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May 8, 2018 · 19m · top-founders

1018 Why He's Making Leap from AdTech to Data As A Service Business Model

Kevin Tan · 11m spoken Nathan Latka · 6m spoken
0:00 / 0:00

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In this episode of The Top Entrepreneurs podcast, host Nathan Latka interviews Kevin Tan, CEO of Eyeota, about building a global audience data platform aggregating 3.5 billion profiles. Kevin shares how Eyeota bootstrapped, raised $12 million in funding, and is successfully transitioning from transactional ad tech revenue to predictable Data as a Service (DaaS) subscriptions.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Nathan holds 37.7% of the talking time here. How this is scored →

Nathan as informed peer 5.6 Guest teaching 3.1 Guest disagreement 2.4 Nathan pushing back 4.0
05100:0010:000:49–4:34 · Nathan as informed peer 5/10 Introducing Kevin Tan, CEO of Eyeota Nathan probes how Eyeota accesses proprietary audience data and clarifies technical industry acronyms like PII. Kevin explains their heuristic data-matching methodologies and privacy compliance across global markets.4:34–7:04 · Nathan as informed peer 6/10 Monetization Models and Platform Integrations Nathan draws on past interviews with adtech CEOs to assert that transitioning from percentage-based transactional fees to SaaS is chaotic and difficult. Kevin reframes Eyeota as a platform company plugged into over 100 DSPs and DMPs regardless of pricing model.7:04–9:07 · Nathan as informed peer 7/10 Fundraising History and Valuation Multiples When Kevin initially declines to disclose fundraising totals, Nathan cites exact public round details and accuses Kevin of claiming a SaaS model purely for valuation multiples. Kevin firmly counters Nathan's mischaracterization of his revenue breakdown.9:08–11:38 · Nathan as informed peer 5/10 Exploring Data as a Service and Predictable Revenue Nathan introduces a popcorn bag metaphor to conceptualize predictable DaaS consumption. Kevin explains how transactional programmatic revenue exhibits high predictability similar to SaaS, citing Trade Desk's public positioning.11:40–14:58 · Nathan as informed peer 6/10 Navigating In-House Media Shifts and Industry Trends Nathan questions whether brands bringing media in-house erodes transactional margins, which Kevin denies due to multi-channel data utility. The discussion shifts to Kevin's previous exit with Adify and the founding journey of Eyeota.14:58–17:15 · Nathan as informed peer 7/10 Go-to-Market Strategy and Distribution Channels Nathan presses for customer counts, calling out Kevin's evasive answer of 'between a dozen and a thousand.' Nathan breaks down the stark mechanical differences between high-touch enterprise deals and low-touch self-serve SaaS distribution.17:15–18:50 · Nathan as informed peer 3/10 The Famous Five Rapid-Fire Questions Nathan conducts the standard Famous Five rapid-fire closing sequence, touching on book recommendations, CEO role models, sleep routines, and lessons learned about business cycles.0:49–4:34 · Guest teaching 4/10 Introducing Kevin Tan, CEO of Eyeota Nathan probes how Eyeota accesses proprietary audience data and clarifies technical industry acronyms like PII. Kevin explains their heuristic data-matching methodologies and privacy compliance across global markets.4:34–7:04 · Guest teaching 3/10 Monetization Models and Platform Integrations Nathan draws on past interviews with adtech CEOs to assert that transitioning from percentage-based transactional fees to SaaS is chaotic and difficult. Kevin reframes Eyeota as a platform company plugged into over 100 DSPs and DMPs regardless of pricing model.7:04–9:07 · Guest teaching 4/10 Fundraising History and Valuation Multiples When Kevin initially declines to disclose fundraising totals, Nathan cites exact public round details and accuses Kevin of claiming a SaaS model purely for valuation multiples. Kevin firmly counters Nathan's mischaracterization of his revenue breakdown.9:08–11:38 · Guest teaching 3/10 Exploring Data as a Service and Predictable Revenue Nathan introduces a popcorn bag metaphor to conceptualize predictable DaaS consumption. Kevin explains how transactional programmatic revenue exhibits high predictability similar to SaaS, citing Trade Desk's public positioning.11:40–14:58 · Guest teaching 4/10 Navigating In-House Media Shifts and Industry Trends Nathan questions whether brands bringing media in-house erodes transactional margins, which Kevin denies due to multi-channel data utility. The discussion shifts to Kevin's previous exit with Adify and the founding journey of Eyeota.14:58–17:15 · Guest teaching 3/10 Go-to-Market Strategy and Distribution Channels Nathan presses for customer counts, calling out