Product teams launching GenAI chatbots and AI copilots face a familiar question: how do we track user behavior? The instinct is to reach for tools already in the stack. Google Analytics tracks website traffic. Amplitude and Mixpanel handle product events. Hotjar captures heatmaps and session recordings. Pendo measures feature adoption. These tools work well for traditional digital products. But GenAI chatbots are not traditional products.
Conversational AI introduces a fundamentally different interaction model. Users do not click buttons or navigate pages. They type questions and receive answers. They ask follow-up questions. They rephrase when they do not get what they need. They express frustration through word choice, not rage clicks. Traditional analytics tools cannot capture this.
This blog explains why purpose-built GenAI analytics matters, how traditional tools fall short, and what capabilities you actually need to understand user behavior in conversational AI.
The fundamental mismatch between traditional analytics and conversational AI
Traditional product analytics emerged in the era of point-and-click interfaces. Every meaningful user action generated a discrete event: a page view, a button click, a form submission. Analytics tools were built to capture, aggregate, and visualize these events.
Conversational AI operates differently. User interaction revolves around typing text and pressing enter, then typing more text and pressing enter again. The meaningful information is not in the action but in the content. Traditional product analytics tools cannot capture the essence of what users are typing.
Consider what matters in a chatbot conversation. What was the user trying to accomplish? Did they succeed? Were they satisfied with the response? Did they have to rephrase their question multiple times? Did they abandon the conversation in frustration? None of these questions can be answered by counting clicks or tracking page views.
Without purpose-built analytics, it felt like we were largely flying blind, like going back 10-15 years ago when we were guessing instead of looking at product analytics.
What traditional analytics tools actually track
Each major analytics platform has strengths for traditional products. Understanding what they measure reveals why they fail for conversational AI.
Google Analytics excels at web traffic analysis. It tracks page views, session duration, traffic sources, and conversion funnels. GA4 added event-based tracking, but the events are still discrete actions like clicks and form submissions. Google Analytics cannot analyze the content of chatbot conversations or determine whether users accomplished their goals. It also struggles to properly attribute AI chatbot traffic, often misclassifying it as direct traffic because many AI platforms strip referrer data.
Amplitude provides sophisticated product analytics including event tracking, funnel analysis, retention cohorts, and user segmentation. It recently added AI Feedback to analyze surveys and reviews, and AI Visibility to track how brands appear in AI search results. However, Amplitude's core model is still event-based. It cannot parse the unstructured natural language data that defines chatbot interactions.
Mixpanel focuses on behavioral analytics. It tracks user actions, conversion funnels, and A/B test results. Some teams have attempted to map chatbot intents to Mixpanel events, treating each intent as a discrete action. This approach provides basic visibility into intent distribution but misses the nuance of conversation flow, user satisfaction, and the reasons behind user behavior.
Hotjar takes a qualitative approach with heatmaps, session recordings, and user surveys. These tools work well for visual interfaces where user attention and click patterns matter. But chatbots do not have meaningful click patterns. Users type in a text box and read responses. Heatmaps and session recordings provide little insight into conversational experiences.
Pendo measures product experience through feature adoption tracking, user paths, and in-app guidance. Pendo recently announced Agent Analytics to measure AI agent performance, which represents a step toward conversational analytics. However, Pendo's foundation is still traditional product analytics, not conversation-native analysis.
What LLM observability tools track
Beyond traditional product analytics, many teams turn to LLM observability platforms. These tools are purpose-built for AI applications, but they focus on system performance, not user behavior.
Helicone is an open-source LLM observability platform that tracks user metrics including active users, session length, request frequency, and explicit feedback. It provides cost, latency, and error monitoring, and supports session grouping for multi-step agents. Helicone excels at engineering visibility but does not automatically detect user intent or measure satisfaction from conversation patterns.
LangSmith is built by the LangChain team and provides tracing that captures full execution traces of LLM applications including all LLM calls, tool calls, and logic steps. It offers custom dashboards for cost, latency, and response quality, plus feedback collection on specific runs. LangSmith is powerful for developers debugging AI applications but does not provide the user-centric analytics that product teams need.
Langfuse is an open-source, self-hostable observability platform that provides trace-level logging, session analysis, prompt management, and cost monitoring. It offers flexibility for teams that want control over their data and custom dashboards. Like other observability tools, Langfuse focuses on technical metrics rather than understanding what users want and whether they succeed.
These tools are valuable for engineering teams monitoring system health. But they answer different questions than user analytics. Observability asks: is the system working? User analytics asks: are users getting what they need?
Why GenAI chatbots need different metrics
The metrics that matter for conversational AI are fundamentally different from web and product analytics. Traditional tools ask: what did users click? Conversational analytics asks: what did users want, and did they get it?
Consider a simple scenario. A user visits your AI assistant, asks a question, receives an answer, says "thanks," and leaves. Traditional analytics would see a successful session: the user engaged, the system responded, no errors occurred. But was the user actually satisfied? Did they get what they needed? Were they being polite while actually frustrated? Traditional metrics cannot answer these questions.
Purpose-built GenAI analytics tracks different signals. User intent detection identifies what users are actually trying to accomplish, whether explicitly stated or implied through conversation patterns. Satisfaction scoring measures whether users felt helped through behavioral signals, not just explicit ratings. Conversation flow analysis maps how dialogues progress, identifying where users succeed and where they abandon. Topic clustering reveals what users discuss most frequently and where gaps exist. Implicit feedback detection captures frustration signals like rephrasing, repetition, and abrupt topic changes.
