An example of extracting topic metadata and isolating the best thumbnail image based on a neural network
Last August, our data exploration tool Segmentation launched and has helped Advanced Analytics users take control of their data and dig deeper into video performance trends.
This month, we rolled out an important new metric: Ad Impressions!
Users can now…
How We Increased Uptime and Decreased Engineering StressWorking in the big data world can be a little chaotic. For us that means a stable flow of 20k events per second can spike to 50k over the course of an hour. It takes only seconds to look through our logs and find a perfect example: Things move fast and we need to be able to adapt and respond immediately when our systems are under stress. When a system goes “red” most people’s first instinct is to call the engineer who built it. Over time this can create silos of knowledge and an uneven load on the engineers. Another side-effect of this engineer-first thinking is that the priority is focused on fixing the problem, missing the importance of communication.
Big Gains in 2016Recommendations are a crucial part of many video publishers’ offerings and that has put a premium on simple and integrated solutions that can be delivered cost effectively at scale. JW Recommendations integrates deeply with each part of JW Player to provide a powerful, real-time and automated content discovery solution built on our vast network of viewer and content data streams.
Architecture of the evaluation tool
Why use Data-Driven Recommendations?Launched in November 2015 to a select group of publishers, JW Platform Data-Driven Recommendations:
- Increase content plays and monetization opportunities where ads are present.
- Use existing content connections and viewer activity to simplify your publishing workflow and increase revenue.
- Provide your viewers with automatically-curated video suggestions that increase audience engagement and are continually updating based on interaction data and content traits.