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“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
Duplicate customer records between NetSuite and Salesforce create inaccurate customer counts, skewed revenue attribution, unreliable segmentation analysis, and compromised customer service through fragmented customer views.
Here’s how to systematically detect, resolve, and prevent customer record duplicates when combining data from both systems.
Provide robust duplicate customer handling using Coefficient
Coefficient provides robust capabilities for duplicate customer handling through flexible import options and data manipulation features that enable clean multi-system data blending despite customer record inconsistencies. The platform supports comprehensive data import using Records & Lists for complete NetSuite customer records, custom field matching for Salesforce IDs, and multi-criteria comparison across customer names, addresses, and contact information.
How to make it work
Step 1. Import comprehensive customer data with identifying fields.
Use Records & Lists to import complete NetSuite customer records with all identifying fields including custom fields containing Salesforce IDs or sync status indicators. Import Salesforce account data with matching fields for comparison.
Step 2. Set up duplicate detection strategies.
Compare customer names, addresses, phone numbers, and email addresses across systems using standardized formatting to improve matching accuracy. Use SuiteQL analysis to write queries that identify potential duplicates based on similarity algorithms.
Step 3. Create systematic duplicate resolution.
Implement master record strategy by designating NetSuite as system of record for financial data, merge complementary data from both systems for complete customer view, and flag potential duplicates for manual review and resolution.
Step 4. Apply advanced duplicate handling techniques.
Handle variations in company names, addresses, or contact information with fuzzy matching. Track parent-subsidiary relationships that may appear as duplicates and maintain audit trails of duplicate resolution decisions.
Step 5. Establish automated maintenance processes.
Set up regular refresh to capture new customer records and potential duplicates, ongoing identification of new duplicate patterns, and cross-system validation to ensure duplicate resolution maintains data integrity.
Transform duplicate management into systematic data quality
This approach transforms duplicate customer management from a manual, error-prone process into an automated, systematic data quality management system. Start cleaning your customer data today with automated duplicate detection and resolution.
