Maximize the Impact of your survey data.

Introduction

One of the most straightforward ways to make compensation decisions is by grounding your plans in market data. While there are a number of data sources available, most organizations choose to purchase and participate in salary surveys to get the data they need.

Survey data is the gold standard for compensation programs for a number of reasons:

• Survey data is time-bound, meaning that it is easier to age and it provides a safe harbour relative to anti-trust legislation.
• Survey providers use transparent, rigorous methodology for data collection and analysis, making it trustworthy.
• Surveys offer the possibility to slice and dice data by location, peer group, and other factors that comp practitioners use to benchmark base pay.

There are a wide array of surveys available for comp teams, from large, multi-national surveys covering a wide array of industries, to heavily focused surveys that zero in on a single industry, company size, or location. Because of the range of surveys available, many organizations will purchase several (or many) distinct surveys to ensure they have effective data coverage for their jobs and job families, locations, and talent competition.

Once you have your data, the big question is, “now what?” Data alone doesn’t make for a successful compensation program, but never fear! This eBook will help walk you through creating a methodology that will make your survey data work for you, so you can inform your compensation plans with the right information.

You can’t pluck raw ingredients from the grocery store shelf and expect them to create a restaurant-quality meal without a little culinary art and science.

Same with compensation surveys. Think of your raw survey data as the ingredients for your compensation plan and your compensation philosophy as the recipe that guides your analysis.

As you begin survey data analysis, the following areas should flow directly from your compensation philosophy:

• Compensation Strategy
  Outline how your compensation decisions will match the stated goals of the overall philosophy.
  
• Data Elements

  Define the role of each data element (e.g., Bonus, Total Target Cash) in your compensation structure.
  
• Peer Groups
  
Identify who your organization competes with for each business unit/function.
  
• Competitive Positioning

  Explain where you aim to position yourself in the market (lead, lag, and match).
  
• Internal Equity
  
Keep your internal pay structures equitable and fair for all employees.
  
• Corporate Governance
  
Summarize your team’s decision making structure and responsibilities.
  
• Pay Segmentation

  Establish differentiation for different groups of positions. For instance, you may use the 50th percentile for the general group of positions, but use the 60th percentile for certain specialized technology positions.

Survey Participation

Survey data may be the gold standard for compensation programs, but it is expensive. You’re not going to get a brand new Cadillac for the price of a ten-year old Hyundai, after all.

One of the ways companies mitigate the expense is through participating in the surveys they purchase. This process involves gathering your job and pay data and inputting it into the survey template. That information is validated, anonymized and added to the overall dataset by the survey provider.

While the work pays off, survey participation can be overwhelming. A compensation technology partner like BetterComp can help you work more efficiently by:

• Assembling your job and pay information in a survey participation template
• Pulling your previously mapped job over each year
• Creating a submission report for your review

Evergreen or Annual Surveys

Surveys are typically one of two types: they’re “evergreen”* or they’re annual. Annual surveys include many of the larger datasets and usually have a participation season that kicks off in February and runs through March or early April. Evergreen surveys gather your participation information when you purchase and participation happens on an annual basis aligned with your contract renewal.

* Evergreen doesn’t mean real-time. Typically, evergreen surveys compile and release fresh data on a quarterly basis.

Align Your Job Architecture

Job architecture and survey data are two peas in a pod for your compensation program. Mapping your levels and jobs to your surveys builds a clear picture of how your survey data lines up with your internal structures, making benchmarking faster and easier.

Starting with a clear internal job architecture sets you up for a smoother, more consistent job mapping process. Create (or annually review and update) a job architecture that clearly outlines the following elements of your organization:

Job Families & Subfamilies: Group jobs based on function or professional discipline (Finance, IT, Marketing, etc.) and Subfamilies (Tax, Security, Market Research, etc.)

Standardized Career Bands (Executive, Management, Professional, Technical, Business Support, etc. Standardized Job Levels (Entry, Intermediate, Senior, Specialist, Expert, etc.) Job Descriptions: Ensure content and detailed descriptions of responsibilities, requirements, and competencies.

