Cleaning Data

Clean First Name

Clean up the firstname specified in the input.

Ensure the name is properly capitalized, with the first letter in uppercase and the rest in lowercase. Remove any emojis, symbols, or extraneous characters.

Trim unnecessary spaces and ensure no nicknames or unrelated text is included.

The goal is to have a clean, properly formatted firstname.

The output can only be the lastName, don’t make any comments.

If you don't have the information output: N/A

Input: {{firstname}}
Output:

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Your input

1. ’   michael’

2. ‘ANNE-MARIE’

3. ‘Tom😊’

4. ‘’

Output

1. ‘Michael’

2. ‘Anne-marie’

3. ‘Tom’

4. ‘N/A’

Where to find this variable?

The first name can typically be sourced from LinkedIn profiles or a B2B lead database like lemlist, where individual names are commonly provided. It’s essential to extract and clean this data for accuracy.

How to use it?

The cleaned first name should be used to personalize communication in cold outreach campaigns. Proper capitalization and format reflect attention to detail and professionalism, potentially enhancing response rates from recipients.

Use cases

Personalized Advertising: Tailor marketing materials and ads to individuals by using their cleaned first names, enhancing direct connection.

Networking Invitations: Send personalized invites to events or webinars using the cleaned first names to increase engagement.

Customer Service: Address customers by their correct first names in support communications to foster a friendly, respectful interaction.

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