47% of marketers think this is the hardest part of Privacy Sandbox (and much more from our webinar with Google, Gameloft, and Tinuiti)

On April 26, 2021, half the mobile marketing world changed as Apple released iOS 14.5 and SKAdNetwork. Something similar will happen for the other half of the mobile universe, likely at some point next year in 2024, when Google flips the switch on Privacy Sandbox on Android and — perhaps simultaneously — Privacy Sandbox on the web. We recently hosted a webinar with Google, Gameloft, and Tinuiti to help the ecosystem prepare. One of the things we learned: what marketers consider to be the hardest part of Privacy Sandbox.

We invited panelists from Google, Gameloft, and Tinuiti to join Singular co-founder Eran Friedman:

  • Kelly Gieschen, strategic partner manager, Privacy Sandbox, at Google
  • Vasil Georgiev, UA director at Gameloft
  • Mollie Sheridan, senior manager mobile app paid search, Tinuiti

As it turns out, the hardest part of Privacy Sandbox could be the same as the most important part: understanding measurement results.

On April 26, 2021, half the mobile marketing world changed as Apple released iOS 14.5 and SKAdNetwork. Like at some point next year in 2024, something similar will happen for the other half of the mobile universe when Google flips the switch on Privacy Sandbox on Android and — perhaps simultaneously — Privacy Sandbox on the web. We recently hosted a webinar with Google, Gameloft, and Tinuiti to help the ecosystem prepare. One of the things we learned: what marketers consider to be the hardest part of Privacy Sandbox.

We invited panelists from Google, Gameloft, and Tinuiti to join Singular co-founder Eran Friedman:
Kelly Gieschen, strategic partner manager, Privacy Sandbox, at Google
Vasil Georgiev, UA director at Gameloft
Mollie Sheridan, senior manager mobile app paid search, Tinuiti

As it turns out, the hardest part of Privacy Sandbox could be the same as the most important part: understanding measurement results.

When we asked participants during the webinar what they thought would be most challenging, here’s what they said:

  1. Understanding measurement results: 47%
  2. Setting up conversion models: 20%
  3. Keeping track of cohorts: 12%
  4. Targeting: 12%
  5. Retargeting: 8%

That and other challenges just in learning what will become the new official system for attribution, targeting, retargeting, and SDK management on Android is stressing marketers out.

how marketers feel about android sandbox

68% are concerned. 17% are terrified, according to the sample of marketers attending the webinar. Only 15% are either “happy” or “fine.” 

The good news is that Singular, Gameloft, and Google are already beta-testing the hardest part of Privacy Sandbox as well as every other part. 

Here are some of the highlights of the webinar:

2 beliefs behind Android Privacy Sandbox

“Privacy Sandbox believes first that user privacy and a healthy mobile ecosystem are not at odds. And, two, that a blunt approach without providing working alternatives does not work and will make users worse off. So with those two principles in mind, we envision technology where privacy takes precedence while businesses can still thrive and be successful.”

– Kelly Gieschen, strategic partner manager, Privacy Sandbox, at Google

Essentially, Gieschen says, it’s about allowing businesses to continue their growth and marketing initiatives without having to use granular user-level data, or capturing device identifiers that could be used for cross-app tracking.

Advertising APIs and context APIs

Most mobile marketers know the core APIs in Privacy Sandbox by now. It is interesting, however, to see how Google insiders approach them: as building blocks for the industry to innovate on top of.

There’s 3 advertising APIs, 2 of which are “relevance APIs:”

  • Topics and Protected Audiences are relevance APIs
  • The third advertising API is Attribution Reporting

Topics provides high-level user signal interests and may be combined with contextual signals and first-party data so that SSPs and publishers can select relevant ads. Then we have Protected Audiences, which supports more granular remarketing use cases, enabling ad tech marketers, developers, advertisers to reach audiences who’ve shown interest in a specific brand or product in a privacy-preserving way.”

– Kelly Gieschen, strategic partner manager, Privacy Sandbox, at Google

Those matter in the MMP space, and there’s some work happening on them, says Singular cofounder Eran Freidman, but the Attribution Reporting API is naturally where marketing measurement companies are going to focus.

“Naturally, it’s the biggest focus for us as an MMP and we’re putting a lot of resources on that, from testing the framework, integrating with the different media partners, or designing the product to… provide the essential performance reporting.”

Differences between SKAN and Privacy Sandbox

“There are similar principles between the frameworks … but if we talk about the differences, there are many,” says Eran Friedman.

Some of the differences:

  • Privacy Sandbox aggregation keys for campaign, creative, placement and optimization data provide far more range than even SKAN 4’s campaign IDs
  • Wanting more data points comes with a cost in Privacy Sandbox: the more values you encode and the more granular you try to go, the more random noise gets injected into the data. In SKAN, there are fewer data points, but once you pass privacy thresholds (SKAN 3) or crowd anonymity (SKAN 4) you get essentially all your data.
  • In Privacy Sandbox you always get some data, even at very low scale campaigns, while under SKAN you need to pass certain minimum install number thresholds. That number is less with SKAN 4 than with SKAN 3, but it remains. The tradeoff for Privacy Sandbox for Android is that at low thresholds, more noise or junk data is inserted.

There’s another key difference between SKAN and Privacy Sandbox that Gameloft UA director Vasil Georgiev highlighted, and that’s testing.

You’ll be able to do far more testing far easier under Privacy Sandbox than under SKAN, simply because you have more ability to encode variables.

“It’s very clear that one of the first differences is that the opportunities for testing will be enormous,” Georgiev says. “We are not going to be limited to the things that we can test.”

From tracking everything to trade-offs

You don’t achieve privacy without cost. There will be loss of signal, similar to what we’ve seen on iOS with ATT and SKAdNetwork. Probably less loss of signal, but loss nevertheless.

