April 25, 2022
When Apple rolled out its App Tracking Transparency (ATT) last year, mobile app marketers were more or less put on notice: convince users why they should allow you to track their behavior, or find another way to target, measure and optimize your campaigns.
Suddenly, brands that relied on tracking to assess the efficacy of specific channels for their campaigns were shut off from a key insight they’d long relied on.
These changes have had a significant impact on tactics marketers use to reach and engage their audience, but savvy marketers are testing, experimenting and succeeding at with alternatives. Let’s look at the impacts of privacy on mobile campaigns, along with some of the tactics to help marketers overcome them.
First, marketers can no longer target via Apple’s Identifier for Advertisers (IDFA), which is the unique device identifier Apple assigns to devices. Therefore, mobile app publishers will need to rely on data that isn’t as precise as the IDFA, and lean into contextual targeting a bit more. But as we’ll see below, this isn’t the challenge that many believe it to be.
Second, if you can’t recognize a specific device, you can’t engage in retargeting and re-engagement campaigns.
Third, you will need to optimize for UA creatives, as the creatives themselves will need to do more of the heavy lifting. Plan on doing a lot of creative A/B testing to see which creatives perform the best for your UA campaigns.
This isn’t to say that privacy has put an end to dynamic creative optimization, however. We still have plenty of attributes that can drive which creative a user sees, including geolocation, channel, time of day and so on.
For instance, the creative for a delivery app can emphasize the convenience of home delivery to users in areas that are soaked in by snow, while users in Florida see one that asks, “why do your own shopping when the beach is calling?”
Fortunately, Apple’s ATT feature doesn’t put an end to smart marketing. With the right partner, app marketers have great options that aren’t reliant on just the user ID to drive growth.
Moloco, for instance, has easily adapted to ATT (and all privacy changes, for that matter) because our machine learning (ML) thrives on sparse data sets. What data do we have to step into the IDFA’s place? Bid requests still contain a wealth of information, including IP address, geolocation, OS, time of day, and so on.
Additionally, we’ve been developing, testing and improving our limited ad traffic (LAT) models even before ATT was released. LAT traffic isn’t a new concept, what’s changed is the percentage of LAT traffic was much lower before iOS 14.5. This groundwork has enabled our customers to thrive post-ATT.
Moloco’s ML is a form that’s known as deep learning, and it’s built on deep neural networks (a fancy way of saying it has a big brain). Our DSP is programmed to self learn and self improve with sparse datasets, so it’s able to adapt and drive performance in the absence of IDFAs.
In fact, ever since ATT went into effect, Moloco has seen a steep increase in iOS spend from our customers. This is a testament to our DSP’s performance.
Finally, while creatives need to be a bit more “generic” we shouldn’t assume this is a bad development. Much of the customization relied on third-party data segments, which were never really accurate to begin with (let’s be honest here). Marketers should focus instead on testing the messaging, themes, images, offers and formats to see which delivers the best results.
Here again ML comes in handy, because it can determine which creative is most effective for the users.
Still, marketers want to know if their campaigns are delivering tangible outcomes, which is why Moloco has integrated with the leading MMPs, and supports SKAdNetwork (SKAN). Through these integrations, we receive postbacks across the entire campaign, allowing us to pinpoint the channels that deliver the most conversions.
What to learn more? Get in touch.
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