Case Study

Transforming Digital Content Engagement with AI-Driven Recommendation Engine​

Maximising Platform Performance​
Tata Elxsi's AI-Driven Engine Boosting Platform Performance
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enhancement in user experience and engagement

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hit ratio​

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to prepare use recommendation rails

Background

The digital content industry is increasingly adopting AI technologies to enhance user experience and engagement. AI-driven recommendation engines are becoming essential, enabling personalised and predictive analytics while real-time data processing delivers dynamic and immediate recommendations.​

A leading digital content provider faced challenges with content fatigue and low user engagement. Users struggled to discover relevant content, leading to shorter session times and decreased satisfaction. To address this, the client sought an advanced AI powered solution to improve user experience and boost engagement through personalised content recommendations.

Challenge

​The client faced several critical issues impacting their platform’s performance.​

Users experienced content fatigue due to the overwhelming volume of content, leading to disengagement. This was exacerbated by low user engagement, as users struggled to discover relevant content, resulting in shorter session times and decreased satisfaction. The lack of personalised recommendations further hindered user satisfaction and retention.​

Industry-wide, digital content providers grapple with similar challenges. The vast content libraries made it difficult for users to find personalised, engaging content. This led to content fatigue, reduced user experience and engagement, and lower retention rate.

Users experienced content fatigue due to the overwhelming volume of content

Solution

​Tata Elxsi developed a comprehensive AI-driven recommendation engine tailored to address the client's challenges. This solution involved an extensive user behavior analysis framework, which gathered and analysed watch history to provide deep insights into user preferences.​

Leveraging a hybrid approach that combined Gen-AI and machine learning, Tata Elxsi designed a content recommendation system capable of delivering highly personalised and accurate suggestions.​

The implementation included a continuous feedback loop to monitor and refine user interactions in real-time within new media design, ensuring constant improvements to the system. Additionally, an efficient data pipeline was created using Azure Databricks for seamless data processing and integration. Real-time stream analytics were also incorporated, enabling the system to provide immediate and relevant content recommendations.

Gen-AI and machine learning-powered content recommendation system delivering personalised content suggestions
User Interface Highlighting Personalized Content Recommendations

Impact

The implementation of Tata Elxsi’s advanced AI-driven recommendation engine significantly enhanced the client's platform performance.​

  • User Engagement and Satisfaction: There was a 35% increase in session duration and 18% rise in user satisfaction​
  • Content Discovery and Fatigue: Content consumption per session increased by 40% with 50% reduction in content fatigue​
  • User Experience Enhancement: There was a 2x improvement in user experience​
  • Hit Ratio: 87% hit ratio was achieved for relevant content recommendations​
  • Recommendation Preparation Time: The real-time content suggestion was less than 100 milliseconds

Services rendered

Tata Elxsi

  • Analysis of User Behavior and Content Consumption Patterns
  • AI-driven Recommendation Engine Development and Deployment ​
  • Platform Integration​
  • Optimisation and Support

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