We are witnessing and participating in a new era of marketing measurement effectiveness. Recent ecosystem changes are driving the community to rapidly advance in a field that had lagged over the past decade. The last four to five years have seen remarkable progress, mainly thanks to innovations from the open-source community and the adoption of Artificial Intelligence (AI) into our everyday lives.

As highlighted in our previous paper, the shift from licensed software to open-source solutions is accelerating, reflected in a 32% increase in searches for "Marketing Mix Modelling " or “MMM” from 2022 to 2023, a trend that continues into 2024.

In response to these evolving trends, companies are progressively refining their approaches to measuring marketing effectiveness, transitioning towards more agile methodologies that emphasize actionable insights. This paper serves as a strategic guide for marketing stakeholders and data science teams, aiming to translate insights into actionable strategies that will foster significant organizational transformation.

To effectively internalize the MMM process, it is essential to align the system with the business's unique needs and goals by considering several key factors:

• Assess the specific characteristics of marketing spend, focusing on data granularity, geographic analysis, and the integration of diverse data types (online/offline, paid/non-paid).
• Determine the necessary level of detail for actionable insights by splitting information by region, category, product type, sales channel, and customer segment.
• Evaluate the ROAS for full-funnel campaigns and the incremental effect of each marketing channel to differentiate marketing activities in the decision-making process.
• Include external sources such as macroeconomic variables, market trends, and seasonal changes to understand all factors impacting sales.
• Account for the long-term effects of brand-building investments, and potential halo effects from marketing efforts on different product lines.

By clarifying these needs, businesses can select the most appropriate MMM methodology and develop a comprehensive and dynamic process that effectively identifies the key factors driving their business.

A comprehensive data strategy is essential for the successful implementation of MMM. This strategy is built on four foundational pillars: Dynamic Modelling Integration, Smart Automation, Agile Approach, and Bias-Free Decision Making.

Automation, driven by AI and machine learning, significantly improves the speed and efficiency of the modelling process. Leveraging AI brings a level of accuracy that was previously unattainable, while an agile approach facilitates timely adjustments to strategies based on market changes. By standardizing complex decisions and reducing the need for analyst intervention in tasks like parameter selection, assumption checking, and trend decomposition, the consistency and precision of the results are enhanced, shifting the approach from assumption-based to AI-driven certainty.

Together, these pillars create a robust and adaptable data strategy that helps organizations effectively internalize Marketing Mix Modelling (MMM).

Integrating business acumen with data is essential for turning insights into action. A combination of AI and human expertise focuses on the primary goal: driving maximum value through a lens of incrementality. This approach is particularly significant in measuring advertising effectiveness, where calibration and alignment with market realities are crucial for achieving reliable results. Open-source tools like Robyn enable the direct integration of experimental results, as well as customized budget allocation and optimization, making them invaluable for strategic decision-making.

Finally, maintaining MMM as an “always-on" process is vital for continuous alignment with evolving strategic needs. For companies that have maintained a consistent refresh of MMM over seven years, we have observed an average growth of 151% in ROAS, underscoring the value of regularly updated models in maximizing return on advertising spend.

To achieve these outcomes, it is crucial to ensure collaboration across teams and identify the right mix of stakeholders to oversee the MMM process. Integrating a steering committee and determining key roles for guiding, supervising, and most importantly, adopting MMM recommendations are fundamental to successfully embedding the model into the company's strategic operations.

In conclusion, innovation fostered by open-source tools unlocks unprecedented opportunities to measure marketing efforts effectively, enabling organizations to understand what’s working and optimize and adapt to new AI-driven marketing strategies. By integrating business acumen and aligning closely with the realities of business operations, companies can become AI-driven marketing mix entities, maintaining a vision where MMM remains an always-on process.