nangu.TV launches Recommendation Engine
May 28, 2013
nangu.TV, the pioneering media platform provider for IPTV and OTT services, has announced its Recommendation Engine addressing the global issue that viewers’ face when trying to navigate their way through vast quantities of television content. nangu.TV’s Recommendation Engine is a key element of its Media Platform and provides operators with a route to greater customer satisfaction and loyalty therefore increasing ARPU.
The challenge that operators face is to keep the viewer entertained with a steady flow of content so that they stay watching the television: we’re all creatures of habit and our viewing tastes are no different when it comes to television as we tend to look for familiarity. Service providers can then take advantage of viewers’ “stickiness” to push additional content their way: a virtuous circle when it comes to viewer retention. If they are satisfied with the television offering and can watch it at their leisure they won’t feel the need to look elsewhere.
Within nangu.TV’s Recommendation Engine the recommendation is based on a combination of algorithms that work together to deliver the end output. It’s important to set the weight of each algorithm to yield the most accurate results. For service suppliers this can mean an increase in video sales, increase in brand awareness, decrease in churn and so on. The weight of each component can be adjusted to make the recommendation more specific to the end result. The algorithms are continually calculating and recalculating the live data. This ensures accurate, up-to-date results based on actual user behaviour, which means the results of searches are changing constantly.
The Recommendation Engine is very simple for the viewer to navigate; they turn on the device and start to type their selection into the search box. Instantly, they’re presented with several choices that may appeal. All the time they are searching and selecting content, a history of their behaviour is being collected to create a user profile. This translates into more accurate recommendations in the future and better quality recommendations for other users.
Zdenek Gerlicky, CTO, nangu.tv, says, “Having access to statistical data about user behaviour is key to providing customer satisfaction. An operator can pre-filter the most relevant content that’s likely to make an impact on the user. As all user data is collected anonymously it makes the user behaviour statistics universal yet rich as they reflect actual user preferences. All this leads to a happier customer and increased ARPU.”