Connection Ranking System for Sales Data Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for accessing and utilizing relationship data in large databases, particularly in business-to-business sales contexts, are limited by their inability to effectively rank and present meaningful connections, leading to inefficient sales processes and missed opportunities.
Innovation Solution
A system that analyzes user connections, scores their strength based on factors like recency and frequency, and ranks them for display, using machine learning to dynamically update and refine the rankings, thereby providing actionable insights for sales professionals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If keyword searching is used to parse useful connection data, then data extraction is simple, but the responsive data sets are of limited usefulness and unwieldly in implementing the task
Solution Approach 1:
The system transforms relationship data from unstructured to structured format, changing the organizational parameters of the data. This allows the data to be ranked and filtered effectively, resolving the contradiction between simple extraction and useful presentation by maintaining extraction simplicity while adding structural organization for enhanced usability
Solution Approach 2:
The system segments the large database into manageable relationship units that can be individually scored and ranked. By breaking down the extensive database into discrete relationship records with specific attributes, the system makes the data both extractable and useful for specific tasks
2Quantity of substance
If relationship data is extracted and identified in an unstructured manner, then data collection is comprehensive, but the data is displayed in an unhelpful way that does not prioritize contacts meaningfully
Solution Approach 1:
The system changes the organizational parameters of relationship data by introducing scoring mechanisms and ranking systems. This transforms comprehensive but unstructured data into prioritized, actionable insights while maintaining data completeness
Solution Approach 2:
The system introduces an intermediary processing layer that scores and ranks relationships between users and contacts. This intermediary mechanism transforms raw relationship data into meaningful prioritized lists, resolving the contradiction between comprehensive data collection and useful presentation
3Extent of automation
If system managed recalls are used for connection data, then data retrieval is automated, but the data is not ranked in a manner that allows for enhanced access and implementation by a user
Solution Approach 1:
The system enhances automated retrieval by adding ranking parameters based on relationship strength scores. This maintains automation while improving access efficiency by presenting data in prioritized order
Solution Approach 2:
The system implements feedback mechanisms where relationship interactions are continuously monitored and used to update scoring algorithms. This creates an automated system that learns and improves ranking accuracy over time, enhancing both automation and access efficiency
Data Source
AI summary
A novel approach to facilitating access to valuable actionable content from a multi-tenant database involves system generated ranking of connection content with associated data retrieval methods and systems, utilizing “connector” scores to rank responsive content. The system “learns” how to optimize retrieving and ranking high value actionable content with experience; and applies optimized scoring parameters to enhance future operations. The computer platform is greatly improved by delivering actionable content that is immediately translated into critical operations and tasks recommended by the system to support transactions for the User.