When we talk to people who are new to MMM and tell them there’s no pixel and we don’t track people at all, we basically see their minds explode.
The idea of trying to track people as well as possible has become so established that it can be hard to imagine how this could possibly work if you’re not familiar with marketing mix modeling.
With digital tracking becoming so institutionalized over the last decade, there is a generation of marketers that have only thought about attribution through multi-touch attribution (MTA), where browser cookies (or similar identifiers) allow advertisers to track the marketing source of their customers.
Truth is, for a while, it worked – until it didn’t. With the implementation of GDPR, the impact of iOS14, and the trajectory towards even more privacy regulations, digital tracking has become broken and an unreliable way of measuring marketing’s impact – especially for larger brands.
Smart and adaptive marketers have seen this shift and have looked for alternatives that don’t rely on pixels and tracking people across the internet. That’s where MMM comes in.
What is MMM (the short version)
Let’s start with some context: marketing mix modeling is a statistical modeling technique that marketers use to determine which channels in their marketing mix deserve credit for sales, in order to reallocate budget to the highest-performing areas. The best consumer brands already use MMM. We even built a database on who’s talking about MMM in their case studies.
The concept of MMM isn’t new – and that’s why it works. Digital tracking is a product of the internet but, if you zoom out, it’s just a bleep in marketing’s history. Marketers had never had the capability to follow people across the buying journey. If you worked for Coca-Cola in the 60s, you had no MTA on your TV, radio, or newspaper ads.
MMM was developed to solve this problem with only aggregated data, and now we’ve circled back to facing the same challenge.
How Does MMM Work without Tracking People?
Here’s how Recast’s CEO, Michael Kaminsky, explains it:
We’re using machine learning and AI to find patterns in your historical observational data. When we look back at it, there will be some days where there are different amounts of marketing activity happening and there are different amounts of new customers that you’re acquiring, or revenue that you’re generating, or new marketing qualified leads you’re bringing in.
We use AI and machine learning algorithms to find those relationships in the historical data. On days when you relatively spend a little bit more on Facebook, how many additional new customers do we generate? On weeks when the podcast drops, do we tend to see an increase in MQLs?
Of course, there are a lot of other sophisticated things that are going on in our models. But the idea is that we’re going to look at the days where your channel mix spend changes and how they’re different, and then we’re going to look at how we acquire different amounts of customers based on different amounts of marketing activity.
And if we don’t see an increase in customers (considering lag periods), we’ll know that spend wasn’t very incremental. And then we’re going to do that lots and lots of times for all of the different marketing channels on all of the days going back into history until we can give you estimates of incrementality across the board so you can make better budget allocation decisions.
Because it doesn’t track people through pixels and cookies, MMM is an effective and privacy-compliant method for consumer brands to measure their marketing effectiveness.
(Modern) Marketing Mix Modeling
While not tracking people across the Internet has not changed, MMM has evolved over the years. Consumer brands used to have Analytic Partners, Nielsen, or some other consulting firm bring them a report every six months and use that plan to determine what to buy in the “upfronts”.
Unfortunately, now the world is a lot more complex and marketing channels are so much more dynamic. We’re all dynamically buying media on a daily or weekly basis and ROI in a marketing channel can change dramatically week-to-week.
That once-every-six-month PowerPoint just doesn’t work anymore, but there are big CPG brands still using them to inform their budget allocation.
So what we’ve done at Recast is take the magic of modern statistics to fully automate that for them. Modern marketers can finally get a real-time read on what’s performing well and what’s not, algorithm changes, new platforms… all in one place and with the scientific rigor they need to make accurate decisions.
Unlocking the Potential of MMM without Tracking
MMM, especially when empowered by Bayesian methods, offers invaluable advantages that traditional tracking cannot provide. First and foremost, it delivers a holistic view of marketing effectiveness. While digital tracking focuses on individual channels or campaigns, MMM looks at the bigger picture. This gives you a comprehensive understanding of how different channels interact with each other and contribute to overall marketing goals.
Moreover, MMM can handle the complexity of real-world marketing data with numerous overlapping channels, campaigns, and external factors. It can effectively untangle this web and attribute outcomes to specific activities, thereby revealing the true incremental impact of each channel.
Finally, as a non-tracking method, MMM protects consumer privacy and complies with strict data regulations. It achieves this without compromising on the richness and depth of marketing insights. By leveraging machine learning and AI, MMM ensures that your marketing strategy remains robust, effective, and future-proof in the ever-evolving regulatory landscape.
TLDR:
In a world where consumer privacy regulations are becoming stricter, digital tracking is failing to provide the right information to marketers. Multi-touch attribution is still the most common starting place for most brands, but if you’re looking for more accuracy, you should consider marketing mix modeling to make sure you’re not wasting your budget.
Without having to track people and by focusing on aggregate data, marketing mix modeling circumvents the downfalls of digital tracking and empowers marketers to make data-informed allocation decisions.
