How We Built an International SaaS Salary Calculator for Our Distributed Team

Adeline Bodemer
Adeline Bodemer
Last updated on 
May 18, 2022
How We Built an International SaaS Salary Calculator for Our Distributed Team
Like anything in your company, your compensation policy should reflect your company’s values. Here’s how we build a public salary calculator to reflect ours.

Like any major topic in your company, your compensation policy should reflect your organizational values.

At Gorgias, we created a compensation calculator that reflected ours, setting salaries across the organization based on 3 key principles:

  1. Compensation should be based on data
  2. Compensation should reflect everyone’s ownership, meaning everyone should have equity
  3. Compensation should be transparent

Since the beginning, we applied the first two: Each of our employees was granted data-driven stock options that beat the market average.

However, we were challenged internally: Our team members asked how much they would make if they switched teams or if they got promoted.

This led to the implementation of our third key principle, as we shared the compensation calculator with everyone at Gorgias.And this was not a small challenge. We’re sharing our process in hopes that we can help other companies arrive at equitable, transparent compensation practices.

We built our compensation calculator using four key indicators

First, let’s get back to how we built the tool. We had to decide which criteria we wanted to take into account. Based on research articles and benchmarks on what other companies did before, we decided that our compensation model would be based on 4 factors: position, level, location, and strategic orientation.

If we had to sum it up all briefly, our formula looks like this:

Average of Data (for the position at defined percentile & Level) x Location index

Position

This is the job title someone has in the company. It looks simple, but it can be challenging to define! Even if the titles don’t really vary from one company to another, people might have different duties, deal with much bigger clients or have more technical responsibilities. Sometimes your job title or position doesn’t match the existing databases.

For some of these roles, when we thought that our team members were doing more than average in the market, we crossed some databases to get something closer to fairness.

Level

To assess a level we defined specific criteria in our growth plan for each job position. It is, of course, linked to seniority, but that is not the primary factor. When we hire someone, we evaluate their skills using specific challenges and case studies during our interview processes.

Depending on the databases you’ll find beginner, intermediate, expert, which we represent as   L1, L2, L3, etc.We decided to go with six levels from L1 to L6 for individual contributors and six levels in management from team lead to C-level executive.

Location

Our location index is based on the cost of living in a specific city (we rely on Numbeo for instance) and on the average salary for a position we hire (we use Glassdoor). Some cities are better providers of specific talents. By combining them, we get a more accurate location index.

When we are missing data for a specific city, we use the nearest one where we have data available.

Our reference is San Francisco, where the location index equals 1, meaning it’s basically the most expensive city in terms of hiring. For others, we have an index that can vary from 0.29 (Belgrade, Serbia) to 0.56 (Paris, France) to 0.65 (Toronto, Canada) etc. We now have 50+ locations in our salary calculator — a necessary consideration for our quickly growing, global team of full-time employees and contractors.

Strategic orientation

We rely on our strategic orientation to select which percentile we want to use in our databases. When we started Gorgias we were using the 50th percentile. As we grew (and raisedfunds), we wanted to be 100% sure that we were hiring the best people to build the best possible company.  

High quality talent can be expensive (but not as expensive as making the wrong hires)! Obviously, we can’t pay everyone at the top of the market and align with big players like Google, but we can do our best to get close.

Since having the best product is our priority we pay our engineering and product team at the 90th percentile, meaning their pay is in the top 10% of the industry. We pay other teams at the 60th percentile.

Some other companies take into account additional criteria, such as company seniority. We believe seniority should reflect in equity, rather than in salary. If you apply seniority in the company index on salaries, eventually some of your team members will be inconsistent with the market. Those employees may stay in your company only because they won’t be able to find the same salary elsewhere.

By crossing several databases, we arrived at a more accurate dataset

 Data is at the heart of our company DNA.

Where should you find your data? Data is everywhere! What matters most is the quality.

We look for the most relevant data on the market. If the database is not robust enough, we look elsewhere. So far we have managed to rely on several of them: Opencomp, Optionimpact, Figures.hr, and Pave are some major datasets we use for compensation. We’re curious and always looking for more. We’ll soon dig into Carta, Eon, and Levels. The more data we get, the more confident we are about the offers we make to our team.

Once we have the data, we apply our location index. It applies to both salaries and equity.

To build our equity package, we use the compensation and we then apply a “team” multiplier and a “level” multiplier. Those multipliers rely on data, of course. We’re using the same databases mentioned above and also on Rewarding Talent documentation for Europe.

Internal communication is key

As we mentioned above, once our tool was robust enough, we shared it internally.

To be honest, checking and checking again took longer than expected. But we all agreed that we’d rather release it to good reactions than rush it and create fear. We postponed the release for one month to check and double-check the results..

For the most effective release, we decided to do two things:

  1. We shared it with one team at a time. This was done to anticipate the flow of questions, though we didn’t receive that many.
  2. We shared it with a lot of humility. . Even if we checked the data many times, we could have missed something, or there could have been something that lacked consistency. We asked everyone to stress-test it and to provide feedback.

Overall, the reactions have been great. People loved the transparency and we got solid feedback.

We released the new calculator in September 2021, and overall we’re really happy with the response. We also had positive feedback from the update this month.

Let’s see how it goes with time.

Next step: sharing it with the rest of the industry

Let’s be humble here: It’s only the beginning. It’s a Google Sheet. Of course, we’ll need to iterate on it. 

In the meantime, you can check out the calculator here.

So far we’ve made plans to review the whole grid every year. However, now that it’s public within the teams, we can collect feedback and potentially make some changes. Everyone can add comments as they notice potential issues.

The next step for us is to share it online with everyone, on our website, so that candidates can have a vision of what we offer. We hope we’ll attract more talent thanks to this level of transparency and the value of our compensation packages


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