Post by account_disabled on Dec 20, 2023 4:42:05 GMT
Many years ago Conversations about mobile performance marketing and user acquisition center around automation. “Create a programmatic campaign and let it run!” “AI and machine learning free up your time to do other things.” That's what User Acquisition Managers say. Manager)) heard for many years But that all changed when Apple released iOS 14.5+. The difficulty of getting users to consent to ad tracking through App Tracking Transparency (ATT) and integration with SKAdNetwork (SKAN) added another layer of complexity. with the UA campaign, but instead saw this as an obstacle. Instead, I see it as an opportunity for UA managers to hone their skills and truly demonstrate their value. More than ever, UA managers need to think critically.
Campaigns you run for iOS users who haven't consented to tracking (and share their IDFA) will look very different from campaigns you run on almost any other device. Conversion thinking and Phone Number List modeling Values require a thorough understanding of UA and marketing. The launch of SKAN and ATT is just the beginning of the trend towards privacy-centricity in the mobile industry. Complexity and ambiguity will continue to define UA for the foreseeable future. Make talented UA managers and their teams shine. Your creativity and knowledge base will be key to your campaign's success. Here are some new challenges and opportunities. Partially for UA teams to navigate a mobile business that is increasingly focused on user privacy, learning SKAdNetwork and aggregated SKAdNetwork data is the only Apple-approved way to obtain hard attribution data. (deterministic attribution data) For installations that do not ship with IDFA, working with SKAN requires the UA team to.
Understand how the aggregated and delayed SKAdNetwork datapoints you receive can be connected to the rest of the data. and your BI stack? Data discrepancies between data sources can be manually identified, improving the entire process using IDFA. Tasks at the heart of UA, such as user segmentation and behavioral modeling, have become increasingly difficult. Understanding these impacts is key to working with SKAN successfully. Additionally, UA teams need to understand SKAN in detail to optimize the conversion value schema for the post-installation attribution payload. This helps you capture as much insight as possible about in-app usage within the first 24 hours, when Apple and SKAN return attribution data. This is key in providing data from which your team can learn and predict usage patterns. And learning these things will inform your conversion value strategy.
Campaigns you run for iOS users who haven't consented to tracking (and share their IDFA) will look very different from campaigns you run on almost any other device. Conversion thinking and Phone Number List modeling Values require a thorough understanding of UA and marketing. The launch of SKAN and ATT is just the beginning of the trend towards privacy-centricity in the mobile industry. Complexity and ambiguity will continue to define UA for the foreseeable future. Make talented UA managers and their teams shine. Your creativity and knowledge base will be key to your campaign's success. Here are some new challenges and opportunities. Partially for UA teams to navigate a mobile business that is increasingly focused on user privacy, learning SKAdNetwork and aggregated SKAdNetwork data is the only Apple-approved way to obtain hard attribution data. (deterministic attribution data) For installations that do not ship with IDFA, working with SKAN requires the UA team to.
Understand how the aggregated and delayed SKAdNetwork datapoints you receive can be connected to the rest of the data. and your BI stack? Data discrepancies between data sources can be manually identified, improving the entire process using IDFA. Tasks at the heart of UA, such as user segmentation and behavioral modeling, have become increasingly difficult. Understanding these impacts is key to working with SKAN successfully. Additionally, UA teams need to understand SKAN in detail to optimize the conversion value schema for the post-installation attribution payload. This helps you capture as much insight as possible about in-app usage within the first 24 hours, when Apple and SKAN return attribution data. This is key in providing data from which your team can learn and predict usage patterns. And learning these things will inform your conversion value strategy.