“Today marketers can track everything they want, everything they can,” says Gieschen. “And that practice would have to fundamentally change. And depending on what marketers want to look at specifically, there may be trade-offs on granularity and richness of information versus noise and delays.”

Some of the trade-offs refer to how much detailed information you, as highlighted above. Some refer to how quickly you want information, says Tinuiti’s Mollie Sheridan.

“If you’re pulling reports more often, there’s going to be less accurate data and Google is going to inject that noise data just to protect privacy,” she says. “You’re going to have to decide if you want to wait longer periods for more accurate data or if you want shorter periods.”

That said, most things marketers want to do today will still work under Privacy Sandbox for Android. The art and the science will be balancing granularity versus aggregation as well as speed versus accuracy to achieve the best possible — not perfect — results for marketers’ attribution needs and ad networks’ optimization needs.

“You’re going to be able to optimize towards the events that you want to optimize towards, whether that be CPI, target ROAS, specific events, they’re still going to be available to you,” says Sheridan. “It’s just going to be the frequency of your reporting and the segmentation of your reporting … balancing that out to make sure that you’re getting enough volume to get the most accurate data available within this privacy-centric framework.”

The good news: actual hard-core high-scale marketers think this is going to work.

“It’s very clear that Google recognizes the minimum viable state of data and they’re trying not to block marketers from continuing to do optimizations,” Georgiev says. “And I also believe that they are trying to avoid making it more complex than it should be.”

The timetable for Privacy Sandbox full roll-out

Short answer: there isn’t one yet.

But Google promises plenty of notice before the full Privacy Sandbox roll-out does actually happen.

“We don’t have any updates that we can share publicly at the moment in terms of when 100% migration would happen,” says Gieschen. “But just as we’ve done in the past, we’ll be giving the ecosystem and partners ample notice prior to any changes regarding the beta and general availability.”

Much, much more in the full webinar, including progress on our Privacy Sandbox beta with Google and Gameloft

Check out the full webinar now to get further details on:

  • Status of our Privacy Sandbox beta test
  • Adapting Android user acquisition campaigns to Privacy Sandbox
  • 30-day measurement windows in Privacy Sandbox
  • How retargeting works
  • Why first-party consent still matters
  • How web to app flows and cross-platform conversions work under Privacy Sandbox
  • Why Privacy Sandbox early adopters will have an advantage, and why that’s different on Android than it was on iOS when SKAN first launched
  • How Singular is building an “easy button” for Privacy Sandbox
  • How to decide between granularity of data and number of events you want to measure and accuracy of data under Privacy Sandbox

 

Meta Install Referrer brings back view-through attribution on Android

Meta is introducing a privacy-safe way to get back user-level click-through and user-level view-through attribution on Android for ads on Facebook or Instagram, the Meta Install Referrer

This significantly expands on the measurement possibilities already available via Google Play Install Referrer and is a big deal. For the first time in years, Android app advertisers will get view-through attribution back on Meta, providing additional details and data to build up a more accurate picture of the results of their campaigns.

Meta Install Referrer (MIR) is supported by Singular as of November 2023.

Here’s a quick comparison of the Google referrer versus the new Meta Install Referrer:

Google Play Install ReferrerMeta Install Referrer
PurposeAttribute Android installs via ads on MetaAttribute Android installs via ads on Meta
Use casesClick-throughClick-through
View-through (most scenarios)
Different session click-through
App storesGoogle PlayGoogle Play
Other Android app stores

Singular supports both the original Google install referrer and MIR, but because MIR includes all Google referrer use cases and adds more, Singular will prioritize using the Meta Install Referrer for user-level attribution decisions. 

It’s important to note that there are some caveats. View-through attribution is available via MIR when using:

  • Advantage+ App Campaigns
  • Broad Targeting Manual App Promotion Campaigns

The supported campaign configuration for Manual App Promotion Campaigns include:

  • Default age setting (18-65+)
  • All genders
  • Country or country group
  • Interest segments, behaviors, and demographics set to broad targeting
  • Custom audiences also set to broad

How the Meta Install Referrer works

Most people understand how the Google Play Install Referrer works, because it’s based on the concept of a click referrer on the web:

  1. User clicks on app ad on a Meta property
  2. Meta encrypts campaign metadata
  3. Meta appends it to the referrer parameter in the Play Store URL
  4. The Play Store URL brings the user to the app listing
  5. The Play Store saves the referrer string
  6. Singular’s SDK reads the referrer from the Play Install Referrer API
  7. Singular decrypts the data for install attribution

The Meta Install Referrer operates differently, but essentially achieves a similar purpose. At a very high level it works something like this:

  1. User views or clicks on an app ad on a Meta property (and then installs the app)
  2. Meta encrypts campaign metadata
  3. Meta saves the campaign metadata to local on-device storage in either the Facebook or Instagram app, wherever the ad was shown
  4. Singular’s SDK (for the installed app) reads the campaign metadata from local storage
  5. Singular decrypts the campaign metadata for install attribution
Google Play and Meta Install Referrer

Privacy and marketing attribution

The Meta Install Referrer is on-device attribution. 

When we looked at the design of MIR we were excited to see it does not need to rely on leveraging a Google Ad ID or other device identifier, nor on the need for transferring any device identifiers to a Meta or MMP server to make it work. As such, we believe it’s a much more privacy-friendly solution than existing GAID or IDFA-based solutions. Another way we think of it, the solution could be said to function pretty much like UTM parameters on a standard link on the web, even in view-through scenarios.

As such, it’s possible other major platforms could adopt similar mechanisms.

The result of this new methodology is that marketers will get a clearer picture of what ads, creative, and campaigns on Meta resulted in conversions, and they’ll be able to gauge the value of their investments better. 

Enabling MIR on your campaigns

Talk to your customer success contact about enabling MIR on your campaigns. If you’re not currently a Singular customer, here’s a good place to start.

