> ## Documentation Index
> Fetch the complete documentation index at: https://docs.data-wizard.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Customer Feedback to JSON

> Transform handwritten or printed customer feedback forms into structured JSON. Ideal for analyzing customer sentiment, identifying trends, and improving service quality.

If you want to try this extractor, you can download it here:

<Card icon="download" title="Download Customer Feedback Extractor" href="#" horizontal>
  `customer-feedback-extractor.json`
</Card>

<Tip>
  [How do I import extractors?](/docs/import-export)
</Tip>

## Document Input Examples

Here are some examples of the types of documents that this extractor can process.

<CardGroup cols={2}>
  <Card title="Customer Feedback Data (PDF or Spreadsheets)" icon="clipboard-user">
    Perform some sentiment analysis on some data you already have. This extractor can help you extract feedback data from PDFs or spreadsheets.
  </Card>

  <Card title="Scanned Feedback Forms" icon="print">
    Paper-based feedback forms collected in-person can be scanned and processed to digitize customer opinions and suggestions.
  </Card>
</CardGroup>

## Example Output

This extractor will produce JSON data that looks like the following:

<CodeGroup>
  ```json Customer Feedback Data theme={null}
  {
      "formType": "Customer Feedback",
      "submissionDate": "2024-09-08",
      "customerName": "Jane Doe",
      "email": "jane.doe@example.com",
      "feedbackText": "The service was excellent, and the staff were very friendly. I especially appreciated the quick response time to my inquiry.",
      "rating": 5,
      "suggestions": "Perhaps offer more variety in your product catalog."
  }
  ```
</CodeGroup>

## Extractor

Here's a template for a customer feedback form extractor with specific settings recommendations.

### JSON Schema

Here's a tailored JSON schema for customer feedback forms. It focuses on capturing key feedback elements such as customer details, ratings, and textual feedback.

<Accordion title="Show Customer Feedback Schema">
  ```json theme={null}
  {
    "type": "object",
    "properties": {
      "formType": {
        "type": "string",
        "description": "Type of the form (e.g., Customer Feedback Form)",
        "default": "Customer Feedback Form"
      },
      "submissionDate": {
        "type": "string",
        "format": "date",
        "description": "Date the form was submitted"
      },
      "customerName": {
        "type": "string",
        "description": "Full name of the customer"
      },
      "email": {
        "type": "string",
        "format": "email",
        "description": "Email address of the customer"
      },
      "feedbackText": {
        "type": "string",
        "description": "Textual feedback provided by the customer"
      },
      "rating": {
        "type": "integer",
        "description": "Customer satisfaction rating (e.g., 1-5 stars)",
        "minimum": 1,
        "maximum": 5
      },
      "suggestions": {
        "type": "string",
        "description": "Customer suggestions for improvement"
      }
    },
    "required": [
      "formType",
      "submissionDate",
      "feedbackText",
      "rating"
    ]
  }
  ```
</Accordion>

### Extraction Strategy

#### Strategy: `simple`

For customer feedback forms, the `simple` strategy is generally sufficient. Feedback forms are typically short and focused, making them suitable for single-pass extraction.

#### LLM Recommendation: `google/gemini-2.0-flash-lite`

`google/gemini-2.0-flash-lite` provides a good balance of cost-effectiveness and performance for processing customer feedback forms. Its vision capabilities are beneficial if handling scanned forms with potentially varied handwriting.

### Context Settings

#### Chunk Size: `25k-50k`

A chunk size of 25.000 to 50.000 tokens should comfortably accommodate most customer feedback forms.

#### Include Text: `true`

Essential for capturing the textual feedback, customer names, and other text-based fields.

#### Include Embedded Images: `false`

Embedded images are unlikely to be relevant in standard feedback forms and can be excluded.

#### Include Page Screenshots: `true`

Page screenshots are recommended, especially for scanned forms. They help the LLM understand the layout and context of different fields, improving accuracy in extracting data from potentially less structured forms.

#### Mark Images with IDs: `false`

Not needed for typical customer feedback extraction.

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**Next Steps**

<Card title="Learn how to extract some data" icon="list-ol" href="./extracting-data">
  Step by step guide to extract data from documents using Data Wizard.
</Card>

<CardGroup>
  <Card title="Extractors" icon="laptop-code" href="./extractors">
    Learn how to define and configure data extraction tasks.
  </Card>

  <Card title="Strategies" icon="code-branch" href="./strategies">
    Understand different data processing strategies.
  </Card>

  <Card title="LLM Provider Configuration" icon="sliders" href="./configure-llm">
    Set up your Large Language Model API keys.
  </Card>

  <Card title="Integration" icon="code" href="./integrate">
    Embed Data Wizard into other applications using iFrames or APIs.
  </Card>
</CardGroup>