Marketing Mix Modeling · Brand Science · On-Demand
Dr. Koen Pauwels
Distinguished Professor, Northeastern University
Athena Dai
Head of Marketing Data Science,
Quicken
Dirk Beyer (Host)
Chief Data Scientist,
LiftLab
You made the rational call. You hit your ROAS targets. The model agreed. But CAC is still climbing, market position is eroding, and the growth you expected never arrived. The culprit? A structural blind spot is baked into almost every marketing mix model in use today.
Traditional MMM captures what's measurable in a weekly window, but long-term brand effects compound over quarters, not days. When your model can't see them, it quietly rewards decisions that undermine your brand, and then prescribes even more lower funnel spend to compensate.
Free · Instant Access
Watch it before your next planning cycle.
What you’ll learn
Dr. Koen Pauwels, Athena Dai, and Dirk Beyer cover the theory, the evidence, and the practical path forward, in plain language.
Understand the structural reason weekly models can't capture brand compounding, and why this isn't a data problem you can fix with more data.
Learn how over-indexing on performance signals drives up CAC, weakens baseline sales, and erodes long-term brand equity on the P&L.
Discover LiftLab's approach to establishing the net present value of long-term advertising effects, calibrated to your brand, lifecycle, and funnel stage.
Dr. Pauwels synthesizes three decades of marketing science into actionable multiplier logic that can be embedded directly into budget optimization.
Walk away with a framework for presenting short-term and long-term returns side by side, making the brand investment case with hard numbers.
Liftlab's approach
LiftLab's platform measures short-term advertising effects, establishes the NPV of long-term contributions using calibrated LT multipliers, and continuously optimizes your portfolio across both.
Regression models and incrementality experiments capture what's happening today, at the channel, tactic, and funnel level, with daily precision.
LT multipliers quantify the brand's compounding value, calibrated to your market position, brand lifecycle, product category, and funnel position of each ad tactic.
Budget recommendations use total advertising effect (ST + LT) as the objective function, balancing short-term returns with long-term brand equity compounding.
