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MMM? Incrementality? Hybrid Measurement is the future for marketing in the age of privacy

By Gadi Eliashiv August 30, 2022
  • What is the future of marketing measurement?
  • How will growth marketers measure impact in the age of privacy?
  • What tools will brands need for both strategic and tactical marketing insight?

The age of privacy

We increasingly exist in the age of privacy, and privacy will increasingly define both how people engage with digital tools, and how marketers measure the impact of their efforts.

But the transition we’re making from deterministic granular data to a more nuanced aggregate reality is challenging. The word privacy has so many meanings and interpretations, and while almost all of them are overwhelmingly positive, in the realm of digital marketing, it also reminds people of uncertainty, complexity, confusion, and limitations. 

It’s the one topic that keeps coming up in every single conversation I have with mobile marketers. The one thing that constantly erodes measurement and attribution. The big caveat. The huge unknown.

Terms like GDPR, CCPA, third-party cookie deprecation, ATT, SKAN 1.0/2.0/3.0/4.0 and Android Privacy Sandbox are now at the core of every decision you make. Some exist thanks to a true concern for consumer privacy, while others are greatly shaped by the competing interests of today’s big tech platforms. 

But one thing is obvious: it’s a tricky landscape to navigate, and the job of growth marketing has only gotten more complex, not less.

For me personally and Singular in general, all this complexity highlights a very clear calling. Our job is to make things simple, and help companies achieve marketing greatness. Let’s be honest: both of those jobs are getting harder. 

But let me be 100% clear: they have not become impossible.

While it might be hard for those struggling in the trenches of SKAdNetwork to believe, I honestly see all of this change as a massive opportunity. And I’m fairly certain that if you are reading this article, you’ve probably seen that optimism across all our content … webinars, guides, Slack groups, workshops, and even in 1:1 consultations. You probably recognize that we spend a lot of time thinking about privacy.

My commitment to our community: we’ve got your back.

And today, two years into this privacy-obsessed era we live in, I wanted to take a moment and describe the vision I have for the future of measurement. But to do that, I have to start by describing what I see in the measurement space today, and how it shapes our POV towards the future.

A fairly divided market on the front line

While for now marketing on Android remains largely the same (and measurement is based on the soon-to-be-gone GAID identifier), Apple’s iOS is where things have changed the most.

In the past, most companies were pretty much on board with the IDFA-last-click model. Sure, we all ideally wanted MTA. We doubted last click. Sure, we wanted SANs like Google or Snap to share impression level data at the IDFA, so we could have proper MTA. Sure, we wanted the App Store to have a “referrer” mechanism similar to the Play Store. But for the most part – the market was aligned. You had an acceptable method of measurement.

But now – the market is heavily divided, again.

Division 1: iOS and SKAdNetwork

The first subject of division is SKAdNetwork (Apple’s privacy-preserving-yet-very-limited Attribution API).

If I had to cluster companies into two groups, I would define them as:

  • Group 1 – yay! We got SKAdNetwork to work
  • Group 2 – ughh! We can’t get SKAdNetwork to work

Let’s be 100% clear:

  • Both groups contain super smart companies
  • Both groups have companies of all verticals and sizes
  • Both companies dislike SKAdNetwork (even if they got it to work!)

I’d even go as far to say that it’s quite popular to hate SKAdNetwork. I mean, it’s difficult, a black box, and easy to screw up. A lot of times you see numbers that don’t make sense (e.g. a $500 CPA), and your ability to make sense of it is limited. 

So why would you love it?

But here’s something that might surprise you: group 1 is bigger than you think. It’s just unpopular to be publicly positive about SKAN. And that makes sense, because even if you get SKAN to work, it’s still worse than what you had before in IDFA. Plus, if everybody else is struggling to make SKAdNetwork function when you can … it just might be a massive competitive advantage.

(Side note: if you’re in the group that can’t make SKAN work, talk to us. We have literally helped hundreds of companies become SKAdNetwork experts and they are getting amazing results.)

Division 2: probabilistic attribution (AKA fingerprinting)

Many companies (particularly ones with bigger brands to protect) have decided to rid themselves of any tracking-based probabilistic attribution that infringes on Apple’s guidelines. They are willingly accepting a potential competitive disadvantage to their business, because they want to play by the rules and stay safe.

