Generative AI in gaming will double or triple the size of the industry: chatting with Unity CEO John Riccitiello

70% of mobile games are built in Unity, says CEO John Riccitiello. And he thinks that generative AI in gaming is going to double or triple the size of the industry, returning the games to the growth days of when 2D shifted to 3D, or what we saw with the introduction of the internet.

Why?

Even more mind-blowing experiences.

“These worlds, when they’re built, will be so compelling,” Riccitiello says. “this is what we’ve been dreaming about since we watched our first Star Trek episode … imagine the holodeck. We’ve been wanting this for a very long time. We’re gonna get it.”

We’re already seeing the massive impact generative AI is having in text generation. Image generation. Video generation. Code generation. Document summarization. Test-taking … whether it’s passing the bar to become a lawyer or the tests you need to take to become a doctor. There’s a $1.3 trillion generative AI boom in just 3 industries — banking, retail, and high tech — and generative AI will unleash the next wave of productivity, says McKinsey, adding up to $4.4 trillion in economic growth annually: more than the UK’s entire GDP.

Generative AI in gaming

Generative AI in gaming looks like help generating art, help creating code, help creating story and dialog, and much more. Especially when you take generative AI from the development studio and insert it into real-time gameplay.

In most games, you realize you’re in a scripted world with severe limitations pretty quickly, Riccitiello says. Even though it’s “still the most compelling form of entertainment in the world,” it has its limits. Insert generative AI into gaming, and that changes pretty quickly.

Think NPCs

“Imagine … that each one of these characters could be as smart as ChatGPT and talk to you about anything,” Riccitiello says.

In FIFA, you can chat with the fans, face off with a heckler, chat up a hot admirer. In GTA, you could talk to the liquor store clerk and get his insights on driving, violence, and crime. In Call of Duty, the soldier in your foxhole now has a whole history, perspective, and character.

The options are endless.

And so are the worlds, which can become truly endless and unbounded, and actually unique to each player and each player’s experience, history, and actions.

“I think this is 10x the potential transformation because I don’t think anybody looks at their games and thinks of them as real worlds. They’re sort of scripted fantasy worlds,” says Riccitiello. “We’re about to find out what happens when we make these worlds fully alive.”

Project Barracuda

One of the technologies Unity is offering: Barracuda. 

Unity has been working on it for over 5 years, and bundles an AI model inside the runtime in anyone’s device, whether on smartphone or console or PC. That runtime is now in over 4 billion devices globally, and what it means is that the computational costs of generative AI — which can be massive — are distributed to each user rather than borne by the game publisher.

Why’d Unity start building this over 5 years ago?

“Sometimes it’s better to be lucky than good,” says Riccitiello.

Subscribe to Growth Masterminds

We chat about much, much more. Subscribe to Growth Masterminds in your favorite podcast app as well as on YouTube, and listen in as we discuss:

  • What Unity is doing for generative AI in gaming (and elsewhere)
  • How developers will work with generative AI
  • The productivity gains we’ll see with generative AI
  • Why generative AI won’t just create whole games for us
  • What’s faster when NOT using generative AI
  • Making games infinite
  • Making NPCs smart
  • Overall growth of the gaming industry despite a downturn
  • And more …

Full transcript: Unity’s vision for generative AI in games

John Koetsier:

How will generative AI change gaming? 

Hello and welcome to TechFirst. My name is John Koetsier. 

No surprise to anybody, generative AI is the hottest thing in tech right now and that might even be especially the case in gaming. Well guess what? Games are hard. They’re incredibly challenging to create. There’s images, video, sounds, objects, characters, dialogue and code itself. All of which can be built faster, cheaper, if not always better, by AI. 

Roblox is doing generative AI, Meta is doing generative AI for the Metaverse if that’s not canceled yet. Microsoft is testing generative AI for Minecraft. What is Unity gonna do? 

To find out, we’re chatting with Unity CEO, John Riccitiello. He’s an OG in gaming, former CEO of Electronic Arts, long history in business, led companies like Haagen-Dazs and Pepsi and I think this is the third time we’re chatting. Welcome, John.

John Riccitiello:

Well, it’s great to be here and good morning, John.

John Koetsier:

Good morning. Excellent. Love the art behind you. Love the topic as well, generative AI. Let’s start with the big picture. What is Unity doing in generative AI?

John Riccitiello:

It’s hard to say what we’re not doing, actually.

John Koetsier:

Good.

John Riccitiello:

Let me focus it for you. So, you know people think of Unity as doing two things in our business. One is helping creators create games or digital twins. So we’re a content creation platform. And the second is we’re a platform for helping people operate and monetize their games and other applications. 

And, you know, we’re the leader in both spaces. So, we’re the benchmark, if you will. Now well over half in mobile, over 70% of games are built in Unity. I’m very proud of that. 

We’ve been doing AI in that space for several years now. And so there are lots of tools inside of Unity that enable people to leverage the best of what AI can offer to advance or accelerate or to improve upon the output that they’re trying to get to, the content they’re trying to build. 

And that lives within our Weta tool chain where most of those tools wouldn’t be possible without components of AI. And it lives within Unity, the tools we use for facial animation and all sorts of things. I’ll talk to you about where that goes, but we’ve been at that for a long time. 

It also lives with our Operate side of the business. We’ve been using neural networks for three years to help developers find users, do it more cost efficiently, to deepen the engagement of their audience. Again, for us this is not a new subject. It started before OpenAI started, before ChatGPT was sort of on the forefront of everyone’s conversation and thought process. And we’re doing a lot more. 

And what I think might be interesting to talk about is where that goes for content creators, the winners and losers and such. But also, I’m going to talk to you, one of the first people I’m going to talk to, about how some of what we’re doing will change the very nature of what a game is and make it a lot more compelling. And I would argue in many ways something that feels so alive that maybe we prove out the thesis that we’re living in a simulation, because we create one that’s every bit as believable as the world we live in today.

John Koetsier:

So there’s a lot to unpack in what you just said there, because what I think you’re saying is that, hey, not only have we been using AI for a long time, we’re going to be using AI a lot and generative AI to help people make games in a lot of different ways, but also we’re putting that into the gaming experience so that it’s real time. There’s real time generative AI happening for maybe dialogue, maybe settings, environments, maybe objects. Did I hear that correctly?

John Riccitiello:

More than the art. Let’s tackle the first part, the creation side, maybe, and then we’ll come on to the worlds that you could build. So I’m sure you and many of your viewers have used tools like ChatGPT or some of the generative art, the hugging face type of things to I wanna see a girl on a bike in the style of Van Gogh or whatever. But when you’re using it, for example, to help you write something, what are you doing? You put in a prompt and you get something back. And then ultimately you take that something you got back and you move it into a Word Doc or a Google Doc or something and then you edit it and make it your own. 

So, defining a couple terms, you’re using a natural language, large language model when you’re doing the first step. You’re using a deterministic tool to make it precise, to make it what you want it to be. And you’re iterating back and forth. Now, I think that’s going to be true for animators, character artists, level designers, lighting people, physics people. They’re going to be a lot more productive because they’re going to continue to use deterministic tools because they need to… you can’t publish the outgrowth of an AI tool. I’m going to dig into that a little bit more in a minute. But they can be two times or three times or ten times more productive using the combination. 

And that’s how I see this going for most creation. And it’s how the tools are used today. 

Now, the difference between, say, a Unity and Word or a Google Doc is pretty profound, though. And so a large language model, whether it’s studying photos or paintings or books or articles, whatever, to crunch, you know, dialogue or scripts, on what it’s studying, the model itself sees everything if it just has the frame. So if it sees the script, it knows all the words. If it sees the painting, it knows all the pixels. Even if it looks at a movie, and right now we’re not at a point where we’re creating efficiently with any of these models’ film, if it sees the 20 frames a second that is on television or film, it’s got all of the data. 

Games are profoundly different than that. You use, I don’t know, we’re playing Call of Duty, you know, you who had X hit me in the shoulder, I duck. Think about what’s going on there. I get hit in the shoulder, my ragdoll physics, I fall to the floor. There’s your input, there’s my input. There’s all that content that is in fact in the frames, in this case, 60 frames a second. 

Then, underneath that, there’s the instructions for lighting, there’s instructions for all those interactions. And for a model to study that, all it really sees is the frames. But there’s a boatload of information that made those frames possible. 

So my sense is, well, while these models will eventually produce simple games, you know, the Flappy Birds of the world, I think the rich and complex things are gonna be very hard for these models to produce for a bunch of reasons. One is they can’t study all the data. The second thing, but I’m going to tell you why we can’t, but they can’t study all the data. And they also kind of skipped the point that you’ve got 100 people or 20 people or 10 people working on a product together and it’s a wildly iterative process.

John Koetsier:

Mm-mm.

John Riccitiello:

Trying to get from it looks like this, how to make it fun, how to make it more engaging and that iterative process for creation that’s existed since literally the beginning of art. People would sketch what they’re going to paint and then sketch it 15 more times. That iterative process, the very process of creation, especially in teams, is always hugely iterative. Those things you can’t just simply, I’ll give you a prompt and we get a product back. 

So Unity is by far the largest and most used editor in the world for creating games. And we’ve already got a bunch of AI tools in our product and we’re going to be introducing… more and more natural language interfaces so you can talk to it and get that juicy, lovely relationship between I get a first draft from something that’s non-deterministic, then I edit it to make it better. 

So, to give you an example, I might want to tell it to give me a character that looks like X or an environment that’s got trees, and that’d be great, but then I’m imagining a war over those trees or using that character. I want to move the river over. Okay, so now, so you might get a first draft and you’re over here on editing and then you edit this, you go back and forth. But now let’s assume that I want fog. All right, now everything uses fog, right? 

It’s like, part of the melodrama of entertainment is all those beautiful foggy intersections with life and it helps people build a better story. I wouldn’t even know how to describe fog, to be honest with you, but within Unity, the deterministic tools are a bunch of sliders. It gets thicker and thinner, more reflective, non-reflective. It takes a second. Do I want to talk to a natural language thing for like a week to figure out how to describe it, or just make it in a second? 

And the workflow I imagine that is going to replace the one that most people use today is that brilliant accelerant, the power that a creator can have, where natural language gets them half the way there. Then they do something over here to make it a little bit better and then they go back over here to close the gap. And then they’ve got it, and they’ve got a build of their game. And then it isn’t fun. And so when they go back and the team edits it and makes changes, and that iterative process is one that I don’t think goes away, I just think that creators are gonna be two to 10 times more productive than they’ve ever been before, which will open all sorts of frontiers for cool new stuff.

John Koetsier:

There’s some really good news in what you’re saying and in how you’re framing it because there’s a lot of concern that AI will cost jobs, and in some cases it will and hopefully open up many other jobs as well. But you have Dropbox CEO saying that we’re in the age of AI that’s one of the reasons why we let go of 500 people, right? 

But what’s interesting about what you’re saying is that it gives you superpowers. And what comes to my mind, I believe it was Red Dead Redemption or maybe it was No Man’s Sky, one of the two. It was a game that was massive in scale and scope for the world and the universe. And it took eight years for hundreds of artists. They had literally 100 musicians scoring all the music that was used in it. And it took eight years to get this game off the floor. 

And how much does the world change? How much does technology change? How much do our tastes change? Eight years and how much investment do you have if you’re building this triple-A game for eight years and you don’t even know if you’re going to get some ROI on it.

And so what I’m hearing and what you’re saying is there’s a lot of tools. That is going to speed up… I need some music, I need a character, I need an object, other things like that that you can work with. That’s interesting, that’s good, because then it’s an interplay between a person, a creator, an intelligent designer, developer, and the machine. So that’s cool. 

The other thing that’s interesting is that a lot of generative AI that is visual is 2D. There’s not a ton that’s 3D. And as you said, not any, perhaps, that understands the interplay, the rules of the universe in a particular game, the rules of what can happen or not happen, why things are happening, what’s going on. 

It’s interesting to imagine a generative AI built within the Unity environment, understanding the physics and other things and the fog and other things like that and the capabilities that you could have in that world.

John Riccitiello:

You make some really interesting points. First off, I wasn’t involved in the production of either Red Dead or No Man’s Sky. But if I recall press at the time, I believe they were, they’re both giant worlds, but I believe they actually use very different methodologies for creating their environments. Red Dead, I believe, was actually quite a traditional process, you know, artists drawing environments. I believe, no matter what you do, most of the environments were algorithmically created. 

I remember at the time, there were some really beautiful things that were algorithmically created, but there was also some comment about how it felt a little lifeless. In a weird sort of way, dead straight up, sort of lacking humanity. I don’t know if that’s actually a legitimate way of looking at it, but I do believe they had different ways of getting it. I do remember some of the criticism. 

Now, within this world, I think, you make the point that humans are going to be involved. 100% agree. And, you know, one of the things that we tell you is I don’t think in this world where, you know, we live at the bleeding edge of the most interesting entertainment media in the world and the content creation end of it, I don’t think anybody’s job in this world, in the creation side, is going to be taken by an AI. But it will get taken by a human using AI. Because those people are going to be more productive, and they’re going to have to be, you know, they’re going to just force multiply their ability to realize what they can conceive. 

Now, you know, within that added productivity, remember the most successful games are multi-billion dollar franchises. And at least what I’ve witnessed in the quarter of a century I’ve been working on building games, which is… a frightening thought right there, is that… given the size of the prize, the most important developers on the planet will put anything in terms of effort and spend to make their product better and more compelling. 

And so my expectation is these ambitious companies are going to use AI to make things even more entertaining and more engaging than it was ever possible before. But you know, maybe that’s a good pivot to what might be possible. more is possible that heretofore wasn’t even imaginable. And I think that’s a super interesting subject.

John Koetsier:

Mm-hmm. Let’s talk about that. And I also want to get to the marketplace, so let’s not forget that piece as well. But let’s talk about that in-game experience. We’ve grown up with games, right? And the dialogue, for one thing, is very stilted, sometimes inappropriate, just doesn’t fit the situation. You’ve got the Jumanji movies, right, where they’re in the game environment, and they get the same guy saying the same thing every single time, doesn’t get it. Imagine something like ChatGPT hooked up to characters who are NPCs that don’t feel like NPCs. Talk about what you’re thinking about for in-game experiences that are powered by AI.

John Riccitiello:

So I’m going to describe to you something beautiful that is going to be both too expensive and virtually impossible to make work, and then I’m going to bridge it to tell you how it actually can work. 

So, virtually every game… not every game, but you know if you play a MOBA this isn’t true because there’s no non-player characters or they’re not a lot of non-player characters… but most games have lots of non-player game characters and then we interact with them and somebody wrote every line of dialogue. They wrote every bit of animation and it very quickly becomes repetitive. 

And, you know, if you’re playing GTA for example, and you pull over to rob a liquor store, the writers, in this case, it was probably Dan Howser, if you go back and audition in the title, he and his team will have imagined, you know, ten interactions and each one has a line of dialogue. Maybe the clerk fights you or the clerk just gives you the money or the clerk runs away and they’ve anticipated that and then they wrote dialogue for that. That’s fine. 

