As Pay Grows More Complex, Analytics Tools Become More Valuable

HR professionals have rarely faced as complex a compensation environment as they do today. Between a growing number of pay transparency and pay equity laws, the rise of skills-driven compensation, and new geography-based pay practices, human resources and comp practitioners require more accurate and up-to-date information about compensation than ever before.

Making pay decisions that attract and retain top talent against that backdrop requires compensation management software that can help HR leaders track ever-shifting market pay trends, diagnose the root cause of pay equity issues, and better inform talent strategy. But not all compensation analytics software is created equal.

With a growing number of planning, benchmarking, and pay equity tools on the market—many of which incorporate varying forms of AI—HR buyers need a good strategy to separate the pretenders from the contenders.

Compensation Benchmarking Tools

Among the most valuable tools in helping to set competitive pay levels are compensation benchmarking platforms. HR historically has relied on annual salary surveys from industry consultants to gather pay data about the wider market, but many tools now available from vendors offer more current and accurate benchmarks amid shifting market conditions.

John Baldino, SHRM-SCP, president of Humareso, an HR advisory firm in Vero Beach, Fla., said there are a handful of key criteria HR buyers should weigh when considering benchmarking software. One factor is how often HR believes it will need to use such data.

“That cadence is important,” Baldino said. “I say that because these software products can be expensive. For example, if you’re a small business with just a few hundred employees, does it make more sense to invest in a benchmarking tool rather than using a compensation consultant? In some cases, the consultant may be more cost-effective if you don’t expect to use the tool much or if you need more consultative support around setting pay ranges.”

The best benchmarking tools also use multiple data sources, Baldino said. “They don’t just lean into one type of researched data,” he explained. “You want software that will accept or amalgamate a number of different researched data points. That provides the most confidence because the disparate data is void of a particular point of view.”

Ensuring accuracy of the pay data also is key, which requires determining whether compensation data in the tool is employee-reported or HR-reported. In the latter case, the data comes from real employee pay stubs and payrolls, which analysts say often gives it a higher level of veracity than when employees report their own pay via online surveys.

Buyers also will want assurances benchmarking tools contain pay data from organizations they’re most competitive with, broken out by factors such as industry, employee size, operating objectives, and funding stage, experts say. 

While traditional salary surveys from consulting firms have advantages, including covering a wide variety of job roles, their downside is timeliness, since many are only conducted annually, and their results published months later.   

“You’ll want a resource that updates at least twice a year at a minimum,” Baldino said. “That’s why having multiple data sources in a benchmarking tool is an advantage, because some of those sources may only update once a year. There also are a growing number of resources that update quarterly or more frequently.”

Matthew Brown, HCM research director at Information Services Group (ISG), an HR advisory firm based in Stamford, Conn., said another key criterion in selecting benchmarking tools is whether providers incorporate skills-based pay data, given that many organizations are moving toward skills-based talent models.

“Understanding whether the pay data is contextualized at the job level and whether it has supporting information from a skills-level perspective will be increasingly important,” Brown said. “Without satisfactory answers to that question, you may find yourself investing in benchmarking data you think is a good match for your organization, only to find after the fact it’s not.”

Lastly, experts say providers of benchmarking data offer what can be widely varying levels of post-sale service and onboarding support, which can be more valuable for companies with little experience applying benchmarking data. It’s important to find out what type of support is available and how much you’ll pay for it.

Adapting to Compensation Trends

The stakes for choosing the right compensation management software have risen in light of recent market trends. According to Payscale’s 2024 Compensation Best Practices study, the majority of organizations (60%) are now publishing pay ranges in job postings, compared to 45% in 2023. Research also shows salary transparency is increasing even in states without related legislation, as more organizations view it as a way to attract and retain top talent, and as more job candidates say they prefer to see pay ranges in job postings.

The Payscale study also reflects the growing influence of skills-based talent models on compensation. One-third of respondents said they no longer require a degree for salaried positions, and almost half (45%) said they don’t consider education a “compensable” factor. 

“As both HR practitioners and software providers continue their trek toward skills-based compensation strategies, the analytics technology will continue to evolve at warp speed to keep up,” Brown said.

The move to remote work during the pandemic also continues to influence compensation practices. The Payscale study found that about half of organizations now use an employee pay strategy based on geography, using market pricing for locales where they have offices.

Choosing Pay Equity Software

More HR pros also are turning to pay equity software in efforts to comply with evolving regulations and help ensure equal pay for equal work among employees of different genders, races, ages, and other protected categories.

HR analysts say that key criteria in choosing such software should include whether the tool features continuous pay monitoring, not just a yearly analysis; if it can conduct root cause analysis of pay inequalities; and whether the software can analyze compensation beyond base salary to include incentive plans, equity, bonuses, and more.

Baldino said the best pay equity tools can evaluate and compare roles with similar duties and responsibilities, not just job titles, to ensure employees receive equal compensation for the same or similar work, factoring in differences such as work experience or training.

Use of the software is optimized by working hand in hand with benchmarking tools, Baldino said. This allows HR to update pay ratios for roles based on duties and responsibilities and in conjunction with leveling frameworks, which define job hierarchies and career levels, and flexible pay bands.

Impact of AI in Compensation

The Payscale report found many organizations continue to approach the use of AI in compensation with caution. Only 7% of study respondents were “totally on board” with using AI for compensation decisions, with 50% saying they were still undecided. In addition, 56% said their top concern with AI is that it will extend bias rather than mitigate it.

Compensation software vendors have long used AI for a variety of purposes. Naomi Lariviere, vice president of product management at ADP, said the company uses AI in its pay equity dashboard to help identify pay inequities across different demographics such as race, gender, and other protected characteristics.

“AI also can be leveraged to help anticipate salary adjustment needs for specific roles or locations based on benchmark data and provide personalized pay recommendations considering factors like comp-ratio and performance ratings,” Lariviere said.

Some experts believe AI still has a ways to go to reach its potential in the compensation arena. “I don’t think we are where we need to be with AI yet,” Baldino said. “Part of the reason is that with many compensation benchmarking datasets, the AI is forced to use job titles rather than job duties and responsibilities in order to quickly gather information, which can create disparities.”

Baldino uses the example of a “marketing manager” title to illustrate the problem. “That title is rarely apples-to-apples because a marketing manager in one organization can look quite different from one in another organization,” he said. “Those variables include whether they actually manage people, size of the company, and more. We have to figure out better ways of providing levels of input so AI can go beyond merely gathering comp data around job titles. Many benchmarking tools using AI still struggle to collect and apply data around specific job duties and responsibilities.”

Dave Zielinski is principal of Skiwood Communications, a business writing and editing company in Minneapolis.