Composite Lead Scoring via Weighted AI Model Merging
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Solution Overview
Problem
Sales representatives face inefficiencies in identifying and pursuing leads due to confusion from multiple metrics from disparate sources, resulting in a low conversion rate of marketing and sales campaign leads into actual revenue.
Innovation Solution
The use of multiple AI engines to analyze input data sets, combine results into a composite score indicating the likelihood of a lead making a purchase within a predetermined time interval, using weighted probability distributions and Monte Carlo methods, and adjusting for accuracy based on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning models use different data sets to generate scores, then the comprehensiveness of lead analysis is improved, but the complexity of interpreting and acting on multiple disparate scores increases
Solution Approach 1:
The patent combines multiple scores from different machine learning models into a single composite score. The system receives scores from multiple models that analyze different data sets, selectively combines these scores using weighted probability distributions, and outputs one unified composite score that represents the lead's propensity to purchase. This merging process maintains the comprehensive analysis benefits while eliminating the complexity of interpreting multiple separate scores.
Solution Approach 2:
The composite score acts as an intermediary that translates multiple complex model outputs into a single interpretable metric. Rather than requiring users to directly interpret multiple disparate scores, the system introduces a composite score that mediates between the complex multi-model analysis and the user's decision-making process, making the information actionable and easy to understand.
2Measurement precision
If multiple machine learning models are used to analyze disparate data sets, then the accuracy of lead scoring is improved, but the device complexity increases
Solution Approach 1:
The system merges multiple machine learning models into a unified composite scoring framework. By combining the outputs of multiple models that each analyze different data sets, the system achieves higher measurement precision while managing complexity through a standardized combination process using weighted probability distributions.
Solution Approach 2:
The composite score generation system serves multiple functions: it integrates data from multiple sources, applies multiple machine learning models, performs statistical combination using weighted probability distributions, and produces a single actionable metric. This multi-functional approach allows the system to achieve high accuracy while maintaining a unified, manageable architecture.
3Reliability
If a composite score is generated by combining multiple scores, then informed decision-making is enhanced, but the computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and standardizing the outputs from multiple machine learning models before combination. The scores from different models are prepared and normalized in advance, allowing the composite score generation to proceed efficiently without requiring complex real-time calculations when leads need to be evaluated.
Solution Approach 2:
The system changes parameters by transforming multiple scores into a standardized composite metric. By applying weighted probability distributions and mathematical transformations, the system converts diverse model outputs into a unified parameter (the composite score) that can be quickly interpreted and acted upon, reducing the time loss despite the enhanced decision-making quality.
Data Source
AI summary
Example embodiments include software for selectively combining outputs for multiple machine learning modules, so as to generate composite values. The composite values may provide more accurate metrics, e.g., composite scores, that may be used to more reliably predict/estimate, for instance, the likelihood of a given lead converting into a customer within a predetermine time interval. In certain embodiments, a math model selectively combines the scores, and the parameters thereof can be selectively adjusted in real time, based on real time data; so as to maintain accuracy of the composite scores. This can lead to enhanced situational awareness, which can be particularly important for enterprise sales representatives, account managers, and so on.