One of the questions that has come up in our workshop series Mobile Growth Lab has been about what lookalikes – and optimization will look like under iOS 14.

As we know, the cohorted post-install events that we all know and are accustomed to will be replaced by conversion values that are not linked to individual users or install times. 

Ad networks such as Facebook & Google will receive these conversion values without timestamps rather than individual postbacks for each completed event (revenue, level 50, free trials etc.)

Until this point, Facebook would build lookalikes based on a user’s similarity to others who were in a seed list – based on their probability of completing events that users in the seed list had completed. So: if your users in the seed list were high value purchasers for your social casino game, Facebook would find other users that actually made high value purchases in other social casino games.

By the way, over the last few months, we’ve increasingly leaned into broad targeting on Facebook rather than lookalikes – especially on accounts with significant history. In essence, this has worked similar to lookalike targeting – if you were optimizing for purchases for your match 3 game in a broad or AAA campaign, Facebook would find other users that made high value purchases in other match 3 games.

So: what happens post ATT under SKAdNetwork?

Under SKAdNetwork, the algorithm won’t know who has already made high value purchases in other comparable apps. BUT you can map events in your conversion value schema to standard events in your MMP for Facebook, Snap, TikTok and other platforms. 

This is to say, your conversion events can be set up to be updated when an event fires in your app (say when a user completes a first purchase event, your conversion value becomes 3 or 011; and for a user completing a first purchase and then getting to level 40, your conversion value becomes 6 or 110).

This event is mapped to a standard event in Facebook – or every purchase (or for that matter non-purchase event) can be mapped to Facebook’s standard event fb_mobile_purchase. This lets you optimize your Facebook campaign for purchases.

So: Facebook’s algo is going to know and understand how many ‘fb_mobile_purchase’ events were fired for each campaign via SKAdNetwork – and which users were targeted by each campaign. It’ll also know from its first party data as to which users saw ads, what their interests and demographics were, and which users engaged with the ads via clicks and video views. 

With this, Facebook forms a probability of which users are likely to engage with which app – and this will let Facebook group users into lookalikes, or target users via purchase or post-install event optimization.

In many ways, this approach is similar to that of probabilistic attribution that we talked about in an earlier edition of this podcast. This is also similar to some of the post-cookie solutions that are being proposed in the web advertising world – which we will hopefully talk about in more detail soon.

Needless to say, this form of optimization and building lookalikes based on SKAdNetwork aggregate data is far less powerful than today’s deterministic optimization and lookalike construction. That said, what is clear is that you will still be able to target event completers – and hopefully the platforms’ ability to target and optimize for event completers will get better over time.

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.

Constructive criticism and suggestions for improvement are welcome, whether on podcasting platforms – or by email to shamanth at rocketshiphq.com. We read all reviews & I want to make this podcast better.

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