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Apr 30, 2018 · 16m · top-founders

1010 The Boston Poker Player Turned B2B Advertiser Breaks $10m Revenue Mark

Patrick Shea · 9m spoken Nathan Latka · 5m spoken
0:00 / 0:00

gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

Nathan Latka interviews Patrick Shea, CEO and co-founder of Adaptive Intelligence, exploring how he bootstrapped a B2B ad-tech data platform past $10 million in revenue through proprietary data triangulation, CPM monetization, and heavy operational automation.

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 36.4% of the talking time here. How this is scored →

Nathan as informed peer 4.3 Guest teaching 3.0 Guest disagreement 1.5 Nathan pushing back 2.5
05100:0010:001:12–3:41 · Nathan as informed peer 4/10 Business Model Overview and Proprietary Data Matching Nathan probes how the company connects offline and online data and asks if they rely on vendors like Clearbit. Patrick explains their proprietary matching mechanism and clarifies their non-SaaS adtech roots.3:41–6:16 · Nathan as informed peer 3/10 CPM Media Model, Impression Scale, and Operating Team Nathan asks about the pricing model and needs clarification on the term 'IO based'. Patrick walks through their CPM range, impression numbers, and client breakdown.6:16–11:55 · Nathan as informed peer 5/10 Bootstrapping Journey, Automation, and SaaS Comparison Nathan attempts to calculate annual revenue using CPM math but mistakenly uses monthly impression figures, prompting Patrick to correct the timeline. Patrick then details their bootstrapping journey, automation efficiencies, and why they choose a media model over pure SaaS.11:57–15:02 · Nathan as informed peer 5/10 Sponsor Break: Acuity Scheduling Efficiency Tool After an ad read for Acuity Scheduling, Nathan asks about profit distribution and competitors like Demandbase. Nathan displays industry knowledge by sharing recent data points about Demandbase's ARR.1:12–3:41 · Guest teaching 3/10 Business Model Overview and Proprietary Data Matching Nathan probes how the company connects offline and online data and asks if they rely on vendors like Clearbit. Patrick explains their proprietary matching mechanism and clarifies their non-SaaS adtech roots.3:41–6:16 · Guest teaching 3/10 CPM Media Model, Impression Scale, and Operating Team Nathan asks about the pricing model and needs clarification on the term 'IO based'. Patrick walks through their CPM range, impression numbers, and client breakdown.6:16–11:55 · Guest teaching 4/10 Bootstrapping Journey, Automation, and SaaS Comparison Nathan attempts to calculate annual revenue using CPM math but mistakenly uses monthly impression figures, prompting Patrick to correct the timeline. Patrick then details their bootstrapping journey, automation efficiencies, and why they choose a media model over pure SaaS.11:57–15:02 · Guest teaching 2/10 Sponsor Break: Acuity Scheduling Efficiency Tool After an ad read for Acuity Scheduling, Nathan asks about profit distribution and competitors like Demandbase. Nathan displays industry knowledge by sharing recent data points about Demandbase's ARR.1:12–3:41 · Guest disagreement 1/10 Business Model Overview and Proprietary Data Matching Nathan probes how the company connects offline and online data and asks if they rely on vendors like Clearbit. Patrick explains their proprietary matching mechanism and clarifies their non-SaaS adtech roots.3:41–6:16 · Guest disagreement 1/10 CPM Media Model, Impression Scale, and Operating Team Nathan asks about the pricing model and needs clarification on the term 'IO based'. Patrick walks through their CPM range, impression numbers, and client breakdown.6:16–11:55 · Guest disagreement 2/10 Bootstrapping Journey, Automation, and SaaS Comparison Nathan attempts to calculate annual revenue using CPM math but mistakenly uses monthly impression figures, prompting Patrick to correct the timeline. Patrick then details their bootstrapping journey, automation efficiencies, and why they choose a media model over pure SaaS.11:57–15:02 · Guest disagreement 2/10 Sponsor Break: Acuity Scheduling Efficiency Tool After an ad read for Acuity Scheduling, Nathan asks about profit distribution and competitors like Demandbase. Nathan displays industry knowledge by sharing recent data points about Demandbase's ARR.1:12–3:41 · Nathan pushing back 2/10 Business Model Overview and Proprietary Data Matching Nathan probes how the company connects offline and online data and asks if they rely on vendors like Clearbit. Patrick explains their proprietary matching mechanism and clarifies their non-SaaS adtech roots.3:41–6:16 · Nathan pushing back 2/10 CPM Media Model, Impression Scale, and Operating Team Nathan asks about the pricing model and needs clarification on the term 'IO based'. Patrick walks through their CPM range, impression numbers, and client breakdown.6:16–11:55 · Nathan pushing back 4/10 Bootstrapping Journey, Automation, and SaaS Comparison Nathan attempts to calculate annual revenue using CPM math but mistakenly uses monthly impression figures, prompting Patrick to correct the timeline. Patrick then details their bootstrapping journey, automation efficiencies, and why they choose a media model over pure SaaS.11:57–15:02 · Nathan pushing back 2/10 Sponsor Break: Acuity Scheduling Efficiency Tool After an ad read for Acuity Scheduling, Nathan asks about profit distribution and competitors like Demandbase. Nathan displays industry knowledge by sharing recent data points about Demandbase's ARR.

