Complying With ATT Is Not Enough

Many of you may have already seen Eric Seufert’s latest and greatest Mobile Dev Memo post about Voodoo being fined by the French privacy watchdog for using the IDFV for advertising purposes without user consent.  Up till now, most of the mobile industry has focused on complying with ATT. As made clear by France’s privacy regulator, CNIL, this is NOT enough, especially because complying with ATT does NOT equate to complying with any privacy law

But, why is that?

Well, ATT says that you can access and do what you wish with the IDFV (assuming it’s not breaking some other Apple policy) even if the user opts out of tracking. The ATT opt out only protects the user’s IDFA. GDPR and the French DPA, on the other hand, make it clear that you cannot do anything with that IDFV without opt-in unless it is:
1. clearly contractual (e.g., the user has already contractually agreed to be tracked) or
2. it’s in the legitimate interest of the advertiser (e.g., the advertiser must use your IDFV for tracking in order to provide the basic functionality that the user expects from the product).
Refer to Eric’s post for more details that support the above assertions.

Now, in terms of measurement, what does this mean for the mobile industry?

  1. Most apps use an MMP SDK for measurement.
  2. MMP SDKs must collect device data in order to measure (whether that data is IP address, etc. for fingerprinting, which is against Apple’s policies, but has remained generally unpoliced on both iOS and Android, or a cross-publisher device ID like IDFA on iOS or GAID on Android).
  3. GDPR and the French DPA state that device data can only be collected after clear user consent unless the company meets one of five other legal bases, the most common of which are:
    a. the company has a contractual obligation to collect that particular data (contractual basis) or
    b. it must collect that particular data in order to provide the expected functionality of the product or service (legitimate interest basis)
  4. Only on iOS are MMP SDKs requesting user consent before collecting the cross-publisher device ID (IDFA), whereas on Android, the cross-publisher device ID (GAID) is collected unless the user specifically opted out, which is an option buried in the settings; on both platforms, most advertisers have the MMP SDK configured to collect other data like IP address for fingerprinting if the device ID can’t be accessed anyway.
  5. Recent GDPR rulings suggest that a contractual basis isn’t applicable even when only using first-party data like the IDFV (which based on ATT, doesn’t require user consent on iOS) to target ads (the European Data Protection Board ruled that the contractual basis wasn’t applicable in Meta’s case) since users were essentially forced to agree to the contract terms in order to use the product, which is expressly disallowed.
  6. Recent regulator advice suggests that a legitimate interest basis isn’t applicable even when only using first-party data like the IDFV to target ads (the Irish DPC advised TikTok to abandon their plans to use the legitimate interest basis for targeting ads with first-party data) since targeting ads ostensibly doesn’t constitute a legitimate business interest.
  7. Measurement is unlikely to be interpreted differently from ads targeting in any significant way in terms of the applicability of the contractual or legitimate interest bases (e.g., measuring the performance of marketing is not necessary to fulfill contract obligations to users nor is it a part of the expected functionality of the product).

What can we conclude from this?

1. All apps are in violation of the GDPR/French DPA if they:
a. Have European users
b. Use an MMP SDK
c. Either:
i. Are fingerprinting on iOS for users who do not consent to ATT (collecting data after an opt-out)
ii. Have any Android users (there is no ATT-equivalent on Android, so no consent framework exists at all)

2. Even if companies only collect data from users who have consented (which would require them to create consent dialogs on Android since the platform doesn’t have a built-in framework like ATT on iOS):
a. Fingerprinting would be rendered unnecessary since the company would already be able to collect the cross-publisher device ID (much more accurate than fingerprinting) with the consent (currently, it’s used as a nefarious backup if the user denies consent)
b. SKAN, which has tons of visibility issues, would be the only viable way to measure last touch on iOS
c. Even worse, last touch measurement on Android would be almost impossible since the MMP would need consent from each user in the publisher app (the app that displays the ad) and the advertiser app (the app that buys the ad) to attribute any user (often called the double opt-in problem), which as we’ve seen after ATT was released on iOS, is incredibly rare.

What can you do?

The mobile industry is approaching another watershed moment. Do you have the right measurement to succeed? Fortunately, Polaris by MetricWorks is a turnkey, privacy-centric incrementality MMP.  Polaris does not need device IDs, painful migrations, heavy lifting, SDKs, or additional skills.  Most importantly, Polaris will help you avoid any issues with privacy regulators because it respects users’ privacy.

