Marketing mix models aim to answer the marketer’s billion-dollar question: where should you spend your budget? Yet most organizations build models and still struggle to translate their outputs into decisions anyone feels confident making. The problem isn’t how advanced the model is. It’s how organizations use it and what they feed into it. The real gap: Application, not technology Today’s customer journey is more fragmented, faster-moving and harder to track than ever. Consumers switch between platforms, devices and channels in ways that don’t follow linear paths, and their decisions are shaped by factors far beyond paid media. Despite this, many organizations still apply marketing mix modeling (MMM) with a decade-old mindset. Annual refresh cycles, siloed ownership and static inputs like channel-level spend, impressions or GRPs remain common. Some models assume linear, time-invariant effects or rely on last-touch logic, which fails to accurately reflect how customers actually move across channels. These legacy assumptions no longer align with faster, more complex decision cycles. Foundational practices still matter when applied thoughtfully. Multi-year data helps establish reliable baselines, and limiting variables supports model stability. But the pace of change in consumer behavior, media and culture means those practices must evolve. New channels, trends, devices and market dynamics constantly reshape how people engage, requiring data that captures emerging channels, real-time behavior and broader market shifts. Dig deeper: What your attribution model isn’t telling you MMM must reflect that complexity. It needs broader inputs, more frequent refreshes and an operating model designed to guide decisions as…