Table of Contents
What Are Payroll Analytics?
Payroll analytics brings immediate financial benefit by efficiently and quickly identifying areas for improvement, from budgeting to payroll fraud. Payroll analytics affects a company’s bottom line and improves profitability. Payroll analytics expand beyond payroll itself by incorporating other factors of payroll, such as compensation and budgeting.
For a large HR department, a payroll specialist will be in charge of payroll analytics. For a small HR department, a generalist will take on payroll analytics or use a third party to run the analytics for them.
Why Are Payroll Analytics Important?
Payroll analytics can be used for many things, like fraud detection or budgeting. Resources such as Excel or a programming language can be used to identify these errors. Payroll impacts everything from financial profitability to higher employee retention. Below are some of the main reasons why payroll analytics is important.
- Detect fraud. Payroll analytics can help detect fraud. It can find duplicate employee records, repeated bank accounts, extreme hours, and more. For example, using VBA in Excel can red-flag any hours above a certain number or any duplicates. Detecting fraud saves the company money and helps identify dishonest employees.
- Find errors. Errors often occur in payroll. Payroll analytics can find the errors, such as an employee with duplicate shifts or missed punches.
- Budgeting. Many companies underestimate their labor costs. Payroll analytics help to more effectively budget for labor costs and growth that might occur.
- Compensation costs. Market data and research give an idea of what compensation costs should be. At the end of the year, payroll analytics compare actual compensation costs to the researched costs. A large difference signals that a company may not be paying employees enough and needs to give raises, or that a company is paying employees too much and may need some reorganization or layoffs.
- Turnover. Payroll analytics sometimes show trends in employees who leave the company. It can show if they’re comparatively underpaid, haven’t received a raise in a while, and more. HR can then address these reasons to reduce turnover in the future.
- Hiring. Payroll analytics can be used to understand the effectiveness of the company’s hiring process. Analytics can show how long it takes for a new hire to be added into the system and get their first, complete paycheck. Improving the company’s payroll for new hires will create a better experience from the start.
Payroll Analytics HR Should Know
There are many different payroll analytics out there. Below are some of the most important ones for HR to know. Knowing these will help you understand payroll analytics and get started implementing it in your business.
Understanding what you should pay your employees is important. You want to be fair and accurate while not overpaying them. Planning and distributing pay and benefits to employees, known as compensation management, helps achieve this. Compensation management does overlap some with payroll analytics, helping with effective hiring and retention. There are many different compensation-specific metrics.
Salary benchmarking, or compensation benchmarking, means matching internal jobs to similar jobs in the market to understand how the salaries compare. Doing this helps determine the market pay rate for each position. When making hiring or promotion decisions, knowing the market pay rate will better help you make decisions about employees’ pay.
Comparing actual labor costs to budgeted labor costs can be insightful. It shows if a company has underbudgeted their labor costs. If this proves true, the company needs to review labor and budgeting procedures to be more accurate.
Tracking attendance helps you identify trends in employee absenteeism. For example, you may see that time off tends to cluster around the end of the year. Certain companies may have deadlines at the end of the year and can create incentives to encourage employees to take more time off throughout the year. On the flip side, seeing that absenteeism is very low can allow you to encourage workers to take full advantage of their time off, motivating them to take breaks and recharge.
There are strict laws and guidelines about payroll, specifically minimum wage and overtime. Only certain employees, like managers and other professionals, can be classified as exempt. Payroll analytics help identify if one of these employees is in the wrong category and is therefore being paid incorrectly. Each job position should have a job description that includes if the employee is exempt or non-exempt. It’s possible that when entered into a system, an employee was marked as the wrong category; payroll analytics can flag if an exempt employee is being paid overtime or if a non-exempt employee is not being paid overtime, helping to correct those mistakes.
Identify Quick Wins
Analytics can be very useful for identifying long-term trends, but a good first step in utilizing analytics is identifying quick wins. Identifying quick wins helps the whole company see the benefit of analytics, gaining more support. For example, observing what benefits are most used by employees enables the company to improve the benefits package. Another quick win would be to see if there is good pay equity between genders and races and promoting further action if not.
Analytics can be hard if there is constant change. Companies that change their payroll provider every year struggle to get accurate data. Companies that change what they want to measure will not have long-term data to provide future action. Being consistent in what you measure and in how often you measure it creates the cleanest and most concise data.
Use the Data
When you have the data, don’t just store it until someone asks for it. Be proactive. Go to a manager to let them know that you see bad pay equity. Go to recruiting to show them how their initial offers compare to the market pay. Being proactive in using and sharing the data makes gathering the data meaningful and demonstrates your value to your colleagues.
Tips for Tracking Payroll Analytics
Here are a few tips to make implementing payroll analytics simpler and more effective for you.
Tip 1: Find Resources
If you haven’t tracked payroll analytics before, look at the resources around you. Does your company use payroll management software that already has payroll analytics? Many payroll managers do, and it’s possible you might just be unaware. Does your company pay for Excel? If so, look online for tips or Excel spreadsheets that have been created to measure analytics. You don’t have to start from scratch.
Tip 2: Start Small
Start small with the number of analytics you track, especially if you don’t have an automated platform to track it for you. Start with one metric and ensure you get the calculations correct. Once you feel comfortable with that one and have it set up, start the next one. Analytics can take time to implement, and that’s okay.
Tip 3: Ask for Help
Believe it or not, other professions already know some of these analytical tools. Someone in finance or accounting may have a lot of experience with Excel and may be able to show you some useful tricks. It’s even possible that at some point, they have performed some sort of payroll analytics.
Tip 4: Be Okay With Learning
Whether it’s your first time using payroll analytics or not, be okay with mistakes. That doesn’t mean you shouldn’t fix mistakes or try to improve. However, when a mistake with your calculations happens, recognize that it is bound to happen. Data can have errors, and even the best analysts can make mistakes in calculations.
Questions You’ve Asked Us About Payroll Analytics
What data is needed for payroll analytics?
Any data that is related to payroll is useful to payroll analytics, including salary, hours worked, overtime hours, bonuses, leave, benefits, taxes, and more. All this data can be used to create other data and help ensure compliance.
Is workplace productivity included with payroll analytics?
Workforce productivity is not a type of payroll analytics. Improving workforce productivity can indirectly affect payroll, but is not a part of payroll analytics. It does, however, tie into payroll analytics.
Katie is currently studying at BYU, with a HRM major and Statistics minor. She works there as an HR research assistant and also works as an HR Generalist at a local company, and both jobs provide her with a wide variety of experiences. Katie’s passion lies in HR and People Analytics, where she can discover and use data to help everyone understand and improve the workplace for a universal benefit.