Media Mix Modeling (MMM) is a well-established marketing measurement approach that has existed for decades, but over the last year, “next-generation MMM” has been quietly gaining steam in the world of mobile. This key marketing tool can help marketers understand how different channels impact business outcomes. MMM provides insights into how various marketing initiatives work together, which can then be used to optimize budgets and reserve spend more effectively.
If you are like many of the app growth marketers in this space, you may know a little about MMM but still have many unanswered questions. Hopefully, you’ve had a chance to tune in to our latest webinar on MMM — it provides an in-depth look at the fundamentals. (If you haven’t listened yet, this is a great place to start!)
This article picks up where we left off in our webinar. We cover some of the most common questions surrounding MMM and lay the groundwork for including MMM in your marketing strategy moving forward.
By the way, perhaps the biggest roadblock cited by advertisers is the technical lift and change management investment required to get their model to a productive point for their business. Here at Branch, we are excited to alleviate that burden by partnering directly with you via our Branch Media Mix Modeling closed beta program. To learn more about participating, reach out to your Branch customer success manager.
MMM isn’t a new concept. What’s changed?
First of all, let’s clarify the name itself. Is it “Media Mix Modeling?” “Marketing Mix Modeling?” Or “Mixed-Media Modeling?” Multiple legitimate names are currently in use, but “Media Mix Modeling” is the most common and what we are calling it at Branch. We also feel it is the most accurate, because using “Mixed-Media Modeling” implies a distinction between mixed-media and non-mixed-media which doesn’t exist in practice.
When it comes to the growing awareness of MMM, three forces are occurring simultaneously:
- Attribution is getting harder.
The primary keys with which we connect data from different parties in the ads ecosystem are drying up faster than we can keep up with. This means that traditionally relied-upon touch attribution approaches are degrading in efficacy.
- The rules keep changing.
The walls haven’t been breached. This means no open-ecosystem exists to rally behind. Rather, we must make sense of platform changes like SKAN for iOS and Attribution API for Android with no dominant reconciliation paradigm to make sense of them (yet).
- The learning curve is steep.
Machine learning has been making meaningful steps forward, but its many applications for accelerating marketing haven’t yet been fully explored. Similar to AI, the efficacy curve is shows mostly gradual and sometimes sudden progress.
And — you guessed it — MMM uniquely benefits from or tackles each of these three forces.
MMM is like MTA, right?
This is a common misconception about MMM that we encounter at Branch. The short answer: From a technical perspective, MMM and multi-touch attribution (MTA) are completely unrelated. But they can address similar business needs when employed correctly.
Others have mentioned previously that advertising IDs (such as IDFA and GAID) are becoming increasingly scarce. In order to adapt to this new reality, marketers need a solution that does not need to directly join individual events together. MMM considers aggregate sets of spend (paid channels), clicks and impressions (organic channels), as well as other signals. MMM then uses machine learning-powered statistical analysis to generate budget allocation recommendations and forecasts.
MTA, on the other hand, compounds the traditional last-touch paradigm. MTA considers all touches leading up to conversion and distributes partial credit between them. This is based on some valuation logic like “linear decay” (giving progressively more credit to later touches) or “U-shaped” (giving more credit to the first and last touches, and distributing the remainder equally).
Our observation is that MTA often garners attention but rarely represents a strong enough value proposition for customers to migrate off last click. And the reality of increasingly scarce advertising IDs means all touch-based methodologies (including MTA) are losing accuracy. We must collectively adapt.
Isn’t MMM a solution for brand advertising that takes months to generate?
There’s a lot to unpack in this one.
The history of MMM did previously resemble this picture. Typically, media agencies contained the requisite reach, data, and resources to provide MMM as a consulting service for advertisers. It was also expensive ($70K-$100K per project), took a long time to get results, and was mostly adopted by brand-focused advertisers heavy in traditional channels like linear TV.
The big downside: When they finally arrived, the results went quickly out-of-date.
The winds have since changed. MMM is now more accessible and flexible via powerful automation — quickly delivering rich, diverse marketing data. With the robust, next-generation MMM tools available today, it is much easier to make timely decisions and optimize your budget without having to wait weeks or months for results.
For Branch customers, data from all marketing channels is gathered in one place — from paid media, emails, social, mobile web, and organic search. Our MMM solution uses this data to run weekly refreshes and help you understand how potential budget allocation changes can drive incremental app growth. This means you’re no longer waiting three months for your next MMM report.
The Robyn MMM framework was built by Meta. Is there a conflict of interest?
This is a great question that indicates a healthy understanding of the importance of unbiased measurement practices.
Branch is building our MMM solution on top of Robyn. Robyn was originally developed by Meta, but it is an open-source MMM code library. This means the code is open for reviews and methodology audits by any code user. By leveraging an industry-standard, open-source model, Branch can ensure we are aligning with MMM best practices to deliver a balanced, impartial, and insightful cross-channel analysis.
Side note: Branch is also excited to be part of Meta’s MMM Incubation Program, a select group of partners working closely with Meta to improve Robyn and develop the future of privacy-first, paid media performance measurement.
This all sounds rather theoretical. How does MMM actually work at Branch?
The main deliverable of the Branch MMM solution is a set of budget allocation recommendations for your channels and a forecast of the impact of those changes. These recommendations can be used alongside your existing, touch-based attribution reports to make more informed campaign investment decisions.
Initially, we’ll ask for 12 months of data that we can use to run the MMM model. We will also verify the data by discussing the model’s inputs and determining whether any additional data should be included. After we run the model for the first time, we will share the results and discuss whether any tweaks or improvements need to be made. The next step is an interactive refinement process until the model delivers results.
MMM is most effective when it sees the full picture of your business. So, if you have other marketing activities like push notification campaigns that aren’t tracked in Branch, you will want to include that data as well. You can expect recurring meetings with our team to help you interpret the model as it “refreshes” (i.e., runs MMM with new data). Those meetings will also act as a standing office hour to brainstorm experiments based on Branch’s recommendations.
How can we trust what the model recommends?
This is the million-dollar question. MMM can seem like a bit of a black box at first, and a recommendation like “shift 10% of spend from Google Ads Search to Apple Search Ads” is not necessarily the type of quantifiable impact analysis most teams are used to. Making budget allocation decisions, however minor, is no small thing.
Our team will work with you over time to ensure the MMM model sufficiently comprehends the nuances of your business model, market, and competition. After you are satisfied with the completeness of the model’s inputs, we recommend first conducting at least one test based on its recommendations, then taking it from there.
Let Branch show you how MMM works
In our increasingly privacy-focused world, MMM can be a powerful tool — for marketers of all sizes — to allocate marketing budgets more effectively. The insights MMM can provide into how various marketing initiatives work together can help you make the best decisions possible when planning your next campaign.
Interested in finding out more about Branch’s MMM closed beta program and how to get started? Just reach out to your Branch customer success manager!