Once your job architecture is created, you’ll need a consistent job mapping catalog before you can make sense of your survey data. This job mapping catalog should record:

• Internal job title & job code
• Internal job family, subfamily and level
• Mapped survey job title, job code, job family & subfamily, and level
• Rationale for mapping (including key matching criteria)
• Mapping level (Exact Match, Closest Match, Composite, or No Match)

Your job mapping catalog is what offers you the opportunity to actually match the data to your organization. Once it’s lined up, you can begin to work with the data and are one step closer to refining your compensation plans!

CHECKLIST

Best Practices for Developing a Salary Benchmarking Methodology for Your Organization

For Each Survey:

• Review Survey Job Descriptions
• Compare survey roles against your internal jobs.*
  

Document the Following Mapping Decisions:

• Internal job title & job code
• Internal job family, subfamily and level
• Mapped survey job title, job code, job family & subfamily, and level
• Rationale for mapping (including key matching criteria)
• Mapping level (Exact Match, Closest Match, Composite, or No Match)

No Job Architecture? No Problem.

You don’t necessarily need a job architecture to use survey data, particularly if you’re just using one survey. If you’re just getting started with survey data, job mapping is usually sufficient, but job architecture just makes job mapping easier in the long run. That said, moving to a market-based compensation program is definitely an indicator that it may be time to transition from jobs to a more formalized job architecture for continued growth and progress. When it comes to the evolution of compensation programs, the reality is that many organizations take what they need from multiple surveys, or even just one, and build out their job architecture based on their needs.

Utilize Roll-Up Jobs for Unique or Blended Roles
Although you’ve taken the time to match your internal job structure with your surveys, there isn’t always a perfect one-to-one match for every job in your organization. Enter: Roll-Up Jobs.

With specialized company jobs, you may want to identify which survey roll-up job best matches your internal position for market pricing. Premium Survey Vendors provide the ability to go from a standard survey job to a roll-up survey job. Depending on the survey, a variety roll-up jobs may be available, such as combining specializations, subfamilies, or families at the same job level, subfamilies and families regardless of level, and even all the way up to a specific job level regardless of the specialization, subfamilies and families.

For global companies, roll-up jobs may be used differently. If survey data is not as robust (insufficient or a small number of organizations providing data), they may want flexibility to move from a standard survey job to roll-up jobs.

If Warranted, Utilize Global Grades or Position Class

In addition to levels, many global companies take advantage of global grades or position classes for countries outside of the US and Canada. These are typically found in premium surveys and provide a consistent framework for evaluating and organizing jobs across all levels, functions, and geographies within an organization. This global framework can be adapted to local market conditions while maintaining internal alignment across countries and business units, and is designed to support fair pay, career development, and strategic workforce planning regardless of geographic location.

Since the global grade and position class function similarly to job levels, the key is creating a consistent job catalog for each survey. Most global companies begin with their Job Mapping Catalog with the standard job codes as the core and then layer in the global grade or position class at the end of the survey job code.

Using the Right Data Cuts

Establishing Comparator Groups or Pay Markets for your jobs is a vital part of staying competitive. Understanding how and who you compete with to attract the right talent will help you determine how to group your company jobs and then select the appropriate data cuts. This will also ensure consistency of the market data when analyzing the competitiveness of your pay.

We rarely have the full picture of what our competitors do to attract and retain talent, but we can know who we compete against, the regions where we are seeking candidates in, and the decisions we make to establish our compensation plans and practices. When grouping jobs based on how you compete for talent, consider one or more of the following elements:

• Industry: Match with companies in the same or adjacent industries.
• Region: Local, regional, or national depending on the role.
• Company Size: Revenue, headcount, or other size proxies relevant to your organization.

Peer Companies: Direct talent competitors or aspirational peers. Peer cuts are custom survey data segments based on a list of comparable or aspirational companies (often competitors, similar industry, size, or talent pool), and are typically available in premium surveys at an extra cost.

