Repeated records
The same contact or company appears more than once, with different levels of detail.
RevOps / Sales Ops
Clean CRM data before it becomes a RevOps problem.
Prepare lead, contact, company and customer files before they reach your CRM. RowDesk helps you clean CSV and Excel data, review duplicate records and compare incoming lists with existing exports before your next import.
- Browser-local processing
- Review before applying
- Original file preserved
Before the importThe problems start
The problems start
inside the file.
RevOps data cleaning is rarely one big correction. It is a series of small decisions that determine which records reach the CRM and in what shape.
01 / Clean incoming dataMake values consistent.
Make values consistent.
Keep decisions deliberate.
Start with the CSV or XLSX file your team received. Scan for issues, then choose the cleanup that fits the columns you intend to import.
Trim surrounding whitespace, remove completely blank rows and configure null-placeholder cleanup. Apply casing or selected-value standardization to specific columns, rather than changing every field in the dataset.
Explore lead-list and customer data cleanupExample cleanup decisions
- Company
- Before:
" Acme Inc. "After: Acme Inc.Trim surrounding whitespace. - Before: ALEX@EXAMPLE.COMAfter: alex@example.comNormalize email casing in the selected column.
- Lead source
- Before: Trade showAfter: EventApply a value mapping your team has chosen.
Formatting cleanup does not fill missing business information or guarantee CRM acceptance. Review warnings and check the target system's required fields.
02 / Review duplicatesOne record to keep.
One record to keep.
A reason you can check.
Remove duplicate contacts from the incoming file before comparing it with your CRM export. Choose the fields that identify a record for this particular workflow.
RowDesk groups records using the selected matching columns and configured normalization. Ignore capitalization or trim surrounding spaces where appropriate; selected fields must match under those rules. Similar-looking names alone are not a fuzzy match.
Explore duplicate groups and keep decisionsKeep the most complete record
RowDesk can recommend the record with the most non-empty values across output columns. A more complete row may be a useful starting point, but completeness is not a guarantee that its values are correct.
Review before removing
Compare the fields, inspect why a record was selected and choose another survivor when needed. Preview the proposed removals before confirming. RowDesk does not automatically merge field values from different records.
Use the right business key
Email may suit a contact list; a stable company identifier may suit company records. Check that your selected columns represent the identity you actually want to compare.
03 / Compare with a CRM exportNew campaign.
New campaign.
Only new records.
Export the relevant records from your CRM, then compare that file with the incoming list in RowDesk. File A is your new lead or customer file; File B is the existing CRM export.
Map fields such as Email to Email Address, choose the matching rules and review new, matched and only-in-B results. An existing export is a snapshot: use a recent one and confirm its scope covers the records you need to check.
File ANew lead / campaign file
File BExisting CRM export
Your decisionKeep only new records from File A
Compare files
Find new and matched records before you import.
File Aincoming-customers.csv12,482 records
File Bcrm-export.csv14,021 records
Match by column
Email (File A)Email Address (File B)
Match options
Ignore capitalization
Trim leading and trailing spaces
1,450New recordsIn File A, not in File B
11,032Matched recordsPresent in both files
2,989Only in BIn File B, not in File A
| Selection | Status | Name | Email (File A) | Email Address (File B) | Company | Source |
|---|---|---|---|---|---|---|
| New (in A) | Sarah Chen | sarah.chen@acme.co | - | Acme Co | File A | |
| Matched | Michael Torres | M.TORRES@GLOBAL.IO | m.torres@global.io | Global Inc | Both | |
| Matched | Priya Patel | priya@vertex.com | priya@vertex.com | Vertex | Both | |
| New (in A) | Daniel Kim | daniel@raftlabs.com | - | Raft Labs | File A | |
| Only in B | Emma Wilson | - | emma@brightpath.com | BrightPath | File B |
Why did these records match?
Email in File A is mapped to Email Address in File B. Leading and trailing spaces are trimmed, then values are compared without case differences. For example, M.TORRES@GLOBAL.IO matches m.torres@global.io. Names are not used; no fuzzy matching is applied.
Illustrative results: 12,482 records in File A and 14,021 in File B, with one record per email key in each file. There are 11,032 matched records, 1,450 new records in File A and 2,989 records only in File B.
Preview keeping only new records before applying the filter. This changes the working dataset in RowDesk, not the CRM or either source file.
Explore mapped columns and two-file comparison04 / Review before importingKnow what changed.
Know what changed.
Then prepare the handoff.
Sales Ops data preparation should leave you able to explain the result. Before downloading, verify which records remain and how the working file got there.
Preview the impact
Check affected rows, changed values and proposed removals before confirming important operations.
Inspect what changed
Review before-and-after values and removed rows, including duplicate removals and records filtered by comparison.
Follow the transformations
See the cleaning, duplicate-removal and filtering steps applied to the working dataset.
Reconsider during the session
Undo transformations during the active session. Your original source file is never overwritten.
Example RevOps workflowFrom incoming list
From incoming list
to a reviewed import file.
A repeatable sequence for preparing a new lead or customer list. Use the steps your file needs, then complete the import in your CRM.
- 01
Export existing data
Download a relevant, recent export from your CRM.
- 02
Load the new file
Open your incoming CSV/XLSX and scan its issues.
- 03
Clean
Choose cleanup for the columns that need it.
- 04
Deduplicate
Review groups and confirm which records to keep.
- 05
Compare
Use the CRM export as File B and identify new records.
- 06
Review
Check changed values, removals and applied steps.
- 07
Export clean CSV
Download the result for your CRM import workflow.
The raw datasets stay on your device in local-processing mode. RowDesk prepares a separate file; exporting from and importing into your CRM are steps you control.
Your CRM, your import processBetter preparation.
Better preparation.
A clearer CRM handoff.
Prepare cleaner files before importing them into your CRM. The output is a clean CSV, ready for you to check against the destination's field and format requirements.
- HubSpot
- Salesforce
- Pipedrive
- Zoho
Check the destination requirements
Confirm required columns, accepted values and record identifiers for the object you plan to import. Clean formatting and reviewed duplicates help with preparation, but do not replace those checks.
Keep the output separate
Download the prepared dataset and use your CRM's import process. RowDesk does not synchronize changes back to the CRM. Explore clean CSV exports.
Related capabilitiesTake a closer look
Take a closer look
at each step.
Make your next CRM import
a better-prepared one.
Clean the file, review the decisions and export with confidence.