Multi-Target ML Model for Sales Opportunity Prediction
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Solution Overview
Problem
Organizations face challenges in prioritizing sales opportunities effectively due to the lack of insights from existing CRM and sales management processes, leading to low opportunity win rates and inefficient resource allocation.
Innovation Solution
A multi-target machine learning model, specifically a multi-output deep neural network, is trained using historical sales opportunity and deal closure data to predict the outcome and duration of new sales opportunities, enabling organizations to prioritize efforts on high-potential deals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional CRM and sales management processes are used to prioritize sales opportunities, then resource allocation is simplified, but opportunity win rates remain low due to lack of predictive insights
Solution Approach 1:
The system performs preliminary analysis by training a multi-target machine learning model on historical sales data before actual sales opportunities arise. The model pre-learns patterns and relationships between various sales features and outcomes, enabling predictive insights to be available in advance when new opportunities need prioritization
Solution Approach 2:
A multi-target machine learning model serves as an intermediary between historical sales data and current opportunity prioritization. The model translates raw historical data into predictive insights about opportunity outcomes and durations, bridging the gap between past performance and future predictions
2Measurement precision
If multiple separate models are used to predict different sales metrics, then model accuracy for each metric may be improved, but system complexity increases
Solution Approach 1:
The patent combines multiple prediction objectives (opportunity outcome classification and opportunity duration regression) into a single multi-target machine learning model. This unified model processes all relevant features simultaneously and generates multiple predictions in one execution, reducing system complexity compared to using separate models for each prediction task
Solution Approach 2:
The multi-target ML model is designed to perform multiple functions: it classifies opportunity outcomes (win/lose) and predicts opportunity durations simultaneously. This multi-functional model handles diverse prediction tasks using a single unified framework, eliminating the need for multiple specialized models
Data Source
AI summary
In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving information regarding a new sales opportunity from another computing device and determining one or more relevant features from the information regarding the new sales opportunity, the one or more relevant features influencing predictions of an opportunity outcome and an opportunity duration. The method also includes, by the computing device, generating, using a multi-target machine learning (ML) model, a first prediction of an opportunity outcome of the new sales opportunity and a second prediction of an opportunity duration of the new sales opportunity based on the determined one or more relevant features. The method may also include, by the computing device, sending the first and second predictions to the another computing device.