Kevin's evasive answer of 'between a dozen and a thousand.' Nathan breaks down the stark mechanical differences between high-touch enterprise deals and low-touch self-serve SaaS distribution.17:15–18:50 · Guest teaching 1/10 The Famous Five Rapid-Fire Questions Nathan conducts the standard Famous Five rapid-fire closing sequence, touching on book recommendations, CEO role models, sleep routines, and lessons learned about business cycles.0:49–4:34 · Guest disagreement 1/10 Introducing Kevin Tan, CEO of Eyeota Nathan probes how Eyeota accesses proprietary audience data and clarifies technical industry acronyms like PII. Kevin explains their heuristic data-matching methodologies and privacy compliance across global markets.4:34–7:04 · Guest disagreement 2/10 Monetization Models and Platform Integrations Nathan draws on past interviews with adtech CEOs to assert that transitioning from percentage-based transactional fees to SaaS is chaotic and difficult. Kevin reframes Eyeota as a platform company plugged into over 100 DSPs and DMPs regardless of pricing model.7:04–9:07 · Guest disagreement 6/10 Fundraising History and Valuation Multiples When Kevin initially declines to disclose fundraising totals, Nathan cites exact public round details and accuses Kevin of claiming a SaaS model purely for valuation multiples. Kevin firmly counters Nathan's mischaracterization of his revenue breakdown.9:08–11:38 · Guest disagreement 2/10 Exploring Data as a Service and Predictable Revenue Nathan introduces a popcorn bag metaphor to conceptualize predictable DaaS consumption. Kevin explains how transactional programmatic revenue exhibits high predictability similar to SaaS, citing Trade Desk's public positioning.11:40–14:58 · Guest disagreement 2/10 Navigating In-House Media Shifts and Industry Trends Nathan questions whether brands bringing media in-house erodes transactional margins, which Kevin denies due to multi-channel data utility. The discussion shifts to Kevin's previous exit with Adify and the founding journey of Eyeota.14:58–17:15 · Guest disagreement 4/10 Go-to-Market Strategy and Distribution Channels Nathan presses for customer counts, calling out Kevin's evasive answer of 'between a dozen and a thousand.' Nathan breaks down the stark mechanical differences between high-touch enterprise deals and low-touch self-serve SaaS distribution.17:15–18:50 · Guest disagreement 0/10 The Famous Five Rapid-Fire Questions Nathan conducts the standard Famous Five rapid-fire closing sequence, touching on book recommendations, CEO role models, sleep routines, and lessons learned about business cycles.0:49–4:34 · Nathan pushing back 2/10 Introducing Kevin Tan, CEO of Eyeota Nathan probes how Eyeota accesses proprietary audience data and clarifies technical industry acronyms like PII. Kevin explains their heuristic data-matching methodologies and privacy compliance across global markets.4:34–7:04 · Nathan pushing back 5/10 Monetization Models and Platform Integrations Nathan draws on past interviews with adtech CEOs to assert that transitioning from percentage-based transactional fees to SaaS is chaotic and difficult. Kevin reframes Eyeota as a platform company plugged into over 100 DSPs and DMPs regardless of pricing model.7:04–9:07 · Nathan pushing back 7/10 Fundraising History and Valuation Multiples When Kevin initially declines to disclose fundraising totals, Nathan cites exact public round details and accuses Kevin of claiming a SaaS model purely for valuation multiples. Kevin firmly counters Nathan's mischaracterization of his revenue breakdown.9:08–11:38 · Nathan pushing back 3/10 Exploring Data as a Service and Predictable Revenue Nathan introduces a popcorn bag metaphor to conceptualize predictable DaaS consumption. Kevin explains how transactional programmatic revenue exhibits high predictability similar to SaaS, citing Trade Desk's public positioning.11:40–14:58 · Nathan pushing back 4/10 Navigating In-House Media Shifts and Industry Trends Nathan questions whether brands bringing media in-house erodes transactional margins, which Kevin denies due to multi-channel data utility. The discussion shifts to Kevin's previous exit with Adify and the founding journey of Eyeota.14:58–17:15 · Nathan pushing back 6/10 Go-to-Market Strategy and Distribution Channels Nathan presses for customer counts, calling out Kevin's evasive answer of 'between a dozen and a thousand.' Nathan breaks down the stark mechanical differences between high-touch enterprise deals and low-touch self-serve SaaS distribution.17:15–18:50 · Nathan pushing back 1/10 The Famous Five Rapid-Fire Questions Nathan conducts the standard Famous Five rapid-fire closing sequence, touching on book recommendations, CEO role models, sleep routines, and lessons learned about business cycles.

