Marketing Mix Models (MMMs) are regaining relevance as AI-driven solutions for data complexity and privacy. Discover how machine learning transforms omnichannel measurement and empowers marketers to optimize investments with confidence.

Omnichannel Studies make a Comeback, but now with the Full Power of Machine Learning in their DNA

The proliferation of media channels and the increasing importance of data privacy are driving marketers to seek alternative strategies for measuring and optimizing their advertising efforts. In this new context, traditional Marketing Mix Models (MMMs) emerge as a viable alternative to address these challenges, enabling marketing professionals to make data-driven decisions with confidence.

Sales evolution

Challenges in Measurement within the Industry

The phenomenon brought by Big Data means we now have access to a vast amount of data to inform decision-making. In advertising, this translates to having much more information about the performance of our campaigns, with access to highly detailed metrics at various levels of aggregation. However, collecting, understanding, and making decisions based on this data presents a significant challenge. Many of the data points may not be relevant to addressing the specific business problem at hand, and the complexity of interactions between different data sources can complicate the development of comprehensive solutions.

Data Generated per Year

On top of that, in recent years, data privacy has become a top priority for all major organizations. Protecting and respecting users, consumers, and potential customers must be a priority for any industry company. Consequently, resources such as cookies are disappearing to make way for less invasive techniques adding a layer of complexity to data handling.

Both of the aforementioned issues apply to any advertising medium: we have more data available than ever before, but simultaneously, respecting the privacy of those generating the data is becoming increasingly important. As a result, managing data to make business decisions is facing a significant challenge. Additionally, over the past 15 years, the number of advertising channels has grown exponentially with the rise of digital marketing, creating a ‘snowball effect’ that further complicates the issue.

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Marketing Mix Models as “the Solution”

We live in a world that generates an ever-increasing amount of data from various sources. If we can handle and harness these data effectively, they would enable us to make informed decisions about our business. The tool that allows us to leverage advertising data is the Marketing Mix Model (MMM). It’s a statistical model that enables us to understand the influence of each of our marketing actions and work on their efficiency and effectiveness in generating results.

Evolution of MMMs

Marketing Mix Models (MMMs) aren’t new; multinational consumer goods companies have been using them for decades to optimize their advertising budgets and gain a better understanding of the performance of the media channels in which they invest.

So, what changed?

The challenges mentioned in the previous section put traditional MMM methodologies to test. With Big Data providing access to more detailed data, marketers now want to use them to answer more granular questions. However, for technical reasons beyond the scope of this article, traditional models may not be sufficient.

Big Data has also brought forth more powerful and modern algorithms commonly known as Machine Learning or Artificial Intelligence. These algorithms enable the handling and extraction of information from the vast volumes of data available, taking MMM studies to a new power lever and unlocking many new features.

Advantages and Disadvantages

MMMs are a powerful tool for gaining a better understanding of the business and making data-driven decisions. However, they should never be seen as the absolute solution to a question but rather as one of the resources available to marketers.

Below, we outline the advantages and disadvantages of traditional MMMs:

Advantages:

• Allows understanding of the effects of advertising on sales or any other variable being measured.
• Interpretability: Traditional MMMs are simple models that can be easily interpreted.
• Quantity of variables: Multiple media channels can be included in the analysis, so the proliferation of channels is not a problem.
• Can model non-linear effects of advertising by transforming data prior to modeling.

Disadvantages:

• The model is not suitable for answering more granular questions as it does not handle collinearity well.
• Highly dependent on the available data. ‘You cannot trust any model if you do not trust the data you have.’
• Projects tend to extend over several months (+6), making it difficult to make short-term decisions.

How to get the most out of them

Here at Zenda, we believe that industry changes compel us to innovate and create better tools to adapt to the environment in which we operate. That’s why we’ve developed an MMM that utilizes Machine Learning, allowing us to work at a higher level of granularity and in less time to effectively address the questions challenging our business, lowering the impact of most of the traditional model disadvantages.

If you’re facing a business question that you’re struggling to answer accurately, there’s likely a solution in predictive models, and you may need to consider the challenge of finding the solution internally. Questions such as:

• Which media channels perform best?
• What are the most important metrics for my business?
• Am I optimally allocating my budget?
• What is the impact of different variables on sales?
• What if I invest more in one medium than another?
• How should I distribute my budget to increase my ROAS by X%?

These are the types of questions that should prompt you to seek a partner to develop a predictive model to unlock the problem or develop an in-house team capable of working in this area. If you operate within the consumer goods, retail, and retail media, banking, or insurance verticals, among others, there are likely many applications that can take your business to the next level of understanding and execution.

Stay tuned for upcoming articles to learn more about applications, success stories, and why developing these capabilities will soon be almost mandatory to stay at the top of the industry.

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