SaaS Interviews with CEOs, Startups, Founders
1018 Why He's Making Leap from AdTech to Data As A Service Business Model
08 May 2018
Chapter 1: What is the main topic discussed in this episode?
This is the Top Entrepreneurs Podcast, where founders share how they started their companies and got filthy rich or crash and burn. Each episode features revenue numbers, customer counts, and other insider information that creates business news headlines. We went from a couple of hundred thousand dollars to 2.7 million. I had no money when I started the company.
It was $160 million, which is the size of many IPOs.
We're a bit strapped. We have like 22,000 customers. With over 5 million downloads in a very short amount of time, major outlets like Inc. are calling us the fastest growing business show on iTunes. I'm your host, Nathan Latka, and here's today's episode. Hello, everyone. My guest today is Kevin Tan.
He's an accomplished entrepreneur and advertising industry technology leader with over 20 years of experience. He's the co-founder and CEO of IOTA, the global leader in audience data with more than 3.5 billion unique profiles across Europe, Asia Pacific, and the Americas. Kevin, are you ready to take us to the top? All set. All right. You bet. What does audience data mean?
Audience data is really information around any consumers. We start off providing audience data for media and advertising consumers, but I suppose data on audiences has grown to be more than just the media consumption. It's really information and profiling of customers' And consumers can be home consumers or business consumers really based on their activities and their demographics.
And who's buying this data? Are we talking consumer brands like P&G or are we talking ad executives to target their ads better?
A combination of all the above. I think it flows down really from a lot of the brands. So the brands can be someone like a CPG brand, like a P&G. It could be a business brand. It could be a retailer. And it's also all the channels in between. The brands obviously are using it for marketing, but they are also using it for their advertising and targeting of their advertising.
It's being used by publishers really across the gamut. Everyone is looking for data to do kind of profiling and targeting, be it content or be it advertising.
And data is a very competitive space. I mean, what relationships have you secured that gives you proprietary access to data other people don't have?
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Chapter 2: What does audience data mean and who buys it?
What's the business model here? We help partners who have data basically turn that into something that can be activated online so they will pay us a fee there. Some of it is a SaaS fee.
Other parts of the fee that we get are selling a lot of this data either for some of those partners or data that we're pulling together and selling under our own brand and selling that on either a flat fee or a cost per thousand or a data as a service fee. There's a lot of different ways to sell data. It depends on really the channels.
If you're selling within advertising, the advertising world typically gets sold just like advertising inventory gets sold, either on a cost per thousand or on a kind of performance type of basis. If you're selling within marketing, again, it could be on a flat fee, on an analytics type of fee where it's a monthly recurring service fee or data as a service. We sell a lot of data.
We also distribute data around different channels for people. And that's, again, like a SaaS type fee.
Are any of these revenue streams clearly the leader for you, or no, they're all pretty even?
I'd say probably the leader for us is where we started from seven years ago globally. That was building out an audience data marketplace. So it's a CPM charging model? That has been CPM. It's been percentage of media spend, and it's also increasingly monthly service fees to some of the brands.
Yeah, so we've had probably 20 CEOs on in this space and they all, like Bill Wise, Media Ocean came on. I mean, they all come from the old world, which is a percentage model. And they're all trying to move to a SaaS model, especially as spend's being moved inside teams themselves.
But I just haven't met anyone who's doing it really well because you're having to manage multiple business models and multiple different channels. And it's a big clusterfuck. Like, how do you figure out what to focus on?
But for us, we consider ourselves more of a technology or platform company. We're integrated into over 100 of the world's leading platforms that use Zoom audience data. So those can be DSPs, they can be DMPs, they can be MarTech platforms, they can be content delivery systems, they can be native ad platforms, mobile platforms.
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Chapter 3: How does the company secure proprietary access to data?
Yeah, well, you know, I think people are, you know, initially when the business started, I think people onboarded data and sold it, you know, on a CPM on a transactional basis because that was the way that media was sold. So it was just really easy to append and buy it because that's the way the advertisers bought it.
When you say data as a service, are you saying this many records over this period of time?
Yes. So what's happening right now is we're selling data. We tend to be selling data as a service into a lot of different brands and advertisers and publishers, et cetera, and businesses who are consuming that data and using it for a wide range of service. They're buying a certain volume of data that they can utilize for whatever purposes are stated in the contract over a period of time.
So it's a recurring monthly subscription time.
Yeah, they're buying in a recurring model a bag of popcorn. Each kernel of popcorn is a different data point. As they eat the popcorn in the bag, it's empty. They then need to buy that popcorn bag again next month. And so it is a bit predictable.
Yeah. I mean, in terms of that way, it's predictable. I think also, you know, if you look at the predictability, even across the transactional stuff that's attached to the ad spend, it is pretty predictable. You know, and we see we certainly see that in our business. It's in terms of spending very, very low churn rates in terms of if you're looking at advertisers on a monthly basis and we have.
thousands and thousands of advertisers buying our data on a monthly basis. But if you look at other businesses, I know Jeff Green over at the Trade Desk is talking a lot about how they have predictability of their revenue, even though it's transactional, it's much more SaaS-based, even though it's transactional.
So I think you start to see this in a lot of areas, but we are actually seeing pretty rapid growth in the SaaS part of our business.
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Chapter 4: What is the business model for data as a service?
Number four, how many hours of sleep do you get every night?
Chapter 5: How does the company make money from its data services?
six okay that's good what's your situation married single you have kids married with the child okay good one kid like like young you're not getting sleep child one kid teenage daughter so yeah that that's not my sleep that's not my sleep issue and how old are you kevin i am uh in my late 40s late 40s okay last question take us back about you know late 20s take us back like 28 ish years what do you wish your 20 year old self knew
Ah, well, I wish my 28 year old self knew. Um, I suppose that, um, that things come up and down in waves. You know, I, um, been through, been through a couple of waves of stuff and, uh, And yeah, when things are good harvest, when things are bad invest.
There you guys have it from Kevin. Things come in waves. Don't ever forget that. He cut his teeth in this space, making and growing a company very large before then exiting, doing his own thing, launching IOTA in 2010. They had headquarters over many different locations. Even right at launch, they now have 65 folks.
Again, looking at a data as a service platform, a SaaS platform with their relationships back with podcasting. publishers. They've got a large data set of 3.5 billion unique profiles across Europe, Asia Pacific, and the Americas with about north of $8 million raised with the current announcement growing quickly. Kevin, thank you for taking us to the top.
Thanks a lot. Bye-bye.
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