Marketing Mix Modeling and You: Understanding MMM

Marketing Mix Modeling is not a new concept; it’s been around since the middle of the 20th century and was popularized when Kraft used it to track the relationship between television ads on different networks and Jell-O sales. But MMM has (obviously) become a lot bigger, a lot more intricate – and a lot more important.

What is Marketing Mix Modeling?

Marketing Mix Modeling (MMM) also known as Media Mix Modeling is a holistic analysis technique that shows a bigger picture of how marketing strategies are actually performing. MMM involves aggregating data from all channels – this enables us to look beyond one set of numbers at a time, so we can have a better understanding of how all marketing channels are working together. Plus, aggregating data makes it possible to consider other influencing factors like seasonality, promotional deals, and more. 

What’s the difference between MMM and MTA?

Multi-Touch Attribution (MTA) is a method of analysis that focuses on the customer journey. With MTA, you’re examining all the marketing touch points that a customer comes in contact with as they move from initial impression to purchase, and determining the effectiveness of every interaction. By looking at each touch point, MTA is a way to analyze how different marketing tactics and channels influence customer decision-making. MMM, on the other hand, provides high-level insights into marketing tactics over a longer period of time, rather than short-term data from individual customer engagements. When putting together an MMM model, we’re usually looking at several years worth of data to identify larger patterns, so we can gain a better understanding of how each channel is actually performing. 

Because MMM is about examining trends, it also helps us predict how certain channels will perform down the line. These predictions enable advertisers to make smarter decisions about how to spend their ad dollars in the future.

Both MTA and MMM are useful in different ways. But both also struggle with the fact that most attribution methods tell different stories. There are a lot of sources of information out there, and none of them agree. Plus, even with something as precise as MTA, you only get data about half of all sales because attribution methods are faulty. And the way attribution source data is presented rarely matches the way marketing teams think about their ad spend.

Wait, what about Media Mix Modeling? Is that the same thing as Marketing Mix Modeling? What makes them different?

“Media” and “Marketing” are often used interchangeably when talking about MMM. But there’s an important distinction.

Media Mix Modeling analyzes the effectiveness of paid media efforts. Media Mix Modeling looks at elements like ad size, ad platform, channel distribution – all the factors that affect paid media performance.

Marketing Mix Modeling, however, is a broader term that includes marketing efforts that you don’t have to pay for. Marketing Mix Modeling factors in paid media, but also takes into account:

  • Non-paid actions like email sends
  • External events like price changes
  • Word-of-mouth marketing from influencers 

Because it incorporates everything, Marketing Mix Modeling can paint a fuller picture of how all marketing efforts are working in concert.

How is Pickaxe MIX different from other MMM models?

Mix is smart, and updates daily.

Pickaxe MIX is a tool we’ve built that looks at all of the patterns in your spending, and identifies the moments where changes in patterns have influenced sales. Based on that information, it creates a cost-per-action (CPA) value for each incremental dollar spent on each network. And then (unlike a traditional MMM model that can take forever to update) Mix adjusts CPA over time with AI-driven analysis. It’s a souped-up, supercharged version of MMM that uses modern data pipelines and AI to give you a view of your data that’s proactive, comprehensive, and intuitive.

Mix is customizable.

Every marketing team has different needs, and a unique way that they summarize their information. Mix allows marketers to create queries and define channels in a way that matches the way their business uses each channel. Mix is customizable, so it can think how your marketing team thinks.

Mix goes beyond just paid media, and consider earned and owned as well.

Lots of marketing efforts aren’t reflected in the paid marketing budget. For example, social media: while ad spend on promoting a social post might be part of the budget, a viral post or an active social team isn’t. Mix can be configured to take these non-elements into consideration when calculating a CPA.

Mix can also factor in email blasts, talk show appearances, co-promotions, influencers, and other marketing efforts.

And Mix can also be customized for other key business strategies!

Our customers have used Mix to model out price changes, paywall settings, new reality show seasons, and new product launches.

Mix even factors in events outside of marketing efforts.

Lots of things can influence engagement that have nothing to do with touchpoints. Inflation, election spending, and Q4 ad prices rising - all of these things can affect whether your customers purchase! Things like seasonality and historic changes are baked into Mix, but when something big and unusual happens (like Covid in 2020), Mix can alert you immediately to the unexpected performance and provide quickly adjusted insights. 

For example: A global streaming service that offers a lot of reality programming noticed a massive spike in sign-ups in May of 2023. At first, this might seem like a fluke. But if you’re a Bravo-head, and you remember the drama around Vanderpump Rules’ Tom Sandoval, you might also remember that the Vanderpump reunion episodes aired that month. This is a major external factor that might throw off CPA in a traditional model, but Mix is able to integrate it and adjust calculations accordingly.

How can Media Mix Modeling work in practice?

Here’s an example:

A global streaming service was facing a problem – they were using multiple attribution methods, and none of them were comprehensive.

For instance, their Web Attribution data and their Ad Network data were telling dramatically different stories about customer engagement. Their Mobile & Cross device attribution was limited to the users that could be tracked by their customer data platform, which included less than half of their customers. And none of these attribution methods took their “above the line” spending into consideration.

So after creating a new attribution methodology to better understand their data, they used MMM to compare their Ad Network numbers with their newly attributed data. We then helped them examine the Life Time Value (LTV) for all their users to figure out which campaigns and channels had attracted the highest (and lowest) value customers. And once that was done, we used Pickaxe MIX to figure out the impact of their ad spend on all these channels.

Going even further

Once data is organized, channels are defined, and advertising targets have been identified, MMM models can be used to establish a baseline performance. The right Mix Model (like Pickaxe Mix) can then intelligently make correlations and find relationships, and then every week, identifies when values are trending above or below that baseline. 
Marketing Mix Modeling (MMM) is a holistic analysis technique that shows a bigger picture of how marketing strategies are actually performing.

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