The biggest challenge facing brands when marketing on mobile is knowing their users. Reconciling the many accounts across screens and devices that belong to each user profile lends a difficult hand in segmented campaign targeting. While mobile devices unfortunately do not have the associated advertiser cookies followed religiously by desktop advertisers, there remain ways to gain insight into mobile user activity in order to properly segment and establish targeted user groups.
Cue the IDFA, or the iOS Identifiers for Advertisers and their Android counterparts. While not entirely the mobile cookie alternative, IDFA’s serve to help advertisers track a specific mobile device where an ad action takes place, such as a click or download. By applying device-level ad action history, advertisers enhance campaigns to drive user receptivity by applying large scales of aggregated data in order to establish targetable mobile profiles.
Amongst the key characteristics of the IDFA’s, it is important to note that this tracks ad actions across mobile web and in-app, giving a holistic view of the users ad activity and insight into personal preferences. Google DoubleClick support forums confirm that while in iPhones the IDFA is activated by default, each user does, however, have the ability to limit or reset their IDFA manually in their phone settings, offering consumers the benefit of choice compared to the now extinct UDID. This ability to self-regulate means information transmitted is not considered personally identifiable information, and users opting in to sharing mobile actions have a predetermined receptivity to targeted ads.
IDFAs to create user profiles for cross platform targeting
In today’s world, we are living with a plurality of devices, constantly switching from smartphone to tablet to computer throughout the day and throughout a single information search. AdTech algorithms serve to aggregate mobile data from multiple sources in order to reconcile accounts and create mobile user profiles. The biggest challenge to algorithm effectiveness is reconciliation of cross-device information. Mining millions of mobile data points to identify connection clues and associate one user profile to each of his/her devices. Each time a mobile user connects to his/her mobile apps, sees a mobile ad, or downloads an app, a data point is generated lending insight into the user’s preferences and tendencies.
By coupling demographic information associated with each IDFA with the user’s known usage preferences, we begin to construct an anonymous user profile. Passing through a funnel, algorithms make further associations with each of the user’s actions. For example, User A clicks on a banner and downloads an ecommerce app. With this knowledge, we have identified the IDFA associated with this user on her smartphone
IDFA XXX profile identification funnel:
- User A clicks on Amazon banner ad and converts, downloading the app
- Apply demographic data: Female, 35-40 years old, lives in an urban area
- Apply mobile usage data: Checks news app daily at 7 AM, mobile usage mainly during the evening, usage spikes from 12 – 1PM
- Reconcile cross-device accounts: User A logs into her Amazon account on iPad, device now known and associated in her cross-platform profile
- Associate any further ad interaction data: Has clicked on Uber ad, no conversion
IDFA’s are the only data tracking that Apple allows advertisers to access, and the App Store began rejecting apps using any other identifying system, thus ensuring both developers and advertisers are playing the same game.
These consumer device IDs alone aren’t enough to effectively target consumers. However, the ad actions and mobile preferences data collected from access to a large pool of such IDFAs enables mobile advertisers to apply additional personal-level insights to their existing data sources to further narrow down specific targetable mobile audiences.