Context.ai
  • What is Context.ai?
  • List of Trackable Metrics
  • Product Analytics
    • Overview
    • Topic Categorisation
      • LLM Topics
    • User Engagement Tracking
    • Foreign Language Support
    • PII Filtering
    • Custom Metadata Filtering
    • Backfill Analytics Data
    • Custom Events
    • API Ingestion Methods
      • Log Conversation
      • [deprecated] Upsert Conversation
      • Thread Conversation
      • Patch Thread Message
      • API Resources
        • Chat Message
        • [deprecated] Tool Message
        • Custom Event
        • Metadata
        • Conversation
        • Thread
    • Embedded API
      • Multi-Tenancy
      • Conversations
        • Series Data
  • Integrations
    • Getting Started
    • Python SDK
    • Javascript SDK
    • LangChain Plugin
    • Haystack Plugin
    • Authorization
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  • Overview
  • Integration Instructions

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  1. Product Analytics

User Engagement Tracking

Overview

You can use Context.ai to track user engagement in your LLM-powered product.

Our user engagement metrics allow you to easily monitor:

  • Volumes of Unique Users: How many unique individuals are using my application?

  • User Cohort Analysis: How frequently are users returning to my application?

  • Top Topics of Most Engaged Users: What are my most engaged users discussing?

  • Most Engaged Users: Who are my most engaged users?

Integration Instructions

To start using user engagement monitoring, add a User ID (user_id) metadata key-value pair to ingested transcripts. The user_id metadata can contain any arbitrary string value.

{
  "conversation": {
    "messages": [ ... ],
    "metadata": {
      
    }
  }
}

Once conversations are ingested, your metrics will become available under the 'Users' tab within the Context.ai UI.

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Last updated 11 months ago

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