Pendo’s complex analytics capabilities allow product teams to move beyond basic page views and understand deep user behavior, journey mapping, and AI-driven interactions.
Here are specific examples of complex analytics in Pendo:
1. Multi-App User Journeys (Pathing)
Pendo can map the user journey across multiple products or different areas of a complex application.
- Example: A B2B company uses Pendo to trace the path of a user from their public marketing site (using Pendo Web Analytics) into their SaaS platform, identifying which marketing pages lead to the highest product engagement
. - Complex Scenario: Analyzing the paths users take after seeing a specific in-app guide, revealing that a “Welcome” guide actually led to high drop-off rather than high adoption of Feature X.
2. Agent Analytics (AI & Conversational Metrics)
As Pendo integrates AI, it offers specialized analytics to measure the performance of AI agents (conversational UIs) alongside traditional UI.
- Rage Prompt Identification: Automatically detecting when a user “rage prompts” (repeatedly asks the agent for help due to frustration).
- “Success” Analysis: Analyzing whether an AI agent’s response resulted in the user successfully completing a workflow (e.g., creating a report) or if they had to revert to a human-driven traditional UI.
- Conversation Clustering: Automatically clustering user conversations to identify unintended use cases for the AI agent.
3. Data Explorer (Custom Behavioral Analysis)
Data Explorer allows for multi-faceted slicing of data, combining feature usage, metadata, and custom events.
- Example: A car rental company filters by a specific custom event () to see which users with a cart value over $500 are dropping off at the “payment” step.
- Cohort Analysis: Segmenting users based on custom metadata (e.g., “Industry: Healthcare”) to compare feature adoption rates between healthcare clients and financial clients.
4. Advanced Funnels and Retention
- Funnels: Instead of just linear step-by-step conversion, Pendo funnels can show where users are failing to adopt a new feature (e.g., Step 1: Click “New,” Step 2: Configure, Step 3: Save).
- Retention/Stickiness: Measuring “Product Engagement Score” (PES)—a combination of adoption, intensity, and breadth—and comparing it across different customer segments (e.g., high-paying vs. low-paying accounts).
5. Embedded Customer Health Dashboards
- Example: Using Pendo to create a “Customer Health Module” that combines product usage data with data from Salesforce, allowing Customer Success Managers (CSMs) to see which accounts are at risk of churn based on dropping engagement in key features.
6. Session Replay with Sentiment Analysis
- Example: Reviewing session replays triggered specifically by “rage clicks” or when a user closes a guide immediately. This allows for qualitative analysis (why did they do that?) combined with quantitative data (how many did that?).
More reading : https://support.pendo.io/hc/en-us/articles/360049997812-Paths