About – Adriver

AdRiver About

Object oriented model of traffic management for real time decision making
Display, Video, Mobile Web, Mobile In-App, Native, Native Video, SmartTV
Pretargeting configuration of traffic flows
Can offer ad
Advanced post campaign reporting and analytics
Each ad request triggers auction with both internal and external bidders SSP

Proprietary taxonomy of segments
Segments can be used for targeting in DSP solution as well as pushed to customer
Target segments are formed based on both own and client data
Real time profiling of third party data
Cluster analysis is performed on elements of taxonomy first party data to reveal marketing insights
Look alike segments generation

Setup configurations ranging from direct orders to multilevel auction
Header Bidding is supported
Most of RTB protocols are supported for communication with bidders
Bid requests can be enriched by first or third party data

ML optimisation towards conversion of traffic
Bidding strategies for 1-st and 2-nd price auction, direct deals, PMP
Operations of advertising campaigns are fully automated
Bid rate is calculated in real time based on conversion predictor
Supports Display, Video, Mobile Web, Mobile In-App, Native, Native Video, SmartTV
2019Q4 average bid-rate =9.7%, win-rate = 11,2% (400B bidreq served)
Embedded real-time decision trees

Viewability specification — IAB/MRC
no MRC certification
Fraud detection: SIVT & GIVT
Integrated with MRC certified verifiers — DoubleVerify and Adloox Fraud detection: SIVT & GIVT

80 bare metal servers, 120K qps scalable to 300K qps.
C++, proprietary components from frontend to backend, in-memory databases.
System architecture supports working from multiple data centers, allowing for geographically distributed system to serve local users

Snapshot of each event consists of 1000 features out of 100K total available features
Trained model is used in real time and needs <40ms per bid request
Models are trained on data from last 4B events
Each bid request results in XXX calculations of primitives on average
Architecture supports any ML methods, including hybrids (ie decision trees, logistic regression, naive bayes, etc)

Contacts