Tony Karrer, a CTO hired gun and head of TechEmpower, wrote a very interesting blog post regarding CTO compensation and equity ownership for venture-based companies, most of them startups. Tony’s post is based on data that Todd Gitlin of Safire Partners made available to members of the LA CTO Forum.
Tony encouraged me to build some interactive dashboards that might shine more / better light on the subject. You will find a live dashboard that you can explore at the end of this post, or you can click here.
Getting our Bearings
The first tab of the dashboard shows the following.
For 2010 we see that:
- There were 564 responses (179 founders and 385 non-founders)
- The largest average total compensation was in Southern California ($225,533)
- The average total compensation was $203,264 for Founders and $217,389 for Non-Founders.
- The average total compensation for CTOs was $219,605 and the average for Engineering VPs was $207,761.
You can also glean additional information by hovering over a mark. For example, if you hover over the big circle in California you can see that there were 303 responses for the San Francisco Bay Area.
You May Be Thinking “Yes, But What About Me?”
While the generalized study of this data may interest a handful of people, the real power comes from applying the visualizations to your particular needs. Indeed, I would argue that putting all the responses into one big vat and taking the average doesn’t do much to clarify any issues or relationships.
Fortunately, the different graphs on this visualizations aren’t just pretty pictures; they are also filters. This means you can click different bars / marks and what you select will apply a filter to the other graphs on the dashboard.
For example, let’s say you are only interested in seeing non-founder CTO compensation for West Coast-based organizations with 50 or fewer employees that have been in existence for seven or fewer years. By selecting different marks and applying the “years” in business filter, you can glean that the average compensation for a respondents fitting into these categories is $201,495.
Note: to clear the filters (or if you make a mistake) click the Revert All button at the bottom of the screen.
Top Quartile? Bottom? Median?
The second tab in the dashboard is a type of scatterplot that shows individual responses from 564 different organizations. By hovering over a mark you can see in-depth information about a particular respondent.
As before, to make this information meaningful to you, apply filters so you just focus on organizations similar to yours. Below is an example that shows compensation by quartile for Enterprise Software companies with between $10M and $50M in revenue and with greater than 50 employees.
Equity and Dilution: Benchmarks
The third tab allows you to investigate share equity based on job title, founder status, organization size, and so on. As with the first visualization, you can select different visual filters to “triangulate” your results, but even without clicking anything it’s easy to see that organization size, profitability, and revenue all have an inverse relationship with equity ownership.
Equity and Dilution: What’s the Story Here?
In the fourth tab we perform a binning analysis and see that founder CTOs and Engineering VPs for all organization types retain a 2% to 10% ownership while the vast majority of non-founders can expect to see a less than 1% share.
Equity and Company Age (and “What About Me?)
The final tab shows the relationship between equity and company age. Not surprisingly, the longer a company has been in business the more likely that shares have become diluted.
As with the other visualizations, the real value comes from applying filters that are applicable to a particular situation. That said, it is fun to explore individual data points.
One data point that really sticks out is at the 14 year / 10% mark:
Despite the large headcount, capital raised, and years in business, this particular CTO has managed to retain 10% ownership.
Give it a Try
The interactive data visualizations may be found below. Try them for yourself and let me know what you think, and let me know if there are other relationships in the data that you’d like to explore.










