Aug 29, 2017 · 30m · top-founders

766: This Company Used Evangelical Christian Mobile Data to Influence US Elections

Anindya Datta · 16m spoken Nathan Latka · 10m spoken
0:00 / 0:00

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

Host Nathan Latka interviews Mobilewalla CEO Dr. Anindya Datta to explore how the company executed a strategic pivot from low-margin programmatic media buying into a high-margin B2B mobile consumer data SaaS platform. Datta outlines Mobilewalla's proprietary data sourcing architecture, lean global operations, and the strategic deployment of consumer mobile tracking in enterprise marketing and political campaign targeting.

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 40.4% of the talking time here. How this is scored →

Nathan as informed peer 5.2 Guest teaching 2.8 Guest disagreement 1.2 Nathan pushing back 2.3
05100:0010:0020:0030:001:23–3:23 · Nathan as informed peer 4/10 Guest Background and Career Overview Nathan prompts Anindya on MobileWalla's core value proposition and business model. Anindya walks through consumer audience generation and raw mobile telemetry data licensing.3:24–5:46 · Nathan as informed peer 6/10 Monetization Models and Pricing Mechanics Nathan analyzes the difference between pay-as-you-go audience batches and recurring SaaS subscriptions, accurately analogizing the pricing structure to API call metering.5:46–7:46 · Nathan as informed peer 7/10 Data Sourcing Infrastructure and Tracking Pixels Nathan immediately drills into COGS and gross margins upon hearing about data exchange partnerships, confirming the data acquisition is done via barter rather than cash expense.7:48–9:51 · Nathan as informed peer 6/10 Early Strategy: Leveraging Media Buys for Data Access Nathan presses on the cold-start problem of how MobileWalla secured early data access without leverage, pushing Anindya to clarify gross media buys versus net revenue take.10:00–12:00 · Nathan as informed peer 7/10 The Strategic Pivot to High-Margin Data Revenue Nathan synthesizes the strategic transition from low-margin media buying to pure data revenue, laying out the exact revenue trajectory and half-year drops across 2014 to 2016.12:00–16:45 · Nathan as informed peer 6/10 Mobilizing Voter Segments in the Presidential Election Anindya reveals their role in the 2016 US presidential election; Nathan repeatedly pushes to discover which party hired them and whether they won, while Anindya carefully deflects under NDA.16:45–19:32 · Nathan as informed peer 5/10 Customer Segmentation, Distribution Partners, and Low Churn Nathan breaks down customer segmentation between nine core SaaS clients and broader Oracle marketplace distribution, verifying enterprise churn dynamics.19:32–21:33 · Nathan as informed peer 5/10 Lean Sales Operations and Zero Paid Marketing Strategy Nathan investigates customer acquisition cost and sales cycles, learning that MobileWalla operates with only three quota carriers and zero paid marketing or PR spend.21:34–23:47 · Nathan as informed peer 5/10 Global Workforce Architecture and Data Corpus Mapping Anindya clarifies how their team in India performs manual point-of-interest mapping rather than standard outsourced engineering when Nathan asks about buyer segments like Walmart visitors.23:47–27:08 · Nathan as informed peer 8/10 Real-Time Get-Out-The-Vote (GOTV) Tracking Capabilities Nathan poses an incisive technical hypothesis about tracking polling place turnout in real time on election day, which Anindya praises as exactly how they powered GOTV operations.27:09–29:21 · Nathan as informed peer 3/10 The Famous Five Rapid-Fire Questions Nathan runs through the standard rapid-fire Famous Five format covering business books, CEO influences, and sleep schedules.29:21–30:39 · Nathan as informed peer 0/10 Episode Recap and Upcoming Show Preview Solo host monologue recapping MobileWalla's financial milestones and promoting upcoming podcast episodes.1:23–3:23 · Guest teaching 4/10 Guest Background and Career Overview Nathan prompts Anindya on MobileWalla's core value proposition and business model. Anindya walks through consumer audience generation and raw mobile telemetry data licensing.3:24–5:46 · Guest teaching 3/10 Monetization Models and Pricing Mechanics Nathan analyzes the difference between