Marketing mix modelling went from old-school to modern over the past five years. The traditional MMM was a spreadsheet exercise produced annually by a specialist consultancy and consumed mostly by the CMO. The modern MMM is a continuously updated model that integrates with marketing operations and informs decisions at multiple levels of the organisation. By May 2026, the modern stack has settled enough that we can describe it usefully.

This is a working view of where the tooling sits and what’s actually useful in production.

What modern MMM is

Modern marketing mix modelling differs from the traditional version in a few specific ways.

The modelling is continuous rather than periodic. The model is refreshed weekly or even daily, not annually. The decisions it informs are operational, not just strategic.

The modelling is open and transparent rather than proprietary. The methodology is documented, the team can interrogate the model, and the assumptions are visible. The black-box consultancy MMM has fallen out of favour.

The data integration is automated. The model pulls media spend, conversion data, external factors, and contextual variables on a schedule. The manual data preparation that used to dominate MMM work has been substantially automated.

The output is actionable at operational tempo. The model produces channel and tactic-level recommendations that the marketing operations team can act on, not just strategic narratives the CMO consumes.

The tooling categories

The modern MMM tooling space has consolidated into a few categories.

The vendor MMM platforms — typically built on top of Bayesian or related Bayesian-adjacent methodologies — are the most common starting point. The major vendors have continued to improve their platforms through 2024-26, with better default model specifications, better integration capability, and better user interfaces. The platforms work well for organisations with reasonably standard data structures and don’t require deep statistical capability inside the marketing team.

The open-source MMM frameworks — particularly the Meta and Google open-source projects — have continued to mature. The frameworks require more internal capability to use effectively but produce excellent results in the hands of capable teams. The total cost of ownership is lower than vendor platforms for organisations with the capability.

The custom-built MMM, using the underlying Bayesian frameworks directly, remains the right answer for organisations with very specific modelling requirements, very large scale, or very particular integration needs. The investment is substantial but the outcome is differentiated.

The hybrid approaches — vendor platform with significant customisation, or open-source framework with vendor support — have become the most common pattern in mid-market and larger enterprises.

What’s actually working

The MMM implementations that have produced real value in 2024-26 share several features.

Strong data foundations. The model only works as well as the data feeding it. Organisations with clean, integrated data on media spend, conversion outcomes, and contextual variables produce useful models. Organisations with messy data don’t, regardless of which platform they use.

Reasonable model specifications. The vendor platforms ship with reasonable defaults. The open-source frameworks require the team to specify the model. Either way, the specification has to reflect the actual marketing reality — which channels interact, which lag effects matter, which contextual variables to include.

Integration with the broader measurement ecosystem. The MMM has to work with attribution, with incrementality testing, with the broader analytics stack. The MMM that operates as an island produces narratives nobody acts on.

Operational integration with marketing planning. The MMM output has to flow into the planning process, the budget allocation process, and the campaign decision-making process. Otherwise the model is producing recommendations that don’t translate into action.

Continuous calibration through incrementality testing. The MMM model needs validation against ground truth. Periodic incrementality tests provide that validation. Models that aren’t validated drift over time.

What’s not working as well

Several common pitfalls show up across MMM implementations that aren’t producing value.

Models that haven’t been calibrated against actual incrementality results. The model produces recommendations but nobody knows if they’re correct. Decisions get made on what’s effectively unvalidated output.

Models that don’t actually inform decisions. The MMM produces insights that get presented at quarterly reviews but don’t change the tactical decisions made by channel teams. The resource investment in the modelling produces narrative not action.

Models that are too complex for the team to understand. The model is producing recommendations the team can’t interrogate. The team either ignores the recommendations or follows them without understanding why. Neither produces good outcomes.

Models that change too often. Each refresh produces different results. The team can’t tell whether the changes are real signal or modelling noise. The credibility of the model erodes.

Models that don’t account for the actual marketing strategy. The model treats every channel and tactic as commensurate when they actually serve different strategic purposes. The recommendations make sense statistically and not strategically.

The capability question

The capability question for MMM has shifted. Five years ago the question was whether to outsource entirely to a specialist consultancy or to build the capability internally. The middle ground has expanded substantially.

The dominant pattern in 2026 is some level of internal capability complemented by external support. The internal team understands the marketing reality, owns the operational execution, and runs the model on a continuous basis. The external partners provide methodology depth, validate the modelling choices, and support the more complex analytical questions.

The internal team capability needed isn’t enormous. A capable analyst with statistical training and curiosity can run a modern MMM platform effectively. The capability needed is more about understanding the marketing reality and being able to translate model output into action than about advanced statistical mastery.

Some organisations have engaged external delivery partners for the initial MMM build and then transitioned to internal operations. This pattern has worked well when the transition is structured properly. The external partner is engaged for delivery and capability uplift, not just for a one-off engagement.

What CMOs actually use the output for

The CMOs getting real value from MMM are using the output for several specific purposes.

Quarterly budget allocation across channels. The MMM provides the directional answer for which channels deserve more or less of the marketing budget. The decisions are still informed by other inputs but the MMM is a primary input.

Annual planning and target setting. The MMM provides the reasonable expectation for what the marketing program can deliver under various scenarios. The planning is grounded in the model’s output rather than in optimistic estimates.

Decisions about new channel investment. The MMM provides the reasonable estimate for what a new channel might contribute, calibrated against the existing channel mix. New channel decisions are better informed than they would be otherwise.

Strategic communications with the CFO and the board. The MMM provides the analytical foundation for marketing budget conversations with finance. The CMO can defend the budget request with data-driven analysis rather than industry benchmarks alone.

Diagnostic insight when something changes. When marketing performance shifts unexpectedly, the MMM provides the structured framework for understanding what changed and why.

The privacy alignment

MMM has become more important relative to user-level attribution as the privacy environment has tightened. The methodology doesn’t depend on user-level tracking; it works at the aggregate level. The continued tightening of the privacy environment has made MMM more attractive relative to attribution.

The CMOs who anticipated this have generally been ahead. The CMOs who continued to invest only in user-level attribution have generally been catching up.

The long-term direction is reasonably clear. Aggregate measurement methodologies will continue to grow in importance. User-level methodologies will continue to face structural pressure from the privacy environment. The MMM stack matters more in 2026 than it did in 2022.

Where this goes

The modern MMM stack will continue to mature through 2026 and beyond. The vendor platforms will continue to improve. The open-source frameworks will continue to add capability. The integration with the broader marketing operations stack will continue to deepen.

The CMOs who have built strong MMM capability are positioned well for the continued evolution of the marketing measurement environment. The CMOs who haven’t are increasingly disadvantaged.

The investment is real. The case for it is strong. The patterns of what’s working are clear enough that there’s no good reason to keep delaying the work.