The further away your colleagues live from your office, the more likely they are to leave within the next year.
I learned that during a discussion of machine learning use cases in HR at the All Things Open conference last year. The presenter was head of data science and people analytics at a financial services firm.
She shared that her team is always thinking about how they can use data to improve their work. That is where analytics come in: Her team uses business intelligence (BI) tools and techniques to build mathematical models using relevant data. Then, they look for correlations in that data to predict future outcomes and act upon them quickly.
Data analytics in HR applies BI tactics—collecting, analyzing, modeling, and visualizing data—to employee engagement. HR leaders apply this range of BI capabilities to data within their specific sector. Teams with higher budgets for tools and talent can use more advanced software features to predict which results are most likely to happen based on the data points that they have.
For example, let’s say you use employee engagement software to track your colleagues’ performance reviews and home addresses. One day, you notice that a star employee has updated their address after buying a new house that’s 30 miles away from your office.
If you’ve used predictive analytics to find the correlation between commute times and likelihood to quit, you can have proactive conversations with that person. Even if you don’t think they seem unhappier, checking in to ask if they need flexible options (like more remote work days) shows goodwill and boosts business loyalty.
Employee turnover can cost a 100-person business up to $2.6 million per year. Meanwhile, the rise of cloud computing over this last decade lowered the barrier to entry for many businesses who couldn’t afford on-premise software. So, if you’re not already using analytics in HR, consider 2020 the year that you should start.
Are you still unconvinced that the investment’s worth it? Research from Gartner (available to clients) says that combining analytics with BI or AI is essential to improve your team’s decision-making:
“BA & AI is not only about improving performance, but also about accelerating your ability to acquire knowledge. In fact, thanks to capabilities from AI and ML, advancements in analytics technologies are not trending to replace human resources. Rather, they are accelerating human resources’ ability to acquire knowledge and improve productivity.”
Before you start using a new analytics tool, you’ll need a clear picture of how you can use it to solve problems in your business. You will also need to readjust your expectations of what—and how much—analytics can do.
It’s true that using analytics in HR can help your team perform certain tasks more effectively. But using software to do too much is its own mess in the making.
As one example, it’s common for companies to use talent management software to weed out “unqualified” candidates by automating the resume-reading process. The intention’s not bad; it lets companies confirm which characteristics are essential for each role. Such software also screens the (sometimes hundreds of) resumes that come in per role, and helps companies save time by presenting select candidates for managers to review.
The problem? Such software often gives too much weight to small differences between candidates. This causes 62% of employers to admit that qualified candidates slip through software’s cracks and never get the job interviews they deserve.
As you head into 2020, you can set your team up for success by:
Auditing your HR team’s processes for managing all aspects of employee engagement, from recruitment and retainment to exit interviews. Then, select two to three processes that you’d like to improve using analytics. Remember that you can’t improve what you don’t measure.
Reviewing the embedded analytics features within your employee engagement software to confirm what’s currently at your disposal. If you’re unsure whether this software offers such features, ask your account executive for help. For example, if your software offers analytics and BI features at additional cost, it might be worth buying them instead of shopping for another tool.
Shopping for self-service BI if your HR software doesn’t have enough analytics features. During product demos, explain the HR goals that you want to achieve using analytics. The more specific you can be, the more likely you are to find the best BI tool for your business’s use cases.
To elaborate, GetApp spoke with* Andrea Misir, i.e. The Millennial Job Coach. In her most recent role, Misir managed internal communication and engagement for over 900 employees. Today, she is a career coach who uses her expertise as a former recruiter to help SMBs find the candidates they need.
Read on to learn the:
Recruitment problem that analytics can help solve
Biggest misconception about automation in HR
Current state of AI within HR software
Most crucial employee engagement problems that SMBs must address in 2020
*We edited this interview for length and clarity.
GetApp: Based on your experience in employee management, what do you believe are the most crucial HR analytics for SMBs to track in 2020? How (if at all) do these differ from past years?
Andrea Misir: I think more SMBs will be paying closer attention to candidate and employee engagement numbers and reading between the lines. For example, if they can see that 5,000 people viewed a job opening but only 50 submitted an application, they need to look into what exactly was in the job spec that scared 99% of their audience. Data and research will be more critical than ever in the coming years.
GA: What's the biggest misconception about automation in HR? (i.e. "robots" scanning resumes to weed out candidates before HR employees see them.) How does this misconception stifle SMBs?
AM: I think the biggest misconception both employers and job seekers have is that they think a tool is going to do all of the heavy lifting in sorting candidates, when that is not at all the case. Any kind of tool is only as smart or effective as the human behind it.
This misconception stifles SMBs, as they might think that they don't have to do anything and that the tool can outright replace them, which is not nor will it ever will be the case. Humans always hire other humans, even if they use software to help them do so.
GA: Describe the current state of AI within HR software. How many vendors have the functionality needed to track the analytics mentioned above?
AM: Any tool can easily track surface value engagement, in terms of views, clicks, and whatnot. But like any dataset, it's all open to interpretation. Numbers never lie, but different people will look at them and draw their own conclusions.
No company (in my opinion, at least) has created a widely used solution that has universal acceptance in the talent acquisition community to effectively filter and attract candidates. Some companies like IBM are working on AI that claims to filter and remove any unconscious bias, but how feasible can the price point of that solution be at this time for SMBs to adopt it?
GA: Do you foresee SMB needs for advanced analytics outpacing the features that HR software offers?
AM: Not at this time, as I think SMBs can do a better job interpreting the data and actually revising their candidate workflow accordingly without relying too much on other tools and software. They definitely need to be more diligent and proactive in making the hiring process as human as possible.