Fingerprinting is dead. What’s your next move?

I’ve seen some folks contend that even though Apple’s latest language is more clear than ever, the phrase “uniquely identifying” means that fingerprinting must be 100% accurate to result in a ban.

Such an interpretation is only possible if you ignore the preceding words “for the purpose of”. Fingerprinting is always probabilistic, but its purpose in attribution is to match a single converting device (e.g., install) to a single ad-engaged device (e.g., click). Failing to uniquely identify a device does not change the purpose of fingerprinting. It is simply an indication that the fingerprinting tech needs improvement.

Also, it’s important to remember Apple’s motivations in all this. It does not benefit them to allow a form of tracking that is not controllable by or transparent to them or their users. Fingerprinting has always been the most anti-privacy tracking method available on both mobile and desktop. Apple didn’t spend all those resources on SKAdNetwork just to allow everyone to attribute and track outside of the ecosystem they control.

No matter how specific Apple’s language gets, there will always be someone interpreting it in just the right way to support their hopes, right up until they are banned from the app store.

Submit a meeting request below to get any questions answered or to hear about our FREE beta program for our incremental measurement solution that doesn’t need IDFA or GAID and can still provide you, DAILY, in incremental form at the source by country level:

  1. Revenue
  2. Installs
  3. ROAS
  4. LTV
  5. Retention
  6. Any other KPI we have data for that matters to your team.


CEO of MetricWorks


IDFA Deprecation Damage Control (Mobile Presence Podcast Interview of Brian Krebs by Peggy Anne Salz)

Thank you to Mobile Presence’s Peggy Anne Salz (noted author, analyst, content marketing strategist, and frequent Forbes contributor) for interviewing our CEO, Brian Krebs on the positive aspects of the IDFA deprecation including our new measurement solution for the post-IDFA world.  See podcast link below.

Here’s a quick summary of the podcast:

With IDFA, marketers will no longer be able to attribute post-install activity, including revenue, directly to a campaign or publisher app. And that means marketers will be in the dark and unable to evaluate important metrics, including retention, DAUs, LTV, and ROAS at the campaign or publisher app levels. Or will they? Not if they harness a “top-down” solution that algorithmically attributes post-install activity to campaigns.  You can listen to the full podcast below or on the Mobile Presence site.

Listen to “IDFA Deprecation Damage Control: Lifting The Lid On A Mobile Measurement Solution For The Post-IDFA Era” on Spreaker.

Submit a meeting request below to get any questions answered or to get feedback on your testing plan for post-IDFA measurement. Remember to:

  1. Upgrade your SDKs as soon as possible.
  2. Stay in touch with the vendors who haven’t yet provided an iOS 14 update.
  3. Continue to be vigilant during this 3+ month IDFA deprecation reprieve.
  4. Test thoroughly. The winners will be those that evaluate and adopt the solutions of the future while everyone else is still exhaling.

Why Are LAT Rates Skyrocketing Before IDFA Deprecation?

You wouldn’t be blamed for not paying close attention to LAT rates over the last week since iOS 14 launched. We were all told that life can continue as normal until early next year. With that, a huge collective sigh of relief was exhaled in unison and the mobile industry went about its business. However, there are a number of SDKs that check the isAdvertisingTrackingEnabled property (toggled by the user via the Limit Ad Tracking setting) before attempting to access the device’s IDFA.

This is a relic of how IDFAs were handled pre-iOS 10. At that time, you could still access the IDFA when LAT was enabled, but you had to pinky-promise not to use it for advertising tracking (although frequency capping, fraud detection, and other uses were still permitted). With iOS 10, IDFAs were zeroed out when LAT was enabled, meaning SDKs were free to pull the IDFA without first checking isAdvertisingTrackingEnabled. Many widely used SDKs understandably continued the legacy practice though.

For some SDKs, that practice continues to this day. When Apple pushed out iOS 14 a week ago, they deprecated the isAdvertisingTrackingEnabled property. See the relevant Apple docs here.  As you can see, now it always returns “no”. Unfortunately, “no” isn’t just a default value that tells app developers that LAT is a thing of the past. To some pre-iOS 14 SDKs, it means that LAT is very much alive and enabled on the device (or advertising tracking is disabled).