Google’s Performance Max adds generative AI for ads: the floodgates are opening for all ad platforms

It seems like just yesterday that one of the core challenges of high-volume growth teams was scaling ad creative. Making the thousands of pieces of art needed for testing was hard, even with some rudimentary automation. But yesterday, Google announced that P-Max is adding generative AI for ads in beta, and it has some super-cool capabilities. 

Over the next few months the floodgates will be opening for adtech platforms to make generative AI for ads a common core feature for all their advertisers. And while right now many of the platforms are sandboxing generative AI created images in creative suites that help marketers make the images they want, soon that will transition to on-the-fly creation of images and content personalized to individual people.

In that, the big platforms will have huge advantages.

Google’s Performance Max and generative AI for ads

P-Max’s generative AI is both impressive and limited. 

It’ll take your brand ambassador and drop her into a corporate, home, beach, or country setting. It’ll generate headlines and descriptions, and let you create core assets to use in multiple ad types and backgrounds. If you already have product assets, P-Max will import them and allow you to try as many variations as you like. Google says it will never generate the exact same image twice, so your competitor won’t have an ad that looks shockingly similar to yours.

(Cue the let-me-try-prove-that-wrong crowd.)

It’s U.S.-only at the moment, and it is rolling out gradually, so not every advertiser will get access this week or even this month. If you’re in a sensitive vertical, such as politics or pharmaceutical, you won’t get access either.

You won’t be able to create images of specific people or celebrities, for obvious reasons, or branded items. (My recent attempt to get OpenAI with Dall-E to make a picture of Oprah failed recently, but Creative Diffusion allowed it.)

everyone gets an LLM for generative AI ads

And all images will be watermarked with SynthID so there’s a track record and accountability to surface the fact that they are artificially created.

What P-Max’s generative AI for ads solution isn’t is a real-time per-person per-product generative AI solution that combines what it knows about Google users and what it knows about advertisers’ products and offers, and crafts a completely personalized one-off ad in real-time or near real-time to maximize relevance.

Join the party: everyone’s doing it

Everyone is joining the generative AI for ads party. Some are further ahead than others in their ability to integrate generative AI with ad tools.

ad platform generative AI
  • Google launched Performance Max generative AI ads; availability starts now
  • Meta launched generative AI in Ads Manager: started last month, rolling out globally “by next year” for background generation, image expansion, and text variations
  • Amazon launched image generation in beta last month, primarily focused on lifestyle backgrounds for product images 
  • Microsoft is adding Copilot to the Microsoft Advertising Platform which will generate new images using Microsoft’s image library, as well as offer conversational chat … debuting in closed beta in “early 2024”
  • TikTok just launched a generative AI “creative assistant” to “to spark creativity and be a launchpad for curiosity” as you make ads for the platform
  • Snap is running text ads in My AI, but not generative AI in ads yet
  • Pinterest hasn’t announced anything yet
  • Reddit hasn’t announced anything yet
  • Big brands are using Dall-E and other tools to make their own generative AIs
  • Agencies like WPP, Publicis, Omnicom are doing the same

Ultimately, this will be a standard feature and a checkbox item on all major platforms, and from most significant ad agencies and ad networks.

The big platform advantage for on-the-fly generative AI, and what’s next

While all advertising platforms will likely add generative AI for ads tools, add-ons, or plugins eventually, the biggest platforms have huge advantages. Not only can they throw more engineers and more money in solving the challenges of bringing generative AI to their advertising tools quicker, they have an on-platform data advantage.

That means that when it comes to on-the-fly generative AI creation, they can do it with much greater knowledge of their users’ interests, habits, and behaviors, giving them a much greater chance of crafting a message that resonates.

There’s still a lot of work to be done, of course, as the platforms themselves acknowledge. One big area of improvement: allowing marketers to define brand colors, imagery, styles, even conversational tones so that the ads they generate fit the brand and build the brand.

“There is still work to do on delivering outputs customized to every brands’ unique voice and visual style,” Meta says. “We’ll need to define new ways of partnering with brands and agencies to help train these models on brands’ unique perspective.”

The other is doing this all in a way that is cost-effective on GPU time. Especially for on-the-fly generative AI, the GPU load is going to be intense.

Snap is already thinking about that, and has plans to do the work on-device. I’m not sure that will work in all cases, but with the kinds of chips that Apple is putting in its devices these days — and the new Snapdragon Gen 3 chip on Android — that’s like to be a possibility at some point.

State of UA 2023: trends in mobile web, CPI, ad formats, and growth strategies

Mobile web for user acquisition is growing, iOS and Android spend patterns are normalizing, and CPIs are dropping. That’s just a fraction of the data-driven insight in the new 2023 State of UA Report, which is based on a $10 billion subset of Singular cost data, trillions of ad impressions, hundreds of billions of clicks, and billions of app installs.

It’s also based on insight into billions of creative optimization decisions from our partner on this report, Apptopia.

state of  UA 2023- ad formats

So what is the state of UA in late 2023?

It’s a weird, weird time, and not just for user acquisition pros.

Spend is down: the global economy is feeling the aftershocks of Covid, military conflict, and increasing political polarization. There’s also still post-ATT and SKAdNetwork disruptions, and visions of more of the same in the coming Privacy Sandbox changes.

state of UA 2023- CPI trends

What’s inside the report?

So in this state of UA report we focus on:

  • Massive ecosystem shifts
  • Verticals most impacted
  • iOS/Android spend changes
  • CPI trends
  • Spend distribution in key global geos
  • Ad format shifts, including
    • Video
    • Banners
    • Interstitials
  • Growth insights based on brands like
    • Calm
    • Sephora
    • Temu

The state of UA is that it’s late in the year

We’re nearing the end of 2023, and the all-important holiday season is just around the corner. The data in this report will help you navigate end-of-year as well as the first quarter of 2024, sharing how user acquisition specialists are adopting mobile web, testing new ads. We also dive into spend distribution between Android and iOS globally as well as in key countries in Africa, Asia, Europe, North America, and Africa.

