AI Lead Generation

How to Clean Lead Lists in Google Sheets Using AI: A Full Guide to Data Normalization

This guide explores how to leverage AI for cleaning and normalizing lead lists in Google Sheets, enhancing data accuracy and improving outreach effectiveness.

In the world of sales and marketing, data is everything. But not just any data—clean, normalized data. Imagine reaching out to a company with the wrong name, or addressing the CEO by their incorrect title. These small mistakes can create big problems, from low response rates to damaging your brand’s credibility. Whether you’re in sales operations, business development, or lead generation, having a clean, accurate lead list is essential for personalizing outreach and improving conversion rates.

Traditionally, cleaning data has been a time-consuming process, relying on methods like regular expressions (regex) or manual formatting in Google Sheets. But thanks to AI-powered tools like TypeCharm, cleaning and normalizing your data is easier than ever. With custom and templated AI prompts, you can standardize company names, people’s names, and job titles with just a few clicks.

In this tutorial, we’ll dive into the following:

  1. Why clean data is critical.
  2. Traditional methods for cleaning data (including a regex tutorial).
  3. AI-powered solutions for lead list normalization using custom prompts.
  4. New AI use cases for cleaning and enriching lead lists.

Why Clean Data is Critical

Let’s start with why you should care about cleaning your lead lists.

  • Personalization: A clean lead list helps you correctly address your prospects, making your outreach more personal and relevant. People respond better when their name and company are spelled right, and when their job title reflects their current role.
  • Improved Response Rates: Personalization leads to better engagement. Clean, consistent data ensures your messaging is accurate, helping to build trust and encourage responses.
  • Segmentation and Targeting: Clean data makes it easier to segment your audience based on relevant criteria like industry, job title, or company size. With messy data, your segmentation may result in incorrect targeting, leading to poor campaign performance.
  • Operational Efficiency: Clean data prevents errors in reporting and ensures smoother operations, making it easier for sales and marketing teams to work together without constantly fixing data issues.

Traditional Methods of Data Cleaning

Before diving into AI solutions, let’s cover some of the conventional methods for cleaning lead lists. This includes using regex, formulas, and manual sorting in Google Sheets. While these methods can be effective, they’re not always the most efficient.

Using Regex for Data Cleaning

Regular expressions (regex) allow you to search for patterns in text. For cleaning data in Google Sheets, regex is commonly used to remove unwanted characters, fix formatting issues, and standardize text.

Here’s how to use regex for some basic cleaning tasks:

  1. Removing Extra Spaces:

    Extra spaces between words or at the start/end of a cell can make data inconsistent.

    Formula:

    =TRIM(A1)

    This removes any leading or trailing spaces from the text.

  2. Correcting Name Order (Last Name, First Name):

    Some lead lists will have names formatted as “Last, First,” and you may want to change them to “First Last.”

    Formula:

    =REGEXREPLACE(A1, "(\\w+),\\s*(\\w+)", "$2 $1")

    This formula switches the first and last names in cells.

  3. Removing Legal Suffixes from Company Names:

    If you want to remove terms like “Inc.,” “LLC,” or “Co.” from company names:

    Formula:

    =REGEXREPLACE(A1, "\\b(Inc\\.|LLC|Co)\\b", "")

    This removes the suffix while leaving the core company name intact.

  4. Standardizing Case:

    Use this to capitalize the first letter of each word in names and titles.

    Formula:

    =PROPER(A1)

    This formula changes “google inc.” into “Google Inc.”

While regex is a powerful tool, it requires some technical knowledge and can become difficult to manage for complex data cleaning tasks. This is where AI-powered solutions come in.

AI-Powered Data Cleaning with TypeCharm

Using AI for data cleaning brings a new level of simplicity and efficiency. With tools like TypeCharm, you can use custom AI prompts to clean and normalize lead lists directly in Google Sheets. Here’s a practical look at how AI can automate this process, saving you time and reducing human error.

AI Use Case 1: Normalizing Company Names

One of the most common problems in lead lists is inconsistent company names. You might see “google LLC” in one row and “GOOGLE” in another. Here’s how to clean and normalize those names using AI prompts.

Prompt:

Persona: Data Analyst

Task: Clean and normalize company names

Steps:

  1. Identify and remove unnecessary legal suffixes like “LLC,” “Inc.,” and “Co.”
  2. Standardize capitalization using title case.
  3. Flag any variations that may refer to different entities (e.g., “Apple” vs. “Apple Corp.”).

Example 1:

  • Input: “google LLC”
  • Output: “Google”
  • Chain of Thought: The AI recognizes that “LLC” is a legal suffix and removes it, while also capitalizing the first letter of the company name for uniformity.

