
Overview
Defining your Channel’s context and topic descriptions enables you to guide Dovetail’s AI to focus on what matters most to you and capture the important topics and themes that your organization wants to track over time.Defining your Channel’s Context
When adding context to any channel, follow these core principles:- Focus on 2-3 main goals that apply across all your data
- Keep it under 400 characters and be concise
- Use natural language with clear structure that helps you understand the content in a glance
- Avoid overly detailed specifics to prevent filtering out other valid insights
- Specify your role to direct AI to analyze data from your perspective
Context Strategies to Consider
- Umbrella Approach: Create broad context that encompasses all team needs:
- Use company-wide objectives as the foundation
- Focus on customer experience or business outcomes
- Avoid team-specific jargon or priorities
- Emphasize shared metrics and goals
- Perspective Integration: Explicitly acknowledge different viewpoints
- Outcome-focused context: Center on business results rather than team functions
- Journey-based context: Organize around customer journey stages
Defining Topic Names & Descriptions
While you can utilize the Dovetail generated topics & descriptions, you are always able to adjust the both the topic names and/or the descriptions (or add your own).Topic Name Best practices
- Use clear wording that immediately conveys the high-level category
- Keep titles concise (2-3 words when possible)
- Use customer language rather than internal jargon
- Avoid overlapping categories that could confuse classification
- Use consistent naming patterns across similar topics
Topic Description Best Practices
- Explain the theme’s scope clearly in 1-2 sentences
- Use keywords that customers might actually use
- Guide the AI on what to look for and what to exclude
- Keep under 200 characters for optimal performance
- Include specific examples but make sure the example and expectation matches the most common meaning in general English
Examples
Below you will find Context and Topic descriptions examples for six of the most common types of data customers- NPS/CSAT
- App Store Reviews
- Support Tickets
- General Product Feedback
- Churn/Cancellation Reasons
- Product Reviews