How Machine Learning Speeds Marketing Mix Modeling

Paul Cowman
Paul Cowman
Sr. Director, Demand Generation, Pecan AI

Marketers are facing uncertainty as the digital ecosystem shifts to new privacy standards and cookieless attribution. A recent report shows that roughly 50% of CMOs still need to get ready for this change. 

Marketing mix modeling (MMM) is re-emerging as an important measurement approach for marketing leaders, but the traditional methods of implementing MMM need to adapt to the current needs of modern marketers. 

This issue was highlighted in a recent Pecan webinar: a concern that the traditionally slow-moving nature of MMM prevents it from being refreshed frequently. But that’s not the case today.

Let’s dive into marketing mix modeling and machine learning, and explore how machine learning speeds up today’s MMM process. We will also explore the pros and cons of using an ML-based MMM solution and provide tips for implementing it.

Understanding the Basics of Marketing Mix Modeling

We talk about marketing mix modeling (MMM) in great detail in another recent blog post. But for a high-level overview, MMM is a statistical technique that measures the impact of different marketing channels on sales or other key performance indicators. It considers various factors, such as marketing spend, pricing, distribution, and external factors, like seasonality, to identify effective marketing strategies. Studies show that companies that use MMM see around a 10% increase in ROI.

Integrating online and offline campaign data in MMM involves combining data from various sources to create a comprehensive view of marketing performance. This approach helps marketers understand the relative impact of each channel and optimize their marketing mix accordingly. Research suggests that integrating online and offline data can increase the accuracy of MMM by up to 20%. By incorporating data from both online and offline channels, MMM provides a holistic view of marketing performance, leading to better decision-making and optimization of marketing spend.

Overall, the process of marketing mix modeling helps businesses gain a better understanding of their customers and their preferences. This approach guides more effective marketing strategies and campaigns tailored to their target audience. Additionally, it helps businesses identify areas of improvement and optimize their marketing efforts for maximum efficiency and effectiveness.

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Understanding the Basics of Machine Learning

At its core, machine learning is a type of artificial intelligence (AI) that enables computers to identify patterns in data using statistical methods. It gathers information from datasets, learns from past experiences and observations, and then uses this knowledge to generate insights and make decisions.

Machine learning algorithms are used to identify patterns in data and make predictions. These algorithms can detect anomalies in data, classify data, and make predictions about future events. By leveraging the power of machine learning, businesses can gain valuable insights and make better decisions.

Where Does Old-Fashioned MMM Fail?

Older marketing mix modeling approaches often took months, if not quarters, to complete because they required large amounts of data and complex statistical analysis. This difficulty level and pace are why this older method was adopted only by large, well-resourced companies who could handle this complex process. Older methods of MMM traditionally took more time due to three reasons: 

  • To conduct a thorough analysis, the model needs to incorporate various variables such as sales data, marketing spending, pricing, distribution, and external factors such as economic trends or seasonality. Gathering this data from multiple sources and cleaning and formatting it (without the help of machine-learning tools) can take significant time and resources. 
  • Once the data is collected and cleaned, it needs to be processed and analyzed using advanced statistical techniques. This step involves building and validating multiple models, testing different assumptions, and evaluating the model’s accuracy and effectiveness. The process can be iterative, with multiple rounds of model refinement and validation, further extending the timeline. Again, without using ML-based methods to expedite the process, this can be very time-consuming and expensive.
  • Additionally, traditional marketing mix modeling approaches require a high level of expertise in statistics and data analysis. Finding the right experts to handle this older method could be challenging.

Overall, the complexity of the data, the analysis techniques, and the need for specialized expertise make “old-fashioned” marketing mix modeling a time-consuming, expensive, and slow process. 

However, advances in AI and data analytics are leading to faster and more efficient modeling techniques, such as machine learning, that make this method much more accessible for all marketing teams.

