Factori AI & ML Training Data | Point of Interest Data (POI) | Global | Machine Learning Data product image in hero

Factori AI & ML Training Data | Point of Interest Data (POI) | Global | Machine Learning Data

Factori
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#
PlaceID
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
ratio_residents
ratio_workers
ratio_others
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Sign In To Preview Data
Volume
420M
MAU
Data Quality
95%
Match rate
Avail. Format
.csv
File
Coverage
248
Countries
History
1
years

Data Dictionary

[Sample] Factori_POI_ Sample_data.csv
Attribute Type Example Mapping
PlaceID
String Walmart @ Miami 8400 Coral Way, Miami, FL 33155, USA
do_date
DateTime 2024-05-01T00:00:00+00:00
year
Integer 2024
month
Integer 5
day_of_week
String All
part_of_day
String Afternoon
n_visitors
Integer 1926
distance_from_home
Float 66492.461
travelled_countries
String {"CUB": 0.175, "MEX": 0.14, "BHS": 0.105, "CAN": 0.088, "...
visitor_country_origin
String {"USA": 1.0}
visitor_home_origin
String {"03202312110131313": 0.019, "03202312111020202": 0.005, ...
visitor_work_origin
String {"03202312110131313": 0.038, "03202312110132103": 0.004, ...
carrier
String {"t-mobile usa": 0.525, "at&t wireless": 0.336, "verizon ...
brand_visited
String {"Ginger People Visitors": 100, "Walmart Visitors": 94, "...
place_categories
String {"Parking Area Visitors": 100, "Petroleum Company Visitor...
geo_behaviour
String {"DIY Enthusiasts": 100, "Family Leisure Enthusiasts": 10...
make
String {"apple": 0.929, "samsung": 0.054, "12": 0.007, "10": 0.0...
model
String {"iphone": 0.896, "ipad": 0.032, "sm-g975u": 0.007, "mobi...
os_version
String {"16": 0.314, "12": 0.138, "13": 0.133, "11": 0.095, "15"...
ratio_age_18_24
Float 0.223
ratio_age_25_34
Float 0.123
ratio_age_35_44
Float 0.145
ratio_age_45_54
Float 0.223
ratio_age_55
Float 0.285
ratio_female
Float 0.068
ratio_male
Float 0.932
ratio_residents
Float 0.021
ratio_workers
Float 0.037
ratio_others
Float 0.942
Product Attributes
Attribute Type Example Mapping
String 108135559 Location ID
Name
Walmart @ Walmart, Miami, FL 33162, USA
Day OF Week
saturday
Part of Day
Morning
n_visitors
274
Distance from home
166407
Vistor home
Lat/Long
Visitor work
Lat/Long
brand visited
Mc Donalds
Demography Ratio
.239

Description

We provide POI Data, which can be used to train AI & ML Models on14M physical locations globally, and unlock wide range of use cases, from marketing to public planning and fraud detection.
Our POI Data connects people's movements to over 14M physical locations globally. These are aggregated and anonymized data that are only used to offer context for the volume and patterns of visits to certain locations. This data feed is compiled from different data sources around the world. Reach: Our POI/Place/OOH level insights are calculated based on Factori’s Mobility & People Graph data aggregated from multiple data sources globally. To achieve the desired foot-traffic attribution, specific attributes are combined to bring forward the desired reach data. For instance, in order to calculate the foot traffic for a specific location, a combination of location ID, day of the week, and part of the day can be combined to give specific location intelligence data. There can be a maximum of 40 data records possible for one POI based on the combination of these attributes. Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method at a suitable interval (daily/weekly/monthly). Use Cases: Credit Scoring: Financial services can use alternative data to score an underbanked or unbanked customer by validating locations and persona. Retail Analytics: Analyze footfall trends in various locations and gain an understanding of customer personas. Market Intelligence: Study various market areas, the proximity of points or interests, and the competitive landscape Urban Planning: Build cases for urban development, public infrastructure needs, and transit planning based on fresh population data. Data Attributes Included: Location ID n_visitors day_of_week distance_from_home do_date month part_of_day travelled_countries Visitor_country_origin Visitor_home_origin Visitor_work_origin year

