158: Jeff Lee: How Calm’s Billion customer message machine unites martech and engineering

Jeff built a billion-message marketing machine at Calm with three people. But it was a journey. Push notifications sparked a three-year battle until a new CPO, unburdened by notification trauma, green-lit the project in six weeks. It was a four-year battle for ML recommendations but the data tells an unexpected story. Jeff also shares tactics for winning over the most skeptical product and engineering teams. It starts with operational empathy by taking a stab at a working prototype of your idea but also recognizing that product and martech decisions often stem from personal bias that requires champions who have experienced positive outcomes.More

153: Sundar Swaminathan: How Uber measures the ROI of marketing according to their former Growth Marketing Data Science Lead

After leading Uber’s Marketing Data Science teams, Sundar shares insights that work for both tech giants and startups. Beyond uncovering that Meta ads generated zero incremental value (saving $30 million annually), they mastered measuring brand impact through geo testing and predicting LTV through first-week behaviors. Small companies can adapt these methods through strategic A/B testing and simplified attribution models, even with limited sample sizes. More

129: Re: Why Martech is Actually for Engineers

Homegrown tools aren’t appealing to marketers, they’re hard to scale, most have a shitty UI and it’s not a recognisable martech tool you can add to your resume. Homegrown martech tools are even less appealing to engineers, they can’t stand the chaos of marketing. We’ll always need for cross-functional translators, disproving the claim that martech is actually only for engineers.More