And if you’re playing a war game and you’ve got all these NPCs and you’re trying to get to another place in the city but these guys are fighting you off and getting in the way, somebody wrote very simple rules around what those things are. Go left, go left, go right, go right, go right, go left. Maybe they respond, if you go right, they go left. But they’ve written a collection of rules and it doesn’t often take all that much effort to figure out the seams. And you realize you’re in a scripted world with severe limitations pretty quickly. Now it’s still the most compelling form of entertainment in the world, but it has its limits. 

And so… what we imagine when we look at AI today is that each one of these characters could be as smart as ChatGPT and talk to you about anything. You could walk from the pitch to the stadium in FIFA and talk to the fans. What do you think? Hey, come out in the pitch and play with me. They can not only talk, you know, a new game, but they can also act in a new way. 

Now, if you could imbue those NPCs with all that, you know, seeming intelligence of a large language model, both in action and in dialogue, you’d have a staggeringly interesting world to live in and play in. And one of the things that would be the first thing a developer would need to do, they put all this intelligence in their games, is now we have these beta periods to iron out the bugs. 

We’re going to need a beta period to train all the NPCs, you know, OpenAI and ChatGPT. Really the first few million people trying to get to be infinitely smarter in terms of how a human would judge it. The first million players playing that game, messing around with it for a couple of days, those NPCs would go from sort of feeling off the reservation to being entirely alive.

And it would be a combination of what the company that developed the game wrote for the backstory, like who is this character? What are these characters, you know, human or non-male, female, whatever gender, whatever they put in there for, it’s ambition. So, back to that GTA liquor store clerk, is he or she a meth addict that’s just trying to make enough for whatever the expression is that they would buy at the end of the day, I’m not in that market? Or are they on summer vacation from an Ivy League school? And they might have a different orientation to what it means to be worth living for, right? So they might behave differently. 

And so you could imagine almost anything happening with that range of choice, but the creator will write that for all of them. Less than the dialogue, less than the interaction, they’ll write that, but they will imbue it with … a learning algorithm that will train it to achieve what it needs to achieve and maybe a game will take a month of doing this. By the way, I would love to be a beta tester when it’s off the reservation, getting it on the reservation, because that would be so much fun too. 

These worlds, when they’re built, will be so compelling, in my view, that it’s going to drive a growth spurt in gaming that, like the likes of which we haven’t seen since 2D became 3D or we saw the internet or we saw mobile. Those are all done, double-triple the industry in a short period of time, this is gonna do that again, because this is what we’ve been dreaming about since we watched our first Star Trek episode in Imagine the Holodeck. We’ve been wanting this for a very long time. We’re gonna get it. 

Now the problem, this is the almost insurmountable problem, but I have a surmount of that problem for you, is that right now, all of that capability is on a server someplace. So you have latency issues and you have cost. Nobody really wants to, you know, we’ve all read about how much money OpenAI has invested with Microsoft to run all of that. 

And this is where sometimes it’s better to be lucky than good, but over five years ago, I was with one of my colleagues, Silvio Druin, and we were talking about AI then, like a lot of people talk about it now. And we decided to start a project to see if we could get an inference engine, a learning AI tool, in Unity’s runtime. And the runtime ships with every game. It’s what drives all of the movement and dialogue and the effects. It takes the creator’s content and turns it into interactive content. 

And we decided to start working on that over five years ago. We had lots of ups and downs. We ultimately built it. We call it Project Barracuda. And what that allows is the runtime that’s on the device, on your iPhone or Android device or PC or console, that runtime can now have essentially an AI model inside of it to drive those things. So it can be done locally. So as those actions are driven locally, it doesn’t cost anything. 

And it happens fast. And we’re unique in the world of having done that. And look, I’ve started a lot of things, didn’t turn out to be quite so prescient. So I’m not gonna claim that I’m a seer of all things. Got lucky on this one. We kept it alive when there wasn’t an obvious use for it because we thought it would be useful. We didn’t know exactly what for. 

But now, as we look at our business around digital twins where they’re gonna want to go from reporting the past to asking about fixing the problem, as opposed to just reporting it. 

Or we were thinking about games that could be smart and alive, like I’ve just described, I can’t wait to play the games that the better creators make that are … it may be more interesting than my friends to hang out with. I mean, apology friends if you happen to be listening.

John Koetsier:

The mind boggles on a lot of different levels there. First of all, having quote-unquote NPCs that are seemingly super intelligent, powered by AI, is incredible. You could meet that clerk that you are going to rob and have a conversation about the meaning of life. You could be in a foxhole with a soldier in Call of Duty and, well, this is my girlfriend and she just broke up with me and here’s the letters. You could have those kinds of human engagements and interactions in a game and it could be powered there. 

And you know what? The nice thing is that people have supercomputers in their pockets. They’ve got supercomputers in their console so they can run that kind of power. 

John Riccitiello:

And the deposit is picked up on four billion of those today. My runtime is already on them. And so the deployment already exists. And so against that deployment, what we need is … I don’t know how long you’ve been a student of the game industry, but I remember when I was a kid in my parents’ living room playing Pong. But it’s always been the case that there’s like a substantial innovation and then there’s a lot of followers. 

And I remember calling Sam Houser when I was playing GTA 3, thinking this is the first open world game I played. I know a lot of times it sort of felt open, but this was the first one I could drive, I could fire it, I could shoot, I could hold up a liquor store. I could do anything I wanted to do. Some things I won’t tell you in live interview format that you could do. And I remember thinking, this changes everything. And it did. 

And so many games have now moved on to that model of an open world because of the freedom that players feel in the environment. And, you know, that was to me a super important milestone in the industry and I was so impressed by the work. 

I think this is 10x the potential transformation because I don’t think anybody looks at their games and thinks of them as real worlds. They’re sort of scripted fantasy worlds, whether you’re playing an RPG like Dragon Age or a sports game or an open world environment like a GTA or you’re playing an endless wonder. Whatever you’re playing, you know, they’re great. But what has happened is the creators have basically taken the long set of rules that we used to read for board games and transformed them into real-time 3D. But they’re still constraints in rules. And we’re about to find out what happens when we make these worlds fully alive in the… in terms of how it feels to the player. I can’t wait.

John Koetsier:

Unleashing games and unbounding games. That is super interesting. I do want to hit on the marketplace. You’ve talked about that and the news that you released I think something like a month ago or so is you will have an AI marketplace or a marketplace for objects that people have created with generative AI. It reminded me a little bit of people made a living on Second Life making clothing and other objects and stuff like that. People made six figure salaries there. Talk about what you’re doing with the marketplace and how that fits.

John Riccitiello:

Well, first off, Unity’s had an asset store for over a decade. And more people make a living in the Asset Store, creating assets and characters and particle effects and all sorts of different things that plug into Unity already. So the marketplace exists for… and virtually nobody, maybe some very large companies, but most game companies, either at prototype stage or through production, end up getting a lot of things out of the Asset Store from these entrepreneurs that have built really cool things. 

And, I don’t know if you’re building a real-time strategy game and you’ve got some sort of a unit out there that might be a fortress or whatever it happens to be, depending on the fiction of the game, and you want that thing to blow up, all right, and you want to have dust clouds and flames and other stuff when it blows. I mean, because it’s kind of fun. Like you’ve blown it up and you wanna see it, you don’t wanna see it disappear, like say, somebody taking a chess piece off the table. You wanna see it melted, broken, and now a team of 10 can spend a month, or a couple weeks maybe, drawing and animating all of those effects. 

They go to the Asset Store, they get paid 20 bucks, it’s done. And so that’s what they’ve been using the Asset Store now for years. And the point that we wanted to make with the Asset Store here, and we’re working with a large number of AI startups and larger skill companies, is that Unity has the largest number of 3D real-time creators on the planet by orders of magnitude. It’s a gigantic audience of creators. 

And we’ve already got a business, and always people have a business catering to that audience so they can build faster. And we’re building and have built a number of AI tools. But we don’t think we’re the only people in the world building AI tools for 3D creation. We think there’s a lot of smart people out there, lots of them. And what we want to facilitate is for a simple API connection or a quick install of an SDK, whatever, depending on the product and the service being offered, that just happens quickly and easily inside of the environment people are already using to build games. 

You know, a lot of things take place outside of that approach too, but then they’re downloading something in their PC or whatever Mac or PC or workstation they’re using to create something. They’re running something over here, they’re trying to figure out some sort of a workflow to get it into the product or to get the outgrowth of that into the product. And that’s a cumbersome, difficult process. 

It’s also really difficult for the AI startup or larger company to even find these people, right? You’ve got to go through this big marketing campaign to say, and we just want to make that easy, as we’ve always wanted to do. The intention here is to expand the marketplace very deliberately around AI tools because I think AI tools are profoundly important. We just want to do anything and everything we can to help our creators be more productive.

John Koetsier:

Cool, cool. Well, this has been super interesting. I think your outlook is calling and many other things as well. Thank you for taking this time. Really do appreciate it.

John Riccitiello:

Alright, well thanks John and have a great rest of your day and until next time.

Meta piloting direct app downloads from ads, no app store required (who needs IDFAs or GAIDs now?)

It has begun. According to a report in The Verge, Meta will be piloting direct app downloads later this year in Europe. That’s straight from a tap on an ad in Facebook to an instant app install on your mobile device, bypassing Google Play and the iOS App Store. This, as the saying goes, changes everything. Specifically, it breaks the app store model that Google and Apple have used to manage, protect, control, and profit from their mobile operating system duopoly.

As I wrote almost a year ago in July 2022, it’s all enabled by the Digital Markets Act. From my post back then:

Google and Apple are most definitely on the target list for exactly this kind of legislation, and will most certainly be defined under the DMA as “gatekeepers” that govern access, to greater or lesser degrees, to their massive mobile platforms.

Specifically in the context of mobile apps, that probably means something like this:

  • People can delete pre-installed apps
  • People will able to side-load apps, or install them just like you might install an app from the internet on a desktop computer
  • Businesses can create independent app stores
  • Apps can use third-party payment processing
  • Apps can interoperate with core services around messaging
  • Apps can use hardware features that platforms might have reserved for themselves
  • People can switch AI assistants

The report says that Meta will start experimenting with Android first, which is less risky because it already allows app side-loading. But you can bet that iOS will be coming as well.

Meta has confirmed the plan in a statement to The Verge by spokesperson Tom Channick:

“We’ve always been interested in helping developers distribute their apps, and new options would add more competition in this space. Developers deserve more ways to easily get their apps to the people that want them.”

Direct app downloads: more money for app developers, better targeting for Meta?

The opportunity here is huge for both Meta and for app developers.

Meta’s pitch, according to the story, is that they will not take a cut of app publisher’s in-app purchases. That’s theoretically an immediate 30% boost in revenue, although in practice payment processors and handling costs will reduce that bump.

(Of course, this could change if the initiative is wildly successful.)

The other implication however is that Meta could regain much of the premium targeting capability it lost when Apple neutered the IDFA by permission-gating it behind App Tracking Transparency.

ATT vastly reduced behavioral signal that Facebook gained from tens if not hundreds of thousands of apps and thereby chopped off a significant percentage of Meta’s competitive advantage versus other, smaller, less content fortress-y ad networks and platforms. One function of the IDFA was to report results over time to Facebook, which then not only allowed it to price its inventory higher — and report higher LTV — but also to build graphs of people and devices (audiences) that Facebook could target more effectively than pretty much anyone else.

By enabling direct download, Meta gains a first-party advantage in immediate attribution reporting, and likely — depending on the SDK Meta includes — long-term behavioral data as well.

But … Meta, meet Apple’s privacy manifests and Google’s SDK Runtime

Direct app downloads sets up an interesting collision between an immovable object and an irresistible force, because Meta will have to deal with privacy manifests on iOS and SDK Runtime on Android at some level.

Apple is introducing privacy manifests in iOS 17 and while they are a function of App Store publishing — so a direct download process shouldn’t need them — they form the basis for Apple blocking access to tracking domains. And you can bet Apple’s not going to give up that right on their devices and in their operating system just because Europe has enabled direct download.

From my post on iOS 17’s Privacy Manifests just recently:

Apple will block network requests to tracking domains if users have not granted permission via App Tracking Transparency.

My guess: Apple will figure out tracking domains that Meta is using and make life difficult. In which case Meta will encrypt all traffic and route it all via a limited set of domains, so that blocking will cause their apps to fail and make Meta uses angry at Apple … and the arms race will ante up, going tit for tat until Meta and Apple reach a new resolution (hopefully) short of mutually assured destruction.

On Android, it doesn’t get any easier, because Google is building in SDK Runtime, which I called a game-changer in February 2022.

From my post back then:

In Privacy Sandbox for Android, processes are isolated. SDKs live in a separate world. Adtech SDKs are no longer able to see and track app usage via persistent identifiers without developer knowledge and consent, and they’re also going to have a much more difficult time collecting perishable identifiers, or factors that can be summed up into a temporary identifier.

SDK Runtime gives Google as well as app developers much more control over how SDKs function, what data they have access to, and where they can transmit it. Privacy Manifests do something similar for Apple. And you can bet if billions of dollars in in-app payments are at risk (as well as, let’s be honest: users’ privacy) those companies will do something to make Meta’s life harder.

Early days: much to be figured out

We are in the very early days of the Digital Markets Act, and how all these things play out remains to be seen.

My bet: it’ll start slow, get hot, and we’ll enter a fairly chaotic time in mobile growth where acquisition, costs, measurement, and marketing will all become massively more interesting, along with significantly more complex. After which everything will settle down to a new normal.

But it might be weird for us old-timers.

  • Imagine TikTok adding apps to their entertainment colossus app
  • Imagine Rovio enabling direct download from Angry Birds of all their new games
  • Imagine Reddit and Snap and Pinterest and Twitter becoming app stores
  • Imagine an influencer like Mr. Beast starting an app store, or a brand like Nike or Microsoft or Amazon doing the same
  • Imagine mega-apps and all-in-one apps actually working in North America and Europe

It’s worth remembering that despite the potential for Meta — and other major non-mobile-OS-owning platforms — here, there’s huge power in the trust that Apple and Google have built for managing downloads, privacy, security, and payments. None of that is easily replaced by direct app downloads from other parties.

But here is yet another reminder for those of us in the world of tech and business: nothing stays the same, and no one stays on top forever.

Innovation is about to kick into high gear. So is competition.

Alt-UA: 4 new mobile user acquisition sources beyond ye olde in-app ads

When you’re on the hunt for new mobile user acquisition sources, you can fish where everyone else is fishing, or you can add some fresh hunting grounds. According to Fluent co-founder Matt Conlin, doing so significantly increases your chance of outsized growth.

“What we’re finding now is that as some of the most sophisticated mobile marketers in the world are realizing, you have to start to fish in new ponds,” Conlin told me in a recent Growth Masterminds episode. “You have to start expanding your media mix. And companies like Fluent have been pioneering what I’ll call the alt UA channels from a very early stage.”

Alt-UA is the new new mobile user acquisition sources?

Sounds good to me.