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

0:00 · Nathan 50.9% · guest 49.1%0:00 · Nathan 50.9% · guest 49.1%3:00 · Nathan 12.2% · guest 87.8%3:00 · Nathan 12.2% · guest 87.8%6:00 · Nathan 42% · guest 58%6:00 · Nathan 42% · guest 58%9:00 · Nathan 17.3% · guest 82.7%9:00 · Nathan 17.3% · guest 82.7%12:00 · Nathan 51% · guest 49%12:00 · Nathan 51% · guest 49%15:00 · Nathan 53.1% · guest 46.9%15:00 · Nathan 53.1% · guest 46.9%
Sharpest disagreement ▶ 13:56 Aiming to Beat Demandbase

When asked if he would consider selling out to Demandbase, Patrick firmly rejects the premise and asserts he would rather take them down.

 Hardest push from Nathan ▶ 6:21 Testing Revenue Math from Impression Data

Nathan aggressively multiplies out the impression and CPM figures to check if Patrick's reported numbers represent actual gross revenue after COGS.

 Biggest teaching moment ▶ 7:03 Correcting Monthly vs Annual Impression Metrics

Patrick corrects Nathan's 2.7 million dollar revenue calculation by clarifying that 300 million impressions was a monthly figure, putting annual impressions into the billions.

 Nathan holds their own ▶ 14:05 Citing Demandbase Revenue Benchmarks

Nathan demonstrates sharp domain intel by sharing proprietary ARR and customer numbers from Demandbase's founder based on a recent interview.
the scores for every segment, with the reasoning behind each
ChapterTopicNathan as informed peerGuest teachingGuest disagreementNathan pushing backWhy
Business Model Overview and Proprietary Data Matching 4312 Nathan probes how the company connects offline and online data and asks if they rely on vendors like Clearbit. Patrick explains their proprietary matching mechanism and clarifies their non-SaaS adtech roots.
CPM Media Model, Impression Scale, and Operating Team 3312 Nathan asks about the pricing model and needs clarification on the term 'IO based'. Patrick walks through their CPM range, impression numbers, and client breakdown.
Bootstrapping Journey, Automation, and SaaS Comparison 5424 Nathan attempts to calculate annual revenue using CPM math but mistakenly uses monthly impression figures, prompting Patrick to correct the timeline. Patrick then details their bootstrapping journey, automation efficiencies, and why they choose a media model over pure SaaS.
Sponsor Break: Acuity Scheduling Efficiency Tool 5222 After an ad read for Acuity Scheduling, Nathan asks about profit distribution and competitors like Demandbase. Nathan displays industry knowledge by sharing recent data points about Demandbase's ARR.

Statements from this episode (8)

Disclosure
Adaptive Intelligence uses in-house matching rather than third-party enrichers
“So it's all proprietary. We do it all ourselves. We do partner with like digital element and then some of the bigger offline data guys that you've probably heard of to help round out the offering, but the actual kind of matching process is proprietary.”
Patrick Shea Apr 30, 2018 ▶ 2:08
Disclosure
Shea: Adaptive Intelligence goes to market at $7 to $9 CPM
“Seven to nine is where we go to market. Obviously with some of our bigger customers that comes down a little bit.”
Patrick Shea Apr 30, 2018 ▶ 5:24
Disclosure
Shea: Adaptive Intelligence delivers roughly 300M monthly impressions
“So let's see monthly. We're probably it's in the hundreds of millions. So, you know, somewhere in the three hundred million range, somewhere around there, but it varies by month. We're definitely heading to four.”
Patrick Shea Apr 30, 2018 ▶ 5:35
Disclosure
Shea: Adaptive Intelligence employs 35 office staff and 5 contractors
“So we've got 35 here in the office and then we have five contractors as well. So those guys are all local Waltham, Cambridge area too.”
Patrick Shea Apr 30, 2018 ▶ 6:09
Disclosure
Shea: Adaptive Intelligence is entirely bootstrapped with zero outside funding
“So we bootstrapped it from the start. We've never taken any funding.”
Patrick Shea Apr 30, 2018 ▶ 6:19
Assertion Not checkable as stated
Shea: Adaptive Intelligence grows 40% to 50% yearly into double-digit millions
“Growth rate year over year is between 40 and 50%. We've doubled since the end of 2015 and like I said, we're in that, we're in the early double digits in the millions.”
Patrick Shea Apr 30, 2018 ▶ 8:49
What-if
Automation allows Adaptive Intelligence to operate with 35 employees instead of 60
“We've got 35 people here, but we don't have any traffickers. We don't have any really basic campaign analysts. Most of that work is all done programmatically with our algorithms and APIs and things like that. So we'd probably be closer to, like, 60 people if w…”
Patrick Shea Apr 30, 2018 ▶ 11:29
Assertion Supported
Latka: Demandbase has 400-600 customers and near $100M ARR
“He was just on, they just, they're between four and 600 customers. They just broke or they're getting close to breaking a hundred million bucks in ARR.”
Nathan Latka Apr 30, 2018 ▶ 14:06
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