Multi-tenant lead scoring framework with ML objective detection
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Solution Overview
Problem
Traditional lead scoring systems rely on manual configuration and may use inaccurate or outdated data, leading to inefficiencies in predicting customer conversion stages, which are not aligned with objective-driven sales processes in CRM systems.
Innovation Solution
A multi-tenant lead scoring framework that utilizes machine learning to build data pools for prediction objectives, trains multiple ML models, and combines them using an ensemble approach to identify and select accurate objectives based on performance, enhancing data quality and alignment with business objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration is used for prediction objectives, then ease of operation is improved, but accuracy of prediction objectives deteriorates
Solution Approach 1:
The system automatically detects and selects prediction objectives using machine learning models that analyze CRM data patterns. The objective detection framework autonomously identifies the most accurate prediction objectives without requiring manual configuration by administrators, thereby maintaining ease of operation while significantly improving accuracy through data-driven insights.
2Device complexity
If manual configuration is used for prediction objectives, then device complexity is reduced, but reliability deteriorates
Solution Approach 1:
The patent replaces manual mechanical configuration processes with automated machine learning-based objective detection. The system uses trained ML models to automatically identify and select prediction objectives from CRM data, substituting human manual operations with intelligent automated systems that improve reliability while managing complexity through standardized frameworks.
3Ease of operation
If traditional lead scoring system is used, then ease of operation is improved, but productivity deteriorates
Solution Approach 1:
The system implements a feedback mechanism where machine learning models continuously analyze CRM data to detect and select the most accurate prediction objectives. This automated feedback loop enables the system to adapt to changing data patterns and business conditions, improving productivity by providing more accurate and timely lead scoring insights while maintaining ease of operation through automatic updates.
Data Source
AI summary
Finding accurate prediction objectives includes building, by a framework application, a data pool for each of a plurality of prediction objectives. A plurality of machine learning (ML) models is trained for each data pool, and each of the plurality of ML models is combined for each data pool. One or more accurate objectives are identified and selected on the basis of a performance of the combined plurality of ML models.