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From IBM To Mastercard; Tech Giants Are Using Predictive Analytics To Reduce Employee Attrition

From IBM To Mastercard; Tech Giants Are Using Predictive Analytics To Reduce Employee Attrition


Predictive analytics has been pegged as the key to addressing employee attrition. It has emerged as the missing link for the human resources department which lacks the analytical ability in bolstering their reporting. Also, the combination of right analytical approach is crucial to address the biggest pain point for HR — retaining talent. The other important issue is also about identifying the employees who have a propensity to leave, and how to retain them.



Predicting employee turnover is one of the most common use cases in HR analytics. The turnover rate can be identified in HR reporting, by assessing various parameters such as employee profile, satisfaction evaluation, performance evaluation, project planning and evaluation, absence and time sheets and communications and interaction schemas, among others. However, a certain amount of attrition is unavoidable and largely unpredictable, says a whitepaper from TCS, since companies can never gather all the data that went into each decision.

Today, data analytics in human resources function has become increasingly sophisticated with enterprises relying on advanced modelling techniques such as neural networks to find out “drivers” that influence target variable. Earlier attrition methodologies relied more on correlation while advanced methods are able to get further information and uncover more complex patterns.

Some Of The Companies That Deployed Predictive Analytics To Counter Attrition Are

  • Earlier in 2016, it was reported that top IT bellwether IBM is heavily investing in predictive analytics tools to counter attrition. Kevin Cavanaugh, VP, Smarter Workforce Engineering, IBM was cited by ET mentioning how predictive analytics can be used to re-target employees who are most productive and are likely to stay.
  • Other companies like SAS also adopted the use of predictive analytics tools to identify employees at the risk of leaving, better known as high-flight-risk employees, screening and hiring better employees and deploying social media to get a real-time understanding of employee engagement.
  • Meanwhile, MasterCard is developing predictive models directed at improving the employee experience through a range of data sources.
  • Meanwhile, data from LinkedIn and other social networks have proven to be very effective for pharma and software companies in predicting ‘high-flight-risk’ employees among their high-potential staff.

Key Applications Of Predictive HR Analytics

1) Forecasting hiring needs: This helps in optimizing the resource and allows growth and margins, by predicting requirements for HR capacity. Through this application, HR managers can develop plans for recruitment, training, and infrastructure development.

2) Loyalty and attrition analysis of employees: Loyalty and attrition analysis is one way of assessing employee retention, by calculating an attrition risk score for individual employees, thereby helping organizations to prevent the potential attrition of high performing employees and ensure business continuity.

3) Employee segmentation and profiling: Accurately segmenting and profiling workforce helps in talent management. Organizations can understand the workforce better and take initiatives tailored to fit employee requirements by segmentation of the existing employee base.

4) Hiring and profile selection: High-cost employee attrition leads to significant losses for the organization. The HR decision makers can come to this decision by analysing their data such as performance and productivity indicators.

See Also

5) Employee sentiments analysis: One of the most effective tools to assess overall employee satisfaction, employee sentiment analysis tracks, and analyzes topics that are most relevant to employee sentiments over a period. Data from internal sources and external data from social media platforms such as Facebook, Twitter, and LinkedIn is used for this analysis.

Drawbacks

  1. According to a research by Fitz-enz and Mattox, approximately 75 percent of HR departments do not have usable base metrics. This means that, for many organizations, there is a big leap from their current state to the appropriate use of analytics.
  2. Even if the skills and ability to conduct these analyses are present, it is still a challenge to gather the data necessary to turn information into results
  3. Before investing in HR analytics, it is important that HR professionals should evaluate both the potential and drawbacks of employing analytical techniques and develop a sound strategy for approaching data collection before leveraging HR analytics

Conclusion

Large enterprises are dependent on new models to understand the dynamic factors that affect their staffing requirements. Keeping this in mind, predictive analytics and predictive modelling can go a long way in helping them understand and appropriately respond to current trends and likely future events in the HR domain, thereby improving business performance.

 


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