File A: your incoming list
Start with the customer or lead file you want to prepare. File A is the working dataset that will be filtered when you choose to keep only new records.
Compare files
Compare two files and see exactly what's new.
Compare CSV or Excel files, map matching columns and choose conservative matching rules. Separate new, matched and only-in-B records before preparing your next CRM or business-system import.
- CSV and XLSX input
- Preview before applying
- Browser-local processing
Two-file comparison workspace example
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.
Start with the outcomeKeep only records
Keep only records
that are new.
You have a fresh lead list and an existing CRM export. Compare the two to find what is not already in the export, without building spreadsheet formulas or manually checking each contact.
File B: your reference export
Load the existing list to compare against. File B supplies the matching keys; it is not a connected CRM account or a live view of that system.
A focused import file
Review the results and retain records from File A without a match in File B. Here, new means new relative to the file and matching rule you selected.
Map matching columnsDifferent headers.
Different headers.
The same matching key.
Your files do not need identical column names. Map fields that represent the same information, then compare on one or more selected fields.
File AFile B
EmailEmail Address
This example compares the mapped email field. Header names can differ; the selected values must match under your rule.
Need a more specific key?
Add another mapping, such as Company Organization. With both selected, email and company must match.
Conservative matchingAccount for formatting.
Account for formatting.
Keep the rule precise.
Choose how selected values are compared. RowDesk uses your mapped columns and configured rules, not approximate names or inferred identities.
Ignore capitalization when configured
Treat uppercase and lowercase versions of the same value as a match when that option is enabled. Leave it off when capitalization is meaningful to your key.
Trim surrounding spaces
When configured, ignore leading and trailing whitespace before comparing keys. This is not aggressive rewriting of names, phone numbers or addresses.
Require every mapped field to match
For a multi-column key, all selected field pairs must match under the configured rule. A similar name alone does not qualify, and there is no fuzzy or AI matching.
Clear resultsThree groups.
Three groups.
A clear next step.
Compare CSV files or Excel datasets and review which records are new, which have matching keys, and which appear only in the reference. Keep the meaning of each group visible before you change File A.
New records
1,450Only in File A
Records in the incoming file with no matching key in File B. These are the records retained when you keep only new records.
Matched records
11,032In both files
Records whose selected keys match across the files. A matching key does not mean every other field is identical.
Reference-only records
2,989Only in File B
Records in the reference export without a match in File A. They are not added to File A when you keep only new records.
File A: 12,482 = 1,450 new + 11,032 matched
File B: 14,021 = 2,989 only in B + 11,032 matched
Illustrative counts from the preview above. Each email key is unique within each example file, so matched records pair one to one.
Understand why a record matched
The explanation should follow the actual mapped field and rule. In this example, Email in File A is mapped to Email Address in File B, and capitalization is ignored.
- Email (File A)
- M.TORRES@GLOBAL.IO
- Email Address (File B)
- m.torres@global.io
Why did these records match?
Email matched after capitalization was ignored. The name is not part of this matching key. Other fields can still differ; this result does not merge or overwrite either record.
Keep only new recordsPrepare the next import.
Prepare the next import.
Leave known records out.
Retain only records from File A whose keys are absent from File B. Preview the rows being kept and filtered out before confirming. File B remains the reference; its records are not appended to your output.
For the illustrated comparison, this keeps 1,450 of File A's 12,482 records and excludes its 11,032 matched records. The action changes your working dataset, not either original source file.
- 01
Review the mapping and results
Check which columns form the key and why records match. If the rule does not reflect your data, adjust it before deciding what to keep.
- 02
Preview the proposed filter
Inspect the new records and the matched rows that will be excluded. Confirm the output count before applying Keep only new records.
- 03
Apply, then review
Apply the filter to File A. The transformation remains reversible during the active session, so you can return to the earlier dataset if needed.
Review and exportCheck what was filtered.
Check what was filtered.
Export what you need.
Move from comparison to a prepared CSV with the context intact. Review the applied transformation and excluded rows before downloading the resulting dataset for your business system's import workflow.
Inspect the comparison action
Use RowDesk Changes to review the applied filter and the rows excluded from your working dataset. Matching rules explain the comparison; the transformation summary explains its impact.
Explore reviewable data changesUndo while the session is active
Undo the most recent transformation to restore the previous state. If later steps have been applied, undo those first to return to the dataset before filtering. Your original files remain unchanged.
Download a separate CSV
Export the retained records as a clean CSV, with relevant change or removal exports when needed. RowDesk prepares the file; it does not write records into your CRM.
Explore prepared CSV exportsBoth datasets stay local in local-processing mode. Raw file contents are processed in your browser, not uploaded to RowDesk servers.
About local processingKeep preparing your dataA comparison is one step.
A comparison is one step.
Finish with confidence.
See what's new.
Import with intention.
Map your columns, review the matches and prepare the records you need.