Thanks to our partner Apptopia we also have dozens of insights into ad format changes, what’s working, what’s changing, and what’s hot.

In addition, we dive into how Calm boosted downloads 83% month over month, and more than doubled them quarter over quarter. We examine how Temu tested new partners and boosted Android installs more than 3,000%. And we look at how Sephora transformed from underdog to top dog in a mobile app install battle with a key upstart competitor. All of this is gold not just for user acquisition managers but also for growth strategists looking to deploy capital, creative, and campaigns at scale to win.

Get the report here

Get the whole State of UA Report here right now.

It’s 33 pages long, but you’ll be able to breeze through in just a few minutes since it’s heavy on charts and long on short, pithy insights.

From the first-ever banner ad to generative AI in advertising

The first-ever banner ad is a long way from generative AI in advertising. But one of the same people who worked on placing that tiny paid piece of internet history is still engaged in the adtech industry. Now, of course, he’s focused on generative AI and other emerging technologies.

The first banner ad

It was 29 years ago in 1994 when AT&T paid actual money for the very first banner ad on HotWired.com, now just Wired. Part of a crystal-ball ad campaign that foretold people working remotely from the beach on laptops, or having video conferencing meetings, the text-heavy banner ad said simply in a rainbow-colored font: “Have you ever clicked your mouse right here? You will.”

first banner ad

It wasn’t even called a banner ad, initially.

The first name was “tile.”

“The original concept was drawn on a whiteboard and we were drawing outlines of the websites that were out there … and we called them tiles,” says Tom Zawacki, now president of enterprise solutions at Data Axle, but formerly employed at Modem Media. “Originally, we said, if we could just take a tile and put the tile on one website and have them click there and go to another website, that would be really cool. And that was it. So that was the original original concept.”

A box, with text, linked to another website.

A fairly humble beginning for internet advertising, you might think. Pretty far from today’s ideas of generative AI in advertising.

Generative AI in advertising personalization

Now of course generative AI is the hottest tech, not the humble banner ad, and the opportunity for creativity and variety is increasing exponentially. Ad creative personalization is one big opportunity Zawacki sees in generative AI.

“One of the nice things about using generative AI is it allows us to increase the volume of production when creating copy and or visual design,” he says. “Forever we’ve been promising the delivery of personalization … what’s gotten our way is the volume and velocity of variables that create these combinations of creative message and visual design that humans just can’t create fast enough.”

I see that too, and my mind is blown by the opportunities that Amazon, for instance, has in generative AI ads. Most Amazon ads are shown on the Amazon platform, of course, which means that Amazon knows a lot about the people seeing them: purchase history, search history, maybe some of what you watch on Amazon Prime Video, maybe some of what you listen to in Prime Music, maybe what you read on Kindle.

Imagine the personalization Amazon can create — especially on-platform — in generative AI ads for the 10s of millions of products it sells. Text and image ads should be relatively easy. Video ads are also fairly doable, with a heavier compute lift.

Personalized playables are probably coming as well.

Off-platform, of course, that’s harder. With ATT, SKAN and Privacy Sandbox taking away access to device identifiers, off-platform personalization will be harder and less targeted. 

But other platforms, walled gardens, and retail media platforms ought to be able to do similar things in their own worlds: Facebook, TikTok, Snap, Pinterest, DoorDash, Uber, and others. The golden age of marketing personalization is likely going to be found in on-platform generative AI in advertising … with the possible addition, for brands, in owned spaces like apps and websites, and permissioned communications.

Generative AI to build creative campaigns

Another question: how will marketers use generative AI to build their core creative?

It’s easy, as I stated in the conversation, to want the machine to do the work. To sort of poke MidJourney with a stick and ask it to do something cool. It’s harder to make excellent, world-class, brand-compliant creative that completely fits your needs for a specific campaign or ad opportunity.

For Zawacki, the combination of biological and artificial intelligence is always going to win.

“IBM Watson did some great research in 2017, taking human intelligence, taking an artificial intelligence … and having them do a series of events,” he says. “And in every case, the combination of humans and AI working together — augmented intelligence … they called it cognitive computing at the time —  won out in all those series of tasks.”

His goal: the Tony Stark model, where you have a human intelligence directing an AI that multiplies and accelerates innovation. We’ll all probably have our own JARVIS — Just A Rather Very Intelligent System — at some point in the near future.

And that will be a game-changer.

“We are using augmented intelligence to turn our clients and our employees into superheroes,” Zawacki says.

Check out the full show

Check out the full show by watching the video above, subscribing to our YouTube channel, and subscribing to the audio podcast on your favorite podcasting platform.

Here’s a quick overview of what we all cover:

  • Introduction and guest introduction
  • The story of the first banner ad
  • The evolution of e-commerce and social networks
  • The impact of business transformation on success
  • The role of AI in business transformation
  • The importance of adapting to technological changes
  • The future of AI in advertising
  • The role of AI in mobile apps
  • The power of AI in creative optimization
  • The challenge of personalization in advertising
  • The importance of quality data in AI
  • The shift from omnichannel to omni-person

Meta will charge 2X average ad revenue for EU subscriptions

Meta will charge the equivalent of $127.40 for an annual subscription to its services in the EU starting in November 2023. That’s essentially double what Meta makes in ad revenue per user in Europe, which is a total of not quite $64 over the past 4 quarters.

Meta announced today that due to changes in EU regulations, it will be offering an ad-free subscription to Facebook and Instagram. The core reason: EU legislators have rejected Meta’s use of “contractual necessity” as a legal basis in Europe for processing user data for personalized advertising and have pushed Meta towards a subscription option which would then offer EU citizens a data-processing-free means of accessing Meta’s global-scale social platforms.

