Customer Feedback Categorization System

Overview

This prompt creates a structured system for analyzing and categorizing customer feedback based on sentiment and topics. It helps maintain consistency in feedback analysis and provides clear justification for categorizations.

Key Features

Sentiment analysis (Positive, Negative, Neutral)
Topic categorization (Product, Service, Support)
Structured XML output format
Built-in justification requirement
Example-based learning

Prompt Template

You are an AI assistant tasked with categorizing customer feedback. Your job is to analyze the given feedback and categorize it based on sentiment and topic. Here's how to proceed:
1. First, you will be presented with a piece of customer feedback:
<customer_feedback>
{{CUSTOMER_FEEDBACK}}
</customer_feedback>
2. Analyze the sentiment of the feedback. Determine if it is:
 - Positive
 - Negative
 - Neutral
 Consider the overall tone, language used, and any explicit statements of satisfaction or dissatisfaction.
3. Identify the main topic(s) of the feedback. The primary categories are:
 - Product: relating to the features, quality, or performance of the product
 - Service: relating to customer service, delivery, or overall customer experience
 - Support: relating to technical support, troubleshooting, or assistance
 Note that a piece of feedback may touch on multiple topics. In such cases, identify all relevant topics.
4. After your analysis, present your categorization in the following format:
<categorization>
<sentiment>[Insert sentiment here: Positive, Negative, or Neutral]</sentiment>
<topics>
<topic>[Insert first identified topic]</topic>
<topic>[Insert second identified topic, if applicable]</topic>
</topics>
<justification>
[Provide a brief explanation for your categorization, referencing specific parts of the feedback]
</justification>
</categorization>

Variables

The prompt contains one main variable:

{{CUSTOMER_FEEDBACK}}: The customer feedback text to be analyzed

Example Usage

Here's an example of how to use this prompt:

<customer_feedback>
I love my new smartphone! The camera quality is amazing and the battery lasts all day. However, I had some trouble setting it up and customer support wasn't very helpful.
</customer_feedback>
<categorization>
<sentiment>Positive</sentiment>
<topics>
<topic>Product</topic>
<topic>Support</topic>
</topics>
<justification>
The feedback is primarily positive, with the customer expressing love for the product and praising specific features (camera and battery). However, there is a negative aspect related to the setup process and customer support, which is why "Support" is also included as a topic.
</justification>
</categorization>

Optimization Tips

Clear Feedback Text: Ensure the customer feedback is clearly formatted and complete before insertion into the prompt.
Multiple Topics: Remember that feedback can be categorized under multiple topics - don't limit to just one if multiple are relevant.
Balanced Analysis: When providing justification, reference both positive and negative aspects if present.
Consistent Format: Always maintain the XML structure for easy parsing and standardization.

Best Practices

Always include specific references from the feedback in the justification
Consider the overall context when determining sentiment
Be thorough in topic identification
Maintain consistent XML formatting
Provide clear reasoning in the justification section

Use Cases

Customer service feedback analysis
Product review categorization
Support ticket triage
Customer satisfaction monitoring
Feedback trend analysis
Quality assurance reviews

Technical Details

The prompt uses a structured XML format for output, which makes it ideal for:

Integration with automated systems
Database storage
Analytics processing
Report generation
Trend analysis

Limitations

Limited to three main topic categories
Three-tier sentiment classification may not capture nuanced feedback
Does not account for mixed sentiment within individual topics