Customer Personas
Learn how Evols automatically generates customer personas from VoC data and calculates product metrics.
Overview
Personas are automatically generated from your customer feedback data by grouping customers with similar characteristics (segment, industry, role). Each persona represents a distinct customer archetype with specific needs, behaviors, and product usage patterns.
Evols calculates dynamic metrics for each persona including revenue contribution and usage frequency from the VoC data.
How Personas Are Generated
1. Customer Segmentation
Personas are created by grouping feedback from customers with similar attributes:
- Segment: Enterprise, Mid-Market, or SMB
- Industry: SaaS, Healthcare, Finance, etc.
- Job Role: Executive, Engineering, Management, etc.
2. Duplicate Detection
The system uses semantic similarity (85% threshold) to prevent creating duplicate personas. If a new persona is similar to an existing one, the existing persona is updated with new data instead.
3. Incremental Updates
When you refresh personas, only new feedback since the last refresh is processed. This makes refreshes fast and cost-effective while keeping personas up-to-date.
Persona Lifecycle
🆕 New
Newly generated personas start with "New" status. These are available for review but not used in voting or decision-making yet.
✅ Active
Active personas used in trade-off voting, Ask Personas, and workbench. Only "Active" personas participate in product decisions.
🚫 Inactive
Hidden personas that are no longer relevant or accurate. These are excluded from all platform features and won't be updated during refresh.
Revenue Contribution
Average Revenue Per Customer
Calculated from revenue data in feedback extra_data fields.
Data Sources
Evols extracts revenue data from the following fields in your feedback data:
Calculation
Updates
When a persona is updated with new feedback, revenue contribution is recalculated using a weighted average based on feedback counts:
Note: If revenue data is not available in your feedback, this field will show "N/A".
Usage Frequency
Product Usage Pattern
Extracted from usage frequency data in feedback extra_data fields.
Data Sources
Evols extracts usage patterns from these fields:
Calculation
Usage frequency is determined by finding the most common pattern across all feedback items for that persona:
Common Values
- Daily: Users who log in or use the product daily
- Weekly: Users who engage with the product weekly
- Monthly: Users who access monthly or occasionally
- Seasonal: Users with periodic or seasonal usage
Note: If usage data is not available in your feedback, this field will show "N/A".
Confidence Score
Each persona has a confidence score indicating how reliable the persona data is based on the amount of feedback:
Best Practices
✅ Review "New" personas regularly
New personas need to be reviewed and marked as "Active" to participate in decisions. Filter by "New" status to see personas waiting for review.
✅ Include revenue and usage data
For accurate metrics, include extra_data fields in your feedback with revenue (arr, revenue, account_value) and usage (usage_frequency, login_frequency) information.
✅ Merge similar personas
If you find similar personas, use the merge feature to combine them. This creates a more accurate persona with higher confidence scores.
✅ Refresh after major data uploads
Click "Refresh Personas" after uploading significant new feedback to keep personas current. The system will intelligently update existing personas rather than creating duplicates.