By | Ben Eubanks – Industry Analyst, Podcaster, and Influencer, Lighthouse Research & Advisory
As an avid follower of the analytics movement in the HR profession, I’ve seen several challenges to adoption. However, one of the most common that I hear from both vendors and practitioners is the use case. In many instances, HR leaders know what they need their analytics to help them prove. But in others, they are swayed by messaging and marketing, following a general trend instead of one tailored to their business needs.
During a recent episode of the HR Happy Hour podcast, hosts Steve Boese and Trish McFarlane talked about the example of employee flight risk and how it instantly becomes the foundation for a discussion on predictive analytics, even if the company’s specific challenges/opportunities don’t revolve around flight risk. With that in mind, I set out to list nine different use cases as a way for practitioners to understand just how powerful analytics can be beyond helping you figure out which of your people might be a retention risk. In addition, it will help vendors to understand some of the common challenges that your prospects and customers might be facing so you can listen to cues and support their concerns appropriately.
I also want to note that this list isn’t based on any specific vendor’s offerings. I don’t know that any one provider has the ability to demonstrate most or all of these, but this is about helping the practitioner first and foremost. Speaking of helping the practitioner, data from an IBM and MIT research study points out that that top-performing companies are three times more likely than lower performers to be sophisticated users of analytics. In addition, the study reported that companies with a high level of HR analytics had:
- 8% higher sales growth
- 24% higher net operating income
- 58% higher sales per employee
How’s that for a business case for people analytics?
Signals vs Use Cases
Before we get too deep, I wanted to make an important distinction. Signals are the various inputs that models and algorithms use to provide predictive insights. Common data signals in HR applications can include:
- Job status
- Job type
- Application source
All of these signals (and quite a few others) can be combined to create complex models that predict human behavior, something that has long challenged employers. Those models are the engine that powers the analytics behind each of these use cases.
Note: This is part of a continuing series exploring the use of people analytics. If you’re interested, here are links to the other pieces:
- Layman’s guide to predictive HR analytics
- Defining analytics: descriptive, predictive, and prescriptive
9 Use Cases for People Analytics
- Retention/flight risk. I’ll get this one out of the way. 🙂 Analytics can show you if a person is likely to depart based on a variety of signals. Bonus: this can be applied pre-hire as well, and I would even make the argument that it’s more valuable pre-hire since you can avoid hiring people that might depart sooner than others, if turnover costs are a primary concern. For example, if workers that commute 50+ miles each way are 80% more likely to depart within 12 months, you might want to factor distance to the office into the selection criteria. As you’ll see, not all answers with analytics are straightforward or simple.
- High potential. Every company would like to know which employees are not only ready for additional responsibility, but also those that will bring high performance to the role. By examining succession, career aspirations, assessment, 360-degree feedback, performance, and other data, companies can determine which of your people are poised for future success. Beyond the basic 9-box grid, this takes into account a wide variety of signals to determine a person’s true potential.
- Improved sales. This strategy involves measuring using previous scheduling and attendance data, correlating it with sales and customer satisfaction data, and using that blend of information to determine the best team makeup for optimum performance. This is especially valuable in retail and food service environments, where shifts are a key part of the makeup of work.
- Workforce planning. You might know your best sources for software developer talent today, but what will be the best sources two years from now? Will you even need those developers then, or will they be robotized like every other job (hint: not being serious here)? Being able to do “what if” analyses on various workforce scenarios and demographics will give companies a better picture of available talent while helping them to remain agile. Instead of planning only for the “perfect” scenario, the team can look at 3-5 likely avenues and make contingency plans for each, including aspects such as talent sources, making use of full-time or on-demand workers, and more. Forecasting the supply and demand for talent is often messy, but it’s valuable. In addition, with more companies thinking not about talent as a full-time person, but as a set of skills, this is even more important.
