Here are my thoughts regarding John Koetsier’s Aug 7th article in Forbes:
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?
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.
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).
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.
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.
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:
Requires few inputs – mostly just a time series of spend and target outcome measurements.
Robust to incongruities among ad channels, both online and offline, in terms of functionality and data availability.
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.
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:
Requires several years of data at a minimum due to aggregation at the week or month granularity.
Not used for quick decision making since it takes weeks or months to update a model with new data.
Usually custom built for an individual advertiser by very expensive specialist consultants.
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:
We believe that measurement of campaigns at the country and publisher app level (when applicable) remains critical for UA decision-making.
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.
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.
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.
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.
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.
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.
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.
We realize that as an advertiser, you are facing a great deal of uncertainty due to Apple’s announcement about iOS14 and IDFA. First, as the provider of the leading UA automation & LTV prediction engine for mobile gaming, we want to reassure you that we’re here to help. For almost 12 months, we’ve been planning in the background for the upcoming deprecation of device IDs including the IDFA. While this change is a seismic shift for the entire industry including advertisers, publishers, networks, MMPs, and analytics and automation platforms like ours, we see this as an opportunity to not only continue providing our current value, but expanding that even more. Second, part of this expanded value that we’ll provide will be based on fixing the issues caused by the last touch attribution model, which the mobile industry adopted long ago and continued to accommodate till present day.
For those not familiar with the above diagram or the implications of Apple’s announcement, let’s recap. Once iOS device IDs disappear to a large extent toward the end of this year, it will no longer be possible to attribute post-install activity including revenue directly to a campaign or publisher app. That means that important metrics like retention, DAUs, LTV, and ROAS will be impossible to evaluate at the campaign or publisher app levels. We are working on “top-down” solutions that algorithmically attribute post-install activity to campaigns/publisher apps so that our platform (UA Command Center) continues to work and deliver value to you without disruption.
Not only will UA Command Center continue to provide accurate LTV predictions and robust optimization algorithms, the solutions that power the algorithmic attribution of post-install activity will also allow us to “fix” the many problems with last touch attribution. In the last touch attribution model, 100% of user activity is attributed to the network that got the last touch just before the user installed the advertiser app. Even though it is very easy to understand, this is often highly misleading and rarely aligns with advertiser value.
We have been and still are in talks with all of the major industry players to ensure proper alignment and the long-term design and success of our solution. We look forward to sharing more information shortly about our solution. Of course, we are very much in alignment with Apple’s move toward greater personal data privacy and are excited about the opportunities it presents.
As part of our ongoing effort to gather feedback from as many perceptive voices in the industry, we invite you to connect with us to ensure that your concerns are addressed and you have a chance to shape the design of our solution.
You have probably seen a lot of buzz lately about automation in mobile user acquisition. So we teamed up with Gamesforum to explore this topic deeper with some of the best mobile gaming UA leaders from top companies like EA (Electronic Arts), Luna Labs and Hyper Digital Partners. Sign up below to watch the recording of this free webinar from June 9th, 2020.
In the recording, you’ll hear these leaders (John Wright of Luna Labs, Daniel Lopez of EA and David Jumper of HyperDP) share their insights about UA automation in mobile gaming. Our very own CEO, Brian Krebs also shares his perspective (as a technical co-founder who has been innovating for over six years) on how automation and AI can be leveraged for successful mobile user acquisition, especially in mobile games.
There has certainly been a lot of buzz lately about automation in mobile user acquisition. So we thought it would be a good idea to explore this topic with mobile gaming UA leaders from top companies like EA (Electronic Arts), Luna Labs and HyperDP. Register today for this FREE WEBINAR on June 9th, 2020 where you’ll hear how these leaders are thinking about UA automation. Big thanks to John Speakman of Gamesforum for hosting this webinar and the panelists (John Wright of Luna Labs, Daniel Lopez of EA and David Jumper of HyperDP) for sharing their insights.
You can check out Gamesforum’s other free webinars on important industry topics on their site. Our very own CEO, Brian Krebs will join that webinar to share his perspective as a technical co-founder who has been innovating for over six years on how automation and AI can be leveraged for successful mobile user acquisition, especially in mobile games.