Your business is sitting on a goldmine of employee data.
No, not salary histories and performance reviews. We’re talking about data that can tell you what’s going to happen in the coming months — who’s thinking about leaving the company, who’s likely to stay, what your top performers do differently…and the list goes on. It’s enough to excite even the most seasoned data scientist.
The only problem? You’re not a data scientist.
You’re a time-strapped business leader who needs to understand what’s happening within your workforce, and you need to get your hands on those insights fast.
Thankfully, there is a solution – predictive workforce analytics. In this guide, we’ll share everything you need to know about predictive workforce analytics including how to implement and use it for your business.
What is predictive workforce analytics?
Predictive workforce analytics is the process of analyzing historical employee data to make predictions about future performance and productivity.
With standard workforce analytics, the focus is on identifying past problems so you can decide how to fix them. Predictive workforce analytics provides a more proactive approach. This type of data analysis allows you to go from focusing solely on what’s happening now to understanding what those trends might mean for the future.
In short: Predictive workforce analytics shows you past and present patterns in employee behavior to forecast what will happen in the coming weeks and months.
Benefits of using predictive workforce analytics
Predictive workforce analytics is an effective way to prevent problems from happening. As you see how your people work and what it means for the future of your company, you can remove guesswork and gut instinct from important business decisions.
Analyzing data from a predictive standpoint also helps you identify trends that would otherwise fly under the radar. Human Resources can get ahead of skills shortages, IT can predict which business technology investments will generate ROI and executives can anticipate how an upcoming change in leadership will impact productivity.
Organizations most commonly use predictive workforce analytics to:
- Increase employee retention. By the time you conduct an exit interview, your employee is probably beyond the point of candid conversation. However, you can review past data to look for possible overlooked issues — such as signs of burnout — to proactively prevent voluntary employee turnover in the future.
- Inform real estate decisions. Are people more productive when they work from home, at the office or a mix of both? By using predictive workforce analytics to answer this question, you can compare past performance based on employee locations to predict what works best moving forward. You then use these insights to guide decisions around office real estate.
- Prepare for peak seasons. Analyzing past peak season trends, such as spikes in customer requests or periods of extended work days help guide everything from lunch break policies to hiring plans.
- Inform policies. Predictive workforce analytics helps you anticipate how employees will respond to different remote work policies, if they’ll benefit from four-day workweeks and even what kinds of training requirements they’ll need.
- Optimize technology. Of the dozens or hundreds of software licenses you pay for, how many of them are people actually using? Build future plans to optimize SaaS spend by looking at past and current trends in technology use..
How to implement and use predictive workforce analytics
Predictive analytics has grown by almost 50% in the last three years — and for good reason. Business leaders who use workforce analytics have a better grasp on everything from burnout risks and retention rates to talent needs and hiring costs.
Still, many companies aren’t using workforce analytics at all due to a lack of skills and resources.
The good news? Thanks to the latest advancements in workforce analytics software, any leader can implement and manage predictive workforce analytics — no deep expertise needed. Once you’re getting a regular dose of insights, you may wonder how you ever made decisions without it.
Successful business leaders use predictive analytics by following these five steps:
- Define your use cases
- Map out data needs
- Find the right software
- Inform your employees
- Set a cadence
Let’s look at each one in more detail.
1. Define your use cases
First, you’ll need to decide what use cases matter to you as a company. There are a lot of options, but it’s reasonable to take an iterative or batched approach to tackling them. You don’t need to analyze every use case to be effective.
Do you want to understand why people are leaving so you can reduce turnover rates moving forward? Are you looking for ways to improve future productivity among current employees before you add headcount? Do you need insights to guide an upcoming hybrid work policy?
Decide first what matters to your workforce. Only then can you move on to step two.
2. Map each use case to data needs
Once you’ve decided what you’d like to predict, the next step is identifying what to analyze.
For example, do you want to forecast how often your office space will be used as you roll out a new hybrid work policy? For that, you’ll need location insights. Do you need to know how an upcoming change in leadership might impact employees? You may want to study past productivity trends to predict future business outcomes based on what happened the last time your workforce experienced a big shift.
Most organizations have a mountain of data at their fingertips, but analyzing too much too fast can quickly lead to overwhelm. Start with the handful of reports you actually need — you can always build out more at a later date.
3. Find the right workforce analytics software
Whether you want to build out full-blown predictive models or simply need to make a few data-driven decisions, investing in workforce analytics software is key.
