SKAdNetwork Limitations and Their Impact on Mobile Attribution

The mobile advertising market had a radical transformation when Apple introduced privacy-driven models, and such policies have completely changed the way marketers monitor user activity. What appeared to be a simple attribution process has turned into a complicated issue and mobile marketers have to find their way to a different paradigm.

The release of SKAdNetwork was a breaking point in the history of mobile marketing that opened opportunities and major challenges to advertisers across the globe. With privacy becoming more restrictive and user data security becoming the primary concern, it can be noted that the scope of SKAdNetwork has been viewed as a major drawback to any marketer who needs to maintain the efficacy of campaign measurement and optimization tactics in the iOS platform.

Understanding SKAdNetwork and Its Purpose

SKAdNetwork is a privacy-friendly attribution system that is created by Apple to quantify the effectiveness of app install campaigns without knowing about the violation of user privacy. In contrast to the traditional methods of attribution based on the device-level identifiers, this framework works on the principles of aggregate data. The system uses the ability to measure campaign effectiveness and keeps the personal information of the users of the system out of the reach of the advertisers and the third parties.

The previous versions that were developed into SKAN 4.0 were advanced with minor changes such as improved conversion value schemas and improved web-to-app attribution. The updates have however not removed several inherent SKAdNetwork limitations that still impact the strategies of mobile measurement partners.

Core SKAdNetwork Limitations Impacting Attribution

Distributed Postback Structure

Among the major constraints is the flow of attribution data in the ecosystem. Postbacks are sent directly to specific ad networks and not to a centralized mobile measurement partner when users either tap advertisements and install apps. This spread implies that the attribution data of marketers who have been undertaking campaigns in the many networks will be scattered on the various platforms making it significantly harder to conduct a thorough analysis than previously.

Granularity Constraints

The framework restricts the scale of the trackable data that basically changes what is measurable by marketers. This system does not have any device-level tracking as it removes the possibility of tracking individual user journeys across touchpoints. The creative-level data is also inaccessible where marketers would not be able to understand which particular variations of ad would provide the most effective results. Campaign monitoring has limited values of potential campaign tracking and ad networks often use most of these values internally to track their campaigns.

Time Window Restrictions

Another challenge of paramount importance among the limitations is attribution timing. The default measurement window can only last 24 hours after the installation but marketers can extend time but this comes with its set of problems. This shortened time is challenging in scenarios that have lengthy conversion cycles where users can take several days before valuable actions are performed. 

Impact with Mobile Measurement Platforms

Limited Multi-Touch Attribution Capabilities

Multi-touch attribution that was done traditionally helped marketers to gain a full picture of the customer experience with several interactions and channels. The contemporary structure only provides rudimentary multi-touch data in form of what some experts in the industry call secondary postbacks which implies that there must have been more interactions with the advertisement by then which had happened prior to installation. However, these restrictions do not allow an in-depth journey mapping across platforms, devices, and long periods, which marketers need to have advanced attribution modelling.

Campaign Optimization Challenges

The cumulative aspect of information in this framework renders real time optimization of the campaign significantly more challenging. At the granular level that used to be available, marketers have no idea which individual publishers, creatives, or audience segments do the most effective work. Advertisers will therefore have to turn to the more extensive measures of campaigns and more lengthy testing times, which will slow down the optimization process and may raise the cost of acquiring customers.

Integration with MMP Tools

Mobile measurement partner platforms have scaled their technologies to conform to these limitations, creating platforms that combine data of several ad networks into a single dashboard. Nonetheless, the underlying constraints imply that the most advanced MMP tools will not be able to bring back the previously existing device-level visibility. Marketers have to change the basic perception of measurement at the user-level into campaign-level trends, and patterns.

Adapting to the New Attribution Reality

Probably modeling, incrementality testing, and conversion value optimization are some of the strategies adopted by organizations that have succeeded in this environment. The SKAN application ecosystems have compelled marketers to create measurement systems that focus on aggregate insights, as opposed to its individual tracking. The change requires new capabilities, better technology layers, and re-evaluation of what attribution has the capacity to provide.

The awareness of what SKAN 4.0 provides and what it can do will help marketers to make the most out of available data and consider the natural constraints. The structure is still under development and every time, the concern addressed is one or two issues and still, Apple has the dedication of protecting the privacy of its users.

Conclusion

The constraints of SKAdNetwork are not only technical limitations, but also a redefinition of the philosophy of mobile attribution. Although these restrictions present the conventional methods of measuring marketing with a challenge, they also introduce the innovation of privacy-oriented analytics methods that will most probably define the future of the industry regardless of the platform.