How to Find Decision Makers in Clay When People Finder Falls Short

How to Find Decision Makers in Clay When People Finder Falls Short

If you've ever struggled to find specific decision-makers using Clay's standard "People Finder," you're not alone. Sometimes the built-in databases just don't cover your niche perfectly, or you need highly specific titles that require an AI agent's nuance.

There's a powerful alternative method that works just as well—if not better—for these tricky cases.

The Challenge: Locked Data

Let's say you're targeting a specific niche (like schools, malls, or specialized firms) and need to find the "Head of School" or a specific Director. You import your list of companies into Clay and run a Clay AI Agent to find these people.

The agent does a great job. It returns a list of decision-makers for each company.

But here's the problem: That data is often returned as a list (array) inside a single cell. You have one row for the company, and a clump of people stuck in one column. You can't enroll them in a campaign like that.

You need to "explode" that list so each decision-maker gets their own row, while keeping the company's information (Website, Name, etc.) attached to them.

Here are two ways to solve this.

Method 1: The "Clay Native" Way (Recommended)

This is the fastest method and keeps everything inside Clay.

1. Find Your List Column: Locate the column where the AI Agent output the list of decision-makers.
2. Take Action: Click on the column header or use the "Actions" menu. Look for an option like "Write each item to a new row in another table".
3. Map Your Data: Clay will ask you where to send this data. Create a new table (e.g., "Decision Makers").
4. Select Columns:
   • Map the decision-maker's name, title, and email from the list.
   • Crucial Step: Make sure to also map the "Parent" columns from your original table—like the Company Name, Website, and Phone Number. This ensures every new row has the company context.
5. Run It: Clay will process the list and create a new table.

Result: You now have a clean table where every row is a unique person, complete with their company data. You can now enrich them with emails or push them straight to your CRM.

Method 2: The "AI Assistant" Way (For Complex Data)

If the data is messy or truncating, or if you want to do complex cleaning before creating new rows, you can use an AI code editor like Cursor.

1. Export to CSV: Export your Clay table (the one with the list in a cell) as a CSV.
2. Upload to AI: Drag that CSV into Cursor.
3. The Prompt: Ask the AI:

   "I have a column here called 'Decision Makers' which contains a list of objects. Please create a new CSV where each item in that list gets its own row. Keep the other columns (Company Name, Website) for each new row."

4. Review & Re-Import: The AI will write a script to process the file and give you a clean CSV.
5. Import to Clay: Upload this new CSV back into Clay.

Note: This method adds a few steps but is a lifesaver if you need to apply complex logic, like "Only keep decision makers if the confidence score is > 80%" or "Reformat the titles before splitting."

Pro Tip: Add Logic Before You Split

Whether you use Method 1 or 2, consider adding a filter before you create the new rows.

For example, if your AI Agent returns a "Confidence Score" for each person, you can tell Clay (or your AI script) to only keep people with a score of High or Medium. This saves you credits on enrichment later by filtering out low-quality matches early.

Summary

Don't let a "list in a cell" stop your workflow. Whether you use Clay's native "Write to new table" feature or wrangle the data with an external AI tool, unlocking this data gives you access to decision-makers that standard databases often miss.