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Every uploaded file receives a quality score from 0-100, helping you understand your data’s readiness for copy generation.

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How Quality Score Is Calculated

The score starts at 100 and deductions are made for:
IssueMax Deduction
Empty rowsUp to 20 points
Incomplete columns (low avg completeness)Varies
Data quality issuesUp to 30 points

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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

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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

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Score Ranges

ScoreRatingRecommendation
80-100ExcellentGreat data quality, proceed confidently
60-79GoodMinor issues, should work well
40-59FairReview data, consider cleaning
Below 40PoorClean data before processing

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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

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How Quality Affects Results

Quality score reflects your input data, not Smelt’s output quality.
Higher quality input data leads to:
  • 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

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Improving Your Quality Score

1

Remove Empty Rows

Delete any rows without meaningful data
2

Fill Critical Fields

Ensure name, company, and email columns are populated
3

Fix Invalid Emails

Correct obviously wrong email formats
4

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.