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Overcoming the Limitations of Audience Targeting

As the migration from web to mobile persists, advertisers are realizing that the unique environment of mobile is not translatable to what was achieved on desktop. We are finding out that apps are much more transactional than previously estimated with evidence of a push towards messages (information) and video rather than standard advertisement displays, yet both still very dependent on consumer insights from device signals (such as location). All of these shifts place enormous pressure on the level and quality of mobile data being collected.

To capture the data, APIs have become pivotal for mobile execution. This is another piece lost in the ad-tech shift, as web-based client-side technologies are obsolete in the mobile world, such as pixels and cookies. Many ad-tech platforms are not architected for mobile and instead, require JS tags to drop cookies for user-level data, and meanwhile, are still left to figure out how to adapt. This will greatly limit the level of data being collected while dimensioning the ability to target users as the mobile ecosystem continues to advance.

From the publisher standpoint, we see concerns around the level of quality with SDKs on the market known as SDK bloat, plus a lack of resources to generate comprehensive audience profiles. Without audience information, publishers struggle to form the non-intuitive conclusions necessary to enrich ad offerings and predict intent.

More regarding Cookies and APIs:

  • 67% of ads served on Safari browser were served with no cookie match
  • 66% of impressions served overall
  • 41% of total mobile impressions served without cookies
  • Compared to 10% cookie-less on desktop

Overcoming Limitations

To date, there is no universal solution for “cookie-less” mobile audiences, although many advertisers and publishers are working with a combination of approaches to maximize data accuracy, especially given the limitations of pro-privacy operating systems and other consumer-interest norms such as opt-out/opt-in user level controls.

The current approaches include:

  • Unique device identifiers (UDIDs), which is a digital indicator specific to a mobile phone or tablet. The challenge is that these ID formats vary according to manufacturer and is not effective for users who have more than one device. Additionally, this method has been phased out due to pressure by Apple and Google to adopt advertiser IDs
  • Android ID and ID for Advertisers (IDFA) are parallel methods of tracking and are exclusively for marketing purposes. They stick like a cookie as the user moves across the mobile browser and mobile apps. On Android, the ID is static and depreciates in about two years. On iOS, the IDFA is consumer controlled and open by default.
  • “Device fingerprinting” is a technique which uses browser environment characteristics, for instance fonts, plugins and screen resolution to create a unique ID. Mobile touch points include country code, device brand, device model, device carrier, IP address, language, OS name, OS version, user agent, and timestamp.
  • Personally Identifiable Information (PII) uses elements including name, phone number, email address or other cross-channel logins to create a “match.” The main definition of PII is that it is data that could potentially distinguish a specific individual and de-anonymize the user.
  • “Lookalike” modeling uses behavioral data and individual-level data to create inferences using broad audience insights to map out attributes. This type of data is typically collected on specified policies and marginalizes the need for persistent
    individual-level targeting.

In 2015, a data platform equipped for mobile is essential for your company to succeed. If you would like to learn more about how Personagraph can help you effectively reach your audience and meet advertising KPIs, please contact us at: for a demo.

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Published inAdTechPublished in 2015Reports and Whitepapers

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