To help a media company increase the lifetime value of their customers, we developed analytics to provide item recommendations from a diverse set of customer sources. This promoted improved customer engagement and retention as well as boosting loyalty.
The customer is a big media company that owns various TV and radio channels, audio podcasts, magazines, and newspapers. They were interested in a personalized recommendation system for their existing users and content consumers. The content type is diverse (TV programs and shows, news articles, etc.). And every user has preferences that have to be understood and taken into con-sideration while recommending a new content item. The challenge is to create such a complex sys-tem that would recognize individual users’ consumption patterns, understand their content prefer-ences and recommend new content items that users are likely to consume. With such personaliza-tion capabilities, the customer is expected to increase engagement and decrease churn.
Solution by AI Superior
We developed a recommendation system that utilizes several factors to provide recommendations. The system has the following capabilities:
- Estimates consumption patterns of individual users
- Understands content preferences of each user (topics of interest, content type, etc.)
- Estimates demographics and technical means which are used by every user to access the con-tent
- Assesses content items similarity from different perspectives
To enable this, we developed several analytical components: NLP-based topic discovery and content tagging module, content items similarity extraction analytics, consumption patterns extractor, col-laborative filtering-based recommender, item-to-item recommender, hybrid recommender that takes into account all the listed modules.
Outcome and Implications
The developed solution allowed the customer to increase the diversity of content consumed by their users by 5% and, as a result, increase the lifetime value of a customer. Additionally, with the help of the developed solution, the customer could directly identify similar groups (clusters) of consumers. Armed with this information, they can target specific audience groups with the content they are most likely to enjoy. They can also estimate potential demand for specific content.