Predictive analytics in HR – retaining employees (Part 5 of 8)

Welcome to 2014! I hope everyone had a wonderful holiday season. I understand the tremendous anticipation and speculation for the next article in this series. It must feel like Christmas morning all over again (insert chuckle, smirk, giggle here)! So without further delay, we will pick up where we left off in 2013 and continue our high level look at predictive analytics. Today, we take a deeper dive into predictive analytics and employee retention.

Predictive analytics in HR – retaining employees

Retaining employees is about more than just keeping your pay competitive. It’s about fulfilling deeper needs that will leave them satisfied in their work, creating engagement, motivation and above all satisfaction. But while we can say some things in general about what will achieve this, and the work of Frederick Herzberg has spelled it out for us since the 1960s, there is still a need to provide focus, both on your own industry and on your employees.

Understanding your industry

The largest scale work on this has yet to be done. To succeed, it needs to be ambitious, looking at trends on an industry-wide scale. Any company’s ability to do it will depend upon what data is publicly accessible, or can be negotiated from other firms on the basis of sharing the results. But it could be hugely beneficial for all concerned.

Assuming that data can be gathered, predictive analytics could be used to find links between employee conditions and retention within different industries, and for different types of employees within them. By comparing retention rates with conditions, pay, career development and other factors, it would be possible to identify, for example, the three types of incentives that most help in retaining educational software developers.

By examining trends over time, and comparing them with the wider social and economic picture, predictive analytics could also foresee trends in the way retention incentives are likely to change. For example, does pay become more important during a down swing, or working conditions during a period of industry instability? Are such changes coming your way? It’s this crystal ball gazing that makes predictive analytics so useful.

Understanding your employees

Putting this into practice involves more than just looking at the big picture. As with much of predictive analytics, it involves taking that big picture and relating it to your own people. Knowing that opportunities for project work help in retaining team managers is all very well, but how can you tell if you are meaningfully providing those opportunities for your employees?

This is where we get into the analytics that is already available to you. After all, you have access to data on your own employees, and analyzing a smaller set like this is easier. You can look for trends in which parts of your organization have trouble with retention, and focus on them. Is it your call center? Your financial managers? Your senior executives?

You will get a better understanding of this by comparing your situation with that of others. Is your high call center turnover something that’s unique to your organization, and that you can fix, or something that’s common in that type of work? And if there are particular incentives that are predicted to help in that area, are you using them? If not, then predictive analytics may be presenting you with the solution.

By relating big picture insights back to teams and individuals you can focus your retention efforts in the right areas, using the right tools.

Of course, none of this will do any good if it isn’t put into practice properly, and that’s what we’ll look at next.

– Mark Lukens

Links or other articles in this series:

Article 1: Predictive analytics – looking to the future in recruitment
Article 2: Predictive analytics in HR – looking at the big picture
Article 3: Predictive analytics in HR – smarter recruitment
Article 4: Predictive analytics in HR – training and development
Article 5: Predictive analytics in HR – retention
Article 6: Predictive analytics in HR – getting it right
Article 7: Predictive analytics in HR – barriers to deployment
Article 8: Predictive analytics in HR – the future

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Mark Lukens, MBA

Founding Partner at Capatus

Mark Lukens is a founding partner at Capatus and located in the New York office. He leads the Capatus’ Global Talent and Advisory practice. He is also an expert in the firm’s research and nonprofit practice. Lukens has more than 20 years of c-level executive and consulting experience delivering strategies and transformational programs to firms ranging from start-up to Fortune 50. He has worked with clients in Europe, North America, South America, and Asia. Lukens worked extensively in various product and service categories including health care, life sciences, government, nonprofit, technology, and professional services. He also advises clients in other industries including commercial and industrial, retail, logistics and transportation, media and more. Lukens serves on several Nonprofit Boards and is a professor at the State University of New York where he teaches in the School of Business and Economics with a focus on marketing, international management, entrepreneurship, HR, and organizational behavior to name a few. Lukens has a technical background as a MCSE and earned an MBA from Eastern University.

@marklukens

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