PeopleImport - Best Practice Matching Steps in Stage 3

Understand that the goal to is to accurately find matching records and update, then insert any new records. It is typically best to "err" on the side of caution, not making the steps too loose simply to find "more matches" and update the wrong records.

1. Fields to Match On

Selecting what fields to match on will depend on what data is included in the input file, and what fields are typically populated in Salesforce. Also, keep in mind that everyone's data is different and what works for one organization may not work for another. When creating matching rules keep the following in mind:

• PeopleImport will only look for matches in Salesforce and within the input file, where ALL the fields being matched in a step are POPULATED
• All the matching conditions within a step are AND'd together, so ALL the fields will need to match for a record to be flagged as a match for that step
  • If the step includes matching on First Name, Last Name, Email, and Phone Number all 4 of those fields need to be populated in Salesforce and in the input file
  • A matching condition can include a "Match Blank" option, which will match a blank to a blank for a particular field, but "Match Blank" WILL NOT match one record with data to one that is blank. In order to do the latter, a matching step needs to be built WITHOUT matching on that field.
• When electing to match on email, ideally should also match on last name, at least BEFORE a step matching on just email alone
  • Last name is a required field in Salesforce so matching on this also will NOT cause less records to be searched
  • This will avoid problems with generic emails, and situations where records may have been cloned in Salesforce, and other fields updated (e.g. first, last name, phone etc), but the email address of the new person was not provided (and the existing email on the cloned record was not deleted).

2. Advanced Mapping Techniques to Find Additional Duplicates

Exact matching is not the only way to match! Please review ALL the various mapping types and options available within PeopleImport to help identify similar Account/Company name, nicknames for first names, similar addresses, similar phone numbers, perform phonetic matches etc.

Looser techniques are best used when more than one field is being matched on.

Here are some tips when matching:

• First Name matches
  • Use the "FirstName" mapping type to match nicknames, e.g. "Mike" to "Michael"
  • The “middle initial” dilemma: Try matching on just the first letter or first word (mapping type “First XX Letters” or "First XX Words") and Last Name
• Last Name matches
  • Add “Alpha-Clean” to match “Smith-Jones” -> “Smith Jones”
  • Add "Fuzzy" to catch spelling errors
  • Company/Account Name Matches
• Use the "Cleaned Account Name" mapping type to find similar names
  • Can match abbreviations to long forms, e.g. Saint -> St
  • Can ignore common suffixes and prefixes, e.g. match "The Hewlett Packard Company" -> "Hewlett Packard Inc"
  • REVIEW/UPDATE the "Account Cleaning List" to include abbreviations, suffixes, prefixes specific to YOUR industry!
    • Also a DemandTools user? Manage ONLY ONE replace list by updating the "Replace List Directory" File Path in PeopleImport Options to point to the DemandToolsData\ReplaceList\ directory
• Add "Transpose" to catch where the order of the words in the name are different but the words themselves match, e.g. University of North Carolina -> North Carolina, University of
• Add "Alpha-Clean" to catch slight differences in punctuation
• Add "Fuzzy" to catch spelling errors
  • NOT RECOMMENDED IF ALL THAT IS BEING MATCHED IN A STEP IS THE COMPANY OR ACCOUNT NAME AND CLEANED ACCOUNT NAME IS THE MAPPING TYPE
• Street Address Matching
  • Use the "Street Address Match" mapping type to match abbreviations to their long forms (e.g. Street -> St)
• Use the "Relaxed Address Match" mapping type to additionally match one street address with a Suite # to another without (e.g. 100 Main Street -> 100 Main St Suite 234)
• City matches
  • Use “Alpha-Clean” to match “St. Charles” with “St Charles”
  • Add "Fuzzy" to catch spelling errors
• Phone Number matches
  • For North American phone numbers use "Relaxed NA Phone Match" to ignore punctuation, leading 1's, area codes and extensions
  • match +1 (781) 458-9999 to 6174589999 x123
  • Since this mapping type IGNORES AREA CODES and returns just the "458-9999" portion of the phone number IT SHOULD NOT BE USED BY ITSELF (only use when additionally matching on other fields to avoid updating the wrong record).
• Email to Website matches
  • Use the "Domain" mapping type to match an email address to a website on a Lead or Account
    • Great option to find matching Accounts in a "New Contact - Existing Account" matching step
    • Will need to map email to account.website in Stage 1 to make it available for matching (be sure to "uncheck" the insert option and change the update option to "Do Not Update")
• Salesforce ID matches, e.g. using the Salesforce Account ID to check for a matching Account
  • If the ID in the input file is 15 digits vs. the full 18 digit ID use the "Salesforce.com ID Match" mapping type
    • This converts the 15-digit ID to it's 18-digit counterpart so it can be matched

More information on Mapping Types can be found HERE

3. Add a New Contact if the Account Exists, Otherwise Create a New Lead

Keep in mind that with the addition of "New Contact - Existing Account" matching steps PeopleImport now has the ability to import new Contacts if the Account exists.

4. Do Not Create Duplicate Accounts When "Create New Contact" is Selected in Final Match Conditions

If there are multiple new Contacts all associated with the same new Account, create just one Account with all the new Contacts, by using the new option "Limit New Account Duplicates by Field".