The Truth About AI In Marketing Measurement: What Works, What Doesn't And What It Costs You | AdExchanger

"The world of marketing measurement is buzzing about AI. But when it comes to complex techniques like media mix modeling (MMM), the field is awash with false promises about the benefits that these new technologies offer. This creates enormous risks for enterprise marketers who regularly base multimillion-dollar decisions on such models. The hard truth is that AI, especially LLMs, produce confident-sounding but often wrong statistical analyses that can lead to poor budget allocation decisions."

"LLMs aren't designed to solve causal inference problems that connect real-world causes to effects. And since that's the fundamental goal of marketing measurement, it means LLM-type models struggle to produce actionable recommendations that consistently improve business performance. Even worse, the hype around AI creates a dangerous distraction from the only question that truly matters in MMM: Is this model helping us invest our media budget in ways that actually yield profit?"

"Still, this doesn't mean AI has no place in marketing measurement. It just means brands need to be sure that they're selecting the right tool for the job at hand - all while maintaining a healthy skepticism of any vendors who claim AI magic will solve fundamental problems. Why "AI-powered" measurement can be dangerous The fundamental purpose of media mix modeling should be simple: to help businesses drive more profit through better marketing decisions. Yet, historically, MMMs have failed to deliver on this promise."

AI and LLM hype threatens reliable marketing measurement by presenting black-box models that lack causal inference capability and rigorous validation. LLMs commonly generate confident-sounding but incorrect statistical analyses that can mislead budget allocation and jeopardize multimillion-dollar marketing decisions. Vendors leveraging "AI-powered" labels can obscure methodologies and avoid necessary model validation, increasing operational risk for enterprise marketers. Brands must assess tools based on causal rigor, validation practices, and demonstrable profit impact rather than marketing claims. AI can have a role when paired with appropriate methods, transparency, and skepticism toward oversold promises.

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