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Compensation and Big Data – The Targeted Merit Budget

Big Data Merit Pay BudgetBig Data is delivering a more intelligent approach to merit budgets, but with a caveat.

Big Data is just that, Big and general, and far from perfect.

At one time or another, we’ve all been victims of computer algorithms that judge us unworthy of credit, or direct marketing to us based on algorithms that really don’t apply.

The same can happen if we set our merit budgets—using analytics– at the granular level. That’s why it’s important, once you’re using more Big Data, to leave some room for manager judgment.


Compensation and Big Data – Bringing Science to Pay

It’s a common belief in HR that compensation is both art and science. With the introduction of Big Data and data visualization, the art portion is becoming a much smaller part of the equation. This trend will profoundly impact the comp analyst’s job.

Say Goodbye to One Size Fits All

Here’s how merit budget presentations used to go:

“OK, after all of this beautiful data we’ve shown you, we recommend a merit budget of 3.25% across the board.”

Umm, no. You can no longer spread merit budgets evenly with a “peanut butter” tactic. The emerging focus on analytics and big data, along with more sophisticated technology, means that multiple solutions for merit budgets and other compensation elements will become the norm.

Technology is making it progressively easier to use analytics to evaluate relationships in your external and internal data. You’ll finally be able to confirm or disprove long held beliefs about the interaction of pay with other factors. One of the inevitable results of all of this will be merit budgets that vary significantly across the organization.

Takeaway: Don’t stop at a beautiful, intuitive presentation of data and conclusions. Follow up with targeted recommendations that make use of those conclusions.

Be Smart in How You Use Your Compensation Research

Most compensation plans do not give a manager a higher merit budget just because s/he has a performance distribution skewed toward higher ratings. In fact, you’re probably chuckling at the very idea. We don’t trust the managers not to manipulate the system.

But, what if you had other measures of performance that could not be so easily manipulated and you knew they positively influenced performance?

Would you use them in the creation of merit budgets? With the advent of big data analysis, that situation is very likely to arise.

If you found, for example, that merit increase size influences turnover among high-performing employees with five to ten years of experience, would you be willing to give managers of those employees a higher merit budget?

And, just as important, give less to the managers whose people were not in that category?


A Simple Compensation Example

Take the simple example of displaying the position to market of different job families. If you present the data in a bar chart, before and after the “all for one” merit budget, the chart will actually change very little.

The jobs significantly below market will stay significantly below market. Internal relationships among jobs will remain the same.


Current Position to Market

(PTM) By Job Family

PTM After “All For One” Merit Budget

PTM After Targeted Merit Budget
(Targeted by Job Family)



Merit based on performance ratings will help move salaries faster for the top performers, but the difference is often so small that it really doesn’t affect the outcome.

Takeaway: Let the data tell you the differences and use that information to develop a targeted merit budget to better align your PTM.

Why San Francisco Salaries are Higher than Other Cities (and it’s not just the Cost of Living) – The Power of Geographic Differentials

What are Geographic Differentials?

Geographic differentials are market-driven pay variations between locations.

Companies use these differences when pricing the same job in different geographic markets. The objective is to control costs where it is not necessary to pay at national levels, or to ensure adequate pay in areas where the market is much higher than the national average.

Now you might think that living in San Francisco, one of the more expensive cities in the U.S., would naturally have higher salaries because of the cost of living alone.

In spite of what many think, it is not an adjustment based on cost of living.

Salary Geographic Differentials

Geographic differentials are usually expressed as a

percentage that’s applied to a national market rate.

For example, a job with a median national salary of $60,000 might pay twenty percent (120%) more, or $72,000, in San Francisco, but ten percent less, or $54,000, in Jackson, Mississippi.

How are Geographic Differentials  calculated?

Geographic differentials are expressed as a percent of the base-line data point.


If research shows that a geographic location pays, on average, 90% of the national average, or in other words 10 percent less than the national average:

  • Plus/Minus Geo Diff: -10%\
  • Geo Diff Ratio:    90%

Note:  You will find compensation professionals use the same term for both calculations, depending on their original training.

