The Exchanges

Q You're talking 15% low, like people churning or the revenue that make up those people?

A Uh, either. So, um, we track the subscriptions because we don't have multi, we don't have different price points. We just have one. So, so we look at subscription churn. 15% per month is really high. 10% is okay. You can kind of, um, and then I think five percent is like world class, right? So that five percent monthly churn for consumer business is great. Um, and you can really build a humongous business doing that. And I think David Pakman, who's a VC at Venrock, wrote a great article, um, on, on just consumer subscription churn. Um, so I would say we're definitely, you know, in the single digits for monthly churn, um, it improves year over year. But yeah, it really comes down engagement, right? So people don't use your product. They're going to cancel.

AI assessment note: “we track the subscriptions because we don't have multi, we don't have different price points.”

Q What do you do? Like, that's what I'm interested in. You've already gotten churn kind of down below 10%, not as low as five percent, below 10% monthly. What are like the other levers you pull to go from eight percent churn down to, you know, five?

A I mean, a lot of it's just engagement, right? So, I mean, it's just about getting your, you know, people use the product and finding value. Um, obviously there are a lot of growth hacky things you could do there as well. Like you could, you know, um, you know, there, there are little things you can do to drive sharing down, like, you know, swipe people, you know, if their card bounces, swipe in on the first or 15th, you know, when they're getting paid. Um, there's little things you could do that, that might get you, you know, uh, some, um, Like incremental improvements in turn, but the big stuff function changes in turn are, are just core product experience.

AI assessment note: “if their card bounces, swipe in on the first or 15th”

Q Okay. Got it. Talk to me more about, uh, about how you're winning these consumers over, you know, a treehouse on the coding side or Udemy on the, you know, business side or creative live on the creative side. Like how are you winning these sectors?

A Um, I think one way to think about it is just our audience. So we focus on what we call the independent class. So these are, you know, either, you know, independents, entrepreneurs, small business owners, freelancers, and, you know, there's two concentric circles, the other millennials. So we feel like we built the best product and the best ecosystem for that audience. Um, and the second one is just kind of like when, you know, you know, you asked me earlier about moats, you know, Because what we do is digital content, right? At the end of the day, it's just, it's just an MP four file that we're delivering. You know, if you really strip out what, you know, you know, that it's a class. If you think about publishing, you think about entertainment, think about music, all of those businesses on their internet, you know, they all fell to subscription models, um, that had the biggest libraries. Um, and those are the ones that usually win over time. So it's more Spotify than iTunes. You know, it's more Netflix than going to Blockbuster online. Um, so we feel like, you know, today, you know, we have the biggest catalog for one price. And we think as we add more classes, we'll have a 100,000, if not a million courses on every single skill for one set price. And that's a great value for, for, for our members.

AI assessment note: “we have the biggest catalog for one price”

Q Well, give us an update where you are today. How many folks do you have paying for the platform every month?

A Um, so we have about three, three and a half million registered users, 17,000 online course classes from about 5000 teachers. We're pretty much, you know, half of our user base is international. Um, you know, business is doing really well. We're doubling year over year, uh, revenues and, you know, you know, way above ten million. Um, so it's doing really well. I think the next, uh, stage of the company is really around growth and scaling. So I think, you know, the early days was around product market fit. And then we started figuring out how to lay down the foundation of scale. I'll say like we are, we are in that, you know, scaling phase right now.

AI assessment note: “we have about three, three and a half million registered users”

Q Yeah. What do you assume like when you do lifetime value or do you do lifetime value calculations and do you use that to inform how aggressive you can be on CAC?

A Uh, for sure. I mean, the, the name of the game is like for a company is like, if you can make your business Pretty predictable. Um, that is, I think the holy grail of scaling. So if you can accurately say that I'm going to invest X amount, I'm going to get Y amount out with, you know, with high probability of confidence or high percentage of confidence, then Then it comes down to how much risk you want to take and how aggressive you want to be. And I think, you know, on one end of the spectrum, it's like Uber where they're, you know, they're going really aggressive because they feel so confident in their curves. And the other businesses that aren't predictable, like, you know, they're not, you know, they're not taking that much risk for being that aggressive or, you know, even sticking, you know, being around in a couple of years. So I think the name of the game is, you know, the more predictable you can make your business, the better. And if you can do that, then you can be really aggressive.

AI assessment note: “Uh, for sure. I mean, the, the name of the game is like”

Q for you to say you're well above ten million bucks in AR at this point. Um, tell me more about the data side of this. So when you have Video and audio content in your platform. Are you doing things like transcribing it and then actually using your search and recommendation algorithms tied to the actual text data? Or do you have something that ties directly into the voice data?

A Um, we do some of that. What it really, what we really look for is quality content, right? So we look at engagement within classes. So let's say you upload a class and, you know, 10 people watch it, but they all watch the whole thing. So we, we can signal that. So that's pretty good. And based on, you know, another user's past viewing history, let's say you're really into, I've seen your, in your background, you have like psychology. Let's say you're really into that. We can pull that class out and recommend it to you because we, we feel pretty confident. It's really good. And if you like it, And you know, that, that, that class funnels all the way to the top. So that's one example on the class layer, but our vision is to create what we call this ecosystem for the new economy. So, um, we want to add a career layer where we can start recommending jobs, other pieces of content for you, other people you should interact with. So the idea is that we can create this like personalized, like school for you, um, around everything you want to learn and who you want to interact with. So that's kind of the vision. And that's where a lot of the data comes into play.

AI assessment note: “we do some of that. What it really, what we really look for is quality”