Buffer’s Salary Formula

Introducing the New Buffer Salary Formula, Calculate-Your-Salary App and The Whole Team’s New Salaries

Let’s address the elephant in the room first: the most notable aspect of Buffer’s salary policy is that it’s 100% transparent. Anybody can go into the spreadsheet linked from the post and see what each Buffer employee is making.

This post is not about salary transparency.

It’s a contentious topic that will earn its own separate post in due time, when I can better formulate my thinking on this particular aspect.

But whether you agree or disagree with their full-transparency policy has little to do with the real learning opportunity here: looking at a very bold attempt to formulaically model “soft” aspects of the Buffer culture into the way Buffer employees are compensated. That is worth studying and celebrating. Let’s begin.

The current version of Buffer’s salary formula consists of 5 core components:

  • Role – talent markets matter. Software developers have a higher (talent) market valuation than accountants, for example. This component accounts for that. (more below)
  • Experience – how good you are at what you do matters. Note that this is not “tenure” (x years of experience doing y), but an assessment of the level of skills based on discussions. Buffer currently uses 4 ties with a multiplier varying between 1x and 1.3x 
  • Dependents – the social dynamic that you live in matters. For every family member that depends on your income you get an extra $3K/year
  • Loyalty – loyalty matters. You get a 5% increase for every year with the company
  • Choice – your risk tolerance matters. Employees have a choice between an extra $10K or 30% more equity

Here’s a representative example:


The role component is worth further exploration as it’s the most complex one. It’s impacted by the following elements:

  • Overall base (35%) – standard US data for a particular role
  • Location base (65%) – accounting for variations in cost of living across multiple locations
  • Role value – Buffer may disagree with the way the market values a certain role (for example, if they think that customer support reps are undervalued by the market). This multiplier allows it to adjust for that.
  • “The Good Life Curve” – (cost of living correction) a $0-$8K adjustment that takes into account the market rate for a certain role in a certain location. This is perhaps the most thoughtful and interesting piece of the equation. The folks at Buffer argue that taking the market rate at face value is unfair. For example, while there may be a 4x cost-of-living difference between SF and Capetown (CPI of 113 vs. 30) there may be a 5x salary difference ($124K vs $25K) leaving the person working out of Capetown worse-off compared to his peer in SF. This component introduces a multiplier that’s meant to address that.

In future iterations the Buffer team plans to take a deeper look at two existing components that have become stale/outdated: the way experience and risk tolerance (“choice”) factors in, as well as two add’l external/environmental factors: taxes and exchange rates.

I’m excited to see where the Buffer team takes this experiment and particularly looking forward to see the next rev of the experience component, being the most “qualitative” component in the equation. Personally, I think their approach already leads to a better outcome than the standard “choose the percentile of market rate you want to be in”.  The biggest challenge the team will have to tackle has to do with complexity. Figuring out a way to keep the formula simple enough to understand while factoring additional drivers and addressing the interplay between them. Otherwise, “actual fairness” and “perceived fairness” will start to diverge.

Buffer’s Salary Formula

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