Companies have invested in customer predictive analytics, but is employee predictive analytics garnering similar attention?
Despite years of research, predictive analytics in HR is only just beginning to go mainstream. A 2020 report by Mercer suggestsOpens a new window that less than half of companies use metrics to predict which employees are likely to leave. Only 18% can predict how compensation will impact employee performance. Employee predictive analytics can help identify these trends in the organization and allow HR to develop strategies to face the issue.
Table of Contents
- What Is Employee Predictive Analytics?
- Employee Predictive Analytics Offers Several Benefits
- 7 Employee Predictive Analytics Trends We Expect in 2020 and Further
- It All Starts with Embedded Predictive Capabilities
What Is Employee Predictive Analytics?
Analytics is a type of technology that applies statistical analytical models to digitally stored information to churn out insights.
As the name suggests, predictive analytics uses historical data to give you a forecast of what’s coming ahead. There are several types of predictive analytics – patient predictive analytics in healthcare, customer predictive analytics at contact centers, and employee predictive analytics in HR.
Employee predictive analytics refers to any technology that can dive deep into employee data to extract useful insights that can help predict future events and their possible outcomes. It may or may not prescribe the best-fit action that HR should take, but it informs HR of probable events that could happen.
Employee data analytics works on three core components:
1. Data sources
Different digital systems feed into the analytics engine, providing the underlying data for transformation and insight generation. These data sources must be updated regularly (near-real-time, daily, weekly, or monthly) to give you the most accurate insights possible.
2. Analytical models
These are the statistical models that are applied to data for analysis. Typically, these models are developed by researchers and data scientists, using mathematical theory to extract meaningful insights from raw data. The models are black-boxed – which means that they are invisible to the user.
3. The user interface
This is the platform that HR uses to access insights from employee predictive analytics. The interface uses techniques like data visualizationOpens a new window and data storytelling to make predictive insights comprehensible even without data science expertise.
Through these three components, an employee predictive analytics tool will collect data, process it, and surface meaningful insights.
Why is this so important?
Employee Predictive Analytics Offers Several Benefits
There are several reasons to adopt employee predictive analytics – which is probably why the use of predictive analytics has increased from 10% in 2016 and to 39% in 2020 (per the Mercer study). Here’s how it could help HR:
1. Stay ahead in a post-COVID-19 labor landscape
During the pandemic, the global workforce scenario was marked by uncertainty. Some sectors saw lay-offs and pay cuts due to a weak economy. Other industries that provide essential services and goods witnessed a dramatic spike in demand, facing a labor crunch. It can be difficult to map hiring volumes in such a climate of flux – making predictive analytics extremely critical.
As we come out of the pandemic and the economy adapts, employee predictive analytics can help to adjust hiring activity and maintain an optimum level.
2. Prevent attrition among your most high-value resources
This is among the top use cases for employee predictive analytics. The technology considers a variety of data like performance trends, engagement feedback, competitor compensation, etc. to detect any flight risk among employees.
This can prove hugely beneficial in the high-potential employee segment, as these resources are difficult to replace, and their exit from your company can cause significant disruption to the business. That’s why several companies already use employee predictive analytics to prevent attrition.
3. Lower labor costs and non-compliance penalties
Here, we are looking at predictive analytics as part of workforce management. Data from everyday employee operations – employee schedules, movements, task completion rates, shift preferences, etc. – can give you an accurate picture of how labor is utilized and where you’re incurring preventable costs.
For example, employee predictive analytics can anticipate a demand spike sometime in the future that could lead to costly (even non-compliant) overtime hours. You can take action to prevent this by adjusting employee schedules preemptively.
4. Design the most impactful learning experiences
Predictive analytics can scan employee learningOpens a new window habits to understand what aids knowledge absorption and retention. Different employees will have varying preferences, depending on their personalities, previous skill levels, and other parameters. Employee predictive analytics tells you which learning experience is most likely to hit the “sweet spot†with a specific type of learner.
5. Keep up with demand without burdening employees
This benefit ties back to labor cost optimization. By using employee predictive analytics, you not only save effort costs, but you also make your employees’ lives easier.
The technology would tell you about future demand and its possible impacts on workforce scheduling. You can share this data with our workforce, allowing employees to plan and reschedule their personal obligations so that they can maintain work-life balance as much as possible.
Broadly, these are among the most compelling benefits of employee predictive analytics. And once you adopt the technology, its impact on micro-processes and everyday decision-making becomes clearer.
If you’re looking to adopt employee predictive analytics in 2020, what are some of the trends you could expect? Let’s find out.
7 Employee Predictive Analytics Trends We Expect in 2020 and Further
Predictive analyticsOpens a new window is a burgeoning area of research, gaining from ideas by the world’s leading academicians as well as enterprises that have amassed huge data repositories (e.g., Google, Facebook, etc.). SaaS platforms will take employee predictive analytics to smaller companies and startups, applying advanced analytical models to gain from the increasingly large data volumes flowing in today. Here are some of the trends we expect this year, as employee predictive analytics gains traction.
