SKAdNetwork
SKAdNetwork (SKAN) is Apple's privacy-centric tool for attribution of mobile app installs and related events.
The SKAdNetwork (SKAN) is an API from Apple designed to help advertisers measure the success of ad campaigns while maintaining user privacy. Apple installs this framework on devices with iOS 14 and later. It allows registered networks to receive data about user installs in response to ads, albeit in an anonymized and aggregated format.
SKAN reports conversions to ad networks, enabling them to attribute installs without resorting to using the device’s ID. It uses a conversion value that measures user engagement levels within a specific time frame. After a user installs an app, the app can use SKAN to update the conversion value, indicating that the user performed a certain level of activity.
Example
Assume an ad network serves an ad for a hotel booking app. A user sees the ad and installs the app. If this user makes a booking within 24 hours, the app records this in the conversion value, which then sends back to the ad network.
Why is SKAN important?
- User Privacy: SKAN upholds users' privacy by not directly linking ad-to-user action. It allows the continued tracking of ad campaign effectiveness without violating user privacy rules.
- Enhanced Advertising: Conversion values from SKAN intentions help advertisers optimize their campaigning strategies.
Which factors impact SKAN?
- Time-Decay Models: Using advanced time-decay models can maximize the usefulness of the limited data from SKAN.
- Incrementality Testing: Complementing SKAN data with incrementality testing can improve campaign optimization.
How can SKAN be improved?
- Delay in Reporting: The SKAN data is reported with a delay, which can affect the real-time decision-making process.
- Aggregated Data: As data provided by SKAN is aggregated, this limits the possibility of granular analysis.
What is SKAN's relationship with other metrics?
The conversion value from SKAN could potentially relate to similar e-commerce metrics like customer lifetime value (CLTV) and average order value (AOV). It can also tie to app metrics, such as user engagement levels and retention rates. It allows e-commerce analysts to understand user behavior related to ad campaigns while upholding privacy norms.
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