Riding the Wave of Innovation: Understanding and Utilizing Media Mix Modeling

“If you can’t measure it, you can’t improve it.” This famous quote is often attributed to the incredible British Mathematician, Lord Kelvin (or William Thomson). While it’s uncertain if he has actually said this exact line, it is undoubtedly true that quantifiable data is a key to success in the current digital era, especially in the evolving world of advertising. As a performance marketing agency, Vovia is determined to utilize different tools, including Marketing Mix Modeling (MMM), to achieve the best business outcome.

What is Marketing Mix Modeling (MMM)?

Marketing Mix Modeling (MMM), sometimes known as Media Mix Modeling, is a statistical approach that leverages aggregated business data to measure the impact of different forms of marketing initiatives on return on investment (ROI). It evaluates the contribution of different channels to the overall business performance, or the key performance indicators (KPIs) you (and your leadership team) care about. Whether you are aiming for app downloads or store sales, MMM helps you understand how various marketing activities, such as TV ads, digital campaigns, Out-of-home etc., impact the KPIs. Most importantly, once we understand the efficiency and effectiveness of different channels, we can optimize the budget accordingly to produce the desired results.

The Difference between Artificial intelligence (AI) and Machine Learning (ML)

Artificial intelligence (AI) has certainly become the buzzword in recent years. If you are interested to know how AI revolutionizes the advertising industry, please read the blog written by my awesome coworker, Jason!

Since November 30 2022, the day ChatGPT was released for public use, AI, or I should say Generative AI, has played a crucial role in our daily life. However, Generative AI is just one of the powerful applications. AI has been impacting our lives long before the launch of ChatGPT, typically in the form of Machine Learning (ML). Think about the recommendation system on Netflix or YouTube, of the “Exclusive For You” offers in the loyalty app of a grocery store or Quick-Service Restaurant. Those are some products of ML, but what exactly is the difference between AI and ML?

According to Google Cloud:

• AI is the broader concept of enabling a machine or system to sense, reason, act, or adapt like a human
• ML is an application of AI that allows machines to extract knowledge from data and learn from it autonomously

In layman’s terms, AI is more advanced and encompasses the idea of a machine that can think like a human. On the other hand,  ML is just teaching the machine how to perform a specific task, for instance, recommend a show to you on Netflix, and provide accurate results (suggesting something you are interested in) by identifying patterns (your viewing history and someone sharing similar tastes and preferences).

Categories in Machine Learning and MMM

In general, there are two main groups of Machine Learning: Supervised learning and Unsupervised learning. To put it simply, the former relies on labeled data to identify a correlation between the input features and output variables, while the latter uses unlabeled data to discover patterns.

For example, insurance claim classification is a common Supervised learning model. By inputting different features, such as gender, age, occupation, and many more, insurance companies predict the probability of claims by using Machine Learning algorithms. MMM is also an example of Supervised learning as it determines the relationship between inputs, for instance marketing spend on different channels, and the outputs, usually sales.

Clustering is a good example of Unsupervised learning. By analyzing a large dataset, the model is grouping users, such as loyalty program members, into segments sharing similarities. Those segments are used for targeting, and that’s exactly how Google or Facebook came up with interest targeting audiences.

Despite the different types of Machine Learning, one rule always applies. Garbage in, garbage out. It sounds clichéd, but the quality of the input features would definitely impact the performance of the model. For MMM, we are usually looking for 4-5 years’ worth of clean data to train the model and enhance the performance.

Conclusion

MMM is a powerful tool that provides actionable insights into the effectiveness of different marketing channels, helps businesses make data-driven decisions and optimizes their marketing strategies. Like it or not, the world is changing faster than we can imagine. In this rapidly evolving landscape, the best thing we can do is embrace the change, learn continuously, and adopt new technologies that help us to succeed.