Improve quality of video recommendations in order to increase time and engagement on site, while decreasing churn.
Today’s consumer is drowning in a sea of options when it comes to streaming movies and shows online. Research shows the average user spends 21 minutes searching for something to watch. When we began working with OVS, our goal was to use Hai’s software and proprietary methodology to provide higher quality recommendations to their users with the target of decreasing both the time users spent searching and OVS’ churn rate.
For that, we created a simple plugin that would display 2 lines of recommendation, one coming from the usual OVS recommendation engines and one providing recommendations coming from Crossing Minds proprietary algorithms. The data leveraged by our team in order to start providing consisted on the online available history of the users testing the plugin.
The test was narrow in terms of the data set given to our algorithm. By only being able to review the watch history of 12 OVS employees, our goal was to prove that even with such a limited data set, Hai would be able to provide higher quality recommendations than the current algorithm used by OVS. Our algorithm not only included the actual video’s chosen by employee’s, but also reviewed watch time, drop rate and engagement.
All the rules to convert implicit feedback to explicit feedback were done independently (no collaboration with OVS). Several assumptions were made internally to define a scale from 0 to 10 that determines if a user liked a show or not (based only on watch history data ‒ drop rate, time spent watching, etc.).
After running the data, the recommendations generated from Hai were displayed alongside the OVS’ current algorithms’ recommendations. The testers job was to select which line of recommendations they preferred. We were thrilled to see that 100% of the testers chose the recommendations generated by Hai, despite the algorithm not being trained on the OVS’s data. This clearly demonstrated the advanced capability of the cross-domain, deep-learning algorithm Hai possesses, and the superior results it provides.