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Agencies & consultants

Turn messy client data into clean, reviewable deliverables.

Prepare client CSV and Excel datasets before CRM imports, migrations and operational handoffs. Clean inconsistent values, review duplicates and compare files, then deliver a separate CSV with a clear understanding of what changed.

Prepare client dataSee how RowDesk works
  • Review before delivery
  • Original file preserved
  • Browser-local processing
The client data handoff

Every client file
brings its own cleanup.

Client data cleaning starts with decisions about the file: what a record represents, which values to standardize and what the final deliverable needs to contain.

Different files from every client

A contact CSV, a multi-sheet workbook and an older customer export can arrive with different structures and assumptions.

Contacts and companies repeated

Records may appear more than once, with useful details spread across entries that need deliberate review.

Formatting that varies by source

Mixed casing and surrounding spaces make otherwise comparable values look inconsistent.

Field values without a clear convention

Different status labels or missing-value placeholders need a cleanup rule agreed for the client dataset.

More than one version to reconcile

A new lead list or refreshed customer file needs to be checked against the data the client already holds.

A handoff that needs an explanation

Clients need context for the final count, excluded records and value changes, not just a file with a new name.

01 / Clean inconsistent files

Make the data consistent.
Keep the meaning intact.

Use RowDesk for spreadsheet cleanup without treating every field the same. Trim surrounding whitespace, remove completely blank rows and choose casing rules for selected columns.

Standardize selected values to the labels your client needs, and configure which null placeholders should become empty values. Preview affected rows and proposed changes before applying the cleanup.

Explore controlled client data cleanup

Agree the cleanup rules first

  1. Identify the important fields

    Keep record identifiers and destination requirements in view before changing columns or values.

  2. Choose the intended labels

    Decide whether labels such as Customer and Active customer should share a value. Similar wording is not enough to assume the meaning is identical.

  3. Review the proposed result

    Check before-and-after examples. Formatting cleanup does not supply missing business information or replace a client decision.

02 / Deduplicate before delivery

Keep the useful record.
Review what leaves.

Choose matching columns for the client dataset, then inspect duplicate groups and compare completeness. Contact email may be a useful key for one job; a company identifier may be appropriate for another.

RowDesk can recommend the most complete record by counting non-empty values across output columns. Manually choose a different record when needed and preview proposed removals before confirming.

Explore duplicate groups and keep decisions

Match on explicit rules

Selected columns must match under the configured rules. You can ignore capitalization or trim surrounding spaces where appropriate; RowDesk does not use fuzzy matching to infer that similar names are the same record.

Completeness is a recommendation

A row with more populated fields is not necessarily more accurate. Review why it was selected and use your knowledge of the client data to make the final choice.

No silent merging

Keeping one record does not automatically combine field values from the others. Review what would be removed so the deliverable reflects the decisions you intended.

03 / Compare client files

Reconcile the next file
with what already exists.

Map matching columns between an incoming file and a reference export. Review records only in File A, records matched in both files and records only in File B.

New leads against a CRM export

Use the new lead list as File A and a recent client-provided CRM export as File B. Review matches, then preview keeping only new records for a new-record import.

Two customer dataset versions

Compare selected keys in a refreshed file with an earlier export to see which records are new, matched or only in the reference. A key match does not mean every field in both versions is identical.

A clear comparison scope

Map differently named fields, such as Email to Email Address, and choose conservative normalization. Check that the reference export covers the records relevant to the client job.

Explore client file comparison and matching rules
04 / Explain the changes

More than a cleaner file.
A result you can explain.

Reviewability gives your handoff substance. Check the changes yourself, then use the available outputs to explain the preparation decisions without relying on memory.

Changed values in context

Review before-and-after values, the affected columns and the operations responsible. Preview important transformations before applying them, rather than discovering their impact at export.

Removed rows with a reason

Inspect what was excluded by blank-row cleanup, duplicate removal or comparison filtering. Explain the difference between the original and final row counts.

