Marketing Mix Model

The proliferation of these models has revolutionized the ecosystem: by increasing accessibility and engaging development, the measurement of media strategies has reached degrees of sophistication never seen before.

The migration from licensed-software to open-source solutions is gaining momentum. A true reflection of this trend is patent in the evolution of searches related to "MMM", with an increase of 32%¹ in 2023 compared to 2022.

This accessibility has led to the expansion of creative, data-driven approaches to marketing measurement, promoting a culture of experimentation and innovation within the industry. As a result, the MMM landscape continues to evolve rapidly, with the open-source paradigm acting as the driving force.

Evaluating open-source use cases unveils three main characteristics:

1. Cost-effectiveness. One of the main attributes of open-source software is its inexpensive nature. Its free availability, even for business applications, makes it a cost-effective and accessible option.
2. Innovation. Open-source software is enriched through communities’ contributions, encouraging the proliferation of innovative ideas and constant improvements. As people from diverse backgrounds contribute, new ideas and improvements emerge.
3. Flexibility. The source code can be modified to suit a user's specific needs, allowing for the creation of bespoke solutions.

However, it is also important to consider certain risks associated with the use of open-source models:

1. Support. Technical support for open-source software can be limited or non-existent. Users often rely on the community for troubleshooting, increasing the degree of uncertainty to solve problems.
2. Integration. Integration with other software components can pose a challenge.
3. Experience. To tailor and adapt an open-source model to the specific needs of an organization, an internal analytics team is imperative.

From a strategic standpoint, companies looking to implement MMM models can consider three main approaches based on their capabilities and workflows:

• In-house: an internal team builds and maintains the solution, requiring business knowledge and technical expertise to achieve positive results. Companies can choose to develop their own models from scratch or adapt and customize an existing open-source model. This approach is gaining popularity and already accounts for a 44% of the total market².
• Outsourcing: consists in a complete outsourcing of the MMM project, delegating its operationalization to a third party, and providing an end-to-end solution to the client. It provides a high degree of satisfaction, with 93% of respondents indicating that their efforts were effective².
• Hybrid: merges the workflow between the company and third parties. While the company leads and maintains the solution, the third-party acts as a provider of guidance on various aspects related to strategy, operationalization, or technical expertise.

Among the three approaches, there is a growing trend in the internalization of models. This dynamic increases the chances that companies will face new challenges, both at an organizational and operational level.

What are the main challenges that a company faces in the internalization of the MMM process?

1) Setting up a team of data scientists is imperative in a successful internalization of MMM.

2) Cooperation with sponsors within the organization is critical to the successful implementation of the MMM.

3) Setting up the infrastructure for MMM-based decision-making involves implementing a range of tools, from databases to scenario simulators with dashboards.

4) Ensure a high degree of collaboration between marketing, business teams, and data scientists.

The key to success in tackling these challenges lies in an actionable framework that guarantees the operationalization of the models:
 

Integrating open-source tools into MMM can significantly transform the way companies measure and optimize their media strategies. However, to make the most of these benefits, the comprehensive involvement of the entire organization is crucial.

Open-source tools allow companies to create their own advanced MMM systems. However, doing so internally presents challenges. Not only does it require having a highly trained team and the appropriate technological tools, but also developing an effective strategy for the efficient use of data. A well-structured plan is essential for defining the project roadmap, from setting up clear goals to managing and maintaining the system on an ongoing basis.

As MMM continues to change in the open-source landscape, it's important to align the analytics strategy with the company's business goals. By fostering effective collaboration and choosing the right strategy, organizations can gain a deeper understanding of the return on investment in their marketing activities. This allows them to allocate their budget more efficiently, optimize their ad campaigns, and ultimately drive business growth.

While implementing MMM can be complex, the long-term benefits of effective data management and strategic decision-making justify the effort.


 

[1] Google Trends. (2023). Trend data for Marketing Mix Modelling. Retrieved
November 22, 2023.

[2] Sagefrog, (2023). 2023 B2B marketing mix report: Data-driven insights for your marketing plan. Sagefrog.

Get in touch:

Davide Fabrizio
Partner Artificial Intelligence & Data

Carlos Real
Director of Artificial Intelligence & Data

Karen Flores
Senior Manager Artificial Intelligence & Data