Have you ever wondered, why the binge-worthy series of Netflix or Prime Video shows on the top of your app homepage on Fridays or even Saturdays, but rarely on weekdays? Or how the bag you have been checking out online for a long time and waiting for the price to come down, just shows up during the Christmas week at a discounted price on your favorite shopping app? Well, in both cases, it’s the power of the recommendation engine that aptly recommends the ideal content/product at the ideal time.
Time-based recommendation system has been instrumental in both streaming as well as in e-commerce business. It not only helps in the discovery of contents/products but also by showcasing the seasonal/timely content on user’s screen, it greatly reduces the average time spent by a user for content streaming or making a purchase. A selected number of Muvi customers who have been using Recommendation Engine, a premium feature from Muvi were quizzed about their experience and they termed the Time-Based Recommendation to be extremely effective in user conversions and quicker purchase. While Muvi is ecstatic about the report, read on to know what exactly is a time-based recommendation? Don’t forget to share your comments below.
What is a Time-Based Recommendation Engine?
Simply put, a time-based recommendation is just another module of the traditional recommender system but more powerful in terms of identifying preference of visitor at a particular time. And the good part is the recommendations only get svelte over time by virtue of the algorithms.
While personalized recommendations are inherent with any recommendation engine, for the time-based recommendation, the data source is gathered by combining the maximum user search for products during a particular period of time or for the matter of a month or a week, with the inclusion of individual preference gathered from their previous purchase/search history.
Streaming platform owners and Time-based Recommendations
Digital content consumption behavior is of late the hotcake among streaming platform owners and everybody is working hard, seeking the help of data scientists and spending millions to decipher the content consumption habits. The importance of content browsing and consumption habit holds a lot of importance especially in the age of live streaming and an era where billions of dollars are invested only for content production. To make them discoverable, relatable and then recommending them as per user interest is an uphill task less traveled by most considering the time and resource it demands. That’s why online store owners often resort to frivolous recommender systems available in the market whose credibility is largely questionable.
Time-based Recommendations across OTT Platforms
Muvi’s Recommendation Engine, powered by Alie, deploys machine learning algorithm to recommend content to individual users with impeccable accuracy. The accuracy it hits is also due to the deep learning involved in the recommendation process that takes into account several factors out of which time-based recommendation is one.
While browsing pattern and content consumption habits are yet to be fully decoded by streaming platform owners, there are a lot of obvious use cases of time-based recommendation. For example, spiritual podcast and devotional content are often recommended by music streaming services in the morning hours. Although recommendations are curated by user persona and individual preferences there is some universal thought process involved in picking content at a particular time. For example, if you like English rap songs, an Eminem song you would often find on your playlist while returning home after a hectic day. Similarly, Netflix’s binge-worthy series often adorn your recommendation list on weekend. Because on weekdays it is highly unlikely for a user to binge-watch an eight-episode series at one go.
According to statistics, 85% of the total consumers prefer being recommended, where time-based recommendation engine holds a major part. It saves users’ time and brings growth to your business. So, it’s up to you who are you turning up to and when?
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