Referral Tracking for Targeted Content Recommendation
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Solution Overview
Problem
Existing online shopping platforms struggle to effectively direct customers to relevant products, especially for unrecognized or unregistered users, as current methods like email-based and shipping-address-based communities are limited in providing targeted recommendations.
Innovation Solution
A computer-implemented method and system that tracks referrals from multiple web sites to a target site, identifies user groups with common interests, collects user activity data, and displays relevant items to users based on their preferences, both members and non-members, on the target site or referring sites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If email-based or shipping-address-based communities are used to group customers, then some targeted recommendations can be provided, but the recommendations are limited and do not adequately direct customers to relevant information
Solution Approach 1:
The patent segments the general user population into distinct communities based on their referral sources (associates). Each associate refers to a specific community with common interests, allowing the system to provide targeted recommendations specific to each community's preferences rather than using broad email or address-based grouping.
Solution Approach 2:
The system uses purchase activity data from community members as feedback to identify popular items within each community. This feedback mechanism allows the system to continuously improve recommendations by analyzing what items are actually popular within each referred community and using that information to guide future recommendations to both members and non-members.
2Adaptability or versatility
If referral tracking is implemented to identify user groups, then targeted content can be provided to specific communities, but the system complexity increases
Solution Approach 1:
The referral tracking system serves multiple functions: it identifies user groups, tracks purchase activity, determines community preferences, and generates recommendations. By making the referral mechanism multi-functional, the patent reduces the need for separate systems for each function, thereby managing complexity while achieving versatile user group identification and targeted content delivery.
3Productivity
If popular items within specific communities are identified and displayed to users, then customer engagement and product discovery are enhanced, but the quantity of data processing and analysis increases
Solution Approach 1:
The system extracts only the essential information needed for recommendations - specifically, purchase activity data that indicates popular items within each community. Rather than processing all possible user data, the patent extracts and analyzes only the relevant purchase patterns, reducing the effective data volume while maintaining high customer engagement through targeted recommendations.
Data Source
AI summary
A computer process is disclosed for selecting items to present or recommend to users based on the referring sites accessed by such users. The process includes tracking referrals of users from referring sites to a target site, and recording the item selections of the referred users from an electronic catalog of the target site. The process may also include analyzing the recorded item selections of the users to identify, for a particular subset of the referring sites, a set of items that correspond to group preferences of users referred to the target site by the subset of referring sites. These identified items may thereafter be presented to users who access a site that is a member of the subset of referring sites.