Compensation is both an art and a science.
Too loose of a structure? It can lead to compensation inequities, bias, rewards for the more outspoken team members, and broader cultural issues.
Too much structure? Every edge case breaks the compensation philosophy and managers can’t make the decisions that aren’t captured in a 1- 4 performance rating.
And one of the most common areas you see this balance between art and science is around merit recommendation logic (for example, a merit matrix).
At some companies, hundreds… or even thousands of employees may be eligible for merit increases.
So you need a framework to make compensation decisions at scale without every manager being a comp expert.
By the end of this article, you’ll know:
Note that we won’t cover who’s eligible for merit or the actual amounts of those merit increases. We’ll need to write some more posts to cover those topics!
Fortunately, we’ve had the opportunity to assist people leaders in hundreds of merit cycles.
A few common trends emerged. We see 3 main ways merit recommendations are structured:
And of course, there’s the “no recommendation” philosophy as well as the “no merit” philosophy, which we won’t cover here!
Let’s break them down one-by-one.
Every employee gets the same recommendation % from the People team, regardless of performance. Managers are given discretionary input.
We typically see this most frequently in a few situations, where companies opt for this model because it’s the easiest to administer and empowers managers to make compensation decisions.
Instead of making rigid recommendations off performance, managers often have the most information about an employee’s performance and can be given the latitude to “peanut butter” compensation increases across the team, or allocate more to those top performers.
Merit increases are directly correlated to an employee’s performance.
Let’s say someone “meets expectations,” the HR team might recommend a 2-4% increase. If someone “exceeds expectations,” the HR might recommend 4 - 6%.
Managers often have discretion within the band, or can even make the case for out-of-recommendation approvals with their manager or HR.
The Standard Merit Matrix tends to strike a balance between art and science. It’s still easy enough for managers to understand and gives them a framework to think about compensation decisions.
Merit increases are based on multiple variables. Those variables can change quite a bit, but the most common ones are performance and position within the compensation band.
For instance, if someone is a performance rating of “4: Exceeds Expectations,” someone below the midpoint of the band may get a 5% increase, whereas someone above the midpoint might get a 3% increase.
At Pave, we’ve also seen companies layer in location and/or job function so that the recommendations change based on performance, position in band, role, and location.
We see this most commonly when companies want to minimize deviation outside of the compensation philosophy.
If the team wants to reduce the risk of two employees in the same role having large discrepancies in pay, this matrix accommodates both performance and bands.
However, make sure you understand the trade-offs of flexibility in this model, given it definitely falls more on the “science” end of the art vs science debate!
Great companies make compensation a competitive advantage. We’ve seen this famously with Facebook, Google, Netflix, Stripe, and more. That’s why of the hundreds of compensation reviews we’ve been able to assist, very few follow the exact same playbook.
If you believe compensation is more of an art than a science, you may opt to empower your managers to make compensation decisions and enable them with L&D.
If you believe compensation is more of a science than an art, you may establish tighter guidelines, sacrificing flexibility for consistency across the organization.
It’s never binary. One compensation philosophy won’t work at another company. It’s all about the types of people your team wants to attract and the level of involvement you want your managers to have in compensation decisions.
For that reason, Pave’s goal is to be the most flexible compensation tool where we can support all types of compensation philosophies so you don’t have to force-fit your philosophy into a tool.
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