The subscription will be optional, Meta said today in an announcement post. The option is simple:

  1. Either pay for the service
  2. Or, consent to targeted, relevant ads

How many subscribers would it take to replace Meta revenue in the EU?

Thanks to Meta’s detailed quarterly and annual reports, it’s easy to understand both how much Meta makes from advertising right now, and how many Europeans would have to subscribe to Meta’s services to replace that ad revenue.

  • Meta has averaged 408 million monthly average users over the past 4 quarters
  • Each user returned an average of $15.99 per quarter
  • Meta made $63.97 per MAU from advertising

Note that total revenue per user is slightly higher than ad revenue per user, simply due to the fact that Meta offers some products for purchase.

meta subscription plan EU

But Meta’s subscription plan would theoretically bring in far more revenue, per average user, than ads.

Subscribers on the web will be charged $10.60/month for an annual total of almost $130 (so far there is no mention of an annual discount, though that could come). In-app subscribers will pay more, but I’m using the web numbers as Meta is following Twitter’s lead in charging more for in-app purchases to cover Apple’s and Google’s cuts.

That $130 is almost twice what Meta makes from targeted advertising per average user.

meta subscription plan EU

At these rates, Meta would need 208,572,327 European users to buy a subscription to replace all ad revenue.

Of course, as we’ve seen from Twitter (OK, X), very few people will subscribe. On X, about 640,000 people pay for premium, formerly Twitter Blue. If we take Twitter Ads Manager’s estimate of 372.9 million addressable users, that’s far less than 1% of users. To be precise, it’s under .2% of users. And there’s very little to indicate that Meta’s users would be substantially different enough to impact the economics on Facebook and Instagram.

This is not about a shift in Meta’s business model

208 million Europeans are not going to start paying the Euro equivalent of $130/year to access Facebook, when you can get it simply by consenting to ads. 

Rather, this is simply about dotting I’s and crossing T’s so that Meta can point to the subscription model and tell European regulators that citizens can totally and completely opt out of data processing for personalized advertising if they choose to pay for the service.

That’s good news for advertisers, who don’t want to lose a valuable way of connecting with consumers, players, customers, and users.

It’s also good news for Europeans, who will continue to have a valuable service that connects them with friends, loved ones, communities, and celebrities, and who can continue to do it for free now that Meta has (almost certainly) cleared a legally plausible rationale for continuing to process user data for personalized advertising.

Comparing the emerging Google and Apple suites for privacy, marketing, and attribution as Google preps IP Protection

Google is starting to quietly signal an upcoming Chrome feature called IP Protection that will act much like Apple’s Private Relay feature, which hides IP addresses to make tracking — and marketing measurement — more challenging. Add IP Protection to Google’s soon-to-come Privacy Sandbox technology, and you’ve got interesting parallels between Apple and Google privacy technology, plus some parallels — and gaps — between the two tech giants’ technologies for marketing and attribution.

Comparing Google’s and Apple’s privacy, marketing, attribution tech

From Google, this set of software and standards includes:

From Apple, this includes:

Clearly, we’re seeing the emergence of separate but often related suites of software, standards, frameworks, and requirements from the tech giants.  These tech giant initiatives are in 2 distinct but very related areas:

  1. Privacy enhancement
  2. Marketing measurement

The key reason for the connection: marketing measurement has typically required tracking, and that tracking has significantly impacted privacy. These tech giant initiatives are intended to rip out granular tracking as a vector for measurement and replace it with something very different: cloaked deterministic evaluation of advertising impact, with some noise sifted into the data, to provide analytics and preserve privacy.

Here’s what I’m seeing so far. (Let me know if I’m missing anything!)

google apple marketing measurement gatekeepers

* See the Privacy Sandbox website: “Privacy Sandbox also helps to limit other forms of tracking, like fingerprinting, by restricting the amount of information sites can access so that your information stays private, safe, and secure.”

These are complex beasts on both sides, with some parts baked in as OS-level components in iOS and Android, some grafted into the app submission and review process, and some that act more as platform-level directives than actual hard-coded realities. They are not monolithic projects or programs that are neatly subdivided, which makes them harder to fully grasp, and to fully understand the overall impacts on privacy as well as marketing measurement.

And, of course, they both deal with the world of mobile apps and the world of the open web, further complicating the overall landscape. 

Intelligent Tracking Prevention vs the new Google IP Protection

Apple Intelligent Tracking Prevention, first introduced in iOS 11 and MacOS High Sierra in 2017, fights cross-site tracking by blocking third-party cookies, quickly deleting many first-party cookies, and blurring device characteristics to make fingerprinting harder. In conjunction with Private Relay hiding your IP address and App Tracking Transparency for requiring permission for the IDFA on mobile, it’s a powerful tool for privacy, plus a challenge for marketing measurement.

Now there’s a similar technology coming from Google for the Chrome browser, increasing an interesting degree of similarity — and divergence — between the Apple and Google stacks for privacy, marketing, and measurement.

The new technology from Google has been signaled in a Google Groups post by a member of the Chromium team. Chromium is an open source browser engine that forms the foundation of Chrome itself, as well as any other Chrome-based browsers, like Microsoft Edge, the Brave browser, and Opera.

“IP Protection is a feature that sends third-party traffic for a set of domains through proxies for the purpose of protecting the user by masking their IP address from those domains,” writes Brianna Goldstein, a senior software engineer at Google.

It’ll be an opt-in feature that will roll out in phases, she says, and will be “just focused on the scripts and domains that are considered to be tracking users.”

Functionally, this will work very similar to Apple’s Intelligent Tracking Protection, Goldstein says. The experiment does not currently impact Android WebView, the technology that allows an Android app to display web content, and will be limited in the beginning to Google’s own domains. It could cause some security concerns, Bleeping Computer notes, because proxied traffic “may make it difficult for security and fraud protection services to block DDoS attacks or detect invalid traffic.”