These capabilities require natural language processing, sentiment analysis, and conversation-aware models that traditional analytics platforms do not have.
The questions traditional analytics cannot answer
When product teams deploy GenAI chatbots, they need answers to specific questions that traditional tools cannot address.
What is the users' primary intent when interacting with the chatbot? Traditional analytics can tell you how many people used the chatbot but not what they were trying to do. Purpose-built GenAI analytics automatically classifies user intents and surfaces the most common requests.
What are the most common follow-up questions after receiving a response? Traditional analytics tracks sessions but not conversation flow. GenAI analytics maps how conversations progress and identifies patterns that indicate success or confusion.
Are users satisfied with the answers they receive? Traditional tools rely on explicit feedback like thumbs up or thumbs down, which most users never provide. GenAI analytics detects implicit satisfaction signals from conversation behavior.
Where do users seem confused or frustrated? Traditional analytics might show drop-off points in a funnel but cannot identify frustration within a conversation. GenAI analytics detects signals like repeated rephrasing of the same question, which indicates the AI is not understanding the user.
Are there opportunities to improve responses based on actual user interactions? Traditional tools show aggregate metrics but not actionable insights. GenAI analytics identifies specific conversation patterns that lead to poor outcomes, enabling targeted improvements.
How purpose-built GenAI analytics works
Purpose-built GenAI analytics platforms are designed from the ground up for conversational interfaces. They capture every interaction between users and AI systems and apply specialized analysis to extract insights.
The process starts with understanding the actions within each interaction. User actions fall into categories: active actions where users explicitly request something, passive actions like copying a response or rephrasing a question, and assistant actions representing the AI's responses. Each action type provides different signals about user behavior.
Next, the platform extracts properties from each interaction. What topic is being discussed? What format or style is the user expecting? What sources did the AI reference? These properties enable segmentation and trend analysis across thousands of conversations.
The platform then maps conversation flow, going beyond individual interactions to visualize how entire conversations progress. This reveals natural dialogue patterns that lead to success, drop-off points where users abandon, and friction moments that signal frustration.
Finally, the platform detects engagement versus frustration. Engagement appears as enthusiastic follow-ups, exploration of subtopics, and use of AI responses like copying text. Frustration appears as repetitive questions, slight rephrasing without moving forward, or abrupt topic changes. Identifying the root cause of frustration, whether knowledge limitations, tone mismatch, or verbosity, enables targeted improvement.
Why Nebuly is purpose-built for GenAI
Nebuly is a user analytics platform designed specifically for GenAI products. It automatically analyzes every conversation between users and AI systems, surfacing user intent, satisfaction, friction points, and conversation quality in a unified dashboard.
Unlike traditional analytics that require manual event instrumentation, Nebuly captures conversations automatically and applies proprietary models to extract insights. Learn more about how to analyze user behavior in your GenAI chatbot: from system metrics to semantic insights.
Nebuly tracks what traditional tools cannot. It identifies the most common user questions and topics across all conversations. It detects when AI responses fall short and surfaces patterns in poor responses. It measures user satisfaction without requiring explicit feedback, using implicit signals from conversation behavior. It maps conversation trends over time, showing how user needs evolve. Learn more about how user analytics differs from web analytics in the AI era.
For enterprises, Nebuly provides the visibility needed to scale AI adoption confidently. A global bank with over 80,000 employees deployed Nebuly to monitor internal AI copilot usage across trading, legal, and HR functions. Within 60 days, the platform identified conversation topics in real time, spotted potential compliance issues, and revealed which departments needed additional training. The bank used these insights to improve the copilot and increase adoption. Explore the full case study.
Nebuly maintains enterprise-grade security with automatic PII removal, encryption, role-based access control, and private deployment options. It holds SOC 2 Type II, ISO 27001, and ISO 42001 certifications. Learn more about Nebuly's security practices.
When to use traditional analytics versus GenAI analytics
Traditional analytics tools still have a place in your stack. Google Analytics remains valuable for tracking website traffic and understanding how users arrive at your chatbot. Amplitude and Mixpanel work well for analyzing how chatbot usage fits into broader product journeys. Hotjar can capture feedback through surveys.
But these tools should complement, not replace, purpose-built GenAI analytics. Use traditional tools for web and product context. Use GenAI analytics for understanding what happens inside conversations.
The combination provides complete visibility: how users find your AI product, what they do when they get there, and whether they succeed. Learn more about how to measure business value from GenAI products.
Conclusion
Traditional analytics tools were built for a different era. Google Analytics, Amplitude, Mixpanel, Hotjar, and Pendo all provide valuable insights for web and product analytics. But they cannot capture what matters in conversational AI: user intent, satisfaction, conversation flow, and the implicit signals that reveal success or frustration.
GenAI chatbots require purpose-built analytics that understand natural language, detect sentiment, and map conversation patterns. These capabilities do not exist in traditional tools because they were never designed to parse unstructured conversational data.
Nebuly provides the user analytics platform designed specifically for GenAI products. It automatically captures and analyzes every conversation, surfacing the insights that product teams, AI teams, and business leaders need to improve their AI products and prove ROI.
For organizations deploying GenAI at scale, purpose-built analytics is not optional. It is the missing piece that transforms deployment into adoption and usage into business outcomes.
Explore how leading enterprises use Nebuly to understand GenAI user behavior by visiting our case studies or book a demo to see the platform in action.
