Market Pricing 101: using general industry and industry specific data

July 30, 2021

With the number of salary surveys available today, plus all the various scope cuts available within the surveys, it can be overwhelming to know what to use. As discussed elsewhere in this Market Pricing 101 series, Establishing a Benchmark Methodology Unique to Your Organization, the importance of selecting surveys and documenting the data cuts and blending method used in order to ensure consistency, accuracy, and efficiency cannot be understated. But, just understanding when and how to use general versus industry-specific data is something that deserves additional discussion.

General industry survey data

In a general industry survey, such as the US or Canada MBD: Mercer Benchmark Database, you have the option to refine your dataset by selecting from a set of industries (e.g., High Tech, Consumer Goods, Life Sciences). In the case of MBD, when you do that, you are taking the full dataset, which includes over 3,000 organizations for the US or 1,100 organizations for Canada, and reducing it down to just those organizations that fit into those industry categories. This data subset, in many surveys, can then be narrowed further to reflect only the size and/or location of companies that are relevant to your organization or the particular jobs you are market pricing.

When using refined data cuts from general industry surveys, you need to consider how you use them, being cautious not to combine data cuts from the same survey, which would, in effect, be double counting the same data. For example, if you choose to use the "All Data" cut and the "Industry: Finance" cut for market pricing the same jobs, you are including the responses from any company that is in the Finance industry cut twice, because they are also included when you do not refine the data and use the "All Data" cut. Typically, when using more than one data scope cut from the same survey you are using them with different jobs or employee segments.

Industry-specific survey data

In an industry-specific survey, such as the US or Canada MCTS: Mercer Total Compensation Survey for the Energy Sector, the dataset already includes only those that identify within the particular industry. Oftentimes, the overall participant set is smaller than in general industry surveys, but the benefits are numerous:

  • A larger number of overall participants in just that industry may be available in the segment than would be found in the general industry report, meaning more statistically significant data.
  • More jobs or variations of jobs would be relevant to that particular industry, making for more-specific job matching.
  • Industry-specific surveys sometimes will include policy and practice information, which will capture practices that differ from the broader industry. For example, only in healthcare surveys will you find information on all the various types of pay that apply to nurses.

Next steps to survey data planning

When using industry-specific surveys, you are typically applying those data to specific employee segments or jobs within your organization — those that require industry experience. If you choose to blend industry survey data with a cut from a general industry survey, you'd typically also be utilizing the data refined by industry scope with the goal of diversifying your data sources.1

Confused yet? You're not alone. Effectively using salary survey data and all the various scope cuts and data elements can be challenging. And consistency is key. Do yourself a favor and and develop a benchmark methodology. The process of developing it will help you think through all of your choices and the process of documenting it will ensure consistency going forward. Best of luck to you!

Need help developing your benchmark methodology? Check out the article Establishing a Benchmark Methodology Unique to Your Organization within this Market Pricing 101 series for some helpful tips and tricks.

1 Best practice is to attempt to collect at least three data points for each job when market pricing.