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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. This article explores why it is often better to work with an experienced partner to develop a reliable MMM solution.
Building an MMM is no simple task. According to Facebook, 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.
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 Without automation, the model will quickly become outdated, reducing its effectiveness.
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.
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.
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.
Building your own MMM solution is a daunting undertaking. It requires substantial investment in time, resources and expertise. Moreover, the model must be automated, insights must be generated and rigorous testing is needed to ensure reliable results.
For these reasons, it is often best to rely on an experienced partner to provide a robust MMM solution. This approach will save you time, effort and money in the long run, enabling you to make better-informed marketing decisions and optimise your ROI.
