Do you know who invented the very first compensation survey?
Arch Patton, the famed McKinsey consultant, began advising corporate boards about compensation after the end of the second world war.
According to his New York Times obituary, Patton developed the first multi industry survey of how top executives were compensated in 1951. Through his survey, which appeared annually in The Harvard Business Review more than a decade after it was introduced, many executives learned for the first time how their compensation compared with that of colleagues in their industries.
Patton’s survey was widely used over the years to help companies develop bonus plans and stock option programs to improve executive performance. You can read all about it in his book, Men, Money and Motivation.
Of course, that was over seventy years ago.
And while that survey was no doubt innovative at that point in history, to say the world has changed dramatically since then would be a profound understatement. Particularly when it comes to compensation.
As Pave CEO Matt Schulman wrote in his post, The Birth of a New Industry: CompTech:
“Companies are spending painful days (sometimes weeks!) each year pulling data from their disparate human resources systems just to be able to participate in old-school compensation surveys hosted in spreadsheets that become outdated the moment they are submitted. Then, another set of archaic spreadsheets were being used to run painfully manual merit cycles. And the end result? Candidates and employees alike were left bewildered about their total comp.”
There’s no way to sugarcoat it.
We believe compensation surveys are broken.
Based on our experience, research, data and the thousands of companies we’ve worked with, here’s why, along with recommendations of how to fix them.
Have you ever bought a car from a dealer before? There’s a classic saying in the auto industry that a new car loses value almost immediately after you drive it off a dealer's lot. The vehicle is depreciated between fifteen and twenty percent.
Salary data is similar. If you look at the traditional public records, you’re effectively looking back in time. Given how fast the labor market is changing, compensation surveys that use static reports hosted in spreadsheets are stale. Salary data becomes outdated as soon as it's published.
And since most comp surveys only run once or twice a year, data from a year ago won’t hold up. Employers need to calibrate their data in real time. Otherwise they risk losing their candidates or employees to competitors with better offers.
One of Pave’s clients recently made an astute observation about this very issue:
“I don’t hate the spreadsheet method when I trust the data that’s in them.”
How much do you trust your salary data? Is your employee compensation pulled live and current to provide the most value?
You’re not alone if it’s not. The existing solutions for getting the right comp data to make decisions, plan compensation and communicate it to employees are subpar, scattershot or nonexistent.
But they don’t have to be.
At any given company, compensation takes a million different shapes, but all the forms live in different places. Ask some who worked in human resources or people ops over the past twenty years. It’s likely their data has historically been captured manually or housed in different systems that didn't talk to each other.
Now, traditional comp survey providers do offer salary data. But for growing companies with lean human resources teams, you’re going to need real time API integrations with all of your systems. One that will constantly refresh data with every new employee.
Without your systems talking to each other, data will need to be pulled from disparate sources and manually stitched together for a comprehensive view of total comp. Large data uploads like this will be cumbersome. Using broken compensation surveys almost certainly means you’ll have to log into multiple systems to extract data and smash it all together in a spreadsheet.
No thanks. You have more important work to do, like keeping your team engaged. If you want to break the yearly cycle of manual surveys that require days and days of pulling down spreadsheets every single merit cycle, it’s time to upgrade your CompTech stack.
Find the right technology that integrates and helps your company stitch together data from all your systems. That way, you can give raises and grants with the full picture of an employee's total rewards and compensation in mind.
In addition to problems with data relevance and systems integration, another reason today’s compensation surveys are broken is the issue of bias and error. If the wrong compensation data gets in the wrong hands, that could hurt internal culture and cause you to lose your job.
Imagine you’re running a merit cycle for two hundred employees. You accidentally send the wrong spreadsheet to the wrong team member. That employee sees sensitive data they’re not supposed to, perhaps about how much more or less their colleagues are earning than they are.
Who’s on the line for that permission violation? Human resources is.
Now imagine you’re running compensation planning for your startup of sixty employees. If your employer doesn't have enough data, there will be a tendency to fill in the gaps in between. And such gut instincts or ballpark numbers are subject to bias. Your salary band data ends up being messy, inaccurate and outdated. Which leads to a “garbage in, garbage out” situation.
As Rachael Murray writes in her phenomenal book, The Practical Guide to HR Analytics:
“People tend to trust their own judgment more than the data and algorithms, even when they know it's less accurate.”
To reduce the likelihood of bias and error, data coverage is critical. When you plan your compensation, be as specific as possible. More specificity means more confidence, and that means smarter decisions. What’s more, understand that compensation is both an art and a science. We’ve written about this distinction extensively, including our popular post, How should merit recommendations be structured?
One pain point Pave hears from our customers is how challenging it can be for people ops teams to gain trust and credibility with their leadership team. Particularly when they are implementing an outdated, broken compensation survey.
Imagine a head of human resources says he blended a bunch of disparate numbers to benchmark employee salary. If he makes that case with the other department heads, those leaders will start poking holes in his data. And if the data is potentially inaccurate in the first place, then the executive team won’t trust anything human resources does in comp going forward. Yikes!
Another one of our customers, a Senior HR Manager at a large tech company, summarized it best:
“Once we implemented Pave, we gained a ton of trust and a ton of credibility with our leadership team. And we just came out looking like rock stars.”
Compensation surveys might have been good enough to skate by years ago, but we live in the age of transparency. Now that people have free access to the data to plan comp, flying by the seat of your pants each year isn’t going to be enough. It’s time to transform the comp world away from a closed system where insights are determined behind closed doors without reference to a broad base of data.
To quote Dr. David Cowan’s inspiring book, Strategic Internal Communication:
“Technology can help overcome human barriers. It creates more transparency and makes it increasingly hard to hide information, money and actions.”
Ultimately, human resources' credibility increases the moment it starts using real time data to inform its decisions. Why trust something that’s six months old versus last week?
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As you can see, compensation surveys have come a long way since the fifties.
But the world is not the same way it was yesterday. In order to build the fair, transparent organizations of tomorrow, we need to properly price employee offers based on real time benchmarking data.
That means no more outdated, stale data.
That means no more programs that don’t integrate into your other systems.
That means no more compensation filled with bias and error.
Compensation surveys are broken, and we’re confident we’ve found a different way.
There are 3.5 billion people who are part of the employment world, and Pave is building the canonical data asset to support all of them.
If you want to wave goodbye to spreadsheet driven, manual compensation surveys, book a demo with us today.