Media mix modelling and its role in modern marketing

How media mix modelling helps to determine the most effective media channel

Media mix modelling (MMM) is an important tool for marketers. It helps to better analyse the effectiveness of different media channels within marketing campaigns. MMM allows you to analyse how channels interact with each other. It also shows the impact of each channel on achieving business objectives. 

As you know, attribution models focus on a single channel. Media mix modelling, on the other hand, considers all marketing efforts. This makes it a powerful tool for optimising advertising budgets and making informed decisions.

How the tool works

MMM relies on analysing historical data, including marketing spending, sales data, changes in trends, and other factors. Using this information, a statistical model is created that quantifies each channel’s contribution to business results, which may consist of increased sales or conversions. 

The key stages in the media mix modelling process are:

1. Data collection. This covers marketing spending, sales, and seasonal and market trends.
2. Data cleansing. This is an important and time-consuming step in preparing data for further analysis.
3. Model building. It is a statistical model that analyses the relationships between channels and their impact on business results.
4. Interpreting the results. Evaluating the contribution of each channel and understanding its role in overall performance.
5. Budget allocation. Applying the findings to optimise costs and maximise ROI.
6. Forecasting. Assessing how changes in strategy may affect future results.

Examples of using the tool

In this way, media mix modelling helps marketers identify synergies between different channels and optimise budget spend. Aaron Whittaker of Thrive Internet Marketing gave an example of how the tool is being used. Using MMM, his team discovered that radio ads increased social media engagement by 25%. This insight allowed them to plan ad campaigns more effectively and use radio at the right time.

MMM also helps quantify the impact of long-term strategies such as brand building. Whittaker found that podcast sponsorship did not provide an immediate return on investment. However, within a few months, the approach significantly reduced the cost of customer acquisition through other channels. 

Peter O’Callaghan of ScrapingBee demonstrated how MMM helps identify regional trends. The modelling identified California and Texas as key regions for lead capture, which helped increase regional engagement by 30% and shorten the sales cycle. This approach continues to guide the brand’s marketing campaign strategy.

Therefore, the correct use of MMM allows for understanding how different channels interact and predicting their future performance. This approach is particularly important for companies developing a long-term strategy.