Can we do Fuzzy Match in Clay?

Clay doesn't currently have built-in fuzzy matching capabilities. The available documentation mentions that Clay doesn't yet have semantic search capabilities, which means you need to include various permutations of search terms to capture all relevant results. [1]

For matching and finding similar data, Clay offers company lookalikes through Ocean.io integration, which can help find businesses similar to your existing dataset. You can also use AI formulas to process and structure data in various ways.

What specific type of data matching are you trying to accomplish - are you looking to match company names, job titles, or something else?

If you need any more help with fuzzy matching or data matching in Clay, please let me know. Would you like to share more details about what you're trying to match or achieve?

I am looking to match Company name and identify potential Duplicates

For company name matching and duplicate identification, Clay offers two key solutions:

1. Normalize Company Names

Use our normalize company names function to standardize company names by removing legal suffixes and prefixes. For example, "Panamax Inc." becomes "Panamax." [1] This creates consistent formatting for better matching. [2]

2. Lookup Integration for Duplicate Detection

Check for duplicates across Clay tables using our Lookup integration. Use "Lookup Single Row in Other Table" to verify if a specific record exists elsewhere, or "Lookup Multiple Rows in Other Table" for checking multiple records at once.

Workflow:

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Lookup will return exact same thing which is definitely there. I am trying to identify Duplicates in same column

For identifying duplicates within the same column, you can use Clay's built-in deduplication feature. Click on your company name column header and select "Dedupe" from the dropdown menu.

Alternatively, you can use our "Normalize and Deduplicate a List" integration, which standardizes data formats and removes duplicate entries within a selected column. This is particularly useful for cleaning large lists and ensuring each unique value appears only once. [1]

For best results, first normalize your company names using our normalize company names function, then apply the deduplication to catch variations of the same company.

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If I apply de-duplication will it remove or first it will identify

Clay's deduplication will remove duplicates immediately, not just identify them. When you click on a column header and select "Dedupe," you'll see the duplicate values and can confirm which ones to delete before they're permanently removed. [1]

Important: Deleted rows cannot be recovered, so proceed with caution. Duplicates are identified based on exact string matches and are case-sensitive.

For automatic removal, you can enable "Auto-dedupe" which will continuously remove duplicates as new data is added.

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If you need any further assistance with deduplication or have more questions about managing your company data, I’m here to help. Would you like to provide more details about what you’re trying to resolve or understand?

yes

Great! To clarify the deduplication process:

Manual Dedupe: Click column header → "Dedupe" → review duplicates → confirm deletion. [1]

Auto-dedupe: Click the Auto-dedupe icon (bottom-right) → "Enable automatic deduplication" → select your company name column → "Save Changes." This continuously removes duplicates as new data is added.

Both methods permanently delete duplicate rows - they cannot be recovered. For company names, consider normalizing first for better matching results.

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