4 alt-UA mobile user acquisition sources

I asked Conlin what new and (relatively) untapped sources he was talking about, and he listed 4:

  1. Rewarded discovery
  2. Editorial content
  3. Influencer marketing
  4. E-commerce environments

Rewarded discovery is exactly what it sounds like: rewarding people with credits that can be redeemable for gift cards and other rewards when they try a new game or app. At scale, that requires a coordinated first-party publishing approach with a common identity across properties, and Fluent says they have 250 million opted-in American profiles. But you can also imagine using the core idea at much smaller scale: offering real-world and virtual incentives for trying a new app or game.

Editorial content is something I’m continually hearing about: an old-school SEO strategy meeting ASO meeting mobile user acquisition. It can be in conjunction with influencer marketing, but doesn’t need to be, and can be executed via video on TikTok or text on the web. 

Just one example:

Last year RoboKiller’s VP of marketing Giulia Porter told me about a completely organic example of editorial content when her phone spam app went viral with 20 million views on TikTok. RoboKiller is an app to intercept spam calls and play funny recordings to trick telemarketers into talking to bots. A TikTok influencer recorded a particular humorous one in which the bot reported as someone at Area 51, the famous military base that’s often been connected with alien visitations to earth, and it went viral, boosting installs massively. And showing that sometimes, the best new mobile user acquisition sources are the ones you aren’t even aware until you see your metric spike.

Influencer marketing can be related to editorial content — think TikTok’s new creative challenge ad initiative — but it doesn’t have to be. It could just be a “sponsored by” or “brought to you by” type of ad.

And e-commerce environments leverage the white space of getting your order confirmation with an offer to do something related in an app.

As Conlin says:

“It’s not always intuitive, but after you make a purchase on a travel site … it says, hey, while you’re in-flight, try this game. You can start to think about the opportunities for expansion into these kinds of alternative UA channels.”

Reaching the non-gamer gamer

Every mobile marketer wants to reach incremental audiences, which is why they’re continually testing new networks. A particularly challenging audience is the non-gamer gamer, says Conlin.

“We know that if you’re fishing the same pond, at a certain point you’re reaching saturation and you reach a point of diminishing returns … we brought in one of our clients from Israel and we did a Q&A. And they have driven tens of millions of installs to their apps. And they started surveying their users. And the fun takeaway was that a lot of these people that are playing these games will not self-identify as a gamer. And so if you think about that concept, where else can we hunt for this non-gamer-gamer outside of the traditional mobile game environment?”

You’re going to do in-app advertising. That’s a given.

But if you’re only focused on in-app advertising, Conlin says, you “easily miss out” on new and highly incremental audiences which new mobile user acquisition sources can provide.

And there’s clearly room for growth: if 50% of people on mobile devices are playing games, that leaves another 50% who just haven’t had the right opportunity to dive in.

Surround sound marketing = new mobile user acquisition sources

I’ll always remember chatting with Vivian Chang a year or two ago. She leads D2C (direct to consumer) at Clorox and is an AdWeek top 50 for 2022. She talked about something she called “surround sound marketing.”

“I am a firm believer that creating a surround sound for consumers is helpful,” she said. “Leveraging influencers, brand partnerships on top of the traditional social and Google and affiliates, really having a lot of different places that have similar but maybe slightly different messaging for a consumer … they’re really understanding us as a brand but then also us as a product and getting unique perspectives across all of those.”

The same is applicable for mobile apps, Conlin says.

If you want to be successful long-term and tap into incremental audiences, you need a diversified media mix, he adds, and that means expanding out of traditional mobile user acquisition channels. That diversification and surround sound effect might be from the methods above, it might be from out-of-home, it might be from connected TV … but each incremental source is another opportunity to reach new people in new ways.

The good thing about different audiences is that they bring different things.

The Facebook user you reach might have high LTV and spend a lot. But they’re probably older, and they probably don’t share as much. The TikTok user you reach might spend less and have a lower LTV. But they’re probably younger and they share more … adding a viral coefficient to your marketing efforts that you can’t easily account for just via ARPU or ARPDAU.

But, ROAS is absolutely key

Surround sound marketing and expanding marketing channels for incremental reach is all well and good, but neither live in a vacuum.

And now that we’re out of the Covid era of incredible growth coupled with virtually free money, ROAS matters more than ever.

“I think as CFOs and CMOs have been tightening the belt and everyone’s looking to maximize profit and preserve as much cash as possible, ROAS is all that matters,” Conlin says. “All these channels that we have invested into over the years have their value, right? Some are higher ROAS, some are lower ROAS, but you have to find the right price so you can extract maximum value from these channels.”

As always, discretion is the better part of valor, and testing, testing, testing before going all-in is critical.

SO. MUCH. MORE … in the entire conversation

Watch the video above. Subscribe to our YouTube channel and (or) subscribe to Growth Masterminds on your favorite podcast platform

Plus, here’s a full transcript of our conversation …

John Koetsier:

What are some new interesting sources for mobile users? 

Hello and welcome to Growth Masterminds. My name is John Koetsier. 

Sometimes it feels like everybody’s fishing in the same sea. They’re hitting the same programmatic pool of app users using the same partners and roughly getting the same results as everyone else. Or just throwing more fishing poles in the same stream. 

How can you break out of the norm and attract new fish? 

Today we’re joined by someone with an interesting resume. He’s got four or five years in various marketing roles and then co-founded a digital marketing company that he has been running for close to 13 years. Lucky 13! The company is Fluent and it’s a top performer in the 2023 Singular ROI Index. 

The person is Matt Conlin, founder and chief customer officer. Welcome Matt!

Matt Conlin:

Thank you, John. Great to be here.

John Koetsier:

Awesome. Interesting history. We were chatting before we started recording. And so many people I had talked to have like 26 experiences on LinkedIn, right. And you’ve sort of got a bunch that you’ve bundled and then boom, 13 years of the same company. 

Talk about that.

Matt Conlin:

Yeah, absolutely. So my digital marketing story started right at college and my first role was at a performance marketing company in 2005, right? So very early era. And I was, I was bit by the entrepreneurial bug. My first company I started at … the founders sold their company for a lot of money to a private equity firm. 

And me and my business partner said, you know what? I think we should look at starting a company in digital marketing. And we didn’t know what it was gonna be yet, but we knew that we had the chops to get it done. And so five years later, we launched Fluent in 2010. And it’s been a wild ride for these past dozen plus years.

John Koetsier:

Wonderful. Very cool. You started, IDFA didn’t even exist. The iPhone didn’t exist. Right? And so there’s been huge pivots in that whole period of time.

Matt Conlin:

Yeah, and it’s pretty wild because when we first launched our business, we started off developing ad serving solutions for web publishers. And we were helping the likes of Pandora and Yahoo Fantasy Football monetize their post transaction sign up. So when someone signed up for a Pandora account, you hit that blank thank you page, we were like, why are you serving a blank page, put a relevant ad experience there. 

And so we started embedding this piece of JavaScript that would render a relevant ad. And we started doing it in mobile web environments. 

And shortly after that, it was probably 2011, we said, well, why don’t we take this capability in-app? So we came up with Mobflow and we were now going to mobile app developers with a lightweight SDK and helping them monetize their high impact moments in between levels or after you’ve completed a certain task with brand ads. So we were embedding brand ads into these high impact moments. 

The problem is I think we were a little bit early. This is 2012, so that adoption was not quite there yet. And it was shortly after that that we made our first major pivot as an organization. And we decided at that point that we are going to become the publisher. We wanted to start developing our own experiences so that we could develop a first party relationship with the consumers, understand them better. and help connect them with incredible products and services. 

And that was a bit fortuitous given all that’s changed with deprecation of cookies and IDFA and everything else. 

And that kind of culminated in explosive growth, little fun story here, but when we started in 2010, we had zero in revenue, a couple million bucks. By the next year, we skyrocketed to $40 million of revenue, right? So we started right out of a cannon. And by 2015, we got acquired by a company that was publicly listed. 

And it was great until it wasn’t. And that at two years in, we said, I don’t know if this is working, right? We developed this great company. We’re growing fast. The synergies weren’t being recognized. And so at that point, the board agreed, let’s find a new suitor. So we went through another process. And similar to other companies you’ve probably featured in this podcast, the company that tried to acquire us was based in China and you can imagine 2017, how well that went over. So that deal got blocked. Then two years after getting acquired, we became a standalone company again and decided to split from our parent. And so we got back control of the company we’ve sold two years prior and we have been running it ever since. 

And so, you know, wild ride …

John Koetsier:

It’s always cool to hear from some of the pioneers and the people who’ve been there before.

It’s funny, I mean, like all the companies that were started, what, 2009, 10, 11, 12, if it was in mobile, it had to have “mob” in the title. I mean, that was just, it was law. It had to have “mob” in the title.

Matt Conlin:

We thought we were brilliant marketers. We’re like, all right, we’ll call this one AdFlow for web. This one’s MobFlow, obviously simple.

John Koetsier:

There you go, done, simple. Branding, signaling, all in one, perfect. Love chatting about that. I think I chatted once with the person who said that she paid for the first banner ad on a website. So love chatting with some of the people who got there early. 

Okay, let’s dive into it. As I kind of alluded to in my intro, most mobile marketing, most dollars that are spent, they follow the path that’s most traveled, right? I mean, in-app ads, SDK networks, the big SANs, all that stuff.

Why is that a concern?

Matt Conlin:

I think it’s now a mature marketplace. And I think that at a certain point, what we see as you spend into any channel, there’s a point where you reach diminishing returns. And no matter how much you invest, you can only get so much back in return. Costs start to rise. ROAS starts to diminish. 

And I think what we’re finding now is that as some of the most sophisticated mobile marketers in the world are realizing, you have to start to fish in new ponds. You have to start expanding your media mix.

And companies like Fluent have been pioneering what I’ll call the alt UA channels from a very early stage.

And we didn’t do it intentionally, right? Some of this was inadvertent, right? Cause you fast forward to today and we are a leading performance marketing company, but we’ve developed a user acquisition platform that’s underpinned by our portfolio of digital sites, apps, and even integrated technology. And what’s interesting is when we first launched our first websites in 2013, 14, we had a survey technology, and we’d ask consumers questions about what types of products and servers they like. And what we found out was lo and behold, these mobile web users, they like to play games. 

And so we struck up partnerships with some of the big behemoths of today back in 2014 and said, can we start to ingest some of your mobile app feeds into a mobile web environment? We think we’ve got an idea. And this started to work. So we started using this mobile web inventory to drive mobile app outcomes. And we’ve continued to innovate and iterate around that ever since. 

And our premise was pretty simple. We’re in the business of driving outcomes for our partners, whether that’s a mobile game studio, a CPG or a QSR. And it’s all about the mechanism. How do you connect with consumers where they are? Because they’re not just in-app, right? They’re in all these different places. And so we’ve been trying to work through that ever since.

John Koetsier:

So that’s pretty interesting because you’ve been significantly ahead of your time. We’ve started to see consolidation in the past few years. We’ve started to see a frantic urge to acquire as much first party data as you can. The content fortresses, those sorts of things, the marketing agencies that are also buying supply and all that stuff, right, in the past few years … but you were doing it five, six, even eight, 10 years ago, pretty interesting. 

So I talked about the path most traveled and the next question is where else should mobile markets be looking? You answered some of that already. You talked about mobile web, anything else? I mean, CTV is an option, right?

Matt Conlin:

Yeah, yeah. So you’re seeing this more and more, right? I think King was probably the pioneer. You started seeing Candy Crush ads on TV years ago. But even now, when you look at Playtika, they are all over connected TV. You see their Bingo titles are prominently featured on daytime television. 

So I think, you know, anyone who’s in the space is starting to look at new channels. For us, what we’re seeing is we play in four unique categories of alt-UA. 

  1. So number one, one of our biggest pillars is rewarded discovery, right? Where consumers are coming to our properties, they’re engaging with mobile games, they’re playing, they’re trying out new products, and we reward them for doing so. They can get credits that they can redeem for gift cards, right, and that’s a huge channel for highly incremental users, great ROAS, incredible scale. 
  2. Number two is editorial content. You’re seeing this more and more now, where it’s, you know, you’re on TikTok, and it says, check out these five great products, these apps you have to try today. You’re gonna love them. So we’re writing our own content, but it’s a mobile web experience that’s driving into the App Store. But in this environment, it’s giving the user a chance to read a bit more about the product, understand if they’re interested or not. It’s very different from the playable that’s reached ubiquity. But we’re seeing that as a big emerging trend, as leveraging content to drive outcomes. 
  3. The third is influencer. And we’ve developed an influencer platform where we’re helping creators. better monetize their following. And mobile apps and mobile gaming has become one of their go-to monetization levers. And you’re reaching a brand new audience that, frankly, sometimes these are newcomers. These aren’t necessarily the people that are, quote unquote, mobile gamers. 
  4. And the fourth is e-commerce environments. It’s not always intuitive, but after you make a purchase on a travel site … it says, hey, while you’re in-flight, try this game. You can start to think about the opportunities for expansion into these kinds of alternative UA channels.

John Koetsier:

Wow, interesting. I would not have thought of that. I would have thought of complementary apps, like, I don’t know, Mapping or Tour Guide or something like that. I’m sure you do some of that as well. But a game while you’re stuck on the plane or something like that is super interesting. Talk about why people should look at these alt-UA channels, if you will. 

And by the way, I think you just named this episode. It’s alt-UA. That’s the name of the episode now.

Matt Conlin:

Hehehe

John Koetsier:

But talk about why they should, because it seems to me in a lot of cases, you’re probably accessing the same people … in not all cases, but in some cases you are. But the context matters, right?

Matt Conlin:

Yep, absolutely. Context matters. But I’ll tell you, so I’ll go back to earlier point, right? We know that if you’re fishing the same pond, at a certain point you’re reaching saturation and you reach a point of diminishing returns, right? So that’s point one, right? You have to expand and have a healthy media mix. 

But two is, think about it through this lens. You’re reaching the non-gamer gamer. We had a QA at the beginning of the year for Fluent’s big annual kickoff. We brought in one of our clients from Israel. And we did a Q&A. And they have driven tens of millions of installs to their apps. And they started surveying their users. 

And the fun takeaway was that a lot of these people that are playing these games will not self-identify as a gamer. And so if you think about that concept, where else can we hunt for this non-gamer-gamer outside of the traditional mobile game environment?

And so I think if you’re focused exclusively on in-app advertising, you can easily miss out on this potential audience, which is highly incremental. You have an opportunity to tap into what’s really an untapped audience of casual players, mobile users. And I think the biggest opportunity is potential newcomers. How do you get more users into the ecosystem? 

I think the latest stat is something like 50% of consumers on smart devices are playing games. That’s another 50% of the connected world to bring over into this ecosystem, right? If they’re not in mobile games already, how do we bring them in?

That’s the opportunity for the industry.

John Koetsier:

I think that’s super interesting. I think that ad blindness could play a role here. Because if you’re in games already and you’re seeing the rewarded ads and you’re seeing the other ads, sometimes you’re just, okay, you’re just skipping over them. Maybe you’re enduring some of them, whatever the case might be. 

