One shopper can hide behind a web login, a loyalty card and a guest checkout; migration unifies those profiles carefully and carries each consent choice across.
Retail and consumer goods migrations fall into two patterns that often overlap. Brands and retailers selling to consumers move customer profiles, loyalty membership, service cases and marketing preferences from older tools into Salesforce, with orders summarized from commerce and point-of-sale systems. Consumer goods companies selling through retail move key accounts, store lists, brokers, distributors and trade promotion history. We match customers across channels, load only the order and product data teams will use, and carry consent exactly as captured. Service cases and returns history come over so agents can resolve issues without asking customers to repeat themselves.
Consumer data is high volume and highly duplicated. One shopper may exist as a web account, a store receipt email, a loyalty member and a guest checkout, each with different details. Matching must be accurate enough to unify profiles without merging different people who share an address or email. Marketing consent carries legal weight under email, text and privacy laws, and preferences often differ by channel and brand. For consumer goods companies, the customer is often a retailer with store hierarchies, category buyers and promotional calendars rather than an individual. Migration also runs against the retail calendar, so peak seasons are off limits for cutover.
Profiles from ecommerce, point of sale, loyalty and service systems are standardized and matched using email, phone, loyalty number and address rules agreed with marketing. Uncertain matches remain separate but linked for review. The outcome is fewer duplicate profiles and a more accurate picture of active customers, which feeds directly into segmentation and lifetime value reporting after cutover and in future campaigns.
Loyalty membership, tier, enrollment source and communication preferences move with capture dates and channel detail. We keep point balances in the loyalty platform, bring over the attributes marketing and service teams use, and test that opt-outs survive every merge performed during identity resolution. Brand-level preferences stay separate when a company runs more than one brand, so a shopper can opt out of one without losing the other.
For consumer goods teams, retailer accounts, banners, distribution centers and store locations are rebuilt as a hierarchy with buyers and category contacts. Broker and distributor relationships are linked, giving account managers a clear view of coverage before trade promotion and sales planning move into Salesforce. Store identifiers are standardized so retailer data can be matched later against sell-through and syndicated data feeds.
Recent cases, returns, warranty claims and product complaints are migrated with order references, product identifiers and resolution. Agents see past issues on the first day, product teams can analyze complaint trends by item and batch, and older closed cases can be summarized rather than loaded individually. Safety-related complaints are flagged so quality teams keep their review trail intact through the transition.
We start with marketing, ecommerce, customer service and privacy leads, plus sales operations for consumer goods teams. Before any production load, marketing approves the identity matching rules after seeing how they behave on a broad slice of real profiles. Consent loads with the customer record, never afterward, and all automated journeys stay off until preferences are validated. Loads run in phases by source system, with reconciliation of profile totals, opt-in status and open cases each time. The final load lands once peak selling has passed, and preference data is frozen briefly so no opt-out slips through between extract and go-live.
Customer accounts and order summaries are extracted for matching and history, and the ongoing integration keeps new orders flowing without re-importing the full catalog.
Store receipts linked to email or loyalty numbers help unify profiles, while full transaction detail remains in the retail data warehouse or sales system.
Order and return references are mapped to service cases so agents can see what happened with a purchase, without migrating every line item.
Email, text and phone preferences have separate legal requirements, and state privacy laws add rights to access and deletion. Migrate each channel's status separately with capture source and date, and let the most restrictive status win whenever two records merge.
Agents have been known to paste card numbers into case comments or attach photos of receipts. Scan and exclude them before loading, follow PCI DSS with your security team, and keep payment handling in systems designed and certified for it rather than in the CRM.
Over-merging customers is harder to fix than leaving a few duplicates. Start with strict rules, review the gray areas with marketing, and loosen rules only when evidence shows they are safe, since merged profiles blend purchase history and preferences in ways that are hard to unwind.
Customer-level summaries and recent orders that service and marketing teams actually use. Full transaction history is better kept in the commerce platform or data warehouse, where it can be queried when needed. Data Cloud can bring purchase signals into profiles without copying every line item into the CRM, which keeps storage and governance manageable as volumes grow.
Preferences are loaded with each customer, reconciled against every source, and tested before any marketing automation is switched on. When sources conflict, the most restrictive preference applies. We also keep suppression lists as their own records, so an unsubscribed address stays blocked even if that customer is later recreated from a new signup at a store or on the website.
Retailer accounts, banners, store lists, buyer contacts, distributor and broker relationships, and any promotion or deal history the team relies on for planning. We standardize retailer names and store identifiers during cleanup so later integrations with syndicated or retailer-provided data can match reliably and account managers trust the numbers. Duplicate store records created by different reps are merged against the retailer's own store list where one is available.
During the busiest selling and returns periods, and around major promotional events or product launches. Service volumes are high, marketing is running campaigns, and staff have little time for training. We schedule the final load for a quieter stretch after peak returns settle, and plan training so frontline teams are comfortable before the next busy season arrives.
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