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The Challenges of Introducing New SKAdNetwork Models in Mobile App Marketing

With the recent introduction of SKAdNetwork (SKAN), the landscape of mobile app attribution has undergone a significant transformation. In this blog post, we’ll delve into the challenges faced by introducing new SKAN models and explore their impact on the mobile app marketing industry.


Understanding SKAdNetwork (SKAN)


SKAdNetwork, or SKAN, is a framework developed by Apple to provide privacy-focused attribution for app install campaigns. Unlike traditional attribution methods that rely on device-level identifiers like the Identifier for Advertisers (IDFA), SKAN operates on a more privacy-centric model, allowing advertisers to track app installs without compromising user privacy.


Effects on the Mobile App Marketing Industry


The introduction of SKAN models has ushered in a new era in mobile app marketing attribution. Advertisers are increasingly relying on first-party data and exploring alternative attribution models to navigate the challenges posed by SKAN. Ad networks and mobile measurement partners (MMPs) are also adapting to new attribution standards, while strategies for success in a SKAN-driven ecosystem are evolving.


Challenges with Introducing New SKAN Models


1. Limited Data Attribution:

The transition to SKAN models presents a challenge due to limited access to user-level data. With SKAN, advertisers can only receive aggregate data on app install events, making it difficult to track individual user interactions and behaviors.


2. Conversion Value Optimization (CVO) Limitations:

SKAN’s conversion value optimization (CVO) poses challenges for advertisers in optimizing campaign performance. With SKAN, advertisers have limited control over optimizing campaign parameters based on post-install events, leading to potential inefficiencies in campaign optimization.


3. Impact on Advertiser Strategies:

The shift to SKAN models requires advertisers to adapt their strategies to a post-IDFA world. Advertisers must find alternative methods for targeting and tracking users, such as leveraging first-party data and implementing contextual targeting strategies.


4. Privacy Concerns:

While SKAN prioritizes user privacy, it also introduces challenges in balancing privacy concerns with marketing effectiveness. Advertisers must navigate the complexities of privacy regulations and ensure compliance while still delivering targeted and relevant advertising experiences.


 Navigating SKAdNetwork Migration: Challenges and Solutions


In the transition to SKAdNetwork (SKAN) 4.0, unforeseen hurdles emerged, notably exemplified by Meta’s rollback from SKAN 4.0 to 3.0 due to a surge in “0” conversion values post-migration. The crux of the issue lies in the complex interplay of multiple SDKs managing SKAdNetwork within advertiser apps, resulting in conflicting updates and unreliable attribution data. To address these challenges, effective coordination, thorough testing, and continuous monitoring are paramount. Advertisers must establish clear communication channels among stakeholders, conduct rigorous testing to identify discrepancies, and implement robust monitoring systems to optimize SKAN performance. By adopting proactive strategies and fostering collaboration, advertisers can successfully navigate the SKAdNetwork migration and harness its potential in a post-IDFA landscape.


Conclusion


The introduction of new SKAdNetwork models presents both challenges and opportunities for the mobile app marketing industry. By understanding the limitations and implications of SKAN, advertisers can adapt their strategies and leverage innovative approaches to navigate the evolving landscape of mobile app attribution. As the industry continues to evolve, staying informed and agile will be key to success in a SKAN-driven world.


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