Factori_Global Location Intelligence Data
| # | id |
name |
do_date |
year |
month |
day_of_week |
part_of_day |
n_visitors |
distance_from_home |
travelled_countries |
visitor_country_origin |
visitor_home_origin |
visitor_work_origin |
carrier |
brand_visited |
place_categories |
geo_behaviour |
make |
model |
os_version |
ratio_age_18_24 |
ratio_age_25_34 |
ratio_age_35_44 |
ratio_age_45_54 |
ratio_age_55 |
ratio_female |
ratio_male |
||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx |
| 2 | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx |
| 3 | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx |
| 4 | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx |
| 5 | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx |
| 6 | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx |
| 7 | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx |
| 8 | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx |
| 9 | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx |
| 10 | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx |
| ... | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx |
Data Dictionary
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
id
|
Integer | 108135561 | |
|
name
|
String | Walmart @ Miami 9191 W. Flagler St, Miami, FL 33174, USA | |
| String | Address | ||
| Integer | 33172 | Postal Code | |
|
do_date
|
String | 01-08-2021 | |
|
year
|
Integer | 2021 | |
|
month
|
Integer | 8 | |
|
day_of_week
|
String | Thursday | |
|
part_of_day
|
String | Evening | |
|
n_visitors
|
Integer | 77 | |
|
distance_from_home
|
Float | 19877.16 | |
|
travelled_countries
|
String | {"CAN": 0.5, "MEX": 0.5} | |
|
visitor_country_origin
|
String | {"USA": 1.0} | |
|
visitor_home_origin
|
String | {"03202312110113033": 0.114, "03202310332330223": 0.057, ... | |
|
visitor_work_origin
|
String | {"03202312110113033": 0.114, "03202310332330223": 0.057, ... | |
|
carrier
|
String | {"t-mobile usa": 0.743, "at&t wireless": 0.229, "verizon ... | |
|
brand_visited
|
String | {"Walmart Visitors": 100, "Subway Visitors": 100, "Teavan... | |
|
place_categories
|
String | {"Convenience Store Visitors": 100, "Burger Restaurant Vi... | |
|
geo_behaviour
|
String | {"DIY Enthusiasts": 100, "Foodies": 78, "Frequent Pizza R... | |
|
make
|
String | {"apple": 0.4, "samsung": 0.4, "generic": 0.057, "oneplus... | |
|
model
|
String | {"iphone": 0.4, "sm-g950u": 0.057, "sm-g960u1": 0.057, "a... | |
|
os_version
|
String | {"14": 0.2, "10.0": 0.143, "10": 0.114, "9": 0.114, "14.7... | |
|
ratio_age_18_24
|
Float | 0.25 | |
|
ratio_age_25_34
|
Float | 0.25 | |
|
ratio_age_35_44
|
Float | 0.188 | |
|
ratio_age_45_54
|
Integer | 0 | |
|
ratio_age_55
|
Float | 0.313 | |
|
ratio_female
|
Float | 0.233 | |
|
ratio_male
|
Float | 0.767 |
| Attribute | Type | Example | Mapping |
|---|---|---|---|
| String | STRING | Location ID | |
|
n_visitors
|
INTEGER | ||
|
day_of_week
|
STRING | ||
|
distance_from_home
|
DOUBLE | ||
|
do_date
|
DATE | ||
|
month
|
STRING | ||
|
part_of_day
|
STRING | ||
|
travelled_countries
|
JSON | ||
|
Visitor_country_origin
|
JSON | ||
|
Visitor_home_origin
|
JSON | ||
|
Visitor_work_origin
|
JSON | ||
|
year
|
STRING |
Description
Geography
History
Volume
| 698 million | MAU (Monthly active users) |
Pricing
| License | Starts at |
|---|---|
| One-off purchase |
$25,000$22,500 / purchase |
| Monthly License | Not available |
| Yearly License | Not available |
| Usage-based | Not available |
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is Factori_Global Location Intelligence Data?
We provide Location Intelligence Data, which helps power geographical information system (GIS) tools and provides data-driven insights across a wide range of use cases, from marketing to public planning and fraud detection.
What is Factori_Global Location Intelligence Data used for?
This product has 5 key use cases. Factori recommends using the data for Location Intelligence, Urban Mobility Analysis, Store Visit Tracking, Store Visit Attribution, and Movement Analytics. Global businesses and organizations buy Location Data from Factori to fuel their analytics and enrichment.
Who can use Factori_Global Location Intelligence Data?
This product is best suited if you’re a Medium-sized Business or Enterprise looking for Location Data. Get in touch with Factori to see what their data can do for your business and find out which integrations they provide.
How far back does the data in Factori_Global Location Intelligence Data go?
This Tabular Data has 1 years of historical coverage. It can be delivered on a daily and monthly basis.
Which countries does Factori_Global Location Intelligence Data cover?
This product includes data covering 249 countries like USA, China, Japan, Germany, and India. Factori is headquartered in United States of America.
How much does Factori_Global Location Intelligence Data cost?
Pricing for Factori_Global Location Intelligence Data starts at USD25,000 per purchase. Factori offers a 10% discount when you buy data from them through Datarade. Connect with Factori to get a quote and arrange custom pricing models based on your data requirements.
How can I get Factori_Global Location Intelligence Data?
Businesses can buy Location Data from Factori and get the data via S3 Bucket. Depending on your data requirements and subscription budget, Factori can deliver this product in .xml, .csv, and .xls format.
What is the data quality of Factori_Global Location Intelligence Data?
Factori has reported that this product has the following quality and accuracy assurances: 90% match rate, 90% horizontal accuracy. You can compare and assess the data quality of Factori using Datarade’s data marketplace. Factori has received 2 reviews from clients. Factori appears on selected Datarade top lists ranking the best data providers, including 10 Best Data Providers for Customer Segmentation, Best Data Providers For Location-Based Marketing, and Top 10 POI Data Providers & APIs.
What are similar products to Factori_Global Location Intelligence Data?
This Tabular Data has 3 related products. These alternatives include Factori Mobility/ Raw Location Data Global mobile location data (1 year history), Tamoco Custom Location Counter - Global Coverage Daily breakdown of unique devices in a location (polygon, radius, geofence), and Global Location Intelligence Analytics & Services. You can compare the best Location Data providers and products via Datarade’s data marketplace and get the right data for your use case.
Factori
Location Intelligence Made Simple
Verified Provider
Trusted by
