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In a conversation with Protocol, Iregbulem discussed revenue concentration in SaaS and why current revenue metrics don’t give investors the full picture.

This interview has been edited and condensed for clarity.

I want to dive into some of the concepts you explore. You wrote about the fat-tailed nature of SaaS revenue. Why are SaaS revenues so concentrated?

If I were to go back to the basics, the reason SaaS revenue is so concentrated is that the distribution of companies that you sell to as a software publisher is very concentrated in terms of a large number of small companies, a moderate number of medium-sized companies and a very small number of very large companies.

You also performed an analysis of the revenue concentration of a subset of public software vendors. Did you find anything surprising?

What was most surprising was the consistency of focus. I think people talk about concentration as if there are a few companies that have revenue concentration issues, and the rest are fine. It turned out that literally every business has a 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 accounted for a fairly significant share, something like 20% being 70% of turnover. It was pretty consistent across a bunch of different companies, so it was a little shocking.

You can work out the theory and why it happens, and you go to the data, and it turns out that’s actually how it happens. So I thought that was really interesting. And that’s why I sometimes think it’s just the natural result of success. The fact that you were able to go public as a software company almost implies that you have to have a pretty high focus.

You wrote that if you pay too much attention to the average customer, it can actually lead you astray. Is it because, as you said, you don’t have the opportunity for higher income and growth?

Taking your own data too seriously can be problematic because there’s a kind of shadow of customers here that you could acquire, and it could dramatically change the look of your economy, if only you did.

People are shocked by the size of software markets. If you look at Salesforce when they went public, in their S-1 they say, “We sell to this market and to this market.” It seems so small in retrospect if you look at them today. They have totally destroyed all those expectations in terms of the markets they have access to and the size of those markets. And I think that’s partly because of this upside surprise dynamic: you get a bigger customer than you’ve ever reached before.

I think it’s actually a very different dynamic on the consumer. This is probably a future blog post I want to write one day, but I think consumer companies tend to surprise a bit on the downside because they tend to acquire their best, rabid customers early. And then to keep growing, you need to find the next marginal customer who has a lower lifetime value, less excited about the product and what you have. And so your economic situation tends to deteriorate over time in many cases.

What’s interesting on the business side is that, if you think of Salesforce, they continue to grow. And that’s what’s interesting. You would think they have already entered this market because they have been around forever. How do they continue to grow? Is there a limit that all of these big SaaS companies are going to hit at some point?

You are just selecting all my future blog posts. That’s actually great, because I literally have a draft right now that talks about the Salesforce example we were both referring to. It’s really interesting. Normally the way people think of marketplaces is that large marketplaces, in terms of number of users, tend to have very little monetization per user, and then marketplaces that have very high monetization per user tend to be more niche in terms of how many people you can acquire. So there is this natural tendency to think that there is a negative correlation between the size of the average customer and the number of customers.

What Salesforce has done that’s really interesting is that by landing in multiple different markets, you can actually increase both the number of customers you have and the monetization per customer across all of those separate markets. Going back to the fat-tailed stuff, as they grow in each of these markets, their monetization per user actually improves over time. And if you do that in enough different places, it’s like planting seeds in each individual market, and in each of those markets, your unit costs improve, almost independently of each other.

You came up with this metric called weighted average contract value that tries to capture a lot of what we’re talking about, which is [that] just looking at the average doesn’t necessarily account for some of these large customers. Why isn’t the standard LCA metric as useful for comparing different companies with different customers?

The reason that calculating the standard average of contract value has limited utility across different companies is that if the underlying concentration across those different companies is very different, the average simply isn’t comparable. It doesn’t tell you the same thing.

In the same way that, again going back to statistics, taking the mean of a normal distribution tells you something different than when you take the mean of a skewed distribution. So people implicitly assume when they say, “Oh, this company has an ACV of this and this company has an ACV of that,” that they have a very similar concentration of revenue. But if they don’t, then you are actually making a real mistake. But this standard average is so easy to calculate that people use it by default.

What kind of insight does looking at the weighted average give a startup than not just looking at the average?

The most interesting finding is that it tells you where your business revenue is most concentrated. In other words, it shows which type of customer is most responsible for the majority of your revenue. It’s very interesting because it tells you where the risk is in the business, where the growth of the business is likely to be, it tells you what bread needs to be buttered, so to speak, and who you really need to pay attention to at.

The average doesn’t really tell you that. It tells you what the typical customer looks like, but not what the typical dollar revenue looks like. And so I think that’s actually a really helpful reframe when you’re like, “OK, here’s all our income, here’s where it’s at. Where should we spend time? Where should we allocate resources? It’s actually all right to have a more customer-centric view. But if you only think about unit economics, or ROI, that’s where this revenue-centric view becomes really valuable.

What are the benefits for investors? As an investor, if I could see their weighted average relative to their average, how might that inform my understanding of the business?

This is a very common mistake I find among investors where they will come across a company, the company will have X number of customers and the standard ACV will be quite small as most of their users are either free users or in a kind of lowest level version of the product. But they have a few significant customers who are spending actual revenue or paying the highest level of one product or another. But since there are so many customers in total, their average number ends up being rather small. And if you, as an investor, don’t dig a little deeper, you can be tricked into thinking, “Oh, these guys aren’t selling to enterprise-type customers, they’re just focused on small businesses in lower quality. income.” In fact, most of the income comes from very good quality customers. Unless you double click and go deeper, you’ll miss that. So I think investors should really focus on that as a metric and should calculate it if they have the data.

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