speaking balance: gold is Nathan, purple is the guest (3 minute bins)

0:00 · Nathan 41% · guest 59%0:00 · Nathan 41% · guest 59%3:00 · Nathan 11.4% · guest 88.6%3:00 · Nathan 11.4% · guest 88.6%6:00 · Nathan 40.6% · guest 59.4%6:00 · Nathan 40.6% · guest 59.4%9:00 · Nathan 50.6% · guest 49.4%9:00 · Nathan 50.6% · guest 49.4%12:00 · Nathan 16.4% · guest 83.6%12:00 · Nathan 16.4% · guest 83.6%15:00 · Nathan 55.9% · guest 44.1%15:00 · Nathan 55.9% · guest 44.1%18:00 · Nathan 62.6% · guest 37.4%18:00 · Nathan 62.6% · guest 37.4%
Sharpest disagreement ▶ 8:06 Kevin rejects Nathan's revenue attribution

Kevin directly challenges Nathan's aggressive assertion that he misrepresented his revenue mix to chase higher SaaS valuation multiples.

 Hardest push from Nathan ▶ 7:11 Nathan overrides Kevin's funding secrecy

After Kevin claims funding numbers are private, Nathan immediately checks his research notes and reads off the exact dates and round sizes publicly on air.

 Biggest teaching moment ▶ 3:33 Heuristic matching across international privacy laws

Kevin explains why standard US PII and direct marketing spines fail abroad, detailing Eyeota's complex heuristic matching across multi-jurisdictional privacy frameworks.

 Nathan holds their own ▶ 16:12 Contrasting high-touch dinners with low-touch seat SaaS

Nathan exposes the vagueness of Kevin's customer range by articulating the concrete operational differences between 50 enterprise client relationships and self-serve $30/month models.
the scores for every segment, with the reasoning behind each
ChapterTopicNathan as informed peerGuest teachingGuest disagreementNathan pushing backWhy
Introducing Kevin Tan, CEO of Eyeota 5412 Nathan probes how Eyeota accesses proprietary audience data and clarifies technical industry acronyms like PII. Kevin explains their heuristic data-matching methodologies and privacy compliance across global markets.
Monetization Models and Platform Integrations 6325 Nathan draws on past interviews with adtech CEOs to assert that transitioning from percentage-based transactional fees to SaaS is chaotic and difficult. Kevin reframes Eyeota as a platform company plugged into over 100 DSPs and DMPs regardless of pricing model.
Fundraising History and Valuation Multiples 7467 When Kevin initially declines to disclose fundraising totals, Nathan cites exact public round details and accuses Kevin of claiming a SaaS model purely for valuation multiples. Kevin firmly counters Nathan's mischaracterization of his revenue breakdown.
Exploring Data as a Service and Predictable Revenue 5323 Nathan introduces a popcorn bag metaphor to conceptualize predictable DaaS consumption. Kevin explains how transactional programmatic revenue exhibits high predictability similar to SaaS, citing Trade Desk's public positioning.
Navigating In-House Media Shifts and Industry Trends 6424 Nathan questions whether brands bringing media in-house erodes transactional margins, which Kevin denies due to multi-channel data utility. The discussion shifts to Kevin's previous exit with Adify and the founding journey of Eyeota.
Go-to-Market Strategy and Distribution Channels 7346 Nathan presses for customer counts, calling out Kevin's evasive answer of 'between a dozen and a thousand.' Nathan breaks down the stark mechanical differences between high-touch enterprise deals and low-touch self-serve SaaS distribution.
The Famous Five Rapid-Fire Questions 3101 Nathan conducts the standard Famous Five rapid-fire closing sequence, touching on book recommendations, CEO role models, sleep routines, and lessons learned about business cycles.