But if you see a different kind of ad, maybe it’s the editorial one that you mentioned, in a different context, it might just kind of go, ah, interesting, might stop you for a moment and capture your attention. I find that super interesting. Do you have some great examples of performance from unlikely channels?

Matt Conlin:

Yeah, so I can tell you high level that these alt channels drive, especially editorial. 

The ROAS, so let’s take a step back. The entire industry has shifted from, I’ll call it growth in ROAS to ROAS, right? I think as CFOs and CMOs have been tightening the belt and everyone’s looking to maximize profit and preserve as much cash as possible, ROAS is all that matters. 

But we still need to grow. And so what we’re finding is all these channels that we have invested into over the years have their value, right? Some are higher ROAS, some are lower ROAS, but you have to find the right price so you can extract maximum value from these channels. 

You made a comment in a previous podcast when you’re talking with Adam Jaffe about advertising less and making more money. And that resonated with me, right? 

Because I think you see enough of those playables in-app. I’m tired. I’m trying to X out. I can’t X out, but to your point, maybe I see it in a different environment and it’s not as intrusive. I’m going to engage this time, but they all play a really important part in the ecosystem and I don’t think you can do just one, right? Because they all deliver towards a healthy UA playbook.

John Koetsier:

It’s super interesting to hear you say that, and it reminds me of when I talked to a D2C marketer. She worked for Clorox of all companies, and they were doing a D2C direct-to-consumer play. 

And she talked about something called surround sound marketing, which is … that she wasn’t using one channel, she wasn’t using two channels, she wasn’t using three channels. She was trying to surround her prospects with her marketing: different channels, different formats, different ad types, different ways of connecting because you’re different than I am. I pick on something that you wouldn’t pick up on, you pick up on something I wouldn’t pick up on. 

And I wonder if this kind of alt UA is a form of surround sound marketing. for mobile apps because I might see something on connected TV. I mean, you’re still doing in-app stuff. You’re still doing some, you’re doing mobile web, you’re doing stuff in other places. Does that resonate? Does that make sense?

Matt Conlin:

100%. I think that’s where we’re going as a UA community here is expanding what we’ve traditionally thought as traditional UA channels, right? And if you want to be successful long-term and tap into these incremental audiences, you have to be diversified.

We actually have a panel on this topic at the app development partners, more big game studios, and talking about the different value they see from some of the traditional programmatic environments versus rewarded discovery versus content. And so I think, I think a lot more game developers are thinking about the nuances and how to, how to price it into their strategy.

John Koetsier:

It brings up the concept of measurement, which we hadn’t planned to talk about. It’s not on our list of potential topics, but it brings up the topic of measurement because it’s not your daddy’s user acquisition. It’s not just not IDFA and not GAID. There may not be an identifier, like influencer marketing for instance, and other things like that. And it may be hard to connect. 

And we’re recording this May 18. It hasn’t hit the blog yet live, but Singular is actually announcing an MMM product literally today …

Matt Conlin:

Exciting.

John Koetsier:

 … which is super interesting and looks incredibly cool. The blog post is coming out in a couple hours. But of course, this show won’t air for a couple weeks at least. So that is interesting. Are you thinking in terms of different measurement methodologies for this surround sound mobile user acquisition?

Matt Conlin:

So MMM is critical. I would tell you that there’s a big difference between the mobile gaming environment versus some of the larger brands that we work with. So most of the big brands are using one of the MMPs that I think are doing a great job of navigating the new world that is iOS and Android. 

And there’s a lot of richness in the insights that they can share back in terms of RPU and ROAS. It helps publishers like Fluent, marketing partners like Fluent, optimize towards the best outcomes possible. When you look at brands, and they’re typically a bit more diversified in their spend, they have to use a more thoughtful mixed media model, because they’re spending on just about every channel you can imagine from out of home.

They’re doing that surround sound. And so we’re now at the early stages of developing our own approach to measure methodology to satisfy the needs of the bigger brands. 

I would say that what we love about mobile gaming and what I personally love about it is how sophisticated and how analytically driven everyone in this industry is, right? They understand their numbers better than most and, and they can translate it in a way that makes sense to the marketing partners and brands kind of struggle with that sometimes, right? And it’s more art than it is science.

John Koetsier:

Well, there’s lots more to be said about that. We won’t get into that too much deeper. I do want to talk about social. And that includes SANs, but it’s not all SANs, right? (Self-attributing networks, for those who aren’t familiar with the word term SANs.) 

Talk about social and virality. How do you approach it?

Matt Conlin:

So Fluent is, while we are a UA platform, we provide a lot of UA solutions, we’re also a large buyer, right? We invest a significant amount of paid media into all channels from your big social platforms to mobile DSPs and everything in between. 

And so we get to see a bit of how different upstream media channels impact the … the quality and the retention of the users we’re sending to our partners.

And I’d say one big takeaway is every single social channel has its own unique attributes. And one thing that we’ve been seeing more and more recently is that the younger TikTok user that’s coming into a Fluent property and then engaging with one of our mobile clients, they are sharing and inviting friends at far higher rate than a Facebook user might. So that Facebook user is going to fit that traditional mold. A lot of our casual game clients who are looking to drive an in-app purchase, they say, my sweet spot is $35 to $45, and they have high discretionary income, and they don’t mind making these purchases. But they don’t share. They don’t share. 

Now all of a sudden, I have a younger user that may not have as high discretionary income, but they’re gonna make sure their friends know about it. They want them to come into the app and compete with them. 

So I think for all those reasons, this is why that diversified media mix is critical, right? You’re getting a different type of user and they’re just gonna have a different LTV curve. They’re gonna have different retentive qualities and you just have to make sure you price it out thoughtfully. And if you do it the right way, the world is our ocean.

John Koetsier:

It’s really interesting to see the mix of science, math, and art, and gut that you have to have in marketing still, because like you say, you get different things from different users. 

You may get the high immediate LTV, the immediate ROAS from those older users. You may not see that from the younger users. They may churn more quickly as well, but they may help you grow the game in certain phases when you want to grow …

Really, really interesting insights.

Matt Conlin:

That’s exactly right.

John Koetsier:

I want to hit on one last thing in our topic here. We’ve already hit on mobile web a couple of times, but some people are viewing that as, hey, on iOS we’re increasingly challenged, certainly in SKAN 3, less so in SKAN 4, with the ability to measure marketing the way that we want to. 

And so, you know, it’s harder for us to invest with confidence. We’re using mobile web and maybe landing pages. You almost talked about that a little bit as well when you talked about an editorial aspect. How do you view mobile web and iOS mobile marketing?

Matt Conlin:

I think there’s, especially as a publisher with the relationship with consumers, there are some key advantages when it comes to measurement in iOS and Android for that matter. But I think because there’s a relationship with that consumer, there’s insights against that consumer, there’s more information to tie an event back to. And so that’s become a key benefit and a differentiator for the folks in the mobile web space that is not available in a lot of the traditional in-app experiences. Right? 

So you can leverage different data points that help you triangulate around who the user is and how do we help to identify more of them. 

If you think back to five years ago, the main focus for the big UA player is all about whale hunting, right? We can identify an IDFA, this is a whale in one app, let me get him over to my new app. And then with the iOS changes, that went away. And while you can’t do that in mobile web, there’s at least a bit more predictability that helps you better understand types of users that are going to lead to similar outcomes once they start playing your game. 

And we think that’s one of the unique advantages that mobile web has, and how do you leverage those insights for the benefit of the consumer to create more relevant experiences for them, and at the same time, for the benefit of the mobile app developer, and making sure they’re seeing the right type of users that are gonna be valuable to them.

John Koetsier:

Very cool, Matt. This has been a lot of fun. Thank you for your time.

Matt Conlin:

Thank you, sir. Appreciate it.

Live SKAdNetwork adoption dashboard: Singular launches SKAN 4 penetration tracker

SKAN 4 launched in October with iOS 16.1, but it’s taken literally the better part of a year for the adtech ecosystem to start adopting and using Apple’s latest attribution measurement solution. To show the industry’s progress and help marketers understand when they want to transition to SKAN 4, Singular has launched a live SKAdNetwork adoption dashboard.

Check it out right here.

SKAdNetwork adoption dashboard

As we can see, Unity and ApplLovin are at or over halfway there on SKAN 4 penetration already, while Smadex, Persona.ly, Appier, and other ad partners are well advanced in their testing and adoption.

The current adoption trendline, however, tells the story: for most ad networks, SKAN 4 adoption is still squarely in the testing phase

SKAN 4 postbacks are just starting to crack the 10% mark across the industry. And that makes a lot of sense: SKAN 4 is a significant shift in not just measurement methodology for marketers but also optimization management for ad networks. Getting it right takes time, and that’s ultimately much better for the industry at large and marketers specifically than a hasty adoption that results in bad campaigns and lower ROAS.

SKAN 4 penetration dashboard

Note: if you filter per network, you’ll often see fluctuations in the percentage of SKAN 3 versus SKAN 4 postbacks. That’s simply indicative of larger-scale testing. Generally those testing phases are followed by reevaluation and changes, and then another round of testing, sometimes at higher volume. The end result will be better results for app publishers and marketers.

SKAN 4 adoption dashboard: details

Singular’s SKAN 4 adoption dashboard analyzes billions of SKAdNetwork postbacks for SKAN version details. At this point we’re not looking at impressions, or the number of devices that are SKAN 4 compatible.

There is a floor beneath which the dashboard doesn’t report data, just to not clutter up the charts. In addition, if an ad network is generating 0-5% SKAN 4 postbacks, the dashboard reports 5% SKAN 4 postbacks. Not seeing a network appear does NOT mean that the network isn’t currently testing SKAN 4, implementing SKAN 4, or running at least some SKAN 4 postbacks .. it just means the network is not running them at scale yet.

As more networks and platforms start running SKAN 4 at scale, we’ll issue periodic alerts and notices about significant changes.

Good news on SKAN 4 adoption

It’s no secret that moving to SKAdNetwork from IDFA-based attribution has been hard for many in the industry, and that one of the key challenges was SKAN 3’s privacy thresholds. The very good news in SKAN 4’s crowd anonymity and coarse conversion values is that you’ll get more data from fewer installs per campaign.

That reduces marketers’ (and networks) testing tax and opens your ability to run more campaigns with greater numbers of variations in creative, targeting, and offers. It also forms the foundation for better analytics on CPI, ROAS, and eventually LTV.

The giants are coming: bookmark and keep checking

It’s great to see that the industry as a whole is progressing, and that virtually everyone (including all those not on the chart yet) is testing SKAN 4.

It’s pretty challenging to look into the crystal ball but we see a clear industry-wide trend from about 5% on April 20 to 9% on June 24. An overly simplistic linear regression on that trendline indicates that global SKAN 4 penetration won’t hit 20% before the end of the year.

However, the good news is that SKAN 4 adoption progress is not linear.

We see significant jumps between May 6 and May 10, for example, where SKAN 4 adoption more than doubled, only to sink back down as the ad networks evaluated the results of their testing and started tweaking their implementations. What we’ll most likely see over the next few months is a number of significant jumps as various ad networks and platforms decide their testing proves out the quality of their SKAN 4 solutions and turn it on for increasingly larger proportions of their traffic.

Keep checking the dashboard for updates!

iOS ad efficiency dropped up to 75% post-ATT: How DSPs use AI to target and optimize ads in the age of privacy

Assume iOS ad efficiency pre-ATT with almost unfettered IDFA access was 1. What is it now, after App Tracking Transparency, and in the age of SKAdNetwork? This was just one of the things I recently chatted about with Persona.ly’s Joseph Iris. Personal.ly is a demand-side platform that focuses hard on machine learning and AI to find profitable advertising opportunities, mostly for user acquisition and re-engagement. And they do it at scale: 3 million queries per second.

Our main topic: how DSPs function in the age of privacy-reduced signal. 

But the relative iOS ad efficiency change since ATT has been a hard puzzle to solve, and I’ve asked multiple mobile experts without getting a straightforward answer. What I mean, of course, is: how efficiently can the adtech ecosystem target an ad at the right person, at the right time, in the right context, and stimulate action?

Iris accepted the challenge.

iOS ad efficiency drop under ATT

There was a significant efficiency decrease, Iris said.

But, there’s an important caveat.

“So if you look at sheer numbers, that’s like 25% efficiency versus what we had before. But prices decreased by even more than that. So again, this kind of balance … proves there’s an opportunity for smarter buyers.”

That’s super-interesting because an ad environment that is much cheaper but only 25% as efficient at pairing the right people at the right time with the right offer is going to be a significantly worse ad environment in every way besides privacy. You’ll see more ads because they’re cheaper and marketers have to show more to achieve the same results, and the ads you do see will be less relevant because marketers know less about you and can’t target as well.

Iris was specifically referencing casual games, and there are other factors at play, so don’t take this percentage as general across-the-board guideline. Apps that are super-popular, published by well-known brands, and appealing to a wide audience are probably much less affected. Apps that are very niche, monetize on a very small slice of their active users, and don’t have a big brand, could be impacted more.

The good news: SKAN 4 will provide more marketing signal. The bad news: SKAdNetwork and App Tracking Transparency still don’t offer anything like Privacy Sandbox from Google, where there’s a privacy-safe on-device mechanism for targeting. It’s not perfect — though it’s improving — but at least is offers something to marketers beyond context, and early guesstimates of ad efficiency drops are in the 10-20% range.

Signals DSPs use to target ads

So how do DSPs manage in such a challenging environment? By using every signal they can, and by remembering yesterday in extremely accurate detail and applying it to today, Iris says.

The signals include:

  • Time
  • Date
  • Major local/global events (e.g., Super Bowl)
  • Session signals, including length of session
  • IP
  • ISP (probably derived from the IP)
  • Device type (particularly important on Android where there’s more diversity)
    • Pixel density
    • CPU cores
    • RAM
  • App descriptions (believe it or not: keep reading!)
  • Device identifiers, where available
  • And pretty much any else possible (more is available on Android than iOS, for example)

The core of the machine learning adtech companies apply is not all that complicated and shouldn’t be over-glorified. It’s essentially applying memory of the past to the likelihood of future events.

“What we’re effectively trying to do is remember yesterday very accurately at extremely high scale,” Iris says. “The assumption of any machine learning based prediction is that reality didn’t change dramatically from yesterday.”

App store descriptions for context (much more reliable than app categories)

One of the key factors is app store descriptions, believe it or not. That’s a key source of context for Personal.ly: largely because context in-app is a very different animal than content on the web, where pages can be spidered, consumed, and categorized.

ASO specialists have perfected the art of moving to a category in which you can be a big dog, and that’s why categories are almost useless for contextual targeting purposes.

“One thing we’re lucky about is that when you’re building your App Store page, the description kind of has to reflect what’s inside the app, otherwise the users are going to be very upset very quickly,” Iris says. “So in order to not be contaminated by ASO, we take the store descriptions themselves.”