Depending on your needs, you may have one or multiple comparator groupings, based on how you source the jobs and the data cuts you typically use to analyze the market data. The following are examples of job groupings for market pricing:

General Benchmark Jobs:
Priced across broad markets with typically easy-to-source candidates.

Examples: HR Generalist, Accountant

Niche, High-demand, or Technical Roles:

May require industry-specific or specialized surveys or data cuts to price jobs competitively.

Examples: Software Engineers, Clinical Researchers Leadership Roles: Often priced against peer or aspirational companies of similar size, revenue, and structure.

CHECKLIST

Best Practices for Documenting Comparator Groups/Pay Markets

Apply your methodology consistently within a Comparator Group and calibrate results with HR business partners and functional leaders to validate accuracy.

Record the Following:

• The different groups
• The surveys used in each group
• If you use more than one data cut per group and survey combination, detail the priority they are to be used.

Apply Aging, Weights, & Adjustments

While comprehensive survey data may not be perfectly aligned to current economic conditions or your internal needs. Data analysis isn’t simply “plug and play”, it may take some tinkering to create the right fit. Applying aging, weights, and adjustments are ways compensation teams can impact the data to meet their needs.

If you're using multiple surveys published at different times, you’ll need to normalize the data to a common date using aging factors. Aging your data calls for a balance between the following external and internal factors:

Inflation
Prices of goods and services change quickly, and while the traditional “cost of living” increases aim to match the rate of this change, taking current inflation into account brings your survey data into today’s market.

Compensation Philosophy
Being confident in your organization’s compensation philosophy helps you determine whether you will lead, lag, or match the market, which will set a benchmark for you as you evaluate your raw data.

Cost of Labor
The higher the overall cost of labor, the more expensive employees are to hire and retain. Your survey provider may offer this data, or you may need to seek it out yourself from a resource like the Employee Cost Index.

Budget
Of course with any spending opportunity, you have to take your budget into account. Aging the data helps you walk the tightrope of matching your budgetary restraints while working with the most updated information.

Salary surveys vary in methodology, scope, and participant data, so on top of the aging factor, there are other adjustments compensation teams may need to consider to make the raw survey data work for their needs. Weights and Adjustments may be appropriate when the survey job is a close but not a solid match to your company job or when the data cut selected does not align with the Comparator Group or Pay Market you use to target your compensation and recruiting efforts.

For example, if you're a mid-sized company using a data cut for large companies, you may need to adjust the values, or weight one cut more or less than another. Other instances where adjustments may be helpful are when:

• Scope of responsibility differs: If the survey job has broader or narrower responsibilities than your internal job, adjust up or down accordingly.
• Levels are mismatched: If your role is half a level above or below the survey job, apply an adjustment.
• Roles hold hybrid responsibility: When one internal job covers duties of multiple survey roles and you choose to use market data for both roles, prorate or weight the data based on time allocation or job importance.

Finally, even if market data is available, some adjustments may be needed to ensure alignment with your internal compensation philosophy:

• To maintain internal job relationships and pay equity.
• To fit roles within established pay bands or job grades.
• To accommodate strategic priorities like paying above market for key or hard-to-fill roles.

CHECKLIST

Best Practices for Making Adjustments

• Document all adjustments clearly: Show your rationale, adjustment percentage, and data sources.
• Use consistent adjustment factors: Avoid arbitrary changes; base adjustments on logic, historical data, or standard practices.
• Validate against internal benchmarks: Check if adjusted data aligns with current pay practices for comparable roles.
• Review annually: Adjustments made one year may not apply to the next due to market shifts.
  

Putting Your Data to Work

By having a strong methodology it allows your team to have confidence as they put this data to work with important decisions around

Market Competitiveness: Knowing your place in the market (and where you want to be) will keep you competitive for hiring and retaining employees

Salary Structures and Pay Ranges: Due to growth, you may need to build or refresh your salary structures, as well as run CompaRatio and range penetration analysis to maintain internal equity

Use Data to Support Comp Cycles: Reward merit, make promotional adjustments, make solid offers for new hire offers, and handle ad hoc evaluations.