For anyone using an SDK that has not yet been updated to support iOS 14, there is a chance that it checks isAdvertisingTrackingEnabled prior to accessing the IDFA. If it does, you will see LAT rates quickly increase since iOS 14 dropped a week ago. This can have a lot of significant ramifications for attribution, campaign optimization, and ad monetization, depending on the purpose of the SDK.

Upgrade your SDKs as soon as possible. Stay in touch with the vendors who haven’t yet provided an iOS 14 update. Continue to be vigilant during this 3+ month IDFA deprecation reprieve. Test thoroughly. Submit a meeting request below if you’d like feedback on your testing approach.  The winners will be those that evaluate and adopt the solutions of the future while everyone else is still exhaling.

Could Opt-Out Be Apple’s Big Leg Up?

Here are my thoughts regarding John Koetsier’s Aug 7th article in Forbes:

IDFA User Consent Messages

First, I am assuming that the Apple Advertising opt-out setting (shown above) refers to Apple as an ad network and publisher. The verbiage in the screenshot mentions “Apple’s advertising platform” and links to Apple’s Advertising & Privacy page, which refers to ads that are delivered by Apple in Apple-owned properties like “the App Store, Apple News, and Stocks“.

What is “personalization”?

There is a burning question that must be answered if one is to determine if Apple is giving itself an advantage or not: What does “turning off personalized ads” mean?  To me, “personalization” means that the advertiser knows precisely who the user is, which requires an IDFA since IDFVs known by the advertiser are invalid in the publisher app. Therefore, by extension, an IDFV alone does not allow “personalization” but does allow the publisher to model each user’s behavior in their own apps, which can be valuable if they are also an ad network.  

If the setting disables all device identifiers for purposes of ad serving, Apple is putting themselves at a disadvantage since every other publisher at least has guaranteed access to the IDFV. I doubt Apple is willing to hamstring their ad product in order to prove their privacy chops, so we can probably dismiss that option. In that case, let’s assume the setting disables Apple’s access to the IDFA only. Then, the question becomes, does Apple gain an advantage over competitors if their apps are opt-out while competitors must receive per-app opt-in to access the IDFA?

What are the benefits of IDFAs to ad network-publishers?

In my opinion, the answer is yes now, and potentially much more so in the future. In terms of this analysis, we should consider Apple’s competitors to be ad networks that also act as publishers such as Facebook (who operate their primary app plus Messenger) or ironSource (who owns Supersonic). Those companies must ask consent to access a device’s IDFA in each of their apps. Assuming the above is true, Apple would have de facto consent in all of their own apps (App Store, Apple News, and Stocks, for now) unless the user digs deep into their settings and revokes it.

So how does greater access to IDFAs benefit an ad network-publisher like Apple? First and foremost, IDFAs enable a much more robust device graph, which acts as a critical foundation for almost every ad platform with a buy-side including ad networks and DSPs. Linking external performance via MMP postbacks to the internal performance data you are already privy to as a publisher creates a far richer model for determining the likely value of each user to each advertiser. Without IDFA access, ad network publishers only see performance data in their own apps.

In addition, common ad network features like frequency capping, suppression lists, and look-alike audience targeting, are either enabled or significantly enhanced by IDFAs instead of IDFVs.

What is Apple’s long term play?

Last Touch Attribution Model. MetricWorks

I believe one of the primary reasons for this move by Apple (should my earlier assumptions prove to be correct) is to bolster the value of their identity management product, Sign in with Apple. On June 30, 2020, it became mandatory in all apps that offer any other third party single sign-on (SSO) option such as Facebook or Google. While it isn’t being discussed much, I think it’s big news since it allows Apple to greatly increase its footprint even into Android and web.

