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          Identity Resolution Match Methods

          Identity Resolution Match Methods

          Match rule methods determine how source data is transformed for comparison during matching. Changing the match method on a field typically changes the consolidation rate based on that field.

          Warning
          Warning Data is transformed for matching purposes when the Fuzzy and Exact Normalized match methods are selected. However, reformatted data is not stored in unified profiles. The format of the value stored in a unified profile is based on source data and determined by reconciliation rules.

          Match methods describe the precision with which data is matched during identity resolution. The anticipated consolidation rate is lower when precision matching is required. Selecting a less precise match method typically raises the consolidation rate, but can result in undesirable matches.

          Up to five match methods are available for use in creating criteria for custom match rules. The match methods available for each object and field vary. Fuzzy match methods aren’t available for fields from the Account object.

          • Exact
          • Exact Normalized
          • Fuzzy - High Precision
          • Fuzzy - Medium Precision
          • Fuzzy - Low Precision

          You can refine custom match criteria further by allowing matches to be made between blank fields.

          Tip
          Tip To determine the most effective match method for your data, configure multiple rulesets with different criteria. Compare consolidation rates and decide which match method works best for your unified profiles.

          Exact Match Method

          The Exact match method is available for all objects and fields. If this match method is used, values match regardless of case.

          Example
          Example

          Because exact match is case-insensitive, these four source data values are an exact match:

          • Maryanne
          • maryanne
          • MARYANNE
          • MaryAnne

          Exact Normalized Match Method

          The Exact Normalized match method is available for specific fields for the Contact Point Email, Phone, and Address objects.

          When this match method is selected, source data is transformed to address issues like trailing spaces, inconsistent formatting, special characters, and more.

          Object Fields with Exact Normalized Match Method Support Normalization Process
          Individual
          • First Name
          • Standardizes capitalization so that matches are case-insensitive
          Contact Point Email
          • Email Address
          • Standardizes capitalization so that matches are case-insensitive
          • Removes white space from the beginning and end of an email address
          • Removes non-alphanumeric characters like quotes (“”) and brackets (<>) from email address
          • For email addresses with the gmail.com domain, removes both period (.) and plus (+) characters from email address
          Contact Point Phone
          • Formatted E164 Phone Number
          • Removes white spaces from phone number
          • Removes non-alphanumeric-characters like asterisk (*) and parentheses (()) from phone number
          • Validates phone number with Google's common Java, C++ and JavaScript library for parsing, formatting, and validating international phone numbers
          • Uses the country code or name for normalization if it’s available or identifiable based on contact point phone or address records.
            • If a country code is provided and mapped to the phone country code field (ssot__CountryCode__c) in the Contact Point Phone DMO, the country code is used for normalization.
            • If the country code isn't available, and if the country name is provided and mapped to the country name field (ssot__CountryId__c) in the Contact Point Phone DMO, the country name is used for normalization.
            • If a country code or name isn't identifiable from data in the Contact Point Phone DMO, and Contact Point Address DMO exists with a valid mapping to country name, the country name in Contact Point Address DMO is used for normalization.
            • If a country code or name isn't available, the phone number is normalized without it.

          For data sources with phone number country codes, map the Phone Country Code field.

          Contact Point Address
          • Address Line 1
          • Address Line 2
          • Address Line 3
          • State Province
          • Country
          • Standardized based on country-specific rules for addresses
          Example
          Example

          These source data values are an exact normalized match:

          Contact Point Email Object
          Source Values Exact Normalized Email Address Field Value
          • <MichelleNoris@gmail.com>
          • "MichelleNoris"@gmail.com
          • michellenoris@gmail.com
          michellenoris@gmail.com
          Example
          Example

          These source data values are an exact normalized match:

          Contact Point Phone Object
          Source Values Exact Normalized Formatted E164 Phone Number Field Value
          • +1 (650) 277-9500
          • +1 650 277-9500
          • 1 (650) 277-9500
          +16502779500
          Example
          Example

          These source data values are an exact normalized match:

          Contact Point Address Object
          Source Values Exact Normalized Address Line 1 and Address Line 2 Field Values Exact Normalized State Province and Country Field Values

          Source 1

          • Address Line 1: 220 Laurier Avenue West
          • Address Line 2: Suite 1000
          • State Province: ON
          • Country: CA

          Source 2

          • Address Line 1: 220 Laurier Ave W
          • Address Line 2: Ste 1000
          • State Province: Ontario
          • Country: Canada
          220 Laurier Ave W Ste 1000 Ontario Canada

          Source 1

          • Address Line 1: 56 Abascal
          • Address Line 2: Calle de José
          • State Province: Madrid
          • Country: ES

          Source 2

          • Address Line 1: Abascal 56
          • Address Line 2: Calle de José
          • State Province: Madrid
          • Country: Spain
          Calle De José Abascal 56 Madrid Spain
          Tip
          Tip Exact normalized matches generally lead to higher consolidation rates than exact matches. To determine the most effective match method for your data, configure multiple rulesets. Compare the consolidation rate of each ruleset and decide which works best for our data.

          Fuzzy Match Methods

          The Fuzzy match methods are available for most fields.

          If any of these match methods are used, matches are based on an Artificial Intelligence (AI) model trained with data from over 150 countries, 3 billion English words, and 20 million names. The AI model is built with the Bidirectional Encoder Representations from Transformers (BERT) Language Model to match common misspellings, diacritical marks, synonyms, and more. The AI model has a 0.7 confidence threshold.

          To allow more control over the granularity of matching, three levels of precision are available: high, medium, and low precision.

          Fuzzy Matching Precision Levels
          Precision Level Description Matching Values
          Low Precision Matches values with loose similarities.
          • Lisa, Liza
          • Cathi, Cathy
          • Lucia, Luc
          Medium Precision Matches values with the same initials, gender variants, shuffled names, and similar subnames.
          • S., Sharon
          • A.M., Anthony Michael
          • Cathi, Cathie
          • Lilian, Liliana
          • Gabriel, Gabrielle
          • José Andrés, Pepe
          • Joey James, James Joseph
          High Precision Matches values across nicknames, punctuation, international abbreviations, international alphabet characters, and cross-cultural spellings.
          • Beatriz, Beatrice
          • William, Bill
          • Mary-Jo, MaryJo
          • Håkon, Hakon
          • Catherine, Katherine
          Tip
          Tip Fuzzy matches generally lead to higher consolidation rates than exact matches. To determine the most effective match method for your data, configure multiple rulesets with different criteria. Compare consolidation rates and decide which match method works best for your unified profiles.
           
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