The Benefits of Machine Learning for Marketing Mix Modeling

The primary benefit of using machine learning for marketing mix modeling is that it enables businesses to analyze vast amounts of data much faster than traditional methods. MMM can help businesses quickly and accurately identify new trends and insights to improve their marketing strategies. Additionally, machine learning reduces the number of manual processes required, making the process smoother and more efficient.

A second and lesser-known benefit is the creation of an MMM-based “what-if” scenario tool. With the power of machine learning, brands can now not only model their marketing mix but also create what-if scenarios to help them plan out the next budget. With ML-based scenario tools based in MMM, brands have the ability to:

  1. Easily simulate different budget allocations
  2. Immediately see the predicted outcomes
  3. Strategically decide on the most profitable budget allocation
Pecan MMM dashboard
Pecan's simulation tools help marketers experiment with new budgeting strategies.

How Machine Learning Speeds Up Marketing Mix Modeling

Machine learning can speed up the process of marketing mix modeling by automating many tasks and using advanced algorithms to analyze large amounts of data.

First, machine learning can automate data processing tasks such as cleaning, formatting, and integrating data from multiple sources. Automated data preparation can save significant time and reduce errors that occur when processing data manually.

Second, machine learning algorithms can identify patterns and relationships within the data that may not be apparent to human analysts. These algorithms detect complex interactions between variables, such as the impact of weather on sales or the effect of seasonality on advertising effectiveness. By automating this process, machine learning can save time and provide more accurate insights.

Third, machine learning can enable faster model building and testing. Traditional marketing mix modeling often involves building multiple models and testing different assumptions, which can be time-consuming. With machine learning, models can be built and tested more quickly using advanced algorithms that can rapidly evaluate different combinations of variables.

Pecan MMM dashboard showing saturation, carryover effects
Pecan's MMM solution reveals channel saturation and carryover effects, plus additional insights.

Tips for Selecting a Marketing Mix Modeling Process

When considering implementing MMM, a fundamental question of “Build vs. Buy” must be addressed. Generally speaking, several variables need to be considered to ensure a successful implementation of a working ML model. 

Some of the most important topics to consider when evaluating an in-house team or outsourcing the work to a third party are:

  1. Core competencies: Is data science a core competency of your business? Machine learning requires a high level of expertise in statistics, data analysis, and programming. Working with an ML SaaS company can provide access to a team of experts with specialized knowledge and experience in building and deploying ML models.
  2. Time and cost: Building an in-house team to develop and deploy ML models can be time-consuming and expensive. Purchasing a solution can be more cost-effective by leveraging existing infrastructure, tools, and expertise.
  3. Scalability: Machine learning models often require significant computational resources and specialized hardware, which can be expensive to acquire and maintain in-house. A SaaS MMM solution can be scalable and easily adapt to changing business needs and evolving technologies.
  4. Quality and reliability: Deploying ML models requires rigorous testing and validation to ensure that they are accurate, reliable, and secure. Working with an ML SaaS company can provide access to advanced testing and validation tools and processes that can improve the quality and reliability of the models.
  5. Innovation: ML platform developers are often at the forefront of new technologies and techniques. Working with these companies can provide access to the latest innovations and best practices in ML model development and deployment.
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Learn more about how today's ML-driven marketing mix modeling empowers marketers to achieve more.

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Overall, working with a SaaS provider for an ML-based MMM solution can provide significant advantages in terms of expertise, time and cost savings, scalability, quality and reliability, and innovation. Companies should carefully consider their needs and resources when deciding whether to build ML models in-house or work with a specialized deployment company. 

If you’d like to learn more about marketing mix modeling and how to get started with this powerful approach in your business, we’ve got some great resources for you! Check out our on-demand webinar featuring Pecan’s MMM experts, download our in-depth guide to MMM, or get a broader perspective on how ML-based methods can support you as a marketing leader in The CMO’s Guide to Predictive Analytics.

Put machine learning and marketing mix modeling to work today. Contact us to set up a time to chat about Pecan and your marketing team’s needs.

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