Incrementality testing as the budget lever

#346 | Dave sits down with Drew Pinta, Director of Growth Data at Ramp, to talk about what it actually looks like to measure marketing when the hardest things to measure are often the ones working hardest. Drew breaks down how Ramp uses incrementality testing and event studies to put real numbers on brand stunts, how they split budget 70/30 between proven channels and experimental bets, and how scanning thousands of Gong calls with AI surfaced attribution insights that contradicted every model they had. He also makes the bull case for why marketers might be better positioned than engineers in the age of AI, and doesn't shy away from the bear case either.

Timestamps

• (00:00) - - Intro
• (04:25) - - Drew's background: from the Fed to growth data
• (07:15) - - Drew's role as a data partner to marketing
• (09:51) - - How Ramp's funnel works
• (11:34) - - Using AI to scan Gong calls and finding attribution was wrong
• (16:15) - - Why measurement should meet the marketing, not the other way around
• (27:50) - - The 70/30 budget split
• (28:50) - - Ramp's channel maturity framework
• (36:20) - - The marketing leader as pilot, data team as instruments
• (39:35) - - The bull case: why marketers are better positioned than engineers in the age of AI
• (43:20) - - The bear case
• (48:20) - - Real AI workflows Ramp's marketing team has built
• (53:14) - - How Ramp enabled the team on AI
• (55:45) - - Guardrails for AI agents

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