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Salary Benchmarking

Three Steps to Building Better Salary Compensation Studies

Apr 6, 2018 3:00:00 PM

 

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In an economy where finding and retaining top talent has becoming increasingly challenging, organizations need reliable, timely data that provides the assurances that they are offering competitive compensation packages. Having the precise and relevant data from quality salary compensation surveys can help organizations understand if, in fact, they are offering competitive salaries and appropriate benefits packages. Associations often provide these salary compensation surveys to their members, which is viewed as a very valuable business intelligence, a benefit often cited as a primary reason for association enrollment.

It’s important to note, that not all salary compensation surveys are built the same and do not deliver the same value. Understanding what your members need, what they value and how best to present that information in both an engaging and informative manner is vital to your study’s success.

STEP 1: COLLECT THE RIGHT DATA FOR YOUR SALARY COMPENSATION SURVEYS

The best benchmarking studies are built from information that is essential, applicable and interesting to your audience. Without these, your salary study has no value. Take the time to determine the right data points for your audience. For salary compensation surveys, a good place to start is with gathering job descriptions, as these vary greatly from industry to industry and will surely be the foundation of the bulk of your study questions.

Also, be sure that you pay close attention to how you’re collecting compensation rates as this is a common area for errors. Most commonly, users may input annual salaries or bonuses in fields asking for hourly rates which radically skews the overall study data. A simple solution is to set value limits for these fields that automatically flag entries that exceed a capped amount, prompting users to re-enter their data.

salary compensation surveys

STEP 2: ASK THE RIGHT QUESTIONS THE RIGHT WAY

How you ask your questions is just as important as what you ask. Once you’ve determined the information you’d like to gather, spend time formulating your questions in the best ways possible to extract accurate data.

Asking for numeric values rather than presenting users with ranges from which to choose is one strategy for delivering better study results. Exact numeric values allow you to create more meaningful filters and conduct better data comparisons when it comes to building reports for your salary compensation surveys. Questions that use clear “yes/no” answers, followed up by “if/then” qualifiers can also be used to extract more meaningful, measurable data.


STEP 3: PRESENT THE RIGHT CHARTS AND REPORTS

While there may be many options for displaying data, not every chart is appropriate for every data type. Some information is best presented in certain formats so that it is more easily understood by the reader.

Salary study data often works well in scatter chart presentations, though there are certainly other options, especially if your industry has special considerations. Knowing the best way to present your results is essential to delivering information in a format that your audience will find easy to read and will be able to quickly pinpoint the key takeaways. Dynamic reporting, where users can choose their own data filters, offers a way for your audience to choose the information and format that works best for their personal needs.

Charts that present data in its most usable format, along with filters that bring meaning, deliver salary benchmarking reports that are both usable and capable of driving strategic initiatives and organizational changes.
 

salary compensation surveys

As you work toward building meaningful salary compensation surveys for your members, take the time to carefully consider the questions that will extract the most relevant and measurable information for their needs. When you build the right reports, your salary study can be a game changer for your members and your association.

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Salary Benchmarking by Dynamic Benchmarking