And so your economics tend to get worse over time in a lot of cases. And then to keep growing, you have to find that next marginal customer that is a lower lifetime value, less excited about the product and what have you. This is probably a future blog post I want to write one day, but I think consumer companies tend to surprise to the downside a little bit because they tend to acquire their best, most rabid customers early. I think it's actually a very different dynamic on the consumer. And I think it’s in part because of this dynamic of surprising to the upside: You land a larger customer than you've ever landed before. They totally blew all those expectations out of the water in terms of the markets they have access to and the scale of those markets. If you look at Salesforce when they went public, in their S-1 they say, “We're selling into this market and that market.” It seems so small in retrospect if you look at them today. People are shocked at how large software markets tend to be. Taking your own data too seriously can be problematic because there’s sort of this shadow of customers over here that you could be acquiring, and it could vastly change how your economics look, if only you were to do that. Is it because, as you noted, you're not getting that opportunity to have that higher revenue and growth? You wrote that if you're paying too much attention to the average customer, that can actually lead you astray. The fact that you were able to go public as a software company almost implies that you must have pretty high concentration. And it's why I sometimes think it's just a natural result of success. So I thought that was really interesting. You can work through the theory and why that happens, and you go to the data, and it turns out that's actually the way it plays out. That was fairly consistent across a bunch of different companies, so it was kind of shocking. It just turned out that literally every company has pretty high customer concentration, not in the sense that there was one customer that was 10% of revenue, but in the sense that there was a subset of customers that were a pretty meaningful share, something like 20% being 70% of revenue. I think people talk about concentration as if there are a couple of companies that have revenue concentration issues, and then the rest are fine. What was the most surprising was the consistency of the concentration. You also did an analysis of the revenue concentration of a subset of public software companies. If I had to go to first principles, the reason why SaaS revenue is so concentrated is because the distribution of companies that you sell into as a software company is very concentrated in terms of there being a large number of smaller companies, a moderate number of moderate-scale companies and a very small number of very large companies. You’ve written about the fat-tailed nature of SaaS revenue. I want to dive into a couple of the concepts that you explore. This interview has been edited and condensed for clarity. In a conversation with Protocol, Iregbulem discussed revenue concentration in SaaS and why today’s revenue metrics don’t give investors the full picture. WACV can tell a startup where most of the revenue is coming from, which customers are most important and where the most risk is. ![]() In the SaaS industry, where it’s common for a small number of customers to account for the majority of a startup’s revenue, calculating an average doesn’t properly account for the influence of large customers. ![]() In one of those essays, Iregbulem outlined a new metric he created called weighted average contract value (WACV), which he argues provides more meaningful information about SaaS revenues than the traditional average contract value (ACV). When not investing in enterprise software like GitLab, Alteryx and SurveyMonkey, Iregbulem spends his time writing about his theories on data and investing. Now, he’s a partner at Lightspeed Venture Partners. A self-taught programmer who started building computers as a teenager, his interests in mathematics, economics and statistics eventually took him from investment banking at JPMorgan to Stanford’s graduate school. Nnamdi Iregbulem is a tech nerd at heart.
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