How did we build a public Compensation Calculator in our international SaaS company?
Our compensation calculator is based on our values, thus it needs to be public
Like any major topic in your company, your compensation policy should reflect your company’s values.
Applying this to Gorgias, we relied on 3 key principles to build our compensation calculator:
- Compensation should be based on data
- Compensation should reflect everyone’s ownership, meaning everyone should have equity
- Compensation should be transparent
Since the beginning, we were applying the first two ones: each of our employees was granted stock options and we were using data to calculate our offers.
However, we were challenged internally: our team members asked how much they would make if they switched teams or if they got promoted.
Hence, we decided to move forward, and recently, we successfully managed to make our third key principle happen! Our compensation calculator is now shared with everyone at Gorgias.
And this was not a small challenge. Let’s see how we got there!
We built our compensation calculator using 4 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 be in charge of more technical stuff. Sometimes your job title or position doesn’t match the existing databases.
For some of them, when we thought that our team members were doing more than those positions in the market, we crossed some databases to get something that looked fairer than what we got.
Level: to assess a level we defined specific criteria in our growth plan for each job position. It is of course a bit linked to seniority, but not only. When we hire someone, we evaluate the skills thanks to specific challenges or case studies during our interview processes.
Depending on the databases you’ll find beginner, intermediate, expert… or L1, L2, L3…
We decided to go with 6 levels from L1 to L6 for IC and 6 levels in management from Team Lead to C-level.
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 indeed better providers of specific talents. The combination of both gives us the location index.
When we miss data for a specific city, then we take the nearest one where we have data.
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!
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. Getting bigger (and raising funds), we got 100% sure that to build the best company, we’d need the best people. And high quality is expensive! Obviously, we can’t pay everyone top of the market and align on big players such as Google but we can do our best to get close to it.
Since having the best product is our priority we pay our engineering and product team at the 90th percentile, meaning we’re among the top 10% of companies who pay the best. We pay other teams at the 60th percentile.
Some other companies take into account other criteria such as seniority in the company. We believe seniority should rather reflect in equity than in salary. Indeed if you apply seniority in the company index on salaries, then eventually some of your team members will be inconsistent with the market and will stay in your company only because they won’t be able to find the same salary elsewhere.
We crossed several databases so that it’s the most accurate
Being data-driven is in our company’s DNA since the beginning.
Where to find the data? Data is everywhere! What matters is the quality of it.
We look for the most relevant data on the market. If the database is not robust enough, we look for others. So far we have managed to rely on several of them: Opencomp, Optionimpact, Figures.hr, Pave. 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 with what we offer to our teams.
Once we have the data and the figures, 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.
How did we communicate?
As we mentioned above, once our tool was robust enough, we decided to share it internally.
To be honest, checking and checking again took longer than expected. But we all agreed that we’d rather release it and have good reactions than rush it and create fear. We postponed the release for one month so that we wouldn’t be too stressed.
Well, we were actually pretty nervous when we released it and decided to do 2 things:
- Share it with a team at a time: so that in case we had a flow of questions, we would be able to answer them. The flow of questions did not happen. But still, we were happy to anticipate the risk.
- Share it with a lot of humility. We didn’t want to act with any triumphalism. Even if we checked the data so many times, we could have missed something, there could have been something that lacked of consistency. We asked everyone to stress-test it and to provide feedback.
Overall, the reactions were pretty good, people loved the transparency and we got good feedback.
It’s been 1 month since the release now and overall we’re really happy.
Let’s see how it goes with time.
It’s only the beginning, we’re looking forward to sharing it on our website
Let’s be humble here: it’s only the beginning. It’s a Gsheet. Of course, we’ll need to iterate on it. So far we’ve planned to review the whole grid every year. However, now that it’s public within the teams, it enables us to rely on feedback and to potentially make some changes. Everyone can indeed add comments.
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 talents thanks to this level of transparency.