Product management teams
Driving product success with facts, not assumptions
In product teams, balancing long and short-term priorities is essential. Enabling product managers to be data-driven is a key success factor supporting everything from understanding churn causes to usage, making strategic investment decisions, assessing customer impact of offering changes and evaluating delivery performance by technology and infrastructure choices.
Manage releases and new versions with confidence
Every release comes with risk, so by tracking startup performance and error rates across platforms and devices, you can pinpoint whether anomalies are caused by code changes or delivery conditions and decide quickly whether to continue, pause, or roll back.
Make smart infrastructure and product decisions
Successful product decisions balance ambition with possible delivery constraints. Map how CDNs, ISPs, and regional network conditions shape user experience, engagement and retention, and justify your infrastructure investments with clear QoE data that proves loyalty impact.
Track feature use by platform
Not every feature works the same way on every device. With product analytics, you can see which features drive engagement on mobile, smart TV, or web, and connect adoption to QoE metrics to distinguish UX/design issues from delivery challenges. This clarity helps prioritize platform-specific improvements, which will have the strongest impact on usage.
Analyze churn and engagement drivers
Analyze engagement with behavioral and QoE insights. By correlating viewing behavior (such as sessions, navigation paths, and drop-off points) with QoE metrics (like buffering or errors), teams can see where users disengage and understand how performance impacts engagement. These insights help making informed decisions around retention strategies and churn-related factors.
Customer journey insights, beyond playback
A customer’s journey doesn’t begin at playback, it starts from the moment they open the app. With Agama’s Product Analytics, you can trace navigation paths, session activity, and drop-off points from browsing through watching, and correlate these behavioral touchpoints with quality signals such as startup time, buffering, or errors. This helps teams uncover friction in the user experience and prioritize changes that support smoother journeys and stronger engagement.
Use cases
Understand patterns of disengagement, identify at-risk users, and prioritize interventions to improve user lifetime value.
- Which playback or app issues correlate with decreased engagement?
- Which content types or features most effectively drive engagement across different user segments?
- When in the subscription lifecycle are users most likely to churn and why?
Leverage analytics and observability data to drive successful onboarding, trial-to-paid conversions, retention, and premium-tier adoption.
- Which onboarding flows or first-use experiences lead to higher conversion and long-term retention?
- Which trial behaviors or early usage patterns predict successful upgrade to premium subscriptions?
- How do device, platform, or playback quality issues during onboarding or early usage affect retention and conversion?
Use detailed viewing and performance data to maximize content consumption and improve user satisfaction.
- Where in a video do users typically drop off, and is the reason related to content or technical issues?
- Which content genres or formats have the highest completion rates across different segments?
- Which playback quality metrics (startup time, buffering, resolution) most directly impact engagement and completion?
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