✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-13
Multi-location businesses, franchise networks, chamber of commerce organizations, and local SEO agencies maintain business directory data in Excel spreadsheets. Converting this data into LocalBusiness schema markup drives local search visibility through knowledge panels, local pack results, and Google Maps integration. dataclean.to reads your Excel business directory natively, handles the Excel-specific formatting challenges that CSV export introduces, and generates validated LocalBusiness JSON-LD for every location in your dataset.
The Problem
Business directory data in Excel has both general spreadsheet quality issues and Excel-specific formatting traps. Phone numbers stored as numbers lose leading zeros and country codes. Zip/postal codes in numeric cells drop leading zeros ("01234" becomes "1234"). Address cells with line breaks contain characters that corrupt CSV export. Business hours entered as time ranges may use Excel's time formatting ("9:00 AM") which exports differently depending on locale settings. Latitude and longitude coordinates stored with Excel's limited decimal precision may place businesses in the wrong block. Merged cells used for multi-location headers create gaps in the data. These issues are invisible in Excel's display but produce invalid or inaccurate schema markup. LocalBusiness schema type specification
How to Fix It
1
Upload your Excel business directory
Import your .xlsx file directly. dataclean.to handles Excel's cell formatting, preserving leading zeros in phone numbers and zip codes that would be lost through CSV export. Multi-sheet workbooks are supported with per-sheet import.
2
Detect Excel-specific formatting issues
The platform identifies cells where Excel's number formatting masks the actual data: phone numbers stored as integers (missing leading zeros and country codes), zip codes truncated by numeric cell type, coordinates with insufficient decimal precision, and address cells with hidden line break characters.
3
Normalize addresses, phones, and hours
Parse addresses into structured PostalAddress components (streetAddress, addressLocality, addressRegion, postalCode, addressCountry). Restore phone numbers to E.164 international format with proper country codes. Convert business hours into OpeningHoursSpecification entries with day and time ranges.
4
Map to LocalBusiness schema properties
Map your columns to LocalBusiness properties: name, description, address (PostalAddress), telephone, url, openingHoursSpecification, image, geo (GeoCoordinates), priceRange, and the specific LocalBusiness subtype (Restaurant, Dentist, AutoRepair, etc.) if applicable.
5
Generate LocalBusiness JSON-LD
Export JSON-LD for each business location with properly nested PostalAddress, GeoCoordinates, and OpeningHoursSpecification objects. Validate with Google's Rich Results Test. Deploy via hosted API for per-page markup or download for manual integration.
Frequently Asked Questions
Why do phone numbers lose digits when I export Excel to CSV?
Excel stores phone numbers formatted as numbers rather than text, which strips leading zeros and plus signs. A phone number like '+1 (212) 555-0100' stored as a number becomes '12125550100' and may lose the leading '1' if formatted differently. dataclean.to reads the cell value directly and reconstructs proper phone formatting.
How does dataclean.to handle business directories with hundreds of locations?
The platform processes Excel files with thousands of rows. Each row generates its own complete LocalBusiness JSON-LD object. For multi-location businesses, this means you can generate schema for your entire location network from a single Excel file, with each location getting properly structured markup.
Can I use a more specific type than LocalBusiness?
Yes, and Google recommends it. If your Excel directory contains a column indicating business type (restaurant, dentist, gym, etc.), you can map it to the appropriate LocalBusiness subtype. dataclean.to supports specific subtypes like Restaurant, MedicalClinic, AutoRepair, LegalService, FinancialService, and many others.
Example: Input → Output
name
address
city
phone
website
City Plumbing Co
789 Elm St
Denver CO
(720) 555-0200
cityplumbing.com
city plumbing co
789 elm st
denver
co
(720)555-0200
www.cityplumbing.com
Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.