Every uploaded file receives a quality score from 0-100, helping you understand your data’s readiness for copy generation.
How Quality Score Is Calculated
The score starts at 100 and deductions are made for:
| Issue | Max Deduction |
|---|
| Empty rows | Up to 20 points |
| Incomplete columns (low avg completeness) | Varies |
| Data quality issues | Up to 30 points |
Factors That Increase Score
- More complete columns (fewer empty cells)
- Recognized fields present (name, email, company)
- Valid email formats
- No empty rows
- Critical fields have data
Factors That Decrease Score
- Empty rows in your data
- Low column completeness (less than 50% filled)
- Invalid email formats
- Many empty cells
- Missing critical columns
Score Ranges
| Score | Rating | Recommendation |
|---|
| 80-100 | Excellent | Great data quality, proceed confidently |
| 60-79 | Good | Minor issues, should work well |
| 40-59 | Fair | Review data, consider cleaning |
| Below 40 | Poor | Clean data before processing |
Viewing Your Quality Score
After upload, the file detail page shows:
- Overall quality score (0-100)
- Column-by-column analysis
- Detected field types
- Duplicate counts
How Quality Affects Results
Quality score reflects your input data, not Smelt’s output quality.
- Better personalization (more context for AI)
- Higher confidence scores on outputs
- Fewer generic results
Lower quality input data may result in:
- More generic outputs
- Lower confidence scores
- More flagged results
Improving Your Quality Score
Remove Empty Rows
Delete any rows without meaningful data
Fill Critical Fields
Ensure name, company, and email columns are populated
Fix Invalid Emails
Correct obviously wrong email formats
Complete Partial Data
Fill in missing job titles, industries, locations where possible
You don’t need a perfect 100 score. Scores above 60 generally work well for most use cases.
