Home / Case Studies / Employee Attrition Model
/ Case Study · Human Resources
An AI-Driven Employee Attrition Model. Workforce Planning, Made Predictive.
/ The engagement
A Machine-Learning Model to Predict Attrition.
The IT industry carries a high attrition rate, and the annual attrition in the tech sector stands at around 15 percent. For an IT-sector client in South Asia, Brainvire streamlined a robust machine-learning model in workforce planning that predicts employee attrition with high accuracy based on data analysis, so companies can retain talent before it walks out the door.
/ About the client
The IT industry has a high attrition rate, a grave concern for companies that foster talent and face losses in human resources and domain expertise. The annual attrition rate in the tech sector stands at a whopping 15 percent, and it is not lowering anytime soon. Brainvire created a predictive data science service that IT companies can leverage to retain employees, thereby reducing attrition rates in the industry.
We streamlined a robust machine-learning solution model in workforce planning to help businesses predict employee attrition. The predictive AI-driven model employs cutting-edge machine learning algorithms to predict attrition with high accuracy based on data analysis.
/ The approach
Design, architecture, and features, shipped as one program.
Clear goals set
We defined clear project goals, aligned the team, and established a timeline for the model before a line of code was written.
Every stakeholder in
We engaged all the stakeholders, including HR, managers, and leadership, to understand the organizational challenges behind attrition.
Watched as it grew
Developing the model required continuous monitoring and evaluation, so the predictions stayed accurate as new data arrived.
Built to last
We ensured the strategies stay sustainable, aiming for a lasting reduction in attrition rather than a one-time dip.
/ What stood in the way
Four problems, solved without a maintenance window.
Select a challenge to see how it shaped the build.
Strategizing a predictive model
Our team faced a dilemma while creating a model that predicted high attrition and assessed the risks at the same time. We streamlined a comprehensive AI-powered model, drawing on exit interviews, environmental management, career development programs, recognition awards, and progress monitoring.
Employee data security
Ensuring the privacy and confidentiality of employee data collected during exit interviews and surveys was critical. We enabled an automated process to clean and integrate the data, lowering security risk while still extracting meaningful insights.
Limited engagement data
We faced difficulty obtaining employee engagement data that was crucial for understanding attrition drivers. We streamlined regular pulse surveys to gather continuous feedback on employee engagement and satisfaction.
Lack of predictive analytics
Predicting attrition patterns and identifying high-risk employees was hard. We built predictive analytics models using historical data to forecast attrition and flag employees at higher risk of leaving.
/ Product screens
The delivered experience.
The interface people actually use.
/01Risk Overview
/02Employee Scoring
/03Retention Board
/04Pulse Insights/ The results
What changed, in numbers.
/ Next step
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