oolo is an AI-powered monitoring platform that detects and analyzes anomalies in user acquisition and monetization data, providing actionable insights to optimize return on ad spend and lifetime value. By automating data inspection, oolo eliminates manual monitoring tasks, allowing growth teams to focus on strategic decision-making and improve overall performance.
Funding
Funding not disclosed
IIFounders
Product
Problem
Mobile game growth teams face challenges in monitoring vast amounts of user acquisition and monetization data, often missing critical anomalies that impact return on ad spend (ROAS) and lifetime value (LTV). Manual data inspection is time-consuming, leading to delayed responses to performance issues and lost revenue opportunities.
Solution
oolo provides an AI-powered anomaly detection platform that automatically identifies and analyzes irregularities in user acquisition and monetization data. The platform delivers actionable insights, enabling growth teams to optimize ROAS and LTV by addressing issues and opportunities in real-time. oolo's AI inspects granular data points, filters out false alarms, and pinpoints the origin of performance deviations, allowing teams to focus on strategic decision-making and proactive optimization. By providing clear root cause analysis, oolo helps teams quickly understand and act on insights to maximize the impact of their interventions.
Target Audience
oolo is designed for user acquisition managers, monetization managers, and growth teams in the mobile gaming industry who need to optimize campaign performance and maximize revenue.
Features
- AI-driven anomaly detection for user acquisition and monetization metrics
- Real-time alerts for unexpected performance changes, both positive and negative
- Root cause analysis identifying the source and impact of anomalies
- Customizable dashboards and reporting to track key performance indicators (KPIs)
- Integration with advertising platforms and analytics providers for comprehensive data coverage
- Forecasting capabilities to anticipate future performance trends
- Automated data inspection to eliminate manual monitoring efforts
- Prioritization of issues and opportunities based on business impact