Fair Offer Part II: Total Compensation
FairOffer.ai is a tool to algorithmically estimate fair compensation — for any title, at any stage of company, anywhere in the world.
Jan 14, 2024
If you like this post, please forward it on and/or share on X :).First time here? This post stands on its own, but it might be useful to check out the context from Part I (out of three parts) here.
After thousands of regressions, analyses and backtests, we’ve determined that there are only 3 inputs (i.e., independent variables) in determining “total compensation” for any employee:
- “Entry level” salary
- Title / Level
- Stage of Company
(For those less familiar, Total Compensation is an industry term for the sum of base salary, incentive compensation such as bonus, and equity.)
While this may seem like a bold claim, it was a simple result of our data science efforts to understand and analyze compensation patterns. As we covered in Part I, only accounting for benchmarks results in spotty, inconsistent coverage (outside of a few roles with lots of data in major metros). This only gets worse when trying to understand total compensation (base + incentive comp + skin in the game).
In this post, we’ll lay out an approach to calculating total compensation based on first principles, which — as you’ll see — is surprisingly predictive of compensation patterns:
- first, we’ll discuss entry level salary, and patterns by each incremental level rises in total comp (i.e., a level-based “progression”)
- then, plotting these levels across job families, in the context of an overarching compensation framework
- finally, tying it all together with a surprisingly simple formula to determine total compensation for each level within your compensation framework
The overall result: FairOffer.ai.
Entry-level or “Level 1” salary
What should the most junior, entry-level employee make at your company? Let’s specifically consider this question in the context of “Zone 1” cities (San Francisco or New York City in the US).
This might seem like a hard question, but it turns out it has a simple answer: minimum exempt salaries are set by regulation in the US. The anchor point for “Zone 1” regions is set by the highest exempt salary, which is California clocking in at $66,560 for 2024 and $68,640 in 2025 (and $64,480 in 2023).
Given this is an objective market standard set by regulation, this is what the algorithm uses for a) entry-level salary, and b) organic inflation adjustments in each year.
Total Compensation: fixed progression between levels
It’s very common — a borderline best practice, if such a thing can exist in compensation! — to sanity check compensation bands according to a certain “progression” between levels. This is a common practice for base salary, but the same core principles hold for total compensation. In fact, when we analyzed patterns in total compensation, we found that these ratios have a strong predictive effect on compensation in adjacent levels. For example, an L7 Sr. Manager might make 1.25x the total compensation of a L6 Manager.
When discussing the progression, we’re referring to the ratio between the mid-points between levels; it’s common to have a range of acceptable compensation oriented around the mid-point.
In this context, it’s not ideal to have a jump that is too small or too big between levels:
- too small (e.g., L6 mid-point = 1.05x L5 mid-point) means employees don’t feel appreciated when they get promoted
- too big (e.g., L8 mid-point = 1.90x L7 mid-point) means challenging impact on financials; a promotion should not result in an employee’s cost doubling
This means that — as you move up the levels in a job family — there is an exponential, not linera growth in compensation. E.g., If a Sr. Manager in Finance makes $150K and a Finance Manager makes $120K, this is not because each level makes $30K more than the one below, but rather that each level makes 25% more.
This “progression index” effectively ends up being a golden ratio across the company. At a given company, we see the same progression regardless of job family; i.e., the same company doesn’t usually have a progression of 133% for product and 129% for sales.
The later the stage of the company, the higher the leveling multiplier.
This progression ranges from ~115% (for very early stage companies) to 135% (for mature companies with top-of-market compensation). This progression index is the primary factor in determining the generosity of a company’s compensation bands.
Entry level roles are fairly similar at any company; but due to the compounding, compensation higher up the chain rises much more significantly at large, mature companies than it does at a small organization. For example, an engineering leveling guide might look like the following at example early stage (where each level makes vs. late stage vs. public company.
This means that if you know a company’s progression (which it shares with its “peer group” companies), and the compensation for a given level then you already know the compensation for every other level in that job family!
Total Compensation for
• L7 = L5 * (progression ^ 3)
• L8 = L5 * (progression ^ 2)
etc.
Mapping levels across job families
Levels (and corresponding) Titles are dramatically different across various functions. This is partially due to semantics, and partially due to the labor market for various roles.
Nevertheless, it’s very clear that a Director of Operations, Director of Engineering, and a Director of Customer Support do not have the same compensation in the market.
Currently, best practice is to create separate compensation levels & total rewards for each function. This may be necessary and prudent.
However, it started to become clear during our analysis that it is better to start with a single ladder of “Baseline Levels,” and simply map back the different titles (and functional levels) to a single ladder. It is not perfect, but it surprisingly gets 95% of the way there.
I was skeptical of this too, but it works reasonably well in most cases; you may choose to tailor individual levels as you customize Fair Offer for your company (as you should!). However, we recommend starting from the approach above, rather than starting from scratch.
There is one huge benefit to setting the Baseline Levels and indexing functional levels & titles to it: making periodic company wide adjustments for inflation, or as you change your compensation framework completely (e.g., going from Series A to B, or from late-stage to public) is dramatically easier with 12-15 levels rather than 100+ levels.
So how would this mapping look? Through a combination of regressions and trial and error, we’ve found that the titles & functional levels cluster in the following way.
Here is a sampling of mid-to-senior levels for illustrative purposes:
Here is the full table of the 200+ Titles covered by Fair Offer today, the Functional Levels, and the Baseline Levels they map to (click this link or the preview image below to access):
There is no “industry standard” but this is the most common set of titles and ladders that we’ve observed. Perhaps one side effect of Fair Offer will be that this will be more standardized against “Fair Offer titles,” but execs and HR teams are understandably quite opinionated on this topic!
Combining the inputs to generate a fair offer
The combination of these formulas are surprisingly simple.
Where
ExemptThreshold = $66,560 (for 2024)Multiplier ranges from 1.15 (founding stage) ~ 1.35 (90th+ percentile public company)L is the Baseline Level, and ranges from 1 (entry level) ~ 12 (executive) according to the mapping table provided above
That’s it, surprising though it may seem.
If you’d like to backtest this, head on over to FairOffer.ai to try out combinations.
Coming Next
In Fair Offer Part III, we'll cover cash-equity splits, and potential ways to address location-based adjustments.
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