The Scenario
You are a data analyst and you've been asked to clean the accounts table before the new CRM goes live. The migration lead dropped a note in the project channel: "company names are inconsistent, needs normalization before import." You open the Excel workbook. The Name column has "Salesforce," "salesforce.com," "Salesforce Inc," "SFDC," and "Salesforce, Inc." all as separate rows, all referring to the same company. There are 150 rows. The migration is scheduled for next Wednesday.
The bad version:
- Write a normalization formula that lowercases names, strips legal suffixes, and runs a fuzzy match against a company reference table you've been maintaining in a separate tab
- Find that the reference table covers 70% of the cases and the formula flags 45 rows as unmatched that are clearly real companies with just uncommon name variations
- Manually look up the remaining 45 rows one at a time, paste canonical names, and try to keep track of which rows you've touched so you don't double-process anything
The CRM migration is blocked on your spreadsheet. Your spreadsheet is blocked on 45 rows of company name cleanup that shouldn't require 45 separate lookups.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads the company names in the workbook and uses PDL's company cleaner to resolve each entry to a canonical name, website, and LinkedIn URL.
Clean every company name in my Excel Raw Accounts table column A using People Data Labs and populate the Canonical Name, Website, and LinkedIn URL columns.
What You Get
- The Canonical Name column populated with PDL's standardized company name for each entry
- The Website column populated with the company's primary domain
- The LinkedIn URL column populated with the company's LinkedIn page URL
- Entries that PDL cannot resolve flagged in a Status column so you can handle them manually before the migration
What If the Data Is Not Quite Ready
Some entries have a legal suffix or punctuation that's throwing off PDL's match
Before cleaning, strip legal suffixes — Inc, LLC, Corp, Ltd, SA, GmbH — from each company name in column A. Then clean the stripped names via PDL and populate Canonical Name, Website, and LinkedIn URL.
You need to keep the original name alongside the canonical form for the migration team to spot-check
For each company name in column A, clean via PDL and write the canonical name to the Canonical Name column. Keep the original entry in column A unchanged so the migration team can verify the mapping before the import.
Some rows should resolve to a parent company that differs from the subsidiary name entered
Clean each company via PDL and write the canonical match to the Canonical Name column. If PDL identifies a parent company, add it to a Parent Company column so the migration lead can decide which level of hierarchy to use in the CRM.
Clean, deduplicate by canonical name, and flag survivors for the import file in one pass
Clean each company name via PDL and populate the Canonical Name column. Group rows that share the same canonical name and mark duplicate rows in a Duplicate column as "Duplicate of row X." Flag non-duplicate rows in a Migration Status column as "Ready to import."
One prompt cleans the names, surfaces the duplicates, and marks what's ready — the CRM admin gets an import-ready table without a back-and-forth review cycle.
Try It
Get the 7-day free trial of SheetXAI and open any Excel workbook with a column of inconsistent company names from reps or form submissions. Ask it to clean each one via PDL and write canonical names back. Then see how to enrich the cleaned companies with firmographic data or go back to the People Data Labs overview.