A 15-minute AI workflow to clean campaign data

You’re about to launch a campaign. The creative is done, the emails are built and everything is scheduled. Now you’re ready to pull the list. You give it a scan and notice all the data hygiene issues.

The name field is full of inconsistencies like “Hi JOHN” or “Hi ,Mary (Mary Jane).” Or you have two contacts from the same company, except one says “Salesforce” and the other says “Salesforce.com Inc.” Then there’s a title field that says “vp marketing” next to another that says “Vice President, Marketing.”

We all know the pain of data cleanup. Most systems will validate emails and block hard errors. But they won’t clean your data in a way that makes it usable for personalization, segmentation or dynamic content.

This is a simple workflow you can run in 10-15 minutes before any campaign using AI and a spreadsheet. No new system or complex setup, just a repeatable way to make your data usable.

You don’t need to run this workflow for every campaign, but you should run it anytime the data looks even slightly off.

When you notice:

• You see inconsistent name formatting (JOHN versus John versus john).
• Company names appear in multiple variations.
• You’re using personalization fields in emails.
• You’re segmenting by company, role or title.
• The list came from multiple sources or imports.

If even one of these is true, run the workflow. Because if 5% of your data is messy and you’re sending it to 5,000 people, that’s 250 broken experiences in one campaign.

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Step 1: Export your list 

Pull your list from your CRM or marketing platform the same way you normally would. Include only the fields that matter for the campaign:

• First Name.
• Last Name.
• Email.
• Company.
• Job Title.
• Any personalization or segmentation fields.

Don’t try to clean anything yet. Export it as a CSV or Excel file and leave it as-is.

Step 2: Upload the file into an AI tool

Use a tool like ChatGPT, Claude or Google Gemini, and upload the spreadsheet directly.

You’re not asking AI to fix everything just yet. You’re using it as a structured assistant to clean specific things in a controlled way.

Step 3: Profile the data 

Before you touch anything, figure out what’s actually wrong. Otherwise, you’ll waste time fixing things that don’t matter. Copy and paste this prompt:

“Analyze this dataset and summarize data quality issues. Focus on:

• Missing values by column
• Inconsistent capitalization (e.g., all caps, all lowercase)
• Duplicate records (based on name, email or company)
• Company name inconsistencies (e.g., “Salesforce”, “Salesforce Inc.”, “salesforce.com”)
• Formatting issues (extra spaces, strange characters)
• Any fields that look unreliable or inconsistent

Give me a short summary and highlight the biggest problems to fix before using this for a marketing campaign.”

What this usually reveals isn’t catastrophic errors, but small inconsistencies at scale. You’ll see patterns like 20% of names not properly formatted, three or four variations of the same company and duplicates that weren’t obvious at first glance.

That tells you where to focus.

Step 4: Standardize the data 

Now you clean the data structure so it behaves consistently across your campaign tools. Use this prompt:

“Clean and standardize this dataset for marketing use. Apply the following rules:

• Capitalize first and last names properly (e.g., “john” → “John”)
• Remove extra spaces before and after text in all fields
• Normalize company names by removing suffixes like Inc, LLC, Corp, Ltd
• Standardize capitalization for company names (e.g., “salesforce” → “Salesforce”)
• Remove obvious duplicate records based on matching email or full name + company
• Ensure consistent formatting across all text fields

Do not delete rows unless they’re clear duplicates. Return the cleaned dataset in a table format and clearly indicate what was changed.”

Step 5: Normalize the fields that drive your campaign

Now focus on what actually affects targeting and messaging. If you’re sending a campaign to marketing leaders, and your title field says:

• VP Marketing.
• Vice President of Marketing.
• Head of Marketing.

Those may or may not belong in the same segment depending on your campaign. Use this prompt:

“Review the cleaned dataset and identify inconsistencies in:

• Company names (different variations of the same company).
• Job titles (e.g., VP Marketing versus Vice President of Marketing).
• Name formatting issues.

Group similar values together and suggest a standardized version for each group. Do not automatically overwrite anything, show recommendations only.”

Now you’re not just cleaning data, you’re making it usable for decisions.

Step 6: Create a review layer

This is the step that prevents you from introducing new errors. AI is good at spotting patterns. It’s not perfect at making judgment calls. Use this prompt:

“Create a review table of records that may need manual verification. Include:

• Potential duplicates that are not exact matches
• Company names that may have been incorrectly standardized
• Any fields where confidence in the correction is low

Add a short explanation for why each record is flagged.”

This gives you a short list of “look here before you send,” rather than forcing you to review the entire dataset.

Step 7: Export and use the clean version

Once you’re done:

• Export the cleaned dataset.
• Keep the original version.
• Keep the flagged/review version.

This becomes your pre-campaign habit, not a one-time fix. Save your prompts. Use the same workflow every time. Add this to your campaign checklist.

Data cleanup is a pain, but save yourself some headache by building a habit of running a quick cleanup pass on every campaign.

You don’t need perfect data. You just need consistent data. Clean inputs lead to better outputs. It’s that simple.