Example 2:

  • Input: “airbnb inc.”
  • Output: “Airbnb”
  • Chain of Thought: The AI removes “Inc.” and applies title case to maintain a consistent format.

Example 3:

  • Input: “Microsoft Corporation”
  • Output: “Microsoft”
  • Chain of Thought: The AI simplifies “Microsoft Corporation” to “Microsoft,” as the legal term “Corporation” is often unnecessary in outreach materials.

AI Use Case 2: Standardizing People’s Names

People’s names can come in various forms, such as “Smith, John,” “Johnny Smith,” or “J. Smith.” AI can help you standardize these names to ensure consistency.

Prompt:

Persona: Sales Operations Manager

Task: Standardize names in a lead list

Steps:

  1. Detect first and last names, rearranging them if necessary (e.g., last name first).
  2. Correct abbreviations to full names where possible (e.g., “Jon” becomes “Jonathan”).
  3. Flag non-human or fake names for review.

Example 1:

  • Input: “Smith, John”
  • Output: “John Smith”
  • Chain of Thought: The AI rearranges the last name-first format to the more conventional first name-last format.

Example 2:

  • Input: “Johnny S.”
  • Output: “John S.”
  • Chain of Thought: The AI detects that “Johnny” is an informal version of “John” and standardizes it for professional use.

Example 3:

  • Input: “Test Testerson”
  • Output: Flagged for review
  • Chain of Thought: The AI flags this name as potentially fake, suggesting human review for accuracy.

AI Use Case 3: Cleaning and Standardizing Job Titles

Job titles can be particularly messy, with abbreviations, mixed capitalizations, or unconventional terms like “Creative Ninja” that need to be standardized.

Prompt:

Persona: Growth Team Lead

Task: Clean and standardize job titles

Steps:

  1. Expand abbreviations like “Mgr.” to “Manager” or “VP” to “Vice President.”
  2. Apply consistent capitalization to all titles.
  3. Flag any unconventional or vague titles for further review.

Example 1:

  • Input: “Sr. Mgr.”
  • Output: “Senior Manager”
  • Chain of Thought: The AI expands the abbreviations “Sr.” and “Mgr.” to their full forms for clarity.

Example 2:

  • Input: “VP Sales”
  • Output: “Vice President of Sales”
  • Chain of Thought: The AI recognizes “VP” as an abbreviation for “Vice President” and expands it to provide the full title.

Example 3:

  • Input: “Head of Creative Operations”
  • Output: “Head of Creative Operations”
  • Chain of Thought: The AI leaves this title as-is, since it’s already in a clear and professional format.

AI Use Case 4: Enriching Data with Company Info

Beyond just cleaning data, AI can also enrich it. For instance, you can use AI to automatically add key company information such as size, industry, or website URL, based on the company name in your lead list.

Prompt:

Persona: Business Development Manager

Task: Enrich company information in a lead list

Steps:

  1. Use the company name to find relevant details, such as the company’s website URL, industry, and employee count.
  2. Populate additional columns in the Google Sheet with this information.
  3. Flag any companies with incomplete data for manual review.

Example 1:

  • Input: “Airbnb”
  • Output:
    • Industry: Hospitality
    • Website: airbnb.com
    • Employees:

10,000+

  • Chain of Thought: The AI cross-references Airbnb with public databases to extract relevant company information.

Example 2:

  • Input: “Tesla”
  • Output:
    • Industry: Automotive
    • Website: tesla.com
    • Employees: 70,000+
  • Chain of Thought: The AI uses the company name to find publicly available data and fills in the relevant fields.

Additional AI Use Cases for Data Cleaning

Here are a few more creative ways you can leverage AI for cleaning and enriching your lead lists:

  • Detecting Duplicates: AI can automatically find duplicate entries in your lead list, even when the names or emails are slightly different (e.g., “John Smith” vs. “Jonathan Smith”).
  • Validating Emails: AI can flag email addresses that don’t follow standard formats or seem suspicious, allowing you to focus on leads with valid contact information.
  • Geographic Data Normalization: If your lead list includes addresses or cities, AI can standardize those fields, correcting common misspellings or ensuring consistency (e.g., “NYC” becomes “New York City”).

Conclusion: Simplifying Data Cleaning with AI

Data cleaning doesn’t have to be a slow, manual process. With AI-powered tools like TypeCharm, you can clean, normalize, and enrich your lead lists in Google Sheets with custom prompts that take the guesswork out of data management. From normalizing company names to validating email addresses, AI makes your data ready for smarter, more effective outreach.

If you’re looking to make your lead generation processes faster, smarter, and more scalable, give AI a try and see the difference it makes in your data quality.

By integrating AI into your data-cleaning processes, you’re not just saving time—you’re also improving the accuracy and effectiveness of your outreach efforts.