- What is workforce analytics?
- What’s the difference between workforce analytics and HR analytics?
- How to apply workforce analytics
- How to implement workforce analytics
- How workforce analytics can benefit organizations
- Examples of workforce analytics in practice
What is workforce analytics?
Workforce analytics is an algorithm-based model that is applied to employee data in order to provide Return-on-Investment (ROI) evidence for workforce-related decisions, as well as gain insight on future workforce planning.
Workforce analytics are typically used in Talent Management where the focus is specifically on employee data.
What’s the difference between workforce analytics and HR analytics?
While both terms (workforce analytics and HR analytics) are sometimes used interchangeably, there is a slight difference between the two.
Workforce analytics is mostly associated with Talent Management and is focused specifically on analyzing people data. Workforce analytics is also closely identified with the reference to analytical software that manages and reports on employee data.
HR analytics is concerned with many areas of the organization at large. This could include day-to-day HR operations, procedure efficiencies, or strategic organizational issues in other areas.
When the need is for HR analytics, the reference should pertain to a wider organizational commitment to full-scale data-collection, reporting and analysis on multi-levels of the organization.
How to apply workforce analytics
Workforce analytics utilizes algorithms that can be programmed and applied to major areas of Talent Management, including:
- Automatically search resumes and unpack applicant information instead of searching for keywords.
- Carry out background checks and social media checks for candidate personality, red flags, or positive indicators.
- Create an automatic shortlist of candidates.
- Search and find talent similar to the top talent already within the organization.
- Reduce recruitment costs with the efficiency of automated tasks.
- Avoid gender-biased decision making in the candidate search.
- Monitor, alert and fix compensation rates for employees who are over-performing but have not yet been acknowledged.
- Monitor and track behaviour for productivity or security protocols.
- Monitor and flag behaviours that characterize employees who may be wanting to leave the organization.
- Set benchmarks for performance and track employee performance to measure their future potential so that support can be provided.
- Identify training and skill gaps in employees so that appropriate training or support can be offered to improve employee satisfaction.
- Map high-performing employees to the requirements and performance specifications of other roles in order to facilitate succession planning.
How to implement workforce analytics
Data on its own does not have any meaning and will waste the time of managers or executives looking at it. To make workforce analytics work, here are the basic steps needed to get started:
1. Start with a workforce problem or question that the organization wants or need to solve.
For example, Can we improve employee engagement by addressing skill gaps? A specific problem or question will help determine what data and statistical models are needed.
2. Determine what information managers or executives would need in order to make a decision about their problem or question.
What should the analysis be reporting on?
For example, in the question posed in Step 1, the information they need might be the relationship of training completion rates to employee survey results.
3. Determine the benchmark or goal for the problem or question.
This means ensuring that historical data or benchmark standards are available in order to compare the collected data. As in our example question, the benchmark might be the successful completion of a specific training course.
4. Decide how to analyze and report the metrics.
This means figuring out what levels or relationships to measure and analyze. To figure out the skill gaps in the organization’s workforce, the metrics might include the training results for different departments, the benchmarks for each department, and a correlation with employee survey results. Is employee satisfaction higher for those who have completed the additional skill training and lower for those who have not completed the skill training?
How workforce analytics can benefit organizations
Workforce analytics can help organizations to more efficiently address challenges or opportunities in employee management. Some of the organizational benefits include:
- Ability to identify potential candidates who best match with organizational needs and work culture.
- Ability to forecast which employees will be high-performers so that the right resources are provided to retain them.
- Determine the need for future organizational needs so that recruitment will satisfy talent requirements.
- Determine what factors indicate employee engagement and job satisfaction in order to sustain a better performing workforce.
- Identify and flag upcoming talent for future succession planning.
Examples of workforce analytics in practice
One company used workforce analytics to better understand how front-line employee performance was impacting customer experience and sales.
They wanted to know: what factors improve sales performance and customer satisfaction?
The data collected included personality traits, daily management procedures, behaviour and customer interaction information.
These metrics were then measured against things like financial incentive and employee development opportunities to determine if a relationship existed.
The results indicated that employee development was one of the most important factors in improved customer satisfaction and sales, and that financial incentives was actually quite low as a motivator.
The study also pinpointed other factors that impact performance, including specific personality traits and length of shift.
Armed with this information, the company was able to make better decisions regarding the management of their sales staff and were able to improve performance.
Another company wanted to address high turnover in some of their important positions.
An algorithm was used to analyze data such as recruitment data, promotion history, tenure, role, performance, salary, job role, and location.
They also used data from another analytical tool that measured areas of social engagement with employees.
They wanted to know: what factors can indicate when an employee is thinking about leaving?
The outcome was that employees who interacted less socially had a higher rate of quitting.
Being able to identify this red flag has enabled the company to intervene in these situations and reduce turnover over time by 25%.
A company in Amsterdam offers a workforce analytics tool geared at recruitment strategies.
Their tool provides sixteen questions to applicants in order to collect data on candidate preferences, such as career growth opportunities, job security, benefits, and salary.
It also collects standard data on academic background and experience.
Companies using the workforce analytical tool can ask themselves what kind of employee they need for a specific role, but also better understand what factors are important to potential candidates.
Being able to satisfy candidate needs enables a company to have a better chance of that candidate signing on with the company.