The Exchanges

Q a four X. Okay. So you basically took MRR times 12 months. That's what Chad means when he says annualized times four X, right? Okay. Interesting. Great. So that's, that's, and, and walk me through when it happened. You just mentioned earn out. So this top tribe might not know what earn out means. What does that mean? And how'd that work in your deal?

A Yeah. So for, you know, the, the standard deal that you hear is a company gets acquired by Google. They go work there for two or three years as they get their, either their cash, uh, incentives or their equity incentives at that new company. Um, and so normally it's like this two year thing. If you read, uh, Zappos and their, and their story, uh, he, he quit and right in the beginning of his earn out. So he took like a fourth of what he sold the business for to go do his next thing. Um, but so for us, our earn out was a little bit interesting because it was essentially a six month total package deal, which is a pretty crazy thing in the industry. So from the day we inked the deal and closed the deal, we were going to have all of our money and all of our equity in six months. And we were going to have a majority of it within two months if we transitioned and revamped the entire product to fit into their ecosystem. So for us, it was essentially, you know, 90% of our, of the earnings were in those first two months, and then the rest was kind of at this six-month, like, maintenance-type mode.

AI assessment note: “our earn out was a little bit interesting because it was essentially a six month”

Q Got it. Okay. So you stopped, you did your own thing. So walk me through the growth of edge rank checker. What was the, what were you actually selling and what was the, you know, how could people buy it?

A Uh, yeah. So we built a Facebook analytics kind of Facebook insights and you know this better than anyone else. Facebook insights at the time was absolutely terrible. So we sat down to essentially build out a website, um, and a web application that enabled us to go do this at scale. And so sat down, started building it. I think it took us about, uh, about a month or two to actually build out kind of version one. Um, I had built the very, very, very smallest prototype, and that was starting to gain some steam, maybe 10,000 free users, uh, using the app kind of really regularly, some big brands took us about maybe two months to build out a kind of a professional version of that. We then launched it, uh, crossed our fingers, went to bed that night thinking that maybe.

AI assessment note: “we built a Facebook analytics kind of Facebook insights”

Q And, and just out of curiosity, because we have a lot of Students that are maybe using their local entrepreneurship clubs to start their own businesses, and they're always having equity debates with co-founders. How did you three decide how much equity each person would get?

A Yeah. So for the first business, it was, it was kind of a really basic process of like, okay, let's start at 33% for each. And then how do we distribute it based on what else we could be doing? Um, and so like my co-founder who was doing all the development, like he could have worked at, you know, Google or something or Facebook of that, of that nature. And so it was like, okay, how do we, how do we shift that over to make sense for him? So we started at 33% each and then we adapted. And I think that's great for a small business model. When you start talking about VCs and series A, Uh, that model changes a little bit.

AI assessment note: “let's start at 33% for each. And then how do we distribute it based on”

Q Okay. Got it. And walk me through that negotiation. I mean, how did that work? I mean, I know we spoke, but did you run a big process after that with a bunch of people?

A So we had basically kind of two horses in the race. Um, uh, simply measured was interested in us. And so it was a social bakers and we were starting to work on Dolly already. And so that we had this interesting proposition where we were going to sell the technology, but not the team. And Some companies were really interested in that and some companies weren't. Uh, and so for me, it was fine trying to find the right companies that, that were interested in just the technology or the branding and social bakers was an awesome fit because they were trying to get into the American market. They really needed something to prove their newsfeed knowledge, uh, worthiness. And a drink checker was the, it was kind of the shining gem for them that fit into this bigger conglomerate that made a lot of other things on unlocked for them.

AI assessment note: “we had basically kind of two horses in the race. Um, simply measured was interested”

Q I mean, are you, when you guys raised, cause it was recently, I mean, are you seeing crazy valuations like that?

A Yeah, I think valuations are a bit high right now, um, as, especially in these kind of Uber for X type economy. And I think it all for me is driven around. I think everyone thinks there's something really interesting with this gig economy and what's going to pan out and which assets are going to work and which ones aren't. And so I think all the investors are saying like, Hey, if, if I can click on this one, it'll, it'll make up for the ones that I don't click on. And so I think we're in this period right now of, of figuring out which of these models are going to work for this space. And a lot of them are, and I'm confident Dolly will be one of them because of the unique asset that we're leveraging, which is pickup truck owners instead of Prius owners, which if you look at Postmates and Instacart and all these other companies, they're all competing for the same person in the same vehicle. I think that's really interesting for like, if you think of your labor acquisition costs, it's a really difficult balance to maintain. So we'll be looking at the different businesses that the asset Makes sense and you can acquire it cheaply. And so I think there's just big bets happening.

AI assessment note: “Yeah, I think valuations are a bit high right now”

Q Well, give me the math equation that you used, I mean, again, and I assume you're thinking, like, You know, MRR times 12 annualized times a discount because churns high times this equals x. What was that math like?

A Yeah. So it was pretty close to that. I think that in, in hindsight, we got a pretty nice deal on the churn. The churn wasn't too much of a, of a negative impact because they felt like a lot of our really old customers were still really interesting value because we saw a lot of people that churn, churn from, from paid to free and then back to paid depending on campaigns. And so we had this little like oscillation, uh, thing in our business where just because we lost them didn't mean we lost them forever. And so I think social bakers looked at that as an upsell opportunity. And I actually had the great Opportunity to talk to with Jan who acquired the business, uh, just, just last week. I was like, Hey, now that everything is that's dust has settled, how did that net out? And he's like, I actually think it was, it was a really strong move for us. It wasn't a home run, but I think we made our money back and then some, and we've been able to unlock some branding out of it too. So, um, but the equation was roughly kind of how you described it, where it was, it was kind of the month of your reoccurring revenue time and annualized out. We had a very small penalty for the churn, right? Because of the interesting opportunity. And then we picked a multiplier.

AI assessment note: “the equation was roughly kind of how you described it”