CRM Data Cleansing: A Step-by-Step HubSpot Cleanup Plan

A CRM data cleansing project should end with a database your team can trust and a short list of decisions that need a human. Start with scope, a backup, and clear rules, and the cleanup becomes a project with an end date.

This plan is for a one-time HubSpot cleanup. It covers contacts and companies, but the same sequence works for other CRM objects. The goal is not to make every record look identical. The goal is to make important records usable, explain the changes, and leave an owner for the problems that automation cannot solve.

1. Define the cleanup before you touch the CRM

Write a one-page brief. Keep it specific enough that another person could tell you when the work is finished.

Decide:

• Which objects are in scope: contacts, companies, or both.
• Which teams and workflows depend on those records.
• Which properties affect routing, reporting, segmentation, or email sends.
• The date range or source systems that need extra attention.
• What is out of scope for this pass.

Do not begin with “clean everything.” A better first project might be “clean active contacts and companies used by the sales pipeline, excluding archived records and custom objects.” You can schedule another pass later.

Use a simple project table to keep the work moving:

Workstream	Owner	Status	Acceptance check
Scope and rules	CRM owner	Not started	Objects and fields are listed
Backup and baseline	Admin	Not started	Snapshot and issue counts are saved
Formatting fixes	Operations	Not started	Sample changes match the written rules
Duplicate review	Sales or marketing owner	Not started	Each merge is approved or rejected
Import and integration audit	Systems owner	Not started	Known sources have a next action
Final review	CRM owner	Not started	Before-and-after results are recorded

The names matter. “The team” is not an owner.

2. Back up and protect the source data

Take a snapshot before making a bulk edit. In HubSpot, you can export records for analysis. Accounts with the required subscription and permissions can also use HubSpot’s CRM data backup feature.

Backups are not a magic undo button. HubSpot says CRM backups include records and property values, but not associations or activity data. Store the file somewhere access-controlled, especially if it contains sensitive values.

Before cleanup day:

1. Save the export or backup name, date, portal, and person who created it.
2. Record the number of contacts and companies in scope.
3. Pause any import or sync that could write to the same properties.
4. Decide who can edit records during the cleanup window.
5. Keep a copy of the original values for any field you plan to change in bulk.

If you cannot explain how to recover from a bad change, you are not ready to make the change.

3. Agree on required fields and cleanup rules

Data cleansing gets messy when people argue about individual records without first agreeing on the rule. Write the rule before looking at the exceptions.

Start with the fields that support a real process. A sales pipeline may need an owner, lifecycle stage, company association, and contact email. A marketing audience may need a valid email and consent status. A company-routing workflow may need a domain and country.

Separate rules into three groups:

Rule type	Example	Default action
Safe format	Lowercase an email or trim spaces	Automate after a sample review
Missing value	Contact has no owner	Route to the process owner
Business decision	Company is active or closed	Manual review

Write down what the cleanup will not change. Subscription status, suppression lists, lifecycle stages, ownership, and factual company details should not be overwritten just because a record looks incomplete.

4. Find invalid and incomplete records

Create a baseline before you start editing. Count the records with each issue, not just the total number of “bad” records.

Useful issue groups include:

• Required fields that are blank for records in an active workflow.
• Emails with spaces, malformed syntax, or a domain that needs investigation.
• Phones stored in mixed formats or without a known country code.
• Names and company domains with inconsistent casing or stray characters.
• Records created by a recent import, form, or integration change.
• Contacts and companies that look like possible duplicates.

Sample a few records from every issue group. A rule that looks obvious in a spreadsheet may be wrong in the CRM. For example, a blank phone number may be acceptable for a lead, while a missing owner is a routing failure.

Record the baseline in a small table:

Issue	Records affected	Risk	Owner	Next action
Email spacing and casing	0	Low	Operations	Preview format fix
Missing owner	0	Medium	Sales ops	Route for assignment
Possible duplicate contacts	0	High	Sales ops	Review pairs
Unknown company domain	0	Medium	Account owner	Confirm manually

Replace the zeroes with your own counts. The point is to make progress visible without pretending that every issue has the same risk.

5. Standardize the easy values first

Start with changes that preserve meaning. Common examples are:

• Trim spaces around names, emails, and domains.
• Lowercase email addresses and company domains.
• Apply title case to simple personal names when the rule does not damage acronyms or brand names.
• Normalize phone numbers when the country is known.
• Remove a URL protocol or path from a company domain when the intended domain is clear.

