Example library
CRM data cleanup before and after examples
Before and after examples for lead source cleanup, lifecycle normalization, and import readiness.
Built for RevOps and operations teams that need a shared cleanup reference before pushing data back into a CRM.
Copy-ready prompt patterns
Normalization examples
Show how messy values become a clean mapping.
- Paid Search, paid-search, and Google Ads collapse into one value.
- Lifecycle fields align to a single approved set.
- Blank owner values are flagged for review.
Cleanup prompts
Use prompts that force reviewability.
- Normalize this CRM export and show the mapping table.
- List rows that need manual review before import.
- Return a safe field-by-field cleanup sequence.
Import checks
Reduce bad imports and accidental overwrites.
- Preserve external IDs.
- Check for duplicate contacts before upload.
- Separate source cleanup from import instructions.
Before and after
Fix this CRM export.
Normalize lead source and lifecycle values, preserve IDs, flag duplicates, and return a safe import checklist with rows needing manual review.
What makes this useful
- Shows the input shape, not just the task name.
- Separates drafting from review.
- Works as a source page for internal linking and external reference.
- Can be reused in recurring workflows.
Common failure cases
Next pages to use
AI for CRM data cleanupClean exported CRM records, normalize lifecycle fields, and prepare lead data for routing, reporting, and imports.CRM cleanup before and after examplesUse before-and-after examples to normalize CRM lead sources, owners, stages, and import fields.CRM lead source normalization with AINormalize messy lead source values into clean reporting categories before CRM import or dashboard updates.CRM deduplication prompts with AIFind duplicate contacts, accounts, and leads in CRM exports while keeping review rules explicit.CRM import readiness checklist with AICheck CRM imports for required fields, duplicate records, lifecycle values, owner fields, and risky rows.