{"id":10658,"date":"2025-12-18T18:49:19","date_gmt":"2025-12-19T00:49:19","guid":{"rendered":"https:\/\/attentionmedia.io\/?p=10658"},"modified":"2025-12-18T18:49:19","modified_gmt":"2025-12-19T00:49:19","slug":"marketing-mix-modeling-has-a-usage-problem-not-a-tech-problem","status":"publish","type":"post","link":"https:\/\/attentionmedia.io\/?p=10658","title":{"rendered":"Marketing mix modeling has a usage problem, not a tech problem"},"content":{"rendered":"
\"Marketers<\/div>\n

Marketing mix models aim to answer the marketer\u2019s 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\u2019t how advanced the model is. It\u2019s how organizations use it and what they feed into it.<\/p>\n

The real gap: Application, not technology<\/h2>\n

Today\u2019s customer journey is more fragmented, faster-moving and harder to track than ever. Consumers switch between platforms, devices and channels in ways that don\u2019t follow linear paths, and their decisions are shaped by factors far beyond paid media.<\/p>\n

Despite this, many organizations still apply marketing mix modeling<\/a> (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.<\/p>\n

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.<\/p>\n

Dig deeper: What your attribution model isn\u2019t telling you<\/a><\/em><\/strong><\/p>\n

MMM must reflect that complexity. It needs broader inputs, more frequent refreshes and an operating model designed to guide decisions as conditions change. The challenge, however, is not the technology itself. The real gap lies in how organizations approach MMM. Too often, models are used to validate past decisions rather than guide future ones. Making MMM effective requires cross-functional ownership, better data access, faster feedback loops and a mindset that treats measurement as a continuous, decision-driving capability.<\/p>\n

The Interactive Advertising Bureau\u2019s \u201cModernizing MMM: Best Practices for Marketers\u201d provides a practical blueprint for doing exactly that, focusing less on modeling theory and more on the impact of decisions.<\/p>\n

What it takes to make MMM decision-ready<\/h2>\n

If your MMM still runs on annual cycles and is only based on campaign performance, you\u2019re already behind. IAB highlights three principles that separate leaders from those that are still relying on outdated methods.<\/p>\n