Insight Explorer
Explore data with Insight Explorer: build filters, visualize trends, and analyze interactions across chat, support, surveys, and forum data for actionable insights.
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Explore data with Insight Explorer: build filters, visualize trends, and analyze interactions across chat, support, surveys, and forum data for actionable insights.
Last updated
Was this helpful?
The Insight Explorer is a powerful feature that enables you to explore your data, uncover patterns, and gain highly specific insights.
You can build complex, custom filters using a variety of attributes, visualize data through interactive charts, and explore individual conversations. Whether you're looking to analyze user behavior, track conversation topics, explore customer feedback, or identify trends over time, the Insight Explorer provides all the tools you need for informed decision-making.
The Insight Explorer lets you analyze diverse types of conversational and interaction data, sourced from various channels and formats:
Language Model Interactions:
Chat-Based Sessions: Ongoing, conversational interactions between users and a language model, similar to chatbots like ChatGPT, covering multiple topics.
Prompt-Response Exchanges: Specific prompts from users for targeted responses, such as data analytics requests or content generation, where the model provides a single, structured reply.
Customer Support Conversations:
Human Agent: Text-based interactions where users communicate with live customer support representatives.
AI Agent: Automated customer service conversations with an AI-powered agent that provides support, answers questions, and addresses user concerns.
Uploaded Data:
Survey Data: Results from customer or market research surveys that offer insights into user preferences, opinions, and demographics.
Feedback Forms: Direct feedback collected from customers about their experiences, preferences, or requests.
Logs from LLM Features: Records of interactions with language models, capturing usage patterns, prompts, and responses for analytics or monitoring.
Community Forum Data: Discussions from online communities, such as support forums or customer groups, providing authentic, community-driven insights.
Scraped Data: Data collected from external sources like Reddit, Trustpilot or other public websites, often providing informal customer sentiment, trends, or user-generated insights.
Users can upload this data themselves via the CSV uploader in the platform, enabling you to seamlessly integrate external datasets and enrich their analysis within the Insight Explorer.
Insight Explorer supports filtering and grouping based on a range of attributes, enabling a tailored view of the data. Key attributes include:
User Attributes: Demographic or user profile data, such as location, subscription type, and other characteristics.
Conversation/Interaction Attributes: Conversation-specific elements, including keywords, topics, query types, and whether the interaction types.
Upload Source: The origin of uploaded data, such as survey results, feedback forms, forum posts, or LLM logs, providing context for deeper analysis.
With these filtering capabilities, the Insight Explorer offers flexibility to analyze conversational data, LLM interactions, and external datasets to uncover patterns, trends, and actionable insights across multiple sources.
User Attributes: Filter data based on user-specific information such as location, subscription type, demographics, or other profile details.
Conversation Attributes: Refine data using keywords, conversation topics, or the type of query, whether from customer support, an AI model, or uploaded interactions.
Uploaded Data Sources: Combine filters for data you’ve uploaded yourself—such as surveys, feedback forms, LLM logs, community forum posts, or scraped web data (e.g. Reddit or Trustpilot)—to uncover insights from various channels.
With multi-layered filtering, you can combine user, conversation, and source attributes to create intricate queries that surface precise insights. For instance, you can identify common customer issues by location and subscription type across different channels or pinpoint the most frequently mentioned topics in user-uploaded feedback forms and support logs.
Interactive Charts: Display your structured data using bar charts and line graphs to see patterns visually.
Grouping and Aggregation: Group data by user, conversation, or other attributes to see totals, averages, or percentages.
Percentage Toggle: Switch between absolute numbers and percentage views for context on relative frequencies.
Trend Analysis: Visualize data over time to detect emerging patterns, spikes, or trends in customer feedback, conversation topics, or other key indicators.
Detailed Interaction Viewing: Access individual interactions to examine user messages/prompts and LLM/agent responses.
Save to Collections: Save relevant interactions into collections to easily share examples of interesting prompts, responses, or conversation patterns.
Analyze Content:
Word Cloud: View a word cloud for prominent themes and frequently used words.
Frequent Questions: Review the list of the most common questions users ask.
Recurring Messages: Identify messages that appear frequently, providing insight into user concerns.
URLs Mentioned: See which URLs are often included in responses, identifying frequently referenced resources.
Category Comparison: Analyze and compare different data categories side by side to identify differences, similarities, or anomalies.
Login: Sign in to your Requesty account.
Navigate: From the dashboard, click on the Insight Explorer tab in the main menu.
Select Filters:
User Attributes: Choose attributes such as location, subscription type, or other demographic details.
Conversation Attributes: Input specific keywords or select conversation topics to narrow down results.
Combine Filters: Mix multiple attributes across user profiles, conversation details, and data sources to build a comprehensive view.
Apply Filters: Click Apply to filter your data based on the selected criteria.
Group Data: Decide how to group your data (e.g., by user, conversation, topic) for meaningful aggregation.
Choose Chart Type: Select a chart type (e.g., totals in a bar chart or trends on a line graph) that best represents your filtered data.
Toggle Percentage View: Switch between absolute numbers and percentage views for context.
Additional filtering: Click on the chart to add additional filters to your data segment.
View Conversations: After applying filters, click on individual conversations to view the whole interaction.
Analyze Content:
Word Cloud: View a word cloud for prominent themes and frequently used words.
Frequent Questions: Review the list of the most common questions users ask.
Recurring Messages: Identify messages that appear frequently, providing insight into user concerns.
URLs Mentioned: See which URLs are often included in responses, identifying frequently referenced resources.
Save to Collections: Select relevant conversations or interactions and save them to collections for easy sharing and further analysis. This feature is especially useful for building examples of common user requests, interesting prompts, or specific conversation patterns.
Compare Categories: Use category comparisons to analyze data segments side by side, enabling insights into different user behaviors, regions, or channels.
Task Analysis: Identify the types of tasks users are submitting to your language model, such as data analytics, information requests, content generation, or customer support inquiries.
Domain Exploration: Understand the domains of knowledge users are querying, such as finance, healthcare, education, or general information.
Prompt Patterns: Discover common prompt structures or phrasings that users employ, helping you improve prompt guidance or optimize the model’s responses for typical user needs.
Feature Requests: Identify recurring suggestions or feedback, especially from surveys and community forums, to guide product improvements.
Trend Spotting: Detect emerging topics or keywords across various channels, signalling new user needs or interests.
Common Issues: Identify frequent user questions or pain points across multiple channels.
Agent Performance: Evaluate human or AI agent responses for effectiveness and consistency.
Geographic Analysis: Determine which locations show the highest engagement and specific conversation topics of interest.
Subscription Trends: Analyze data based on subscription types to refine marketing and retention strategies.
Combine Filters: For highly specific insights, use multiple filters simultaneously to pinpoint trends across different data types.
Regular Updates: Regularly refresh filters and data for up-to-date insights as new data streams in.
Save Configurations: Save filter configurations to quickly revisit complex views and share them with your team.
Leverage Collections: Use Collections to save, share, and revisit specific conversations or data segments for ongoing analysis and reference.