On the flip side, everyone knows there are still companies that employ fingerprinting methods on a certain portion of their traffic. 

But let’s be real: it’s obvious that this portion is getting smaller and smaller. 

According to our data, SANs command 80%+ of the ad spend, and they are already 100% SKAN compliant and do not reveal data that can be used for tracking. The remaining 20% consists mostly of some fairly large and public ad networks, and these too operate with SKAN for the most part. 

So while some might be doing fingerprinting, they’re chasing an ever-shrinking part of the market. At risk of stating the obvious, this is not a winning long-term strategy.

Division 3: MMM and incrementality

In the wake of the IDFA deprecation, measurement technologies like Media Mix Modeling (MMM) and incrementality quickly became a hotly debated topic.

Media Mix Modeling is a data science-heavy process. It takes aggregate spending data, aggregate revenue data, other ecosystem parameters, and then outputs an estimated ROI by channel (or campaign, or more). The appeal is natural given its lack of dependency on IDFA, or any special access to platform data. Plus, it only needs aggregate spend and revenue data (something that’s very easy to get if you use a platform like Singular).

We all know the reality: MMM is not new, and has existed for many years now.

Predominantly MMM has been used to help Fortune 100 corporations answer very complicated media mix questions (think about connecting TV ad spend to body lotion sales at Walmart in Nebraska). What is new, however, is the new-found motivation to explore MMM, and see if it can be transformed from an almost archaic enterprise-only services-heavy solution, to a more modern light-weight SaaS product that can be made available to app developers suffering from the lack of insights provided by SKAN today and Privacy Sandbox on Android tomorrow.

Die hard fans of MMM believe this is the only way to look at your true ROI, and therefore measure the true incrementality of your media. Others simply say they are using a simplified MMM model for iOS applications simply because SKAN doesn’t work, and it can’t be relied on.

And while there’s definitely a growing number of fans, there are also plenty of critics: companies that tried MMM and found the numbers to be highly inaccurate (at least, not without a good deal of mostly manual ‘adjustment’). MMM can take a ton of investment to get right, and even its advocates have literally told me there’s still a lot more interest than actual adoption.

And incrementality?

It’s worth mentioning one more divide in regards to incrementality: some say the only gold standard for measuring incrementality is to run A/B tests against an audience, at the same time, and measure the conversion results of each cohort. The only problem is that it’s now extremely difficult if not impossible on iOS, and even in the Android world you can only accomplish it via intensive cooperation from your ad channels. So I’d be wary of tools promising you incrementality measurement through means of audience A/B testing in this era of privacy.

But let’s be clear: there is value in both media mix modeling and incrementality when adapted to the specific worlds and needs of mobile marketing. And, both can also be extremely valuable when used to measure and allocate spend in not just mobile and not just digital marketing channels but all marketing channels, including TV, connected TV, streaming media of all kinds, out of home, web, and even extremely traditional channels like print or flyers.

Hybrid measurement is the future for marketing

I think it’s obvious that we’re now at a point in time where the discussion shouldn’t be about which single measurement methodology we should use. Rather, it’s about the when, where, and how we use multiple measurement methodologies together.

Hybrid measurement
Hybrid Measurement: 3 pillars

That’s why I believe that the future of marketing measurement is what we’re choosing to call Hybrid Measurement. I could probably go on for hours about Hybrid Measurement, and truth be told – there’s still a lot more to learn and uncover as we put this to test with customers, but at its core, there are 3 key concepts we’ve settled on:

  1. Unified data infrastructure
  2. Multiple measurement methodologies
  3. Reporting and insights serving multiple views and multiple purposes

Unified data infrastructure

You probably wouldn’t be shocked if I told you that at Singular we always believed that greatness in marketing involved bringing a lot of different signals into a single place. Since our inception in 2014, that has been the design principle of our product, and we successfully created the world’s best platform to deal with marketing data coming from literally thousands of different sources.

