OTT Platform Case Study | AI-Driven Solutions by PRIMOTECH AI

Introduction In the rapidly growing Over-The-Top (OTT) media service industry, personalized content recommendations are crucial for enhancing user engagement and retention. This case study explores how an OTT platform leveraged AI and machine learning to develop a sophisticated content recommendation system.

Project Overview

1. Background The OTT platform, offers a vast library of movies, TV shows, and documentaries. Despite the extensive content, users often struggled to find relevant titles, leading to decreased user satisfaction and increased churn rates. This aimed to improve its recommendation system to provide a more personalized viewing experience, increase user engagement, and reduce churn.

2. Objective

Methodology

1. Data Collection

Collected a vast amount of data, including

User Data: Demographics, watch history, ratings, search queries, and interaction times.

Content Data: Genre, director, cast, release year, and metadata.

User Data: Time of day, day of the week, device type, and location.

2. Data Processing

The data was preprocessed to handle missing values, normalize data formats, and encode categorical variables. Feature engineering was performed to create additional features such as user viewing patterns and content popularity trends.

Model Development

Implemented a hybrid recommendation system combining collaborative filtering, content-based filtering, and deep learning techniques.

1. Collaborative Filtering

User Collaborative Filtering: Recommendations based on similarities between users' viewing habits.
Item Collaborative Filtering: Recommendations based on similarities between content items.

2. Content-Based Filtering

Recommendations based on the similarity between the content watched by the user and other available content.

3. Deep learning

Used to capture non-linear user-item interactions.
To model sequential user behavior and predict the next likely item a user might watch.
For analyzing user reviews and content descriptions to extract sentiment and thematic elements.

4. Context-Aware Recommendations

Utilizing contextual data to tailor recommendations based on the user’s current context (e.g., time of day, device type).

Input

User Data
Content Data
Contextual Data

Ensemble Model

Collaborative Filtering
Content Based Filtering
Deep Learning Model
Context Aware Recommendation

Output

Increased User Engagement
Reduced Churn Rate
Enhanced User Satisfaction
Optimized Content Strategy

Evaluation and Optimization

The models were evaluated using metrics on a validation dataset. A/B testing was conducted to compare the performance of the new recommendation system with the existing one. Continuous monitoring and feedback loops were established to retrain models based on new data and changing user preferences.

Results

1. Increased User Engagement

The personalized recommendations led to a 25% increase in the average watch time per user.

2. Reduced Churn Rate

The improved recommendation system contributed to a 15% reduction in the monthly churn rate.

3. Optimized Content Strategy

Insights from user preferences helped make informed decisions on content acquisition and production, leading to better alignment with user interests.

4. Enhanced User Satisfaction

User surveys indicated a 30% increase in satisfaction with content discovery.

Conclusion

By leveraging AI and machine learning, the OTT Platform significantly enhanced its content recommendation system, leading to increased user engagement, satisfaction, and retention. The hybrid approach combining collaborative filtering, content-based filtering, and deep learning proved to be effective in providing personalized recommendations. Continuous optimization and adaptation to user behavior were key to the system's success.

This case study highlights the importance of a data-driven approach in the OTT industry and the potential of AI and machine learning in transforming user experience and business outcomes.