Relationship Graph for Cross-Selling Insights
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Solution Overview
Problem
Current systems fail to effectively leverage the value of human capital by tracking and analyzing relationships between entities, leading to unrealized cross-selling opportunities and slow innovation due to the inability to utilize collective intelligence within and outside organizational firewalls.
Innovation Solution
A system and method for analyzing electronic relationships between entities on a network by collecting interaction information, generating behavioral attributes, and displaying relationship characteristics, which includes a server configured to collect interaction data, generate attributes, and provide data for display, enabling the determination of relationship strength, type, and value based on communication modes and volumes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static CRM systems and siloed databases are used to categorize relationships, then data storage and management are simplified, but the ability to leverage human capital and generate cross-selling opportunities is limited
Solution Approach 1:
The patent merges data from multiple siloed systems (CRM, billing systems, contact databases) into a unified relationship graph that tracks interactions across entire networks. This consolidation enables comprehensive analysis of human capital and cross-selling opportunities while maintaining manageable system architecture through modular graph processing components.
2Productivity
If relationship data is stored in siloed systems like CRM applications and contact databases, then each system remains simple and focused, but cross-selling opportunities are unrealized because sales representatives are not aware of relationships that exist
Solution Approach 1:
The patent introduces a relationship graph as an intermediary layer that sits between siloed systems and end-users. This graph structure mediates information flow by aggregating relationship data from multiple sources and presenting it in an accessible format to sales representatives, enabling them to see connection information without requiring changes to underlying siloed systems.
3Adaptability or versatility
If organizations maintain strict firewalls to protect internal information, then security is improved, but collective intelligence cannot be leveraged because employees and partners outside the firewall cannot be analyzed
Solution Approach 1:
The patent extracts relationship interaction data from various communication sources (email, IM, VoIP, social networking) and represents it as a neutral relationship graph. This extraction process separates the analysis function from the secure internal networks, allowing collective intelligence to be leveraged from external partners while maintaining security firewalls through non-intrusive data collection and representation.
Data Source
AI summary
Systems and methods are provided for measuring the level of relative activity (relationship) between two entities (e.g., people, companies, organizations, etc.) in a group as compared with others in that group or in a subset of that group. A group or subset of a group can be defined manually or automatically by the program. Once the activity is measured, it is further analyzed to generate behavioral attributes (e.g., trust, respect, mutually enjoyable company or personal relationship, reciprocity and shared experience) of the relationship. These attributes may be employed to derive characteristics such as the strength of the relationship for each pair of entities. The relationships and characteristic and/or attributes may then be displayed in a simple to understand manner.