Mobile attribution and affiliate marketing: an evolving landscape

How do mobile attribution and affiliate marketing fit together?

A properly configured mobile advertising campaign becomes an integral part of the development of any (and especially e-commerce) business because at least half of the end users use mobile devices to interact with the advertiser. Attribution of user actions is a crucial component of such campaigns, enabling app publishers to track user behavior and optimize campaigns to provide optimum customer experience. This approach is particularly important in the affiliate marketing channel, where affiliates can use a variety of tactics independently of the advertiser to generate traffic and sales.

Advertisers must have reliable attribution data to pay out commissions to successful partners and monitor their performance.

But, as affiliate marketing, mobile apps, and mobile attribution evolve, the strategies to measure and optimize these pathways also must adapt. 

How does mobile attribution work?

The process of mobile attribution tracks and analyzes how users interact with mobile marketing efforts, from their first engagement with an ad or link to their subsequent actions, such as app installs or in-app purchases. Its application is essential for understanding which mobile channels and campaigns effectively drive user behavior, enabling marketers to refine their strategies for better outcomes.

In the context of affiliate marketing, mobile attribution addresses the complexities of tracking user activities across different devices and platforms, ensuring that affiliates are accurately credited for their contributions.

There are two key types of mobile attribution: 

• Classic mobile attribution allows partners to track and analyze the performance of users who initially installed the app through their specific channel, linking the attribution directly to the install event. However, if a user already has the app installed on their device, partners do not receive any commissions or rewards for bringing that user back to the app through their web links or other apps.
• Re-engagement mobile attribution on the other hand, enables partners to launch campaigns aimed at increasing user retention within the app. This type of attribution allows partners to utilize ad channels to encourage users to return to the app and make additional purchases, thereby enhancing user engagement and driving more revenue.

How does affiliate marketing fit in?

Affiliate marketing is a performance-based marketing strategy where businesses reward affiliates for driving traffic or sales through their marketing efforts.

Traditionally, this model has been heavily web-centric, relying on cookies to track user activities across websites. With the advent of smartphones and the exponential growth of mobile internet usage, affiliate marketers needed to take a new approach to tracking and attribution, as in the mobile environment as cookies are less effective than in a legacy desktop one due to various technical constraints and privacy regulations. Also this broader definition of partners necessitates more sophisticated attribution models to accurately measure the contributions of each partner.

Therefore, mobile attribution plays a crucial role in affiliate marketing by providing accurate performance measurement.

Affiliates can be rewarded based on the actual in-app actions they drive, such as purchases, registrations, or subscriptions, rather than just clicks or installs. This performance-based approach aligns incentives with business goals, ensuring that partners are motivated to drive high-quality user actions that contribute to long-term value.

For instance, an e-commerce app can use mobile attribution to measure the effectiveness of its influencer marketing campaigns.

By tracking the entire user journey from an influencer’s social media post to app install to a purchase within the app, the business can identify which influencers drive the most valuable users and optimize their marketing spend accordingly.

For instance, users who installed the app via influencer campaigns could have been more likely to participate in the flash sale, driven by the influencer’s charisma. On the other hand, their brand loyalty could be more shallow compared to users converted by other channels fully controlled by the brand. Advertisers should put this into consideration when calculating their LTV and appropriate actions.

Another example would be a mobile gaming app which leverages mobile attribution to understand which ad networks deliver users who make in-app purchases.

By analyzing the data, the app identifies the top-performing ad networks and reallocates its budget to maximize revenue. Game developers could discover for instance that players who installed the game via YouTube ads are more likely to make in-app purchases after completing the first few levels, whereas those from mobile ad networks engage more in special events. This can guide targeted offers and in-game content tailored to different user segments.

Challenges specific to mobile attribution and affiliate marketing

While mobile attribution shares some fundamental principles with desktop attribution—such as tracking user behavior, attributing conversions to specific campaigns, and optimizing marketing efforts—it also presents unique challenges and requires tailored approaches to be effective.

Applications run in separate, isolated environments that do not interact with the web browsers’ cookie storage. Additionally, users often switch between apps and websites, creating fragmented data trails. Privacy concerns and regulations like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States have also imposed restrictions on data collection and tracking, further complicating attribution efforts.

One of the most critical differences between mobile and desktop attribution is the importance of mobile deep linking.

Deep linking refers to the ability to direct users to a specific content within an app, rather than just opening the app’s home screen. This functionality is vital for the success of mobile advertising campaigns that aim to increase user activity, such as boosting the amount users add to their shopping cart or re-engaging users who have been inactive.

