Response enrichment adds additional context and classification to your raw survey responses. This helps you organize, filter, and analyze data more effectively.
Types of Enrichment
Quality Flags
Automatic indicators of response quality:
Flag | Meaning |
High Quality | Thoughtful, detailed responses |
Standard | Normal quality, usable data |
Low Quality | Short, rushed, or unclear responses |
Flagged | Potential issues requiring review |
Personas
AI-generated consumer segments based on response patterns:
Identifies common respondent types
Groups similar respondents together
Provides insights into audience segments
Labels
Custom tags you can add to responses:
Organize responses by custom criteria
Mark for follow-up or review
Create custom segments
Themes
Topics and patterns extracted from qualitative responses:
Auto-detected from open-ended answers
Used for breakdown analysis
Searchable across responses
How Quality Assessment Works
Automatic Scoring
The system evaluates response quality based on:
Factor | What It Measures |
Completion time | Too fast may indicate rushing |
Response length | Very short may lack detail |
Consistency | Contradictory answers flagged |
Engagement | Pattern of thoughtful vs. random |
Quality Indicators
In the response viewer, you'll see:
Overall quality score
Specific quality flags
Areas of concern highlighted
Using Quality Flags
Filter high quality:
Focus analysis on best responses
Use for qualitative deep-dives
Prioritize for highlight selection
Review flagged responses:
Check for data quality issues
Decide to include or exclude
Document exclusion reasons
How Personas Work
Persona Generation
After collecting responses:
AI analyzes response patterns
Identifies clusters of similar respondents
Creates persona profiles
Assigns respondents to personas
Persona Profiles
Each persona includes:
Name - Descriptive label (e.g., "Quality Seekers")
Description - What defines this group
Size - Number of respondents
Key traits - Distinguishing characteristics
Using Personas
Personas help you:
Segment analysis by customer type
Identify target audiences
Understand different perspectives
Tailor recommendations by segment
See Data Breakdowns for using personas in charts.
Adding Custom Labels
Creating Labels
Navigate to the Responses tab
Select a response
Click Add Label or the tag icon
Enter a label name or select existing
Save
Label Uses
Research organization:
Mark responses for specific analysis
Tag interesting quotes
Note follow-up needs
Quality control:
Flag for review
Mark as reviewed
Note data issues
Custom segmentation:
Create your own segments
Tag by specific criteria
Filter for targeted analysis
Managing Labels
Labels are unique per study
You can add multiple labels to one response
Filter responses by label
Remove labels when no longer needed
Theme Extraction
How Themes Are Identified
From open-ended responses, the system:
Analyzes text content
Identifies recurring topics
Groups similar mentions
Creates theme categories
Theme Examples
From a product feedback question:
Theme | Responses Mentioning |
Price/Value | 45% |
Ease of Use | 38% |
Customer Service | 27% |
Quality | 24% |
Using Themes
Themes enable you to:
Understand what respondents talk about
Filter to specific topic areas
Use as breakdown dimensions
Track topic sentiment
Viewing Enriched Responses
In Response Table
The response table shows:
Quality indicator column
Persona assignment
Applied labels
Theme tags (for qualitative)
In Individual Responses
When viewing a single response:
Full quality assessment
All applied labels
Persona details
Theme associations
Option to add/remove labels
In Analysis
Enrichment data flows into analysis:
Filter charts by quality level
Breakdown by persona
Compare by theme
Include labels in exports
Best Practices
Quality Review
Review flagged responses before analysis
Document exclusion criteria
Keep exclusions minimal and justified
Note impact on sample size
Using Personas
Don't over-segment (3-5 personas usually sufficient)
Validate personas make sense for your research
Use personas for comparison, not just description
Consider combining with demographic data
Labeling Strategy
Create a consistent labeling scheme
Don't over-label (keep it manageable)
Use labels for actions, not just description
Review and clean up labels periodically
Theme Utilization
Verify auto-detected themes make sense
Combine similar themes if needed
Use themes to guide analysis direction
Connect themes to quantitative findings
Exporting Enrichment Data
What's Included
Exports can include:
Quality scores
Persona assignments
Custom labels
Theme associations
Export Format
In CSV exports:
Quality score column
Persona column
Labels column (comma-separated if multiple)
Theme columns (one per theme or combined)
Using in External Tools
Enrichment data enables:
Advanced segmentation in BI tools
Custom quality filtering
Persona-based analysis
Theme mapping and visualization