Private Click Measurement and SKAN vs Privacy Sandbox everywhere

Despite the fact that Apple absolutely needs privacy to be its crucial calling card as it expands its mobile universe to an ever-more personal PC that you wear on your face with no fewer than 12 cameras on it and in it looking both at your world and your face — plus 6 microphones — the company understands that that advertising drives free apps and the free web.

And that requires measurement, because advertisers need validation that they are getting ROI.

Google, of course, as an ad network primarily — at least in terms of revenue — never needed to learn that lesson.

Private Click Measurement measures both web-to-web and mobile app-to-web ad clicks, providing an 8-bit identifier on the source for up to 256 simultaneous ad campaigns per website or app, and a 4-bit identifier on the conversion, enabling measurement of 16 different conversion events. There’s a built-in time delay of between 24 to 48 hours, similar to SKAdNetwork, and measurement postbacks for both advertiser and ad network are handled in-browser and on-device. 

Along with SKAdNetwork for mobile apps — which I won’t talk about here since we’ve covered it pretty exhaustively on the Singular blog — Apple is iterating through an increasingly richer advertising measurement framework. Yes, PCM pales in comparison to cookies (first or third-party) and SKAN pales in comparison to unfettered IDFA access, but that’s the point: they’re privacy-safe, and Apple will continue to add features over time.

On the other side of the fence, Privacy Sandbox on Web and Privacy Sandbox on Android are full-fledged initiatives to redefine the basics of how advertising works. Apple’s initiatives are more about mitigating adtech’s problematic capabilities; Google’s are about reinventing the world within which adtech exists. 

That’s why adtech experts say Privacy Sandbox will break more than Apple’s SKAN, but ultimately be less disruptive. 

Again, I won’t go into huge detail on Privacy Sandbox in this post: we’ve done it extensively already (focused, of course, on Android and not so much web, because mobile is where Singular primarily lives):

The one big obvious difference between the two suites in the marketing measurement area is that Google has provided capability for needed functionality in advertising and marketing: targeting and retargeting. It’s privacy-safe, which means it’s limited and restricted, but it’s there. Apple, on the other hand, while it will offer the ability for a retargeting signal in SKAN 5 so you know you’ve marketed to an existing user, player, or customer, does not offer any capability for targeting at scale in a privacy-safe way, or retargeting existing or former users.

That, perhaps, will wait until SKAN 6 or SKAN 7?

Google & Apple’s privacy/marketing/measurement suites: parallels and divergences

Ultimately when you boil down the privacy requirements of our evolving digital marketing ecosystem, you need a combination of items to limit tracking via cookies, identifiers, or device characteristics.

  1. Device ID obfuscation (IDFA, GAID)
  2. Device characteristics blurring (ITP, Privacy Sandbox)
  3. Device location masking (Private Relay, IP Protection)
  4. Privacy-safe marketing measurement (SKAdnetwork, Private Click Measurement, Privacy Sandbox)

As far as all of that goes, there are clear parallels between the Google and Apple technology platforms. Despite the fact that Apple banned third-party cookies much earlier (2020) and that ITP has been in-market for years, as has Private Relay, Google’s coming Privacy Sandbox along with IP Protection will achieve roughly the same results. (Note: likely those technologies will be applied with varying degrees of vigor: Google after all makes almost all of its revenue from advertising, whereas Apple makes almost all of its money from devices, but the broad strokes are similar.)

But there are clear divergences as well, like for the same reason just mentioned.

Google’s Privacy Sandbox is in essence a reinvention of the entire advertising model, as we’ve already said. It’s something that has been called a 360-degree advertising suite by InMobi’s Sergio Serra:

“Privacy Sandbox for Android is a complete advertising suite … it goes 360 degrees from targeting, retargeting, fingerprinting crackdown, and attribution.”

That’s clearly beyond the scope of Apple’s ATT and SKAdNetwork, which focus entirely on privacy and privacy-compliant marketing measurement, disregarding targeting or retargeting.

The emerging privacy-safe marketing infrastructure means we need hybrid measurement

Put it all together, and you have the emerging privacy-safe advertising infrastructure. 

It’s defined by:

  • Increasing respect for the individual and therefore, respect for the individual’s privacy
  • Decreasing data-gathering capabilities for the adtech ecosystem
  • Decreasing ability to track people from site to site and app to app
  • Increasing marketing measurement complexity
  • Growing reliance on semi-independent attribution frameworks and technologies (Privacy Sandbox, SKAN)

All of this is happening while we’re seeing increasing complexity in marketing mix, moving from just web or just mobile to web AND mobile AND CTV and outdoor AND custom SMS AND retail media AND influencer AND desktop AND console AND more and more channels and platforms … all of which is pumping the tires of the growing need for media mix modeling (MMM).

It’s also increasingly requiring what Singular calls hybrid measurement: marketing and advertising attribution based on a multiplicity of platform, cost, campaign, delivery, attribution, and first-party signals. Some of those are derived from deterministic sources such as SKAN or Privacy Sandbox, even if they are aggregated and noise has been added. Some are based on probabilistic technologies, like MMM itself. And others are based on deterministic signals that are the most accurate and detailed and precise of any that a marketer could hope for: your own first-party data.

All of this is a tremendous shift that is literally pulling the rug out from under the feet of marketers. But it’s both an industry and global legislative shift that won’t stop.

The one thing Singular can guarantee in all the change is that we will be providing everything you need for marketing measurement, optimization, and growth.

From privacy thresholds to crowd anonymity, plus much more SKAN 4 help: Singular’s Eran Friedman on AdBites

https://youtu.be/CgcWou01FA8

Singular CTO Eran Friedman spent some time with Redbox CTO Samual Chorlton on the AdBites podcast. The topic: everything SKAdNetwork, especially SKAN 4 help for those working on a transition from SKAN 3.

