Employee Attrition Prediction via Automated Training Recommendations
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Solution Overview
Problem
Current human capital management systems are unable to effectively address the underlying causes of employee attrition risk, as they lack the capability to provide concrete and automated solutions for mitigating factors such as burnout and time management issues, relying on manual and inefficient methods.
Innovation Solution
A system that connects internal and external data sources to predict employee attrition risk, generates training course recommendations using learning management system data, and presents these recommendations to client devices, utilizing artificial intelligence, machine learning, and deep learning to model employee circumstances and suggest targeted actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual approaches with spreadsheets are used to track employee attrition data, then flexibility and customization are improved, but time consumption and human error increase
Solution Approach 1:
The patent replaces manual mechanical operations (spreadsheets, manual data entry, manual analysis) with an automated computer-based system that performs data collection, analysis, and recommendation generation automatically, eliminating time-consuming manual processes while maintaining flexibility through configurable parameters
2Extent of automation
If HCM systems perform heuristics analysis on attrition data, then automated reporting is improved, but actionable solution generation deteriorates
Solution Approach 1:
The system implements feedback loops where attrition risk predictions trigger specific actionable recommendations, and the outcomes of these recommendations are fed back into the system to refine future predictions and suggestions, creating a closed-loop system that continuously improves both automation and actionable guidance
Solution Approach 2:
The system performs preliminary analysis of employee data to identify attrition risks before problems fully manifest, and pre-generates actionable recommendations based on predicted risk factors, enabling proactive intervention rather than reactive problem-solving
3Measurement precision
If companies identify employees at risk of burnout, then early detection is improved, but concrete corrective steps deteriorate
Solution Approach 1:
The system applies local quality by providing customized, individualized recommendations tailored to each employee's specific risk profile and circumstances rather than generic advice, making corrective steps highly relevant and actionable for each individual case
Data Source
AI summary
The systems and methods described herein provide for predictions of employee attrition within an enterprise. In one embodiment, the system connects to one or more internal data sources within the enterprise and one or more external data sources outside of the enterprise, receiving internal feed data from the internal data sources and external feed data from the external data sources, the internal feed data relating to a number of employees. The system then determines, based on at least the internal feed data, a number of attrition risk factors for the employees; receives LMS data corresponding to training courses being offered; generates one or more training course recommendations for the employees; and presents the training course recommendations to one or more client devices.