The Exchanges

Q And are they, can you, when you go through each of these models, are these all SAS models?

A That, that part is a SAS model. We sell it as a subscription. And we sell it either direct or through, uh, our channel partnerships. We have over 55 channel partners who, uh, sell, uh, our analytics SaaS product, if you will. The other product, audience solutions, is, uh, sometimes SaaS, but more, more, um, more frequently not SaaS. It is, is a, uh, a product that's bought on a CPM, which is a cost per thousand. Media world, what that means is they, they're buying data and they pay for it as they use it for every thousand impressions that they use our data to target an ad. They pay us a certain cost per thousand. So it's a variable price. Uh, but what we have seen is that the more subscriptions that we sell, the more revenue, um, we drive on the, um, the variable piece of our business as well.

AI assessment note: “The other product, audience solutions, is, uh, sometimes SaaS, but more, more frequently not SaaS.”

Q So this is really for your own internal purposes. I mean, how have you changed the make of the company in the face of the virus? Have you laid people off, furloughed, like what strategic decisions?

A Yeah. Interestingly enough, you know, we haven't done, we haven't had to do anything like that. We haven't, um, done any furloughs. We haven't laid anyone off. Uh, businesses is as, as, as usual for us right now, we, we actually had a very strong Q one. In fact, in March, in the last two weeks in March, even we closed more new logos, new customers than we've really ever closed before. And I think that's a strong testament to what our team has built and the value of the product. And I think that Now, more than ever, it's really important for companies to try to identify what's working, what's not working, which companies are really going to buy from them or not. They have less resources. Everyone's looking at their budget, and we're able to identify what companies are really interested in, who it's likely to buy or not. So, fortunately, things haven't, haven't, haven't changed there. We have, I will tell you, on our own budget, the big obvious thing is that we had a, we had a big budget for a larger, a largest part of our budget was for events. You know, we do a lot of events. And for a small company like ours, I call ourselves small still under 500 employees. Um, we, we, we had a big hole in our budget as all these events got canceled. We are redeploying some of that money to other tactics that are the gen oriented. Um, but we won't deploy all of it. We'll, we'll, we'll hold on…

AI assessment note: “We haven't, um, done any furloughs. We haven't laid anyone off.”

Q Cool. So 20, 20 14. And I want to get more into the spin out story there in case there's entrepreneurs listening, thinking about doing that same kind of structure. But before we do that, uh, in a nutshell, what does Bambora do and what is the revenue model? How do you make money?

A So at the core of what we do, we're a data co-op. And what that means is we have partnered with Uh, many, many, uh, publishers and very notable large ones like the Wall Street Journal, uh, CBS, Quinn Street, um, Bloomberg recently. They all pool their behavioral, uh, and BD data together so that they have more scale and they know more about their users and they can build insightful, uh, innovative products for their advertisers and marketers. So that's at the core of what we do. Uh, we have three, three different products, if you will, that are the Bumball family. There's a surge analytics where we can identify using all the data that we have access to. We can identify what companies In the world are interested in your products. So if you're a B to B marketer, let's say IBM or Lenovo, and you're trying to sell a product like servers, we can actually identify which companies out of two companies in the world are most likely to be in market for servers in the next three to six months, let's say.

AI assessment note: “So at the core of what we do, we're a data co-op.”

Q are doing right now, where they were all built on kind of taking, it depends on their scale, like MediaOcean, it's less than a percent. Some people are taking up to 20, 30, 40%, or 40 cents on the dollar of ad spend going through their platform. They're all trying to invent SaaS platforms now to aggregate this data and make it more valuable. You're generally following those same footsteps?

A Yeah, and you bring up an interesting point, ad tech, which So we, we are, we're unique in the sense that we're not specific to ad tech. We actually, our data flows through the whole marketing and sales stack, right? So we, we partner with, for example, in the marketing space, Marketo. We, our data flows into Marketo. So when we meet with a CMO, Unifying sales and marketing. We can speak to a CMO and say, hey, you can, there's a lot of use cases for our data. Let's find a place to start. Let's find a place that works. But the nice thing is they can use our data to unify, I think, internally and externally so they can target the right buyers at the right companies at the right time for programmatic display, for email nurturing. That's where the Marketo and Eloqua has come in. For sales, we integrate it to CRM. So different than some of those other point Solutions that mentioned, uh, we're really more agnostic to the ecosystem, but also in every part of sales and marketing.

AI assessment note: “we're unique in the sense that we're not specific to ad tech.”

Q Interesting. So why would the parent, what was in it from the parent company then? What did they get out of all this? Did you buy this piece of tech from them or?

A So again, so I actually, The cap table of the original company was very similar to this one. Um, it was all individuals. I didn't, right. It was mainly me and a bunch of angels. We didn't have a complicated cap structure with preferred shares, all common shares. There was no, um, VC, uh, or, you know, private equity or anything like that involved. So I was able to do that and prove that it would make sense to spin it out as a separate entity and, uh, let it, let it. And at the time, by the way, we had no revenue. You know, we're really, it was in its infancy. It was a little bit more than an idea at that point.

AI assessment note: “The cap table of the original company was very similar to this one.”

Q Your tech, though, is cookie based? Sorry? Cookie based? IP address based?

A We use a lot of different tactics. Um, you know, at the core of what we do, there's a few, a few pieces of technology that we've, we've, we've built, uh, first is an NLP engine, so we are able to identify, and I should back up for 1:02, the reason that we're able to do what we do is because Over the last five years, we've built out a data cooperative. We've worked with thousands of B to B publishers, uh, from some of the biggest B to B brands you've ever heard of to some very niche, uh, B to B publishers. Um, we, we cover essentially anywhere that a business professional will do research. It's pretty much who we work with.

AI assessment note: “We use a lot of different tactics. Um, you know, at the core”