Machine Learning Prediction of Client Life Events From Disparate Data
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Solution Overview
Problem
Existing CRM systems fail to efficiently develop client relationships by manually sifting through transaction data to identify new clients or enhance existing client relationships, missing opportunities for revenue growth.
Innovation Solution
An AI system analyzes transaction data and external sources to identify commonalities and predict client life events, using unsupervised learning for initial training and supervised learning for ongoing refinement, enabling targeted marketing and relationship development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sales people manually comb through large amounts of data to identify client prospects, then they can identify potential new clients, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical data analysis with an AI-based automated system. The machine learning model processes transaction data, external data sources, and identifies client relationship opportunities automatically, eliminating the need for sales people to manually comb through large amounts of data while significantly improving productivity and reducing time loss.
Solution Approach 2:
The system enables self-service by allowing the AI model to autonomously analyze data, identify patterns, and generate client relationship development opportunities without requiring human intervention in the analysis process. This automation allows the system to serve itself in identifying prospects and enhancing existing client relationships.
2Productivity
If sales people engage in personal interactions to identify new client prospects, then they can build relationships, but the method is not scalable and less efficient
Solution Approach 1:
The patent substitutes personal mechanical interactions with an automated AI system that processes data from multiple sources including transaction data and external data sources. The machine learning model identifies client relationship opportunities automatically, making the process scalable and more efficient compared to manual personal interactions while reducing the complexity burden on sales people.
3Measurement precision
If transaction data is structured for accurate recording, then transactions can be recorded accurately, but the data is not organized for identifying client relationship development opportunities
Solution Approach 1:
The patent applies segmentation by separating the original transaction data structure from the relationship development analysis. The system maintains the original accurate transaction recording structure while creating a separate processed data representation that is optimized for identifying client relationships. This allows both accurate recording and relationship analysis without compromising either function.
Solution Approach 2:
The patent introduces an intermediary processing layer between the transaction data and relationship analysis. The machine learning model acts as a mediator that transforms transaction data into actionable insights about client relationships, enabling the data to serve dual purposes: accurate recording and relationship development identification.
4Ease of manufacture
If existing client data is used for single purpose, then data management is simple, but opportunities for relationship development are missed
Solution Approach 1:
The patent implements universality by enabling transaction data to serve multiple functions: original transaction recording and client relationship development analysis. The machine learning model processes the same data for both purposes, extracting both transactional accuracy and relationship insights, thereby increasing productivity and identifying revenue growth opportunities without complicating data management.
Data Source
AI summary
A system for identifying life events for individuals based on relationships found in data. The system includes a database containing data records and fields and identifying individuals involved in each record. A supplemental source of data including location and social media data is also included. The database and the supplemental source of data are provided to a computer which executes a machine learning algorithm configured to identify life events for the individuals based on clusters in the data, where the machine learning algorithm provides output data identifying the life events, and rating values for each of the life events for each of the individuals. A communication system algorithm sends actionable communications to particular ones of the individuals based on the output data. Unsupervised learning may be used for initial system training, and supervised learning for ongoing training.