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CRM administrators

Prepare cleaner data before it enters your CRM.

Inspect and prepare contact, company, lead and customer files before an import, migration or bulk update. RowDesk helps you clean CSV and Excel data, review duplicate records and understand the changes before downloading a separate prepared file.

Prepare your dataSee how RowDesk works
  • Browser-local processing
  • Preview before applying
  • Source file unchanged
The import checklist

Catch the file problems
before the import job.

CRM import preparation starts with understanding the file, not immediately changing it. Check the problems that could affect record identity, field consistency and the final handoff.

Duplicate contacts and companies

Repeated records may contain different amounts of information. Removing a row without reviewing it can discard the better record.

Inconsistent field formatting

Extra spaces, mixed casing and different labels for the same value make a file harder to reconcile with your import conventions.

Blank or malformed values

Missing identifiers and invalid-looking values need attention before the file is checked against required CRM fields.

Different column names

One file uses Email and another uses Email Address. Field names need to be mapped deliberately when comparing exports.

Records that already exist

An incoming list can overlap with current CRM data, even when the file has no internal duplicates.

Cleanup that is hard to explain

A final spreadsheet alone does not show which values changed, why records were removed or which operations were applied.

01 / Clean and standardize

Prepare consistent fields.
Choose the changes.

Clean CSV before CRM import with operations configured for the file in front of you. Trim surrounding whitespace and remove completely blank rows, then review changes that depend on business meaning.

Normalize casing for selected columns, configure null-placeholder cleanup and map selected values to your preferred labels. Standardizing a status field should be a deliberate choice, not an automatic rewrite of every text column.

Explore column-specific CSV and Excel cleanup

Before you apply cleanup

  1. Choose the scope

    Pick the columns and operations that match your import conventions.

  2. Preview the impact

    Check affected rows, proposed value changes and any completely blank rows selected for removal.

  3. Check business requirements

    If you need to validate CSV before import, also check required fields, identifiers and accepted values in the target CRM. RowDesk data-quality checks do not guarantee acceptance by its importer.

02 / Customer data deduplication

Review the group.
Keep the right record.

Choose matching columns that identify the contacts or companies in your file. Inspect duplicate groups and compare their fields before deciding which records to remove.

RowDesk can recommend the most complete record, based on non-empty values across output columns. You can manually choose another record when completeness does not reflect the best business information.

Explore duplicate review and completeness

Use conservative matching rules

Selected columns must match under the configured rules, such as ignoring capitalization or trimming surrounding spaces. RowDesk does not use fuzzy matching to treat similar names as the same company.

Preview removals before confirming

Check the duplicate groups, records involved and rows proposed for removal. Keeping one record does not automatically merge field values from the others.

Keep CRM changes separate

To remove duplicate CRM records from an export, RowDesk works on that file. It does not delete or merge records inside the live CRM. Any downstream migration or update remains under your control.

03 / Reconcile incoming records

Different exports.
A clear comparison.

Use a current CRM export as your reference. Compare the incoming CSV or XLSX against it to understand which records are new and which already match existing data.

  1. Choose the two files

    File A is the incoming dataset. File B is the existing CRM export, covering the records you need to check.

  2. Map matching fields

    Map Email to Email Address, or choose another suitable key. Compare on one or more fields with configured casing and whitespace rules.

  3. Review the result sets

    Inspect new records in File A, matched records and records only in File B. For a new-record import, preview keeping only new records from File A.

For a bulk update, matching identifies records already represented in the export; it does not update their CRM fields. Preserve the identifiers your CRM needs and configure the update in its own import process.

Explore column mapping and file comparison
04 / Review every transformation

A prepared file.
An explainable result.

Review changed values with their before-and-after context, inspect removed rows and follow the applied transformation history. You can see the impact of cleaning, duplicate removal and comparison filtering instead of inspecting only the final file.

Preview important operations before applying them. If you reconsider, undo during the active session. Your original source file remains unchanged; this is not permanent cloud audit-history storage.

RowDesk workspaceSample data
customer-export.csv1,248 rows · 6 columns
Local mode

Changes

Review your working dataset before export. Illustrative session, not a live file.

Original rows
1,248
Current rows
1,200
Rows removed
48
Values changed
5
Transformations
6
Five changed values across four retained rows. Quotation marks show surrounding spaces.
Source rowColumnBeforeAfterOperation
12Company" Acme Inc. "Acme Inc.Trim whitespace
34Company" Northwind "NorthwindTrim whitespace
12EmailJORDAN@EXAMPLE.COMjordan@example.comEmail casing
57EmailSAM@EXAMPLE.COMsam@example.comEmail casing
89EmailLEE@EXAMPLE.COMlee@example.comEmail casing

5 changed values across 4 retained rows. Customer IDs remain unchanged.

Active-session history1,200 rows ready for review

Illustrative review: 1,248 original rows become 1,200 current rows after 8 blank rows, 25 duplicates and 15 existing matches are removed. Five values changed across four retained rows; six transformations were applied.

Explore changed values, removed rows and session undo
Example CRM admin workflow

From source export
to a checked handoff.

Keep preparation separate from the live system. The same sequence can support a new-record import or CRM data migration cleanup, with the final import controlled in your CRM.

  1. 01In your CRM

    Export current CRM data

    Choose a recent export with the record identifiers and fields needed for comparison.

  2. 02In RowDesk

    Load the incoming file

    Open the contact, company, lead or customer CSV/XLSX you need to prepare.

  3. 03In RowDesk

    Scan

    Inspect the dataset and identify formatting or data-quality issues.

  4. 04In RowDesk

    Clean

    Choose whitespace, casing and selected-value cleanup, then preview the impact.

  5. 05In RowDesk

    Deduplicate

    Review matching groups, compare completeness and confirm removals.

  6. 06In RowDesk

    Compare

    Map fields to the existing export and review new and matched records.

  7. 07In RowDesk

    Review changes

    Verify changed values, removed rows and the operations applied.

  8. 08In RowDesk

    Export clean CSV

    Download the current prepared dataset as a separate file.

  9. 09In your CRM

    Import into CRM

    Check required fields and identifiers, then run your chosen import or update process.

Your CRM import process

Work toward the format
your CRM needs.

Prepare cleaner files before importing them into your CRM. Check the destination's requirements for contacts, companies, leads or customers before using the final CSV.

  • HubSpot
  • Salesforce
  • Pipedrive
  • Zoho
  • Microsoft Dynamics

These are examples of systems teams may prepare files for, not connected destinations. RowDesk does not write changes back to the CRM or synchronize records automatically.

For bulk updates, retain the appropriate record identifiers. For migrations, confirm accepted values and required fields in the new system. Explore prepared CSV exports.

Prepare locally.
Keep your source intact.

Your raw dataset is processed in the browser and is not uploaded to RowDesk servers in local-processing mode. Review important transformations before applying them and export a separate file without overwriting the source.

Read about local processing
Related capabilities

Prepare the file.
Stay in control of the import.

Clean, reconcile and review your next CRM dataset before export.

Prepare your dataExplore more team workflows