Comprehensive Business Database for Building Sales and Marketing Strategies.
MapInfo Business Points Data is a comprehensive database containing more than 15 million geographic points of business locations throughout the U.S. This data allows you to see business locations in a given geographic area, and gather valuable information on those businesses such as business name, address, SIC code, employee sizes for both the business location and parent company family, sales volumes for both the location and parent company family, ownership structure, and more.
This level of detail makes Business Points Data ideal for:
- Analyzing market opportunities
- Analyzing competitive threats
- Building sales and marketing strategies
- Assigning sales territories
Business Points Data 8.0 Feature Highlights:
- Updated Business Data
- MapInfo, GeoResults, Inc.
For Business Points the total number of business records is approximately 15,130,000 – with 100% containing Business name, SIC Code (8-Digit), NAICS Code, Address information, Employee sizes and Sales volumes, at both the individual location and parent company.
Over 90% of the total business records geocoded to street-level accuracy!
Site Employee Validation/Re-Distribution Process
Validating employee values in 15 million business records is a daunting task. However, MapInfo Business Points employs a proprietary method for reigning in as many unreasonable employee values as possible into a reasonable range. This is performed by first looking at businesses which belong to a corporate family (Wal-Mart, McDonalds, Home Depot, etc.), then at single-site businesses which belong to a generic profile (barber shops, dry cleaners, florists, etc.), then at single-site businesses which don’t meet a generic profile but belong to a specific industry as determined by their 8-digit SIC code (lawyers, general hospitals, post offices, etc.). Once the employee value outliers are identified, they are shifted back into an appropriate position along the employee distribution curve.
What is the result? The methodology maximizes the reasonableness of employee values throughout our entire national dataset. It is not enough that an employee value was reported to be X – the question is: ‘Is X reasonable?’ The results are that the employee values fit in a reasonable expectancy curve, and allow the most accurate analysis possible.