Since Apple’s ATT Framework, mobile advertisers have been innovating new ways to serve ads to the right user, in the right place, at the right time — without relying on user-level data provided through a unique identifier, like the IDFA. This new focus on contextual — versus deterministic — data signals has propelled contextual advertising. In fact, the global contextual advertising market is set to reach $335.1 billion by 2026.
Keep reading to learn more about contextual data and how it can be used to run successful mobile programmatic campaigns post-IDFA.
What is mobile contextual data?
Contextual data, like probabilistic data, is made up of contextual signals. Contextual signals are privacy-compliant data points that relay useful information about the environment in which a mobile ad appears. This might include the app or mobile web URL on which the ad shows up. It might also include broader information about a user’s ad experience, such as their location, device type, operating system, battery level — even whether their device is plugged in and charging and what text size their screen is set to.
While contextual data might be having a renaissance today, it’s nothing new. In fact, even prior to iOS 14.5, contextual signals were available to mobile marketers. There’s just been a renewed focus on mobile contextual data since Apple’s ATT Framework. Why? Because prior to the deprecation of the IDFA, DSPs could access user-level data about a user’s behavior in apps and on the mobile web — including what apps they browsed, for how long, etc. Today, unless a user opts in to sharing their IDFA, app marketers do not have access to the deterministic data available through their device ID. Instead, what marketers do have access to — regardless of whether a user opts in or not — is contextual data.
How do DSPs use contextual data to engage and retain mobile users?
While access to the data used in mobile programmatic advertising has changed, the advertising process itself has not. Many DSPs use the same bidding formulas and prediction models to serve hyper-targeted in-app ads to high potential mobile users. What’s changed are the signals being fed into DSPs’ bidding models. That is, unless a user opts in to sharing their IDFA — contextual signals rather than deterministic signals are fed into machine learning (ML) models to bid on users.
An example use case of contextual data
A hypercasual game app is looking to retarget customers that have downloaded their app but haven’t made an in-game purchase yet. The game’s KPI is to retarget users to re-open their game and make an in-app purchase (IAP) within 30 days of reactivation. They launch a retargeting campaign with a mobile DSP. Every time there is a bid request, the DSP receives a set of contextual signals from the ad exchange about the impression opportunity.
The DSP’s bidding strategy uses predictive ML models that have been trained on the game’s existing user base with desired characteristics — e.g. players that have made an in-game purchase within 30 days of download or reactivation. These models identify common features between contextual signals, the DSP’s historical impression data and existing users to determine in real time whether or not to bid on an impression and by how much.
For example, one characteristic the hypercasual game identifies as indicative of a high potential user are people who open the game and play for short sessions in the middle of a week day. This indicates players who are likely to come back to the app during moments of free time. Following the correlation, the game app’s DSP bids higher on impression opportunities that occur midday and midweek.
How to make the most of your contextual data in the post-IDFA age
No matter what data is available, DSPs will innovate ways to use it to advertise to in-app audiences. In the new age of data privacy, what will give advertisers an edge will be how their DSP partners use their ML models to make new and unique connections between available data and audiences. Here are some tips to make the most of your contextual data.
Know your audience
The better you know your audience, the more insight you can squeeze from your contextual data. This is where the power of robust audience profiles cannot be overestimated. If you know your audience tends to be young professionals who play your game during short stretches of time during the week, then you can better use contextual data to scale new pockets of ad inventory. Another example: if your audience tends to fall in an older demographic then bid higher on impressions for devices that show a larger text size.
Customize your ATT prompt
Yes, contextual data is having a moment. But, that doesn't mean deterministic data isn’t still available when users opt-in to sharing their IDFA. Furthermore, opt-in rates are much higher than originally expected. In contrast to the dire predictions of IDFA opt-in rates being as low as 0-20%, the average opt-in rate for users worldwide is 46%. That means that almost half of all iOS users trust apps enough to share their IDFA for a more personalized experience.
To stretch the value of your contextual data even further — customize your ATT opt-in message with a pre-permission prompt. This enhances your chances of users opting in to sharing their IDFA by giving them more information about how you’ll be using their data. In fact, Adjust found a promising 65% of users opted in when shown a pre-permission prompt during an app’s onboarding flow.
Work with a DSP that can do contextual targeting
Programmatic technology has come a long way since app advertising first began. Now, DSPs can create robust bidding formulas. These formulas combine contextual and deterministic data with historical impression learnings to serve hyper-targeted ads to users regardless of whether they’ve opted into sharing their IDFA. With that said, work with DSPs with advanced programmatic bidding technology who have a history of mobile performance.
Takeaways
Contextual signals are privacy-compliant data points that relay useful information about the environment in which a mobile ad appears.
- Contextual data might be having a renaissance today. But, even prior to iOS 14.5, contextual signals were available to mobile marketers. There’s just been a renewed focus on mobile contextual data since Apple’s ATT Framework.
- Many DSPs use the same bidding formulas and prediction models to serve hyper-targeted in-app ads to high potential mobile users. What’s changed are the signals being fed into DSPs’ bidding models.
- Tips for making the most of your contextual data:
- Know your audience
- Customize our ATT Prompt
- Work with a DSP that can do contextual targeting
Looking to use contextual targeting in the post-IDFA age?
Our retargeting DSP, ReAppi, blends contextual data with historical impression data to serve targeted ads that win. Reach out to us to get started.
