Market Pricing and Salary Surveys, Part II

Market-based compensation

Market-based compensation uses salary survey data to match pay with rates paid in the external market. It’s not a job factor evaluation system but can be used to develop an internal job worth hierarchy.

Its advantage is that it takes into consideration external practices/competition, a necessity for organizations that must offer competitive pay. Its disadvantage is that it relies on accurate survey data and internal pay equity is of less importance.

In a perfect world, you’d be able to find market data for all of your positions. You’ll probably obtain data, however, on a sample of your job titles, which is why many companies rely on benchmark jobs to determine market rates. Benchmarking is identifying a selection of your positions that have “benchmark” characteristics. For example, positions that are common across industries, represent a sizable workforce, are common among different employers or competitors for your talent, and represent a range of your jobs. “Benchmarking” a selection of common positions can greatly assist in matching positions. If the benchmarked positions represent a broad enough range of your organization’s jobs, the ranking method of job evaluation can flush-out pay ranges for those positions where survey data is not available. In other words, you use salary data for the benchmark jobs to establish salary ranges for comparable positions within your organization.

Another option is to use data from multiple jobs that, when blended, represent a non-benchmark position. For example, if you have an accountant position but the incumbent in that position also is responsible for purchasing materials and supplies, you can blend the data for a “benchmark” accountant and a “benchmark” purchasing agent to create a market rate for your hybrid position by combining a percentage of each.

Salary surveys

Selecting two or more appropriate survey(s) is the first step in market-based pricing. Using more than one salary survey provides a more accurate view of the market and reduces the potential for error when assigning a salary rate or range.

The next challenge is matching your organization’s positions to the positions available in the survey. You’re probably not going to find every title in your organization in any one salary survey, or maybe in any salary survey. Even when you do find your job titles, you have to be compare the job duties and requirements to ensure it’s a match for your job. A good rule of thumb is that if the job description matches 80% or more of your job then it’s a strong match. If it matches less than 70%, though, it’s not a match and you need to keep looking for a closer match for your position.

Salary surveys provide an array of data averages and percentiles. In compensation comparisons the median survey rate is most commonly used because it is less influenced by high or low pay extremes. Other data elements such as the 25th percentile, the 75th percentile and the mean can be helpful. But selecting data is a process that can be as simple or complicated as the organization elects. It’s important to determine your market position as part of establishing your pay philosophy/strategy before beginning a market analysis of your jobs so you can consistently apply market data to your positions.

Good salary surveys group data by organizational characteristics such as staff size, industry, and location. Use of the data from these groupings can create a composite average that profiles the organization. This sort of data helps provide a match of participating survey organizations to the “profile” of the organization.

Most salary surveys include many of the same elements. For example:

  • Effective data of the data
  • Job Summary including education & experience
  • Vital data including the mean, median (50th) , 25th Percentile, and 75th Percentile
  • Data Sorts including location, budget, number of  employees, organization type, and number of survey participants

Below is a view of what salary survey data may look like.

Check back next week for the conclusion, part 3 …


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