A Microsoft extension to the ANSI SQL language that includes procedural programming, local variables, and various support functions.
see:
https://help.salesforce.com/s/articleView?id=sales.managing_duplicates_overview.htm&type=5
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Hi all,
In our Salesforce CRM setup, we often get duplicate customer records from different sources. What is the best approach to identify and prevent duplicate records while maintaining data accuracy?
Thanks in advance!
A Microsoft extension to the ANSI SQL language that includes procedural programming, local variables, and various support functions.
A good approach in Salesforce is usually a combination of prevention rules, matching logic, and regular data cleanup rather than relying on only one method.
Most teams start with Salesforce Duplicate Rules and Matching Rules to detect duplicates based on fields like:
phone number
company name
customer ID
You can configure these rules either to:
warn users before saving a duplicate
or block duplicate creation completely
For better accuracy, many organizations also:
standardize data formats first (phone numbers, addresses, naming conventions)
use validation rules to reduce inconsistent entries
schedule periodic duplicate scans and merge reviews
integrate third-party deduplication tools for larger datasets
One important point is balancing strict duplicate prevention with usability. If the matching logic is too aggressive, legitimate new customers may get blocked accidentally, especially when records come from multiple channels or integrations.A good approach in Salesforce is usually a combination of prevention rules, matching logic, and regular data cleanup rather than relying on only one method.
Most teams start with Salesforce Duplicate Rules and Matching Rules to detect duplicates based on fields like:
email address
phone number
company name
customer ID
You can configure these rules either to:
warn users before saving a duplicate
or block duplicate creation completely
For better accuracy, many organizations also:
standardize data formats first (phone numbers, addresses, naming conventions)
use validation rules to reduce inconsistent entries
schedule periodic duplicate scans and merge reviews
integrate third-party deduplication tools for larger datasets
One important point is balancing strict duplicate prevention with usability. If the matching logic is too aggressive, legitimate new customers may get blocked accidentally, especially when records come from multiple channels or integrations.