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:

Total Weighted Score = (Score₁ * Weight₁ + Score₂ * Weight₂ + ... + Scoreₙ * Weightₙ) / Total Weight

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.

Dulicate Scoring Fields

Example

Record	Name [Weight = 100]	Email [Weight = 100]	Account.Name [Weight = 200]	LinkedIn URL [Weight = 100]
A	Samantha	[email protected]	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%

More on the models used by Delpha for Duplicate scoring