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The Skyline G Blog: New ideas and perspectives focused on results
by Thuy Sindell, PhD. and Milo Sindell, MS.
Thousands of companies fail to achieve their objectives. Overwhelming competition, lack of sophisticated technology, and poor execution all play a significant role.
However, it’s shocking that the most vital aspect of company success - human resource - is often overlooked and neglected.
Companies not understanding their organization, outdated HR systems, practices, and processes not aligning with the changes in their environment - are huge hindrances that severely impact success.
By implementing HR analytics, organizations can gain an in-depth understanding of people driven business functions and formulate effective strategies to ensure sustainable growth.
In this article, we take an in-depth look at what HR analytics are. We also discuss the role HR analytics plays in aligning human capital with organizational objectives. Keep reading to learn more about HR analytics, the benefits of this process for organizations, and the most influential HR metrics for quantifying HR and people driven data.
HR, or human resources, analytics is the study of HR function metrics to improve an organization’s HR practices.
The process of HR analytics involves collecting and analyzing raw human resources data, then translating it into practical, data-backed insights an organization can apply to:
This analytical process enables HR to restructure routinely collected data and measure the contribution of HR efforts in reaching organizational goals.
HR analytics falls within the scope of digital HR, which involves optimizing processes by leveraging machine learning, artificial intelligence, and cloud technologies. As HR systems and technologies evolve, adopting a data-driven approach becomes more cost-effective. Organizations also have access to more data sources to identify trends, evaluate leadership performance, and predict business outcomes.
Most HR professionals use the terms “HR analytics,” “people analytics,” and “workforce analytics” interchangeably. However, each of these concepts conveys a slightly different approach to data analysis.
While people analytics and workforce analytics aim to improve employee retention, employee engagement, and overall business performance, these concepts don’t necessarily fall under the HR analytics definition.
People analytics extends beyond analyzing human resource functions to collect data related to the individual. Data scientists use people analytics to analyze topics such as overall wellbeing, work-life balance, and fairness. For example, during the COVID-19 pandemic, people analytics has enabled HR professionals to identify stressors that employees experience while working from home.
Another difference between HR analytics and people analytics is that the latter applies to stakeholder groups outside the organization. For example, an HR professional can use people analytics to interpret data on customers or the employees of a strategic business partner.
Unlike people analytics, workforce analytics focuses on all work-related aspects within the organization, including its employees. As a result, HR professionals can interpret historical data through workforce analytics and formulate a predictive model for making future decisions. Workforce analytics also assists HR departments in doing the following:
Workforce analytics applies to an organization’s entire workforce, including on-site employees, remote employees, independent contractors, and consultants. The metrics of workforce analytics and HR analytics may overlap, but workforce metrics don’t include HR function metrics such as training efficiency.
Incorporating HR analytics within an organization is primarily the responsibility of the HR department. HR teams input data when onboarding new employees or tracking turnover and retention patterns. However, in the company the HR department is not the only user of HR analytics:
HR analytics is a multi-dimensional, ongoing process consisting of the following integrated steps:
Below, we look at each of these steps in detail.
Collecting relevant and high-quality data is the first step in the HR analytics process. Human resources data fall under two categories, namely:
Internal data sets for collection and aggregation by the human resources department include:
Internal data sources can be organized by a data scientist according to relevant data points for your organization.
External data sets:
The HR manager can obtain external data by liaising with other departments within the organization. Collecting and aggregating external data is critical to put internal data in perspective, and it can explain trends and patterns detected during the analytics stage.
Critical external data sets to collect and aggregate include:
The data collection process should include aggregation, which means organizing and compiling data from various sources into a single HR database that is easy to measure and analyze.
After collecting internal and external HR data, the next step is measuring the data using key HR metrics. The objective of this stage is to determine the effectiveness and value of HR initiatives and measure them in a practical way. Using HR metrics is essential to track data over time, detect trends, and make informed business decisions.
Like barometers for organizations, metrics indicate whether the various operations and departments are meeting the organization’s targets and standards. However, metrics only measure data and track activity. HR metrics feed into the analytics process, while analytics gives insight into improving metrics.
The HR metrics that managers should use to measure data depend primarily on the organization’s needs. If the HR department is in the process of developing an HR analytics strategy, it can take several steps to determine which metrics would make the highest contribution to business value.
Ideally, HR leaders should partner with stakeholders, including the chief officers and heads of departments, to formulate a list of metrics upon which to measure HR data. The organization’s key performance indicators are integral to selecting the most optimal metrics to support HR analytics.
Below, we look at the most common HR analytics metrics.
Revenue per employee is among the critical monitoring metrics. It measures the organization’s total revenue for the last 12 months divided by its number of full-time employees. This metric indicates an organization’s efficiency at generating revenue through its employees.
Profit per employee is a similar metric to revenue per employee, but it measures the organization’s net income for the past 12 months divided by the number of full-time employees. This metric is particularly insightful if an organization wants to gauge the outcome of extensive HR efforts.