App Store and Google Play descriptions have to accurately describe the apps features and capabilities, so the DSP ingests all those descriptions and parses them for contextual relevance. The eventual output is a mapping of which apps are contextually relevant to each other, and therefore which apps (that are advertising) might be appealing to users in a similar or related app. That sounds simplistic, but I’m sure there’s all kinds of non-linear connections that enable the DSP to know that ads in a hiking trails app shouldn’t necessarily only be other hiking-related apps, but also accommodations, food, maps, points of interest, shoes or boots, camping gear, and so on, all with varying degrees of calculated contextual closeness.

Unicorn creatives and the testing tax: 10%

A common question from marketers around optimizing creative and offers: how much should I spend on known winners, and how much should I spend on testing for new heroes?

For Personal.ly, it’s about 10% of your budget.

“Over time as the champion or champions become more statistically significant they get more of the weight,” says Iris. “They get, let’s say, up to 85%, 90% of the traffic. And the other remaining 10% is still left there for exploration for the option of new champions to emerge.”

I’ve often chatted with marketers about unicorn creatives: those ads that for some reason just continue to perform month after month, even quarter after quarter. It’s a good situation: you have a great ad unit that just continues to perform and essentially print money, but it’s a super-frustrating situation because you literally can’t seem to beat the ad with a better one.

Iris has seen the same thing:

“​In some cases, we can have social casino apps that have the same champion for a year. That just happens sometimes. The stars align, something about how the coins are dropping from the skies, just getting people to install, and you’ll see situations where the table of champions doesn’t really change.”

Metrics deathmatch: CTR versus CVR 

There’s an odd inverse connection between clickthrough rate and conversion rate, Iris says, that likely most mobile marketers have noticed upon occasions: high CTRs equal low CVRs.

Not always, but often.

“The predictions are always diametrically opposed to one another, right?” says Iris. “So if a user has a high probability of clicking or installing, he’s gonna have a low probability of becoming a high value user in most cases. It’s very rare that the stars align and all the probabilities are high.”

Stars aligning, of course, is really nice. But it’s rare, even when you’re buying high-quality traffic from respected ad networks and supply side platforms. 

Especially when you’re trying to optimize for a low CPI.

Which is why smart marketers pay attention to CTR, but don’t give it too much weight. The key metric is CTI, click to install ratio. That’s more challenging on iOS in the era of SKAdNetwork attribution, because the signal is delayed, but it’s still possible use, Iris says.

So much more: watch, subscribe, listen

There’s so much more depth in our conversation. Watch the video above and subscribe to our YouTube channel

Also, subscribe to our Growth Masterminds podcast on your favorite platform to get the audio interviews we do with leading experts in growth, marketing, and adtech.

Plus … a full transcript of our conversation

If you read faster than you watch or listen … here’s a full transcript of my conversation with Joseph Iris, who leads machine learning at Persona.ly.

Note that it’s largely machine-generated, so may contain errors.

John Koetsier:

What actually happens when a demand-side platform engages machine learning to boost your bids? 

Hello and welcome to Growth Masterminds. My name is John Koetsier. 

Using machine learning, of course, in mobile advertising and to drive bidding is super interesting. There’s less data to drive decisions than ever before. Makes it more and more critical to use each possible piece of data you can get and use it well. So how do DSPs do it? 

Here to chat is Joseph Iris. He’s the director of ML products at Persona.ly.. Welcome, Joseph.

Joseph Iris:

Thanks John, nice meeting you.

John Koetsier:

Great to meet you as well. 

Let’s start with the signals. What signals feed into machine learning for bidding?

Joseph Iris:

So it all starts in what we get in the requests from the exchanges, right? Obviously up until recently with everything with iOS and privacy, the device ID was like the biggest factor in it. 

But over time, as we go into a more privacy-oriented ecosystem, then that signal is becoming less and less significant and you can’t really rely on it anymore. 

So other than that, you have signals regarding the user’s connection, which ISP is coming from. Those things, even though they sound very … not necessarily related to the users, like actual engagement with ads, sometimes those things are useful as well. You get additional contextual signals from each exchange. 

Sometimes it, back in the day, used to even include the level of battery still left, but those things are kind of not there anymore. But basically they try to give you additional science into what sort of mindset the user is inside. One problem there is that it’s not unanimous across the board, like each exchange tries its own different stuff. 

So the things that you can rely on, you can use machine learning in order to manufacture around features around what that publisher means. It connects your relationship with the app you’re promoting. It’s a prior performance, and basically you can learn a lot from that. 

There’s device enrichment, which is significant. So on iOS it’s not really relevant because you have a very limited list of devices, but on Android it’s insane, right? We have tons of vendors, tons of models. As time progressed, you were able to, let’s say, differentiate between a high value phone from a feature phone pretty easily, but it’s becoming more difficult as manufacturing costs decrease and you can get really strong non-brand phones. 

So with device enrichment, we take the device UA stream, which is obviously not connected to anyone’s identity, so that’s going to stick around. And we can enrich it with the pixel density of the device, the number of cores, the RAM, and everything to create a profile of the device. And this way we can create different device segmentation, again, to differentiate between high-value users and also connect it to the context. 

Other than this, there are session signals that are coming from the exchanges as well because they do know where the user is placed inside the session so that is very informative in regards to the probability of clicking, installing and everything like that …

John Koetsier:

So when you say session, are you talking about how long somebody’s been in a particular app or how active they are?

Joseph Iris:

Yeah, I know that some exchanges try to do this at a multiple app level, but again, in the future it’s not gonna be that way, it’s gonna be just for the current publisher, it is what it is.

John Koetsier:

Exactly.

Joseph Iris:

I do wanna set the tone of the usage of machine learning for our use case, right? 

Because … marketers and me in my past life tend to over glorify any tool that you would actually use to do anything. And with machine learning it’s easy because it sounds like it’s from the future and stuff like that. Especially with all the buzz around it with ChatGPT and everything. 

And a layperson, not necessarily a layperson, even technical people using this tool would be amazed by its capabilities. The way I like to describe it to prospects and people that don’t really understand the industry, like my wife, no offense, but obviously she’s not really connected, she’s a dog trainer, you know. 

There’s a big gap. So what we’re effectively trying to do is remember yesterday very accurately at extremely high scale. So the assumption of any machine learning based prediction is that reality didn’t change dramatically from yesterday. And when it comes to at least our use case, there weren’t any significant breakthroughs in terms of like the underlying math. Under all of this compute and how we can now scale training much faster and it’s like a click to set up a huge cluster of devices on the cloud or on whatever infrastructure you use. It’s all the same concepts that most people know around statistics. 

So this should really reduce the entry barrier to at least discussing it because when you frame it that way, it’s no longer this magical black box that you can’t understand. It’s basically a tool trying to remember yesterday. That’s much more portable and that’s the reality.

John Koetsier:

Is there any other data that you use that is maybe contextual data or other data that isn’t necessarily confined to what you’re getting from an exchange? You know, there’s time of day, there’s seasonality, there’s other things like that. There’s also, and you mentioned this, your assumption is that today is pretty much like yesterday. 

Well, if today is a Super Bowl, today is not like yesterday, right? So are you feeding in things like that?

Joseph Iris:

Yeah, so you need to factor in really dramatic events that change stuff. You have features for that. You enrich your data with whether it is today a holiday or is today like a dramatic event in some market and that way it could expect it. 

But the reality of being able to adapt to those things quickly is by training incrementally instead of retraining your data like every day or every hour is to basically stream your learning and we operate in that way. So if reality is starting to change, imagine like the beginning of COVID, stuff changed right?

So as long as your pipeline is adaptable and it understands that the latest current trends are more important, and that is always done with weights that you apply to the more recent samples, then you’re pretty much fine. 

But yeah, other than this, a lot of stuff that we do with context comes from us preparing for the future of let’s call it interest groups, the same way that Google calls it in their plans for the privacy cloud, is to create cohorts based on their engagements with … with segments of categories. I can’t really call it categories because store categories are filled with lies, and you kind of need to create your own if you really want …

John Koetsier:

They really are filled with lies.

Joseph Iris:

Yeah, that’s the ASO people. I mean, I’m a fan, don’t get me wrong, but it makes our job more difficult. We need to win.

John Koetsier:

I am not a fan. I am definitely not a fan. I’m, I’m almost like, you know, where’s, where’s Apple or Google as a dictator saying, this is your category, stick in your category. It’s like people picking new categories all the time.

Joseph Iris:

One thing we’re lucky about is that when you’re building your App Store page, the description kind of has to reflect what’s inside the app, otherwise the users are going to be very upset very quickly. You remember the days of the fake ads.

John Koetsier:

Yes.

Joseph Iris:

That doesn’t end well eventually, right?

John Koetsier:

Are those days over? I’m not sure they are.

Joseph Iris:

Kind of, kind of, kind of, kind of. They’re not fully over, but it’s not, you know, there were a few months where it was like basically everything. 

So in order to not be contaminated by ASO, we take the store descriptions themselves. And this is where we can actually use robust models that we don’t necessarily understand what happens under the hood, to understand the context. So when a couple years ago, you don’t know this and time goes by so quickly, I think it was even three years ago, when the whole scan conversation started, we understood, okay, it’s time to adapt to a reality where the user no longer exists.

[We created a] solution around using the store descriptors because we said, okay, you’re not going to change that into something that doesn’t make sense. It always has to consider the features that you’re offering, the theme, and what makes you different. 

One of the most important things in machine learning is this sentence, which is amazing if you think about it. It’s trash in and trash out. If you input the wrong data into your model, you’re going to get something completely useless. The technologies are all established, as I said before. There are no groundbreaking things really happening. You’re just computing faster and getting more accurate. But again, the concepts are the same.

You load up your input, you make sure it’s relevant, you help the machine learning to stay relevant, you’re gonna get good outputs out of it. So what we did, and I’d be happy to demo it, this is around how we use context to the best of our ability. I’ll describe it first and then I’ll use stuff to show you. We take the store description of the promoted app, and we also scrape all the store description of all the apps in the wild where we can actually have access to the inventory. We then create a vector representation. We embed it into something mathematical that we can use. That represents its context. And then when you have two vectors, you can measure the distance between them. So this allows us to say, for example, if you’re promoting a boxing app, anything that has the words boxing or fighting are going to be very, very close, and they’re going to have a high score. 

It’s like a score between one, zero to one. So it’s going to be really near one. 

Then you go further away, you will go to other sports, you go further away, you go to sports news until you reach stuff that are completely unrelated. So when we designed it this way, we thought about A, not wanting to take stuff ourselves because that sounds like a nightmare to maintain. A lot of companies do that, not just in our industry. 

Tagging is like a huge … it’s becoming an industry. Tagging and notation … I think that’s how we would call this whole field. 

We don’t want to do that stuff. We’re too lazy, I guess. We want something that’s automatic. So we basically build this process that constantly scrapes the stores for any changes or new apps. We feed this into this already established data set. And for each new app, we can say, okay, yeah, this is its context. Then we had a case study …

John Koetsier:

There’s probably a product right there actually, which is a new way of categorizing apps, which is just: I’m not categorizing them how they say they’re categorized, I’m categorizing them how they’re actually categorized.

Joseph Iris:

Yeah. Yeah. There’s definitely room to add the insights into the industry there. We didn’t go that far. It’s basically proprietary … so yeah, I can show you. Let me find the button to do that stuff. 

This is accessible for a website, just Google context distance calculator with the top score because apparently people don’t use that. So yeah, inside the app description you have keywords and the keywords, we actually detect them by their frequency. There’s a term in machine learning and managing text in natural language processing.

That’s called TFIDF. Again, sounds crazy, but it’s very simple. It’s term frequency inverse to the document frequency. It’s basically the reality of the world against the entire corpus . Uh, so if a world is more rare, It would get higher weight because it expresses the context higher. So we use the model called Elmo. A lot of the models in this field, in adtech come from MuppetNet, so it started with Elmo, then it was Burt, and then it, so it’s kind of funny, I don’t know why they use that, because they teach words, I guess, that kind of makes sense if I think about it. 

So we use this already established model in order to create the embedding, and we apply the weights according to the rarity of the words, because these express the context. So as you can see in this example, words that appear less, and weights, this way we get an app representation and we can measure distances. 

And if I take a couple of demos that I have here, if we take Homescapes for example, you can see that it’s going to find, we show only like the top 20, so usually you only select the top 20 the closest ones, you can see obviously some of the direct competitors and some stuff that like use similar features but not necessarily the exact same theme. 

If you go for dating, and I chose Bumble, I didn’t use a dating app ever because I’m old, but I think that’s a popular context, right? The keywords are very upfront and they tell you exactly what they are. And when it comes to things that are more complex, this can of course hit or miss, but the right thing to do here when you design a campaign using this sort of tool for either SKAN or probabilistic attribution is to just build your campaign structure around this and use it as a feature in the model. 

So eventually it understands, okay, if this is a close context, is it good or is it bad for my performance? Because sometimes it doesn’t necessarily have to be good, but just accordingly, a bit less, bit more. But yeah, this was our way of making it like future proof and not needing to keep doing it manually. 

John Koetsier:

It’s super interesting to hear this understanding of context in the app world. Because of course, there’s always been context on the web, right? And context on the web is pretty easy because as you’re deciding what ad to put on a page, you know a lot about that domain. You know a lot about the content on that page. It’s easily scrapable. It’s easily understandable. And so you have a lot of contextual data. 

But in-app doesn’t have those kind of realities, doesn’t have those kind of accessible pages to a scraper or something like that. So it’s a super interesting way to look at context and how it works. Love it. 

Talk about creative. How does creative come into your models?

Joseph Iris:

Yeah, so when we originally designed the system, we started just with A-B testing, but very quickly we understood that it’s not … I mean, you can build all sorts of automations around it to make it effective and make sure it’s statistical, and you have people that make careers out of it. 

And yeah, sometimes that can be the right tool, but in our use case, when everything moves really quickly. We understood we have to leverage more advanced technologies. So the way we approached it, and by the way, we look at it very differently from a UA manager. For us, the creative is a tool. For UA managers, it’s a tool as well, but there’s a lot more thought going into what you’re putting inside it. There are huge art teams.

John Koetsier:

Brand … does this look like our app … all that stuff.

Joseph Iris:

So that makes sense, right? I mean, that needs to happen. But we’re at this, we need to focus on taking what we get and just making it the most useful tool for us to collect observations and samples faster and more effectively. 

So in order to train our models for new partners, where we kind of have a cold start problem, we have to collect samples quickly. Otherwise they’ll be like, I’m not gonna spend $50,000 exploring with you because I don’t see anything happening. So we built a solution. There’s a different field, the more advanced fields in machine learning called reinforcement learning. That’s the field that’s used for training bots that you’d play against in video games and stuff like that. Because it has mechanisms to give the machine rewards and punishments based on its actions. a double bombastic name, a multi-armed bandits, which is actually coming from the one-armed bandit analogy of a slot machine …

John Koetsier:

Yes …

Joseph Iris:

… because the theoretical problem they were trying to solve was which slot machine do you play with to increase your odds. So in effect, what it is again, it’s much more simplistic. We have the capability to know each creative CTR and IPM in real time. It’s not simple to store this data and serve it very quickly and update it with each new observation that you get, each new impression click, etc. We figured that part out and we were able to scale it. 

