deteriorates
Solution Approach 1:
The patent divides the customer base into buying communities based on business type, enabling targeted product recommendations for each community. This segmentation improves recommendation accuracy while maintaining system simplicity through automated business type extraction from company names.
Solution Approach 2:
The system uses self-service by automatically extracting business type information from company names without requiring additional customer input. This approach improves product recommendation accuracy for specific business communities while maintaining system simplicity.
3Measurement precision
If customized catalogs tailored to specific business communities are created, then product relevance is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary action by extracting and storing business type information from company names in advance. This preparation reduces data processing complexity during catalog generation while maintaining high catalog relevance to specific business communities.
Solution Approach 2:
The system uses self-service by automatically extracting business type information from company names without requiring manual input. This automation improves catalog relevance while minimizing data processing complexity through efficient automated procedures.
Data Source
AI summary
A buying community is established by discerning customers having the same or similar business types. The business type of a customer may be discerned from a corporate, business, or other entity name that is associated with the customer. Once a buying community is established, behaviors of customers within that buying community are discerned. These behaviors may include product purchasing behaviors, on-line navigation behaviors, on-line searching behaviors, etc. The discerned behaviors of the customers within the buying community may then be used to provide catalogs customized for those members and/or on-line services customized for those members.