Marketing Mix Modeling

The concept of using AI models to estimate the sales-effect of marketing is called Marketing mix modeling. At Magpie we combine Machine learning marketing mix modeling with Bayesian Marketing Mix modeling.

Unify with attribution insights and experiment results to get a strategic, long-term view of what drives sustainable growth.

Measure the true impact of every channel

Marketing Mix Modeling (MMM) is a powerful data science tool that quantifies the contribution of every marketing channel. Whether your goal is orders, new customers, or leads. Instead of relying on tracking data, MMM analyzes how spend across channels drives outcomes, even when attribution falls short.
MMM doesn’t just tell you what’s working today—it models the real-world dynamics that shape your marketing performance:
Delayed effects: Channels like TV often create results weeks later, while platforms like Google Ads drive immediate conversions. MMM captures these differences so you can see the full picture.
Baseline performance: Understand how much growth would remain if marketing stopped entirely—what your brand and loyal customers generate on their own.
Diminishing returns: Every channel has an optimal investment level. MMM identifies the point where additional spend stops paying off.

Marketing Mix Modeling (MMM) is a powerful data science tool that quantifies the contribution of every marketing channel. Whether your goal is orders, new customers, or leads. Instead of relying on tracking data, MMM analyzes how spend across channels drives outcomes, even when attribution falls short.
MMM doesn’t just tell you what’s working today—it models the real-world dynamics that shape your marketing performance:

Delayed effects: Channels like TV often create results weeks later, while platforms like Google Ads drive immediate conversions. MMM captures these differences so you can see the full picture.

Baseline performance: Understand how much growth would remain if marketing stopped entirely—what your brand and loyal customers generate on their own.

Diminishing returns: Every channel has an optimal investment level. MMM identifies the point where additional spend stops paying off.

Magpie's unique Marketing Mix Modeling approach

MMM is an umbrella term for many modeling techniques, and this is where Magpie stands apart. We combine Machine Learning and Bayesian methods to build models that are both powerful and practical.

Machine learning lets us uncover channel interactions and isolate marketing’s real value from seasonality and market shifts. Bayesian modeling brings consistency and transparency, allowing us to integrate external data like experiment results and to quantify uncertainty.

This combination turns MMM into a living decision-making system, not just a one-off analysis. It’s what makes our models uniquely actionable, delivering insights that hold up under real-world complexity.

MMM is an umbrella term for many modeling techniques, and this is where Magpie stands apart. We combine Machine Learning and Bayesian methods to build models that are both powerful and practical.

Machine learning lets us uncover channel interactions and isolate marketing’s real value from seasonality and market shifts. Bayesian modeling brings consistency and transparency, allowing us to integrate external data like experiment results and to quantify uncertainty.

This combination turns MMM into a living decision-making system, not just a one-off analysis. It’s what makes our models uniquely actionable, delivering insights that hold up under real-world complexity.

Marketing Mix Modeling

Marketing Mix Modeling

Measure the true impact of every channel

Measure the true impact of every channel

MMM doesn’t just tell you what’s working today—it models the real-world dynamics that shape your marketing performance:

Delayed effects: Channels like TV often create results weeks later, while platforms like Google Ads drive immediate conversions. MMM captures these differences so you can see the full picture.

Baseline performance: Understand how much growth would remain if marketing stopped entirely—what your brand and loyal customers generate on their own.

Diminishing returns: Every channel has an optimal investment level. MMM identifies the point where additional spend stops paying off.

MMM doesn’t just tell you what’s working today—it models the real-world dynamics that shape your marketing performance:

Delayed effects: Channels like TV often create results weeks later, while platforms like Google Ads drive immediate conversions. MMM captures these differences so you can see the full picture.

Baseline performance: Understand how much growth would remain if marketing stopped entirely—what your brand and loyal customers generate on their own.

Diminishing returns: Every channel has an optimal investment level. MMM identifies the point where additional spend stops paying off.

Magpie's unique Marketing Mix Modeling approach

Magpie's unique Marketing Mix Modeling approach

MMM is an umbrella term for many modeling techniques, and this is where Magpie stands apart. We combine Machine Learning and Bayesian methods to build models that are both powerful and practical.

Machine learning lets us uncover channel interactions and isolate marketing’s real value from seasonality and market shifts. Bayesian modeling brings consistency and transparency, allowing us to integrate external data like experiment results and to quantify uncertainty.

This combination turns MMM into a living decision-making system, not just a one-off analysis. It’s what makes our models uniquely actionable, delivering insights that hold up under real-world complexity.

MMM is an umbrella term for many modeling techniques, and this is where Magpie stands apart. We combine Machine Learning and Bayesian methods to build models that are both powerful and practical.

Machine learning lets us uncover channel interactions and isolate marketing’s real value from seasonality and market shifts. Bayesian modeling brings consistency and transparency, allowing us to integrate external data like experiment results and to quantify uncertainty.

This combination turns MMM into a living decision-making system, not just a one-off analysis. It’s what makes our models uniquely actionable, delivering insights that hold up under real-world complexity.