How to Automate Tagging UX-Related Feedback with n8n

## Introduction

In product teams, managing user feedback effectively is crucial for enhancing user experience (UX). However, manual tagging and categorization of feedback often leads to delays, inconsistencies, and missed insights. Automating the tagging of UX-related feedback not only streamlines the process but also empowers product managers, UX researchers, and engineers to prioritize improvements efficiently.

This article provides a step-by-step tutorial on building an automation workflow using **n8n** to automatically tag feedback related to UX. We’ll integrate common feedback channels, use natural language processing (NLP) to detect UX relevance, and update your database or messaging tools accordingly.

## Use Case and Benefits

**Problem:** Product teams receive vast amounts of user feedback across various channels (e.g., emails, Google Forms, support tickets). Manually reading and tagging this feedback for UX relevance is time-consuming and error-prone.

**Who benefits:**
– Product Managers can quickly focus on UX issues without sifting through unrelated content.
– UX Researchers get structured data to analyze patterns.
– Developers gain clarity on priority fixes or features tied to usability.

**Outcome:** An automated, scalable system that tags incoming feedback as “UX-related” or “Non-UX” and routes it appropriately.

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## Tools and Services Integrated

– **n8n:** Open-source automation tool orchestrating the workflow.
– **Feedback sources:** Can be Gmail (emails), Google Sheets (form responses), or Zendesk (support tickets). For this tutorial, we’ll use Gmail.
– **Natural Language Processing:** Use an API like OpenAI GPT or Google Cloud Natural Language API to analyze feedback content.
– **Google Sheets:** To log feedback and tagging results.
– **Slack:** Notification channel for UX-tagged feedback.

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## Step-By-Step Tutorial

### Prerequisites

– An n8n instance setup (locally, cloud, or n8n.cloud).
– Access to Gmail API credentials (or OAuth) to connect n8n.
– API access for an NLP service like OpenAI.
– Google Sheets setup for logging feedback.
– Slack workspace and app webhook for notifications.

### Step 1: Trigger on New Feedback

**Node:** Gmail Trigger

– Configure the Gmail Trigger node in n8n to watch for new emails in the inbox or a specific label/folder dedicated to user feedback.
– Set polling interval according to feedback velocity (e.g., every 5 minutes).

### Step 2: Extract Email Content

**Node:** Set or Function

– Extract the email body text from the Gmail node.
– Optionally clean the text (remove disclaimers, email signatures) using regex in a Function node for better NLP results.

### Step 3: Analyze Content for UX Relevance

**Node:** HTTP Request (OpenAI or Google NLP API)

– Send the cleaned feedback text to the NLP API.
– For OpenAI (GPT-3/4), craft a prompt that evaluates if the feedback relates to UX aspects like usability, interface, navigation, responsiveness, etc.
– Expect a clear classification or confidence score as output.

**Example prompt:**
“Classify the following user feedback as ‘UX’ or ‘Non-UX’. Provide a confidence score: \nFeedback: ‘{{FEEDBACK_TEXT}}’”

– Parse the API response to extract tag and confidence.

### Step 4: Conditional Branching Based on Classification

**Node:** If

– Use an ‘If’ node to check if the tag equals ‘UX’ and confidence > threshold (e.g., 0.7).

### Step 5A: Log UX Feedback

**Node:** Google Sheets

– Append the feedback data (email sender, timestamp, feedback content, tag) to a Google Sheet.
– This serves as a structured repository for further analysis.

### Step 5B: Notify UX Team on Slack

**Node:** Slack

– Send a formatted message to a Slack channel (e.g., #product-ux-feedback).
– Include key info: sender, snippet of feedback, link to original email if possible.

### Step 5C: Handle Non-UX Feedback

– Optionally log non-UX feedback in a different Google Sheet tab or ignore.

### Step 6: Mark Email as Processed

**Node:** Gmail

– Apply a label (e.g., ‘Processed’) or move the email to another folder to avoid reprocessing.

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## Workflow Breakdown

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## Common Errors and Tips

– **API limits:** NLP APIs may have rate limits; implement retries and backoffs.
– **False positives/negatives:** Regularly review classification accuracy; tweak prompts or confidence thresholds.
– **Email parsing issues:** Email bodies can contain HTML or unusual formatting; test your cleaning functions extensively.
– **Authentication:** Ensure OAuth tokens (Gmail, Slack) are refreshed and valid.
– **Error handling:** Use n8n’s error workflow triggers to catch and alert on failures.

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## Scaling and Adaptation

– **Multiple sources:** Expand triggers to other platforms like Zendesk or Google Forms.
– **Custom taxonomy:** Extend NLP logic to tag more specific UX topics (e.g., performance, accessibility).
– **Database Integration:** Replace Google Sheets with a database (e.g., Airtable, PostgreSQL) for advanced querying.
– **Dashboarding:** Integrate with BI tools (Looker, Tableau) for visualizing feedback trends.
– **Machine Learning:** Store feedback and tags to train custom ML models for improved accuracy.

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## Summary and Bonus Tip

Automating the tagging of UX-related feedback using n8n saves time and increases the product team’s responsiveness to user needs. By linking common tools like Gmail, NLP APIs, Google Sheets, and Slack, you create a robust pipeline from feedback ingestion to team notification.

**Bonus Tip:** Periodically export tagged feedback to retrain and fine-tune your classification prompt or model—this incremental learning approach keeps the automation aligned with evolving product terminology and user concerns.

Harness these steps to build a scalable and intelligent feedback tagging system that drives better UX improvements and product success.

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How to Automate Tagging UX-Related Feedback with n8n

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