Predicting Employee Net Promoter Score Changes
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Solution Overview
Problem
Existing methods for analyzing the impact of employee well-being programs on employee Net Promoter Score (eNPS) are inefficient, requiring excessive time and computational resources, and are unable to determine the most relevant drivers for eNPS, making it difficult for organizations to quantify the value of these programs effectively.
Innovation Solution
A system and method using a machine learning model trained on historical data to quantify the impact of employee well-being programs and participation on eNPS, allowing for predictive insights into how modifications can improve eNPS, with a user-friendly interface to input baseline criteria and generate adjusted eNPS scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or brute force approaches are used to analyze data related to eNPS, then comprehensive analysis can be performed, but the time required exceeds the time during which the data remains meaningful
Solution Approach 1:
The patent replaces manual or brute force mechanical analysis approaches with a machine learning-based automated system. The machine learning model programmatically analyzes vast amounts of employee data to predict eNPS changes, eliminating the time-consuming manual processes while maintaining comprehensive analysis capabilities.
2Quantity of substance
If brute force analysis is used to process vast amounts of employee data, then all available data can be considered, but excessive computing resources are required
Solution Approach 1:
The patent transforms the approach to data analysis by changing from brute force processing to machine learning-based predictive modeling. The system identifies key driver variables and their weights, processing only the most relevant data parameters rather than exhaustively analyzing all available data, thereby reducing computational resource requirements while maintaining analytical depth.
3Loss of information
If traditional qualitative methods are used to determine how employee experiences translate into eNPS, then subjective insights can be gathered, but bias and inaccuracy increase
Solution Approach 1:
The patent replaces subjective qualitative assessment methods with an automated machine learning system that objectively analyzes employee data. The model programmatically determines how employee experiences translate into eNPS predictions, eliminating human bias and improving measurement precision through consistent, data-driven analysis.
4Measurement precision
If manual analysis approaches are used to identify relevant eNPS drivers, then detailed examination can be performed, but productivity decreases due to excessive time requirements
Solution Approach 1:
The patent replaces manual driver identification processes with automated machine learning algorithms that programmatically analyze data to identify key eNPS drivers and their relative importance. This substitution maintains detailed examination capabilities while dramatically increasing analysis speed and productivity.
Data Source
AI summary
An employee net promoter score (eNPS) adjustment system and method adjusts an employee net promoter score (eNPS) using a predictive model. The predictive model identifies key eNPS driver variables and adjusts a programmatically generated quantification of employee engagement and/or sentiment toward an employer based upon received data representing the key eNPS driver variables.