A visible transformation history

Follow the sequence of applied operations and undo during the active session when needed. Save the outputs you need before closing or resetting; RowDesk is not a permanent cloud history archive.

Explore change review before client delivery
Clean client deliverables

The prepared dataset.
The supporting context.

The main deliverable is a clean CSV containing the current reviewed dataset. It is a separate output, not an overwritten source file or an edited Excel workbook.

Changed-row, removed-row and audit-summary exports are Pro features. Keep those supporting outputs separate from the file intended for import, and share them through your established client handoff process.

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

Export

Current dataset and supporting outputs. Illustrative session, not a live file.

Original rows
1,248
Final rows
1,200
Rows removed
48
Values changed
5
Transformations
6

Clean CSV

customer-export-clean.csv

1,200 rows / 6 columns / UTF-8

Prepared output

Removed rowsPro

48 rows excluded from output

Changed rowsPro

4 rows with 5 changed values

Audit summaryPro

6 operations and final counts

Review this export summary

1,248 original rows minus 8 blank rows, 25 duplicates and 15 matched existing records leaves 1,200 rows. Five cell values changed across four retained rows. Six applied transformations include a column rename. Supporting row sets are separate from the clean CSV, not additional rows to import.

Export creates a separate file. Your original source remains unchanged.

Illustrative delivery: 1,248 original rows become 1,200 final rows after 48 removals. Five values changed across four retained rows, and six transformations explain the result. Supporting row sets are not additional rows to import.

Cleaned CSV

The retained records and reviewed values for the next import or operational handoff.

Removed rows

A separate output for inspecting the records excluded during preparation, where applicable. Pro.

Changed rows

The rows whose values changed, useful for follow-up checks. Several changed cells can belong to one row. Pro.

Audit summary

A readable account of operation order, key parameters and counts to support your explanation. Pro.

Explore clean CSV and supporting exports
Example agency workflow

From received file
to reviewed deliverable.

A repeatable preparation sequence for CRM migration work, customer data cleanup or a routine spreadsheet handoff. Use the steps that fit the client's file and delivery requirements.

  1. 01

    Receive client CSV/XLSX

    Confirm the source, intended destination and fields the client needs to preserve.

  2. 02

    Scan issues

    Open the file and inspect its structure and data-quality findings.

  3. 03

    Clean

    Choose column-specific cleanup and preview the proposed changes.

  4. 04

    Deduplicate

    Review matching groups and confirm which records to keep.

  5. 05

    Compare against existing data

    Map matching fields to a reference export and check new versus existing records.

  6. 06

    Review changes

    Inspect changed values, removed rows and the applied operations.

  7. 07

    Export clean deliverables

    Download a clean CSV and the supporting outputs available on your plan.

  8. 08

    Hand off or import

    Deliver through your client process or use the destination system's importer.

Client systems

Different client systems.
A deliberate file handoff.

Prepare cleaner files for the systems your clients already use. Keep destination-specific requirements in view while preparing contacts, companies, leads or customer records.

  • HubSpot
  • Salesforce
  • Pipedrive
  • Zoho
  • Microsoft Dynamics

Prepare CSV for a client CRM import

Confirm required columns, accepted values and identifiers for the destination. CRM migration data preparation does not replace the destination system's validation or import settings.

Keep the import under your control

RowDesk prepares a file. It does not connect to the client CRM, write records back or synchronize changes automatically. Use the client's approved import and delivery process.

Privacy and client data

Process locally.
Preserve the original.

The raw dataset is processed in your browser and is not uploaded to RowDesk servers in local-processing mode. Preview important changes before applying them and export a separate file.

Your client's original CSV or XLSX remains unchanged. Manage received files and downloaded deliverables according to your client's handling requirements; local processing does not replace those responsibilities.

Read about local processing and source-file safety
Related capabilities

Deliver cleaner client data.
Know what changed.

Prepare the next file with review built into the workflow.

Prepare client dataExplore more team workflows