For example, organization A employs 25 people and generates a profit of $1 million per year. However, after investing in employee training, annual profits increase to $1,25 million. In this case, training resulted in a profit per employee increase from $40k to $50k.
The offer acceptance rate is a percentage of the extended employment offers that are accepted. This metric indicates the efficiency of the hiring process. For example, if an HR department has a high offer acceptance rate, it means the team:
When calculating the offer acceptance rate, it is critical to document the offer before going through the pre-closing work with a candidate. For example, suppose HR only extends offers to candidates if they are confident the candidates will accept. In that case, the offer acceptance rate will provide little insight into the organization’s value proposition or the efficiency of the recruitment process.
Time to fill is a metric indicating the average number of days between receiving the job requisition and the candidate’s offer acceptance. This metric indicates the organization’s hiring efficiency and provides insight into which recruitment strategies and sourcing methods deliver the quickest results. However, it is critical to balance the time to fill metric with hiring measures such as cost and quality.
Time to hire is the average number of days between candidates’ application and their job acceptance. Where time to fill indicates the speed of the hiring process, time to hire indicates how quickly the hiring team can identify the best candidate for the position.
The time to hire metric helps to identify weak spots in the hiring process and allows the hiring team to answer the following questions:
Employee churn analytics is integral to human resource management and deals with an organization’s overall turnover in staff as members leave and new employees come on board.
The voluntary employee turnover rate is the percentage of employees who choose to leave the organization over a specific period. An unusually high turnover in comparison with historical employee churn may indicate issues within the organization, including:
A high employee turnover can be costly to an organization. The recruitment, hiring, and training processes are expensive, and new hires take time before they contribute to profit generation.
Involuntary turnover is the percentage of employees whom the organization terminates over a specified period. A high involuntary turnover rate typically indicates issues with the organization’s recruitment strategy.
Training cost per employee includes the organization’s total training expenses over a specific period divided by the number of employees who underwent training. HR leaders have to consider training costs per employee in conjunction with other metrics and variables, including employee engagement, revenue per employee, and the resulting change in employee productivity.
The human capital risk metric indicates the gap between the organization’s objectives and the skills of its workforce. Employee turnover and lacking employee experience are among the inherent risks. Other areas of risk include:
This metric indicates where policy changes, actions, or deliberate measures are necessary to mitigate human capital risk.
Managing absenteeism plays a critical role in human resources management. With this productivity metric, HR leaders can gain insight into overall employee health and happiness and identify the need to address factors such as:
The absenteeism metric is the total number of absent days divided by the number of scheduled workdays over a specified period.
The third stage of the analytics process involves analyzing data from metric reporting to identify the patterns and trends that impact organizational growth. HR analytics offers several methods for analyzing data:
Descriptive analytics reflects past data, providing historical context to help stakeholders interpret an organization’s current performance. This type of human resource analytics is typically a visual representation—for example, charts, graphs, and dashboards.
Diagnostic analytics, or root cause data analytics, looks at the reason for historical trends and patterns. For example, if an organization’s sales staff declines at the same time as a reduction in base salaries, there is likely a causal relationship between these events.
Predictive HR analytics takes descriptive and diagnostic analytics into account to formulate potential future trends and patterns. For example, if the C-suite plans a reduction in basic salaries, predictive analytics allows HR leaders to forecast a large-scale resignation among salespeople.
Prescriptive analytics builds on prescriptive analytics by suggesting possible courses of action and the implication of each potential strategy for the business. Prescriptive analytics results from a data-driven approach and eliminates human decision-making based on illogical biases.
Without action, data analytics is of very little value to an organization. Therefore, the last step of the analytics HR process is implementation. During this stage, HR leaders need to collaborate with various role-players to incorporate the actionable insights from descriptive, prescriptive, and predictive analytics into organizational decision-making.
For example, if lack of feedback and recognition cause employees to leave, formulating and implementing an organization-wide feedback system would be critical to reducing the staff turnover rate.
The most common recruiting challenges that hiring teams face include:
Implementing HR analytics into the recruitment process allows hiring teams to address the above challenges while reducing employee churn over the long run.
Organizations
Human resource management can implement various types of HR analytics to optimize staffing in organizations:
HR analytics benefits organizations in several ways. By incorporating analytics, HR can achieve the following:
Ultimately, HR analytics implementation is critical to increase efficiency and increase organizational output, ensuring that the organization grows sustainably over the long run.
Human resource teams can effectively get started with HR analytics by implementing the following steps:
Common reasons for HR analytics failure include:
Being aware of and avoiding the above issues will help to ensure the success of an HR analytics project.
The cost of HR analytic solutions depends on the size of the organization, its number of employees, and whether HR staff members need additional training.
However, successful HR analytics will improve the recruiting process, reduce attrition, and boost productivity for the long haul, ensuring profit and business growth.
The sophistication of on-demand data collecting, aggregation, and visualization solutions allows organizations to implement HR analytics in less than a year.
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