What is a Self Attributing Network (SAN)?

A self-attributing network refers to an advertising tracking setup that allows publishers and marketers to directly identify and attribute app installs to ad campaigns without relying on third parties.

Typically, attributing app installs to media sources involves using attribution platforms offered by companies like Adjust or Appsflyer.

These require SDK integration and cost money.A self-attributing network lets publishers track install attribution directly through their own technology stack and user IDs. This avoids third-party fees and data sharing.

How Self Attributing networks work?

At a high level, self-attributing app tracking involves these components:

User identifier – The publisher tags all ad impressions with a unique user ID or device ID they can track.

Campaign tags – The ads include campaign IDs and parameters that identify the creative, placement, etc.

SDK install tracking – The app SDK fires an install event with the referrer campaign info.

Publisher data pipeline – The pipeline stitches together impressions, clicks, installs, and events by user ID.

Attribution calculation – With their own data, publishers can directly attribute app events to ad exposure without third-party attribution.

By leveraging unique user IDs and centralized tracking, publishers with self-attributing networks have more control over attribution data and avoid sharing data with other parties. This approach provides flexibility but also requires significant in-house technical investment.

Standard Ad Networks Vs Self-Attributing Networks (SANs):

Standard Ad Networks:

Use black box machine learning models to optimize ad targeting and predictions. The internal logic is opaque to users.
Focus only on clickthrough rates and conversions. User trust or an explanation of why they receive certain ads is not a priority.
Have full access to user data which raises privacy concerns. There is no transparency into what data is collected and how it is used.
Make broad assumptions about user intent and interest based on their demographics and online behavior.
Often spread annoying or irrelevant ads due to imperfect targeting methods.

Self-Attributing Networks (SANs):

Can explain why a user is being shown a specific ad using natural language. Increased transparency builds user trust.
Maintain privacy by only accessing minimal user data required for targeting. Keep data compartmentalized within reasoning submodules.
Go beyond click maximization and consider user experience. Seek to make ads relatable by explaining relevance based on user attributes.
Make nuanced inferences about user preferences and behaviors using multimodal data analysis. Not reliant on demographics alone.
Curate ads tailored to an individual’s interests while avoiding assumptions. Explanations provide visibility into the targeting.
Enable feedback loops so users can correct the SAN’s reasoning and improve accuracy over time.

In summary, self-attributing ad networks aim for transparency, user experience, and privacy while standard networks focus on efficiency and scale often at the cost of user agency and understanding. As consumers grow wary of creepy ads, SANs provide a path to building trust through explainable ad targeting.

F.A.Q

Q1: How do self-attributing networks work?

A1: Self-attributing networks attach unique IDs to marketing interactions. They use these IDs to track customers across devices and channels. This lets them map out each customer’s full journey from ad view to buy. The tracking shows exactly how marketing leads to sales.