Preview a sample before applying the rule to the full set. Keep the rule and result in the change log. HubSpot also has a Format data workflow action for supported plans, but a workflow is one part of a cleanup plan. It does not replace the baseline, review queue, or final check.

6. Review duplicates instead of guessing

Duplicates deserve their own workstream because a merge can change history, associations, and ownership.

HubSpot automatically deduplicates contacts by email address and companies by domain name in common creation paths. Imports can also use Email, Company domain name, or Record ID as a match key. HubSpot’s deduplication guidance explains the exact behavior and the limits of each method.

For the cleanup project:

1. Start with strong matches, such as the same contact email or company domain.
2. Put name-and-phone matches in a review queue.
3. Compare activity, associations, consent, owner, and recent engagement.
4. Choose the record to keep and record why.
5. Reject uncertain pairs instead of merging them to make the count smaller.

The duplicate manager and bulk merge features vary by HubSpot subscription and permissions. If your portal does not have the right tool, create a review file and handle merges one pair at a time.

7. Audit imports and integrations

A cleanup is temporary if the same source keeps creating the same bad values. Look at records created or changed after recent imports, form updates, and integration changes.

For each source, record:

• Which properties it writes.
• Which unique identifier it uses.
• Whether it can create new records without an identifier.
• Which values it sends in a different format.
• Who owns the source and can change its mapping.

When you prepare the next import, use the right match key. A missing Email, Company domain name, or Record ID can turn an update into a new record. Test a small file first, inspect the results, and only then run the full import.

8. Decide what not to automate

Some changes look efficient but create more work later. Keep these out of automatic cleanup unless a person has approved a clear rule:

• Merging possible duplicates based on name alone.
• Replacing an email address because a domain looks old.
• Assigning an owner from an uncertain territory guess.
• Changing lifecycle stage, lead status, or subscription status.
• Filling an industry, job title, or company size from a weak source.
• Deleting records that have bounced or opted out.

Automation is a good fit for a narrow formatting rule. It is a poor fit for a decision about what a person or company means.

9. Test on a sample, then run the change

Choose a small sample that includes normal records and edge cases. Do not test only the clean-looking rows.

For each proposed rule, check:

• The new value is what the rule promised.
• Blank values remain blank when they should.
• Acronyms, brands, extensions, and international formats survive.
• Associations, owners, consent, and activity are unchanged.
• The change log identifies what happened without copying more personal data than needed.

After the sample passes, apply the change in a controlled batch. Save the batch name, rule version, timestamp, and number of records changed.

10. Document exceptions and owners

Every cleanup finds records that do not fit the rules. That is normal. Create an exceptions list instead of forcing them into the nearest category.

An exception record needs:

• The record ID or a link to the record.
• The issue that blocked automatic cleanup.
• The person who must decide.
• A due date or next review date.
• The acceptance condition for closing it.

This list keeps the project honest. “Cleaned” should not mean “we stopped looking.”

11. Measure the before-and-after result

Run the same checks you used for the baseline. Compare issue counts, not just the number of records changed.

Your final review should answer:

1. Did the count of high-risk issues fall?
2. Were any records changed outside the approved scope?
3. Did any import or workflow recreate the old pattern?
4. How many exceptions remain, and who owns them?
5. What grade does the database receive now?

Simple Data Hygiene can scan contacts and companies, show an A–F grade, list the issues behind the grade, and preview safe formatting fixes. Use the scan as a repeatable baseline, not as a substitute for business decisions or duplicate review.

One-time cleanup versus ongoing hygiene

One-time cleanup	Ongoing hygiene
Establishes a baseline	Checks for drift after new changes
Repairs existing records	Catches new issues near the source
Handles a backlog of exceptions	Gives owners a regular review queue
Has a start and finish date	Runs as part of normal operations

Finish the one-time project with a small maintenance plan. A weekly scan and digest can show whether the same fields are getting worse again. Read the Simple Data Hygiene setup guide to run a scan and add record cards inside HubSpot.

CRM data cleansing project checklist

• Scope, owners, and excluded objects are documented.
• A backup or export is stored securely.
• Baseline counts are saved by issue type.
• Required fields and formatting rules are written down.
• Safe changes were tested on a mixed sample.
• Duplicate matches were reviewed instead of guessed.
• Imports and integrations have a match key and an owner.
• Exceptions have a next action and acceptance condition.
• Before-and-after checks use the same definitions.
• An ongoing scan or review cadence is scheduled.

CRM data cleansing works when it has a finish line. Make the rules visible, keep risky decisions with the right owners, and leave behind a baseline that tells you when the data starts to drift again.

Scan your cleaned HubSpot database with Simple Data Hygiene.