Topic Analytics helps teams identify recurring themes, customer intents, and friction points in conversations. When set up thoughtfully, Topics give you more than just labels — they help you:

• Track real customer issues and feedback with clarity
• Detect sales opportunities and buying signals early
• Understand how users respond to campaigns or service flows
• Route conversations more effectively to the right teams

By following best practices when creating and managing Topics, you’ll generate insights that are focused, actionable, and aligned with your team’s goals — whether you work in support, sales, operations, or marketing.

This guide will walk you through how to define, structure, and continuously improve Topics to help your team work smarter.

Before you start: define what you want to track

Start with a clear understanding of what you want your Topics to help you uncover. Ask yourself:

• What kind of questions do we want to answer?
  • For example: “Why are customers churning?” “What are common product concerns?”
• What themes or moments in the conversation matter most to our team?
  • For example: complaints, pricing questions, feature requests, intent to buy
• Who will use this data, and how?
  • For example: marketing reviewing campaign feedback, support flagging complaints, sales prioritizing high-intent leads

Once you know what outcome you’re aiming for, you can create topics that clearly surface those patterns — and avoid ending up with vague or noisy insights.

Structuring topics: how granular should you go?

Topics in SleekFlow Analytics consist of only two levels:

1. Topic name: This defines what you want to track.
2. Topic criteria: These are the phrases that help identify conversations related to that topic.

What you choose to set as your "topic" depends on the level of insight you're aiming for. The criteria you add should always be the next level down in specificity.

To help illustrate this, here's a visual example showing how a high-level business goal — in this case, tracking post-sales support issues — can be broken down into topics and matching criteria.

This diagram shows how a single goal can lead to multiple topics, each defined by distinct phrases customers might use in conversation. You can adjust the level of specificity depending on what you want to measure and improve.

Example by use cases:

Notes on language and translation

Topic Analytics currently does not support automatic translation of keywords. If your business serves customers in multiple languages, you’ll need to manually include keywords in each language when setting up your topic criteria.
 

Best practices

Here are some practical guidelines to help you create meaningful, maintainable, and action-oriented Topics. These are general rules — feel free to adapt them to your team's specific goals and workflows.

1. Align topics with business goals: Create topics that answer meaningful business questions or help specific teams act faster. This ensures your data works for you — not the other way around.
    
2. Keep topics focused, not too broad: Avoid lumping different intents into one catch-all topic. Vague labels make it harder to take action or find root causes.
    
3. Use real phrases customers actually say: Topic matching is powered by keyword detection — so use real language pulled from chat logs, FAQs, or CRM notes.
    
4. Track buying intent separately: Buying signals should have their own dedicated topic so they don’t get buried in general conversation tags.
    
5. Review and refine regularly: Topics aren’t one-and-done. Customer language evolves, business focus shifts — your topics should too.
   1. Merge overlapping topics to reduce noise
   2. Split bloated topics that mix unrelated intents
   3. Update keywords as your product or audience changes
       
6. Avoid redundant or confusing topics: Duplicated or unclear Topics can lead to inconsistent data and confusion across teams.
    
7. Use Topic Analytics to challenge assumptions: Let the data guide you. Use topic trends to confirm whether your team’s assumptions match what customers are actually saying.

Use cases and examples

Here are real-world use cases to help you better understand how different teams can apply Topics in Topic Analytics to drive insights and actions:

Common challenges and how to work around them

Recap

Setting up meaningful topics in Topic Analytics helps you turn conversations into clear, actionable insights. Whether you’re tracking refund requests, sales interest, or campaign feedback, well-structured topics let you:

• Focus on the exact signals that matter to your team
• Route issues or leads more effectively
• Respond to customer needs faster and more strategically