Community-Based Catalog Customization via Business Type Segmentation
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Solution Overview
Problem
Existing systems fail to provide community-specific catalogs and online services that effectively cater to the unique product needs of businesses with similar types, leading to inefficiencies in product recommendations and searches.
Innovation Solution
A system that discerns customer business types from company names to create customized catalogs and online services by analyzing purchasing histories and behaviors within specific business communities, tailoring product offerings and search results to match the community's specific needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic catalogs and online services are provided to all customers, then system complexity is reduced and ease of operation is improved, but product recommendations become less relevant and customer satisfaction decreases
Solution Approach 1:
The patent segments customers into distinct buying communities based on business type classification. By dividing the customer base into groups with similar product needs and purchasing behaviors, the system can provide customized catalogs and services for each segment without requiring complete customization for every individual customer, thus improving recommendation relevance while managing system complexity.
Solution Approach 2:
The system performs preliminary classification of customers into buying communities based on their business types before generating customized catalogs. This advance segmentation allows the system to pre-organize product recommendations and services according to community characteristics, eliminating the need for complex real-time customization and improving both relevance and operational efficiency.
2Adaptability or versatility
If customized catalogs are created for each individual customer, then product recommendation relevance is improved, but system complexity and processing requirements increase significantly
Solution Approach 1:
Instead of creating unique catalogs for each individual customer, the patent segments customers into buying communities and creates customized catalogs for each community. This approach maintains high catalog customization and recommendation relevance while dramatically improving generation efficiency by reusing community-level templates and product selections across multiple customers with similar business types.
3Measurement precision
If detailed business type analysis is performed to create buying communities, then product recommendation accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs business type classification and buying community formation as a preliminary action before catalog generation. By classifying customers into communities in advance based on their business types and analyzing their collective purchasing behaviors beforehand, the system achieves high classification accuracy while avoiding repeated analysis during catalog creation, thus reducing overall processing time.
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 keyword within a corporate, business, or other entity name that is associated with the customer or from purchasing interests commonly shared by customers. 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.