CRM Data Migration and Reconciliation

About the customer

Commerce.com, Inc. is a leading public Software-as-a-Service (SaaS) provider that delivers an open, AI-driven digital commerce ecosystem for global merchants.

Overview

Commerce.com needed to modernize its CRM environment by transitioning from Salesforce Classic to Salesforce Lightning. As part of this transformation, Intellious led the migration of qualified customer, sales, and operational data from the legacy platform to Lightning, focusing on data quality, migration accuracy, traceability, and business continuity. The approach included data assessment, cleansing, object and field mapping, controlled data loads, and automated reconciliation.

The Challenge

The Salesforce Classic environment held years of historical data, much of it incomplete, outdated, duplicate, or inconsistent, and not all of it suitable for migration. Complex relationships between accounts, contacts, opportunities, and other objects meant records had to move in the correct sequence while preserving those dependencies. Commerce needed to identify which data qualified for migration, keep poor-quality legacy data out of Lightning, validate records at both the field and record level, and give the team clear visibility into migration exceptions.

How Intellious Helped

Intellious assessed the source data to define migration scope and qualification criteria, cleansed and prepared records before migration, and mapped objects and fields between Classic and Lightning. Data moved through repeatable, controlled migration cycles, validated by an automated reconciliation framework that flagged record and field-level discrepancies before final cutover.

Business Impact

The migration improved the quality and reliability of data in Salesforce Lightning while reducing the risk of carrying over duplicate, incomplete, or outdated records. Automated reconciliation accelerated exception reporting and cut manual validation effort, giving Commerce confidence in the completeness of its migrated data and a repeatable framework for future releases.