The Art of Measuring What Truly Works

When assembling a team before a performance, a conductor must personally ensure that every musician before him is the best of the best. The orchestra may sound harmonious as a whole, yet a trained ear notices something off. Perhaps the trumpeter is underperforming, or the drummer is slightly off rhythm? This is where the most fascinating part begins - evaluating each musician individually. And, with a limited budget, the conductor must make a difficult decision: who stays, and who must leave.

This is exactly how MMM (marketing mix modeling) works - an analytical method that transforms chaotic, fragmented data into a clear and structured picture. It allows marketers to study how different marketing channels influence sales or other key brand metrics by analyzing historical data over a specific period.

In practice, a brand’s marketing strategy is broken down into its smallest components - budget, advertising types (targeted ads, influencer campaigns, etc.), pricing, and any other influential factors. For instance, MMM may reveal that Instagram ads boosted sales by only 5%, while influencer collaborations drove a 20% increase. The logical conclusion: reduce the Instagram budget and focus more on influencer marketing. This approach enables brands to optimize spending, reduce waste, and maximize the efficiency of every dollar invested.

The roots of marketing mix modeling go back to the offline era - when advertising relied on TV, radio, magazines, and billboards. As time passed, traditional methods gave way to digital ones: Excel spreadsheets were replaced by Google Ads dashboards. We no longer measure how many people heard us on the radio - we track views, clicks, likes, and impressions. This gave rise to MMM for digital - a model that accounts for online metrics such as click-throughs, reach, engagement, and frequency. It helps brands identify which channels truly drive profit and which merely drain the budget.

Just as water cannot exist without hydrogen, modern MMM is inseparable from the concept of incrementality - the idea of measuring only the growth directly caused by advertising, excluding other influences like seasonal demand or emerging trends.

This creates the classic pair: incrementality vs MMM. While MMM analyzes past data, incrementality focuses on the present, relying primarily on experimental methods such as A/B testing, holdout groups, and controlled experiments.

Thanks to AI, we are now witnessing a new stage in the evolution of marketing mix modeling. Artificial intelligence can process millions of data rows, exponentially increasing not only the speed of analysis but also the level of competition for consumer attention. What was once an intellectual race has now become a technological one - forcing marketers to invest not only in their knowledge but also in the tools that bring that knowledge to life.

Yet at the heart of it all remains the human factor. Feeding an AI model with data and expecting a miraculous outcome - a kind of digital Deus ex machina - is not enough. Every algorithm, no matter how advanced, still needs a skilled expert to guide it in the right direction.

MMM continues to be one of the most effective frameworks for measuring marketing performance, while AI serves as a powerful extension of its capabilities. If your brand needs expert consultation, feel free to reach out to us at info@cedodigital.com.