Specific Topic Deep-Dive

Use Smart Columns to Precisely Classify or Extract Topic-Specific Feedback

When analyzing open-text feedback, you may want to isolate comments related to a specific topic, such as pricing, onboarding, churn risk, usability, or billing.

While automatic topic assignment helps explore themes, some use cases require more control over how a topic is defined or extracted.

An LLM-powered Smart Column allows you to apply custom logic to either:

• Classify comments (e.g., Yes/No, labels)
• Extract specific comments related to a topic
• Return structured outputs based on defined criteria

Practical Use Cases

You might use a Smart Column when you want to:

• Detect churn risk only when cancellation intent is clearly expressed
• Flag pricing complaints only when dissatisfaction is mentioned
• Extract comments describing implementation challenges
• Identify feedback about a specific process stage (e.g., onboarding phase)
• Separate product issues from service-related issues

These scenarios often require stricter logic than topic clustering provides.

Best Practices

1. Step 1: Define the Topic Clearly

   Avoid vague instructions such as:

   “Extract comments about pricing.”

   Instead, define the scope:

   Identify comments that refer to pricing structure, subscription costs, perceived value for money, or price transparency.

2. Step 2: Add Inclusion and Exclusion Criteria

   Example structured prompt:

   Determine whether the comment refers to [TOPIC].

   
   
   

   Include comments that clearly describe or evaluate defined elements of [TOPIC].

   
   
   

   Exclude comments that mention the topic only incidentally or focus primarily on other themes.

   You can then configure the output as:

   • Yes/No classification
   • Summaries
   • Extracted comments
   • Structured categories