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Marketing Mix Modelling can be delivered in three ways: as a SaaS platform, through a consultancy and as an open-source framework you build yourself. Each carries a fundamentally different cost model, delivery cadence and level of control. SaaS platforms offer always-on access, automated data ingestion and self-service scenario planning. Consultancy-led models offer strategic support but typically deliver insights quarterly or annually at a higher cost. Understanding the difference helps you choose the right approach for your organisation's scale, technical resources and measurement priorities.
Marketing Mix Modelling has been around for decades. The underlying methodology — using statistical analysis of aggregated data to measure the contribution of marketing and non-marketing factors to business outcomes — has not changed fundamentally. What has changed is how it is delivered.
Traditionally, MMM was a consultancy exercise. An agency or specialist firm would collect your data, build a model, run the analysis and deliver a report. This typically happened once or twice a year. The insights were valuable but arrived too late to influence the campaigns they were meant to inform, and the process started again from scratch the next time you needed an update.
The shift to SaaS has changed what MMM can do for an organisation. When measurement runs continuously on an automated platform rather than as a periodic consulting project, it stops being a reporting tool and becomes an operational capability. The question is no longer just which methodology is most accurate — it is which delivery model fits the way your marketing team actually works.
Three delivery models exist in the market today: SaaS platforms, consultancy-led solutions and open-source frameworks. Each has a different cost structure, a different cadence and a different level of internal resource requirement.
Before looking at the differences in detail, it helps to understand what each model is.
A SaaS MMM platform is a software product you access on a subscription basis. Data flows in automatically from your channels, models update on a regular schedule and insights are available in dashboards your team can access at any time. You own the interface and the outputs. Specialist support is available but the day-to-day operation sits with your internal team.
A consultancy-led MMM is a service delivered by an external team of analysts or data scientists. They build the model, run the analysis and present the findings. The relationship is project-based or retainer-based, with deliverables arriving at agreed intervals. Strategic depth and senior expertise are the main advantages. Speed and cost are the main trade-offs.
An open-source MMM framework, such as Google Meridian or Meta's Robyn, gives your data science team the code to build their own model. There is no licensing fee but significant internal resource is required to build, maintain and interpret the model. For a deeper look at whether this route makes sense for your organisation, see DIY MMM: Should You Build Your Own Marketing Mix Model or Use a Platform?
The table below sets out how the three delivery models compare across the capabilities that matter most for enterprise marketing teams. This is Objective Platform's own assessment, based on our experience in the market.
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A few things are worth highlighting from this comparison.
Always-on access is the single biggest practical difference between SaaS and consultancy-led MMM. When your model updates continuously rather than quarterly, you can see the impact of a campaign that launched last week rather than waiting months for the next report cycle. This changes how marketing teams use measurement — from explaining past decisions to informing current ones.
Transparency is the second major differentiator. A consultancy typically delivers conclusions without showing the full methodology behind them. A white-box SaaS platform makes every variable, coefficient and assumption inspectable, which means your Head of Data Analytics can audit the model and your CMO can defend the outputs in a CFO conversation without having to rely on the vendor's authority.
Experiment data integration is increasingly important as privacy regulations reduce the availability of user-level tracking data. Calibrating an MMM with geo-lift tests and other incrementality experiments significantly improves model accuracy. Research published in the Harvard Business Review found that calibrating MMMs with ad experiments can improve return-on-ad-spend estimates by up to 25% across industries. A SaaS platform that integrates experiment results directly into the model makes this calibration practical rather than theoretical.
MMMs excel at working with aggregate data, but accuracy depends on how well the model is calibrated to your specific channels and audience. As privacy regulations tighten and user-level tracking becomes less reliable, calibration through structured experiments has become the most reliable way to keep model outputs accurate.
Geo-based experiments — which test the effect of advertising activity in specific geographic regions while holding others constant — are one of the most practical calibration methods available. They are offered natively by Google and Meta and are widely used by enterprise advertisers precisely because they generate clean incremental data without requiring individual user tracking.
For organisations running highly targeted digital campaigns or focusing on niche audiences, calibration adjustments can be substantial. The more your media mix diverges from broad-reach channels, the more important regular recalibration becomes.
How often you need to recalibrate depends on your media spend and the number of channels you are running. A useful rule of thumb: the higher your monthly spend and the more channels you operate across, the more frequently your experiments should run. A SaaS platform makes this practical by allowing experiment results to be fed directly into the model as calibration inputs, updating outputs without requiring a new consulting engagement.
The shift from consultancy-led to SaaS MMM is not simply about cost or convenience. It is about what kind of measurement organisation you want to build.
Consultancy-led MMM is well suited to organisations that need strategic depth, want a senior external team to own the measurement process and are comfortable with quarterly or annual insight delivery. The trade-off is speed, cost and dependency on an external relationship.
SaaS MMM is well suited to organisations that want measurement to be an internal operational capability — something their own team runs, owns and acts on continuously. The trade-off is that internal adoption requires investment in training and change management.
At Objective Platform, we have seen both approaches work well in the right context. What we have also seen is that organisations which move from consultancy-led to always-on SaaS measurement typically achieve a 15% reduction in marketing spend without impacting results and a 20% potential uplift in incremental ROAS, across the more than €10bn in marketing spend we have optimised on our platform. Those outcomes come not from the methodology alone but from the frequency and accessibility of the insights.
For a fuller picture of how SaaS platforms compare to specific consultancy-led providers in the market, see Marketing Mix Modelling Solutions Compared: An Enterprise Guide for 2026.
What is the difference between a SaaS MMM platform and a consultancy-led MMM?
A SaaS MMM platform provides continuous, self-service access to marketing measurement through a software interface with automated data ingestion and regular model updates. A consultancy-led solution delivers measurement as a bespoke service, with analysts producing periodic reports. The economic model differs significantly: SaaS operates on predictable subscription fees while consultancy solutions typically involve billable hours and custom project scopes.
How often does a SaaS MMM model update?
Modern SaaS MMM platforms update models continuously as new data arrives, typically on a weekly or daily basis depending on configuration. This contrasts with traditional consultancy-led MMM which typically delivers insights quarterly or annually. Continuous updates mean marketing teams can see the impact of recent campaigns and adjust budgets in real time rather than waiting for the next report cycle.
What is MMM calibration and why does it matter?
Calibration is the process of using external data – typically from structured experiments like geo-lift tests – to improve the accuracy of an MMM. Without calibration, models rely entirely on historical data patterns which may not reflect how your channels perform at current spend levels or in current market conditions. Research from the Harvard Business Review found that calibrating MMMs with ad experiments can improve ROAS estimates by up to 25%.
What are geo-based experiments and how do they help calibrate an MMM?
Geo-based experiments test the effect of advertising activity in specific geographic regions while holding others constant, creating a controlled comparison that isolates the incremental impact of that activity. The results provide clean causal data that can be fed directly into an MMM as calibration inputs. They are privacy-safe by design and are offered natively by platforms including Google and Meta.
Is open-source MMM a viable alternative to SaaS or consultancy?
Open-source frameworks like Google Meridian and Meta's Robyn offer complete methodological transparency at zero licensing cost, but require dedicated data science resources to build, maintain and interpret. For organisations with strong internal data science capability, they can be a viable route. For organisations without that capability, the total cost of ownership is typically higher than a SaaS platform when internal time and infrastructure are factored in.
