The landscape of Marketing Mix Modeling (MMM) is evolving rapidly. In 2024, we are witnessing a marked shift not just in the statistical methods used but in how MMM is applied within organizations. Two significant trends are shaping this change: the rise of automated or semi-automated MMM solutions and the proliferation of open-source packages that empower in-house data science teams to build their own models.
At ScanmarQED, we’ve spent the last decade positioned at the intersection of outsourced expert modeling, internal client capabilities, and the use of machine learning for model fitting. While today’s advancements are promising, they come with inherent risks. To truly harness the power of MMM, brands must look beyond simply building models and consider the entire process of using MMM to make reliable, data-driven business decisions.
What is Marketing Mix Modeling?
A Marketing Mix Model (MMM) is a powerful tool that helps businesses quantify the effectiveness of their marketing activities and optimize their strategies. By measuring the impact of various channels like television, digital media, and promotions, MMM enables companies to allocate their marketing budgets more efficiently, ensuring that investments are directed toward the most effective tactics.
The promise and pitfalls of automated MMM solutions
The emergence of semi-automated Marketing Mix Models is reshaping the landscape by enabling a broader range of professionals - data scientists and even general marketing analysts - to construct their own models. This shift towards automation and often “in-housing” is empowering companies to potentially make better-informed decisions and achieve more effective marketing outcomes. However, the stark reality is that open-source packages and commercial automated software solutions frequently fail to deliver the anticipated business gains. The reason? A successful MMM implementation requires much more than just a statistical model.
The comprehensive MMM process: more than just model building
MMM is not a plug-and-play solution; it’s a complex process that necessitates a careful balance of data management, model development, and insight generation. Let’s explore each of these critical components:
- Data Management
Automation plays a crucial role in managing vast volumes of data from multiple sources, which reduces the risk of human error and ensures data integrity. However, the importance of selecting the right data and preparing accurate metrics for MMM cannot be overstated. In many cases, this aspect is overlooked despite being one of the most challenging and crucial steps in the modeling process. Automated data pipelines can streamline this, but expert oversight is essential to ensure that the data inputs are both relevant and correctly structured.
2. Model DevelopmentWhile automated tools can enhance the speed and accuracy of model development by applying sophisticated algorithms consistently, they cannot replace business judgment.
MMM is distinct from other data science tasks because the information within the data is rarely robust enough to eliminate the need for human insight and a solid theoretical framework for how marketing works.
Experienced marketers and data scientists must still interpret model outputs and apply their understanding of market dynamics to make informed decisions.
3. Insight GenerationAutomation has the potential to deliver insights faster and with greater accuracy, but it’s the people who ultimately generate actionable insights. Cross-functional teams using automated tools require proper training and continuous support from seasoned MMM practitioners to translate model outputs into strategic business actions. Without this expertise, the value derived from even the most advanced models can be limited.
ScanmarQED’s approach: striking the right balance
At ScanmarQED, we have been successfully delivering semi-automated MMM solutions for years. Our approach is flexible, offering a range of solutions from pure self-service models for sophisticated clients, to hybrid solutions where ScanmarQED experts work hand in hand with the brand team, to outsourced work that is fully managed by ScanmarQED. This adaptability allows us to meet the diverse needs of our clients, supporting them in areas where they excel, such as data management, while providing the necessary guidance in more specialized areas like initial model development and insight generation.
Moving forward with caution and confidence
As the MMM landscape continues to evolve, companies must approach automation with both enthusiasm and caution. While new tools and technologies offer unprecedented opportunities, they also require careful implementation and ongoing oversight to deliver the desired results. A successful MMM strategy goes beyond the model; it requires a comprehensive approach that integrates data management, model development, and insight generation, all guided by experienced professionals.
By striking the right balance between automation and human expertise, companies can unlock the full potential of Marketing Mix Modeling, making more informed decisions and driving superior marketing outcomes.
The evolution of MMM is a reminder that while technology advances, the fundamentals of good decision-making-grounded in expertise, judgment, and a holistic approach - remain unchanged. As companies charter this new landscape, those who blend innovation with experience will be best positioned to succeed.
