Analytics refers to the interpretation of data patterns that aid decision-making and performance improvement. HR analytics measures the impact of HR metrics, such as time to hire and retention rate, on business performance.
Human resources is a people-oriented function and is so perceived by most people. But for those who think that the HR team’s contributions are limited to extending offer letters and onboarding new hires, HR analytics can prove them wrong. When used strategically, analytics can transform how HR operates, giving it insights and allowing it to contribute to an organization’s bottom line, in an even more active manner.
If you are looking to get started with HR analytics, here’s where you can begin.
Table of Contents
Section I: What Is HR Analytics?
To understand the essence of HR analytics and to explain how it impacts business performance, we asked Mick Collins, Global Vice President, Workforce Analytics & Planning Solution Strategy and Chief Expert at SAP SuccessFactors, to break it down for us.
“The role of HR – through the management of an organization’s human capital assets – is to impact four principal outcomes: (a) generating revenue, (b) minimizing expenses, (c) mitigating risks, and (d) executing strategic plans.
“HR analytics is a methodology for creating insights on how investments in human capital assets contribute to the success of those four outcomes. This is done by applying statistical methods to integrated HR, talent management, financial, and operational data.”
The difference between HR Analytics, People Analytics, and Workforce Analytics
The terms HR analytics, people analytics, and workforce analytics are often used interchangeably. But there are slight differences between each of these terms. It would help you to know the difference to be able to assess the most relevant data to their function.
HR Analytics: HR analytics specifically deals with the metrics of the HR function, such as time to hire, training expense per employee, and time until promotion. All these metrics are managed exclusively by HR for HR.
People Analytics: People analytics, though comfortably used as a synonym for HR analytics, is technically applicable to “people” in general. It can encompass any group of individuals even outside the organization. For instance, the term “people analytics” may be applied to analytics about the customers of an organization and not necessarily only employees.
Workforce analytics: Workforce analytics is an all-encompassing term referring specifically to employees of an organization. It includes on-site employees, remote employees, gig workers, freelancers, consultants, and any other individuals working in various capacities in an organization.
In the HR context, some workforce analytics metrics and HR analytics metrics may overlap, which is why the two terms are often used as synonyms. The goal of the two may also be the same. For instance, data on employee productivity and performance informs both HR and workforce analytics, and the goal is to improve retention rates and enhance the employee experience.
II. How Does HR Analytics Drive Business Value?
HR has access to valuable employee data. How can this data be used to enable change in the organization?
There is a great deal of discussion on replicating the consumer experience in the employee experience. Essentially, the data pertaining to consumer behavior and mindset can inform strategies to maximize sales by capitalizing on those factors. Similarly, the data that informs the HR function can be used to improve the employee experience, and in turn, maximize business outcomes.
Collins offers an example of how HR analytics can be used to enhance business value. “HR analytics could be used to measure investments in reskilling, which will deliver the right competencies to support a new revenue model, using data-driven insights to modify the training offering as sales results emerge.” This is definitive granular data that can not only impact the bottom line, it can also transform employee engagement in an organization.
“As such,” Collins continues, “you might think about the ‘ROI’ of HR analytics being that of increasing the business value derived from using data for talent decisions.”
Learn More: Top Trends Driving the Demand for HR Analytics
Common metrics measured by HR analytics
Several HR metrics contribute to business value, but the key question when measuring these metrics is this: what does the business need? This question can be best answered by having a conversation with business leaders. A strategic collaboration between the C-suite and HR leaders will help determine the HR analytics strategy. Based on the key performance indicators (KPI) of the organization, HR can then propose the metrics that can influence these KPIs.
It is important to note that the C-suite sees a clear connection between the need for analytics and the impact it will have on the bottom line. As an HR practitioner, you will need to build a case for why tracking metrics related specifically to the people of the company is critical. For instance, the C-suite may not be interested in the number of people who have left the organization voluntarily. What might interest them is how many of these employees were in strategic positions or were highly skilled, the duration of their employment, what led to their exit, the cost of replacing these employees, and finally, how all these events affect company profits.
