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Since Apple introduced its ATT privacy framework to give app users more control over their data, ad tech businesses have been tasked with making tradeoffs to comply with new data restrictions while still meeting their growth goals.
Still, while mobile advertisers can no longer use personal IDs to target the 70% of iOS users who have not consented to be tracked, there are other tools at their disposal — such as contextual signals and probabilistic attribution — to identify and target quality audiences across the mobile ecosystem.
That being said, in-app advertising might seem less efficient with the deprecation of the Identifier for Advertisers (IDFA). But with the right data, strategies and partners, it’s not only still a viable growth strategy but a vital one.
What’s changed after iOS 14.5
Under the new privacy restrictions, app advertisers can no longer rely on the IDFA to provide them with device-level data to serve relevant advertising to users on iOS devices. Since advertisers can no longer track a user’s activity across apps on iOS — including their clicks, downloads and conversions — they also have less ability to measure the effectiveness of their ads and use that information to optimize their campaigns and ad budgets accordingly.
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This loss of efficiency means lower conversion rates, but also cheaper CPMs (cost per 1,000 impressions). So, while scaling campaigns to beat KPIs might be more complex, the business of app growth can be cheaper than it was before.
Performance marketing is different, not worse
Beginning with iOS 14.5, while advertisers might not have access to device ID data, they can still use contextual signals to show ads to quality users. What are contextual signals? They’re privacy-compliant data points that relay useful information about an ad opportunity, such as location, device type, and information about the environment in which an ad is shown (that is, characteristics of an app or website).
With this type of data, advertisers can utilize contextual targeting — matching an ad to an impression opportunity to accurately predict the probability of a user engaging with an ad. From there, they can determine the amount to bid for each impression.
Because users are automatically opted out of IDFA tracking, advertisers can no longer rely on the device ID to access data about how a user interacts with an ad, nor target audiences one-to-one based on in-app events. Instead, machine learning (ML) models are leveraging new contextual signals to make effective predictions.
While this has made in-app advertising less efficient, iOS advertising is still hitting or exceeding advertisers’ ROAS goals. For example, at LifeStreet, we’re seeing fewer conversions per advertising dollar spent on device ID-less traffic, but our CPMs are about 2.1 times lower. This nets out to 10% higher ROAS on media spend without a device ID. While the impression-to-conversion rate has changed, the decrease in cost has helped advertising on iOS improve its effectiveness.
New data, new competitive landscape
Contextual signals can also be combined with other metrics. For example, the number of interactions made with a specific feature of an ad identifies which part of the creative is adding value. Of course, this isn’t as precise as using the IDFA, but technologies like ML make it possible to ingest these signals and predict in real time the value of each ad impression with nearly the same level of accuracy as device ID-powered advertising.
Furthermore, the competitive landscape of mobile advertising is far more level than it’s ever been. Today, all ad tech players — not just those outside of the walled gardens (Facebook, Google) — have less information about users than before. This has created space for smaller, niche players with specialized historical ML models and agile algorithms to compete with the tech giants.
For this reason, marketing platforms that have seen the most success following the deprecation of the IDFA are those that continue to invest in improving their models’ performance by adding more predictive signals. The ability to produce new signals that can be used to train models to improve their predictive accuracy continuously will drive more effective bidding, enabling lower CPIs and higher user quality, and ultimately increase ROAS for their advertisers.
How to set up your mobile advertising strategy for success
Now that the rules of the mobile advertising game have changed, so too have the methods of winning it. What are the strategies for successful mobile advertising in today’s privacy-enabled ad tech landscape?
Re-evaluate incrementality and experiment with your media budget
As noted above, the economics of mobile advertising have shifted since iOS 14.5. That means it’s smart to re-evaluate the tangible impact of your spending, and experiment with your media budget. Be open to testing new channels and specialized partners. Work closely with your mobile measurement partner (MMP) to maximize available attribution data, and understand the incrementality of each partner’s campaign performance, which will help enable future campaign success.
Be open to working with independent ad tech companies
Today, the playing field is more level, allowing smaller, niche tech providers to secure stronger results than were previously possible. For advertisers, there’s an opportunity to partner with independent ad tech companies that can offer custom solutions to fit their needs rather than the “one-size-fits-all” approach of the walled gardens.
Demand more from your growth partners
Finally, advertisers need to be demanding of their partners, overseeing how they work and making sure advertisers get the information they want, not just the performance. With Apple’s — and soon Google’s — ongoing privacy changes, strong performance today does not guarantee strong performance 18 months from now.
It is critical advertisers ask themselves: Am I working with the right partners? Are they transparent and passing data I can use to scale future campaigns? Do their process and expertise instill trust? If the answers to those questions are not in the affirmative, it might be time to reconsider your current marketing mix.
Despite increased privacy, the future of mobile advertising is bright
The mobile games industry is projected to see exponential growth into 2029. The advertisers who make the most of this boom will be those whose partners can efficiently test the contextual signals they receive from ad exchanges and use them to iterate their predictive modeling. In our current age of increasing user privacy, the methods of successful mobile advertising might have changed, but the ability to succeed has not. Ad tech has always been full of trailblazers who thrive in moments of uncertainty, and this moment will be no different.
Levi Matkins is CEO of LifeStreet.
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