Three ideas for a smoother integration of predictive analytics in HR

The what, why, and how of retention management analytics

predictive analyitcs in retention management — Two women of color talking in a meeting
Photo by Christina Morillo from Pexels

You and I, we both love analytics. We both believe that predictive analytics is the future of HR. And we both believe that predictive analytics is especially a game-changer for retention management. Unfortunately, not everyone in HR seems to be as excited about software, spreadsheets, and statistics as we are.

In this article, I will share insights from my latest research project, conducted in autumn 2020 as part of my Master in Intercultural Studies at Aarhus University, Denmark. Specifically, I will present three problems that hinder HR teams to integrate predictive analytics software and three potential solutions.

Also, I invite you to share your ideas, insights, and struggles about integrating predictive analytics in retention management with me for my further research. Send an email to and I am happy to share more of my research with you!

Problem 1: Data Enthusiasts vs. HR Traditionalists

When talking to data analysts in the HR field or reading through the plethora of blog articles about predictive analytics, I often noticed how these experts positioned themselves against “traditional” HR. They described HR professionals, who were not using analytics, as backward, outdated, reactionary, and operational. These “HR Traditionalists” were also accused of flying blind, making decisions on a whim, or based on intuition. In contrast to that, professionals in favor of analytics, who I labeled “Data Enthusiasts”, attributed themselves as strategic, fact-based, and effective.

As Data Enthusiasts myself, I understand how talking to skeptics can quickly turn into a frustrating experience. After all, the integration of predictive analytics seems inevitable and promises great competitive advantages. So it is sometimes hard to understand why not everyone wants to dive into it better yesterday than today. However, a dismissive and disrespectful tone will only deepen the divide and make the integration process even more painful and slow.

Solution 1: Respect and kindness go a long way

Yes, voluntary attrition software is a game-changer that will make retention management a lot more successful. But in the end, the software can only give you insights — your HR team still has to make decisions and act accordingly. Thus, successful integration goes beyond technical integration. It also includes getting your HR colleagues on board and combining their best skills and years of experience with the software’s benefits.

By focussing on the HR team's assets instead of its shortcomings, you not only create respectful communication but also harness the best of both worlds. In the context of retention management, you can for example use their interview competencies to understand better why employees might leave — after all, the software only gives you quantitative data that you best supplement with qualitative data. Or they can set up training and development sessions for managers on how to talk to their employees with a high-risk score for voluntary attrition. Also, your HR colleagues might be more informed about the company’s culture and diversity strategy and can therefore make more sound decisions based on the software’s insight.

Problem 2: Scepticism and desinterest

When I interviewed HR traditionalists to get their side of the story and what is their stand on predictive analytics, I was surprised that they almost in unison agreed to see the digitalization of their profession as inevitable. One of my interviewees even stated she wished she had learned more statistics in her liberal arts degree. What then, makes it sometimes difficult to warm up everyone in the HR team for a tech-solution?

The short answer is, people have different values and interests. One of my interviewees explained that she is very much open to any technical solution as long as it helps her to do a better job for the company’s personnel. She sees her professional identity as people-minded and service-oriented. Therefore, her main motivation and driver is to help the people in her company. This emphasis on people-orientation led me to re-label the HR Traditionalists as “Human Enthusiasts”.

Revisiting my interviews with Data Analysts and re-reading the blog articles, I noticed how they all presented the benefits of predictive analytics for retention management from a business-oriented perspective. The main pro-arguments always revolved around plummeting turnover cost and getting a cutting-edge competitive advantage. Now, these outlooks might prompt you and me drooling but leave Human Enthusiasts emotionally cold — albeit understanding these benefits on a rational level.

Solution 2: How to speak the language of Human Enthusiasts

So how can we win Human Enthusiasts for using predictive analytics in retention management? Let us slip into the shoes of our more people-oriented HR colleagues and rediscover the benefits of our beloved retention software from their point of view.

Benefit 1: Saving people the trouble of finding a new job

Looking for a new job is always a hassle. With predictive software, you will spare your employees the dreadful journey of combing through job platforms, enduring interviews, and the risk of facing new struggles with a new employer.

