When Singapore became independent in 1965, DBS was founded three years later as a local bank before serving the region in the 1980s.

As ASEAN’s “Most Valuable Bank Brand” for the 7th consecutive year, the S$12bn brand built a complex, intricate algorithm that predicts attrition rates from 600 data points within the 11000-strong organization.

Using data to predict employee attrition is nothing new: there a plethora of startups offering software and Mercer’s Global Talent Trends Study 2019 revealed that using artificial intelligence in predicting employees’ risk of leaving remains on the wishlist of many companies, even though the technology is still nascent. HR professionals are involving analytics and data are a lot more over the past few years.

According to LinkedIn, that will continue to grow.

It is an uphill struggle to continue preserving competitive advantages and protecting intellectual capital — retaining the best talents is always the top of many lists of HR challenges.

Data scientists have been creating machine learning models and algorithms for a long time: the only variables are the industry and the purpose. Some companies use data to predict sales and others who use data to predict an employee’s fit to a company.

However, there are many dynamics and nuances that go into how HR professionals can use data to help them create the right action plans. How can numbers and spreadsheets point towards the right general direction to driving employee retention?

The Matrimony with Data

Big data and machine learning have been dominating many industries over the past few years, be it in sales, marketing or creative. The reality is that such technologies have become a fundamental and data-driven decision making in leaders is almost a requirement in today’s world.

The Turnover Propensity Index by Holtom & Allen

Working with a talent intelligence firm, Prof. Brooks Holtom and Prof. David Allen embarked on researching into how big data and machine-learning algorithms could predict whether an employee is likely thinking about quitting — the turnover propensity index (TPI) is created as a real-time indicator, grounded on predictive models in academic research.

The TPI is built around turnover factors that include personal and organizational. Another contributing factor is job embeddedness, which is how deeply connected people are to an organization. Along that line, it is tied very closely to core components in employee engagement.

Based on their assessments of the turnover factors, they used machine learning to identify how likely an individual is to be receptive to new job opportunities. A TPI score is given to the individuals in their sample and they ran two studies to see how accurate their predictions are.

Through a logical conclusion, one would expect that the higher someone is on the TPI, the more likely he is to quit.

Hence, they decided to send out e-mail invitations to a sample of 2,000 employed individuals. These individuals have been identified by the algorithm on how likely they are to open an invitation to view available jobs tailored to their specific skills and interests.

Those who were rated as most likely to be receptive to opening the e-mail invitations were twice as likely to open than, in comparison to those rated as being the least likely to be receptive.

They measured the click-through and realized that amongst those who opened the email, those rated as most likely to be receptive were significantly more likely to click through it.

Using the remained of their sample, over three months, they realized that people with high TPI scores were “63% more likely to change jobs” and they were “40% more likely to quit”, compared to the people with low TPI scores.

Employee Attrition & Performance Analytics by IBM

Using a data set from an employee survey from IBM, a team of four used classification models to predict if an employee “is likely to quit could greatly increase the HR’s ability to intervene on time and remedy the situation to prevent attrition”.

Taking into account the mixed employee architectures in IBM, many people attempted to create an ML model through Python. According to the Performance Analysis, the model is successful in effectively classifying 89.12% unknown sets.