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You are about to launch a campaign. The creation is complete, the emails are built and everything is scheduled. You are now ready to extract the list. You analyze it and notice any 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 “vice president of marketing” next to one that says “vice president of marketing.”
We all know the difficulty of data cleaning. Most systems will validate emails and block hardware 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 that you can run in 10-15 minutes before any campaign using AI and a spreadsheet. No new systems 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 every time the data seems slightly off.
When you notice:
- You see inconsistent name formatting (JOHN vs. John vs. John).
- Business names appear in several variations.
- You use personalization fields in emails.
- You segment by company, role or title.
- The list came from several sources or imports.
If any of these are true, run the workflow. Because if 5% of your data is messy and you send it to 5,000 people, that’s 250 broken experiences in a single 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. Only include fields that are important to the campaign:
- First name.
- Surname.
- E-mail.
- Business.
- Job title.
- All personalization or segmentation fields.
Don’t try to clean anything yet. Export it as a CSV or Excel file and leave it as is.
Use a tool like ChatGPT, Claude or Google Gemini and download the spreadsheet directly.
You’re not asking the AI to fix everything yet. You use it as a structured wizard to clean specific items in a controlled manner.
Step 3: Profile the data
Before you touch anything, figure out what’s 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 per column
- Inconsistent capitalization (e.g. all uppercase, 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 appear unreliable or inconsistent
Give me a brief summary and highlight the biggest issues that need to be addressed before using it for a marketing campaign.
What this usually reveals is not catastrophic errors, but small inconsistencies on a large scale. You’ll see patterns like 20% poorly formatted names, three or four variations of the same company, and duplicates that weren’t obvious at first glance.
This tells you what to focus on.
Step 4: Standardize the data
You now clean up the data structure so that it behaves consistently across all your campaign tools. Use this prompt:
“Clean and standardize this dataset for marketing purposes. Apply the following rules:
- Capitalize first and last names correctly (for example, “john” → “John”)
- Remove extra spaces before and after text in all fields
- Standardize business names by removing suffixes such as Inc, LLC, Corp, Ltd.
- Standardize capitalization of company names (for example, “salesforce” → “Salesforce”)
- Remove obvious duplicate records based on matching email or full + company name
- Ensure consistent formatting across all text fields
Do not delete rows unless they are clear duplicates. Return the cleaned dataset as a table and clearly indicate what has been changed.
Step 5: Standardize the fields that drive your campaign
Now focus on what actually affects targeting and messaging. If you’re sending a campaign to marketers and your title field says:
- Vice President of Marketing.
- Vice President of Marketing.
- Marketing manager.
These may or may not be 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 of Marketing or Vice President of Marketing).
- Name formatting issues.
Group similar values together and come up with a standardized version for each group. Don’t overwrite anything automatically, only show recommendations.
Now you don’t just clean data, you make 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 for making judgments. Use this prompt:
“Create a review table of records that may require manual review. Include:
- Potential duplicates that are not an exact match
- Business names that may have been poorly standardized
- All fields for which confidence in the correction is low
Add a brief explanation of why each record is flagged.
This gives you a short “look here before sending” list, rather than forcing you to look at 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 reported/revised version.
This becomes your habit before the campaign, not a one-time solution. Record your prompts. Use the same workflow every time. Add this to your campaign checklist.
Data cleanup is a pain, but save yourself some headaches by getting into the habit of performing a quick cleanup on each campaign.
You don’t need perfect data. You just need consistent data. Clean inputs lead to better results. It’s that simple.
