Challenge
A video content distribution company with its own streaming platform was struggling with insufficient personalization of its offerings and difficulties in managing user data. The lack of advanced recommendation tools and integration with the analytics platform meant that users were receiving generic recommendations that did not match their interests. In addition, the lack of data centralization made it difficult to effectively monitor the effectiveness of campaigns promoting new content.
The company decided to implement an automated content recommendation system, integrating it with its existing streaming platform and analytics tools. The main goals were to personalize recommendations, centralize data and optimize marketing processes.
Action
- Integration of the recommendation system with the streaming platform
The company, with the support of recommendation system experts, integrated a state-of-the-art recommendation tool with a streaming platform. The system automatically synchronized users' data, viewing history and content preferences.
- Data centralization
Thanks to the integration, all data about users - their preferences, viewing history, content ratings and interactions with the platform - has been collected in one place. This has allowed the company to better understand user behavior and interests, and to more precisely tailor content recommendations.
- Personalization of content recommendations
The recommendation system analyzed the collected data to provide users with personalized suggestions for movies and series. Thanks to advanced algorithms, users received recommendations tailored to their previous choices and preferences.
- Marketing campaign automation
The company implemented automated marketing campaigns that included: notifications of new episodes of favorite series, special offers for new users, reminders for unfinished screenings, and personalized subscription offers. Automating these processes greatly improved communication with users.
- Analysis and optimization of activities
The recommendation system provided detailed reports on the effectiveness of recommendations and marketing campaigns. The company was able to track metrics such as click-through rates on recommendations, average content viewing time, and number of new subscribers - allowing it to optimize marketing activities and adjust its recommendation strategy on an ongoing basis.
Results
By implementing a content recommendation system, the conversion rate on the platform's website increased by 18%. Users spent more time viewing content that matched their interests, which increased engagement.
The centralization of data made it possible to more accurately analyze user behavior and more effectively customize content offerings. This translated into better personalized recommendations and higher user satisfaction.
Automated marketing campaigns increased the number of subscribers by 22% and improved the engagement of existing users with personalized offers and reminders.
The automation of marketing processes allowed the team to focus on the strategic aspects of the platform's development, while reducing operating costs and increasing the efficiency of marketing activities.
