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We’re past the point where analytics were merely an opportunity to apply data to decision-making. There’s no longer any uncertainty on whether we should invest in an analytics tool. When you can back your decisions with concrete and insightful data, you don’t need to question the value of analytics.

Gender Pay Equality

So why write about this topic when experts in HR and Sales have already realized that using analytics can help improve performance and make better decisions? The thing is, most of us know that people data has potential, but very few of us really know how to leverage it. Linking performance to compensation requires you to analyze the different datasets of your people data, such as employee attributes, performance data, comp data and benchmark data, all in one place.

In this post, we’ll walk you through a few examples that illustrate the value of drilling down into data for better people decisions – because it is, after all, your people who drive the success of your company.

The first hurdle to overcome when trying to make more effective use of your people analytics is dealing with your system’s data quality. Disparate HR systems don’t help very much and it’s important to find a centralized solution that lets you put together all your employee data, clean it up, and measure it.

Once you’ve centralized your performance and compensation data, you can follow Peter Drucker’s words – “What gets measured, gets managed.” Start by understanding the way your processes are currently running. Then, combine different metrics to make a difference in the way you manage compensation.

3 examples that explain why a data-driven approach to performance & compensation can be beneficial to your organization:

Ensure Your People Are Being Paid Fairly

A hot topic for reward and compensation teams, gender pay gap is an employee attribute that is easy to identify. Its standard metric is derived from comparing the average salary of men and women who have similar jobs within similar environments.

However, for your organization to be truly efficient in fair pay, other dimensions need to be added when analyzing gender equality. These include indicators such as performance attainments, performance ratings, how long the employee has been in the position, seniority, absentee rate, promotion rate, professional experience, and more.

Looking at metrics beyond the issues of equality can provide you with unexpected insights. We found the following results while examining potential gender pay gaps for a B2B telco provider:

The company's sales managers consisted mostly of men between the ages of 35 and 45, who were based in the same region.

Women sales managers, on the other hand, were 10 years younger, had previously worked in different regions, and were showing much greater potential to motivate salespeople with average or weak performance levels.

This discovery led the company to streamline career path and succession planning processes by promoting more women as sales managers.

Identify Correlations Between Benefits and Job Satisfaction

We measured the impact of compensation on employee performance with a company that was into the third year of its benefits program but was still struggling to find a meaningful way to motivate its employees. Although the program ran for a long duration, we found that it contributed to employee engagement only temporarily. After a certain point, we noticed an inexplicable drop in efficiency for 70% of the employees.

In order to understand why this was happening, we looked deeper into the people data using beqom analytics, and compared the engaged population (30%) versus the disengaged population (70%). The correlation between compensation and job satisfaction showed us that the people who were focused more on their compensation, and less on improving their professional skills, were the same ones who were disengaged.

This analytical view helped the company redefine its benefits program with a strategy focused on compensating its employees not just in line with their existing job roles and skills, but also keeping in mind what they are striving to achieve in the future.

Use Data to Understand Why Your Employees Quit

Data analysis can yield unexpected but valuable results. Correlations can be found among various types of employee data, but only managers are in a position to explain the reasons for the results and confirm the accuracy of this information. It is only after this that an action plan can be established, not before.

To back this theory, we used beqom analytics to study a B2B sales team where a strong correlation was found among resignations, those leaving the company all had relatively high performance ratings. The field investigation revealed low levels of engagement among this population of salespeople whose performance and skill levels were high.

A targeted questionnaire addressing the reasons behind the resignations, and a post-resignation interview with each employee who had quit revealed that those who exited were frustrated. They perceived they were not adequately compensated for their higher performance, as opposed to lower performers. They felt that the various caps and levels in the compensation plans were holding them back unfairly.

Based on these findings, the company revised their compensation plan and tweaked their payout curve to make sure high performers earned a well-deserved compensation.

In terms of human capital, decisions should be taken on an individual basis. Most often, the value of managers' and field employees' knowledge is underestimated.

As a Comp & Ben Manager, it’s essential for you to back your statements with solid data and avoid situations in which you’re sharing viewpoints and opinions instead of hard facts.

“Without data, you’re just another person with an opinion.”

W. Edwards Deming.

Analytics will help you provide your organization with the proof to drive decision-making. It’s time you found the right solution to help you make the most of your people data.