Benefit 2: Creating a culture of honest and solution-oriented communication

Employees don’t spontaneously decide to quit their job. A termination letter is always the result of weeks and months of being frustrated, hoping for better times, and searching for better opportunities. What if HR could interrupt this unpleasant work experience by nudging managers to have honest talks with those employees that show a high risk of leaving?

So far, pondering about quitting your job has always been taboo as it is often seen as disloyalty. However, our work culture is changing and job-hopping has become the norm. A software that can show the risk of voluntary attrition for every employee facilitates more honest communication — after all, we probably all pondered about quitting at one point in our careers. It also allows HR to take the right measures before skilled workers are leaving.

Benefit 3: Giving people what they care about

Incentive programs are a great instrument to appreciate employees and to strengthen their commitment. However, incentives work only if they speak to the individual interests and values of each employee. Retention management software gives insights into the factors that are most likely to drive an employee's voluntary attrition. Turn these around and you might be able to give your employee the incentive that he or she was missing. That can mean a promotion for one person or remote work options for another.

Benefit 4: Step up your diversity game

Diverse talent has often a higher risk of leaving. Use predictive analytics to identify the subunits where the highest turnover of diverse employees occur and what the software suggests as their major attrition factors. Now, the software is probably not smart enough to identify discrimination or a slightly divisive work climate in the subunit. But it can be your starting point for a conversation about how to make the work environment more inclusive.

Benefit 5: HR can become less biased

Now, this will be a controversial statement but I think using software will make retention management — or HR in general — less biased in the long run. Most HR professionals probably received training or education in some form about how to be less biased in their job. However, biases are just part of the brain’s filter to reduce information and to make sense of the world. We can work on our biases, but can we ever fully overcome them? Software has more learning capacity than one individual human being. Software is the sum of an incomprehensible amount of data and many smart people writing its code. Now, if the data is biased and the coders are biased, the software will be biased too. But if a trained, self-aware team works together on this software integrating their different perceptions, the sum of this diverse teamwork will be less biased than each individual on the team. Again, there are great debates about biased algorithms going on so feel free to disagree!

Benefit 6: Doing more of the fun stuff

The prospect to learn yet another software and spending more time on a computer might not be received well by your HR team. But having a retention management software means what digitization always means: less repetitive, menial tasks and more time for the fun, “human” work.

Problem 3: Lack of competencies

The first two solutions mostly help with getting the right mindset for you and your team. Another important aspect is the lack of competencies such as data literacy, analytical skills but also the interpretation and communication of data. Most HR teams today do not have people with these skills as predictive analytics is still a rather new field for them. Even worse, we will soon experience a pipeline problem as the demand for these skills will rise but the education system has not yet adapted.

Solution 3: Skill up your HR team

So how do we get skilled HR professionals who can harness the full potential of predictive analytics in retention management?

  1. Identify the business-oriented and /or tech-savvy (read Data Enthusiasts) HR professionals in your team and develop their skills with adequate training.
  2. Borrow data analysts from other departments. Chances are good that your finance or marketing department has some skilled workers, who have worked with predictive analytics before. Maybe you can “borrow” them for your HR team?

What is your experience?

As in any good research process, I ended my research with more questions than answers. For example, how do employees judge the use of predictive analytics in their own company? On one hand, I can imagine that people find it intrusive. On the other hand, I could even see retention management used as a selling point in employer branding. After all, trying hard to keep your personnel in their jobs would be perceived as a good thing, right?

Also, I am curious about how the data is interpreted and actions are decided. For example, one company learned that people over 40 are less likely to leave the company. In a training session with managers on how to use the retention management software, this insight sparked the idea to hire only 40+ year-olds from now on — which was of course not the desired conclusion…

Finally, I have decided to continue my research by writing my Master's Thesis on this topic. So feel free to contact me with any ideas and questions you have. More specifically, if your company has not yet integrated predictive analytics for retention management, what are the preventing obstacles? And if you are already ahead of the curve in the retention game, what are your struggles with using the software?

Tell me about your struggle with using predictive analytics for retention management. Contact:

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