Workforce Analytics: Examples, Benefits, and Trends

Workforce analytics refers to the process of collecting workforce-related data types, both from internal and external sources, analyzing them, and turning these insights into actionable information

Applying workforce analytics

Categories

Workforce analytics data can be applied to three major categories: recruitment, retention, and talent management.

Recruitment

In terms of recruitment, workforce analytics tools allow you to:

• Scan public resumes automatically;
• Perform background and social media checks;
• Shortlist potential candidates;
• Match potential candidates with current talent better;
• Decrease recruitment costs;
• Mitigate race and gender bias.

Retention

In terms of retention, workforce analysis helps with:

• Tracking compensation rates for underacknowledged overperforming employees;
• Observing behavioral patterns for productivity;
• Identifying behavioral patterns that show early signs of potential resignation.

Talent management

In terms of talent management, workforce analytics opens the door for:

• Setting checkpoints regarding performance to measure and support future potential;
• Uncovering training or skill shortage to offer proper learning opportunities.

What are the steps to introduce workforce analytics in your business?

Workforce analytics uses historical data collected either by your organization or externally to improve financial performance and optimize operations. Some specific examples will be discussed in the next section.

There are different software solutions and statistical tools that allow measuring, characterizing, and organizing employee data, but the basic steps of the process are similar:

1. Identify a problem or question you want to address.
2. According to the problem or question, decide what data would help you address it.
3. Build a workforce analytics dashboard.
4. Find a relevant objective or benchmark to compare your results.
5. Decide on the right analysis method and report your results.

Examples

Let’s assume that you are a business manager who wants to know if highly trained employees exhibit improved employee engagement.

The next step would be to identify what information you need to measure this; in this case, you should get access to the relationship between your two variables, training completion data and employee survey results.

Comparing your results with benchmarks (other historical results or the completion of training courses), you need to collect and analyze the employee data from all of your company’s departments.

One of the main data analysis methods, in this case, would be a correlation analysis between employee satisfaction and the training completion rate. This would help you identify whether highly trained employees are more satisfied compared to employees who did not do or successfully finish their training sessions.

Another example would be to identify potential red flags regarding employee turnover. Let’s assume you can analyze whether social engagement, promotion history, salary, and job role affect employee turnover. Using an algorithm, you would be able to know which one of these factors you need to address to enhance the employee retention rate.

The importance of workforce analytics

As briefly mentioned above, workforce analytics aims to aid macro-level planning to improve business operations and performance. Apart from these benefits, related to saving time and expenses, the importance of workforce analytics also refers to intangible aspects, including creating a cohesive team and improvements in the corporate culture.

For instance, gathering employee sentiment information through surveys or questionnaires allows recognizing how satisfied employees are under current working conditions. If a certain field is lacking, you can aim to improve it. If a certain field is thriving, you can use it for recruitment purposes.

This encourages better business performance since happier employees are more motivated and efficient. This also helps managers identify what attracts an efficient workforce to craft suitable strategies to motivate employees, enhance job satisfaction, and more.

The benefits of workforce analytics

As explored in the previous sections, workforce analytics comes with company-wide benefits because of its large scope. Some of the main benefits include:

• Decreasing retention rate by identifying turnover triggers, such as lack of training, lack of promotion opportunities, and more.
• Learning which recruitment strategies are efficient.
• Finding areas where labor costs can be decreased by implementing more efficient processes.
• Predicting the required future workforce by having an insight into the turnover rate.
• Optimizing and improving employee experience and satisfaction.
• Enhancing the overall business performance through HR-related actions, such as identifying and retaining top-performing talent.

There are also numerous benefits of workforce analytics for your team. You can improve the work culture and environment by offering them better-suited training, granular feedback to help them improve work performance and access promotion opportunities, and empower employees to have a more significant contribution to your firm’s objectives.

Predictive workforce analytics

One of the main types of workforce analytics is known as predictive workforce analytics. This refers to conducting workforce analytics to make predictions so you can forecast using historical information. The main statistical models used in this type of workforce analytics are machine learning and data mining.

Predictive workforce analytics is used by numerous businesses to improve employee experience and optimize their processes. It is often conducted via regression analysis, pattern matching, multivariate statistics, among other methods that help firms understand what is likely to happen in the future, both in terms of risks and talent outcomes.

Prescriptive and diagnostic workforce analytics

Apart from predictive analytics, business leaders also employ prescriptive analytics and diagnostic analytics.

The first one uses the same data as predictive workforce analytics, namely historical data. The difference is that prescriptive analytics is used to plan the best course of action after you conducted predictive workforce analytics to make forecasts.

In other words, it is seen as the next step to find ways of mitigating challenges and addressing workforce issues. The main purpose, as a result, is to ensure that you meet your desired objectives.

Diagnostic analytics is a past-looking method of identifying the causes of your workforce failure or success. This is also extremely important for businesses because it can show you clear workforce trends, so you can then leverage this information to optimize your business performance.

Workforce analytics vs HR analytics

Workforce analytics is more evolved than HR analytics software. It has a larger scope, so you can use it to improve your team at an individual level, collectively, and even boost the entire firm’s productivity and operational efficiency.

One of the primary purposes of leveraging workforce analytics is to improve the retention rate of top-performing employees.

Workforce analytics is linked to other HR analytics areas, such as performance management. However, the main difference between different types of HR analytics and workforce analytics is the macro focus of the latter one.

In other words, workforce analytics consists of firm-wide insights into the workforce.

Workforce analytics has a larger scope than HR analytics.

Workforce analytics tools and software

The concept of workforce analytics evolving is not a surprise since there are numerous software solutions that use employee-related information to allow HR professionals to improve their operations.

Some of the most popular ones are Visier People, Anaplan, and ActivTrak.

Workforce analytics tools enable analyzing data using statistical methods, turning it into actionable insights. These tools and software are used to improve your hiring abilities, retention rate, optimize the workforce, and more.

Workforce analytics software consists of machine learning and artificial intelligence that helps you generate rich, predictive insights for improvement and optimization.

There are numerous software solutions on the market, but most of them have several basic functions:

• Automated data collection and analysis;
• Predictive analysis results (for macro-level planning);
• Benchmarking for efficient and accurate comparison;
• Reporting and data management.

Workforce analytics trends for 2022

About 80% of executives in the healthcare industry stated that their investments in big data have been worthwhile. More than half also mentioned that their firms successfully measure the benefits from these investments.

In addition to this, research also shows that 37% of HR managers make data-driven decisions. As a result, it is safe to predict that modern workforce analytics is quickly being adopted by more and more businesses across all industries.

The following trends will show you how workforce analytics is evolving.

The main trends for 2022 include:

• Mass adoption of AI-powered systems to improve the candidate experience, including chatbots, recruitment automation tools, and even screening tools.
• An increasing role of the HR department for the company’s performance. In other words, firms must adopt a data-driven approach to remain competitive and successful, including workforce analytics to identify skill gaps, future workforce demand, and more.
• The increasing competition across all industries requires firms to strengthen their long-term talent planning strategies. HR managers using workforce analytics will be able to accurately predict expertise demand, cut labor costs, and optimize the data-driven recruitment process for many years to come.

Summary

All in all, business leaders now have access to abundant information. The large amounts of data make it difficult to collect and analyze, which is why numerous firms now use workforce analytics projects to collect, organize, and extract actionable insights regarding their employees.

Data-driven decisions help to improve workforce management, make the business outcomes more efficient, and improve its performance in the long run.