Different Compensation Programs Need Different Market Data

by on June 26, 2009 · 0 Comment POSTED IN: HR Info Center

Surveys Help Provide Foundation to Compensation Programs

When you look at market data for compensation programs, look at certain considerations. First and foremost, look at respondents. The organizations that participated in the survey, do they match our clients in terms of geographic area, industry and size of organization. If so, it’s probably an appropriate survey to use in the analysis for the purchase.

Also look at sample size but not just sample size alone and run an analysis on the respondent-base and how stable it is. In other words, sample size is how many participants we have. If there’s an unstable respondent-base set, that trend may have more to do with the turnover in the survey than anything that’s going on in the market. In other words, it may be statistical noise.

Is the survey regularly conducted? Most salary surveys are conducted on an annual basis. Some are updated on a quarterly basis. Some occur every other year. Some organizations are starting to move towards a live data approach to surveying compensation programs. Meaning that they’re constantly accepting new participants and aging factor is applied to old participants. And so, the data is always considered current and relevant to compensation programs.

Next is value. And this is really an important one. There’s an IT survey out there that costs $35,000 and it’s obsolete the following year. If you’re working with a healthcare client, it’s not worth a value in a $35,000 survey for IT, just not that critical to have that kind of expense for data. However if you’re a large IT consulting firm, that may be critical data and $35,000 is a drop in the bucket because if that data causes you to make better decisions about compensation programs and causes you to keep some of your top-level guys who are critical to the success of my organization, you’re going to have that value. Depending on the organization, depending on budgets, depending on industry, data can be expensive, so that cost-benefit analysis needs to occur.

Finally and very importantly is the scrubbing methodology. Quality surveys tend to look at outliers and tend to review the quality of the data being submitted. They tend to work directly with HR departments and get data feeds. They ensure that the job matches are appropriate and consistent. They do not rely on self-reported data.

Typically that reliable data will oftentimes come from major consulting firms — Mercer, Wyatt, Hewitt, Towers Perrin, for example. Sometimes they will come from surveying firms. An example of that would be comp data surveys for example. They are exclusively in the compensation programs and benefits survey business. Again these tend to be statistically validated, that they go through extensive scrubbing and do not accept self-reported data.

In addition, one of the processes to go through to is conducting a standard deviation analysis of multiple survey sources. For example, with an accountant, you may have three different surveys that all have data for an accountant. If you pull that data and the first survey says, the median of the market is $40,000 and the second survey says its $42,300 and our third source says its $74,000. The standard deviation of those matches is going to be very large because we have a huge outlier.

That causes you to dig a little deeper on that third survey source and say, you know what? Is that is your accountant position in Mountain Home, Arkansas and the data are primarily in Los Angeles maybe that’s why there are such a discrepancy. Whatever the reason for the discrepancy, that standard deviation analysis is going to point it out.

Edited Remarks from “How to Set Pay Ranges That are Fair and Effective” by Edward Rataj

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