Multi-Head Lead Prediction Model for Delayed Qualified Leads
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Solution Overview
Problem
Machine learning models for predicting lead and qualified lead conversions in B2B environments face challenges due to sparse training data, delayed conversions, and false negatives, especially when using data from multiple sources with potential discrepancies and privacy concerns.
Innovation Solution
A multi-head machine learning model is employed, comprising a first head to predict the likelihood of a user becoming a lead and a second head to predict a qualified lead, with techniques like differential privacy and a delayed qualified lead model to handle data scarcity and privacy issues, and a pipeline approach to model user interactions across multiple stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single machine learning model is used for both lead and qualified lead prediction, then model complexity is reduced, but prediction accuracy deteriorates due to the different characteristics of the two prediction tasks
Solution Approach 1:
The patent divides the prediction task into two separate prediction heads within the machine learning model: one head for predicting lead likelihood and another head for predicting qualified lead likelihood. This segmentation allows each head to be optimized for its specific prediction task while sharing the same feature extraction backbone, thereby maintaining prediction accuracy without excessive complexity increase.
Solution Approach 2:
The patent implements a multi-functional model where a single machine learning model performs multiple prediction tasks (lead prediction and qualified lead prediction) simultaneously through multiple output heads. This universal approach allows the model to leverage shared features for both tasks while maintaining specialized prediction capabilities, resolving the contradiction between model complexity and prediction accuracy.
2Quantity of substance
If data from multiple sources is used for training, then the quantity of training data increases, but data quality deteriorates due to potential discrepancies and privacy concerns
Solution Approach 1:
The patent introduces differential privacy as an intermediary mechanism that processes data from multiple sources before training the model. This intermediary adds controlled noise to the data, protecting privacy concerns while maintaining the utility of the aggregated data from multiple sources, thus resolving the contradiction between data quantity and data quality.
Solution Approach 2:
The patent employs data transformation techniques that modify data parameters from different sources to a unified format and scale. By standardizing features, handling missing values, and normalizing data distributions across different sources, the model can effectively utilize large quantities of training data while maintaining consistent data quality standards.
Data Source
AI summary
In an example embodiment, a delayed qualified lead machine learning model is trained to predict, for any particular piece of interaction data, a likely delay between the interaction time and a time at which an indication of a qualified lead is provided (if one is to be provided). Thus, for example, the delayed qualified lead machine learning model may predict that, given a particular user's interaction with a particular piece of content, the user is likely to be labeled as a qualified lead within 40 days if the user will become a qualified lead at all. This prediction can then be used to exclude the interaction data from the training data for a separate machine learning model without excluding other pieces of interaction data whose predicted delays might have been shorter, helping alleviate the data scarcity issue.