Stitch Scores table

The Stitch Scores table has all scores assigned by Stitch during identity resolution, including scores that are not associated with an Amperity ID. Use this table to help understand why records are not associated with an Amperity ID.

Use with Stitch QA

Use the Stitch Scores table to understand why blocking used or did not use a foreign key or separation key to block or unblock two records.

Compare records

Use a query similar to the following to compare scores for two records:

1SELECT * FROM Stitch_Scores
2WHERE source1 = 'Table_Name' AND pk1 = '123abc456def'
3AND source2 = 'Table_Name' AND pk1 = '789ghi012jkl'

where the values of pk1 and pk2 are for the records you want to compare.

Add table

A passthrough table adds a domain table to your database. Passthrough domain tables often have a field for the Amperity ID, but otherwise are the same as the fields in the source domain table.

To add the Stitch Scores table

  1. From the Database Editor, click Add Table.

  2. Name the table “Stitch_Scores”.

  3. Set the build mode to Passthrough, and then select Stitch Scores.

  4. Hide the table from the Segment Editor by verifying that Show in VSE? is unselected.

  5. Click Activate to update the customer 360 database with your changes.

Column reference

The Stitch Scores table has the following columns:

Column name

Data type

Description

Amperity ID1

String

The unique identifier assigned to clusters of customer profiles that all represent the same individual. The Amperity ID does not replace primary, foreign, or other unique customer keys, but exists alongside them within unified profiles.

The Amperity ID for the first of two compared records.

Note

The Amperity ID is a universally unique identifier (UUID) that is 36 characters spread across five groups separated by hyphens: 8-4-4-4-12.

For example:

123e4567-e89b-12d3-a456-426614174000

Amperity ID2

String

The Amperity ID for the second of two compared records.

Match Category

String

A match category is applied to individual record-pair comparisons discovered by deterministic and probabilistic matching strategies during identity resolution.

Match Category

Description

Exact

Amperity has the highest confidence. Records represent the same person because all profile data exactly matches.

Excellent

Amperity has near perfect confidence that records belong to the same person, despite some profile data not matching.

High

Amperity has high confidence that records match, despite some profile data not matching.

Moderate

Amperity has moderate confidence that records match, due to weak or fuzzy matches between unique customer attributes, such as email, phone, or address.

Weak

Amperity lacks confidence, but if asked to guess, Amperity would assert these records do belong to the same individual, because they match on non-unique customer attributes, such as name, state, or ZIP code.

Non-match

Amperity has high confidence that these records do NOT match, because core profile data is in conflict.

Match Type

String

The score assigned to matched records is the match type. Possible values: “scored”, “scored_transitive”, and “trivial_duplicate”. Records assigned a “scored” value are directly connected by deterministic or probabilistic matching. Records assigned a “scored_transitive” value are transitively connected.

PK1

String

PK2

String

Score

Float

A score has a value from “0.0” to “5.0” that represents the strength of the match Stitch assigns to a record pair during identity resolution. A higher score always indicates a stronger match. This holds across the entire range: a 3.0 match is one point stronger than a 2.0 match, just as a 3.1 match is one point stronger than a 2.1 match.

The record pair score correlates to the match category, which is a classifier applied by Amperity to individual record pairs. The record pair score corresponds to the classification:

  • 5 for exact matches

  • 4 for excellent matches

  • 3 for high matches

  • 2 for moderate matches

  • 1 for weak matches

  • 0 for no matches

A higher value reflects a stronger signal from the machine learning model, so a score of 4.58 is a stronger match than 4.55. When two record pairs receive the same score and cannot both merge (for example, because of a conflicting given name), Stitch resolves the tie deterministically by selecting the match with the lower alphanumeric cluster ID, so results stay stable across runs.

Source1

String

Source2

String