In summary, for most app companies, the only real options to avoid similar massive fines are:
    1. Block access to European users completely (avoid jurisdiction of European regulators).
    2. Remove MMP SDKs from all apps and completely cease measurement activities.
    3. Continue using MMP SDKs, but ensure no device data is collected unless consent is granted (e.g., disable fingerprinting), meaning only deterministic last touch would be available and only for the few users that the MMP has double opt-in for (this may not even be possible for many MMPs at the moment and you’d still need a custom consent dialog for Android since there’s no ATT equivalent).
    4. Migrate completely to measurement methods that don’t require the collection of device data such as SKAN (iOS only), MMM, and geo lift testing (avoid collecting device data for the purpose of measurement altogether).

    If you’d like to discuss this topic further, feel free to book a time or contact us.

    Photo by Marija Zaric on Unsplash 

tvScientific partners with MetricWorks to boost ROI on CTV

The cord-cutter revolution has seen over 128M+ US viewers transition to watching TV via streaming services, representing 63% of all US TV viewing time. Because CTVs are by definition connected to the internet, this astonishing shift in consumer behavior has created a massive opportunity to democratize access to TV’s awesome power as an advertising medium, complete with self-serve capabilities and digital-like performance measurement. The tvScientific platform will play a major role in enabling millions of digital-first advertisers to participate in CTV advertising for the first time.

tvScientific has developed the most sophisticated CTV performance advertising platform, allowing businesses to prove out the power of TV advertising.  The platform simplifies and automates CTV buying and optimization, leveraging massive data to prove the actual value of TV advertising.  “Value is proved with ROI and this is why measurement down to the granular level continues to be the center of conversations when it comes to CTV,” said Jason Fairchild, CEO and co-founder of tvScientific.

With this partnership, tvScientific continues to enhance marketers’ ability to accurately gauge campaign effectiveness through incrementality measurement. Now, with MetricWorks, tvScientific can give marketers vastly improved performance visibility to CTV. This is due to the fact that the performance is incremental rather than last touch. MetricWorks has quite a bit of data about how wrong last touch can be when comparing performance across various traffic types, including tvScientific’s platform. Our partnership now rectifies this last touch issue and leads to improved ROI/ROAS for marketers.  “With the 108M streaming households market presence of CTV, digital marketers should be looking at CTV as another attractive performance channel in their daily marketing mix,” said Jason Fairchild, CEO and co-founder of tvScientific. “tvScientific’s partnership with MetricWorks will provide performance marketers with deep and comprehensive insight into campaign performance across platforms while proving that CTV has truly been undervalued due to limitations in last-touch attribution.”

MetricWorks is the leading provider of MMM-based incrementality measurement which closes the gaps in last touch measurement. MetricWorks’ incrementality MMP, Polaris, in particular, is a turnkey, drop-in addition that doesn’t require any extra SDKs, device IDs or heavy migration lift.  

With the significant market presence of CTVs, marketers should be looking at CTV as another attractive channel to boost their marketing ROI. This partnership between MetricWorks and tvScientific, makes it far easier than before for marketers to take full advantage of the CTV channel opportunity.  You get the best of both worlds with tvScientific’s self-managed solution coupled with MetricWorks’ fully automated, granular, daily incrementality measurement outputs that are tactically engineered for successful daily marketing effectiveness.

MetricWorks’ incrementality MMP, Polaris automates every step from designing experiments and calculating ground truth to training econometric models and deriving incrementality results. Incrementality is delivered in the form of the KPIs you rely on today including ROAS so that your UA or marketing decision-making processes don’t need to change. Polaris is also designed to offer an easy, turnkey “MMP experience” so that you can get started in 24 hours. No IDFAs or GAIDs. No migrations, no extra SDKs, and no heavy implementation lift. Polaris integrates directly with your last touch MMP to provide a single repository for side-by-side comparisons of your last touch and incrementality data. “Today’s digital marketer needs to have a holistic understanding of how each platform is performing in a marketing mix and MetricWorks is tactically engineered for successful daily marketing effectiveness,” said Brian Krebs, CEO and co-founder at MetricWorks. “We’ve seen CTV significantly undervalued by last-touch across our customers, often because the bulk of its impact is felt higher in the funnel. The partnership with tvScientific will enable marketers to apply performance marketing strategies to advanced television and leverage media mix modeling to drive ROI on an undervalued channel.” 

