The Art & Science of Recommendation Logic in Compensation Planning

Compensation 101
March 14, 2022
min read
Pave Team

Pave has the opportunity to assist people leaders in hundreds of merit cycles each year.

And we’ve found that when it comes to data driven compensation planning, there’s a delicate balance between art and science.

Particularly when it comes to recommendation logic.

If you’ve ever bought anything on Amazon, listened to a song on Spotify, or watched a show on Netflix, you’ve encountered recommendation logic.

On virtually all ecommerce sites and streaming platforms, this is the item based algorithm that tells you, “the people who viewed this book, also viewed this book.”

Or maybe, “the people who liked this movie, also liked this movie.”

The machine learning systems entice users with relevant suggestions based on the choices they make.

In compensation planning, recommendation logic works similarly.

This algorithm is the unique group of variables upon which companies base an employee’s comp change. This might include salary, variable, bonuses, and even new equity grants. Typically, this algorithm is created to reward strong performers and account for changes in various market conditions.

As a hypothetical example, the recommendation logic might be something like this:

If an employee exceeds expectations for performance, they receive a 10% raise.
If an employee meets expectations for performance, they receive a 3% raise.
If an employee is below expectations for performance, they receive a 1% raise.

Now, the recommendation logic will be specific to each company. We’ve written about the three most common types of recommendation logic. And we recently ran an analysis across all of our cycles to find the most common forms of recommendation logic that also made sense for managers, ended up with the happiest customers, and supported equitable, data driven compensation. (More on that in a future post!)

But despite differences across industries, there are several larger principles around the art and science of this practice that hold true for all organizations. 

We believe recommendation logic is the key lever for how companies:

  • Convey their compensation philosophy to their managers
  • Give managers a reference point from which to anchor compensation decisions
  • Apply their unique compensation philosophy to their employee base
  • Convert the theory of how people are paid into practice to influence decisions
  • Deliver equitable pay to all team members

In short, recommendation logic helps companies practice what they preach.

Unfortunately, many human resources teams aren’t always sure how to approach this effort. They may not know what they want insofar as comp changes, especially if they’ve never run a merit cycle before.

We’ve also heard stories and seen firsthand how companies attempt to build recommendation logic themselves based on unreliable metrics and tools like guesswork, instinct, using hundreds or even thousands of spreadsheets to inform decisions, and so on.

That outdated approach ultimately makes the recommendation process too complicated and potentially biased. People teams can no longer afford to make emotional decisions around compensation. They need a logical, data driven intermediary.

This is why the burgeoning field of comptech is so thrilling for everyone here at Pave. Human resources has finally been innovating at a higher rate in the past 5-10 years, and we’re only scratching the surface.

Today, the technology exists to assure companies won’t compromise on their compensation philosophy because of platform limitations. 

We feel privileged to build products that equip managers to be anchored around a sensible comp increase that fits with company guidance, and empowers them to make appropriate comp suggestions. 

These same results are possible for your company. The art and science of recommendation logic can be a key lever for how you plan, communicate and convert your philosophy into reality.

It’s certainly no Amazon or Netflix, but with the right tool, the right recommendation logic can support equitable, data driven compensation. 

And help your company differentiate through the power of fair pay.

Learn more about Pave’s end-to-end compensation platform
Pave Team
Pave Team
Pave is a world class team committed to reinventing the world of compensation and help build a more transparent future of work.

Become a compensation expert with the latest insights powered by Pave.

(function (h, o, t, j, a, r) { h.hj = h.hj || function () { (h.hj.q = h.hj.q || []).push(arguments) }; h._hjSettings = { hjid: 2412860, hjsv: 6 }; a = o.getElementsByTagName('head')[0]; r = o.createElement('script'); r.async = 1; r.src = t + h._hjSettings.hjid + j + h._hjSettings.hjsv; a.appendChild(r); })(window, document, '', '.js?sv='); !function () { var analytics = = || []; if (!analytics.initialize) if (analytics.invoked) window.console && console.error && console.error("Segment snippet included twice."); else { analytics.invoked = !0; analytics.methods = ["trackSubmit", "trackClick", "trackLink", "trackForm", "pageview", "identify", "reset", "group", "track", "ready", "alias", "debug", "page", "once", "off", "on", "addSourceMiddleware", "addIntegrationMiddleware", "setAnonymousId", "addDestinationMiddleware"]; analytics.factory = function (e) { return function () { var t =; t.unshift(e); analytics.push(t); return analytics } }; for (var e = 0; e < analytics.methods.length; e++) { var key = analytics.methods[e]; analytics[key] = analytics.factory(key) } analytics.load = function (key, e) { var t = document.createElement("script"); t.type = "text/javascript"; t.async = !0; t.src = "" + key + "/analytics.min.js"; var n = document.getElementsByTagName("script")[0]; n.parentNode.insertBefore(t, n); analytics._loadOptions = e }; analytics.SNIPPET_VERSION = "4.13.1"; analytics.load("0KGQyN5tZ344emH53H3kxq9XcOO1bKKw");; } }(); $(document).ready(function () { $('[data-analytics]').on('click', function (e) { var properties var event = $(this).attr('data-analytics') $.each(this.attributes, function (_, attribute) { if ('data-property-')) { if (!properties) properties = {} var property ='data-property-')[1] properties[property] = attribute.value } }) analytics.track(event, properties) }) }); var isMobile = /iPhone|iPad|iPod|Android/i.test(navigator.userAgent); if (isMobile) { var dropdown = document.querySelectorAll('.navbar__dropdown'); for (var i = 0; i < dropdown.length; i++) { dropdown[i].addEventListener('click', function(e) { e.stopPropagation(); this.classList.toggle('w--open'); }); } }