Without deep linking, mobile users may experience friction in their journey, leading to a poor user experience and reduced effectiveness of the campaign. For example, if a user clicks on a promotional ad offering a discount on a specific product but is only directed to the app’s home screen upon opening it, they may struggle to find the product or lose interest altogether.

This not only decreases the likelihood of conversion but also wastes advertising resources.

In contrast, with proper deep linking in place, the same user would be taken directly to the product page within the app, where they can easily apply the discount and complete the purchase. This seamless experience is more likely to result in a successful conversion, making the advertising campaign much more effective.

Another key difference lies in the fragmentation of user journeys on mobile devices. Users often switch between apps, browsers, and devices, making it challenging to track their actions accurately. Unlike desktop environments, where cookies can easily follow users across websites, mobile environments require more sophisticated attribution methods, such as fingerprinting, device IDs, or server-side tracking, to piece together the entire user journey.

To address these challenges, affiliate marketing and mobile attribution platforms must integrate with various mobile-specific technologies and data sources. This includes supporting app-to-app deep linking, handling cross-device attribution, and ensuring compliance with privacy regulations that impact how user data is collected and processed.

Where do we go from here? Trends to watch …

There are two major trends in attribution that are relevant to affiliate marketing. Marketers should look out for these in the coming months and years:

Mobile attribution now takes into account and respects the privacy of users and is gradually moving away from tracking through linking a specific target actions to a specific device IDs towards probabilistic modeling. 

• Traditional methods of mobile attribution relied heavily on deterministic tracking, where specific actions—like an app install or in-app purchase—were directly linked to unique device identifiers, such as Apple’s Identifier for Advertisers (IDFA) or Google’s Advertising ID (GAID). While this method provided high accuracy, it also raised significant privacy concerns, as it involved tracking individual users across various apps and platforms.
• With the introduction of stricter privacy regulations, along with platform-specific privacy updates like Apple’s App Tracking Transparency (ATT) framework, there has been a shift towards more privacy-friendly attribution methods. This has led to the rise of probabilistic modeling in mobile attribution.
• Probabilistic modeling aggregates and analyzes various anonymized data points—such as time of installation, device type, and location—to infer which marketing activities led to specific user actions. Instead of relying on a one-to-one match between a user and their actions, probabilistic models use statistical methods to estimate the likelihood that a certain campaign led to an install or purchase. While not 100% accurate, this method is the best that can be in balancing between user privacy and providing actionable insights to marketers. As this trend continues, mobile attribution platforms are developing increasingly sophisticated probabilistic models to enhance accuracy while remaining compliant with regulations.

Since the mobile device market already occupies a huge share of all affiliate marketing, the focus of application owners is shifting towards measuring the effectiveness of re-engaged users (re-engagement campaigns) from the usual attraction of “installs.”

• Traditionally, app owners and marketers placed significant emphasis on driving new installs as a primary metric of success. However, as the market has matured, the value of simply acquiring new users has diminished in comparison to retaining and re-engaging existing users. Re-engagement campaigns are designed to bring lapsed users back into an app, encouraging them to spend more time using the app, make in-app purchases, or continue where they left off. The logic behind this shift is that acquiring new users can be more costly and less effective than nurturing and retaining existing users who have already shown interest in the app.
• To measure the effectiveness of re-engagement campaigns, app owners and marketers focus on metrics supplementing the reactivation rate, like session length and frequency, feature adoption rate, and in-app purchases post-re-engagement. Session length and frequency indicate how deeply re-engaged users are interacting with the app, while the feature adoption rate shows whether users are exploring and using new or key features after returning. Tracking in-app purchases among re-engaged users helps assess the campaign’s direct impact on revenue, providing a comprehensive view of how effectively the campaign has revitalized user activity and engagement. Of course, universal metrics like ROAS or LTV are also applicable.
• Re-engagement campaigns can be highly personalized, leveraging user data to deliver targeted messages and offers. For instance, a mobile gaming app might send push notifications to users who haven’t played in a while, offering them a special in-game bonus to encourage them to return. Similarly, an e-commerce app might use re-engagement strategies to remind users of items left in their cart or offer personalized discounts based on past shopping behavior.

This is a guest post by Anna Gidirim, CEO of Admitad, in partnership with Nastassia Shkampletava, Technology Partnerships Manager at Admitad.

If you’d like to learn more about how Singular can support affiliate marketing and mobile attribution, feel free to get in touch with us today.