Conviva recently introduced Predictive Intelligence: automatic identification of factors that strongly drive conversion in user workflows. This is the first in a series of posts about the technical ideas behind it.
Recent discussions position context graphs as a foundational abstraction for agentic AI and a trillion-dollar opportunity. These discussions are a great starting point, but they are incomplete. Richer context matters for agents.
Conviva’s Data Feeds provide a rich, compact source of data that is easy to work with and cost-effective. Conviva aggregates every aspect of each user’s interaction into a single line, allowing you to focus on high-value analysis.
Introducing Conviva Cohort Replay (Beta): Turn anomalies into answers with Conviva Cohort Replay (Beta) — Watch real user sessions to avoid wasted analysis and optimize performance.
Discover how a single atomic counter caused a major slowdown in Conviva's high-scale streaming analytics platform. This post unpacks the concurrency pitfalls behind a mysterious latency spike and offers lessons for Rust developers working with DAGs, actor models, and shared state.
Discover how automated high-cardinality cohort analytics can prevent misleading product insights, avoid Simpson’s paradox, and drive smarter decisions with Conviva’s next-gen Operational Data Platform.
Conviva recently presented a paper on "SEAM-EZ: Simplifying Stateful Analytics through Visual Programming" at the premier international conference of Human-Computer Interaction, ACM CHI 2024. We showcased our novel “no code“ platform, SEAM-EZ, for creating stateful analytics. SEAM-EZ stands for Stateful Event Analytics Made Easy; a tool that makes what used to be a complex problem seem easy.
When designing a domain-specific enterprise-grade conversational Q&A system to answer customer questions, Conviva found an either/or approach isn’t sufficient.
CTOs and operational teams are facing two critical challenges: 1) high analytics and observability spend with low return on investment (or without the expected impact) and 2) low operational efficiency due to disconnected decision-making.
The key to delivering optimal experiences to every user is to comprehensively measure and react to changes in their Quality of Experience in real-time. But, that can mean scanning millions of unique user data points to identify anomalies, detect patterns, and address the root causes of issues. It's becoming more difficult for operations teams to keep up with the speed and scale of user data they need to analyze. That’s where AI comes in.
In the fast-paced world of digital streaming, the ability to scale and adapt is crucial for delivering high-quality user experiences. With millions of users engaging with dozens of apps at a time, operations teams need the scale and agility to monitor and optimize every individual user experience in real-time. It’s a massive scale challenge.
How does one find issues that affect a smaller set of users but it is still significant enough to impact overall viewer engagement?
