Turn Unstructured Customer Feedback into Actionable JSON

Product teams are drowning in a sea of unstructured customer feedback. It flows in from every direction: support tickets, app store reviews, social media mentions, NPS surveys, and direct emails. This data is a goldmine—a direct line to your users' needs, frustrations, and brilliant ideas. But manually sifting through it all is a slow, inconsistent, and unscalable nightmare.

Valuable insights get lost. Bug reports are missed. Feature requests go untracked. By the time you’ve manually collated the feedback, the opportunity to act on it may have already passed.

What if you could build a powerful, automated workflow to instantly transform this chaotic stream of text into clean, structured, and actionable data? With a single API call, you can turn customer feedback chaos into developer-ready JSON, primed for your product roadmap, bug tracker, or analytics dashboard. Let's explore how.

The Challenge: The Chaos of Customer Feedback

Unstructured text is notoriously difficult for machines to parse. The same basic feedback can be phrased in countless ways. Consider these real-world examples:

• Support Ticket: "Hi team, my main dashboard won't load after the update, and the new CSV export feature is really confusing to find."
• App Store Review: “★★☆☆☆ Great app, but I wish I could just export my reports to a PDF. The current export system is clunky.”
• Tweet: “@YourApp the latest update broke the login flow! I can't access my account. Pls fix!! #bugreport”

Hidden within this text are distinct, valuable data points:

• Feedback Type: Bug Report, Feature Request, Usability Complaint
• Features Mentioned: Dashboard, CSV Export, PDF Export, Login
• Sentiment: Negative, Neutral
• Urgency: High ("can't access my account")

Traditional methods like using regular expressions (regex) or keyword matching are too brittle. A simple change in phrasing or a typo can break your entire script. Manual tagging by a support team is slow and prone to human error and inconsistency.

The Solution: An AI-Powered Extraction Workflow with extract.do

This is where AI-powered data extraction changes the game. Instead of relying on rigid rules, extract.do uses AI to understand the semantic meaning and context of the text. This allows you to build a resilient and intelligent workflow:

1. Ingest: Your system receives raw text from any source (email parser, Zendesk API, Twitter stream).
2. Extract: You send this text to the extract.do API with a simple schema defining the data you want.
3. Act: The API returns a clean JSON object. You can then pipe this structured data directly into other tools: create a Jira ticket, add to an Airtable, trigger a Slack alert, or visualize it in an analytics dashboard.

Building the Workflow: A Practical Example

Let's build the core of this workflow. Our goal is to take a raw customer support email and extract a structured summary for our product team.

Step 1: Define Your Target Data Structure

First, decide what information is most valuable. We don't need to know how to parse it; we just need to describe what we want. We'll create a schema to capture the feedback type, sentiment, specific features mentioned, a summary, and urgency.

Our target schema looks like this:

{
  "feedback_type": "string",
  "sentiment": "string",
  "features_mentioned": "string[]",
  "summary": "string",
  "is_urgent": "boolean"
}

Step 2: Make the API Call with extract.do

Now, we take a piece of unstructured feedback and send it to the API along with our desired schema. The extract.do API does the heavy lifting of understanding the text and mapping it to your structure.

import { Do } from '@do-sdk/core';

// Raw feedback from a support ticket, email, or survey
const customerFeedback = `
  Hi team, I'm having an issue with the latest update. The new reporting
  feature seems really powerful, but it's a bit confusing to set up. Also,
  since the update, my main dashboard is loading very slowly.
  This is becoming a real blocker for my team's morning stand-up. Thanks.
`;

// Simply define the insights you want to extract
const structuredInsights = await Do.extract('extract.do', {
  text: customerFeedback,
  schema: {
    feedback_type: 'string', // e.g., 'Bug Report', 'Feature Request'
    sentiment: 'string', // 'Positive', 'Negative', 'Neutral'
    features_mentioned: 'string[]', // An array of features mentioned
    summary: 'string', // A one-sentence summary of the user's main point
    is_urgent: 'boolean', // Is the user blocked or expressing high frustration?
  }
});

console.log(JSON.stringify(structuredInsights, null, 2));

Step 3: Get Actionable JSON Output

In seconds, the API returns a perfectly structured JSON object, ready for automation.

{
  "feedback_type": "Bug Report",
  "sentiment": "Negative",
  "features_mentioned": [
    "Reporting",
    "Dashboard"
  ],
  "summary": "The user finds the new reporting feature confusing and is experiencing very slow loading times on the main dashboard, which is blocking their team.",
  "is_urgent": true
}

Notice the intelligence here. The AI correctly identified:

• Two distinct features (Reporting, Dashboard).
• The sentiment as Negative.
• The urgency as true by understanding the context of "real blocker."
• It even synthesized a concise summary—something impossible with regex.

From JSON to Action: What's Next?

Once you have this clean, structured JSON, the possibilities for automation are endless:

• Automate Triage: If feedback_type is "Bug Report" and is_urgent is true, automatically create a high-priority ticket in Jira or Linear.
• Track Feature Requests: When feedback_type is "Feature Request", add the summary and features_mentioned to a Canny board or an Airtable base to quantify demand.
• Monitor Product Health: Send the sentiment and features_mentioned to your analytics platform to build dashboards that track customer sentiment around specific parts of your product.
• Keep the Team Informed: Post a nicely formatted summary of urgent issues directly to a #product-feedback channel in Slack.

Why extract.do Is the Right Tool for the Job

Building this kind of workflow is incredibly simple with the right tool. Here’s why extract.do excels:

• Resilience: Unlike brittle scrapers, our AI understands language. It won't break if a customer uses different phrasing or your feedback form changes slightly.
• Simplicity: You don't need to write complex rules, train a custom model, or manage parsing logic. You simply ask for the data you want in the format you need.
• Flexibility: It works on any text source. Use the same schema to process data from emails, documents, and website content without changing a line of code.
• Scalability: The API is built to handle high-volume, real-time processing, so you can get immediate insights as feedback flows in.

Stop letting valuable customer feedback get lost in the noise. It’s time to build an intelligent system that listens, understands, and helps you act.

Ready to turn your data chaos into structured, actionable insights? Get started with extract.do and build your first workflow in minutes.