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KEY HIGHLIGHTS

🎭 What is the Topics API?
🧩 Why is the system structured this way?
🔦 How will the Topics API affect performance on Android?

FULL TRANSCRIPT BELOW

What do Google’s newly announced Privacy Sandbox initiatives mean for marketers?

As you may have read, Google recently announced its Privacy Sandbox initiative for Android (it had announced its Privacy Sandbox for web back in 2019), which is a set of tools and technologies to enable privacy friendly advertising.

This is not surprising – given that many expected Google to follow in Apple’s footsteps after ATT. And because Google is an advertising company unlike Apple, the changes are far more clearly thought out and less disruptive than ATT.

While there still are open questions, the details of actual implementation is still unclear, and the rollout is still 2 years away – it’s time to start thinking about longer term implications of these far reaching changes for mobile app based business.

There are a couple of key components of the Privacy Sandbox – and we want to unpack the implications of these for marketers.

Today we’ll talk about Topics API, which is one of what Google calls Privacy Preserving APIs, and is a key part of the Privacy Sandbox. We’ll talk about how this fundamentally changes targeting – and what this means for marketers. (In coming episodes we will speak about the other components of Google’s Privacy Sandbox as well).

What is the Topics API?

The Topics API lets ad network SDKs target different users by the topics associated with them. Think of this as interest based targeting – except that given the specific implementation, the interests or topics are defined in a way that does not disclose personally identifiable information.

Here is how this works:

Each app has up to 3 topics associated with it, which can be ‘read’ by the ad tech SDKs present in the respective app. 

Now if I as a user am using the 7 apps listed above, the Topics API generates the list of my ‘Topics’ as a user.

So: a ‘Topic’ is defined for each user – based on their recent interactions with specific apps.

SDK1(Applovin) is integrated in app A(ESPN). Jane opens ESPN this morning. Applovin SDK is initialized.

The Topics API for ESPN + Jane surfaces the topic ‘Soccer’.

SDK1 gets a bid request: “would you like to show an ad for a user on ESPN whose Topics include ‘soccer’?’ – and the SDK can choose to respond with an appropriate ad(say match tickets via Ticketmaster).

**

Jane starts playing FIFA this evening. SDK2 is initialized.

What the advertising SDK sees: FIFA + Europe(so: the SDK does NOT know that this is the same person who accessed ESPN in the morning to buy match tickets via Ticketmaster). 

The SDK can choose which ad to show to a user who likes the topic Europe – and decides to show an ad for Booking.com.

**

If Jane buys from both ads that she sees, ad SDK sees:

someone with topic = Europe bought tickets to travel to Europe via an ad on FIFA;

someone with topic = Soccer bought soccer match tickets via an ad on ESPN.

The SDK will not know that this is the exact same person.

**

The SDK can conclude though that the FIFA app converts well for sports related ads shown to users with sports related topics -> and this converts twice as well as the combination of ESPN(publisher app) – Europe(topic) – travel(advertiser app).

Plus: platforms could combine this with first party data(Facebook could combine some of this with interest targeting on Facebook, Google search with intent, and TikTok and Youtube with video view data) – so platforms will have quite a bit of data to train their ML models around to refine their targeting.

You can see *why* this is structured the way it is. In Google’s words:

The reason that each app gets one of several topics is to ensure that different apps get different topics, making it harder for apps to cross-correlate the same user. For example, app A might see topic T1 for the user, but app B might see topic T2. This makes it more difficult for the two apps to determine that this information is associated with the same user.

**

Great. How does this affect campaign performance if you’re running ads on an ad network on Android?

As you can see, the GAID is nowhere in the picture here. And yes, while this lets you collect enough data on publishers and topics to train your ML models, the one thing that you should absolutely remember is that non-purchase signals are very very poor predictors of purchase behavior. We’ve seen this across apps and across verticals – so you should expect performance to be worse than now.

Besides, each user has 5 ‘topics’. If you look at the example above, the 5 ‘topics’ an SDK is accessing about a user are a very small sliver of a user’s persona. If the topic assigned to me is Europe, is that because I’m an American wanting to visit the continent? Because I’m an economist doing research? Am I reading a book about Europe? It’s going to be hard to pinpoint which of those contexts applies to me.

So you should absolutely expect performance deterioration compared to today – although this framework is significantly more powerful and well thought out than SKAdNetwork, which does not address targeting at all – and also does a ham handed job of managing attribution(which we will talk about in a subsequent episode).

**

I hope this episode gives you an idea of what’s changing with targeting via the Topics API in the Privacy Sandbox. 

We’ll talk about other changes in the Privacy Sandbox in upcoming episodes – so stay tuned.

A REQUEST BEFORE YOU GO

I have a very important favor to ask, which as those of you who know me know I don’t do often. If you get any pleasure or inspiration from this episode, could you PLEASE leave a review on your favorite podcasting platform – be it iTunes, Overcast, Spotify or wherever you get your podcast fix. This podcast is very much a labor of love – and each episode takes many many hours to put together. When you write a review, it will not only be a great deal of encouragement to us, but it will also support getting the word out about the Mobile User Acquisition Show.

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Thank you – and I look forward to seeing you with the next episode!