Hit play to watch it now, and keep scrolling for some of the highlights …

Data return to advertisers: from SKAN 3 to SKAN 4 help

SKAN 4 help is coming.

In SKAN 3, as we know, Apple provided privacy thresholds to anonymize users. Low volumes of conversions from a campaign results in few or no conversion values. That works, but it punishes smaller advertisers, reducing the feedback they receive from their ad campaigns and lowering their trust in ROI and ROAS numbers.

In SKAN 4, privacy thresholds become crowd anonymity.

SKAN 4 crowd anonymity

“The idea is for anyone to be able to use SKAN. If you’re just beginning, you have barely a budget, you’re just testing things, you’re going to get some limited information, but not too much,” Friedman says. “But as you scale and you need to become more advanced, you’ll get more and more granular information for optimizations.”

Under SKAN 4, just 15 installs per campaign will start to give advertisers at least some data: at least a coarse conversion value: low, medium, or high. It’s not much, but at least it’s some signal to start calibrating and optimizing.

SKAN 4 crowd anonymity and source identifier

If that provides confidence to boost your ad spend, you’ll get more conversion values and will not just get coarse but fine values: 64 potential values. Increase scale even more, and you’ll get source identifiers, providing more detailed data you can use to tag campaigns, geos, or ad placements. And that tagging informs campaign optimization and improvement: getting more of what you want.

SKAN 4 help: defining terms in SKAN

One of the more challenging parts of SKAN in general is learning the language. That’s especially true for people who are new to mobile marketing, but it’s also the case for veterans of the industry, because many of the terms are new, or used in different ways.

So Friedman defined the terms for the AdBites audience:

Conversion values

A number that you choose that represents the value of a user. When SKAdNetwork encodes that number into a postback, and your MMP decodes it for you, you get clues about the effectiveness of an ad campaign.

Coarse conversion values

Low-volume campaigns like those we just talked about can only have coarse conversion values: 3 potential values like low, medium, or high to represent user value, and therefore campaign effectiveness.

Fine conversion values

When campaign volume is high, SKAN 4 permits more data to be encoded into conversion values: not just the 3 possible values of coarse conversion values, but the same 64 possible values that were available in SKAN 3.

(Note: in SKAN 4, you can only get a fine conversion value for the first postback. The second 2 postbacks are always going to be coarse conversion values.)

Source identifier

In SKAN 4, the source identifier is additional data you can get from your campaigns. Like conversion values, it is connected to crowd anonymity: high volume supplies more potential data than low volume.

SKAN 4 conversion values

If you achieve high crowd anonymity, your source identifiers will be 4-digit numbers that you can encode with data about your campaigns, geos targeted, ad sets used, ad placements, and more.

What an MMP does for you under SKAN

When SKAN first came out, some thought it meant there would be no need for MMPs anymore. After all, SKAdNetwork can send postbacks right back to advertisers themselves, potentially short-circuiting the need for independent results measurement.

Complexity turned out to be one of the core challenges. Plus the ability to be able to interpret advertiser models for ad networks so they could optimize based on known good results.

That’s one of the core reasons SKAN 4 help is so desperately needed.

“This is where we believe it’s the perfect kind of world for MMPs to provide the technology and management of all the SKAdNetwork framework: basically using the APIs, managing those conversion values, getting back those postbacks, and essentially trying to abstract all those technical terms and details so the advertiser doesn’t even need to think in terms of those encoded numbers and all the details, and they just get kind of the bottom line,” Friedman says.

That means campaigns, installs, dollars, registrations: human terms.

Plus, given the privacy-centric obfuscation of SKAdNetwork, including randomness Apple adds to the numbers, being able to use Singular’s AI-driven modeling in SKAN Advanced Analytics restores missing data in marketing measurement while not impacting user privacy.

SKAN 4 adoption: yet to scale

One other topic the two hit on the podcast: SKAN 4 adoption, which is lagging right now for many ad networks and especially the big platforms.

“I think all of them for sure are working to upgrade to SKAN 4,” says Friedman. “Some of them have, for example, started beta testing SKAN 4 and have selected advertisers that are already working with and running SKAN 4 campaigns. Others have done full launches and we already see most of their traffic has arrived to SKAN 4 … it’s on a network by network level.”

The timelines I’ve heard most industry experts mention are in the Q1 2024 range. More on that, likely, in a future Singular blog post, but the key point is that if you’re needing SKAN 4 help, you still have some time.

Looking for guidance on your SKAN 4 transition?

Watch the video above, but also go check out our SKAN 4 transition guide here. It will give you all the details you need to get started.

Once you’ve kicked that off, book a session with a Singular expert to go through your planned implementation, and how Singular can help make it all much, much easier.

App monetization insights: What can we learn from 24 trillion in-app ad bid requests?

App monetization changes from genre to genre of app and across different countries. Globally, casual games almost exclusively monetize via rewarded ads and interstitials while hypercasual games lean more on banner ads, and social apps monetize across a wide range of ad units but at a much lower scale.

But the data varies significantly from country to country.

I love data. I’m a sucker for reports that tease insights based on massive gobs of evidence about what’s actually happening across our digital ecosystems.

So when I saw a recent report by Kayzen citing insights based on …

  • 24 trillion mobile ad bid requests
  • For 630,000 apps
  • Touching 1.4 billion people
  • Built via 10 billion machine learning decisions

… I had to have a chat with one of the authors. Click play on the video above (and subscribe to the Growth Masterminds podcast), then keep scrolling to see what I learned from Kayzen’s Tomas Yacachury.

App monetization varies from country to country

app monetization insights

One of the super-interesting parts of the report is the app monetization profiles of different app types. Globally, games offer vastly more inventory than social apps or tool and utility apps and monetize better from rewarded and interstitial ads than banners or native ads.

But the profile changes from country to country.

