✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-13
Business directories, franchise networks, multi-location brands, and local SEO agencies maintain business listing data in CSV spreadsheets. Converting this data into LocalBusiness schema markup is one of the highest-impact structured data implementations for local search visibility. Google uses LocalBusiness structured data for knowledge panels, local pack results, and Google Maps integration. dataclean.to transforms your CSV business directory into validated LocalBusiness JSON-LD, handling the address, phone, and hours normalization that these listings require.
The Problem
Business listing data in spreadsheets is consistently inconsistent. Addresses are entered as single strings ("123 Main St, NYC, NY 10001"), split across columns with varying conventions, or abbreviated differently ("St" vs "Street", "Apt" vs "#" vs "Suite"). Phone numbers mix formats: "(212) 555-1234", "212.555.1234", "+1-212-555-1234", "2125551234." Business hours are written as "Mon-Fri 9-5", "M-F: 9:00AM-5:00PM", "Open 7 days", or stored in seven separate columns per day. These inconsistencies are normal for spreadsheet data but completely incompatible with schema.org's PostalAddress, E.164 telephone format, and OpeningHoursSpecification requirements. LocalBusiness schema type specification
How to Fix It
1
Upload your business directory CSV
Import your CSV file with business listing data. Include columns for business name, address (single or multi-column), phone number, website URL, business hours, description, and any additional attributes like category, rating, or images.
2
Detect address and contact patterns
dataclean.to identifies address columns (whether combined or split), phone number formats, URL fields, and opening hours patterns. The platform determines whether your addresses need parsing (single column) or consolidation (multiple columns).
3
Normalize addresses, phones, and hours
Addresses are parsed into structured PostalAddress components: streetAddress, addressLocality, addressRegion, postalCode, addressCountry. Phone numbers convert to E.164 international format. Business hours are structured into day-time OpeningHoursSpecification entries.
4
Map to LocalBusiness schema properties
Map your columns to LocalBusiness properties: name, description, address (PostalAddress), telephone, url, openingHoursSpecification, image, priceRange, geo (GeoCoordinates), and the specific LocalBusiness subtype if applicable (Restaurant, MedicalClinic, AutoRepair, etc.).
5
Generate LocalBusiness JSON-LD
Export JSON-LD for each business listing with properly nested PostalAddress, GeoCoordinates, and OpeningHoursSpecification types. Deploy via the hosted API for automatic per-page markup, or download files for manual integration into your business listing pages.
Frequently Asked Questions
Should I use LocalBusiness or a more specific subtype?
Google recommends using the most specific subtype that applies. For example, use Restaurant instead of LocalBusiness for restaurants, or Dentist instead of MedicalBusiness. dataclean.to supports specific subtypes, and using them gives Google more precise information about your business type.
How does dataclean.to parse single-line addresses into structured components?
The platform uses pattern recognition to identify street numbers, street names, unit numbers, city names, state/province abbreviations, postal codes, and country identifiers within a single address string. Results are validated for completeness and flagged when parsing is ambiguous.
Can I generate LocalBusiness schema for hundreds of locations?
Yes. dataclean.to is designed for bulk data processing. A franchise with 500 locations or a directory with thousands of listings can be processed in a single import. Each row produces its own complete LocalBusiness JSON-LD object with all nested types.
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.