Matching Fields
Matching Fields define which fields are used to identify duplicate records. They are mandatory for record processing.
Creating Matching Fields
On the next step of the Matching Rules wizard, you can create matching fields:
- Select an Object Field from the dropdown
- Choose a Matching Type based on the field's data type
- The available matching types are automatically determined by the selected field's data type
After configuring matching fields, you can optionally add Matching Filters to limit which records are processed.
important
- At least one matching field must have the matching type "Equals, ignore case"
- Only fields of the same data type are available for matching
- Fields of the Long Text Area data type cannot be selected as matching fields
How Matching Works
Each matching type has its own threshold and field score:
- When a field's score is greater than or equal to the threshold, the match is considered valid
- The system calculates scores by comparing field values between records
- Empty, blank fields, and strings less than 3 characters long are not processed
How to Choose a Matching Type
Selecting the right matching type is crucial for accurate duplicate detection. Here are some guidelines:
- Exact, ignore case - Use for fields where values should match exactly (e.g., Email, Account Number). At least one field in your matching rule must use this type.
- Fuzzy types - Use when data may have variations, typos, or formatting differences (e.g., Names, Company Names, Addresses).
- Formula Field types - Use when you need to match across multiple fields (e.g., Email List, Phone List). Requires creating a custom formula field first. Learn more.
tip
Best Practice: Combine exact matching (for unique identifiers like Email) with fuzzy matching (for names and addresses) to catch duplicates even when data has minor variations.
Matching Types Reference
The following table describes all available matching types, organized by category:
Basic Matching Types
| Matching Type | Threshold | Description | Example |
|---|---|---|---|
| Exact, ignore case | 100 | Exact case-insensitive matching. | Abc and abc → score 100%Abcd and abc → score 0% |
| Fuzzy Name | 80 | Fuzzy search applies phonetic encoding algorithms and edit distance. Handles name variations and typos. | Matt and matt → score 100%Matt and matty → score 100%Matt and matthew → score 94%Mattie and matthew → score 91% |
| Fuzzy Company Name | 80 | Company Name is broken into tokens which are compared using phonetic encoding algorithms. Word order is not taken into account. Case-insensitive. Strings mentioned in the Ignored Items tab are skipped (LLC, Inc, Corp, etc.) | Abc LLC and abc → score 100% (LLC is skipped)Abc Usa Corp and usa abc Corporation → score 100% (Corp and Corporation are skipped, word order ignored) |
| Fuzzy Text | 70 | Computes a score based on edit distance. Case-sensitive. Recommended for emails and short texts. | matt@abc.com and matthew@abc.com → score 80%matt.white@abc.com and matt_white@abc.com → score 94% |
Address and Location Matching Types
| Matching Type | Threshold | Description | Example |
|---|---|---|---|
| Fuzzy Phone | 70 | A phone number is broken into sections (International code, Area code, Next 3 digits, Last 4 digits). Each section has its own score. All scores are summed up into the final field score. Handles formatting variations. | 1-415-555-1234 and +1-415-555-1234 → score 100%1-415-555-1234 and 1-415-555-5678 → score 70%14155551234 and +1(415)-555-1234 → score 100%Extensions are automatically skipped |
| Fuzzy Zip | 80 | A zip code is broken into sections (First 5 digits: 90%, Next 4 digits: 10%). Handles ZIP+4 format variations. | 12345 and 12345-6789 → score 90%12345 and 1234-5- → score 90%12345 and 12340 → score 0% |
| Fuzzy Street | 80 | Address is broken into sections (Street Number, Street Name, Street Suffix, Suite Number). Handles abbreviations and formatting differences. | 123 Main Street Suite 1 and 123 main st. ste 1 → score 80%123 Main Street Suite 1 and 123 main road ste 1 → score 75%P.O Box 123 and po box 123 → score 100% |
Advanced Matching Types
| Matching Type | Threshold | Description | Example |
|---|---|---|---|
| Email List [Formula Field] | 100 | Splits a string (custom formula field) of emails by semi-colon, checks for duplicate addresses, and confirms if at least one match is found. | matt@abc.com;matt@gmail.com and matt@gmail.com; → score 100%matt@abc.com; and matt@gmail.com; → score 0% |
| Phone List [Formula Field] | 100 | Splits a string (custom formula field) of phone numbers by semi-colon, checks for duplicate numbers, and confirms if at least one match is found. | 1234567;7654321 and 1234567; → score 100%1234567; and (123)4567; → score 0% |
| Digital | 100 | Removes all non-digit characters from a string then applies exact matching comparison. Strings less than 3 consecutive digits long are not taken into account. | SN123-456 and S.N. 123456 → score 100%SN12-34 and SN12-34 → score 0% (too short) |
Ignored Items
The Ignored Items section contains strings that are skipped during Fuzzy Company Name matching.
- Most common abbreviations like LLC, Inc, Corp, etc. are already included
- You can add your own records to get more accurate results
- Limit: Up to 500 records can be added