pay-as-you-go audience batches and recurring SaaS subscriptions, accurately analogizing the pricing structure to API call metering.5:46–7:46 · Guest teaching 3/10 Data Sourcing Infrastructure and Tracking Pixels Nathan immediately drills into COGS and gross margins upon hearing about data exchange partnerships, confirming the data acquisition is done via barter rather than cash expense.7:48–9:51 · Guest teaching 3/10 Early Strategy: Leveraging Media Buys for Data Access Nathan presses on the cold-start problem of how MobileWalla secured early data access without leverage, pushing Anindya to clarify gross media buys versus net revenue take.10:00–12:00 · Guest teaching 2/10 The Strategic Pivot to High-Margin Data Revenue Nathan synthesizes the strategic transition from low-margin media buying to pure data revenue, laying out the exact revenue trajectory and half-year drops across 2014 to 2016.12:00–16:45 · Guest teaching 5/10 Mobilizing Voter Segments in the Presidential Election Anindya reveals their role in the 2016 US presidential election; Nathan repeatedly pushes to discover which party hired them and whether they won, while Anindya carefully deflects under NDA.16:45–19:32 · Guest teaching 3/10 Customer Segmentation, Distribution Partners, and Low Churn Nathan breaks down customer segmentation between nine core SaaS clients and broader Oracle marketplace distribution, verifying enterprise churn dynamics.19:32–21:33 · Guest teaching 2/10 Lean Sales Operations and Zero Paid Marketing Strategy Nathan investigates customer acquisition cost and sales cycles, learning that MobileWalla operates with only three quota carriers and zero paid marketing or PR spend.21:34–23:47 · Guest teaching 4/10 Global Workforce Architecture and Data Corpus Mapping Anindya clarifies how their team in India performs manual point-of-interest mapping rather than standard outsourced engineering when Nathan asks about buyer segments like Walmart visitors.23:47–27:08 · Guest teaching 4/10 Real-Time Get-Out-The-Vote (GOTV) Tracking Capabilities Nathan poses an incisive technical hypothesis about tracking polling place turnout in real time on election day, which Anindya praises as exactly how they powered GOTV operations.27:09–29:21 · Guest teaching 1/10 The Famous Five Rapid-Fire Questions Nathan runs through the standard rapid-fire Famous Five format covering business books, CEO influences, and sleep schedules.29:21–30:39 · Guest teaching 0/10 Episode Recap and Upcoming Show Preview Solo host monologue recapping MobileWalla's financial milestones and promoting upcoming podcast episodes.1:23–3:23 · Guest disagreement 1/10 Guest Background and Career Overview Nathan prompts Anindya on MobileWalla's core value proposition and business model. Anindya walks through consumer audience generation and raw mobile telemetry data licensing.3:24–5:46 · Guest disagreement 1/10 Monetization Models and Pricing Mechanics Nathan analyzes the difference between pay-as-you-go audience batches and recurring SaaS subscriptions, accurately analogizing the pricing structure to API call metering.5:46–7:46 · Guest disagreement 1/10 Data Sourcing Infrastructure and Tracking Pixels Nathan immediately drills into COGS and gross margins upon hearing about data exchange partnerships, confirming the data acquisition is done via barter rather than cash expense.7:48–9:51 · Guest disagreement 2/10 Early Strategy: Leveraging Media Buys for Data Access Nathan presses on the cold-start problem of how MobileWalla secured early data access without leverage, pushing Anindya to clarify gross media buys versus net revenue take.10:00–12:00 · Guest disagreement 1/10 The Strategic Pivot to High-Margin Data Revenue Nathan synthesizes the strategic transition from low-margin media buying to pure data revenue, laying out the exact revenue trajectory and half-year drops across 2014 to 2016.12:00–16:45 · Guest disagreement 3/10 Mobilizing Voter Segments in the Presidential Election Anindya reveals their role in the 2016 US presidential election; Nathan repeatedly pushes to discover which party hired them and whether they won, while Anindya carefully deflects under NDA.16:45–19:32 · Guest disagreement 1/10 Customer Segmentation, Distribution Partners, and Low Churn Nathan breaks down customer segmentation between nine