A robust workforce analytics solution will not only collect the data for you, but also translate it into easy-to-digest dashboards. All you have to do is turn on the reports you need and make a plan to review them weekly, monthly or quarterly. This will allow you to benchmark typical employee behaviors, spot anomalies and take action on small issues before they have a chance to become bigger problems.
4. Inform your employees
When getting started with predictive workforce analytics, it’s important to prioritize transparency and trust. The point of workforce analytics software is not to keep close tabs on employees.
Rather, you’re working with employees toward a shared goal of productivity, engagement and career advancement. Focus on privacy-first data collection and let your employees know about your plans from day one. This step also serves as a safeguard to help ensure your business stays compliant with state and federal privacy laws.
5. Set a cadence
For predictive workforce analytics to be successful, it has to become routine. Whether you analyze the data weekly or quarterly, this is one activity you won’t want to skip.
This is true for all industries, departments and employees — including knowledge workers and self-starters. Here’s why:
It’s often tempting to say “I don’t care how you work as long as you get results.” But unfortunately, there’s a hidden flaw in this approach. While autonomy can fuel productivity, a lack of personal productivity metrics leaves you blind to future outcomes. You simply can’t keep achieving results if you don’t know how work is happening along the way. This can lead to over-hiring, under-hiring, spending money on software that’s never used, investing in training never adopted and creating policies that aren’t followed.
You can avoid many of these pitfalls by regularly reviewing past and present data to predict future trends.
4 Real-life examples of predictive workforce analytics
What kinds of predictions can you make with workforce analytics? Most fall into the three categories of people, processes and technology. Common examples include:
- Preventing voluntary turnover
- Replicating top performers
- Speeding up new hire onboarding
- Predicting future office use
Let’s take a closer look at each one.
1. Preventing voluntary turnover
By the time a manager or HR department recognizes signs of burnout, it’s already too late. And unfortunately, this is a common risk among high performers.
Many of your hardest-working employees are unlikely to speak up. They say “yes” to more work without complaining, but burn themselves out behind the scenes — until one day you’re faced with the huge problem of finding a replacement at one-half to two times the cost of that person’s annual salary.
Predictive workforce analytics arms you with the insights you need to prevent the kind of burnout leading to turnover. You can evaluate what happened the last time a great employee left voluntarily, and use those insights to plan a course of action moving forward. You’ll also know if a team has historically worked long hours or weekends, and can proactively encourage them to take breaks or help them redistribute workloads.
2. Replicating top performers
What does the recipe for success look like based on your top performers? Who’s regularly meeting or exceeding department goals? With predictive workforce analytics, you can look at the day-to-day work habits of those employees over the course of each quarter and use them to help guide other team members as you plan future projects. This can include updating workflows based on what top performers do, or seeing if they’re using any additional tools to assist with their responsibilities.
3. Speeding up new hire onboarding
Understanding what your new hires need to be fully onboarded — and predicting how long it will take — is key to successful transitions. What happened the last time you hired a person for the same team? How long did it take them to be operating at full capacity, and what does that mean for the next hire?
Predictive workforce analytics can tell you all this and more. With these insights, you can create an accurate plan for shifting and offloading responsibilities from tenured staff to your new team member.
4. Predicting future office use
Is your company thinking of implementing a hybrid work policy? For some individuals and teams, that might be an essential step toward greater productivity. If the historical data shows you that Slack and Teams communication is lighter on days when in-person meetings occur, that’s a strong indication people are getting the information they need during those scheduled times. In this example, you’ll know maintaining office space for meeting use is the right answer.
On the other hand, you might see that productivity dips for team members with longer commutes to the office. In these instances, you might use the data to inform more flexible policies and reduce the amount of real estate you’re paying for.
Either way, you’ll have predictive workforce analytics to forecast how different teams will respond to working in the office vs. working from home — and make plans accordingly.
The future of predictive workforce analytics with ActivTrak
Without the right data, business leaders will continue to make one-size-fits-all decisions favoring certain types of workers — the return to office requirements benefiting some employees but not others and the unlimited PTO policy ignoring overworked overperformers who are unlikely to recognize when they need to take a break.
Those approaches may work when jobs are scarce, but will lead to employee revolt the minute the economy picks up and recession fears are over.
Predictive workforce analytics has the power to change all that.
If you want to prepare your business for long-term success, data-driven decisions are critical. ActivTrak provides a full range of insights, from productivity reports and Location Insights to Impact Analysis and Capacity Planning, to help predict future outcomes.