More Than Data

DIY Marketing Mix Modelling: A Step-by-Step Tutorial

Jan 10, 2026By Chelsea Liu
Chelsea Liu

Introduction to DIY Marketing Mix Modelling

In today's dynamic marketing landscape, understanding what drives your sales is crucial. Marketing mix modelling (MMM) offers insights into the effectiveness of various marketing channels. While traditionally the domain of specialized analysts, you can now embark on a DIY journey to create your own MMM. This guide will walk you through the steps needed to build a basic model, empowering you to make data-driven decisions.

Before diving into the process, it's important to note that MMM involves statistical analysis to understand the impact of different marketing tactics. With the right approach, you can maximize your marketing ROI.

data analysis

Gathering Your Data

The first step in creating a marketing mix model is gathering the necessary data. You'll need both sales data and information on your marketing efforts. This could include:

  • Sales figures over a specified period
  • Advertising spend for different channels
  • Promotional activities
  • External factors like holidays or economic changes

Ensure data accuracy and consistency. Clean data is essential for building a reliable model.

Choosing the Right Tools

Once your data is ready, select the tools you'll use for analysis. Popular options include Excel, R, or Python. Each has its strengths:

  • Excel: User-friendly, best for simpler models.
  • R: Powerful for statistical analysis, with numerous packages available.
  • Python: Offers flexibility and integration with other tools, ideal for complex models.
software tools

Building the Model

With your tools in place, it's time to build the model. Start by specifying your objective. Are you looking to understand the impact of digital advertising, or perhaps offline promotions? Clearly defining your goal will guide your analysis.

Next, choose a statistical method. Regression analysis is commonly used in MMM, allowing you to identify relationships between variables. Construct a regression model to estimate the impact of each marketing channel on sales.

Running and Interpreting the Model

After setting up your model, run the analysis. This might require some trial and error to find the best fit. Once the model is running smoothly, interpret the results. Look for insights such as which channels yield the highest returns and how external factors influence sales.

business analysis

Implementing Insights

With insights in hand, it's time to adjust your marketing strategy. Allocate resources more effectively by focusing on channels that deliver the best results. Consider testing different scenarios to see potential outcomes of various strategies.

Remember, MMM is an ongoing process. Regularly update your model with fresh data to ensure it remains relevant and accurate.

Conclusion

DIY marketing mix modelling is an invaluable tool for businesses looking to optimize their marketing strategies. With the right data, tools, and analysis, you can gain a deeper understanding of what drives sales and allocate your marketing budget more effectively. Start your journey today and unlock the potential of data-driven decision-making.