## Introduction
In product development, user session replays are invaluable for understanding how real users interact with new features, identifying usability issues, and validating design decisions. However, organizing and reviewing session replay data manually can be time-consuming and error-prone, especially for fast-moving product teams that want timely insights.
This article guides you through automating the process of logging and organizing user session replays for feature review using n8n, a powerful open-source workflow automation tool. The automation workflow described here benefits product managers, UX researchers, and product engineers by providing an automated, structured feed of session replays linked to specific feature usage for more efficient analysis.
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## Problem Statement
Manually sifting through raw session replay data to find recordings related to a particular feature consumes time and delays feedback loops. It’s challenging to track when and how often new features are used, and users may give incomplete or biased reports about their experience.
### Who Benefits?
– Product teams wanting rapid feedback on new features.
– UX researchers analyzing real user behavior.
– Customer success teams monitoring feature adoption.
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## Tools and Services Integrated
– **n8n**: To orchestrate the automation workflow.
– **Session Replay Platform API** (e.g., LogRocket, FullStory, Hotjar): To fetch session replay data via API.
– **Google Sheets**: To log and organize replay metadata for review.
– **Slack (optional)**: To notify the team about new relevant session replays.
– **HTTP Request node**: For API calls in n8n.
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## Overview of the Workflow
1. **Trigger:** Scheduled time interval (e.g., daily) using n8n’s Cron node.
2. **Fetch session replays** for a target time range from the Session Replay Platform API.
3. **Filter replays** that involve usage of the specific feature by analyzing events or URLs.
4. **Log replay metadata** (session ID, timestamp, user info, feature used) into a Google Sheet.
5. **Notify the team** via Slack with links to new session replays.
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## Step-by-Step Tutorial
### Prerequisites
– Access to n8n (self-hosted or cloud instance).
– API access credentials to your session replay tool.
– Google account with access to Google Sheets.
– Slack workspace and incoming webhook URL (for notifications).
### Step 1: Set up the Trigger (Cron node)
– Create a new workflow in n8n.
– Add a **Cron** node.
– Configure the Cron to run once daily or at your preferred frequency.
### Step 2: Fetch Session Replay Data
– Add an **HTTP Request** node after the Cron node.
– Configure it to make a GET request to your session replay tool’s API endpoint for fetching session data.
– Example: For LogRocket, use `/api/v1/sessions` with a query parameter for the time range.
– Pass authorization headers or API key as required.
– Set query parameters to limit results to the last day or the desired period.
– Test the request in n8n to verify you receive session replay data.
### Step 3: Filter Replays Related to the Feature
– Add a **Function** node to process the API response.
– Parse the session events or URLs to identify interactions with the feature.
– For example, check if specific event names or URL paths associated with the feature appear.
– Filter out sessions not including the feature.
– Return an array of session objects relevant to the feature.
#### Example JavaScript snippet in Function node:
“`javascript
return items.filter(item => {
const sessionData = item.json;
// Assuming ‘events’ is an array of user actions
return sessionData.events.some(event => event.name === ‘FeatureX_Used’);
});
“`
### Step 4: Log Relevant Data to Google Sheets
– Add a **Google Sheets** node (requires prior Google Sheets OAuth setup in n8n).
– Configure it to append rows to a predefined spreadsheet.
– Map fields like:
– Session ID
– Timestamp
– User ID or anonymized identifier
– Feature interaction detail
– Replay URL (if available)
– This sheet serves as a living database of session replays related to the feature for team review.
### Step 5: Notify the Team on Slack (Optional)
– Add a **Slack** node configured as “Send Message.”
– Format a message summarizing the number of new replays logged and include links.
– Connect the Slack node to run after logging to Google Sheets.
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## Common Errors and Tips
– **API Rate Limits:** Session replay services often limit API calls. Implement pagination and respect rate limits. Use `Sleep` nodes between requests if needed.
– **Authentication Failures:** Double-check API keys and OAuth credentials.
– **Data Volume:** For large volumes, consider batching or incremental fetches rather than pulling all sessions at once.
– **Feature Event Identification:** Confirm that your session replay tool reliably captures events that denote feature usage, and that your function node filters accurately.
– **Error Handling:** Add error triggers or catch nodes to alert you if the workflow fails.
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## Scaling and Adapting the Workflow
– **Multi-Feature Tracking:** Extend filtering logic to handle multiple features by parameterizing event names or URL paths.
– **Enhanced Analytics:** Feed the Google Sheets data into BI tools or dashboards for trend analysis.
– **Integration with Bug Trackers:** Automatically create tickets in Jira or GitHub for sessions showing errors or crashes.
– **User Segmentation:** Add filters to segregate sessions by user segments, device type, or geography.
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## Summary
Automating the logging of session replays associated with specific features accelerates the product feedback cycle and empowers teams with actionable insights from real user interactions. With n8n, integrating APIs and tools like Google Sheets and Slack allows you to build a flexible, customizable workflow to monitor feature adoption efficiently.
**Bonus Tip:** Implement webhooks if your session replay platform supports them, enabling near real-time logging and notifications rather than scheduled polling—making your automation even more responsive and resource-efficient.
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By following this guide, product teams can systematically gather and review session replays to identify UX improvements early, ensuring better feature launches and enhanced user satisfaction.