1. Company culture will shift to normalize data-driven forecasting
This is the foundational trend in employee predictive analytics adoption. The majority of companies don’t have the culture needed to weave predictive analytics into their everyday operations. That’s why they resort primarily to ad hoc applications.
Now, there will be a widespread move toward data-driven culture, where senior leaders, middle managers, and team leads regularly use data to support their expertise-driven decisions – in a standardized manner. In other words, expert opinion won’t be enough. Companies will turn to predictive analytics for employee management decisions as part of their operational culture.
2. Cross-disciplinary collaboration will enrich the underlying datasets
One of the most effective ways to increase analytics accuracy is by enriching the underlying datasets.
Let’s say an HR function has collected ten-year data records on employee engagementOpens a new window . This might be enough to predict future engagement trends. But imagine if you interpolated this data with compensation records from the finance team, covering the same period. It would give you richer insights, forming correlations between engagement, pay, and how to optimize this combination.
Going forward, various enterprise functions will come together to power employee predictive analytics, with active participation even outside of HR.
3. HR will guide the predictive analytics revolution enterprise-wide
According to contemporary research, HR is already leading the pack when it comes to analytics adoption. Harvard Business Review (HBR) reportedOpens a new window that HR is among the most data-driven functions in business, even slightly ahead of a traditionally quantitatively oriented function like finance.
We can expect this trend to enter employee predictive analytics as well. HR teams will now be at the helm of predictive analytics usage across the company, calling for collaboration with finance, IT, marketing, and other disciplines (see previous point). We could even imagine a centralized predictive analytics function (a more specialized version of the centralized analytics teams that exist today), inspired by HR-led innovation.
4. Dedicated employee predictive analytics solutions could be on the cards
We mentioned how SaaS platforms would take employee predictive analytics to a wider audience, beyond academic research and large enterprises with sizable data repositories. There is still some distance to cover before we achieve this. Right now, most HR analytics platforms come with value-adding predictive capabilities (not core features), which deal with employee data in a limited way.
Could we see the rise of dedicated predictive analytics solutions now? The answer is a resounding YES. Already, there are standalone platforms like OneModelOpens a new window that are designed solely to process people data, apply AI analysis models, scale analytics, and furnish insights. This could be a growing trend in 2020.
5. There could be regulations around using predictive analytics
The ethical issues around predictive analytics cannot be denied. Let’s say you’re using an AI-based predictive hiring platform to analyze a candidate against existing employee data. There is a risk that the tool may be biased, or the candidate cannot prepare for the assessment, as they are unaware of the tool’s assessment criteria.
To address this, governments are now coming up with rules and regulations around the use of predictive analytics, AI, and other autonomous technologies in the workplace. Just like the AI JOBS Act 2019 Opens a new window that is scheduled for discussion, we expect regulations around employee predictive analytics as well.
6. Predictive analytics will make bespoke benefits a reality
Benefits administration has long been a central cog for HR. The right benefits can persuade top-tier talent to join your company and keep high-value employees engaged in the organizational culture. But it is near-impossible to deliver the right benefits if you are following a one-size-fits-all model.
Enter employee predictive analytics. A predictive analytics solution would be able to look at past employee needs, process current employee profiles to gauge expectations, and predict the best-fit benefits package for every employee. Couple this with automation, and you have bespoke employee benefits at your fingertips.
7. HR departments could start hiring full-time data engineers
Right now, predictive analytics exists in one of two ways in an enterprise. Either it is scattered across business units, with technical teams housing the majority of analytics expertise. Or, there is a central, shared analytics function where HR vies with other departments to obtain analytics support.
But as the need for employee predictive analytics becomes more imminent, HR departments could be looking to achieve analytics self-sufficiency – which means hiring data engineers. Employee data engineers will transform historical and real-time data collected from different devices, platforms, and digital touchpoints on the employee lifecycle to make the data analytics-ready. And this could strengthen HR’s position as an internal flagbearer for predictive analytics (see point three).
It All Starts with Embedded Predictive Capabilities
The future looks bullish for employee predictive analytics. Companies are fast realizing that they are sitting on a mine of monetizable HR data – which continues to stay idle due to the lack of robust analytics. The cloud makes computing resources more accessible and affordable than ever before so that companies don’t need to invest in heavy infrastructure.
With an extensive data repository on the one hand and robust computing power on the other hand, predictive analytics can churn out extremely accurate insights on employee movements, needs, and aspirations.
The first step, we believe, is embedded predictive capabilities. By integrating predictive capabilities in existing HR platforms, companies can:
- Familiarize HR professionals with predictive analytics, making it a part of the operational culture
- Democratize access to analytics insights so that HR can make every decision data-drive
- Adopt predictive analytics in phases, without a steep learning or investment curve
These trends could unlock dramatic benefits for HR. Employee predictive analytics doesn’t require implementation only for the sake of innovation – its business benefits make it a good HR tech use case for years to come.
How are you using employee predictive analytics at your organization? Tell us on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We would love to hear your views!