The evolution of marketing mix modeling (MMM): A pathway to privacy-first measurement

What is old can be new again with technological developments. This is how marketing mix modeling has made a successful comeback.

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. Using aggregated historical data, MMM helps marketers forecast returns, simulate various business scenarios, and optimize future budgets.

MMM has a long history; the concept was first introduced in the 1950s and rose to popularity in the 1980s. For a long time, only the largest companies have adopted this practice on a wide scale because traditional MMM was time and effort-intensive. However, in recent years, more and more companies – regardless of their size – are turning to MMM to drive growth in a privacy-first landscape.  

How has the latest technological advancements brought the new golden age of MMM? Why did MMM evolve to be provided as a software solution? Read on to find out.

💡 To get a general idea of what MMM is, check out our white paper.

Traditional marketing mix modeling methods

Due to the complexity of variable selection and model building, MMM requires a great deal of time, resources, and expertise. Hence, many marketers interested in MMM have either outsourced every step to external parties or handled everything in-house.

Using full suite consulting service

MMM has long been and is still provided by major consulting firms. As end-to-end suppliers, they deliver the full breadth of an MMM project from receiving data from their clients to designing an appropriate model and analyzing the results. The best part about this approach is that the clients have access to experts for hands-on support along every step of the project.

However, as with most easy solutions, it is far from ideal. A typical MMM project run by consulting firms involves a very time-consuming process and enormous operating costs. Highly experienced econometricians and statisticians manually validate at least a year’s worth of data, tailor-make a model around the client’s business needs, and write detailed analysis reports. Obviously, none of this is free of charge.

Moreover, once finalized and deployed, the model is updated only on a quarterly or semi-annual basis, thus making it difficult for marketers to respond to the rapidly changing market situations. With such high hurdles, the utilization of MMM has basically been exclusive to big-budget advertisers developing long-term marketing plans.

Building internal MMM capabilities

On financial costs alone, conducting MMM in-house may seem like a more reasonable option. Other than the already existing labor costs for internal staff, which is a sunk cost, the additional costs are minimal. In addition, you can have full control over your data, thus increasing agility, avoiding risks of information leakage and equipping yourself with always-on measurement.

Even though the benefits of building modeling capabilities internally are definitely worth the effort, it’s easier said than done. In-house MMM as well requires a substantial amount of cost, time and resources. For instance, if your organization does not have any data scientists, you can only set out by recruiting the right people. What’s more, they will have to keep developing and updating the MMM model rather than the essence of the business – the product or service itself.

Furthermore, “in-house” could mean an absence of a second or third opinion, which often serves as an antidote to bias. It is also possible that some critical external factors are neglected, and thus the analysis results are associated with lower objectivity.

Marketing mix modeling: reborn

As explained above, traditional MMM methods could be exhausting in many ways. Hence, fast-growing early-stage startups or small-budget advertisers had limited access to MMM.

For a long time, this was not a major problem because there were other measurement approaches available in the market. Most mobile measurement partners (MMPs), for example, have long relied on device-level data for effective attribution.

However, with the mobile marketing world leaning into data privacy protection, users now have the option of disabling ad tracking and opting out of personalized ads. In other words, data collection has become increasingly difficult and the need for a privacy-friendly method has arisen.

As MMM runs off aggregated data from various sources rather than granular user behavior data, many are paying attention to the approach with the aim of perfecting their performance measurement. Consequently, the demand for a new generation of MMM has grown among both small- and big-budget advertisers from various industries.

Back in the game with SaaS solutions

Decades have passed since the introduction of MMM and the world has changed over time. The 21st century, in particular, has witnessed unprecedented technological developments that have revolutionized our way of life. Capitalizing on such innovation, MMM was repackaged as an easy-to-use SaaS solution and started to receive more attention.

The most distinguishing feature of SaaS MMM is that it requires less cost, time, and resources than traditional MMM. These days, thanks to advances in cloud computing, companies no longer have to build a server themselves, and cost and performance data can be auto-collected. In other words, modern MMM is much more streamlined and affordable.

SaaS MMM also allows marketers to expedite analysis and adapt to market changes in a timely manner. In order to speed up the marketing flywheel, you need to be able to assess and update your strategy whenever necessary. This means that efficient and effective performance measurement is a precondition for marketing optimization and informed decision making.

Presenting the Airbridge Marketing Mix Modeling (MMM)

With a mission to help marketers around the world discover their true sources of growth, Airbridge provides MMM as a user-friendly software solution at a reasonable price. Airbridge MMM not only enables marketers to focus on the more important tasks but also contributes to the protection of mobile users’ digital privacy.

In fact, Airbridge is the perfect MMM provider because it is a mobile measurement partner (MMP) that is already integrated with major ad platforms. By embedding the Airbridge SDK within your apps, you can auto-collect your performance and cost data and track in-app events to further train your MMM model. Even if you import data manually, the modeling and reporting processes are automated, meaning you will get your analysis results in the blink of an eye.

Airbridge MMM, powered by proprietary machine learning algorithms, never stops improving to drive your marketing success. The model is tailored to suit your business needs based on online and offline channel-level data as well as external factors such as macroeconomic trends and seasonality. You also have full access to key information about your MMM model, from analysis target and input channels to training status and last training time.

Furthermore, Airbridge MMM results are presented on a user-friendly dashboard. The Marketing Mix Analysis report, updated daily, shows the incremental contribution of each channel without relying on user-level data. The weekly and monthly Budget Optimization report finds the best marketing mix to maximize your budget potential. The agility of Airbridge MMM leads to actionability, and marketers can use MMM in their everyday decision-making situations.

Find out more about Airbridge MMM:
👉 How Marketing Mix Modeling Enables Privacy-First Measurement
👉 Airbridge’s Unified Measurement Stack: Marketer-Centric Approach to LTA, MTA, and MMM

Modernizing MMM to protect user privacy

SaaS MMM is bringing about changes to the mobile marketing industry by overcoming the limitations of traditional MMM.

With lower hurdles to MMM, it may be the norm to measure without relying on third-party cookies or ad identifier details in the future. This is a win-win for all because users can have control over their data while advertisers can assess performance and optimize campaigns no matter what.

Privacy-first performance measurement has been made easy with the return of MMM. If you would like to know more about how SaaS MMM can benefit your business, talk to our team today.