Facebook’s value as an advertising platform is largely based on the wealth of data they have on each user tied to a Facebook user ID. That value is only truly realized when those Facebook user IDs can be associated with IDFAs, providing the glue between Facebook’s first party data and the mobile advertising world. If Sign In with Apple gains wide adoption as Apple hopes, expect to see advertisers get much more value out of Apple’s ad platform. Even though they’ve promised not to utilize a user’s history of sign-ins for advertising, just the list of apps and websites connected to the user’s Apple ID alone is extremely valuable to advertisers.

Don’t be surprised if Apple’s next move is the return of iAd in some form.  Adding external publisher traffic to the mix similar to what Facebook Audience Network does for Facebook would certainly allow Apple to multiply the value of their IDFA advantage in combination with a successful identity management product.







[White Paper] iOS 14 – The Catalyst For The Evolution of Measurement

Embracing Change

The mobile advertising industry has always been a dynamic one that has attracted brilliant minds to embrace its constant change and bring to life businesses that wrestled with the messiness of advertising data to deliver increasing value as they evolved.  Now is no different.  So we think it is important that as we approach this IDFA/iOS14 cliffhanger, we commit once again to center ourselves, continue to embrace change and innovate.

Let’s begin by taking stock of the positives of this upcoming IDFA change that Apple has postponed till early next year.  Firstly, it offers increased user privacy.  This is in alignment with our core values as end users ourselves and as a mobile ad tech company.  Secondly, it allows the industry to ditch the issues with last click attribution and forge a new chapter of measurement innovation.

Thirdly, we want to reassure you that MetricWorks’ platform, UA Command Center (UACC), will NOT be negatively affected by iOS14 and will still provide all of the value it does today.  Additionally, our new measurement module will ensure that you have granular revenue visibility in order to continue to successfully optimize and execute. You will see more detail about this lower down in this post.

Our Quest For The Future Of Measurement

The planned IDFA restrictions, likely to roll out in early 2021, introduces a huge but welcome change to the mobile advertising ecosystem. After an Apple user upgrades to iOS 14, any iOS 14-compatible app that wants to access their device’s persistent identifier (IDFA), most often for the purposes of retargeting, user profile linking, or measurement, will need to ask for their permission (as shown in the example below).

IDFA User Consent Messages

This presents an interesting challenge for the industry since so many players in the ecosystem rely on the IDFA to varying degrees. At MetricWorks, we’ve focused on how measurement data will flow in the new post-IDFA world. Our ability to provide end-to-end UA automation including LTV prediction and bid optimization for our advertisers, at the country and publisher app level offered by SDK networks, is contingent on accurate, granular measurement. Based on the spirit of Apple’s new terms, we decided to evaluate this challenge with the assumption that MMPs will no longer be able to send us events with attributed channel, campaign, country, and publisher app information. In its current state, SKAdNetwork, Apple’s on-device measurement offering, does not seem to provide enough granularity, nor does it handle post-install events well, making retention and LTV prediction impossible.

Early in our search for a suitable measurement solution, we identified incrementality as an important piece of the puzzle.  For those not as familiar with the concept – incrementality refers to the incremental value directly caused by each advertising touchpoint in the user journey. This is impossible to measure in the last touch attribution model (Fig.1 below), which credits the final touchpoint with 100% of the value for the user without consideration to the possibility that the user could have been acquired with fewer, more valuable touchpoints or even zero advertising as an organic install.

Last Touch Attribution Model. MetricWorks

A methodology known as incrementality testing is an ideal solution to prove the causal relationship between advertising touchpoints (ad buys) and uplift.  It prescribes a rigorous scientific process similar to randomized clinical trials used by pharmaceutical companies where a population of users is randomly split into a test group that is delivered an ad and a control group that receives a “placebo” (often an unrelated public service announcement or PSA). However, it can be costly since, if you’re displaying PSAs, you still have to pay for those impressions.  There is an even bigger problem with the advent of iOS 14. Most forms of incrementality testing require a large list of device IDs so that the audience can be split. iOS 14 will make this difficult to accomplish.

