How does the Duplicate scoring work
Learn how Delpha calculates duplicate scores using field weights, fuzzy vs exact matching, and the impact of mandatory fields on similarity scoring.
How Is the Duplicate Score Calculated in Delpha?
Understanding how Delpha computes the Total Weighted Score helps you fine-tune duplicate detection accuracy and control what records are flagged as potential matches.
Formula
The total score is a weighted average of all matched fields:
This score determines the similarity percentage between two records.
Score Impact Factors
Mandatory Checkbox
Each field can be marked as Mandatory for evaluation:
Not Mandatory (default): If either record has an empty value, that field is excluded from the score.
Mandatory: Field is always used, even if one or both records have missing data. This can lower the overall score due to the missing information.
Example Scenarios
Scenario 1:
Field: LinkedIn URL
Mandatory: ✅
Both Records: Empty
➡ Score is penalized due to missing data.
Scenario 2:
Field: LinkedIn URL
Mandatory: ✅
Both Records: Not empty
➡ Score calculated normally using fuzzy or exact logic.
Use the mandatory setting for high-impact fields to strengthen matching accuracy, especially for strategic identifiers like email, LinkedIn URL, or phone.
Example
Record
Name
[Weight = 100]
[Weight = 100]
Account.Name
[Weight = 200]
LinkedIn URL
[Weight = 100]
A
Samantha
Cogicog
B
Samamtha
-
Cogicog
Matching %
90%
0%
100%
0%
Partial Score
Optional: 0.9*100
Mandatory: 0*100
Optional
Ignored
Optional: 1*200
Mandatory: 0*100
Optional: Ignored
With Email & LinkedIn = Optional -> (0.9*100 + 1*200) / (100 + 200) = 96.7%
With Email & LinkedIn = Mandatory -> (0.9*100 + 0*100 + 1*200 + 0*100) / (100 + 100 + 200 + 100) = 58%
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