Did you know, according to a report by Podcast Insights, there are about 48 million podcasts available on the internet? There are literally thousands of podcasts available, and unlike music and movies, where one can watch a little preview or a trailer, with podcast one has to read every description and title to decide whether to listen to the rest of the podcast or take a random guess. Basically, a lot of time is spent before anyone can start listening to podcasts, which more often than not leads them to be disinterested. What could be the possible solution to this? A recommendation engine. More importantly, a podcast recommendation engine.
A tool that analyzes the usage behavior of users on a website along with likes and dislikes with the product or services present on the website and then, based on different algorithms, creates a list of recommendations. There are different algorithms used in a recommendation engine, such as content-based filtering, collaborative filtering, and hybrid filtering. Learn more about different types of algorithms and how recommendation engines work by reading this whitepaper on recommendation systems.
A recommendation engine that uses content-based filtering to recommend podcasts to its users is known as Podcast Recommendation Engine.
Note: It is essential to understand that a recommendation engine doesn’t work as ‘one size fits all. For example, a music application’s recommendation engine is trained to recommend music, and Netflix’s recommendation engine is trained to recommend movies. The factors that affect its decision will be different than that of a Movie recommendation engine or a book recommendation engine.
Did you know that over 62 million people alone in the US listen to at least one podcast a week? (Source: Edison Research). This number can be easily regarded as a milestone. However, this number is only a fraction of listeners who listen to music or watch movies. The podcast industry has witnessed major growth in the past few years however, the numbers are still low in comparison to other similar industries. Giants like Spotify and Pandora are now constantly updating their recommendation engines and adopting new methods to promote podcasts on their websites.
Users cannot find relevant podcasts at the right time like they do while listening to music or watching movies. Time and patience are the important factors here. So what is the use of a podcast recommendation engine? To reduce the time a user has to search for a good podcast to listen to. This will help in increasing the popularity of on demand podcast, and more listeners will join in, leading to an increase in total revenue.
Every website gathers information about usage trends of the users. For example, what this user listened to the last time, or simply visited. The recommendation engine tracks all this information and a profile for each user. Following this, the recommendation engine clusters all similar podcasts together. How will it recognize the similarity? There can be different methods such as podcasts from the same person are clustered together, podcasts including some similar words can be clubbed together, and of course, according to the metadata. So, next time when the user visits the website, based on the last viewed podcast, the recommendation engine will find similar podcasts and recommend them to the user. Also, note that in order to give personalized recommendations, it is important to have some information about the user. A new user entering the website might not be able to receive personalized recommendations. However, new users can browse the list of ‘Top 10 most listened to podcasts’ or ‘Motivational Podcasts’, etc.
‘One size fits all’ doesn’t work with regards to the recommendation engine feature. It is important to recognize the difference between different recommendation engines and integrate it with your website. Start now by exploring recommendation engines and their functionality. Visit Alie – an AI-based recommendation engine that offers multiple algorithms, which makes it easier to adapt to any industry. Take the 14-day free trial of Alie now to see how drastically your recommendations can improve.
Written by: kritika
Kritika Verma is an Associate Content Writer and works with Muvi Marketing Team. She is an inbound marketing professional and ensures high-quality traffic on the Muvi website through her blogs, articles, and more. She has an engineering background but always had a knack for writing. In her free time, she is either on Quora or on chess.com (Mostly losing).
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