Choosing Your Peers: A Guide to Strategic Benchmarking

You’re tasked with benchmarking an open position at your college or university. How do you determine your college or university’s peers? Do you typically choose peers based on where you’re located, whether your institution is private or public, or how many students are enrolled? Or do you focus on specifics, such as degrees awarded, research spending or athletic divisions? With so many criteria for benchmarking, the peer-selection process is far from straightforward.

Peer selection has been described as both an art and a science. An analysis by The Chronicle of Higher Education found that the overlap between the peers an institution selects and those that select it in return rarely reaches 50%. Another analysis found that, although the average peer group consists of 18 institutions, group sizes vary drastically, ranging from a single peer to as many as 100. These inconsistencies underscore how much institutions differ in selecting their peers.

Getting peer selection right is crucial for initiatives like benchmarking compensation, right-sizing the workforce and comparing benefits. This guide helps compensation professionals in higher ed navigate the complexity surrounding peer selection. We demystify the process by drawing on expert insights to reveal how peer groups are created and used, bringing clarity to a traditionally opaque practice.

Introducing Our Experts

The CUPA-HR research team interviewed four data-savvy compensation experts across higher ed to learn about their peer-selection processes. The participant institutions represented in these conversations varied in size, location and public versus private control.

We asked our experts about:

• What criteria they consider when selecting peers
• What stakeholders are involved in the selection process
• How they tailor their peer groups based on benchmarking and strategic planning goals
• How they distinguish between peer and aspirant institutions
• How frequently they review and update their peer groups
• What important lessons they learned from their benchmarking experiences

How Your Colleagues Choose Their Peers

Determining which institution constitutes a true peer is rarely a simple task. Based on our interviews with compensation experts, the following factors influence peer selection in higher ed. These criteria can be applied in different ways depending on the benchmarking goals.

Common Criteria Used in Peer Selection

The most common institutional criteria considered when building peer groups include:

• Total expenses
• Private vs. public control
• Research activity and spending
• Student full-time equivalent (FTE) count
• Classification (doctoral, master’s, baccalaureate, associate’s)
• Geographical location

Using an Individual Criterion to Identify a Specific Peer Set

The above criteria may be applied individually, such as institutions within a certain range of total expenses. For example, Iowa State University commonly uses a peer group drawn from DataOnDemand comprised of institutions in the fourth quartile of total expenses to generate benchmarks.

Alternatively, you can define mutually exclusive groups to compare benchmarks across broader categories, such as contrasting the East South Central and West South Central census divisions to account for regional variations.

Using Criteria in Combination to Refine Peer Groups

Combining criteria to refine peer groups allows you to filter for peers that capture multiple characteristics. For example, Wake Forest University typically selects private, not-for-profit doctoral institutions within the Southeast region. Likewise, Virginia Tech uses a peer group composed of public doctoral-level institutions with comparable total expenses.

Consortium Peers and Externally Managed Peer Groups

The compensation experts we interviewed often rely on their consortium memberships to define peer groups that reliably compare to their own institution. A few of the consortiums mentioned were: Associated Colleges of the Midwest (ACM), the Great Lakes Colleges Association (GLCA), the Associated Colleges of the South (ACS), the University Innovation Alliance (UIA) and the Association of Public and Land-grant Universities(APLU).

Governing bodies also commonly identify peer groups for individual institutions. For example, Iowa State University’s peer groups are shaped in part by the state Board of Regents, which oversees Iowa’s public universities. The State Council of Higher Education for Virginia (SCHEV), which formally establishes and approves peer groups for Virginia’s public universities, identifies peers for Virginia Tech’s benchmarking. Similarly, the State University System of Florida identifies peer institutions that the University of Central Florida uses to benchmark against.

Stakeholder Influence on Peer Selection

Although compensation expertise resides within HR, the selection of peer group criteria is typically a collaborative process involving a broad range of stakeholders at the institution. Key contributors usually include leadership from the president’s and provost’s offices, finance and budgeting, and institutional effectiveness. Even though hiring managers and HR business partners may not directly impact peer selection, their role-specific knowledge can shape target salaries depending on business needs, competition for talent or other variables.

“We talk to the managers and the people who are in the units and understand the work, having them explain to us: What are the issues? What does the work look like? What are the challenges that you’re facing?”

