Employee Turnover Prediction in Retail

Internal data mining and new methods of automated data analysis provide organizations with the opportunity to discover the employees who are most likely to quit and take action to retain them.

Historically, employee turnover is a problem for companies, because it brings new hiring costs, overtime costs, and losses due to low productivity. Today, the amount of data available for collection, and modern machine-learning techniques mean the employee turnover prediction problem is solvable. Based on log records machines “label” each employee as “1” for turnover or “0” for no-turnover. Alternatively, the model shows turnover probabilities instead of binary results.

One of the most important tasks for employee turnover prediction is to build an engine for collecting extensive data, which can include regular records of very different kinds:

• Age
• Sex
• Job title
• Salary
• Bonuses
• Performance metrics
• Company type
• Number of employees
• Job experience
• Educational level
• Field of education
• Living cost rate
• Social media data (LinkedIn, for example)

Building an accurate employee turnover prediction solution is a complex task that includes initial analysis, database collection, applying and training a machine-learning model on available data, testing, acceptance, and further improvement phases. BitRefine group offers a full cycle of services, ensuring the best possible performance of prediction solution. This powerful tool provides the opportunity to direct the close attention of HR to a number of employees with the highest probability of resigning and find the right way of motivating them before it’s too late.