Marketing seems to be a bit more challenging every single day. You try your best to break through fragmented media consumption habits by juggling a dozen different channels at once. Yet, data privacy comes up, and your ability to track and measure performance is tightened. You want to maximize the impact of your marketing efforts, but you’re not sure which activities are driving profit. If this sounds like you, we’re here to help.

Powered by advanced machine learning, Airbridge MMM unlocks powerful insights to help you make current and future marketing decisions with confidence. Keep on reading to find out what marketing mix modeling is and why you need to add Airbridge MMM to your measurement stack to stay ahead in the ever-changing marketing world.

What is marketing mix modeling?

Marketing Mix Modeling (MMM), sometimes referred to as media mix modeling, is a statistical analysis technique that assesses the impact of marketing inputs on desired business outcomes such as sales, conversions, and installs. Taking various factors into account, from channel-specific ad spend and promotions to seasonality and market elasticity, MMM provides a holistic perspective into growth. In essence, it is a top-down big-picture approach that uses aggregated historical data to help marketers forecast returns and optimize future budgets across an assortment of channels.

💡 To get a more detailed explanation on the concept of MMM, check out our white paper.

Why is marketing mix modeling important?

The rise and fall of marketing mix modeling

The concept of MMM was first introduced in the 1950s and saw a rise in demand in the 1980s, back when it was hard to have enough insights into user behavior. To be more tactical with their ad spend, marketers worked with econometricians and statisticians from consulting firms and received modeling recommendations.

However, the popularity did not last due to operational challenges. Built on tech stacks and data scientists, one MMM project could cost millions of dollars. It also took a long time to count all the variables, build models tailored for each company, and interpret the results. That wasn’t all – manual data transfer was not only cumbersome but also had a lot of room for error. The hurdles were high and the use of MMM was practically exclusive to large advertisers developing long-term strategies.

The impact of privacy changes on marketing mix modeling

By this point, you might be assuming MMM is an outdated technique. The thing is, it does have a long history, but it’s on everybody’s lips these days. Since the iOS 14.5 updates, the App Tracking Transparency (ATT) initiative has shifted the digital marketing landscape and paved the way for the return of MMM.

During the past decade, targeted advertising allowed marketers to drive user engagement and build brand awareness. However, since April 2021, all iOS apps have been required to ask user permission to access the Identifier for Advertisers (IDFA) and collect user-level data. In fact, as of March 2022, more than half of iOS users worldwide had decided to opt out of tracking. The impending deprecation of third-party cookies on Chrome and regulations like the GDPR and CCPA are adding to the difficulties. Such changes also place limitations on last-touch attribution (LTA), which relies on device-level tracking.

All of the above have pushed marketers today to look for new measurement approaches that meet their objectives but still protect users. After a long search, their eyes fell on MMM, an oldie but goodie.

Bringing SaaS to marketing mix modeling

MMM is clearly suited for modern day marketing – it uses aggregated channel-level data rather than detailed user-level data. However, as aforementioned, not everyone can afford the time and money that traditional MMM projects require. This is why many next-gen tech companies like Airbridge are providing MMM as a software solution, to make MMM more accessible by lowering the hurdle.

In specific, Airbridge MMM has the following competitive advantages:

• Usable: The solution is cost-effective and easy to implement.
• Adaptive: Data can be fed to the model as you prefer.
• True-to-life: The model can be calibrated to reduce the gap between the model and reality.
• Practicable: The solution not only analyzes but also predicts and prescribes.

Why should you use Airbridge MMM?

Reason #1: Usable, affordable, and convenient

Since business outcomes usually have annual seasonality and trend, traditional MMM models needed at least one year worth of data. Building a model using such a large amount of data was time-consuming, which meant MMM was not sufficient for day-to-day optimization.

On the other hand, the Airbridge MMM model, driven by Airbridge’s proprietary machine learning algorithms, can be trained with only six months worth of data. This significantly reduces the modeling cost, whether it be time or money. Moreover, several types of reports based on MMM results are each updated on a daily, weekly, and monthly basis. Marketers can draw further insights from these reports to respond to sudden changes and adjust strategies accordingly.

Reason #2: Adaptive and flexible

Future-proofing measurement with MMM is incredibly easy for those already using Airbridge. As a mobile measurement partner (MMP), Airbridge provides an API for full S2S integration and the Airbridge SDK which can be embedded within your apps to auto-collect performance and cost data. Airbridge also tracks all in-app events and uses this additional relevant data to train the MMM model.

Don’t worry even if you’re not working with Airbridge yet. You can run the analysis as long as you have six months worth of data on channel-level spending, daily clicks, daily impressions, etc. Prepare your data with a CSV file and import it to Airbridge, and the remaining modeling and reporting processes are automated. Even if you import data manually, Airbridge MMM will deliver results faster than traditional MMM projects.

No matter how you feed data, the Airbridge MMM model will never stop improving to show you the most up-to-date results. Powered by advanced machine learning, the model will be tailored to suit your business needs and contribute to your success.

Reason #3: True-to-life, reliable, and accurate

MMM quantifies the correlation between marketing inputs and historical business outcomes. In other words, the model, which is an imperfect representation of reality, can be subject to the correlation vs. causation fallacy. To provide sensible outputs, Airbridge calibrates the model with alternative approaches to understanding the marketing mix.

Calibration generally involves well-constructed experiments such as lift tests. Even though these guarantee the highest reliability and accuracy possible, they often incur financial costs and slow down the entire analysis process. Hence, for those who can’t afford the luxury of running real-world experiments, Airbridge calibrates the MMM model with incrementality measurement results brought out using propensity score matching (PSM), which is a quasi-experimental method.

Reason #4: Practicable and actionable

Airbridge MMM is much more agile than traditional MMM, and agility leads to workability. Using three different types of reports, marketers can make an effective use of MMM in their everyday decision-making situations.

Marketing Mix Analysis

The Marketing Mix Analysis report, updated every day, scrutinizes the incremental contribution of each channel without relying on user-level data. Not only digital channels but also non-digital channels such as TV, radio, billboards and magazines can be attributed with MMM. Choose from performance indicators spanning across installs, orders, revenue and eCPI to refine your analysis.

Budget Optimization

The weekly and monthly Budget Optimization report finds the best marketing mix to maximize your budget potential in the future. As the model already has your historical cost data, all you have to do is to decide what percentage of your current budget to allocate as your future budget. After putting in the number and previewing the selected amount, you can simulate the model and see how much your marketing performance will increase by optimizing according to the prescribed budget plan.

👉 Fashion meta-search platform meliz said Airbridge MMM proved to be a reliable privacy-first solution for their cross-channel marketing measurement. The results showed that the team would have seen a 5% increase in installs if they followed the MMM-prescribed budget plan. Find out the details in this case study.

Model Manager

With the Model Manager report, you can get a quick overview of key information of your MMM models such as analysis target, input channels, training status, and last training time. Click on “View Details” for extra details like input data type and calibration status.

Quick start your MMM journey with Airbridge

MMM is just one type of marketing measurement approach you can work with, but it’s an important one to consider.

By examining marketing data holistically, MMM allows marketers to break silos in analyses and prove how their efforts help overall business. Moreover, it is a privacy-safe option that all marketers have been looking for in a privacy-first era.

If you’re interested in trying it out, Airbridge MMM is the way to go. Talk to our measurement experts and set up a data-driven strategy that will get you the most return on your investments.