And this way you start off without knowing anything, but as soon as you get the first signal you have a champion and you can start giving it more of your traffic. So you’re not, you’re effectively A-B testing in real time with much more than two variations and you can adjust very quickly. 

So over time as the champion or champions become more statistically significant they get more of the weight. They get, let’s say, up to 85%, 90% of the traffic. And the other remaining 10% are still left there for exploration for the option of new champions to emerge. In some cases, we can have social casino apps that have the same champion for a year. That just happens sometimes. The stars align, something about how the coins are dropping from the skies, just getting people to install, and you’ll see situations where the table of champions doesn’t really change, like number one is number one. 

But in other cases where you introduce new, you know, the creative teams that are a bit more, how do you call it, adventurous, you can have things shifting all the time. So we had to build something that can always explore. This is a pretty robust solution as is. I mean, as far as we can tell, it really fits the use case. 

One thing that’s missing for me, ironically, based on all of the conversation so far is context. because this solution is designed for one champion at a given time. But of course, when you’re targeting users, you have much more than one persona. So the next iteration we’re working on, and I’m hoping to release this quarter, is basically adding context into this. So when you’re selecting the champion, you’ll have different cohorts that get different champions based on their features. you’ll get the additional boost.

John Koetsier:

Super, super interesting. It’s funny you talked about those creatives that are just winning for like a year or something like that. I’ve called those unicorn creatives.

And I’ve seen that from marketers in the past where there’s been just this one unicorn creative they can’t beat, they just can’t beat it. They try, they’re beating their heads against a wall and they can’t win against this one creative.

It’s a good problem to have.

Joseph Iris:

Yeah.

John Koetsier:

It means that something is just working really, really well … but it can be frustrating for marketers. It’s also interesting that using about 10% of the budget for testing is kind of the testing tax, right? You need to do that, you need to find your next unicorn creative, your next one, that works really well. 

I guess the big question is, what are the signals that mean success to you as a DSP? Is it a click? Cause that’s pretty easy to game, right? Somebody just shoots up an SKOverlay when you look at this playable ad, you didn’t do anything with boom, you’re in the app store almost, right? You know, and other things like that …

Joseph Iris:

Happens on Android as well.

It’s not just iOS now. It’s not just SKOverlay. Like you said, like clicks are, I mean, for years, clicks haven’t been like what they used to be. You know what I mean? A click doesn’t necessarily mean intent.

John Koetsier:

Did they ever?

Joseph Iris:

I mean, yeah, I know what you mean. At some period in time, they probably were. But when we came into this field, this was like after like six, seven years of tech experience already. 

But building this programmatic tool at this crazy scale when you’re processing three million queries a second and you have still have room to grow. It teaches you things quickly like reality hits you hard … can life comes at you fast as you say … so very quickly we assumed okay let’s assume we got rid of all the BS in adtech, right? 

We’re not buying anything fraudulent, we’re directly integrated with all the major SSPs, you know, Unity, AppLovin, and all those good guys, right? So we said, okay, it would be enough just to get a good CPI and from there, everything’s gonna work itself out, right? 

These are real humans, the apps convert at like 5% from install to purchase, we gotta be fine. 

No. Definitely not, especially when you’re trying to optimize towards a lower CPI. 

The predictions are always, I think you say diametrically opposed to one another, right? So if a user has a high probability of clicking or installing, he’s gonna have a low probability of becoming a high value user in most cases. It’s very rare that the stars align and all the probabilities are high. 

And we were like those marketers from before, breaking our heads against the wall of why these users that are clicking are not really installing or paying. We figured out that, yeah, you can’t rely on those signals. Not [completely], because obviously they affect attribution. But you need to treat them with a grain of salt. And with all sorts of tools in machine learning, give them less weight. 

So the full string of predictions we do for a single ad request consists of predicting the auction price for bid sharing. We can get into that later. That’s really interesting in and of itself. 

Predicting the probability of a click, an install given a click, a post-install event, or even an LTV or something about quality and the value it reflects for the advertiser depending on his KPI, and the probability of a view through attribution. 

But that’s a lesser part of this. because you multiply the first three and viewthrough comes in at the end. So when you’re looking at this, the things that matter most are the CTI, that conversion, the click to install, and the post-install one. So those are given significantly higher weight in anything that we consider. Everything else is a tool in order to get those targets so we can train effectively.

John Koetsier:

It’s funny because as you’re talking about that, as you’re talking about the signals that you could potentially look at, and then the one that you really care about, this click to install, and your machine learning model is trying to compute all that in a couple hundred milliseconds.

Joseph Iris:

Yep.

John Koetsier:

And I’m thinking you’re boxing in the dark while you have a blindfold on. while your hands are tied behind your back.

Joseph Iris:

Yeah

John Koetsier:

How many other hurdles can I put in your way?

While you’re standing on a ladder over a thousand meter fall because you don’t know anything about that person who’s viewing that ad or potentially viewing that ad because of course we’re in the era of privacy on SKAdNetwork. You just don’t know, you have to go in this context and that’s soon gonna be the case largely the case on Android as well as Privacy Sandbox comes in there. 

Are you using any SKAdNetwork data? Does that impact anything that you’re doing in real time in those couple hundred milliseconds?

Joseph Iris:

So right now it’s still limited. 

John Koetsier:

So that’s a no, right?

Joseph Iris:

It’s not a no at all because obviously we have to prepare for the future. I mean, yeah, we still live in a different reality, but we have to prepare for tomorrow or rainy day, however you wanna call it.

John Koetsier:

Yes.

Joseph Iris:

The same concept of weighting … it works in the same way now. So because those signals are not specific at all, even with SKAN 4, like the lowest level, it’s very different from anything deterministic or probabilistic where you can still tie it to a transaction. So you have to treat it with a grain of salt and build your scheme around it. And that’s what we do.

So with this new structure of SKAN 4, where you do get more detailed information. So again, we weight the signal according to how much it’s, they say coarse and what’s the other word …

John Koetsier:

Fine.

Joseph Iris:

Yeah, yeah, so the fine, so those get higher weights, coarse ones still get some reward, but they’re not as close to the significant ones. And we can use it.

It’s just, so this shift towards less signal for us was scary at first, but when you saw the dynamic of the market changing so rapidly. So what happened when first when this whole ATT came into play, all the budgets went to Android, right? And the prices on iOS plummeted.

John Koetsier:

Yes, we know that.

Joseph Iris:

That means that even if you can’t classify the same way you could before, as long as you can still classify, you can play the game. So I

John Koetsier:

You know, and the funny thing was that the smart money stayed on iOS, the smart money stayed on iOS because just because you couldn’t measure success, didn’t mean you didn’t have success.

Joseph Iris:

It was just very scary.

John Koetsier:

So if you a) had some faith or b) had alternate means of measurement, as in maybe MMM, media mix modeling or other things like that, or c) figured out SKAdNetwork really, really quickly – because you can make it performant if you know how to do it and if you have the right tools to do it – you had a huge advantage for a couple months there, maybe even existing to a certain extent till today because there’s still marketers that have stayed away from it, still haven’t figured out SKAdNetwork, then you had a big advantage. 

But it’s hard for me to understand how SKAN data can make it into your machine learning models because not only is it aggregate, and therefore not tied to a specific device or anything like that … it’s also delayed … and the delays in SKAN 4 are significant. We’re talking easily 35 days in some cases.

Joseph Iris:

So with adtech and machine learning, you kind of have to get ready for delayed feedback and what’s called sense of data out of the box because that’s the way attribution works. So when you have a click attribution in the open for seven days and you want to train so frequently, you  have to have tools built in, assuming that the impressions you’re getting now can turn into installs later. So just adjusting to that is not so difficult. Adjusting to the reality that you can’t connect an install to an impression obviously is much harder. 

But as long as you map to the lowest degree you can, based on how you get the data back from Apple. When it was 100 IDs, we had a case study with Tilting Point where we were actually able to leverage that and get better performance than the normal iOS traffic at the time. But then it was mostly because it was the savvy users not getting ads, not just everyone, because they knew how to opt out behind like eight screens inside the settings on iOS. 

So, you factor it in this way, you use weights in order to give higher importance to where you actually know the publisher app or the ad set and the deeper levels and hope for the best. 

Just kidding. 

John Koetsier:

Hahahaha!

Joseph Iris:

But I mean, you still need to design the campaigns in a way where you still get enough signal to keep this going. But again, the prices always adjust to your capability, to our capability and any performance based buyer that actually has machine learning capabilities because we set the tone, right? 

Performance buyers are the only ones that are able to beat crazy high CPMs on UA. Retargeting is a different story, but on UA we’re the only companies that can say, okay, this impression hides a hundred IPM under it somehow, right? 

So, you asked before about like the decrease, so I can give you some numbers actually about like what the impact was.

John Koetsier:

Yeah, and here’s context because this was before we started recording. 

I’ve been wondering for some time on, you know, where are we in terms of ad efficiency on iOS specifically? And we’ll talk about Android in a year or two or something like that. 

But you know, if our level with IDFA pre-ATT was one. Right? Let’s say that our efficiency was one. What is our efficiency now with SKAN 3 and maybe thinking about SKAN 4? Is our efficiency 0.5? Is it 0.3? Is it 0.7? Is it a range, depending on how well we understand scan and how to advertise in this reality? Throw it over to you.

Joseph Iris:

So obviously there are a lot of factors that go into it. One being like the prominence of the promoted app. The more popular it is, the easier it is to actually work with SKAN. But generally speaking, I can give you an example that I know the numbers of, because I looked at a lot. 

Casual games, of course, are a big part of our mix when we, like, with our UA partners. So when we look at our ability to classify and discriminate and bid higher and lower based on the predicted IPM or post-install event probability, the IPM range we would see for casual games, the lowest probability to the highest would be between basically no installs, 0 IPM to around 20. This was pre-ATT and this is the reality in Android to some degree.  

Post-ATT, it decreased from 0 to 5. So if you look at sheer numbers, that’s like 25% efficiency versus what we had before. But prices decreased by even more than that. So again, this kind of balance is about and proves there’s an opportunity for smarter buyers.

John Koetsier:

Wow. So interesting, so interesting. 

So you think about the broader impacts of that, right? There’s obviously the specific impacts in terms of adtech, in terms of publishers and advertisers. OK, we’re 25% as efficient, but our costs drop more. So in the end, we don’t really care about that. 

What are the bigger environmental impacts? Ads are cheaper. You’re going to see more ads, right? Ads are less effective. Are you going to see worse ads? We don’t have time to get into all that right now. but that’s absolutely fascinating. It’s something I’ll probably dive into in a blog post or something like that. 

We have to bring this to an end. This has been super interesting. It’s been super informative. I absolutely love the things you’ve been talking about. We have to bring this to an end. I wanna end here. What signals are most predictive? Is it context in terms of the app description, app listing? Is it something else? What signals do you find are most predictive?

Joseph Iris:

So in reality, most of them by themselves are not meaningless, but are not enough to get you what you need. 

So, the classic example, like one of the first tasks you would run as a machine learning practitioner would be to predict the price of a house. Right? That’s like the classic use case. And the features for that are like the number of rooms. and how many stairs are in the house, is there a basement, stuff like that. And you create an equation that says, based on each of these different things, what’s the price going to be based on prior knowledge. The reality eventually comes from all of these interactions between these features. 

So at face value, let’s say if a user is just at the start of a session, that means nothing. But if he’s in the start of a session, and this is a similar context to the context he’s playing right now, and it’s 8:00 PM and the Super Bowl was yesterday, for example, then at this point you have a very specific reality that can suddenly bring you that 20, 30, 40 IP.

It’s never a single feature by itself. It’s always a combination of a few things. They usually come around the session. The session is very strong, as long as you know how to use it because users’ attention spans are short. So usually in most contexts, the beginning of a session is better because they’re more open to learn about new things, but it has to come in conjunction with the context they’re in, their overall context or like the context of the cohort and still taking into account things sound like they’re meaningless, like the ISP as I said before, but again, when you combine it with all of this and you have enough information, they can get you to this very specific case where, yeah, like again, the stars align. 

I use that sentence a lot, but you kind of need that when you’re designing. So as long as …

John Koetsier:

Essentially, what you’re saying is that machine learning and finding the right ad for the right person at the right time is basically astrology, because the stars have to align. And all the factors …

Joseph Iris:

Yeah, but I mean, I wouldn’t, you know, when I read every, I used to do it when I was younger, you know, just open the newspaper and read like the predictions. So yeah, those are, the ones you get there are very generic, right? You’re going to have a bad time, you’re going to lose something, you know …

John Koetsier:

You will meet somebody new today!

Joseph Iris:

So with adtech, you’re trying … yeah, so in our case, yeah, you need to be more specific, but kind of, yeah, because if you think about what astrologists are doing, it’s just, yeah, so in many cases, last week, you probably, you know, these things happen. You were frustrated, you know, usual things that happen in everyday life are going to happen tomorrow. Yeah, yeah, yeah. So, so kind of. But yeah. It is very mathematical and very scientific method oriented. 

I mean, it doesn’t matter how much signal you have, if you set up the correct tools, you clean up the data correctly, you work with the reality that you have and you adapt quickly, which is the most important thing in the world right now, I think, with everything changing so rapidly, you can compete. And that’s why I work so long, so such long hours, because I believe we can do this even more effectively.

John Koetsier:

Excellent, excellent, excellent. Well, we’ve gone from science to astrology to fortune cookies to, you know what, do your homework and good things can happen.

Joseph Iris:

Yep.

John Koetsier:

Joseph, this has been super informative and also quite a bit of fun. Thank you so much.

Joseph Iris:

Thanks John, happy to be here.

Singular CEO Gadi Eliashiv and CTO Eran Friedman on WWDC 2023 and iOS 17 privacy updates (and yes, we’re all buying Apple Vision Pro)

What do we know now about Apple’s iOS 17 privacy plans from WWDC 2023? Well, we know that Singular CEO Gadi Eliashiv and CTO Eran Friedman are lining up to buy an Apple Vision Pro in early 2024. And we know that SKAN 5’s re-engagement attribution will be more useful than I initially thought.

Our recent LinkedIn Live on Apple, privacy, and WWDC 2023 is now available. (Also, check here for more insights on privacy manifests and other technical details.)

Here are just a few of the highlights:

iOS 17 privacy: on SKAN 5 and re-engagement

I wasn’t personally super-excited after learning more about SKAN 5 and re-engagement attribution in iOS 17: no audiences, no targeting criteria, just identification of generic marketing campaigns that happen to re-scoop up existing app users.

But Singular CTO Eran Friedman saw something different:

“We have a customer in Korea … they’re kind of the Amazon shopping of Korea. They’re installed in 90% of the devices there. 