Whitney Grote, a compensation analyst at Iowa State University explains, “We talk to the managers and the people who are in the units and understand the work, having them explain to us: What are the issues? What does the work look like? What are the challenges that you’re facing?”

HR business partners provide frontline expertise on role-specific demands, competitive talent landscapes and departmental needs to inform decision-making. Because HR business partners provide critical context — such as whether there has been high turnover in a role — their insights can lead the compensation team to adjust benchmarking strategies and target more competitive salary ranges.

For instance, although The State Council of Higher Education for Virginia determines the primary peer group at Virginia Tech, it is not always the most appropriate metric for athletic positions. In these instances, Virginia Tech may focus their benchmarking on NCAA Division I institutions to maintain competitiveness with peers in athletics.

Choosing Which Peer Groups to Use

Compensation teams typically use a consistent set of peer groups to benchmark roles. For instance, Iowa State University strictly adheres to three peer groups. The Iowa Board of Regents identifies 10 peer institutions for Iowa State that are land grants with high research activity. They also have a peer group submitted to the Integrated Postsecondary Education Data System (IPEDS) that consists of 29 institutions; this group includes the 10 peers identified by the Board of Regents in addition to other high research activity institutions. The third peer group used by Iowa State consists of institutions in the fourth quartile for total expenses. Although these three groups may be used in combination to derive a single benchmark, the compensation team at Iowa State doesn’t deviate from this established set, ensuring fairness and pay transparency across their benchmarks. If data fall outside the expected range for a role, the university reviews the position to ensure the grade level is accurate.

Some institutions follow an ordered approach to generate benchmarking data from their established peer groups. “There’s a set path we use to make sure we’re being consistent with the data that we’re using,” said Sara Lucy, compensation manager at Virginia Tech. The compensation team at Virginia Tech ensures standardization through an internal written methodology. When Virginia Tech encounters insufficient data, they use a predetermined sequence, moving from narrow to broader groups. They consistently begin with the SCHEV-established group, followed by a group of public and land-grant universities, and finally, a group of doctoral institutions with total expenses exceeding $900 million.

Adjustments to Peer Groups

The process and frequency for assessing and updating peer groups varies across institutions. At Wake Forest University and the University of Central Florida, peer groups are assessed on an ongoing basis. The University of Central Florida has a formal assessment every two years and these are performed across several stakeholders including the president, provost, chief analytics officer and chief human resources officer. At Virginia Tech, any changes to the composition of their peer groups are managed and communicated by the Office of Institutional Effectiveness. At Iowa State, peer groups are assessed every two-three years or when an update to a group prompts a revision (such as Carnegie Classification changes). Compensation experts noted that peer group adjustments are relatively uncommon.

When to Pivot to Aspirational Peers and Local Market Data

In addition to using peer groups that consist of comparable peers, compensation experts may select aspirational groups, particularly when extending a competitive offer. Aspirational groups may be defined as institutions that have elevated salaries, slightly higher total expenses, or greater research output compared to one’s own institution. For example, Virginia Tech uses data from institutions with higher salary levels, such as private doctoral institutions nationwide, when a specific role is experiencing chronic turnover.

National-level academic peers may sometimes provide benchmarks that are too low to be competitive, so the University of Central Florida uses data from local organizations that more accurately reflect their cost of living.

Institutions may also need to assess market data from local organizations. Because the University of Central Florida is situated in the high-cost Orlando metropolitan area, national-level academic peers may sometimes provide benchmarks that are too low to be competitive. To address this, UCF frequently uses benchmarking data from local organizations that more accurately reflect their cost of living. Similarly, Wake Forest University takes into account the lower cost of living in their state even though their institution has relatively high total expenses. Due to the lower cost of living, national benchmarks may lead to salary targets that are too high. To close this gap, they also use local data to reflect their area’s cost of living. Although local data may not come from fellow higher ed institutions, it’s often critical for appropriate benchmarking based on the college or university’s location.

Insights and Reflections from Compensation Experts

The compensation experts we interviewed shared several important lessons they’ve learned from their benchmarking experiences.

Maintaining Transparency and Consistency

Some experts underscored the value in using standardized and transparent peer groups and processes. By maintaining a public set of peer groups and following a structured process for obtaining data, institutions can develop compensation benchmarks that are consistent, equitable and transparent. Transparency fosters institutional trust among faculty and staff while ensuring a unified approach to determining compensation across the institution.