Statements from this episode (11)

Assertion Not checkable as stated
Eyeota tracks 3.5 billion unique users across 30,000 global publishers
“We track 3.5 billion uniques across the planet and our data is based out of a variety of different things. I suppose the base levers, base level is based on relationships with over 30,000 publishers around the globe.”
Kevin Tan May 8, 2018 ▶ 2:23
Assertion Not checkable as stated
Eyeota handles the most offline data onboarding outside the US
“Another part of our business is the onboarding of offline data, and we're the company that does the largest amount of this outside the United States, although we do it in the U.S. Now.”
Kevin Tan May 8, 2018 ▶ 3:09
Disclosure
Eyeota's audience data marketplace remains its primary revenue driver
“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.”
Kevin Tan May 8, 2018 ▶ 5:39
Opinion
AdTech companies struggle transitioning from percentage-of-spend to SaaS models
“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 spends being moved in inside teams themselves. But I just haven't met anyone who's doing it really well, because you're having to manag…”
Nathan Latka May 8, 2018 ▶ 6:04
Assertion Contradicted
Eyeota integrates into over 100 global audience data platforms
“We're integrated into over a hundred of the world's leading platforms that you use assume audience data. So those can be DSPs, they can be DMPs, they can be MarTech platforms, they can be, you know, content delivery systems, they can be native ad platforms, mo…”
Kevin Tan May 8, 2018 ▶ 6:29
Disclosure
Eyeota raised $12 million total through its Series A round
“You know, the last, as of the Series A, we'd raise twelve million dollars.”
Kevin Tan May 8, 2018 ▶ 7:28
Disclosure
Eyeota's fastest-growing revenue streams are Data as a Service and SaaS
“The fastest growing areas are the areas where we're selling data as a service, or we're selling things on a SaaS basis where we're distributing data or doing onboarding as a service.”
Kevin Tan May 8, 2018 ▶ 8:52
Assertion Not checkable as stated
Eyeota has thousands of advertisers buying its data monthly
“We have Thousands and thousands of advertisers buying our data on a monthly basis.”
Kevin Tan May 8, 2018 ▶ 10:17
Assertion Not checkable as stated
Eyeota transactional revenue resists erosion from brands bringing media buying in-house
“We're not seeing it because our data is being sold not only through advertising and media and to a whole bunch of other channels where It makes sense for them to buy it on a use case by use case basis. So, you know, PM or the like. So that part of our business…”
Kevin Tan May 8, 2018 ▶ 12:00
Disclosure
Founders bootstrapped Eyeota using proceeds from the sale of Adify
“Initially, we bootstrapped because we'd all made money off of the sale of the previous company.”
Kevin Tan May 8, 2018 ▶ 13:29
Assertion Not checkable as stated
Eyeota's SaaS customer count doubled over the past two years
“In terms of total customers, I mean, you know, I think the growth has probably doubled over the last two years since we started doing this.”
Kevin Tan May 8, 2018 ▶ 15:07
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