A Leading Beverage Manufacturer Reduced Employee Attrition by 5% With a Custom ESAT Analysis Tool

CPG CPG analytics Attrition Analysis Employee Satisfaction ESAT Custom AI Application ESAT Analyzer




Custom AI Application

ESAT Analyzer

Overview of the ML Classification Attributes


Fig 1: Overview of the ML Classification Attributes

Data Pre-processing: TheMathCompany worked with the client to collate the needed data from the respective stakeholders. Then, existing, disparate data was analyzed to identify areas that required corrective measures and an improved quality of data and data collection.

The data was cleaned thoroughly, in adherence with data compliance regulations, and thereafter used for feature engineering.

ML Classification Framework: A custom ML framework was set up, which utilized the Basket of Algorithms approach to automatically custom-select the best algorithm for every model build.

Attrition Analyses: By using a Champion Algorithm, results were generated to identify at-risk employees, key drivers of attrition, and each driver’s respective contribution to attrition rates. For instance, analyses revealed that peer compensation contributed 19% to attrition, while career progression in comparison to peers, manager performance, and goal achievement contributed 17%, 11%, and 8% each.

Overview of the ML Classification Attributes


Fig 2: Overview of the Functioning of the ESAT Analyzer

Upon implementation, the ESAT Analyzer’s automated dashboard enabled the HR team to track progress and conduct more than 100 “Stay Interviews” within a month. By taking the needed action to prevent attrition, predict risk & drivers of attrition, and so on, employee attrition was reduced across the client company by 5%.