core SaaS clients and broader Oracle marketplace distribution, verifying enterprise churn dynamics.19:32–21:33 · Guest disagreement 1/10 Lean Sales Operations and Zero Paid Marketing Strategy Nathan investigates customer acquisition cost and sales cycles, learning that MobileWalla operates with only three quota carriers and zero paid marketing or PR spend.21:34–23:47 · Guest disagreement 2/10 Global Workforce Architecture and Data Corpus Mapping Anindya clarifies how their team in India performs manual point-of-interest mapping rather than standard outsourced engineering when Nathan asks about buyer segments like Walmart visitors.23:47–27:08 · Guest disagreement 1/10 Real-Time Get-Out-The-Vote (GOTV) Tracking Capabilities Nathan poses an incisive technical hypothesis about tracking polling place turnout in real time on election day, which Anindya praises as exactly how they powered GOTV operations.27:09–29:21 · Guest disagreement 0/10 The Famous Five Rapid-Fire Questions Nathan runs through the standard rapid-fire Famous Five format covering business books, CEO influences, and sleep schedules.29:21–30:39 · Guest disagreement 0/10 Episode Recap and Upcoming Show Preview Solo host monologue recapping MobileWalla's financial milestones and promoting upcoming podcast episodes.1:23–3:23 · Nathan pushing back 1/10 Guest Background and Career Overview Nathan prompts Anindya on MobileWalla's core value proposition and business model. Anindya walks through consumer audience generation and raw mobile telemetry data licensing.3:24–5:46 · Nathan pushing back 2/10 Monetization Models and Pricing Mechanics Nathan analyzes the difference between pay-as-you-go audience batches and recurring SaaS subscriptions, accurately analogizing the pricing structure to API call metering.5:46–7:46 · Nathan pushing back 3/10 Data Sourcing Infrastructure and Tracking Pixels Nathan immediately drills into COGS and gross margins upon hearing about data exchange partnerships, confirming the data acquisition is done via barter rather than cash expense.7:48–9:51 · Nathan pushing back 4/10 Early Strategy: Leveraging Media Buys for Data Access Nathan presses on the cold-start problem of how MobileWalla secured early data access without leverage, pushing Anindya to clarify gross media buys versus net revenue take.10:00–12:00 · Nathan pushing back 3/10 The Strategic Pivot to High-Margin Data Revenue Nathan synthesizes the strategic transition from low-margin media buying to pure data revenue, laying out the exact revenue trajectory and half-year drops across 2014 to 2016.12:00–16:45 · Nathan pushing back 5/10 Mobilizing Voter Segments in the Presidential Election Anindya reveals their role in the 2016 US presidential election; Nathan repeatedly pushes to discover which party hired them and whether they won, while Anindya carefully deflects under NDA.16:45–19:32 · Nathan pushing back 3/10 Customer Segmentation, Distribution Partners, and Low Churn Nathan breaks down customer segmentation between nine core SaaS clients and broader Oracle marketplace distribution, verifying enterprise churn dynamics.19:32–21:33 · Nathan pushing back 2/10 Lean Sales Operations and Zero Paid Marketing Strategy Nathan investigates customer acquisition cost and sales cycles, learning that MobileWalla operates with only three quota carriers and zero paid marketing or PR spend.21:34–23:47 · Nathan pushing back 3/10 Global Workforce Architecture and Data Corpus Mapping Anindya clarifies how their team in India performs manual point-of-interest mapping rather than standard outsourced engineering when Nathan asks about buyer segments like Walmart visitors.23:47–27:08 · Nathan pushing back 1/10 Real-Time Get-Out-The-Vote (GOTV) Tracking Capabilities Nathan poses an incisive technical hypothesis about tracking polling place turnout in real time on election day, which Anindya praises as exactly how they powered GOTV operations.27:09–29:21 · Nathan pushing back 1/10 The Famous Five Rapid-Fire Questions Nathan runs through the standard rapid-fire Famous Five format covering business books, CEO influences, and sleep schedules.29:21–30:39 · Nathan pushing back 0/10 Episode Recap and Upcoming Show Preview Solo host monologue recapping MobileWalla's financial milestones and promoting upcoming podcast episodes.