The full press release is available here.  If you are looking for an edge over your competitors that will elevate your marketing effectiveness, contact us today to see how easy it can be:

Meet Us At MAU Vegas

Well, it is finally the week of MAU and we are excited to meet you in Vegas! MetricWorks is proud to be a Gold Sponsor of MAU Vegas. There are many opportunities to meet our team and learn how you can close the gaps in your measurement stack with our MMM-based incrementality MMP, Polaris.

For a full listing of what MetricWorks is up to at MAU, visit MetricWorks At MAU22. Here are the quick highlights.

1) Don’t miss our MAU Speaking Session with our CEO, Brian Krebs on Wed, June 8th @ 11:40am in Terrace Ballroom 152. Brian will describe “Why It Is Time To Close The Gaps In Your Measurement Stack With MMM-Based Incrementality Measurement”

2) Drop by our Booth 644 to win fantastic daily prizes! Book a time to grab a drink with us.

3) Be among the first 50 to get an exclusive peek on our webinar at the very first Incrementality Industry Report. Sign up now for this exclusive webinar and the report that will follow.

4) Get our FREE GUIDE on How Marketing Mix Modeling Can Fill the Gaps of Attribution.

Hit us up with any questions or for a demo of Polaris at See you soon at MAU! 

Polaris Turns One!

Exactly one year ago, we publicly launched Polaris on VentureBeat (thanks again, Dean Takahashi). At the time, it was the first of its kind: a measurement product pairing media mix modeling (MMM) and geo lift experiments to provide incrementality performance metrics in the same form factor as an MMP. Actually, it remains the only product of its kind, one year later.

While we didn’t fully open up the floodgates until Q3 2021, the past year has been a wild ride. The response from the mobile industry was far greater than I could’ve imagined. Our partnership with Meta and collaboration with their world-class Marketing Science and MMM teams have helped us to continually improve Polaris and the science behind it.

Our growth over the last 2 quarters since we went fully live is nothing short of incredible. We’re adding new people to our amazing team and expanding to new regions across the globe. We’ve onboarded so many notable customers over these months it’s been an absolute blur. None of this would have been possible without my teammates at MetricWorks. I’m humbled to be a part of this journey with all of you.

To learn more about Polaris, submit a demo request or visit our Polaris product page.

How does incrementality enhance UA prediction and optimization?

When COVID-19 hit the world, digitalization skyrocketed. People had to find new ways to communicate, be entertained, and live their life from home. Brands also had to find new ways to survive, grow and interact with their customers. Just like that, the number of apps boomed and so did the competition. It won’t slow down any time soon. That’s why apps need to advertise successfully. Otherwise, it is difficult to differentiate from the competition and acquire new users.

So here’s the secret sauce for successful advertising: innovative UA driven by innovative measurement. Innovative UA provides the ability to make smart, and fast decisions today that outperform the competition tomorrow. This new type of decision-making is powered by prediction. Prediction itself is fueled by innovative measurement that is accurate, unbiased, granular and grounded in scientific methods that have been honed over many years. This type of measurement is known as incrementality.

What is incrementality?

You may have heard about incrementality and wondered what all the fuss is about.  Well, in the post-IDFA world, last touch is certainly a flawed and unreliable form of marketing measurement.  Fortunately, incrementality does not depend on IDFAs or GAIDs, or arbitrary attribution window. It is far more accurate than last touch or SKAN in powering your UA prediction and optimization.


Incrementality is the lift, in terms of any KPI (such as ROAS), over all other media spend plus organic demand. It’s really all that matters. If certain media spend is cannibalizing organic lift or overlapping with other media, true value can be significantly impacted.  

MetricWorks’ incrementality MMP, Polaris automates every step from designing experiments and calculating ground truth to training econometric models and deriving incrementality results. Incrementality is delivered in the form of the KPIs you rely on today including ROAS so that your UA or marketing decision-making processes don’t need to change. Polaris is also designed to offer an easy, turnkey “MMP experience” so that you can get started in 24 hours. No IDFAs or GAIDs. No migrations, no extra SDKs, and no heavy implementation lift. Polaris integrates directly with your last touch MMP to provide a single repository for side-by-side comparisons of your last touch and incrementality data.

Why are traditional MMPs and SKADNetwork not enough anymore?

