How can I utilize interest data for personalized recommendations?

Using interest data within the Brand Control Center allows brands to deliver highly relevant, AI-driven recommendations across their Community Media Network. This directly improves:

• User engagement: Users see content that matches their preferences
• Content discoverability: Relevant videos and communities surface faster
• Retention & session time: Personalized feeds keep users coming back
• Conversion & monetization: Targeted recommendations improve clicks and actions
• Scalable personalization: AI continuously adapts recommendations as user behavior evolves

Guide: Step-by-step usage

Capture and enable interest data

Before using recommendations, ensure interest signals are available:

1. Enable User Profile & Interests in application

Navigate to Manage > Category in Brand Control Center.

2. Capture interest signals via:

• Onboarding selections (explicit interests)
• User interactions (views, likes, shares, watch time)

Allow AI to build interest profiles

Once enabled:

• Brand Control Center automatically creates dynamic user interest profiles
• Interests are continuously updated based on:
  • Content consumption patterns
  • Community participation
  • Engagement signals

Apply interest data to recommendation surfaces

You can utilize interest data across multiple touchpoints:

1. Personalized Feeds

• Rank and display videos based on user interests
• Prioritize high-affinity content categories

2. Community & Group Recommendations

• Suggest relevant communities based on user preferences
• Increase join rates with contextual discovery

3. Content Suggestions

• Recommend similar or related content after user interactions
• Drive deeper engagement within sessions

4. Notifications & Re-engagement

• Send personalized alerts based on user interests
• Promote trending or new content in preferred categories

Configure placements and experiences

Using Brand Control Center (especially within Grow and Onsite modules):

• Configure where recommendations appear:
  • Homepage feeds
  • Embedded widgets (Carousel, Feed, Standard Wall)
  • In-app experiences
• Apply filters:
  • Interest category
  • Community relevance
  • Content type

Optimize using analytics

1. Go to Analytics Dashboard in Brand Control Center
2. Track:
   • CTR (Click-through rate) on recommended content
   • Engagement by interest category
   • Retention and session duration

Use these insights to:

• Refine interest mapping
• Improve content tagging
• Adjust recommendation strategies

Specs & Limitations

System Behavior

• Recommendations are dynamically generated using AI models
• Interest profiles evolve in real-time based on behavior
• Works across feeds, communities, and notifications

Validation Rules

• Requires sufficient user interaction data
• Content must be properly tagged/classified
• Interest signals must be enabled in Brand Control Center

Limitations

• Cold-start users may receive generic recommendations initially
• Misclassified content can reduce recommendation accuracy
• Over-personalization may limit content diversity if not balanced

Example Scenario (Use Case)

A media brand wants to improve engagement across its video platform.

• Users interact with content across categories like tech, gaming, and finance
• Brand Control Center builds interest profiles automatically
• The platform starts:
  • Recommending similar videos
  • Suggesting relevant communities
  • Sending targeted notifications

Result:

• Higher CTR on recommended content
• Increased watch time
• Improved user retention