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Building a Marketing Mix Model in-house gives you control and flexibility, but the investment required is routinely underestimated. A basic model takes between 12 and 22 weeks to build, requires a team with specialist skills across data science, economics and marketing, and needs ongoing automation and maintenance to stay useful. Open-source tools like Google Meridian and Meta Robyn lower the technical barrier but do not eliminate the operational burden. For most enterprise marketing teams, the question is not whether DIY MMM is possible, but whether the ongoing cost of maintaining it is a better use of resources than a dedicated platform.
Marketing Mix Modelling (MMM) is a powerful tool that helps brands analyse the impact of their marketing efforts using business KPIs. There are several reasons why a brand might choose to build an MMM solution in-house rather than rely on an external partner.
For instance, some brands may have unique data requirements they believe cannot be met by external providers. Building an MMM in-house also offers greater control over the model and the insights it generates. However, creating an in-house MMM solution can be both costly and time-consuming. Before making this decision, it is worth understanding exactly what is involved and where teams most commonly underestimate the effort.
This article covers the five main challenges of building an MMM in-house, how open-source tools like Google Meridian and Meta Robyn change the calculation, and what to consider when deciding between DIY and a dedicated platform.
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Building an MMM is no simple task. According to Meta, creating a relatively basic MMM, including data collection, model development, testing and validation, takes between 12 and 22 weeks. This timeline can be extended further if data is not properly organised, cleaned and integrated. The process therefore requires a substantial time investment, which can significantly impact resources.
That timeline assumes clean, well-structured data and a team that already understands MMM methodology. In practice, most teams spend additional weeks on data preparation alone before model development can begin.
Building an MMM as a one-off effort is insufficient. An effective MMM requires automation. Data ingestion, processing and model updates should be automated where possible. This ensures that new data is collected and fed into the model seamlessly, keeping it up-to-date. Without automation, the model will quickly become outdated, reducing its effectiveness.
This is where many in-house builds stall. The initial model gets built and used for a period, but as media mix evolves and new channels are added, maintaining the automation layer becomes a significant ongoing task that competes with other data team priorities.
Having a model is just the first step. Additional tools are required to generate actionable insights. Statistical results alone are insufficient. You need visualisations, reports and dashboards to interpret the data effectively. These tools help you understand how each marketing channel contributes to overall sales and identify underperforming channels. Without these insights, the value of an MMM diminishes.
The gap between a working model and a decision-ready insight layer is often underestimated. Building dashboards, scenario planning tools and budget optimisation outputs on top of the model requires additional development work that is rarely scoped into the initial build.
Diagram showing the five layers of a full MMM platform stack and where DIY builds typically stop, highlighting that the insight layer and optimisation tools are rarely included in in-house builds
Creating an MMM demands a diverse range of expertise. Advanced statistical and data science skills alone are not enough. You need a deep understanding of marketing, economics and consumer behaviour to build a robust model. Additionally, knowing which data sources to use, which variables to include and how to validate the model are critical. Building an effective MMM requires a team with a wide variety of skill sets.
This combination of skills — econometrics, marketing domain knowledge and data engineering — is rarely found in a single person and often needs to be assembled across a team. Hiring or developing this capability takes time and adds to the total cost of ownership.
To ensure reliable results, MMMs must be tested across various brands and industries. An inaccurate MMM can reduce marketing efficiency, leading to unnecessary costs. Robust testing ensures the model can handle different scenarios and provide accurate insights, regardless of the brand or industry. Without proper testing, the reliability of the model is compromised.
Validation is not a one-time exercise. As your media mix changes, as new channels are added and as market conditions shift, the model needs to be recalibrated. This is where triangulation with incrementality experiments becomes particularly important — experiments provide the external validation that keeps the model honest over time.
Open-source MMM tools like Google Meridian and Meta Robyn have made it significantly easier to get a model running without building from scratch. Both use Bayesian methodology and are freely available, which has made DIY MMM more accessible for teams with strong data science capability.
However, open-source tools address the modelling problem, not the full operational challenge. You still need to:
Google Meridian in particular requires significant in-house data science capacity to run effectively. For teams that have this capacity, it is a credible option. For teams that do not, the operational burden tends to outweigh the cost savings.
For a broader comparison of MMM approaches including open-source alternatives, see our guide to marketing mix modelling solutions.
The decision between building in-house and using a dedicated platform is rarely about capability alone. Most enterprise data science teams could build a working MMM given enough time. The real question is whether that time is better spent elsewhere.
Key questions to ask before committing to a DIY build:
If the answer to most of these is no, the total cost of a DIY build is likely to exceed the cost of a dedicated platform without the guarantee of a better result.
For a closer look at how in-house builds compare to the approach Objective Platform takes, see our post on why a SaaS marketing mix model outshines the classic approach.
Building an MMM in-house is a legitimate option for organisations with the right team, the right data infrastructure and a clear plan for ongoing maintenance. For most enterprise marketing teams, however, the operational reality of keeping a DIY model accurate and decision-ready over time makes a dedicated platform the more practical choice.
If you want to understand how Objective Platform compares to a DIY build for your specific situation, get in touch with our team.
What is DIY MMM?
DIY MMM refers to building a Marketing Mix Model in-house using internal data science resources, rather than working with an external platform or partner. Common approaches include building from scratch or using open-source tools like Google Meridian or Meta Robyn.
How long does it take to build an MMM in-house?
According to Meta, building a basic MMM including data collection, model development, testing and validation takes between 12 and 22 weeks. This assumes clean, well-structured data and a team with relevant expertise. In practice, data preparation often extends this timeline further.
What is the difference between Google Meridian and Meta Robyn?
Both are open-source Bayesian MMM frameworks. Google Meridian is Google's implementation, designed for scalability and integration with Google data sources. Meta Robyn is Meta's implementation, built around the R programming language. Both require significant in-house data science capability to run and maintain effectively.
What expertise do you need to build an MMM in-house?
An effective in-house MMM requires expertise across econometrics, data engineering, marketing domain knowledge and statistical modelling. This combination is rarely found in a single person and typically needs to be assembled across a team. Ongoing maintenance adds further demands on that team's time.
When does a dedicated MMM platform make more sense than DIY?
A dedicated platform tends to make more sense when your data science team does not have econometrics expertise, when you need insights on a regular rather than annual basis, when you want to run scenario planning and budget optimisation on top of the model, or when the ongoing maintenance of a DIY build would compete with other priorities.