“You see those radar charts in different countries and they are completely different, right … India and the U.S. are completely different,” Yacachury told me.”

USA vs India vs Germany

In the U.S., casual apps monetize largely via rewarded ads and interstitials, with a bit of banner thrown in. In India, banner is huge, followed by native, with a very little bit of rewarded and interstitial as well.

From the advertiser perspective if you’re looking for the biggest possible audience, casual games let you access about a third of all daily active mobile users in the U.S. In India, however, focusing more on tools and utility apps will capture almost two-thirds of daily active users.

In terms of ad formats banners and interstitials will cover 70% of the India market, while in the U.S. advertisers need to select a more balanced portfolio of ad unit types, and app publishers need to enable that broader portfolio to be able to ad space.

In Germany, it’s all about weather.

“So German users are very much concerned about the weather because you see, you see a lot of weather apps amongst like those apps that actually provide the highest reach,” Yacachury says.

Germany is also an exception to the global rule that iOS inventory is more expensive. In Germany, there are plenty of high-end Android phones, and Android inventory for banner ads and native ads are often more expensive than iOS inventory, so app monetization strategies need to adjust.

Interestingly, programmatic reach is highly atomized in the German market, Yacachury says. No single app reaches even 7% of the total available daily active users.

SKAN support, IDFA, and IDFV

SKAN support currently sits at 85% of all iOS bid requests, which is significantly high and, of course, likely to only continue trending higher. But almost all of that is SKAN 3, as we’ve repeatedly shown.

app monetization SKAN 4 adoption

Interestingly, about 25% of bid requests have IDFA availability.

“What we see there is that IDFV … on non-IDFA inventory, it’s available on 75% of those bid requests, whereas it’s only available on 39% of IDFA traffic,” Yacachury says. “So it’s actually useful that … the coverage of IDFV is quite high on non IDFA inventory.”

Of course, beware of using the IDFV in France, where use of the IDFV now requires end-user consent.

App monetization: Singular can help

If ad monetization is a significant part of your revenue, Singular can help. Not only is ad monetization now available in Singular’s free tier, Singular’s admon solution provides quick and highly accurate insight on ad monetization, offering a much better picture of your LTV and ROAS.

Check out the full ad monetization solution, and let us know if we can help.

Do high-CTR playable ad units work?

Earlier this year I wrote about bad ads: high-CTR playable ad units that refuse to disappear, that interpret every touch as a click, or that crash the game you’re playing. The concern I had at the time was the tragedy of the commons: bad ad experiences ruining the mobile ad ecosystem. The question I didn’t answer at the time was: do these high-CTR playable ads that pop up insanely high click-through rates actually work?

In other words, do they achieve what advertisers want to achieve: installs and revenue?

High-CTR playable ads: data

Recently for a webinar with Kaizen, I had an opportunity to pull some data and analyze it for insights to exactly this question: do bad ads work well? Emotionally, I wanted the answer to be no. But I also wanted to let the data speak. Here’s an overview of what I found.

The data was from a campaign for a mobile gaming app:

  • 88 million ad impressions
  • Mix of ad partners including search, big social networks, and SDK networks
  • $110,000 in spend

Here’s the first thing I found:

  • High CTRs are correlated with low CVRs
    • CTRs in the 60-70% range got CVRs of .4%, .6%, 1%, or even .07%
    • CTRs in this range generally originated from SDK networks
  • Low CVRs are correlated with high CVRs
    • CTRs of .5% or 1.5% are associated with CVRs such as 18%, 28%
    • CTRs in this range generally originated from the traditional blue-chip big platforms

The obvious question, of course, is whether a much higher CTR — even with a much lower CVR — result in similar performance?

A simplistic example: 1,000 clicks and 100 clicks look very much the same in the end if the conversion rate is 10X on the 100 clicks compared to the 1,000 clicks.

high ctr ads

It turns out that the answer is yes.

I looked at the ratio of impressions to installs and installs to impressions to see how many impressions were needed from a particular network in order to get an attributed install. The result: 2 of the 3 SDK networks with high CTR playable ads actually do have higher install rates per impression than traditional large platforms.

And not just by a little: between 2X to 6X.

Wait … what about revenue?

I wanted to take it a little farther: all the way down-funnel to revenue. Does this still hold true?

So I compared the number of top-funnel impressions hitting people’s eyes to bottom-funnel dollars: money in your pocket. The question: how many impressions does it take with different ad networks to end up with $1 of publisher revenue after a player clicks an ad, installs an app, and converts to some form of revenue?

The result: for this campaign, most of the high CTR networks required fewer impressions to drive positive ROAS.

Far fewer.

high CTR playable ad units

That is, of course, why they’re doing what they’re doing in high-CTR playable ads: multiple clicks with multiple invocations of SKOverlay.

But … there’s a big caveat here

Comparing ad units is not always apples to apples.

Most of the ad units from the SDK networks were rewarded ad units, generally with playable ads, and pretty much always with aggressive end cards, which monetization expert Felix Braberg says are almost more important than the ads themselves. Many of the ad units from the traditional big publishers were banners or videos with much less aggressive end cards.

Rewarded ads have guaranteed engagement.

  • They demand more attention (you have to click out of them)
  • They receive more attention (you might play them, and you pay at least enough attention to get your reward)

The other big caveat: I’d really need to look at much more data to draw very detailed conclusions with a high degree of certainty.

What I can say is that there’s definitely a reason app and game publishers are using high-CTR ad units. They may not be the healthiest for the overall ad ecosystem, or for consumers’ impressions of advertising and app monetization based on ads, but they do work.

Want to see the full webinar?

If I may say so myself, it was a pretty good webinar with high-level participants, including:

  • Claire Rozain, Carry1st
  • Felix Braberg, 2 & 1/2 Gamers
  • Adam Gray, Nimbus
  • Tomas Yacachury, Kayzen

Check out the full webinar here.