AI powered Recommendation engine: UX Personalization for digital content
Video service providers spend a lot of money on expanding their content catalogs, but is it worth the investment if nobody watches? It might seem easy, but one of the challenges video businesses face is to direct individual users to the content they really want to watch at just the right moment. Most services use tools designed on content based recommendation algorithms that make recommendation engines’ suggestions based solely on an association to something the user watched in the past.
To get your audience to watch content that is tailored to their individual requirements or tastes and increase their engagement, you need to go a step further and offer them a personalized experience on your video platform. You’ll need a richer understanding of your audience’s viewing behavior in order to offer them the most accurate recommendation engine and a positive user experience.
Lucky for you, the data already exists and you just need to use it in a smart way: define the proper data strategy, gather your audience’s data from multiple data sources, filter and organize that data, slice your audience data to better understand viewing patterns and obtain the kpis that will really have an impact on your user’s experience.
AI and machine learning algorithms can help you fully understand and process that data. AI can predict future behavior, from preferred content topics and similar clusters to favorite genres, times of day… to differentiate, for example, between child consumption habits that tend towards children’s content in the mornings, and adult viewing habits at night, or between the days of the week each consume content. This way, you can make better recommendations off of existing data, offering a personalized experience the current viewer is more likely to engage with.
These recommendation engine powered by AI and machine learning algorithm will let you not only offer Contextual recommendations for your users tailored to their consumption habits but also offer an ultra-personalized experience by configuring individualized digital content and track recommendation performance through performance analysis of recommended items.
How to get contextual recommendations for your users?
You can offer personalized recommendations adapted to different user consumption scenarios to increase user satisfaction, retention and engagement and offer your audience contextual recommendations tailored to their consumption habits. These are some of the kinds of recommendations you can provide your audience depending on the context:
- Recommendations by user similarity and product similarity
- Recommendations by day of the week
- Recommendations by time of day
- Recommendations by device, etc.
Machine learning technologies also allow you offer an ultra-personalized experience by configuring individualized digital content:
- Weighted personalization by product category
- Personalized curated content
- Product blacklists
- Recommendation grid configuration, etc.
The future of digital media services will not only meet user’s expectations but exceed them, offering a seamless, personalized streaming experience. Content offering and quality of service will be tailored to the habits and preferences of each individual viewer. innovation is the cornerstone of video streaming, and as technology evolves, OTT services will provide an engaging viewing experience unlike anything we know today. All of this thanks to the consumer engagement data.
Gathering the data to improve recommendation engines based on machine learning algorithms will also help to optimize the performance of your marketing campaigns, and opens up the opportunity for more customized recommendations and information viewers might like by email, sms, or pop ups, thus increasing the engagement the service providers are after.
Learn more about Jump Personalizer and how to build an effective recommendation engine powered by AI and machine learning algorithms here.
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