The general issues with traditional MMPs and SKADNetwork is that they are built on the simplified yet flawed view of the world (see Fig. 2a below) and they ignore the fact that conversions are the product of a cohesive media mix (Fig. 2b below) rather than a single ad.

Last touch attribution ignores this complex reality, producing random results that are skewed toward self-attributing networks and dependent on arbitrary attribution windows. This has major impacts on business value. Due to this, UA optimization and prediction are also affected. 

In simpler terms, an inferior measurement signal results in inferior optimization and prediction. Tempr. is a fantastic prediction and automated optimization solution. However, to get the most out of your investment in Tempr., it is critical that you feed it a superior accurate signal tied to business value, namely incrementality. This ensures that Tempr. will make the best predictions and optimizations for you based on the best measurement inputs from MetricWorks Polaris.

Prediction: today’s decisions, with tomorrow’s data

The focus on the protection of personal information increases, but marketers still need to understand how to run successful ad campaigns. And we’ve great news: with prediction, marketers can get an accurate estimate of how their campaigns will perform, before wasting any budget or time. Even with limited data.

Where does prediction come from?

To be fair, prediction is no novelty for big app studios. Their teams usually design their own internal BIs. Most of them have data scientists working on predictions, because key decisions for the future should never be made blindfolded.

But here’s the thing. Any app studio, no matter the size, the language, the revenues, the structure, the MMP, etc., should have affordable and easy access to predictions. Not just the big guys. That’s why Tempr. – a predictive tool that adapts to each app vertical, and their KPIs – was created. Picture it: one big team helping UA Managers make today’s decisions, based on tomorrow’s data.

How does prediction work?

Prediction gives clarity on what’s going to happen next. The core principle is to use the past to predict the future

This marketing approach uses data science to forecast the strategies that are the most likely to succeed. In a simple way, here’s how it works:

1: Historical data mapping
The past is what we call historical data. On the graph below, each black dot represents the revenues made on a specific date.

2: Historical data understanding
The aim is to find trends, seasonality, and any other metrics that affected the past revenues – taking into account bids, budgets, traffic, app verticals, KPIs, etc.

3: Calculation
With machine learning, a mathematical formula that reflects the trends and seasonability of the graph in one curve is calculated. The objective is not to precisely follow each dot but come to a global result like the blue curve on the graph.

4: Data enrichment
The source’s data is enriched with data from MMPs such as MetricWorks for more accurate results.

5: Prediction
The prediction model powered by machine learning create thousands of scenarios with changing parameters and suggests the best bid-, budget-, and creative combination that will achieve the highest results (ROAS or CPE) in the future.

Fig. 3: How predictions work

Fig. 3: How predictions work

Benefits of prediction in UA
Now that the privacy era is on, Attribution and Measurement are more and more limited with SKAdNetwork. To make competitive decisions, mobile marketers need specific data. Not only yesterday or today’s data, but also tomorrow’s. 

UA Managers have to make assumptions on their users journey all the time. How active they will be, what type of events they like, and how much revenue they will generate. And since they don’t exactly know what to expect, they use A/B testing. Experimenting, testing, retesting, deleting. And wait to see what works, and what doesn’t. Which can be very costly.

However, when marketers associate predictions and A/B testing, they limit as much as possible the risks, the time-consuming guesswork and the loss of money along the road. Because prediction shows the successful path to follow, and adapts to the environment every day. And most importantly, Tempr.’s predictions give actionable insights.


More than just prediction: actionable insights

To predict the future is good. To recommend exactly what to do in order to achieve greater results? Even better.

Tempr. recommends campaign optimizations based on the predictions. The algorithms predict the combinations of bid, budget, and creative that will have the highest returns or that will decrease costs to achieve a certain event (first listening, download, etc.).

Marketers can finally drop the doubts and let predictions run the thousands of scenarios for them instead.

Fig. 4: Basics of Tempr.

Key takeaways

With prediction, apps increase their competitiveness, their efficiency, and ad campaign profitability. Mobile marketers can focus on what’s important: design better strategies, expand into new markets, and develop new features and apps. As predictive marketing fills in the information gaps left wide open by privacy rules, it’s now a vital practice to adopt.

How incrementality and prediction can work together

As we mentioned above, inferior measurement signal results in inferior optimization and prediction.  Incrementality’s superiority lies in its ability to model complex reality where media sources do interact, both beneficially and detrimentally, due to high audience overlap. Marketing can also both boost and cannibalize organic demand. Therefore feeding Tempr. with a superior incrementality measurement signal from MetricWorks ensures that your predictions and optimizations are far better than those derived from the SKAdNetwork signal.

