Reducing Churn with personalized search

As broadcasters and digital publishers face a variety of challenges when it comes to their video content, the question of how to reduce churn becomes all the more critical for business strategies. With the increased relevance of artificial intelligence and a new technological era just starting, the gjirafatech team has decided to tackle these main challenges with an addition to their end-to-end video platform. 

Content Discovery is made simple and possible through our AI Modeling engine pre-integrated into VP. Through AI Modeling, we deliver a Recommender that includes information about the user and the user behavior. It creates highly accurate predictive models that aim for the user finds the content most interesting to them, either as a follow-up Content Suggestion or even through the search function. 

You wonder what that looks like in an explicit example. Let’s say you log onto a Video Platform that offers the latest Movies, and you tend to watch lots of movies, including a specific actress, let’s say Maryl Streep. When you start searching for a movie by entering a word, for example “Love…” it will first show you movies that include Meryl Streep and the search term, as you are more likely to want to watch that one.

Another example of the follow-up recommendations would be if a user watches a video on a news site, it would recognize that even though the user is watching a video on a financial topic, they are most likely to continue watching a political video as this is their predictive pattern. The recommendations that the user gets will understand this and suggest an ideal mix of video recommendations to keep them on the site and reduce churn.

Through AI modeling engines, many different scenarios can be solved where the goal is to reduce churn and even increase engagement. The issue of content discovery will be in the past as the AI engine learns more and more over time and gets the right content to the right user to keep them consuming. 

Learn more about our AI Recommendation here: https://vp.gjirafa.tech/features/gjirafatech-integrations