Reports

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Reports overview

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Guide analytics

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Performance metrics

Track information about guides created and how users interact with them.

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Guide creation trends

View the number of guides created across different time periods:

• Time-series graph: You can view a visual representation of guide creation over the last 7 or 30 days.
• Daily breakdown: You can see peak usage periods and identify patterns in user needs.
• Trend analysis: You can understand whether guide usage is increasing or decreasing over time.

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Guide effectiveness

Monitor how users interact with generated guides:

• Average steps per guide: You can track the complexity and depth of guides created for users.
• Completion patterns: You can understand how far users progress through multi-step guides.
• User engagement: You can analyze which types of guides generate the most user interaction.

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User feedback analysis

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Feedback sentiment

A pie chart visualization showing the distribution of user feedback:

• Positive feedback: This shows the percentage of users who found guides helpful.
• Negative feedback: This highlights areas where guides may need improvement.
• Feedback trends: This displays how sentiment changes over time.

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Time period selection

All guide analytics can be viewed for flexible time ranges:

• Last 7 days: This provides recent performance data and immediate trends.
• Last 30 days: This offers broader patterns and monthly insights.

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Frequently asked questions

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Question analysis

The dashboard automatically analyzes all user conversations to identify the most common inquiries:

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Top 5 questions

View the most frequently asked questions across all user interactions:

• Question ranking: Questions are ordered by frequency of occurrence.
• Auto-generated answers: The system provides AI-powered responses based on your knowledge base.
• Context awareness: Questions are grouped by topic and user intent for better organization.

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Question insights

Understand what users need most:

• Knowledge gaps: This identifies questions that frequently appear but lack comprehensive answers.
• Topic clustering: Related questions are grouped together for better understanding.
• Trending topics: This shows emerging questions that indicate new user needs.

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Using FAQ insights

Leverage frequently asked questions data to:

• Improve knowledge base: You can add missing content for commonly asked questions.
• Optimize responses: You can enhance existing answers based on user question patterns.
• Content planning: You can create targeted content addressing user needs.
• Product development: You can identify features or information users seek most.

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Conversation logs

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Comprehensive conversation history

Access detailed logs of every user interaction with Pointer:

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Conversation metadata

Each conversation entry includes:

• Date and time: This shows when the conversation occurred.
• Conversation title: The system generates an auto-generated title based on the main topic.
• Length: This displays the number of messages exchanged.
• Guide presence: This indicates whether a Copilot guide was generated during the conversation.
• User feedback: This shows positive or negative sentiment from the user.

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Expandable details

Click on any conversation to view:

• Complete message log: You can see the full conversation history between user and AI.
• Guide details: You can access the step-by-step breakdown of any guides created.
• User context: You can view information about the user who initiated the conversation.
• Response quality: You can see analysis of AI response effectiveness.

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Conversation management

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Pagination options

Navigate through conversation logs efficiently:

• Default view: The system displays 10 conversations per page.
• Extended views: You can choose to display 25 or 50 conversations per page for faster browsing.
• Chronological order: The latest conversations appear first.

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Filtering and search

Find specific conversations using:

• Date ranges: You can filter by specific time periods.
• Feedback type: You can show only positive or negative feedback conversations.
• Guide presence: You can filter conversations that included guide generation.
• User segments: You can view conversations from specific user groups.

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User tracking analytics

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Individual user insights

When user tracking is enabled, view detailed information about each tracked user:

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User profiles

Access comprehensive user information:

• Basic information: This includes name, email, avatar, and profile details.
• Custom fields: This covers plan type, company information, and other custom data.
• Account status: This shows user role, subscription level, and access permissions.

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User activity

Track individual user engagement:

• Conversation history: You can view all conversations initiated by the specific user.
• Recent events: You can see the latest user actions and interactions.
• Feature usage: You can determine which Pointer features the user engages with most.
• Session patterns: You can analyze the frequency and duration of user sessions.

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Credit usage analytics

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Usage breakdown

Monitor your Pointer credit consumption with detailed analytics:

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Daily usage graph

A comprehensive bar chart showing credit usage over time:

• Time period: This provides a daily breakdown of credit consumption.
• Category split: Each bar is divided into three colored sections:
  • Search credits: These are used for knowledge base queries and information retrieval.
  • Embedding credits: These are consumed during content processing and indexing.
  • Chat credits: These are utilized for AI conversations and response generation.

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Usage patterns

Identify trends in your credit consumption:

• Peak usage days: You can understand when users are most active.
• Category distribution: You can see which features consume the most credits.
• Growth trends: You can track how usage changes over time.
• Budget planning: You can forecast future credit needs based on historical data.

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Cost optimization

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Usage insights

Optimize your credit allocation by understanding:

• Feature efficiency: You can determine which features provide the best ROI.
• User patterns: You can see how different user segments affect credit consumption.
• Scaling impact: You can understand how user growth affects overall credit usage.
• Optimization opportunities: You can identify areas where you might reduce unnecessary consumption.