Here are some common metrics tracked by HR analytics:
1. Revenue per employee: Obtained by dividing a company’s revenue by the total number of employees in the company. This indicates the average revenue each employee generates. It is a measure of how efficient an organization is at enabling revenue generation through employees.
2. Offer acceptance rate: The number of accepted formal job offers (not verbal) divided by the total number of job offers given in a certain period. A higher rate (above 85%) indicates a good ratio. If it is lower, this data can be used to redefine the company’s talent acquisition strategy.
3. Training efficiency: Obtained from the analysis of multiple data points, such as performance improvement, test scores, and upward transition in employees’ roles in the organization after training.
4. Training expenses per employee: Obtained by dividing the total training expense by the total number of employees who received training.
5. Voluntary turnover rate: Voluntary turnover occurs when employees voluntarily choose to leave their jobs. It is calculated by dividing the number of employees who left voluntarily by the total number of employees in the organization.
6. Involuntary turnover rate: When an employee is terminated from their position, it is termed “involuntary.” The rate is calculated by dividing the number of employees who left involuntarily by the total number of employees in the organization.
7. Time to fill: The number of days between advertising a job opening and hiring someone to fill that position.
8. Time to hire: The number of days between approaching a candidate and the candidate’s acceptance of the job offer.
9. Absenteeism: Absenteeism is a productivity metric, which is measured by dividing the number of days missed by the total number of scheduled work days. Absenteeism can offer insights into overall employee health and can also serve as an indicator of employee happiness.
10. Human Capital Risk: This may include employee-related risks, such as the absence of a specific skill to fill a new type of job, the lack of qualified employees to fill leadership positions, the potential of an employee to leave the job based on several factors, such as relationship with managers, compensation, and absence of a clear succession plan.
Common data sources HR analytics solutions
Broadly, the data required by an HR analytics tool is classified into internal and external data. One of the biggest challenges in data collection is the collection of the right data and quality data.
I. Internal data
Internal data specifically refers to data obtained from the HR department of an organization. The core HR system contains several data points that can be used for an HR analytics tool. Some of the metrics that an HRIS system contains includes:
1. Employee tenure
2. Employee compensation
3. Employee training records
4. Performance appraisal data
5. Reporting structure
6. Details on high-value, high-potential employees
7. Details on any disciplinary action taken against an employee
The only challenge here is that sometimes, this data is disconnected and so may not serve as a reliable measure. This is where the data scientist can play a meaningful role. They can organize this scattered data and create buckets of relevant data points, which can then be used for the analytics tool.
II. External data
External data is obtained by establishing working relationships with other departments of the organization. Data from outside the organization is also essential, as it offers a global perspective that working with data from within the organization cannot.
1. Financial data: Organization-wide financial data is key in any HR analysis to calculate, for instance, the revenue per employee or the cost of hire.
2. Organization-specific data: Depending on the type of organization and its core offering (product or service), the type of data that HR needs to supplement analytics will vary. For example, says Collins, “HR leaders at a global retailer should power their analytics engine with store revenue and costs and customer experience data, whereas HR at a construction company might pursue operational – health and safety – data and data related to contingent labor costs.”
3. Passive data from employees: Employees continually provide data that is stored in the HRIS from the moment they are approached for a job. Additionally, data from their social media posts and shares and from feedback surveys can be used to guide HR data analysis.
4. Historical data: Several global economic, political, or environmental events determine patterns in employee behavior. Such data can offer insights that limited internal data cannot. For example, the recession in 2008 was a global event that changed the way employees perceived jobs or “work.” The freelance, start-up and gig economies took off as people continued to lose their jobs. Data from such a critical historical event can help predict how the workforce may react to similar shifts in the future. It can then be used to identify trends in the current workforce and predict voluntary and involuntary turnover.
The complete HR analytics cycle
For HR leaders keen to get started with using HR analytics for data-based decision making, here are some tips:
I. Create a collective mindset
Before the operational and mathematical aspect can kick in, HR leaders must prepare their teams and organizations for a work flow driven by analytics. While the discussion with the C-suite for the need for analytics is one part of the change, the other is preparing your team to deal with the amount of data that they will now be using to measure the change. This is a crucial aspect of digital transformation. Getting the team started on small projects and asking them to create the reports that they will discuss with business leaders is a good way to begin.
