Mastering Privacy-Friendly Targeting
>Mastering Privacy-Friendly Targeting
Privacy has always been an important factor and will continue to play an important role. It is important but also tricky to be mindful of privacy while ensuring that brands reach the relevant audience. Effective targeting is the go-to for brands to reach relevant audiences. Read on to learn more about the importance of effective targeting in app marketing, different privacy frameworks, and strategies.
Effective targeting in app marketing is mainly to run targeted ad campaigns that employ several different ads where each one is targeted towards specific audiences based on their preferences, responses, and trends. Achieving this requires marketers to continuously test and tweak campaigns to adapt to the requirements in order to gain the best results.
The concerns surrounding user privacy among consumers have been hugely growing, according to a survey conducted in March 2021, 52.4% of consumers are somewhat concerned about their online privacy in comparison to previous years.
The Significance of User Privacy:
Privacy has seen an ever-changing landscape. Moreover, privacy and customer trust are highly correlated in today’s business climate. It is also important to note that MediaMath’s 2023 report indicated that 74% of respondents are more likely to trust brands that give importance to using personal information with a privacy-safe approach by prioritizing user privacy.
Although there is a high need for data privacy, customers also value being recognized, understood, and targeted with personalized experiences. In order for brands to provide these experiences to their users a certain amount of user data has to be shared by the consumers — and users are consenting to this in return for a better and more personalized user experience.
Exploring the Frameworks:
We’ve seen several changes in the way user privacy is handled in the marketing landscape. One of the recent frameworks that has come into the picture is the privacy sandbox.
Privacy Sandbox has two major areas of focus which includes Privacy Sandbox on Android and Privacy Sandbox for the Web. For app marketers, the area that is most relevant would be Privacy Sandbox on Android. Let’s dive deeper into the various aspects of Privacy Sandbox on Android —
- Attribution Reporting API: Facilitates tracking ad campaign effectiveness for reporting conversions, gaining optimization tips, and detecting invalid traffic and ad fraud.
- Protected Audiences API: Allows targeting tailored, first-party audience groups for remarketing based on previous app interactions while keeping data confidential from third parties.
- Topics API: Facilitates interest-driven advertising using interest signals derived from the user’s app usage directly on the device.
- SDK Runtime: Allows app developers to incorporate third-party SDKs while retaining control over permissions and data access, thus preventing undesired data collection and covert tracking. This also speeds up the dissemination of SDK updates.
IOS and SKAN:
SKAdNetwork or SKAN is Apple’s API and Apple’s privacy framework for ad measurement and attribution. It provides advertisers with aggregated insights without having to compromise on user privacy. This solution/framework was introduced for advertising campaigns run on iOS and Apple introduced this in May 2018.
In April 2021, iOS 14.5 was introduced along with ATT or App Tracking Transparency Framework which was implemented as part of Apple’s privacy efforts. ATT basically requires users to opt in through a prompt where the user can choose if they want to share their device ID or IDFA with the app marketers or advertisers.
RevX Strategy:
At RevX, we approach IOS retargeting with our Probabilistic Model.
RevX’s Probabilistic Model:
This model follows a step-by-step approach that includes data collection, synthetic ID generation, audience building, and real-time bidding & ad delivery.
Data Collection:
The first step of the process involves the collection of data where we postback events collect user data for various IOS parameters such as –
- IP Address
- iOS Version
- Device Model
- Carrier Information, and more excluding the IDFA
Synthetic ID generation
As part of this second step of the process, machine learning is used to create a unique synthetic ID for each user which supports retargeting and enables probabilistic user identification.
Audience building
The next step is all about audience-building, here the custom-built audience is created to align with the campaign’s objectives. Moreover, this facilitates a smooth and precise ad delivery to all the relevant user segments.
Real-time bidding & ad delivery
We don’t stop at audience building, as the final part of this process involves real-time bidding and ad delivery. As part of this part of the process, when a user clicks on an ad, a real-time bid is placed after comparing their device data to their synthetic IDs. This enables higher match results when it comes to the display of ads.
To know more about retargeting IOS users for your brand and how RevX can facilitate this smoothly with our probabilistic model, reach out to our team!
In Conclusion:
In the ever-evolving privacy world, app marketers need to become familiar with the existing frameworks and become innovative with advertising campaigns to build effective campaigns that deliver results without compromising user privacy while following the guidelines. App marketers have to find innovative ways to deliver great user experiences through personalized advertising campaigns that make the consumers valued and seen. The middle ground for this can be found by thoroughly understanding and following the privacy frameworks that are in place and building campaigns that are focused on building trustworthy relationships between consumers and brands.
Hansika Sana
Marketing Manager, Content
