Problems & fixes · CRM
Common problems when migrating to a different CRM
Last updated 25 October 2026 · 2 sources listed · Reading time about 3 minutes
Short answer
CRM migrations fail less often on technology than on data quality, mapping and change management. Typical problems are duplicate and stale records, mismatched field types, lost relationships between contacts and accounts, broken attachments and activity history, and automations that do not translate. Reduce risk by cleaning before moving, mapping explicitly, migrating a trial subset first, and keeping the old system readable until the cutover is verified.
Who this is for: An operations lead or sales manager planning to move customer data from one CRM, or from spreadsheets, into a different system.
What to evaluate
Duplicate and stale records
Importing everything imports the mess. Duplicates multiply when two sources use different identifiers.
How to check: Run a duplicate report on the source and decide merge rules before any import.
Field mapping and data types
Picklists, dates, currencies and multi-select fields rarely match one-to-one, and mismatches silently truncate or reject values.
How to check: Build a mapping sheet for every field with the target type and the transformation rule.
Relationship integrity
Contacts, accounts, deals and activities link through identifiers. If they break, history appears orphaned.
How to check: Sample fifty linked records after a trial import and confirm each relationship is intact.
Activity and attachment history
Notes, emails, calls and files hold context that reps depend on, yet many imports carry only core fields.
How to check: Confirm what the import tool carries for activities and attachments, and what needs a separate path.
Automation and workflow logic
Rules, assignment logic and scoring are tied to the old platform and must be rebuilt, not copied.
How to check: Inventory every automation and document its trigger, condition and outcome before the move.
User permissions and ownership
Record owners and sharing rules decide who sees what. Wrongly mapped owners hide or expose data.
How to check: Map each source user to a target user and verify access with test accounts.
Comparison
| Approach | Good when | Watch out for |
|---|---|---|
| Big-bang cutover | Small dataset and a simple process | Little room for rollback if a flaw appears after go-live |
| Phased by team or region | Several groups with different workflows | Temporary dual systems and unclear source of truth |
| Core records only, history archived | Old activity is rarely consulted | Reps lose context and may distrust the new system |
| Full history via specialist tooling | Compliance or long-cycle sales rely on history | Longer timeline and mapping complexity |
| Parallel run | High-risk revenue operations | Double entry fatigue and divergence between systems |
Decision worksheet
Rate up to three options against this guide’s own criteria. It runs in your browser and saves only on this device. It is a thinking aid, not a recommendation.
1. Have you checked each criterion?
0 of 6 criteria checked.
2. Compare options (0 = fails or unknown, 3 = meets it well)
| Criterion | |||
|---|---|---|---|
| Duplicate and stale records | |||
| Field mapping and data types | |||
| Relationship integrity | |||
| Activity and attachment history | |||
| Automation and workflow logic | |||
| User permissions and ownership | |||
| Total (max 18) | 0 | 0 | 0 |
Questions to ask before you commit
- What does the import tool carry across for activities, notes and attachments?
- How are record identifiers preserved so links between objects survive?
- Can we run a trial import into a sandbox and repeat it?
- How are duplicates handled during import, and can the rule be configured?
- What is the plan if validation fails after cutover, and how long can the old system be kept readable?
- Who is responsible for each step, vendor, partner or our team?
- How will user logins, ownership and permissions be mapped?
- How will we prove that totals and sample records match after migration?
Typical trade-offs
Clean before migrating versus clean afterward
- Upside
- Cleaning first prevents polluting the target and speeds adoption.
- Downside
- It delays the project and needs decisions about what to discard.
Migrate history versus archive it
- Upside
- Migrated history keeps context available inside the daily tool.
- Downside
- It raises cost and complexity, and low-value history can clutter records.
Freeze edits during cutover versus keep working
- Upside
- A freeze guarantees the migrated snapshot is accurate.
- Downside
- The team loses working time and may enter data on the side.
Common mistakes
- Treating migration as a technical export and import. Assign business owners to decide which data matters and who verifies it.
- Skipping a rehearsal. Run at least one full trial import into a sandbox and fix the issues it exposes.
- Forgetting integrations. Billing, support and marketing tools must be repointed and tested after cutover.
- Not telling users what changes. Provide a short guide and a place to report problems during the first weeks.
- Decommissioning the old system immediately. Keep read access until reports and counts have been reconciled.
Related questions
How long does a CRM migration usually take?
It depends far more on data cleanliness and the number of automations than on record count. A tidy small dataset with a simple pipeline can move quickly, while multiple sources, custom objects and integrations take much longer. Plan in stages: cleanup, mapping, trial import, user acceptance testing, cutover, and stabilization, and expect the stabilization period to reveal gaps you did not predict.
What should we do with old, inactive contacts?
Decide with a rule rather than record by record. Many teams set an inactivity threshold and a business reason, then archive or delete those that fall outside it. Check any legal retention obligations first, and respect consent and erasure requests. Keeping a read-only archive outside the live system is often a sensible middle path.
How do we verify the migration worked?
Reconcile counts by object type, compare totals for important numeric fields, and check a random sample of records against the source, including linked contacts and activities. Then have a handful of real users work their normal day on the data. Record the discrepancies, fix them, and repeat the checks after the final import.
Can automations be exported and reused?
Rarely in a directly usable form. Each platform has its own logic model, so the practical approach is to document the business rule behind each automation and rebuild it using the target's tools. This is also a chance to retire rules nobody uses, which reduces ongoing maintenance.
Sources and further reading
These pages were reachable when we last checked. They informed definitions and rules referred to above; they do not endorse this guide. Where rules differ by country, check the authority for your location.
- Information Technology Laboratory — National Institute of Standards and Technology, retrieved 27 October 2026
- Data protection in the EU — European Commission, retrieved 27 October 2026
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