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

0:00 · Nathan 68.2% · guest 31.8%0:00 · Nathan 68.2% · guest 31.8%3:00 · Nathan 28.9% · guest 71.1%3:00 · Nathan 28.9% · guest 71.1%6:00 · Nathan 29.7% · guest 70.3%6:00 · Nathan 29.7% · guest 70.3%9:00 · Nathan 25.4% · guest 74.6%9:00 · Nathan 25.4% · guest 74.6%12:00 · Nathan 40.8% · guest 59.2%12:00 · Nathan 40.8% · guest 59.2%15:00 · Nathan 23.4% · guest 76.6%15:00 · Nathan 23.4% · guest 76.6%18:00 · Nathan 25.6% · guest 74.4%18:00 · Nathan 25.6% · guest 74.4%21:00 · Nathan 22.7% · guest 77.3%21:00 · Nathan 22.7% · guest 77.3%24:00 · Nathan 71.5% · guest 28.5%24:00 · Nathan 71.5% · guest 28.5%27:00 · Nathan 50.5% · guest 49.5%27:00 · Nathan 50.5% · guest 49.5%30:00 · Nathan 100% · guest 0%30:00 · Nathan 100% · guest 0%
Sharpest disagreement ▶ 13:27 Guest firmly deflects election winner inquiry

Anindya repeatedly refuses Nathan's persistent baiting about whether MobileWalla's presidential campaign client won or lost the election.

Hardest push from Nathan ▶ 13:18 Host presses on political alignment and campaign outcome

Nathan directly challenges Anindya's confidentiality by pushing him to disclose if they backed the winning or losing presidential candidate.

Biggest teaching moment ▶ 12:43 Guest explains algorithmic evangelical voter classification

Anindya educates Nathan on tracking 290 denominations and monitoring weekly church visits across 1,400 swing-state locations to define evangelical mobile audiences.

Nathan holds their own ▶ 23:47 Host deduces real-time GOTV mobile tracking capability

Nathan accurately outlines the exact technical mechanics of real-time polling booth monitoring, drawing direct praise from the guest.

the scores for every segment, with the reasoning behind each
ChapterTopicNathan as informed peerGuest teachingGuest disagreementNathan pushing backWhy
Guest Background and Career Overview 4411 Nathan prompts Anindya on MobileWalla's core value proposition and business model. Anindya walks through consumer audience generation and raw mobile telemetry data licensing.
Monetization Models and Pricing Mechanics 6312 Nathan analyzes the difference between pay-as-you-go audience batches and recurring SaaS subscriptions, accurately analogizing the pricing structure to API call metering.
Data Sourcing Infrastructure and Tracking Pixels 7313 Nathan immediately drills into COGS and gross margins upon hearing about data exchange partnerships, confirming the data acquisition is done via barter rather than cash expense.
Early Strategy: Leveraging Media Buys for Data Access 6324 Nathan presses on the cold-start problem of how MobileWalla secured early data access without leverage, pushing Anindya to clarify gross media buys versus net revenue take.
The Strategic Pivot to High-Margin Data Revenue 7213 Nathan synthesizes the strategic transition from low-margin media buying to pure data revenue, laying out the exact revenue trajectory and half-year drops across 2014 to 2016.
Mobilizing Voter Segments in the Presidential Election 6535 Anindya reveals their role in the 2016 US presidential election; Nathan repeatedly pushes to discover which party hired them and whether they won, while Anindya carefully deflects under NDA.
Customer Segmentation, Distribution Partners, and Low Churn 5313 Nathan breaks down customer segmentation between nine core SaaS clients and broader Oracle marketplace distribution, verifying enterprise churn dynamics.
Lean Sales Operations and Zero Paid Marketing Strategy 5212 Nathan investigates customer acquisition cost and sales cycles, learning that MobileWalla operates with only three quota carriers and zero paid marketing or PR spend.
Global Workforce Architecture and Data Corpus Mapping 5423 Anindya clarifies how their team in India performs manual point-of-interest mapping rather than standard outsourced engineering when Nathan asks about buyer segments like Walmart visitors.
Real-Time Get-Out-The-Vote (GOTV) Tracking Capabilities 8411 Nathan poses an incisive technical hypothesis about tracking polling place turnout in real time on election day, which Anindya praises as exactly how they powered GOTV operations.
The Famous Five Rapid-Fire Questions 3101 Nathan runs through the standard rapid-fire Famous Five format covering business books, CEO influences, and sleep schedules.
Episode Recap and Upcoming Show Preview 0000 Solo host monologue recapping MobileWalla's financial milestones and promoting upcoming podcast episodes.

