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:
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.
With user embeddings, platforms can:
Personalized feeds achieve measurable gains on key metrics versus broadcast feeds:
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.
The "Interest Journey" algorithm gradually shifts users into deeper content areas as their interests develop:
Adapting content over time keeps users engaged as they explore new interests and themes.
The "Win-Back" algorithm re-engages inactive users:
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.