Right Content at the Right Time
Today’s social platforms take a generalized approach to feeds, showing all users the same trending content. This broadcast-style delivery results in stagnant homogeneity, failing to meet diverse user needs.
In contrast, TikTok achieved explosive growth through true personalization at scale. The app adapts each user's feed to their taste using signals like views, follows, and micro-level engagements. This creates a seamless individual journey and the feeling of infinite personalized content.
Without AI-powered hyper-personalization, social platforms struggle to:
- Compete for a share of attention against endless content choices. Generic feeds cause tuning out.
- Adjust relevance for heavy users versus casual viewers. One size doesn’t fit all segments.
- Match evolving user interests over time. Static feeds fail to meet changing needs.
- Promote network effects through relevant connections. Misaligned matches prevent virality.
FirstBatch User Embeddings bridge users and the right content. By locating users in embedding space based on interaction signals, proximity algorithms can deliver individualized journeys optimized for engagement, retention, and growth.
Capabilities Enabled
With user embeddings, platforms can:
- Craft continuously adapting feed algorithms personalized to each user like TikTok.
- Balance relevance using known interests while expanding to new areas.
- Build accurate models using real-time interaction data.
- Enable instant personalization without requiring explicit user registration.
- Analyze different user segments to optimize relevance for each.
- Provide the feeling of endless personalized content discovery as users scroll.
Business Impact
Personalized feeds achieve measurable gains on key metrics versus broadcast feeds:
- Higher engagement rates from relevant content matching user interests
- Reduced subscriber churn by sustaining participation in long-term
- Stronger viral effects by connecting users to relevant peers
- Increased advertising performance through contextual targeting
Let’s examine two example algorithms that aim to solve common issues social platforms face. Without effective personalization, social platforms often struggle with:
Low engagement rates as feeds fail to adapt to users' evolving interests over time. One-size-fits-all streams cause tuning out.
High subscriber churn as users feel their needs are no longer met by static content. Lack of relevance pushes users to shift attention elsewhere.
Increase Engagement with "Interest Journey"
The "Interest Journey" algorithm gradually shifts users into deeper content areas as their interests develop:
- Exploration: Users discover posts across the broad catalog.
- Sampling: Variety from multiple topics sustains engagement.
- Discovery: Early personalization based on initial interactions.
- Dedicated: Focused interest areas emerge for deeper content.
- Hyper-Focus: Catering fully to the core interests of heavy users.
Adapting content over time keeps users engaged as they explore new interests and themes.
Reduce Churn with "Win-Back"
The "Win-Back" algorithm re-engages inactive users:
- Monitors for drops in interaction across segments.
- Understand disengaging behaviors based on inactivity.
- Delivers personalized prompts and content to re-activate them.
- Continues adaptation if the user starts engaging again.
Proactive and personalized re-engagement helps reduce churn by bringing users back into active status.
With tailored algorithms, social platforms can drive ongoing participation by matching users with engaging content suited to their evolving interests over time.