Statements from this episode (15)

Assertion Not checkable as stated
Acxiom and Oracle buy raw mobile behavioral data from Mobilewalla
“You know, large companies like, ah, you know, Axiom and Oracle, they just buy raw mobile data from us.”
Anindya Datta Aug 29, 2017 ▶ 3:06
Disclosure
Mobilewalla charges enterprise clients $25,000 monthly for raw data feeds
“When a large company will come and tell us that we want to know every month all the locations that you see people at, we tell them, okay, well, we're going to give it to you as a subscription, sign up for 12 months and pay us 25,000 dollars a month.”
Anindya Datta Aug 29, 2017 ▶ 3:56
Disclosure
Mobilewalla barters data with ad exchanges to access lucrative bid streams
“All, all, all barter. So we give them back valuable information and they in turn, let us look at their bit stream to be able to create audiences.”
Anindya Datta Aug 29, 2017 ▶ 6:29
Disclosure
Mobilewalla requires ad network clients to embed tracking pixels to harvest data
“What we have them do in order to be able to profile their users, we require them to put a little pixel, a little mobile wallet pixel in every ad created the surf, right? So when you are consuming New York Times content on your iPad, and an ad gets surfed to yo…”
Anindya Datta Aug 29, 2017 ▶ 6:48
Assertion Not checkable as stated
Mobilewalla's ad pixel tracks location and infers user travel speed
“Not accelerometer now, Nathan, but it definitely includes location data. But you know, but even from location, we can judge speed, right?”
Anindya Datta Aug 29, 2017 ▶ 7:20
Disclosure
Mobilewalla operated as a key data arm in the 2016 presidential election
“I cannot give you details because we are under, but I'll tell you that we were a very key data arm for one of the major parties.”
Anindya Datta Aug 29, 2017 ▶ 12:12
Disclosure
Mobilewalla tracked church attendance to segment evangelical voters in swing states
“So, so the definition we were given was, take a swing state like Florida, right? And there are like, whatever, 1400 evangelical churches. So to be an evangelical Christian, you need to have been observed in an evangelical church at least once a week for the pa…”
Anindya Datta Aug 29, 2017 ▶ 13:04
Assertion Not checkable as stated
Mobilewalla has nine subscription SaaS customers paying up to $41,000 monthly
“So we have nine customers who have subscription accounts, right? And they pay anywhere between 15,000 dollars, 8500 dollars a month to 41,000 dollars a month.”
Anindya Datta Aug 29, 2017 ▶ 17:03
Assertion Not checkable as stated
Mobilewalla is a top contributor to Oracle's BlueKai digital data marketplace
“So we are one of the biggest contributors to the Oracle blue guy marketplace with the largest digital data marketplace in the world.”
Anindya Datta Aug 29, 2017 ▶ 17:23
Assertion Not checkable as stated
Mobilewalla generates $250,000 monthly across SaaS subscriptions and audience data sales
“No, 150 gain in, in, in SAS plus a hundred gain audience.”
Anindya Datta Aug 29, 2017 ▶ 18:15
Assertion Not checkable as stated
Over 250 different organizations purchase audience data segments from Mobilewalla
“So in the hundred gain audience, we have over. 250 different organizations buying from us.”
Anindya Datta Aug 29, 2017 ▶ 18:22
Assertion Not checkable as stated
Mobilewalla is practically the only source of non-US mobile audience data
“We are pretty much the only source of mobile audience data outside of the US.”
Anindya Datta Aug 29, 2017 ▶ 20:07
Assertion Not checkable as stated
Mobilewalla operates with zero paid marketing, SEO, or hired PR spend
“There's no variable, there's zero variable paid marketing spend. We've knocked down a penny of SEO. We have not, We don't have a, even a hired PR firm that is doing anything. So zero spend.”
Anindya Datta Aug 29, 2017 ▶ 21:19
Disclosure
Get-out-the-vote services generated the bulk of Mobilewalla's 2016 election revenue
“The biggest amount of money we generated in the election was for what is known as get out the vote, the GOTV part of the election, right?”
Anindya Datta Aug 29, 2017 ▶ 24:16
Disclosure
Mobilewalla monitored swing state polling locations in real-time for campaign teams
“What we did for this particular party, who spent way less on, feet on the street than the other party, we were monitoring every polling booth, every polling location in a certain number of states, And we were telling the ground team who showed up to vote and w…”
Anindya Datta Aug 29, 2017 ▶ 24:26
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