Dynamic Employee Attrition Prediction via Enterprise Graphs
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Solution Overview
Problem
Current human capital management systems lack dynamic and reliable data sources for predicting employee attrition, relying on manual methods, static surveys, and subjective feedback, which are often inaccurate and infrequent, making it difficult to prevent attrition in a timely manner.
Innovation Solution
A system that connects internal and external data sources to generate normalized feed data, creating enterprise graphs and risk assessment models to predict attrition probabilities, providing actionable insights and suggested actions for mitigating employee attrition using machine learning and artificial intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual approaches and static surveys are used for employee attrition analysis, then the system complexity is low, but the measurement precision and reliability of attrition prediction are poor
Solution Approach 1:
The patent replaces manual mechanical approaches (spreadsheets, manual surveys) with automated machine learning systems that dynamically process multiple data feeds. The ML model automatically ingests data from diverse sources including employee feedback, performance metrics, and engagement surveys, eliminating manual data collection and analysis while significantly improving prediction accuracy.
Solution Approach 2:
The system integrates multiple data collection functions into a single unified platform that handles employee surveys, performance data, engagement metrics, and feedback analysis simultaneously. This multi-functional approach consolidates previously separate manual processes into one comprehensive system that improves measurement precision without proportionally increasing complexity.
2Reliability
If dynamic real-time data collection from multiple sources is implemented, then the reliability and timeliness of attrition insights improve, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent introduces machine learning models as intermediary components that automatically process, normalize, and analyze data from multiple diverse feeds. These ML intermediaries translate raw data from various sources (surveys, performance systems, engagement platforms) into reliable attrition risk insights, managing the complexity of integrating multiple data sources while improving data reliability.
Solution Approach 2:
The system implements self-service data collection and processing where the ML model automatically ingests, cleans, and analyzes data from multiple feeds without manual intervention. The system self-manages the complexity of real-time data processing by autonomously handling data normalization, feature extraction, and risk assessment, reducing the operational burden despite increased processing requirements.
3Productivity
If comprehensive data feeds from multiple sources are integrated, then the productivity of attrition prevention actions improves, but the loss of time for data collection and analysis increases
Solution Approach 1:
The patent implements continuous real-time data collection and dynamic risk assessment that proactively identifies attrition risks before employees actually leave. The system continuously monitors multiple data feeds and updates attrition probabilities in real-time, enabling organizations to take preventive actions earlier rather than reacting after employees have already decided to leave, thus improving prevention productivity without time loss.
Solution Approach 2:
The system maintains continuous operation by constantly ingesting data from multiple feeds and dynamically updating attrition risk assessments. This continuous processing eliminates gaps in monitoring and ensures that the most current information is always available for prevention actions, maximizing the effectiveness of attrition prevention while minimizing delays through automated real-time analysis.
Data Source
AI summary
Systems and methods describe providing for the dynamic prediction of employee attrition within an enterprise organization. The systems and methods involve connecting to internal data sources within the enterprise and external data sources outside of the enterprise; receiving internal and external feed data relating to a number of entities within the enterprise, including at least one or more employees of the enterprise; converting the internal and external feed data into normalized feed data within a single format; generating enterprise graphs based on the normalized feed data, with each enterprise graph representing relationships between entities within the enterprise at a point in time; generating a risk assessment model related to changes in the enterprise graphs over time; and providing to enterprise users, based on the risk assessment model, one or more attrition probabilities for at least a subset of the employees of the enterprise.