The Vendor Name Chaos Problem
Look at these transaction descriptions from the same vendor:
AMAZON.COM*AB12CD34
Amazon Marketplace
AMZN MKTP US*AB12CD34
Amazon Web Services
AMAZON.COM PMTS
AMZ*Prime Membership
amazon business purchase
That'sseven different variationsof Amazon. Without normalization, your expense reports show seven separate vendors, making it impossible to track total Amazon spending or identify spending trends.
Regular expressions + AI solve this by identifying patterns and consolidating variants.
Building Vendor Normalization Rules
Pattern Matching Approach
Create regex patterns that capture all vendor variations:
Amazon Pattern
Pattern: (?i)(AMZN|amazon|AMZ\*).*
Matches:
✓ AMAZON.COM*AB12CD34
✓ Amazon Marketplace
✓ AMZN MKTP US*AB12CD34
✓ Amazon Web Services
✓ AMZ*Prime Membership
Normalize to: "Amazon"Starbucks Pattern
Pattern: (?i)(starbucks|sbux|sq \*starbucks).*
Matches:
✓ STARBUCKS #12345
✓ SQ *STARBUCKS COFFEE
✓ SBUX Store 456
Normalize to: "Starbucks"Square Payments Pattern
Pattern: SQ \*(.+?)(?:\s+|$)
Extracts vendor name after "SQ *":
- SQ *COFFEE SHOP → "COFFEE SHOP"
- SQ *RESTAURANT ABC → "RESTAURANT ABC"AI-Enhanced Normalization
Combining Regex with AI Intelligence
Use regex to pre-filter, AI to make intelligent decisions:
Hybrid Approach Prompt:
"I have these vendor variations. Using the regex pattern(AMZN|amazon|AMZ).*, I've identified these as Amazon:
- AMAZON.COM*AB12CD34
- AMZN MKTP US*AB12CD34
- Amazon Web Services
Should all be normalized to 'Amazon', or should 'Amazon Web Services' be separate since it's a different service? Provide business logic reasoning."
Common Vendor Patterns
| Vendor | Regex Pattern | Normalized Name |
|---|---|---|
| PayPal | (?i)paypal.* | PayPal |
| Stripe | (?i)stripe.* | Stripe |
| Costco | (?i)(costco|wholesale #\d+) | Costco |
| UPS | (?i)(ups|united parcel) | UPS |
| Verizon | (?i)(verizon|vzw) | Verizon |
Handling Edge Cases
Multiple Locations
Should "Starbucks #12345" and "Starbucks #67890" be separate or combined?
Regex approach:Extract store numbers
Pattern: STARBUCKS #(\d+)
Group 1: Store number
AI decision: Keep separate if tracking by location matters,
otherwise normalize to "Starbucks"Parent Companies vs Subsidiaries
AI can help determine relationships:
- Whole Foods → Amazon (subsidiary)
- Instagram Ads → Meta/Facebook
- YouTube Premium → Google
Real-World Implementation
Google Sheets Method
=IF(REGEXMATCH(A2,"(?i)amzn|amazon"),
"Amazon",
IF(REGEXMATCH(A2,"(?i)starbucks|sbux"),
"Starbucks",
IF(REGEXMATCH(A2,"(?i)paypal"),
"PayPal",
A2)))AI Bulk Normalization
For one-time cleanup of historical data:
"Here are 50 unique vendor name variations from my bank statements. Group them into normalized vendor names. Use these regex hints:
- Anything matchingAMZN|amazon→ Amazon
- Anything matchingSQ \*→ Extract name after asterisk
- Anything matchingPAYPAL \*→ PayPal
Return a mapping table."
Best Practices
- Create a master vendor listwith canonical names
- Build regex patternsfor each canonical vendor
- Test patternsagainst 6 months of historical data
- Use AI to catch unmapped vendorsand suggest patterns
- Review monthlyfor new vendor formats
Conclusion
Vendor name normalization is essential for accurate expense reporting and vendor analysis. By combining regex pattern matching with AI's contextual understanding, bookkeepers can automatically standardize thousands of vendor variations, saving hours of manual work while improving data quality.