For example, Virginia Tech’s compensation team uses a standardized methodology to validate and justify salary ranges, and they share these processes with staff and faculty. The university maintains a public repository of resources outlining their pay philosophy, compensation strategies and peer groups.

Considering Nuance

Developing a nuanced understanding of how an institution’s profile aligns with — or diverges from — its peers is also critical. For example, compared to other institutions with similar total expenses, Wake Forest University has a smaller student FTE count than its peers. If an institution has a very large student FTE, it may not be a useful peer for Wake Forest, despite similarities in overall expenditure.

Identifying Cost-of-Living Differences

Another common consideration is the local cost of living. Wake Forest is located in North Carolina, which has a relatively low cost of living. When setting a salary range, they may need to adjust for living costs when comparing to data against peers in more expensive states. But for the University of Central Florida, institutions with similar student FTE counts and research activity may not serve as appropriate benchmarking peers if there are significant cost-of-living differences between their locations and Orlando.

Compensation professionals need to identify the unique characteristics that differentiate their institution and consider how those differences should shape peer group composition. Accounting for these distinctions may lead to variability in the criteria prioritized during peer selection or necessitate subsequent adjustments to benchmarks.

Peer Group Size

Finally, it is important to have peer groups that are large enough to yield meaningful data. Depending on the position of interest, benchmarking data can be limited, so expanding peer groups to include more institutions might be necessary to ensure reliable data. Compensation professionals have to strike a balance between having sufficient data and making sure their peers share comparable characteristics.

DataOnDemand – Your Source for Strategic Benchmarking

CUPA-HR offers hundreds of thousands of data points through DataOnDemand — the industry standard for benchmarking in higher ed. Whether you are new to using data or a seasoned pro, DataOnDemand helps you get the data you need for valid and actionable benchmarking.

Creating Peer Groups Using DataOnDemand

Using Pre-Built Peer Groups

Many pre-built peer groups are available to all DOD users under the Public Groups section. These are particularly useful for benchmarking against standardized criteria, such as consortia and census divisions. These public comparison groups also offer quartile groupings for total expenses as well as student, staff and faculty FTE. These pre-built groups provide a quick, easy-to-navigate starting point for exploring your data before you dive into more customized comparisons.

Creating Custom Peer Groups

One of the key features of DataOnDemand is the ability to create custom peer groups (called comparison groups in DataOnDemand). These comparison groups are then used to generate reports that show your comparison group’s aggregate data and, if your institution participated in CUPA-HR surveys, provide a side-by-side comparison with your own institution’s data.

DOD offers two methods for building custom comparison groups: the New Group Wizard and the New Group From Scratch.

The New Group Wizard allows you to build custom comparison groups based on criteria such as Carnegie classification, research activity, geographic location and survey participation. You can also define specific FTE ranges for students, staff and faculty — enabling you to target, for example, doctoral institutions with 6,000-8,000 students or highly residential campuses in New England.

The New Group From Scratch feature allows you to build comparison groups by selecting individual institutions by name or IPEDS ID. The New Group From Scratch is ideal if your governing board has authorized a specific list of peers. Simply search for and select those institutions to populate your comparison group.

Both the New Group Wizard and New Group From Scratch methods let you preview institutional names and key characteristics (e.g., total expenses, Carnegie classification, FTE counts) before finalizing a peer group. (Learn how we protect data anonymity.)

Once a newly built comparison group is saved, it can also be shared with all other DOD users in your institution. DOD subscription access can also be granted to external consultants working with your compensation team.

Takeaways

Here are some key takeaways and practical tips we gathered from conversations with compensation experts.

• Define and prioritize relevant criteria (for example, total expenses, student FTE) that capture the unique attributes of your own institution.
• Consult stakeholders to inform your peer group selection.
• Evaluate whether roles with specialized functions — such as athletics — might necessitate unique, non-standard peer groups.
• Collaborate with HR business partners to identify contextual factors, like high turnover, that may influence the target salary ranges.
• Consider generating benchmarks with a consistent set of peer groups to promote fair and transparent salary targets.
• Formally define and distinguish between peer institutions and aspirant institutions when benchmarking for competitive positions.
• Incorporate local market data to determine cost-of-living adjustments for benchmarks.
• Use high-quality data sources, such as CUPA-HR’s DataOnDemand, to build custom peer groups and ensure the delivery of valid and reliable data.

Tags:

• Workforce Planning