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This dataset covers 3,464 verified records spread across 88+ districts in Ohio. 3,464 records carry a verified phone number and 1,212 include a business website — directly usable for outreach, segmentation, and CRM import.
The highest-density districts are Mahoning (685 listings), Knox (342 listings), Highland (228 listings), Preble (171 listings), Trumbull (137 listings), Wood (114 listings), Seneca (98 listings), and Franklin (86 listings).
Additional coverage spans Washington (76), Ashtabula (68), Greene (62), Jefferson (57), Shelby (53), Morrow (49), Adams (46), Holmes (43), Lawrence (40), Guernsey (38), Ottawa (36), Wayne (34) , plus 68 more districts.
| Districts | Total | With mobile | With website |
|---|---|---|---|
| Mahoning | 685 | 685 | 240 |
| Knox | 342 | 342 | 120 |
| Highland | 228 | 228 | 80 |
| Preble | 171 | 171 | 60 |
| Trumbull | 137 | 137 | 48 |
| Wood | 114 | 114 | 40 |
| Seneca | 98 | 98 | 34 |
| Franklin | 86 | 86 | 30 |
| Washington | 76 | 76 | 27 |
| Ashtabula | 68 | 68 | 24 |
| All districts (88) | 3,464 | 3,464 | 1,212 |
| Districts | Total | With mobile | With website |
|---|---|---|---|
| Mahoning | 685 | 685 | 240 |
| Knox | 342 | 342 | 120 |
| Highland | 228 | 228 | 80 |
| Preble | 171 | 171 | 60 |
| Trumbull | 137 | 137 | 48 |
| Wood | 114 | 114 | 40 |
| Seneca | 98 | 98 | 34 |
| Franklin | 86 | 86 | 30 |
| Washington | 76 | 76 | 27 |
| Ashtabula | 68 | 68 | 24 |
| Greene | 62 | 62 | 22 |
| Jefferson | 57 | 57 | 20 |
| Shelby | 53 | 53 | 19 |
| Morrow | 49 | 49 | 17 |
| Adams | 46 | 46 | 16 |
| Holmes | 43 | 43 | 15 |
| Lawrence | 40 | 40 | 14 |
| Guernsey | 38 | 38 | 13 |
| Ottawa | 36 | 36 | 13 |
| Wayne | 34 | 34 | 12 |
| Gallia | 33 | 33 | 12 |
| Fairfield | 31 | 31 | 11 |
| Butler | 30 | 30 | 11 |
| Hamilton | 29 | 29 | 10 |
| Allen | 27 | 27 | 9 |
| Belmont | 26 | 26 | 9 |
| Licking | 25 | 25 | 9 |
| Clark | 24 | 24 | 8 |
| Defiance | 24 | 24 | 8 |
| Ross | 23 | 23 | 8 |
| Muskingum | 22 | 22 | 8 |
| Portage | 21 | 21 | 7 |
| Hocking | 21 | 21 | 7 |
| Darke | 20 | 20 | 7 |
| Madison | 20 | 20 | 7 |
| Paulding | 19 | 19 | 7 |
| Richland | 19 | 19 | 7 |
| Columbiana | 18 | 18 | 6 |
| Pickaway | 18 | 18 | 6 |
| Athens | 17 | 17 | 6 |
| Pike | 17 | 17 | 6 |
| Fayette | 16 | 16 | 6 |
| Huron | 16 | 16 | 6 |
| Carroll | 16 | 16 | 6 |
| Union | 15 | 15 | 5 |
| Champaign | 15 | 15 | 5 |
| Crawford | 15 | 15 | 5 |
| Lake | 14 | 14 | 5 |
| Lucas | 14 | 14 | 5 |
| Noble | 14 | 14 | 5 |
| Tuscarawas | 13 | 13 | 5 |
| Harrison | 13 | 13 | 5 |
| Williams | 13 | 13 | 5 |
| Warren | 13 | 13 | 5 |
| Meigs | 12 | 12 | 4 |
| Montgomery | 12 | 12 | 4 |
| Jackson | 12 | 12 | 4 |
| Fulton | 12 | 12 | 4 |
| Geauga | 12 | 12 | 4 |
| Coshocton | 11 | 11 | 4 |
| Stark | 11 | 11 | 4 |
| Vinton | 11 | 11 | 4 |
| Henry | 11 | 11 | 4 |
| Lorain | 11 | 11 | 4 |
| Erie | 11 | 11 | 4 |
| Clermont | 10 | 10 | 4 |
| Morgan | 10 | 10 | 4 |
| Brown | 10 | 10 | 4 |
| Logan | 10 | 10 | 4 |
| Van Wert | 10 | 10 | 4 |
| Monroe | 10 | 10 | 4 |
| Hancock | 10 | 10 | 4 |
| Sandusky | 9 | 9 | 3 |
