In this interview, the speaker explains that network effects arise from the interactions between users, not from the product itself. Early‑stage founders should first identify the core interaction they aim to facilitate and the two user groups involved. Once the interaction is defined, liquidity—where adding a user makes the platform more valuable for everyone—signals the presence of network effects and can be measured even at seed stage. The speaker categorizes marketplaces into low‑frequency and high‑frequency types and outlines concrete metrics for each. For low‑frequency marketplaces, key indicators are the search‑to‑fill rate (the share of visitors who complete a transaction) and the utilization rate (percentage of available inventory booked). These metrics should be tracked over time to see an S‑curve rise, indicating growing liquidity. The nature of supply matters: high‑touch supply (limited inventory, e.g., Airbnb, Uber) allows clear utilization measurement, while low‑touch supply (effectively unlimited, e.g., Udemy) requires alternative proxies such as share‑of‑wallet or course‑booking rates. Supply density (supply‑to‑demand ratio) interacts with these metrics, helping founders spot optimal balance. For marketplaces where users can be both buyers and sellers, the conversion rate between roles is also informative. High‑frequency marketplaces add another metric: core interaction frequency (how often users transact within a chosen time window). Finally, founders must select the appropriate time granularity—weekly, monthly, or annual—based on how often users engage with the product, as illustrated by Airbnb’s annual retention versus Deliveroo’s monthly frequency. The overall framework equips founders to detect, track, and eventually leverage network effects in the earliest stages of building a marketplace.
Chapters
How should founders be thinking about network effects at the earliest stages? And maybe, you know, how can they start to see them kind of, you know, take shape or form and maybe even start to measure them?
Well, the first thing you gotta do as an early-stage founder is recognize that products and companies don't have network effects, interactions have network effects. So figure out what interaction it is that you're actually trying to create, which are the 2 users you're trying to connect, How far apart do they need to be? What's the nature of that interaction? And that really will define the structure of your network effects, right? First, what category it falls into and also if it's more scalable, more defensible or not. I have a quiz on breadcrumb.vc, it's about 10 questions. If you go through it, you'll get a quick overview of kind of what that looks like. And so that's one bit of it. In terms of when you know you have network effects, like once you have liquidity, in theory, you have network effects. They might not be massively defensible at that scale just yet because you're a seed startup, but they are, they do exist and they're measurable at that stage. If you define a network effect as adding a user makes the product more valuable for all users, right? So what metrics there are for that, again, there's a quiz for this as well, but you can broadly categorize marketplaces into sort of 2 types, right? There's high-frequency marketplaces, low-frequency marketplaces. If it's a low-frequency marketplace, there's 2 things that really tell you if you have enough liquidity or not, if your network effects started to kick in. There's a metric called the search to fill, which is of all users that end up on a particular landing page or marketplace, what percentage end up making a booking or transaction? And people ask me, great, what's the benchmark for that? The answer is there is none. You can't have a benchmark for that because What a search to fill is for an Uber or an Airbnb will differ widely. It's very, very specific to the kind of marketplace you have. Instead, you need to look at the trajectory. This thing needs to follow an S-curve. Once the S-curve starts, that curve starts pointing up, you know you have the beginnings of liquidity. Once it starts to tap out, you know that kind of adding more supply or demand is not really going to meaningfully move the needle, at least in that category of supply. or in that particular region depending on the kind of marketplace you are, right? So that's one metric. The second metric is the utilization rate. In particular, if you have what I call high-touch supply or limited amount of supply, right? So Airbnb and Uber are good examples of that. You would basically measure what percentage of available inventory or SKUs have been booked in a And again, see if that follows an S-curve. Once it starts going up, you know you've got the beginnings of liquidity and when it starts curving up, you can say you have network effects at that point because adding more supply and demand is genuinely changing user behavior. If you have what I call low-touch supply where the amount of supply is technically unlimited, like a Udemy, for example, right? A course is on, the number of students that can take it is infinite. There you don't really have a good measure for the denominator there, right? Or how much inventory you have available. You could either measure what percentage of your courses have received a booking. That's one thing you could do. Or you could measure share of wallet. What percentage of that instructor's earnings or revenue is coming from you? That can be tricky to measure because you have to figure out third-party data in addition to your own. So you can do that via surveys, but that's not something that's going to give you sort of a reliable S-curve. So that's one of those things just to keep in mind. And then you need to look at these 2 in combination with your supply density, which is basically supply to demand ratio or the number of listings for each category, whichever one sort of makes sense in your case. So as that changes, that supply to demand ratio, you'll see variations in the utilization rate in the surge to fill. And so you'll know what's the optimal amount, because if you have sort of too much demand, then something's going to break, or too much supply, something's going to break. You can also measure percentage of buyers who become suppliers and vice versa if you have that kind of marketplace where users play both roles. Now that's for low frequency. High frequency, all of that still applies except for one more thing. which is purchase frequency or what I call core interaction frequency, which again should follow some sort of an S-curve. How many transactions do people make in a given time period? The last thing you need to keep in mind is that what time period do you actually need to look at this data for? Is it weekly, monthly, quarterly? That's very specific to the marketplace you have. So for example, if Airbnb looked at Purchase frequency per month, that would give you nothing. Or if you look at retention per month, that would give you nothing. They look at annual retention cohorts. That's your purchase frequency on Airbnb. Whereas for Deliveroo and Uber, monthly frequency is good enough because that's the frequency of use. And even might even be able to look at it weekly, but that's probably overkill for most marketplaces. So you need to align sort of the time granularity with the frequency of use for your product.
