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2 min read

Key questions to consider when implementing marketing mix modelling

This article was updated on: 18.04.2024

Before diving headfirst into marketing mix modelling implementation, it’s essential to consider several crucial questions. From understanding the intricacies of variable selection to aligning modelling objectives with your business goals, thoughtful consideration of the following points can pave the way for more accurate and actionable insights.

Below, you’ll find a list of key questions to consider before embarking on your MMM journey.


What model boundaries are needed for analysis? Should the model be built at a national, regional, or individual level? How do model boundaries affect the accuracy and interpretability of the results?

Data granularity

What level of granularity is needed for modelling? Shall we use daily, weekly or monthly data? Daily data can be hard to model, and monthly data isn’t granular enough.

Variable selection

Which marketing and non-marketing variables should be included in the model? How do different marketing channels interact with each other and with external factors? Are there any significant drivers of sales that need to be accounted for? Do these variables explain the dependent variable enough?

Parameter estimation

Parameter estimation is the essence of any statistical model. How are the parameters estimated in your model? Which parameters are estimated and which are chosen by the MMM practitioner? Can the practitioner justify their choice of parameters?

How does seasonality affect sales? Are there any long-term trends that need to be captured in the model? How can seasonality and trends be separated from the effects of marketing activities?

Model assumptions

What assumptions are made about the relationship between marketing inputs and sales outputs? How robust are these assumptions to changes in market conditions?

Model validation

How will the model be validated and tested? If our model recommends increasing the Meta budget by 10%, could we do it for validation purposes?

Stakeholder buy-in

How will the results of the market mix model be communicated to stakeholders? What level of understanding and involvement do they have in the model-building process?

Continuous improvement

How will the MMM be maintained and updated over time? What mechanisms are in place to incorporate new data and insights into the model? Frequent tests help to validate the model.

Cost-benefit analysis

What are the costs of building and maintaining the MMM? What are the expected benefits in terms of improved decision-making and return on investment?

Statistical modelling as a whole (not just MMM) is difficult. A good marketing mix model requires in-depth planning and careful consideration. As you delve into the realm of marketing measurement, it’s essential to lay a solid foundation by addressing key considerations before implementing your marketing mix model strategy.

By carefully considering these thoughts and questions, you’ll be better equipped to construct a robust measurement framework that accurately captures the impact of your marketing initiatives.