In particular, as described in Fig. 5 above, with MetricWorks’ incrementality serving as the measurement signal, Tempr. will be able to more accurately identify situations where you might be underspending on a good media source or overspending on a bad media source.

Fig. 6: MetricWorks x Tempr.

The perfect partnership between Tempr. and MetricWorks

This recent partnership between Tempr. and MetricWorks offers mobile UA teams the best of both worlds with superior prediction and optimization by Tempr. being driven by superior incrementality by MetricWorks. “We’ve seen first hand the impact of poor mobile measurement. Therefore, our goal with MetricWorks Polaris is to provide true-north measurement that innovative UA-focused solutions like Tempr. can count on to deliver excellence to their clients in the post-IDFA era,” said Brian Krebs, CEO of MetricWorks.

To move towards a powerful predictive-data-driven decision model, it is crucial for marketers to have tools that provide an accurate understanding of how users interact with their apps. This partnership provides just that, giving marketers the ability to not only understand their app user behavior but to also take action on that information to improve their efficiency and maximize their ad revenues. Prediction-driven UA is undoubtedly linked with reliable data to start with. Therefore we are very happy about our partnership with MetricWorks that offers a very promising, and complementary technology to the well-known attribution platforms, helping mobile marketers to take the right decision,said Cloé Dana, CEO of Tempr.

Together, MetricWorks and Tempr. create the most efficient mobile UA solution on the market today. If you are looking for an edge over your competitors that will elevate your UA performance, contact us today to see how easy it can be:

Vincent Schmiedhausler
Sales Director at Tempr.

Chris Hoyt
Chief Growth Officer at MetricWorks

Video: How To Apply MMM To Mobile Apps

While there seems to be considerable confusion out there about MMM (Media Mix Modelling), it is actually very relevant and applicable to mobile app marketing.  To hear more about how to apply MMM to mobile apps, and why MMM can be more easily applied to mobile apps than to CPG companies watch this conversation between Shamanth M. Rao and Brian Krebs (CEO of MetricWorks). Full video recording available below.

Thanks again to Shamanth Rao (CEO of Rocketship HQ) for hosting MetricWorks’ CEO, Brian Krebs on The Mobile User Acquisition Show.  This is an informative episode that explains why Last Touch/SKAdNetwork is NOT enough for successful post-IDFA measurement, how MetricWorks has seen first-hand the success of media mix modelling for mobile apps, especially at levels of scale far smaller than that of most CPG brands, in which space these models have traditionally been used.  In this episode, Brian describes some of the ways in which these models are specifically applicable to mobile marketing campaigns and argues that these measurement tools are not only useful post-IDFA, but also provide distinct advantages over other measurement tools used by mobile marketers. 

MetricWorks’ own measurement solution, Polaris also makes it easy for you to enjoy these distinct advantages.   It requires really low effort on your part and we can get you started in 24 hours. No need for device IDs, migrations, extra SDKs, or heavy implementation lift. Keep your existing KPIs and marketing processes. Polaris can even do a side-by-side comparison of your incrementality data versus your last touch data. It integrates directly with your last touch MMP to pull the necessary data into a single repository along with your incrementality data. Email us today for a demo

Measurement granularity is a major gap within SKAdNetwork and we’re glad to see The Mobile User Acquisition Show draw attention to this major issue. Our incrementality MMP, Polaris which can co-exist with SKAdNetwork, offers a comprehensive, granular and accurate alternative that addresses the following glaring issues with SKAdNetwork:

  1. SKAdNetwork leaves marketers with only 100 campaign IDs of granularity
  2. It does NOT offer a way to cohort KPIs by install date
  3. Critical post-install performance KPIs that are foundational to your mobile growth strategy today like retention, revenue, LTV, and ROAS, have disappeared and been replaced by a single conversion value with its own significant limitations.

Does your measurement plan address the above gaps?  Contact us today to discuss how our measurement solution, Polaris can boost the effectiveness of your marketing and help you stay ahead of your competition.

You can also listen to the full podcast on iTunes or view the episode notes at The Mobile User Acquisition Show.  Please submit your questions below.  We look forward to answering them and making the future of your measurement brighter!