For Compensation Professionals: Two Mistakes to Avoid in Market Pricing

In the field of employee compensation, “market pricing” refers to the techniques of assessing the going pay rate for an organization’s jobs within the labor market(s) most relevant to the organization.? We also refer to this as compensation benchmarking.

[For this article, I’m assuming appropriate compensation surveys or other data sources have already been selected and an organization’s jobs have already been carefully matched and leveled against the survey jobs.]

As a consultant and former internal practitioner who has worked with many organizations over the years, these are the two biggest market pricing mistakes I’ve seen in practice:

-????Not having a pre-defined, strategic methodology for market pricing, and

-????Not QA’ing for reasonableness after the fact.

Mistake #1: Not having a pre-defined, strategic methodology for market pricing. This is stating the obvious, but without thinking through, deciding upon, and documenting ?a defined methodology, an organization risks inconsistent approaches to determining appropriate pay levels. ?Inconsistency can lead to inequity.

Within any given compensation survey, there are numerous decisions about how to “slice” the data before pulling it.? There is no one right answer to the questions that follow – but they should align with your organization’s overall Compensation Philosophy and Business Strategies.? Further, consideration should be given as to how these data selection decisions should be differentiated based on Job Level and/or Job Family. Such data selection decisions include:

a. Geographic Location –? Are you a “remote-first”, fully distributed company or are your employees largely located in a handful of core geographic locations?? Assuming U.S. only for this purpose, do you want to pursue a National jobs-pricing model (and, if so, based on U.S. averages, or high-end markets, or ??), a Regional one, or more Localized one?? Why or why not and how would you differentiate based on the type or level of job??

[Typically, lower level positions are priced based on their local area. ?One reason is to ensure compliance with locality-based minimum wage laws.? Another is the theory that employees in these jobs are more likely to look within their local area – vs. a wider geography - for a new job when they are ready for a change.]

b. Industry – How critical is having experience within your organization’s industry to the positions being assessed?? And, how common is it for employees in those positions to leave for other organizations in the same industry?? This decision will vary based on the specific Job Function or Job Family that you’re pricing, and often Job Level as well (e.g., a CFO vs. an Accounting Assistant).?

For example, Human Resources is a staff function that is typically “industry-agnostic” (with exceptions for niche areas like labor relations in healthcare, or certain technical or scientific recruiters). Further, how narrow do you want to parse your Industry selection?? The narrower you get, the smaller your sample size, but your data is more targeted.

c. Organization Size – Larger surveys allow you to narrow your data by the size of organization using metrics such as revenues, operating expenses, and headcount.? For which kind of jobs would this level of distinction be relevant?? Generally, we view Executive level jobs this way.? What metric is most relevant to your business and what size range is both reasonable to consider and yields adequate sample sizes?

d. Data Measures – Compensation folks usually pull the 25th, 50th, and 75th percentiles as well as the Mean/Average. ?Which measure goes into determining your overall Market Composite is based on your Compensation Philosophy.? But having these stats available to view is helpful when there’s a need to drill down further to investigate “squirrely” data (a highly scientific term!).? Some quant jocks even pull the Standard Deviation.?

[I don't recommend targeting an unusual percentile like the 60th in your Compensation Philosophy.? Unless your surveys have very large sample sizes, the data becomes more unreliable or squirrely here.? Instead, consider something like:? “We target pay at 10% above median benchmarks”, pull the 50th, then add 10% at the end.]

e. “Incumbent-Weighted” or “Organization-Weighted” Data – This one is often debated ad nauseum by compensation data nerds.? I think the easiest way to understand the difference between the two is as follows:

“Incumbent-Weighted”: ?This answers the question, “What does a typical person in this position make?” or “What’s the going rate for workers in this position?”? An “incumbent-weighted” median (for example) is the true median when all incumbent pay rates submitted by participating organizations matching to the given position are sorted from high to low.? It doesn’t matter if 100 incumbents come from the XYZ Corp. while 4 come from ABC Inc.? So, though ironically named, in effect, this data gives more weight to the organizations with more incumbents.

“Organization-Weighted”:? This answers the question, “What does a typical organization pay for this position?” or “What’s the average (or median) pay that organizations are paying for this position?”? Here, a typical approach (it can vary based on the survey) is for a statistic to be determined for each organization submitting data – regardless of the number of incumbents each has - so that the overall reporting numbers treat each organization equally (again, ironically named).?

f. Sample Sizes – A consistent methodology also includes a decision as to the minimum sample sizes the organization is willing to accept.? For each position reported, this is both the number of employers reporting data as well as the number of individual employees for which data was reported.? This count is important for two reasons:? First, survey publishers are required by the federal government to exclude any jobs/breakouts for which fewer than five employers provided data.? Second, the more data, the more consistent and accurate the results will be.

g. Other - Finally, decisions need to be made regarding what to do with the raw data after you pull it. ?I won’t get into the detail here, but these include:?

-????How to treat “hybrid” jobs,

-????Whether (and how) to apply “premiums” or “discounts” to job matches or not,

-????Whether (and how) to “age” the data or not; and

-????How to combine the data from your different survey sources into an aggregate composite.

Mistake #2: Not QA’ing for reasonableness after the fact.

It’s important not to take the data collected and the final composites at face value.? Do a reasonableness check.? Most importantly, highlight any case where there’s a significant difference between the market composite and your own organization’s incumbent pay.?? In such cases, it’s critical to doublecheck the accuracy of the survey benchmarking (job matching).?

I’ve often found that jobs in organizations “morph” over time, without the benefit of the hiring manager working with HR to update the job description and position title to more accurately reflect the work being done today.? (This is why I usually vet benchmark job matches with line management before pulling data – this goes a long way to education, buy-in and acceptance of the results.)

Another tip:? Look for any significant variations in reported pay between surveys for the same position and assess why this may be.?

Once you’ve concluded that your data is in as good a shape as it can be, then it’s time to have discussions with HR business partners and line management to further explore possible reasons for discrepancies before making any final pay or grade recommendations.

This is all part of the art – and the science – of strong, professional compensation work.