HubSpot doesn’t have native fuzzy matching capabilities for company imports, so variations like “LLC” vs “L.L.C.” or “Corporation” vs “Corp” create duplicate records during Excel imports.
You’ll discover how to build custom fuzzy matching rules in spreadsheets that identify similar company names before they reach HubSpot, preventing duplicates from being created.
Build fuzzy matching workflows using Coefficient
Coefficient enables sophisticated fuzzy matching by letting you create custom similarity scoring in spreadsheets before importing to HubSpot . This prevents the duplicate companies that HubSpot’s rigid import rules would otherwise create.
How to make it work
Step 1. Import existing HubSpot companies as your reference dataset.
Use Coefficient to pull current company data including names, domains, and IDs. This creates the baseline for comparing against your Excel import data.
Step 2. Build name standardization formulas.
Create formulas to normalize company names: =TRIM(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(UPPER(A2),” LLC”,””),” INC”,””),” CORP”,””))). This removes common suffixes and standardizes formatting for better matching.
Step 3. Create similarity scoring logic.
Build formulas that calculate matching confidence using functions like LEN() and SEARCH() to compare standardized names. Set thresholds like 85% similarity to identify potential matches that need review.
Step 4. Use conditional exports based on matching scores.
Set up Coefficient’s export actions to UPDATE records above your similarity threshold and INSERT records below it. This ensures high-confidence matches update existing companies while truly new companies get created.
Prevent duplicate companies with smart matching
Fuzzy matching catches variations that HubSpot’s exact string matching misses, keeping your company database clean and accurate. Start building custom matching rules that work better than HubSpot’s native import limitations.