Media Mix Modelling
MMM (Media Mix Modelling)
A statistical regression technique that models the historical relationship between marketing spend across all channels and business outcomes to quantify each channel's contribution and optimise budget allocation. MMM requires no individual-level tracking data, making it privacy-compliant and able to measure channels like TV and outdoor that resist digital attribution. It is typically run quarterly and produces spend elasticity curves for each channel.
How Media Mix Modelling works in practice
MMM gained renewed relevance after iOS 14.5 privacy changes degraded the signal quality of digital attribution, making channel-agnostic, privacy-safe measurement essential for understanding total marketing impact. Modern MMM platforms (Meridian by Google, Meta's Robyn, Northbeam, Analytic Edgehave) have reduced the time and cost required to run MMM from months-long consulting engagements to continuous, automated models updated with weekly data. The primary output is a spend elasticity curve for each channel — showing the point of diminishing returns for additional investment — which directly informs budget allocation decisions. MMM and incrementality testing are complementary: MMM provides portfolio-level channel allocation insights while incrementality tests validate specific channel or campaign contribution.
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Let's talk →This term sits in the Analytics category, which means it is most useful when evaluating measurement design, attribution quality, reporting accuracy, and decision-making. The goal is not to memorize the label. The goal is to know when it should change a decision, a page, a campaign, or a measurement setup.
Related terms
The rule that determines how credit for a conversion is assigned to different marketing touchpoints in the user journey. Choosing the right model affects how you allocate budget across channels and evaluate channel ROI.
A controlled experiment measuring the true causal impact of a marketing channel by comparing a test group exposed to the campaign against a holdout group that is not. Incrementality testing answers "how much revenue would we have generated without this channel?" — a question attribution models estimate but cannot definitively answer. Holdout tests are offered natively by Meta (Conversion Lift) and Google (Geo Experiments).
The revenue generated for every dollar spent on advertising. Calculated as (Revenue ÷ Ad Spend) × 100. A ROAS of 400% means $4 earned for every $1 spent — a key metric for evaluating paid channel profitability.
Attribution approaches that assign conversion credit to multiple touchpoints rather than a single source. Models include linear, time-decay, and position-based, each weighting interactions differently across the journey.
Put Media Mix Modelling to work
Understanding Media Mix Modelling is one thing — operationalising it across tracking, acquisition, and conversion is another. Explore the full range of digital marketing services, including SEO & content consulting, paid media management, and analytics & CRO. Or work directly with a digital marketing consultant in Dubai on building growth systems that actually compound.
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