| Putnam | 9 | 9 | 3 |
| Clinton | 9 | 9 | 3 |
| Hardin | 9 | 9 | 3 |
| Medina | 9 | 9 | 3 |
| Delaware | 9 | 9 | 3 |
| Summit | 9 | 9 | 3 |
| Miami | 9 | 9 | 3 |
| Cuyahoga | 8 | 8 | 3 |
| Auglaize | 8 | 8 | 3 |
| Marion | 8 | 8 | 3 |
| Ashland | 8 | 8 | 3 |
| Wyandot | 8 | 8 | 3 |
| Scioto | 8 | 8 | 3 |
| Perry | 8 | 8 | 3 |
| Mercer | 6 | 6 | 2 |
| All districts (88) | 3,464 | 3,464 | 1,212 |
The Decorators database for Ohio, United States includes 3,464+ verified businesses with company name, address, city, state, phone, email, website and GPS coordinates. The file is delivered as a CSV ready for CRM import or BI analysis.
The Decorators database for Ohio, United States contains 3,464+ verified businesses as of 2026, each with phone, email, address, website and GPS coordinates. The full record count is shown on this page above.
The complete Decorators database for Ohio, United States is available on this page from Data2Sales AI as an instant CSV download. Every record carries verified contact details and is delivered immediately after checkout.
Each record includes: Business name, Street address, City, State / Region, Postal code, Country, Phone number, Email address and more. The file is delivered as a UTF-8 CSV that opens in Excel, Google Sheets or any standard CRM importer.
Every record is cross-referenced across public business registries, Google Maps and licensed directory partners before it ships. Closed businesses, disconnected phones and duplicates are stripped on every monthly refresh, so the dataset reflects the live decorators footprint in Ohio, United States.
Yes. The Decorators dataset is rebuilt on a rolling 30-day cycle. New entrants come in through registrar filings; closures are detected from negative signals and dropped before export.
Yes — the dataset is licensed for commercial use after purchase. Common use cases include cold outbound, geo-targeted advertising, market research, supplier sourcing and territory planning. Only publicly listed business contact information is included.
You receive a UTF-8 encoded CSV. It opens in Excel, Google Sheets, Power BI, Looker Studio and every major CRM (HubSpot, Salesforce, Zoho, Pipedrive). XLSX is available on request.
Within 30 seconds of successful payment. The CSV is generated fresh from the live database at purchase time. You also get 7 days of unlimited re-downloads from a secure link sent to your email.
Data2Sales AI's World Wide Data database for Ohio, United States consolidates 3,464+ verified records into a single, verified contact list. The engine crosses multiple sources — official websites, business registries, social profiles, licensed directories — and validates each record monthly, so your sales pipeline starts with accurate data instead of a spreadsheet of guesses.
Buy multiple neighbouring states in a single file. Cheaper than the country pack, wider than any one state.
Last Update:
Jul 08, 2026 13:38 PM
Published:
Jul 06, 2026 11:50 AM
Rows (Regular):
1,559
Rows (Extended):
3,464
Category:
Roll-up datasets — broader coverage at a higher tier are highlighted below.
