Welcome to the "Transform" phase of FETE! Now that you've mastered finding and enriching data, we're moving into the crucial step that makes all that data actually usable.

You can think of data enrichment as gathering ingredients, while transformation is cooking them into a delicious meal.

Data enrichment pulls in the data. Transformation gets it ready for action.

You've probably heard the phrase "garbage in, garbage out." Well, the Transform phase is where we prevent that garbage from ever making it to your outreach campaigns or CRM.

Without proper transformation, you'll end up with messy CRM records, inconsistent messaging, and poor targeting that undermines all your hard work.

In this lesson, we'll show you how to use Clay's built-in normalization tools to clean up common data quality issues like:

• Variations in company names
• Inconsistent formatting
• And messy contact information

Clean data leads to better analyses, more effective outreach, and ultimately better business outcomes.

🛠️ Clay's Native Cleaning Tools

Let's start with Clay's native cleaning tools. These are your first line of defense against messy data, and the best part? They're completely credit-free.

These tools handle deterministic transformations—rule-based, predictable changes that follow consistent logic. Since these functions operate by parsing existing data and executing code rather than reaching out to external providers, Clay can offer them without any additional charge.

Here's how to access and use these tools:

Navigate to the "Add enrichment" panel, select the "Normalize" option in the sub navigation, and choose from the pre-built normalization options.

📋 Most Important Normalization Tools

Let's walk through the most important normalization tools:

First, normalize company names. How often have you encountered company names cluttered with legal suffixes or unnecessary prefixes? This function tackles that issue head-on.

For example, "Panamax Inc." becomes simply "Panamax," and "Cora, a company of Blank" becomes just "Cora." This cleanup not only makes your data more uniform, but it also prepares it for seamless use in email copy and other communications.

Here's how it works: Select "Normalize Company Names" from the pre-built options, choose the appropriate input column containing your company names, and run the action across your rows. The result? Clean, consistent company names ready for your next campaign.

These clean names improve downstream copy and segment logic significantly. Instead of writing "Hi, I noticed Panamax Inc. recently raised funding," you can write "Hi, I noticed Panamax recently raised funding"—much more natural and professional. It also improves CRM consistency and email personalization by ensuring your company names are standardized across all systems.

Second, normalize whitespace to ensure consistent spacing across all your text data. This eliminates extra spaces, tabs, and line breaks that can break automation or make data look unprofessional.

Third, normalize phone numbers to standardize formats, removing or adding parentheses and dashes as needed. This ensures your phone data works consistently across different systems and calling tools.

Fourth, normalize locations to create uniformity in how addresses and locations are represented. This is particularly valuable if you're doing geographic segmentation or routing leads based on location.

Clay's native normalization tools cover most standardization needs efficiently and without using any credits.

🎯 Conclusion

That's a wrap on data cleaning and normalization using Clay's built-in tools!

You now know how to use credit-free normalization functions to clean up the most common data quality issues.

Clean data sets the foundation for everything that comes next—more accurate targeting, better personalization, and smoother automation workflows.

In our next lesson, we'll explore AI formulas for more advanced transformation scenarios. While built-in normalization tools handle most use cases, AI formulas step in when things get more complex.

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