Pulling data from mediation, network, GAM, OMS, Google Analytics, and HB sources, oolo forms a holistic but granularly-informed understanding of your revenue ecosystem.
Data is examined in view of historical behavior, seasonality, setup, metric inter-relations, channel mix, human interventions, and other factors.
Understanding the data implications of business activities and business implications of data events, oolo models expected performance across millions of data permutations.
Comparing inbound data to modeled predictions, oolo documents and dissects significant deviations.
Anomalies are automatically investigated ― revealing their origins and business impact. Attention is called to qualifying anomalies via alerts that outline the issue, its root cause, financial significance, affected covariates, and suggested interventions.
Comparing live data to predictions, oolo detects developing anomalies in real time.
oolo requires no client-side technical integration. Simply provide ad server and/or mediation access.
oolo can also be integrated to your Prebid module by adding a small (oolo-provided) script to the page.
Training on current and past publisher data, oolo maps metric relations and models "healthy" behavior patterns. Machine learning is used to forecast performance and set data expectations — across all relevant metrics & dimensions.
Pairing oolo’s industry expertise with your business dependencies & operating logic, oolo calculates the revenue impact of each anomaly.
Monitoring the issue from its earliest expression, oolo traces chain reactions to their core and explains how the first domino fell.
Role-based alerts are sent to the relevant person detailing the issue, its root cause, its cost, and recommended actions. Optimization recommendations take into account:
