Video is one of the most consumed and requested formats by users on the Internet. More and more people are watching online videos on platforms like Netflix, Amazon Prime, or TikTok, among others. However, with so much content to choose from, users can feel overwhelmed or lost when deciding what to watch. That’s why offering personalized video recommendations is a key strategy to improve the user experience, increase engagement, and foster loyalty to your platform. In this article, we will explain how to offer personalized video recommendations to increase user engagement and loyalty.
What are personalized video recommendations?
Personalized video recommendations are content suggestions that match each user’s preferences, interests, and behavior. These recommendations are based on the analysis of data collected about users, such as their viewing history, ratings, searches, demographic profile, or location. The goal is to offer each user the most relevant and suitable content for them at any given moment.
Personalized video recommendations can be displayed in different ways, such as grids, carousels, banners, or notifications. They can also be placed in various locations, including the homepage, category pages, detail pages, or search results pages. The important thing is to make the recommendations visible and accessible to users.
What are the benefits of personalized video recommendations?
Personalized video recommendations have several benefits for both users and the platforms that provide them.
Improve user satisfaction and trust
Users feel more satisfied and confident with a platform that offers them content that is interesting and enjoyable to them. This builds a closer and more lasting relationship between the platform and the users.
Increase user engagement and loyalty
Users who receive personalized recommendations tend to watch more videos, spend more time on the platform, and return more frequently. This increases user engagement and loyalty, lowers churn rate (abandonment), and increases the customer lifetime value (CLV).
Boost conversions and revenue
Users who receive personalized recommendations are more likely to perform desired actions on the platform, such as signing up, subscribing, making purchases, or sharing content.
How to offer personalized video recommendations?
To offer personalized video recommendations, you need a system that can collect, process, and analyze user and content data, and generate and display appropriate recommendations for each user. This system can be created using artificial intelligence (AI) and machine learning (ML) techniques , which allow learning from data and improving recommendations over time.
There are different types of recommendation systems , such as content-based , collaborative-based , or hybrid systems . Each has its advantages and disadvantages , and the most suitable one can be chosen based on the type and amount of available data , the size and diversity of the catalog , and the objectives and context of the recommendations .
To offer effective personalized video recommendations , consider the following aspects :
Relevance
Recommendations should be relevant to the user , meaning they align with their current interests and preferences . This requires constantly updating the user profile and available content .
Diversity
Recommendations should be diverse , offering users different options and a variety of content . Avoid over-specialization and redundancy , and introduce a certain level of novelty and surprise .
Explanation
Recommendations should be explained , providing the user with a reason or motive for the suggested content . This can be done using information such as title , description , image , rating , or similarity to other content the user has liked .
Personalization
Recommendations should be personalized , adapting to the user and the moment they receive them . Consider the user’s context , such as location , device , time , or mood .
At JUMP DATA DRIVEN VIDEO , a business data management platform designed specifically for video service players , we offer JUMP Personalizer , a system that uses AI to provide personalized and contextual content suggestions for each user.
With this solution , you can increase conversion rates by 25%, reduce search times, and encourage content consumption, thus engaging less committed users , among other benefits . If you want to learn more about offering personalized video recommendations to increase user engagement and loyalty , don’t hesitate to contact us.
