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Fine-Tune your marketing mix models to make data-driven decisions

As Chief Marketing Officer, you are responsible for ensuring that your budget is invested in the most effective and efficient way, ultimately resulting in higher ROI and greater success for the company. One of the best ways to achieve this is by using marketing mix modelling (MMM). It is a statistical technique used to optimize media budget and optimize marketing campaigns. MMM uses historical data to measure the impact of past campaigns, analyze the results, and identify what works and what does not. With MMM, marketers can identify which channels perform the best, measure the return on investment for each channel, and determine the optimal budget to allocate to each channel.

What you can achieve with marketing mix models

Marketing mix modeling (MMM) can be used to make data-driven decisions about future media investments.

marketing mix models

Gauge the Impact Advertising Channels Have on One Another

The MMMs provide insights into how changes in one area may impact performance in another channel. By combining data from all sources, including TV, radio, print and digital media campaigns, models allow you to identify which channels have the greatest influence on sales and other KPIs. This helps you understand where best to invest your marketing budget for maximum return and accurately assess how effective each channel is as part of an overall strategy.

Digital Strategy

Measure the Influence of Competitors Advertising

The MMMs provide an understanding of customers’ reactions to particular price changes and promotions made by competing companies or brands. With this information, your business can anticipate its competitor’s next moves and adjust its own strategies accordingly in order to remain competitive in the market.
marketing mix models
Budget Optimization

The MMMs can provide insights into how to allocate resources more effectively across different channels and campaigns. They allow for evaluation of past campaigns, identification of top performing channels and activities, as well as providing insights into how future campaigns should be structured to maximize their return on investment. As they are capable of assessing multiple campaign variables simultaneously, they can help identify unexpected correlations between elements such as type of promotion that could assist in future planning efforts.

marketing mix models

Forecast the Outcomes of your Campaigns

Another use case for MMMs is to forecast the outcome of a campaign before it launches or continues. With an MMM, marketers can estimate the projected effect a particular activity will have on sales (e.g. increasing budget) by producing various scenarios, allowing you to potentially make adjustments in order to maximize the success of the campaign. This forecasting power helps you better anticipate potential outcomes and make more informed decisions about your campaigns and budgets.
marketing mix models

Uncover Synergies Between Media Channels

The MMMs enable you to better understand how changes in your marketing mix result in a change in sales volume, revenue and profit for products or services. With this understanding, you can determine which combination of media channels is most effective at driving increased ROI from marketing activities. For example, a MMM may indicate that spending more on display ads will lead to an increase in brand awareness but also suggest that there is a decreasing return as additional budget is spent on display ads. This kind of insight helps marketers more effectively allocate their budgets across different media types and allows them to identify any potential synergies between channels so they can maximize the effectiveness of their efforts.

You can use the MMMs’ insights to create targeted campaigns that leverage a customer’s behaviour while minimising wasted spending on ineffective efforts.

How to build advanced marketing mix models

Having understood the various utilities of the marketing mix models, we will now explore the various pillars that are essential to building an effective advanced marketing mix model.

1. Gather and use high-quality data

When building an advanced MMM, it is vital your data is accurate. Data should come from sources you can trust which could include your own Analytics database, market surveys or industry-specific sources that have a track record of reliable accuracy.

Once you have access to the necessary data, we will analyse its reliability in order to gain insights and start building the models. A robust MMM should be able to extract valuable information from the data such as trends in consumer behaviour or economic conditions, or correlations between marketing tactics and sales results.

2. Consider the customer journey

When building advanced marketing mix models, it is important we consider the full customer journey in order to make your models accurate. This means taking into account all of the touchpoints that a customer has with your brand, from awareness and interest all the way through to purchase and beyond. By understanding how each of these touchpoints contributes to the overall customer experience, we can build a model that accurately predicts how changes to your marketing mix will impact customers’ behaviour.

3. Align your MMM with your business structure

MMMs merge media and business perspectives into one unique model to provide a holistic understanding of the factors that contribute to and impact your business growth. As such, your MMM should be tailored to your business model by accurately reflecting the media and non-media variables that are at play in the context of your business. We will make use of a methodology that upholds industry standards that will underpin the construction of your models.

4. Leverage AI

One of the biggest issues with a traditional MMM is that it has a high degree of human bias. On the other hand, modelling every day or week is time-consuming and not feasible as a team. You should instead automate your code and techniques through the use of our AITA technology so that they can be scaled easily.

5. Ensure flexibility

To avoid overfitting data or selecting the incorrect variable, we will use industry-standard regularisation techniques such as lasso or ridge regression to improve the accuracy of the model’s predictive abilities. For this reason, our models offer more flexibility with increased detail and robust results. We aim to strike a balance between analytical and business variables for an intuitive understanding of your market.

Why work with us to build your MMMs?

Our team of data scientists have the experience and expertise to build highly flexible, scalable and accurate MMMs. Collectively, we have worked with some of the biggest brands in the world. We take a data-driven approach to everything we do by using the latest analytical and statistical techniques and tools to ensure that your marketing mix model is as accurate and reliable as possible.