“They’re saying that we don’t have app install campaigns. It’s irrelevant for us. We only have engagement campaigns. You know what? They barely get any SKAN postbacks at all, right? They feel that this mechanism is irrelevant for them. So now when they actually have some visibility to how many reengagements are coming from SKAN, they feel that can be powerful for their use case, right?”

Makes sense to me, and it’s great that there’s a strong use case for it. It still seems like a feature that’s more useful for giants than minnows, and hopefully there will be more to the story at some point. SKAN 6, anyone?

Privacy manifests: great news for SDK vendors

If you’re on the side of the angels and not being naughty about privacy, iOS 17 privacy manifests are actually a good thing.

Singular CTO Eran Friedman:

“As an SDK vendor … for us it’s great news.

We’ve been getting questions like: what should I fill in my privacy nutrition report, or how should I handle that? And all of this has been hand handled manually by the app developers with our support. I think privacy manifests is a great way to first of all automate or streamline a lot of this process.”

That’s especially true for app developers with a larger number of SDKs in their apps.

The other positive regarding SDKs: signing them will give developers confidence that they’re using the right SDK, not a fake one, and not at risk of SDK injection attacks.

Tracking domains: sub-domains will work

Tracking domains are new in iOS 17. Any endpoints you use for tracking purposes will need to be declared in privacy manifests, and if people using your apps don’t accept tracking via Apple’s ATT pop-up, all traffic to those domains will be blocked.

That’s potentially problematic if you’re doing multiple things — including some that are essential to your app’s functionality — from a single domain.

Good news from Singular CEO Gadi Eliashiv:

“It’s not just google.com, for example. You could actually define sub-domains. So you could say I have a domain for tracking, like tracking.example.com, and I have a domain that doesn’t do any tracking … non tracking.example.com. 

“And then you can think about it .. let’s say that you have one of your endpoints in your SDK, your subdomain receives IDFA, then you’re probably going to use this IDFA for tracking, right … versus if you have an endpoint that only exists to serve SKAdNetwork or doesn’t receive anything that can be used for tracking, then you would put that in the non tracking subdomain.”

There’s still some complexity and ambiguity in iOS 17 privacy because fingerprinting, which is against Apple requirements, only requires an IP address and a user-agent at base level. So a level of compliance and integrity is still required. Plus, Apple is instituting Required Reason APIs so that accessing datapoints that could make fingerprinting more granular and closer to the device/person level will require explanation.

The future is private

Required Reason APIs is just one more step in Apple securing its ecosystem for privacy. But it’s probably far from the last step.

From Eran Friedman:

“It’s kind of a step towards [blocking fingerprinting]. It shows some of Apple’s intentions, and if I had to guess, I would say that maybe like in the next WWDC, maybe a year from now … we also see that being blocked, which is why we always keep encouraging our own customers to focus on privacy preserving APIs, focus on SKAN, on long-term solutions rather than legacy, problematic, methodologies.”

Make sense, and yet another reason to not only get very good at SKAN today, and Privacy Sandbox tomorrow, but also MMM.

Enhanced private browsing: potential challenges

Enhanced private browsing in Safari on iOS 17 will add a bunch of features. 

First, it will lock your private browsing windows when you’re not using them behind Face ID or a passcode (so a lot of significant others are going to be asking partners to unlock their phones). More relevant to adtech, enhanced private browsing also “completely blocks known trackers from loading on pages, and removes tracking added to URLs as you browse,” Apple says

All of this happens in private or incognito mode right now, which few people use for day-to-day use (although I know some people who always browse like this.)

If Apple extends this beyond private mode, however, some functionality could start to break on websites that use UTM parameters for essential purposes.

What should app developers and marketers do now?

Keep up to date, and talk to their vendors.

From Gadi Eliashiv:

“Every year when WWDC happens, every vendor in the space rewrites their roadmap given whatever Apple announces. And because of where we are, we have to stay really close to the changes. So I guess what I’m saying to marketers and to developers is use your trusted vendors to give you advice.

Because we probably spend 1000X more time thinking about these things than each developer, because we have to. That’s our job, that’s our livelihood. So use us to figure out how to plan.”

Looks like you’re already starting on that process. There’s one more thing you can do right now …

Much more in the full video

Check out the full video for all the details that Gadi and Eran chatted about, and to start your own planning process around how to prepare for iOS 17.

Watch the full WWDC 2023 video and subscribe to our YouTube channel.

Performance user acquisition in 2023 and beyond: 5 expert opinions

51% of mobile marketers in our recent Kickass UA in the Privacy Era webinar think that platform-provided measurement frameworks like SKAN and Privacy Sandbox on Android are the future of marketing measurement for performance user acquisition on mobile. 60% think MMM is a big part of the picture, while two-thirds were hot on first-party data in this pick-all-that-apply survey.

Only 12% said fingerprinting was part of the future.

And that was before WWDC 2023 and iOS 17’s privacy manifests, required reason APIs, and traffic blocking to tracking domains. 

So what do the performance user acquisition experts say? 

We assembled 5 of the best:

  • Jayne Peressini, UA consultant and formerly at EA, Dapper Labs, Draft Kings, Machine Zone, and more
  • Shamanth Rao, CEO and founder of Rocketship HQ
  • Claire Rozain, founder of Global Warming Games, senior UA lead at Carry1st, and formerly at Rovio, Gameloft, Pixel United, and Match
  • Philip Weiskirchen, SVP and Jammp
  • Gadi Eliashiv, CEO and co-founder at Singular

How many mobile marketers are experts at performance user acquisition using SKAN?

Performance user acquisition was hard with IDFA. It’s now much harder under SKAdNetwork.

I’ve seen estimates as low at 35% in the fairly recent past: that only 35% of UA professionals can successfully run user acquisition under SKAN. But there’s definitely been some upskilling, according to Jammp’s Philip Weiskirchen:

“We are seeing roughly 65% of all our clients in the U.S. — which is obviously an iOS heavy market — successfully running SKAN campaigns at the moment.”

That sounds not too bad, but it means that 35% of advertisers are failing to run effective SKAN campaigns: a significant issue before WWDC 2023, and a critical one after Apple’s recent privacy announcements. (Or, when iOS 17 drops.)

For those who still aren’t, Jayne Peressini has some bad news:

“I think there are a lot of overly confident marketing groups out there that think they can get away with things that are just gonna go away … like fingerprinting or other means.”

Presumably that number is decreasing given what we now know about privacy manifests and require-reason APIs in iOS 17: you’ll have to declare the APIs you’re calling, and those that are particularly privacy-sensitive — and useful for fingerprinting — are going to need to have plausible and accurate usage rationales.

In other words, the hammer is finally (mostly) dropping on fingerprinting.

The good news: SKAN is enough on iOS, according to some

This would have been a super-controversial point just a few months ago, but particularly with SKAN 4’s additional data and postbacks, SKAdNetwork is enough to run ROI-positive user acquisition campaigns on iOS. At least if you’ve cracked the code, says Jammp’s Philip Weiskirchen:

“I believe SKAN is enough if you have figured out the setup.”

Jayne Peressini agrees.

“I think less is actually more in this case. It’s just a matter of agreeing on a source of truth. You agree on the methodology and you move on.”

In other words: it’s not perfect, but it’s a measuring stick that can make iOS UA performant. With a good setup and with modeling for missing data, this makes a lot of sense … especially with SKAN 4. The modeling is still a critical component, particularly under SKAN 3 where despite the framework’s limitations, Rovio still managed to get accurate predictions to drive spend

The also-good news: if you need more, more is available

Not all experts agree about everything, shockingly, and in this case RocketShip HQ’s Shamanth Rao is one of them. Rao thinks that SKAN is not enough for scaled-up advertisers.

“If you are at scale and on multiple channels, you do absolutely need additional measurement methodologies.”

The good news here is that multiple measurement methodologies are available. 

One that Rao employs with clients is media mix modeling based on Meta’s open-source Robyn package. For Singular clients, the even-better news is that they can start using hassle-free zero-implementation media mix modeling with automated onboarding from Singular. (Thanks to the fact that all your cost, campaign, ROI, and conversion data is already collected, aggregated, and calculated by Singular.)

One challenge: once you employ multiple methodologies, you start having to answer questions about which forms of measurement are more valid, as Peressini pointed out. 

Claire Rozain agrees:

“I think it’s also a matter of picking your battles.”

One potential way to resolve that conflict might be use cases: daily/weekly campaign optimization with more direct and quicker forms of measurement such as SKAN, the IDFA remnant you might have if you pop the ATT question, and — in the future — Privacy Sandbox on Android. And, for longer-term allocation and incrementality measurement, a dose of MMM.

But … we all need to get comfortable being uncomfortable

Not only is measurement getting harder, mobile user acquisition leads will need to get comfortable with uncertainty. It’s not the perfect message to give to a CFO, but it is the reality. As Rao says:

“Understand that nondeterministic measurement is the present and the future. And I think people should just get used to the new reality.”

That’s also an opportunity, however, according to Peressini.

“I want to see a lot more failure in this industry … this is a really hard thing to solve … start getting messy with it and start testing a bunch of stuff.”

In other words, stop hanging on to old methods that you know are getting phased out and start building your next framework that will … accepting some bumps along the way, and understanding that the old ways are not coming back. Which is not easy, of course, and probably why Rozain adds that she thinks “you need to be a positive kickass marketer” and power through the challenges.

One good thing about Privacy Sandbox when it comes

Just like we have referrers on the web so a website understands where a visitor is coming from, it looks like Google will keep the Google Play Store referrer, Eliashiv says.

Which is huge:

“That means that if somebody clicked on your ad and landed in your app, they’ll still have that information. That means that click based attribution is still deterministic at the user level. Now you can argue: does that represent the full picture? Probably not because viewthrough’s not gonna work with this referrer … people still need to go to Privacy Sandbox but if the referrer stays, that’s also an insane game changer because you haven’t lost that much fidelity.”

Much more in the full webinar

As always, there’s much more insight in the entire performance user acquisition webinar.

Access it instantly on-demand right here: Kickass UA in the Privacy Era.

iOS 17 privacy manifests announced at WWDC 2023: here’s how they work and when they’ll be required

Apple announced new privacy manifests for SDKs as well as apps today at WWDC 2023. It’s a move that will have massive privacy implications as well as massive app developing, publishing, and marketing implications.

I’m going to dive into what they are, how they work, and what they’ll require of app publishers as well as SDK developers.

Long story short:

  • Everyone’s going to need to declare what their apps and SDKs do
  • With certain particularly sensitive privacy-impacting SDKs, you’ll need to state why you’re using them
  • You’ll have to state whether your app uses data for tracking
  • Whether or not your app is tracking, you’ll need to select categories of data you are collecting
  • Apple will block network requests to tracking domains if users have not granted permission via App Tracking Transparency

There’s a lot of nuance and detail around all of these things, so keep reading for more details and explanation …

New Apple requirements: privacy manifests

Everyone, both app developers and SDK makers, will have to create privacy manifests that Apple will aggregate together at the point of app publishing.

From Apple’s statement on privacy manifests:

“We’re introducing new privacy manifests — files that outline the privacy practices of the third-party code in an app, in a single standard format. When developers prepare to distribute their app, Xcode will combine the privacy manifests across all the third-party SDKs that a developer is using into a single, easy-to-use report.”

Apple is requiring this from SDK makers because app developers who incorporate third-party SDKs into their apps are likely unaware of all the code and all the uses of those SDKs. Privacy manifests will help app developers understand the implications of including each SDK and provide them information they need to create their app’s specific Privacy Nutrition label.

Everyone must declare reasons for API uses

If you’re using APIs that collect information that might be used for fingerprinting, you’re going to have to declare why you need it.

If you’re a small flashlight utility app that for some reason needs the amount of free disk space … be prepared to get very creative. (By which I mean: good luck. You’re going to lose access to that API at some point during the App Store app submission process, and for good reason.)

Via Apple:

“Apps referencing APIs that could potentially be used for fingerprinting — a practice that is prohibited on the App Store — will now be required to select an allowed reason for usage of the API and declare that usage in the privacy manifest.”

There are many perfectly innocuous reasons for knowing device type, OS version, screen size, or other potentially privacy-threatening pieces of information. Just be sure that you’re using what you say you’re using, and you’re using what you say you’re using in the exact way you say you’re using it.

Apple will name & shame data-hungry SDKs

In order to put some teeth into these new requirements, Apple says it will publish a list of privacy-impacting SDKs at some point in 2023. While the public statement indicates that Apple will name names, behind the scenes it’s not 100% clear that the list will be made up of specific named SDKs or categories of SDKs.

For now, count on the former.

From Apple:

“We’ll publish additional information later this year, including:

  • A list of privacy-impacting SDKs (third-party SDKs that have particularly high impact on user privacy)
  • A list of “required reason” APIs for which an allowed reason must be declared
  • A developer feedback form to suggest new reasons for calling covered APIs
  • Additional documentation on the benefits of and details about signatures, privacy manifests, and when they will be required”

The list of required-reason APIs will be very informative, as it will be not only the APIs that Apple thinks are privacy-sensitive in general, but also the ones that Apple believes could be used for fingerprinting purposes. There’s good news if you use a required-reason API but for a purpose that isn’t tracking and isn’t suggested by Apple: you’ll be able to submit feedback on new reasons to add.

New privacy-focused processes are becoming part of the app submission process 

With privacy manifests, there are going to be a few new steps in the App Store submission process.

1. First, you’re going to need to make a privacy manifest

You’ll have to declare whether or not your app — or the third-party SDKs used in it — use data for tracking as defined by the App Tracking Transparency framework. If you are, you’ll have to set NSPrivacyTracking to true.

As a reminder, Apple defines tracking as linking data you collect with other user or device data collected by other companies.

Here’s the definition:

“Tracking refers to the act of linking user or device data collected from your app with user or device data collected from other companies’ apps, websites, or offline properties for targeted advertising or advertising measurement purposes. Tracking also refers to sharing user or device data with data brokers.”

Apple provides the following examples of tracking:

  • Behavioral targeting of ads
  • Sharing location or email with a data broker
  • Creating and sharing audiences
  • Device graph generation

And Apple provides a few examples of data collection that are not considered tracking:

  • Data stays on-device
  • Data used only for fraud detection/security
  • Data used for credit score

Presumably there are many more types of data collection that are not defined as tracking, as long as you’re not linking data your app or SDKs collect with data from other companies, or selling your data to data brokers.

2. If you are tracking, you’ll have to list the data you are collecting in your privacy manifest

This starts by listing the domains that you’re sending data to via the NSPrivacyTrackingDomains array. That’s simply a list of URLs that your app or your SDKs connect to that aid in tracking.

Here’s an important point:

If you connect to a tracking domain but your users have not granted permission for tracking via App Tracking Transparency, Apple will block any calls to those URLs. Via Apple: “If the user has not granted tracking permission through the App Tracking Transparency framework, network requests to these domains fail and your app receives an error.”

Another important point:

Since you might collect some data that is tracking and some data that is not tracking, ensure that you or your measurement vendor has different endpoints for different purposes. In other words, skan.vendor.com and tracking.vendor.com. Apple will block tracking domains for apps in the same way it blocks tracking in Safari with ITP, Intelligent Tracking Prevention. But this ITP for apps, if you will, is intelligent enough to differentiate between virtual domains.

