Specific Topic Deep-Dive

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

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.

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