Tata Sky introduces personalised content recommendations using ThinkAnalytics
MUMBAI: Tata Sky has introduced a new feature that enables personalised content recommendation on Tata Sky Mobile App (iOS/Android) and watch.tatasky.com. Tata Sky is using personalised content discovery and recommendations from ThinkAnalytics.
Tata Sky chose the ThinkAnalytics solution to enable an experience where users can quickly and easily find content online and on mobile devices, boosting overall viewer engagement. The experience is personalized through the use of ThinkAnalytics’ AI and machine learning technologies and based on the subscriber’s previous viewing behavior.
The ThinkAnalytics recommendation engine now helps subscribers discover content based on the genres, language and even cast preferences based on his or her last visit to the platform. Also important to Tata Sky was ThinkAnalytics’ proven ability to scale to the operator’s base of millions of users, as well as its track record in helping video service providers boost engagement and loyalty.
Tata Sky Mobile App and watch.tatasky.com give subscribers access to 400+ live channels and over 5,000 TV and movie titles on their laptops and iOS/Android mobile devices.
ThinkAnalytics CTO Peter Docherty commented, “Tata Sky is using our enhanced metadata management solution and viewer behavior analysis to differentiate its OTT offering and demonstrate technology leadership in India. Not only do users now benefit from an experience tailored to their tastes, but we are helping Tata Sky’s business win with increased viewer engagement.”
Initially, in English, the service has planned support for additional languages in the future.
ThinkAnalytics is powering a variety of use cases including Personalised Banners, Channels For You, Live Now For You, Catch-up For You, Movies/Shows for You, and Trending Events based on the shows and channels users like to watch. There’s also ‘Don’t Miss’ which brings up episodes of the shows that are watched regularly so that the user never misses an episode of their favorite show.