Most leaders want to start building a compensation program by declaring a philosophy. “We’re equity heavy.” “We lead on base.”
But if you don’t know what data is underneath those statements, you’re not describing a philosophy. You’re declaring a preference with no foundation under it.
Before anything else—this includes setting pay ranges, leveling, any conversations with Finance and your executive team—you need to decide what surveys you’re going to use as your anchor and why. This is Step 1, and it’s non-negotiable.
Here’s why it matters so much: your data source determines your market definition. And your market definition determines everything that comes after it.
Leading this work for two very different types of organizations—growth tech companies and venture capital—the right data sources looked completely different in each context. In VC, investment-focused compensation surveys were the primary source for investor talent because the labor market for that talent pool is distinct from the broader technology market. That said, for more standard corporate roles that are seen in tech—GTM, G&A, etc.—a non-investment source was also used since those roles were most often being hired more typically out of rather than other venture funds. Additionally, I found VC survey data to be generally weak for roles outside of investment. In growth tech, global technology and sales surveys anchored the work. Using the wrong survey for your industry or talent pool doesn’t just give you bad numbers, it also gives you false confidence in bad numbers, which is worse. As such it is very important, albeit tedious, to be very intentional in this step by assessing the many vendors available, their offerings, and their data integrity.
One important caveat: the data is simply that—data. Compensation surveys are a guide, not a prescription. The goal isn’t to follow them at face value or apply them like peanut butter across your entire organization. They exist to inform a philosophy that is both compelling to the right talent and affordable for the business. Also, how competitive is a company that is just following benchmarks to the tee without any forms of differentiation?
A few questions to pressure test your data sources before you commit:
- Does this survey represent the companies I’m truly competing with for talent—not just by industry, but by stage and size? A perfect example of a pitfall I have often seen is being a startup using only early-stage tech data when they are consistently trying to capture talent from a sector or even a publicly traded company with a generous RSU program. While they may not be able to match dollar for dollar, they can determine the right mix they can afford that is also compelling, but need to know what they are up against.
- Does it have enough data points in my specific job families and regions to be statistically meaningful?
- Does the data provider have a good reputation for support when help is needed to translate some of the data’s quirks—and there will be some, always.
- Does the data provider include companies that matter to you and can they create custom competitor cuts for you?
- How frequently is it updated, and does that cadence match how quickly my market moves?
One survey is rarely enough. Primary and secondary sources give you the ability to cross-reference and catch anomalies before they become expensive mistakes. Remember, the data comes first. Everything else is built from it.
Next up: Step 2—mapping your people to levels using the survey’s definitions. Not your titles. Not what someone negotiated. The survey’s definitions.