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# How to Dig Into Compensation Analytics to Answer Specific Questions

This is a lesson from our forthcoming ecourse on compensation analytics, and builds on our previous post on five comp analytics to track. Be on the lookout for the course launch, which includes helpful examples and insightful exercises — coming soon!

In the last lesson, we overviewed five compensation analytics that help track whether your organization is moving in the direction you’re targeting. In this lesson, we’ll identify ways to dig deeper into a few analytics to pinpoint specific issues in your organization. For each reviewed analytic, we’ll explore a different variable or “slice” of the organization. Most are interchangeable, so the following are meant to be examples of things you might examine.

## Market-Ratio by Location

It’s possible to determine market-ratio for a group of employees. In this example, we’ll group by location.

Imagine: You’re struggling to keep employees in your Boise, Idaho location, but you practically can’t get rid of employees in your Salt Lake City, Utah location. After calculating the average market-ratio for each location, you quickly spot the problem: The average market-ratio for the 35 employees in Boise is 0.76, or 24 percent below market, while the average market-ratio for the 30 employees in Salt Lake City is 1.34, or 34 percent above market.

Now you have it: proof that you’re paying high in one place and low in another. The numbers explain your challenges to recruit and maintain natural attrition in each place respectively. Before you jump up and down, remember that paying high may be justified if you also have high performance, big results and rare-but-relevant skills. You’ll want to see if you can uncover some valid reasons for paying high in Salt Lake City before developing a plan to resolve the problem. If you identify a problem to fix, tracking market-ratio by location for a few years is a good idea.

## Compa-Ratio by Function

Compa-ratio can also be used for groups of employees. For this exercise, we’ll group by function — a specific type of job that may cross departmental lines.

Imagine: The department coordinators at a university have all gotten together and said that they are systematically underpaid, while all the faculty are overpaid. You research the claim by determining the compa-ratio for the group of department coordinators and the faculty group. Because you’re looking at each employee’s pay relative to the midpoint of the range, you don’t have to worry about comparing jobs at the same organizational level (or pay).

You determine that department coordinators have an average compa-ratio of 1.03, while the faculty group has an average compa-ratio of 0.98. Kudos to you for having compa-ratios that are relatively close to 1; the larger the group, the closer they should be to 1. After looking at average tenures, evaluation scores and proficiency levels within each group, you’re able to confidently claim that there is no systematic bias between the two groups. Perhaps there is a problem with some subsets within the groups — the coordinators and faculty within Arts & Sciences, for example. This is another place where tracking more specific measures might be prudent until the rumbling dies down.

## Range Penetration by Manager

One of the biggest advantages to range penetration is that it’s a number that’s typically easier for employees and managers alike to understand. As such, when investigating manager-to-manager problems, range penetration is often a good measure to use so that eventually you can explain the results more easily.

Imagine: The employees of manager A have complained that their peers reporting to manager B are consistently getting bigger increases. You calculate the average range penetration for the group of employees reporting to each manager and find that for manager A’s twelve employees, the average range penetration is 45%. For manager B’s twelve employees, the average range penetration is 65%. You look beyond just position in range to understand if there may be any trends that explain — legitimately — the reason for the difference.

You find that the average tenure for manager A is higher and the average performance ratings are lower. You may not yet be at the answer until you look more closely at the performance ratings. If the ratings are based on measurable goals, it will be easier to uncover whether the employees reporting to manager A truly have lower results than those reporting to manager B. Surfacing the challenge and monitoring the average range penetration, tenure and performance for both managers is wise in this case.

Location, manager and function are typical places where you can find pay issues — either differentiating too much or not enough without good reason. You may find that you have to combine factors to uncover issues in your organization. What if manager A has mostly female reports, while manager B has mostly male reports — is there a pay equity issue? Or, consider that in the function example above, function and department (or school) were cross-referenced.

This is again a place to figure out what matters most to your organization. Identify some useful factors to track and add those to your dashboard so you can quickly check that you’re still on the road to success.

## One Caution

Pick just a few, easily trackable analytics. You don’t want to go down the rabbit hole of tracking more analytics than you can do anything with, and you don’t want to be stuck gathering data indefinitely. Identify the top ones that will make an impact in your organization and check them at least monthly.

Some would stop here and call it good. The danger in doing that is that if you don’t go one step further, you may fall short of taking the action needed to resolve the issues you uncover. In the next blog, we’ll talk about using compensation analytics to tell the story of where your organization is thriving and where it may need a little help. This critical step is the one that poises you for action so don’t miss it!

Image: Pixabay

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