Ensure that your group is consistent in using this term. For purposes of example, we are going to use the Geographic Differential ratio.

Calculation of Market Rates

Some companies adjust their market data for geographic differences before reporting it to management or using it to develop their compensation program.

(Of course, this would not be appropriate for market data that is already adjusted for geographic differences, such as “by region” cuts of the data).


National Salary Range Example

Salary structure differences

This is one of the most commonly used methods to address geographic differentials.

  • Compensation professionals create a “core” range, based on their headquarters location or a national average, and then create structures for each geographic region starting with the core range points.
  • This recognizes differences in average pay levels for each location, allowing you to manage different control points, or slot jobs up or down through salary grades on the basis of local market data.
  • Usually, the different geographic locations are grouped into salary areas or plans and ranges created for each grouping.


Salary Range Geographic Differnetials


Individual pay adjustments and differentials

Companies typically increase the pay for individuals when they move to a location that demands higher rates of pay. It is rare to go the other direction adjusting pay downward when someone goes to a lower pay location.


Mary H. is transferred from her job in Buffalo, New York to the same job in New York City.

The company has only one salary range for this job, which is closer to Buffalo pay rates than those in New York City. In order to make up for the difference in pay levels between the two locations, Mary is given a 10% raise to go to New York City.

(Typically, Mary is not asked to take a salary reduction if she returns to Buffalo, unless it’s made clear in the original move).


Temporary pay adjustments or re-assignment bonuses

These are typically used when an employee is on assignment in a higher wage area. If the change is made to the employee’s salary, the wage adjustment will continue until the employee is re-assigned, returns to the prior job or transfers to another location.


Mary H. from the above example receives a 10% increase in pay while she is on assignment in New York City. When she returns to  Buffalo, her pay is reduced.

Supplemental payments

Supplemental payments are often used when an employee is on temporary assignment in a higher paying geographic area.

Supplemental payments differ from temporary pay adjustments in that supplemental payments are usually used in very short assignments (A few days to a few weeks) and provided in one or two payments rather than a temporary salary increase.

What should you consider when choosing a geographic differential methodology?

  • Are you recruiting nationally or locally for each job or job grouping? National averages may still work for you in the former case.
  • If you’re creating your own geographic differentials, are there industry differences that may account for some of the differences you’re seeing in your data?  The industry difference may have originated from a survey whose participants are from one or two key industries only.
  • Do they apply to non-exempt, lower paid jobs?
  • Or are there actual differences for higher paid positions from different cities?

How are differentials  used? For creating ranges?  Or applying to market data?

They’re used both ways, based on a variety of different circumstances. Much depends on your company’s compensation philosophy, history, politics or, sometimes, whim.

Where can you obtain Geographic Differential market data?

There are two ways to obtain the differential from external resources. You can calculate the differentials based on the survey data you have for various geographic regions…or you can use formulas provided by one of the big consulting firms such as ERI, Towers Watson or Mercer.

It’s better not to rely on government created tables (Bureau of Labor Statistics, etc) because they have other factors built in that can exaggerate the differences, and the data is often too old by the time it’s published.

Making Geographically Differentiated Pay Equal – Begin with a Sound Methodology

The basic principle you need to work from is that employees expect equal pay for equal work. Geographically differentiated pay can still be equal, but you need a sound methodology to back it up. Meantime, subjective arguments can be thrown at you from managers with their own agendas.

Compensation professionals have heard managers argue it both ways:

“I must pay more in rural locations to attract the good people from the metro areas”


“I must pay more in large metro areas because the competition is stiffer for talent”

This latter argument usually wins the day in a contest of wills. However, the better handle you have on your geographic differential statistics, the more you can keep your organization’s policies on track with the real world, based on real numbers.

Watch Your Words with Geographic Differentials

Be careful when naming the different geographic groupings — use non-offensive language. Remember that status and ego are easily affected by categorizations in the compensation world, and you don’t want to go around slapping on labels that make some people feel their jobs are somehow less valuable than those at other locations.

This actually applies to all labels you use in the compensation arena – and business in general today. In a socially driven world, communication about compensation is of critical importance.