The New Era of Marketing Mix Modeling

Introduction

For years, Marketing Mix Modeling (MMM) has been a mysterious art handed off to specialists. Agencies or third-party providers managed the models, and brands waited for periodic reports to tell them how their marketing dollars were performing. But now, things are shifting. More brands are bringing MMM in-house, and it’s changing everything about how they approach marketing.

Drivers of Change

Why are brands choosing to take control of something they once happily outsourced? In one word: speed. Well, maybe three words: speed, control, and flexibility.

When MMM first became popular, data was scarce. You might have weekly or monthly numbers, so it made sense to work with a partner who would run the models on their own schedule. But now, data comes in constantly. With digital channels, brands can get data every minute. When you’re moving that fast, waiting weeks for an updated report is like navigating with last month’s map.

In-housing MMM lets brands update their models in real-time. They can see exactly how their campaigns are performing, make adjustments on the fly, and test new ideas without waiting for approval from someone else. It turns MMM from a lagging indicator into a tool that can drive immediate decisions.

Control is another big factor. Traditionally, MMM has been a bit of a black box. Most brands only saw the output, not the inner workings. They didn’t know how the model was set up or if it was giving them the full picture. In-housing gives brands access to the raw data and the model itself. They can tweak it to fit their specific needs, try out new data sources, or look at results from a different angle. They’re no longer dependent on the agency’s priorities or methodologies.

Flexibility is the third big driver here. Most agencies have a standard approach to MMM because it’s efficient to run models the same way for each client. But brands are not all the same. Some rely heavily on digital ads, while others might see better returns from TV or in-store promotions. An in-house team can customize the model to fit the unique needs of their business. They can even adjust the model for seasonality, regional differences, or any other factor that’s important to them. They’re no longer limited to a one-size-fits-all approach.

Enabling the Transition

The rise of platforms like Alviss AI is what’s making this shift possible. Alviss AI offers brands and agencies a way to do MMM in-house without needing a team of data scientists. It’s a platform that combines the latest machine learning techniques with an intuitive interface that anyone can use. Alviss AI handles the hard stuff — data integration, model setup, algorithm selection — so that marketing teams can focus on what the data is telling them.

There’s another side benefit to in-housing MMM: transparency. When brands outsource their MMM, they’re trusting a third party with sensitive information about their marketing spend, performance, and customer behavior. In-house MMM means they keep control over that data. They’re not handing over the keys to their marketing strategy to someone else. And in a world where data privacy and security are top concerns, that control matters more than ever.

We’re seeing a new era in marketing, one where brands have the tools and the data to make their own decisions. In-house MMM is just one part of this shift, but it’s an important one. It gives brands control over their data, lets them respond faster, and frees them from a one-size-fits-all approach.

Brands that bring MMM in-house are discovering it’s not just a way to analyze past campaigns. It’s a tool for shaping their strategy in real-time. And in a competitive market, that can make all the difference.

This post is part of a 6 part series called “Mastering Marketing Effectiveness with In-Housed MMM”. The posts are outlined below.