II. Bring in data scientists
The data scientist is expected to become an integral part of HR teams. They are best suited to assess the viability of an analytics solution. They can also ensure the robustness of the statistical modeling and predictions.
As Collins says, “data scientists will play an invaluable role in creating a culture of analytics across HR. As the role of HR business partners and generalists evolves to include skills such as data strategy, analysis, and communication (articulating ‘the story behind the science’), the data scientist will serve as the coach, mentoring their colleagues across HR in how to understand, and apply, the insights.”
III. Start small
A great technique to convince stakeholders that HR analytics can drive business value is to first implement a small project successfully. Called “quick wins”, these projects can deliver tangible results in a short amount of time with high impact.
IV. Get clearance from the legal team
The sort of data collection that HR analytics uses is governed heavily by compliance laws. Some legal considerations to keep in mind when implementing an HR analytics solution are:
- Employee privacy and anonymity
- Consent from employees about the amount and type of data being collected
- Establishing the goal of data collection and informing employees accordingly
- IT security when using third-party software to run HR analytics
- Location of the HR analytics vendor – with whom the data will be stored – and their compliance with local laws
Collaborate with the legal team of your organization to ensure ethics and compliance norms are followed.
The key features of an HR analytics solution
Any HR analytics solution that will be used at scale must have certain components.
1. They answer the business questions the C-suite asks. This may require that you invest in a solution to address each question, leading to investments in multiple analytics solutions for granular data on each question. Alternatively, you may choose a solution that can assess multiple metrics to answer each business question.
2. They are easy to use by individuals who are not data scientists. An accessible solution created for laypersons is ideal when they want to assess any one or more metrics without interrupting the workflow of the data scientist.
3. They are cloud-based rather than on-premise. A cloud-based solution also aids accessibility without heavy IT integration. This grants HR the autonomy to use the solution as and when needed.
4. They are powered with statistical analysis and machine learning technology. Big data platforms require advanced data management systems powered by machine learning and natural language processing. This allows the technology to learn and reason autonomously, revealing insights that data scientists can then analyze.
5. They are based on predictive analytics. “[Predictive analytics is] The practice of extracting information from existing data sets to determine patterns and forecast future outcomes. Analysts use statistical methods to forecast future alternatives – will the current termination rate continue at the same pace or might we expect a surge of exits as the job market strengthens?” explains Collins.
6. They are powered with visualization technology. A visual representation of vast amounts of data can allow for better understanding of trends and events. The complex data processed through an analytics engine requires advanced visualization software, as it cannot be presented in simple charts and presentations.
7. They are available through a subscription model. Subscription models of software as a service (SaaS) platforms are useful because they easily allow you to access the latest upgrades in technology. They also eliminate the significant upfront expense of purchasing an analytics solution and may be a more cost-efficient way of investing in analytics.
Section V: So, Should You Invest in an HR Analytics Solution?
HR analytics offers some undoubted benefits. It allows HR teams to significantly streamline processes that reduce costs, reduce attrition, and consequently improve the bottom line. With task automation, you are freed up to innovate and explore the human aspect of human resources without spending time on tracking mountains of data from multiple sources. Overall, the use of HR analytics can result in an improved employee experience that directly translates into improved business outcomes.
However, HR analytics also presents some real challenges. As Collins tells us, “While HR is ambitious about the use of predictive HR analytics, two HR leaders I recently spoke with said ‘we want to be able to predict everything!’ The vision for how analytics will become an HR core competency is being constrained by limited consumption (insights being shared only within the four walls of HR) and action (the research does not lead to a program change or new investment). There is still much progress to be made.”
In addition, because data is siloed across the organization and conversations about the goal of implementing analytics are unclear, the valuable data HR requires for analytics is often underutilized.
The challenge is in waiting to actually see results. Predictive analytics is likely to take a minimum of 24 months to show meaningful results. So, the time to get started with HR analytics is now.
By making a strong business case to key stakeholders, as an HR practitioner, you can leverage the power of analytics to become a strategic business partner who contributes to the business.