- Training customization/performance improvement. For a long time, training departments have been focused on completions, a poor indicator of impact. What if we instead took a comprehensive look at training data to see which employees improved post-training, what amount of the improvement can be traced back to the specific training opportunities, and if the training improvements can be replicated in other employees? In terms of the employee experience, systems can now deliver personalized training based on individual interests, past training, job-required training, geography, date, work projects, search history, training taken by similar individuals, and other signals.
- Retirement likelihood. Nobody wants to lose their most senior, seasoned workers. Yet we often throw up our hands and feel helpless to predict or halt the flow of talent out of our organizations. Predictive tools can help us identify which workers might be likely to retire based on more than just guesses based on someone’s age. We can also factor in eligibility for specific retirement benefits, recent changes in role or salary, and other signals that might indicate someone is going to leave soon. This is a more specialized use case than general flight risk and can be more valuable if your organization is one that has a significant amount of knowledge and wisdom locked away with your older workers.
- Pay for performance. Does it work in general, just for specific roles, or not at all? Measuring this is the difference in plugging a new pay rate into your accounting system or HRIS and estimating how that new pay rate will affect performance/productivity. Several years ago Kimberly-Clarke (#151 on Fortune 500) changed its approach to compensation for this very reason. While the initial transition from “pay for tenure” was rocky, it eventually became standard operating procedure. Wouldn’t it be helpful to see this almost as an ROI calculation? For example, for every $1 we pay someone in this type of job, we see $1.65 in productivity/performance gains?
- Global mobility. It’s well-known that global assignments often fail, which drives up this already-costly practice even further. And many companies only examine at the surface level, seeing a success or failure or some other basic data around costs. But talent mobility can be measured in a variety of ways, from evaluating after an assignment to see which employees are retained and what kind of positions they take to those competencies that are more likely to make an assignment successful. Creating a full picture of your talent’s competencies and aspirations can help to lay out a game plan for success in global mobility assignments, where success can often be elusive. A friend at IBM is currently working on a global assignment in Singapore, and he was offered the position in part based on his specific competencies; however, it was also based on his own interests/aspirations to work in the APAC region, which blends multiple angles of this puzzle and drives up the chances of success.
- Hiring and selection. Wells Fargo uses a variety of inputs to predict which candidates will be high-performing employees post hire, including background experiences, career motivation, performance, and life/work skills. Another great example is Opower, which was able to determine the right number of interviewersrequired to ensure a selected candidate was more likely to perform better on the job (in their case, it was 5+, anything less and there was no correlation between interview and job performance). As mentioned in #1 above, finding out these kinds of insights before hiring is best, as it can help to reduce the costs of turnover and training by bringing in the wrong talent in terms of skills or fit. Lighthouse survey data on talent acquisition analytics shows that recruiting measurement is still in its infancy for most organizations, despite them knowing the importance of the practice.
These are just nine out of many potential uses for talent analytics. During a recent conversation with a cofounder of a talent analytics company, we came up with half a dozen examples from very general to very specific. In the end, we both agreed that each company would be best served by observing, analyzing, and breaking down its own specific questions and problems to determine how analytics might be able to help them solve their talent challenges. My hope is that this list helped to generate some ideas about how implementation of this data-driven approach might help your organization to improve its practices and HR service delivery. Questions? Comments? Chime in below or reach out at firstname.lastname@example.org directly.
Originally published by Ben Eubanks @ LinkedIn and republished with kind permission.
I’m a human capital management industry analyst helping companies and vendors with strategy, content, and more. I have worked as an analyst for more than seven years with five of those in an independent capacity.
Previous experience working as an in-the-trenches leader in the human resources field has provided a broad range of opportunities to lead HR in smaller organizations, government contracting firms, and the nonprofit sector. I’ve had my hands in pretty much everything at some point: recruiting, benefits, employee relations, coaching, and the rest of the spectrum you run across in an HR shop.
I’m also co-founder of the HRevolution movement, an event that has attracted hundreds of attendees from around the globe to work together and explore the future of HR, work, and business. During the evenings, I write at upstartHR.com. I’m an HR blogger with a passion for leadership and culture–two things that can make or break your organization.