Think of this as a super-smart referee for your mobile ads. It watches every tap, install, and purchase across apps and channels, then decides which ad truly deserves the credit—while trying not to expose or misuse people’s personal data.
Traditional mobile attribution struggles to correctly credit which ads drive installs and in-app revenue, especially with privacy restrictions (IDFA/GAID limits, ATT, GDPR) and cross-channel user journeys. This leads to wasted ad spend, fraud, and poor optimization decisions. AI-based attribution uses patterns in aggregate signals to improve accuracy of campaign performance measurement without relying as heavily on user-level identifiers.
Access to large-scale attribution logs and performance data from many advertisers and networks, plus integrations into the broader ad tech ecosystem (SDKs, MMP connections, ad networks) create a data and workflow moat that is difficult for new entrants to replicate quickly.
Hybrid
Vector Search
High (Custom Models/Infra)
High-throughput ingestion and real-time scoring of events at scale under strict latency and privacy constraints; maintaining model accuracy as platforms (iOS/Android, SKAdNetwork, Privacy Sandbox) change signal availability.
Early Majority
Positioned around using AI/ML to improve attribution accuracy and privacy compliance in a post-IDFA world, likely emphasizing probabilistic modeling, fraud detection, and privacy-preserving analytics beyond what rule-based or purely deterministic attribution systems provide.