Bitmovin announced two new features for Bitmovin Analytics: Server-Side Ad Insertion (SSAI) Analytics and Real-Time Observability. The features aim to address the media and entertainment technology’s need for more seamless ad playback, real-time data to measure audience engagement, and the ability to proactively discover issues with streaming quality, as the company has claimed in a statement.

The objective of Bitmovin’s SSAI Analytics is to help users monetize their SSAI workflows by collecting data relevant to debugging ads and measuring audience engagement. SSAI Analytics’ engagement feature shows the number of ad plays, plays per quartile, and abandonment rate. Each metric can be filtered to identify commonalities in engagement between viewers, devices, stream types, and ad servers. Both debugging and engagement data is available at a session level, allowing for analysis down to the level of individual ads within an ad break. The data is also available in real-time to try to empower fast decision-making and early detection of issues.

“Advertising-based business models have gained real momentum in the last few years. Yet, while Bitmovin’s latest Video Developer Report showed that advertising is the top opportunity for innovation, ad insertion is the top challenge,” said Stefan Lederer, CEO and co-founder of Bitmovin, “Bitmovin’s SSAI Analytics has arrived at the right time, and will help our customers with SSAI ad architecture to optimize their ability to monetize their content by providing real-time debugging and engagement data into ad performance and viewer engagement.”

Real-time observability is powered by Bitmovin’s alerting infrastructure and the real-time data that Bitmovin Analytics captures from video players. It’s designed to help users identify issues with streaming quality, performance, or monetization. Its objective is to offer a more proactive and wide-reaching approach to understanding changes in streaming quality. Combining observability with Bitmovin’s deep, session-level data means that root causes could be found quickly and resolved before they affect global viewership. Observability incidents can also be filtered by a range of breakdown filters to understand commonality, for example, in devices, networks, and streams affected.

“Real-Time Observability transforms video analytics from being reactive to proactive when ensuring quality of experience,” added Lederer, “It provides users with an incredible depth of knowledge about their video streaming infrastructure provisioning and the demands placed on it by pinpointing issues before they even occur. As a result, it will positively impact customers’ bottom line, reduce viewer dissatisfaction and minimize churn.”