You’ll also have to list the types of data you are collecting, and you’ll have to do that according to a taxonomy defined by Apple.

3. Whether or not you are tracking as defined under ATT, you’ll have to list this data in privacy manifest

You’re declaring what data your app and any third-party SDKs collect. You’ll also have to list details about that data in your privacy information file using the NSPrivacyCollectedDataTypes array.

Apple will require that you include the following details:

  • The type of data you’re collecting
  • Whether it’s linked to your users’ identities 
  • Whether it’s used to track by app or by SDK 
  • And, the reason you collect the data

4. Whether or not you are tracking as defined under ATT, you’ll also have to report the categories of data your app and any third-party SDKs are collecting

That data will fall into any of these Apple-defined categories:

  • Contact info
  • Health & fitness data
  • Financial info (credit card)
    • Note: not when a third party does it: “If your app uses a payment service, the payment information is entered outside your app, and you as the developer never have access to the payment information, it is not collected and does not need to be disclosed.”
  • Location data
    • Fine
    • Coarse
  • Sensitive information (ethnicity, disability, etc.)
  • Contacts
    • Address book
  • User content
    • Emails
    • Messages
    • Photos
    • Gameplay content
    • Audio data
  • Browsing history
  • Search history
  • Identifiers
    • User ID (screen name, account ID, customer number)
    • Device ID
  • Purchases
  • Usage data
    • App engagement
    • Advertising data (what ads user has seen)
  • Diagnostics
  • Other data … “any other data types not mentioned”
    • I suspect this could be a fairly large catch-all category

5. Whether or not you are tracking as defined under ATT, you’ll have to report the reasons why your app and any third-party SDKs are collecting data

Those reasons could include:

  • Third-party advertising
  • First-party advertising
  • Product personalization
  • App functionality
  • Any other purposes

6. Finally, you’ll create your privacy report

Or, to be more precise, Xcode will aggregate all of the above data from your app declarations and any privacy manifests in any third-party SDKs that your app references and use it to create your full privacy report.

Privacy manifests FAQ

Apple’s new privacy manifests raise a ton of questions. Here’s what we think we know so far about a few key ones, although this of course is subject to change as we learn more officially from Apple.

  1. Does getting IDFV count as tracking?
    Yes
  2. If you’re not tracking, but you are using a Required Reason API, do you still have to declare it in your privacy manifest?
    Yes
  3. What are the privacy-impacting Required Reason APIs?
    Apple will publish a list later this year.
  4. Will SDKs have their own SDK submission process like the app submission process?
    No
  5. Are there specific APIs or methods that Apple considers to always impact privacy?
    Apple will provide more information later this year.
  6. Should you fill out a privacy manifest for a framework that doesn’t access any data?
    It’s strongly recommended to, yes.
  7. Is ATT changing at all in iOS 17?
    No
  8. Does marketers’ deeplink management and measurement for their own web links to their own app change under iOS 17 and privacy manifests?
    No
  9. Can apps manage how chatty their SDKs are now?
    Essentially yes, because any traffic to defined tracking domains will fail unless users have opted in to tracking via ATT.
  10. When will all of this rollout?
    Apple will start sending out informational emails this fall, and enforcement will begin in spring of 2024.

There’s almost certainly more to learn and share, so we’ll keep this updated with any new information we hear or insights we find.

Apple announces SKAN 5 at WWDC, plus privacy manifests for third-party SDKs in an anti-fingerprinting move

Apple announced SKAN 5 at WWDC, which makes me wrong. But it barely flickered across the screen and wasn’t actually mentioned verbally, which makes me feel better. Apple also announced Privacy Manifests for third-party SDKs, building on the Privacy Nutrition labels introduced a year ago, and made it clear that they were part of Apple’s enforcement of its anti-fingerprinting privacy stance.

SKAN 5 is coming

The next major iteration of Apple’s SKAdNetwork framework for mobile app attribution made it into a screenful of API updates that Playrix marketing producer Slava Gataulin caught. But no speakers in the WWDC opening keynote actually mentioned SKAdNetwork, and it’s not clear which session at WWDC will dive into the topic, if any.

SKAdNetwork 5.0 in the WWDC 2023 keynote

I was fairly certain that SKAN 5 would not appear at WWDC 2023 because the reality in the ecosystem right now is that we are still in the very early stages of getting SKAN 4 operational and out the door. As of April 19th, not even 1% of postbacks were SKAN 4 compatible, and the needle has not moved very far in a month and a half.

There are two good pieces of news about SKAN 5, however, that mobile marketers will welcome.

Re-engagement support is coming

97% of mobile marketers in a recent Singular webinar told us they wanted Apple to borrow features from Google’s Privacy Sandbox on Android, and re-engagement has been at the top of the list every time we’ve asked marketers what they want in SKAN 5.

Finally, they’re getting what they want. SKAN 5 will enable re-engagement.

“In addition to measuring downloads, we know it’s important to understand how advertising can bring users back into your app,” Apple says. “SKAdNetwork 5 will support measuring re-engagement. In addition to measuring conversions after a user downloads your app, you’ll also be able to measure conversions after a user opens your app by tapping on an ad.”

skan-5-reengagement

Based on the limited description given, it seems that this is re-engagement of people who already have your app, not retargeting of people who might have once installed your app but since deleted it.

A major question that immediately comes to mind, of course, is targeting for the re-engagement: how will you ensure that people who have your app installed will see your ads as opposed to random people who have not? If Apple supports something there, that would be interesting — and reminiscent of Privacy Sandbox’s Topics API. Hopefully we’ll know more soon, and at this point absent any further documentation from Apple I’m guessing that there is not a targeting component in SKAN 5.

In either case, any additional functionality in SKAdNetwork is a bonus.

And there’s an entirely different use case for measuring re-engagement, as Singular CTO Eran Friedman says: incidental re-exposure.

“In today’s world without IDFAs, you can’t target ‘only users who don’t have my app,’ so marketers are bound to show some ads to existing users. But these users never generate SKAN postbacks, and marketers have no clue when it happens.”

The new functionality solves an important gap in SKAN for large advertisers, Friedman says. And measuring these incidental re-exposures to ads will also yield a clearer ROAS picture for ad campaigns measured with SKAN 5.

SKAN 5 won’t change much from SKAN 4

Right now it looks like adding the re-engagement capability is the only change we’ll see from SKAN 4 to SKAN 5. That, frankly is very good news since, as we know, there are growth teams that aren’t even up to speed on SKAN 3 yet, and others on SKAN 3 who haven’t thought deeply about what to do under SKAN 4.

There’s been a lot of change in the last few years for mobile user acquisition teams: this is a welcome reprieve which goes very nicely with the one additional feature that marketers probably wanted most.

(Plus, it makes my pre-WWDC prediction almost true!)

Privacy Manifests for third-party SDKs

In a new move, Apple says it’s going to be requiring third-party SDKs to provide Privacy Manifests which will be combined to help app developers write their Privacy Nutrition labels

“to help developers understand how third-party SDKs use data, we’re introducing new privacy manifests — files that outline the privacy practices of the third-party code in an app, in a single standard format. When developers prepare to distribute their app, Xcode will combine the privacy manifests across all the third-party SDKs that a developer is using into a single, easy-to-use report.”

In addition, apps that use APIs that could be used for fingerprinting “will now be required to select an allowed reason for usage of the API and declare that usage in the privacy manifest.” They’ll have to accurately describe that usage and only use the data for stated purposes.

The solution, of course, is to use SKAN 4, adopt new privacy-safe measurement technologies like MMM, and use hybrid measurement technologies that are privacy-safe and lean on your own first-party data.

While the technology is vastly different here, this rhymes with some of the moves Google is making in Privacy Sandbox for Android with SDK Runtime: finding ways to manage and control what third-party SDKs do with private, personal data.

Along the same lines, Apple is going to be validating SDKs:

When using third-party SDKs, it can be hard for developers to know the code that they downloaded was written by the developer that they expect. To address that, we’re introducing signatures for SDKs so that when a developer adopts a new version of a third-party SDK in their app, Xcode will validate that it was signed by the same developer. 

This process won’t just happen in private. Apple says that later this year, it will publish a list of privacy-impacting SDKs : “third-party SDKs that have particularly high impact on user privacy.”

That’s likely a shot across the bow now for the adtech ecosystem to get its affairs in order quickly.

WWDC 2023: 4 things Apple could change in SKAdNetwork, ATT, privacy, and adtech

WWDC 2023, Apple’s World Wide Developer Conference is just around the corner on June 5. Given that Apple launched SKAN 3 at WWDC 2020 and announced SKAN 4 at WWDC 2022, it’s possible that we’ll see some updates in just a couple of weeks. Naturally, if this early pattern of major changes every 2 years holds up, we probably won’t see SKAN 5 — especially since SKAN 4 has yet to be really implemented across the ecosystem — but there’s an outside chance that we may still see some tweaks.

And if not in SKAdNetwork per se, then possible more generally in the areas of privacy or additional adtech features like those Google is implementing in Privacy Sandbox for Android.

Here are 4 things that we could potentially see changes in at WWDC that are relevant to the growth marketing and adtech spaces.

1. Possible general privacy changes at WWDC 2023

Apple has made privacy a core component of its brand promise. That plays well on mobile and in today’s global regulatory environment, but it also opens up expansion opportunities for Apple in fintech (Apple Card and more) and health (Apple Fitness but much more), both of which require higher levels of consumer trust.

What could Apple potentially do here at WWDC 2023?

  1. Active fingerprinting ban
  2. Expanded ITP
  3. Expanded Private Relay
  4. Additional app safety initiatives similar to the SDK Runtime from Google in Privacy Sandbox on Android
  5. ATT changes

Fingerprinting ban

An active fingerprinting ban would add teeth to Apple’s stated antipathy to the tracking technology, and it could be accomplished in multiple ways, including by expanding Private Relay. Arguably, this is not highly necessary, since most of the major ad networks don’t provide data that can be used for fingerprinting already.

Intelligent Tracking Prevention expansion

ITP is Apple’s Intelligent Tracking Prevention, which foils trackers on the web by blocking third-party cookies as well as some first-party cookies and non-cookie tracking data kept in Safari’s local storage. An expansion could mean Apple blocking measurement URLs being accessed by apps or by SDKs in those apps. 

The result, of course, is that more of the data that measurement SDKs transmit would move to server-to-server connections, which would be more opaque than measurement URLs.

Private Relay expansion

Private Relay is an Apple VPN for web traffic that obscures data about people from websites they’re visiting. Apple could potentially expand this from web traffic to app traffic, though that would add significant cost since we spend much more time in apps than on the web, and generally send and receive much more data. Also, it could add latency to applications where speed is critical, like multiplayer games.

(This could be avoided by per-app or per-service reversions to non Private Relay traffic, which Apple already does on the web for websites, countries, or ISPs that don’t support the technology.)

I’m on the fence on this one — I’ve been pretty certain in the past that Apple would at least somewhat expand Private Relay — but there are some challenges to making it work well.

SDK Runtime-like technology on iOS

I can’t imagine Apple not liking the capability that SDK Runtime would provide in terms of being able to vet and control SDKs. And, you could argue that providing a separate app and SDK approval process is cleaner and easier for app developers, and offers less risk to app publishers.

On the other hand, it loads more risk onto Apple’s shoulders, because Apple would now be in some sense certifying the safety of SDKs in addition to apps via some kind of submission and approval process.

Ultimately, I can see this happening on iOS, but probably not this year at WWDC 2023.

ATT changes

Theoretically, Apple could change some of the wording or operation of ATT, but I’m not seeing a lot of calls for that now.

2. SKAN improvements (and ultimately SKAN 5)

It’s just too early given SKAN 4’s barely-there position in the adtech ecosystem right now to see major changes from Apple at WWDC 2023, so don’t hold your breath for anything here. Marketers and ad networks have their hands full implementing SKAN 4, so muddying the waters with yet another version would be counterproductive.

In short: don’t expect SKAN 5 this year.

But that doesn’t mean marketers don’t have their wish lists for SKAdNetwork additions and improvements, and a few of those might include functionality for deferred deep linking, additional web to app features including expansion beyond just Safari, and perhaps just general increased simplicity throughout the solution.

3. Additional adtech components and initiatives at WWDC 2023

It’s worth noting as we get closer to the launch of Google’s Privacy Sandbox for Android that where Privacy Sandbox is a more-or-less 360-degree framework for advertising, SKAdNetwork is just a measurement solution, with some optimization thrown in. Privacy Sandbox offers solutions for targeting, optimization, measurement, and retargeting as well as privacy, while SKAN was built to reengineer measurement following platform changes — App Tracking Transparency — that crippled measurement while fixing a privacy problem. 

But ATT also crippled targeting, audiences, and retargeting.

The question is: will Apple pull a Google and offer solutions for those parts of the advertiser’s toolbox?

Google’s doing that via Topics API for targeting and Protected Audiences (formerly Fledge) for retargeting. But Google is an ad network, and therefore thinks like an ad network, while Apple is a company that merely owns an ad network, and one that is a small fraction of its overall business.

My guess: Apple does not offer solutions for these other components.

Other adtech changes that Apple could consider include one that would win it some goodwill in the advertising industry but hamper Apple Search Ads: moving ASA to SKAdNetwork measurement. Fairness might indicate this would be appropriate, but Apple has been fairly consistent that since ASA uses first-party data and does not share it to other parties, this is unnecessary.

Another would be a rebranding of Apple Search Ads as Apple Ads and an expansion to Apple’s music, podcasting, TV, and potentially other products. Apple employees privately say this is unlikely, but there is a significant revenue opportunity here. In addition, and perhaps more importantly, there’s a competitive moat to put up against other podcasting players such as Spotify by offering podcasters a share of the revenue, helping them with monetization.

4. Changes to Apple’s App Store fee structure

I would not be surprised to see additional changes to App Store revenue share agreements with developers within the next year or so. A key driver here is third-party payment processing being driving in countries like the Netherlands and Korea, but sure to expand to many more countries (and larger ones) with the introduction of Europe’s Digital Markets Act

A possible Apple strategy would be to reduce App Store fees in order to make using the official iOS App Store exclusively more attractive as third-party apps stores and third-party payment processing becomes more broadly available.

That’s particularly important if Apple wants to maintain control of the Apple privacy story on iOS, because third-party app stores would likely not implement App Tracking Transparency.

While Apple can control some things via the platform and APIs available, ATT is largely enforced via the App Store submission process, and third-party app stores would potentially considerably change the privacy story on iPhone and iPad.

Announcing these at WWDC 2023 — the developers’ conference — could make some sense, although it’s a bit early yet in terms of when the legislation will actually take effect.

Summing up: probably a quiet WWDC 2023 on the privacy, ATT, and adtech front

There’s a lot Apple could do. Given the timing and the complex competitive and regulatory frameworks within which Apple is working, I don’t expect a lot at WWDC 2023.

However, I’ll be pleasantly surprised if we do get some significant news in early June.