ML Candidate Prediction System for Engagement Probability
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Solution Overview
Problem
Entities face inefficiencies and high costs in identifying candidates likely to engage with them due to large populations and resource-intensive manual processes, where many potential candidates have a low likelihood of engagement.
Innovation Solution
The use of machine learning techniques to predict the probability of candidates taking specific actions, such as becoming members or purchasing products, by analyzing demographic and enrichment data, and training models based on previous candidate outcomes, allowing for personalized candidate lists to be generated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to identify candidates, then entities can potentially contact all possible candidates, but the time and resources expended are excessive and inefficient
Solution Approach 1:
The system performs preliminary actions by pre-calculating engagement probabilities for all possible candidates using machine learning models before the actual candidate selection process. This allows entities to quickly identify high-probability candidates without manual screening, resolving the contradiction between thorough identification and time efficiency.
Solution Approach 2:
The patent replaces manual mechanical processes of candidate screening with an automated machine learning system that processes candidate data and predicts engagement probabilities computationally. This substitution dramatically improves productivity while reducing the time loss associated with manual evaluation of each candidate.
2Reliability
If entities contact all possible candidates, then no potential engagement opportunities are missed, but resources are wasted on candidates with low likelihood of engagement
Solution Approach 1:
The system applies local quality by differentiating candidate evaluation based on individual characteristics and predicted engagement probabilities. Instead of treating all candidates uniformly, the system identifies and focuses resources on specific high-probability candidates while reducing or eliminating contact with low-probability candidates, thus capturing real engagement opportunities without wasting resources.
Solution Approach 2:
The patent changes the parameter of candidate selection from binary (contact/not contact) to probabilistic (engagement probability threshold). By calculating and comparing engagement probabilities against threshold values, the system dynamically determines which candidates warrant resource investment, maintaining reliability of capturing opportunities while optimizing resource allocation.
3Measurement precision
If machine learning models are trained on comprehensive data, then prediction accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system extracts and utilizes only the most relevant features and data elements from comprehensive candidate information for training machine learning models. By identifying and extracting key predictive features rather than processing all available data, the system maintains high prediction accuracy while reducing computational complexity and resource requirements.
Data Source
AI summary
Examples are disclosed that relate to determining probabilities for possible candidates taking an action of interest. One example provides a computing system comprising a logic subsystem and a storage subsystem comprising instructions executable to obtain a list of possible candidates and enrichment data for each possible candidate. The instructions are further executable to, for each possible candidate on the list of possible candidates, determine a confidence regarding an identity of the possible candidate based at least on the enrichment data, when the confidence regarding the identity of the possible candidate satisfies a threshold condition, determine, by inputting information regarding the identity and the enrichment data into a trained machine learning model, a probability that the possible candidate will take an action of interest, and when the probability meets a threshold probability, add the possible candidate to a list of candidates, and output the list of candidates.