Another powerful tool in the measurement toolkit that also considers incrementality is media mix modeling (MMM). This technique uses regression models to find correlations between ad spend and business value. As a “top-down” technique (Fig. 2), versus a “bottom-up”(Fig. 3) approach like last touch attribution that works at the device level, it eschews device IDs in favor of aggregated data, and is therefore naturally aligned with user privacy. As you can see in Fig. 2 and Fig. 3 below, both top-down and bottom-up measurement techniques attempt to allocate the same users and app activity data (installs, opens, revenue) to the same four campaign/publisher app combinations in the proper proportions, but come up with different answers.

Top-Down Measurement Process By MetricWorks
Bottom-Up Measurement Process By MetricWorks

Key Idea: Based on the measurement output of the two different methodologies, we can see that they somewhat agree on the value of ironSource campaign B, publisher C (red). However, ironSource campaign A, publisher A (purple) didn’t get credited much in the bottom-up last touch methodology (just 1 install with little revenue) while top-down shows that, even though it might not be getting the last touch, it is providing significant incremental value. On the flip side, bottom-up attribution gives Vungle campaign A, publisher A (orange) a solid amount of credit for last touches, but statistically, it is providing almost no incremental value. We would have acquired those users either through other campaigns or organically anyway.

Coming back to the top-down media mix modeling (MMM) technique (Fig.2), let’s look at its advantages:

  1. Requires few inputs – mostly just a time series of spend and target outcome measurements.
  2. Robust to incongruities among ad channels, both online and offline, in terms of functionality and data availability.
  3. Can be used to predict the change in outcomes as a function of different spend inputs which is quite handy for planning purposes including what-if analysis.
  4. Can also be used in conjunction with other algorithms to optimize towards a given goal, which can be used for budget allocation optimization.

MMM has its own problems though, which also eliminated it from contention in our quest for the future of measurement. When we attempted to apply it to mobile app advertising, some major issues became apparent including:

  1. Requires several years of data at a minimum due to aggregation at the week or month granularity.
  2. Not used for quick decision making since it takes weeks or months to update a model with new data.
  3. Usually custom built for an individual advertiser by very expensive specialist consultants.
  4. Can only prove correlation, not causation (eating seafood may be highly correlated with personal wealth, but that doesn’t mean eating a lot of seafood will make someone more wealthy).

While MMM wasn’t a perfect fit, we recognized early on that the MMM concepts held a lot of potential. The key is that we found that most of the downsides could be mitigated through a combination of creative feature engineering and automated model validation through constant backtesting and live experimentation.

Keep in mind that the entire measurement process including modeling and validation must be completely automated in order to be scalable. No need to worry though because our existing automation technology in UACC is being enhanced to make this a powerful reality for you.

Our Solution For Mobile Measurement

Thank you for bearing with us and making it this far.  Let us now look at the high level overview of our solution:

  1. We believe that measurement of campaigns at the country and publisher app level (when applicable) remains critical for UA decision-making.
  2. Compatibility with the current iOS14 technical specifications and alignment with the spirit of the new rules are equally crucial for any future measurement solution  including ours.
  3. Regression models similar to those called for by media mix modeling can be augmented with techniques that address problems unique to mobile app advertising in order to finally deliver a measurement solution that considers incrementality.
  4. Using daily data helps solve the data volume issue inherent to MMM and allows the model to be updated more quickly so it can inform the types of fast decision-making required by UACC’s LTV prediction and bid optimization features.
  5. Backtesting allows us to automatically evaluate a wide array of approaches and select only the most accurate models based on their ability to predict known historical outcomes.
  6. Automated controlled experimentation enables live testing of incremental value predictions in the real world so that models can be validated and improved by rejecting poor models and feeding results back into the next model.
  7. iOS14 will not handicap any of the existing capabilities of UACC. All of our UACC platform capabilities remain intact including analytics (dimensions, metrics, etc.), LTV prediction, and bid optimization.  The only difference is that we will be providing a new measurement module (based on our analysis outlined above) that ties aggregated cohort activity with the channel, campaign, country, and publisher app dimensions, instead of the MMP.
  8. Our measurement module will be provided as an option at the app level so that advertisers can rely solely on MMP measurement for their Android apps, should they wish.

We will continue to share details of our progress and invite you to connect with us to ensure that your concerns are addressed, and that you have a chance to shape our solution.