To properly build the hybrid measurement vision, there is a long list of critical data inputs:

  • Marketing spend data
  • Marketing delivery data (impressions, email views, deeplink opens)
  • Permitted granular measurement signals (IDFA, GAID, cookies)
  • Aggregated privacy-safe measurement signals (SKAN, Android Privacy Sandbox, ITP)
  • Revenue data online & offline
  • Customer data (engagement, CAC, LTV, cross-platform activity)
  • Ecosystem data (economy, weather, consumer behavior, seasonality)

I can confidently say that our starting point in building this vision is very strong. Our unified data infrastructure is the best in the market, our existing offering of measurement methodologies and the sophistication with which we combine them is unparalleled, and our reporting and insights layers have been stress-tested and iteratively improved for almost a decade.

But that doesn’t mean we’re done. Far from it, we’re just beginning.

Multiple measurement methodologies

Instead of relying on a single view of performance (which already today is not really a possibility given the data fragmentation in iOS), there will be multiple views that employ multiple methodologies using all the data mentioned above, and serving different purposes:

  • Views and methodologies based on permitted granular attribution data where available (Web, iOS, Android, 1st party data, PC, Console, CTV, Cross-Device data) that offer last touch or multi-touch attribution
  • Views and methodologies based on aggregated privacy-safe measurement signals such as SKAdNetwork, Android’s PSA, Safari’s PCM
  • Views and methodologies based on data science layers running on top of all the measurement signals available (such as our “SKAN Advanced Analytics”)
  • Views and methodologies involving Media Mix Modeling which are useful for evaluating the high level ROI of channels and campaigns, determining incrementality, and informing decisions like budget allocation

And as you might have noticed, we’re increasingly adding layers of data science to our measurement methodologies. Nowadays it’s mostly focused around SKAdNetwork (with our SKAN Advanced Analytics product) and Media Mix Modeling. But in the future it will expand to additional privacy-safe technologies as they start capturing more adoption (e.g. Android Privacy Sandbox, Private Click Measurement, etc).

Reporting and insights serving multiple views and multiple purposes

The obvious question is: does hybrid measurement sum up into a single source of truth for all my marketing activity?

Right away? No. In the future? Yes. But it will take some time.

Off the bat, these data sets were not designed to fit with one other. Imagine taking permitted granular attribution data (e.g. IDFA), combining with aggregated privacy-safe measurement data (e.g. SKAdNetwork), and marrying it to statistical outputs from an MMM model. You simply have no clear way of identifying the overlap between these data points. One is device level, the second is an aggregated cohort level which is still somewhat deterministic, and the last is a complete statistical model looking at correlations between spend, revenue, and additional factors.

Therefore, merging them is extremely challenging to do today without a lot of hand waving. So far, any initial attempts we’ve seen to do so, even for a limited subset of this data (like merging IDFA and SKAdNetwork data) are overly simplistic and quite inaccurate.

As an industry, we’re still at a relatively early stage of proper SKAdNetwork adoption, and that means we’re a long way from having every app developer run their own MMM model successfully. Essentially there hasn’t been a platform up until now that has taken on the challenge of supporting all these multiple views in such a comprehensive way, so there are no real past experiences to learn from.

But, at the end of the day it’s very clear to us that marketers want and need a way to make sense of these numbers in unison, and that will be one of the goals for our data science, engineering, and product teams for the months and years to come.

When will Hybrid Measurement be released?

This will be a gradual process, and I envision three phases:

  1. Phase 1: Enable access to more methodologies through our common data infrastructure and data science layers (AKA our Hybrid Measurement platform). The focus here would be on actual adoption as opposed to just hype.
  2. Phase 2: Reimagine reporting visualizations for multiple methodologies. This is already being surfaced and worked on today, and will become even more important when more methodologies become available.
  3. Phase 3: Develop ways to combine results from multiple views and methodologies into a single insight engine based on all available data sources. 

These phases will not be completely sequential, but it’s clear that learnings from phase 1 will greatly impact phases 2 and 3.

Final words

We are EXTREMELY excited about this vision.

Due to skill or luck (probably both :)) we have built the perfect platform to enable us to carry out this vision. It has always been the natural expansion path for our platform, and the industry as a whole, and privacy was the perfect catalyst to make it happen.

It’s beautiful to see how data science is becoming embedded in everything we do nowadays, and how things are getting more sophisticated. On the flipside, we must remain committed to simplicity and accessibility which is what the next generation of measurement solutions must provide.

Stay tuned as we’ll be sharing more about this over the next weeks and months.
And until then, you can find us in all the usual spots: slack, webinars and of course our blog.

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