Country Coverage

Africa (58)
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cabo Verde
Cameroon
Central African Republic
Chad
Comoros
Congo
Congo (Democratic Republic of the)
Côte d'Ivoire
Djibouti
Egypt
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Libya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mayotte
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Réunion
Saint Helena, Ascension and Tristan da Cunha
Sao Tome and Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
South Sudan
Sudan
Swaziland
Tanzania, United Republic of
Togo
Tunisia
Uganda
Western Sahara
Zambia
Zimbabwe
Asia (50)
Afghanistan
Armenia
Azerbaijan
Bahrain
Bangladesh
Bhutan
Brunei Darussalam
Cambodia
Cyprus
Georgia
Hong Kong
India
Indonesia
Iran (Islamic Republic of)
Iraq
Israel
Japan
Jordan
Kazakhstan
Korea (Democratic People's Republic of)
Korea (Republic of)
Kuwait
Kyrgyzstan
Lao People's Democratic Republic
Lebanon
Macao
Malaysia
Maldives
Mongolia
Myanmar
Nepal
Oman
Pakistan
Palestine, State of
Philippines
Qatar
Saudi Arabia
Singapore
Sri Lanka
Syrian Arab Republic
Taiwan
Tajikistan
Thailand
Timor-Leste
Turkey
Turkmenistan
United Arab Emirates
Uzbekistan
Vietnam
Yemen
Europe (51)
Albania
Andorra
Austria
Belarus
Belgium
Bosnia and Herzegovina
Bulgaria
Croatia
Czech Republic
Denmark
Estonia
Faroe Islands
Finland
France
Germany
Gibraltar
Greece
Guernsey
Holy See
Hungary
Iceland
Ireland
Isle of Man
Italy
Jersey
Latvia
Liechtenstein
Lithuania
Luxembourg
Macedonia (the former Yugoslav Republic of)
Malta
Moldova (Republic of)
Monaco
Montenegro
Netherlands
Norway
Poland
Portugal
Romania
Russian Federation
San Marino
Serbia
Slovakia
Slovenia
Spain
Svalbard and Jan Mayen
Sweden
Switzerland
Ukraine
United Kingdom
Åland Islands
North America (13)
Belize
Bermuda
Canada
Costa Rica
El Salvador
Greenland
Guatemala
Honduras
Mexico
Nicaragua
Panama
Saint Pierre and Miquelon
United States of America
Oceania (25)
American Samoa
Australia
Cook Islands
Fiji
French Polynesia
Guam
Kiribati
Marshall Islands
Micronesia (Federated States of)
Nauru
New Caledonia
New Zealand
Niue
Norfolk Island
Northern Mariana Islands
Palau
Papua New Guinea
Pitcairn
Samoa
Solomon Islands
Tokelau
Tonga
Tuvalu
Vanuatu
Wallis and Futuna
Other (9)
Antarctica
Bouvet Island
British Indian Ocean Territory
Christmas Island
Cocos (Keeling) Islands
French Southern Territories
Heard Island and McDonald Islands
South Georgia and the South Sandwich Islands
United States Minor Outlying Islands
South America (42)
Anguilla
Antigua and Barbuda
Argentina
Aruba
Bahamas
Barbados
Bolivia (Plurinational State of)
Bonaire, Sint Eustatius and Saba
Brazil
Cayman Islands
Chile
Colombia
Cuba
Curaçao
Dominica
Dominican Republic
Ecuador
Falkland Islands (Malvinas)
French Guiana
Grenada
Guadeloupe
Guyana
Haiti
Jamaica
Martinique
Montserrat
Paraguay
Peru
Puerto Rico
Saint Barthélemy
Saint Kitts and Nevis
Saint Lucia
Saint Martin (French part)
Saint Vincent and the Grenadines
Sint Maarten (Dutch part)
Suriname
Trinidad and Tobago
Turks and Caicos Islands
Uruguay
Venezuela (Bolivarian Republic of)
Virgin Islands (British)
Virgin Islands (U.S.)

History

1 years of historical data

Volume

5 million POI's
420 million MAU

Pricing

Free sample available
10% discount if you buy via Datarade
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

Small Business
Medium-sized Business
Enterprise

Quality

Self-reported by the provider
95%
Match rate

Delivery

Methods
S3 Bucket
Frequency
daily
weekly
monthly
quarterly
Format
.csv

Use Cases

Categories

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Frequently asked questions

What is Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data?

We provide POI Data, which can be used to train AI & ML Models on14M physical locations globally, and unlock wide range of use cases, from marketing to public planning and fraud detection.

What is Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data used for?

This product has 5 key use cases. Factori recommends using the data for Geofencing, Location-based Advertising, Urban Mobility Analysis, Foot Traffic Analytics, and Point of Interest (POI) Mapping. Global businesses and organizations buy Foot Traffic Data from Factori to fuel their analytics and enrichment.

Who can use Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data?

This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Foot Traffic 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 AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data go?

This product has 1 years of historical coverage. It can be delivered on a daily, weekly, monthly, and quarterly basis.

Which countries does Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data cover?

This product includes data covering 248 countries like USA, Japan, Germany, India, and United Kingdom. Factori is headquartered in United States of America.

How much does Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data cost?

Pricing for Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning 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 AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data?

Businesses can buy Foot Traffic Data from Factori and get the data via S3 Bucket. Depending on your data requirements and subscription budget, Factori can deliver this product in .csv format.

What is the data quality of Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data?

Factori has reported that this product has the following quality and accuracy assurances: 95% Match rate. 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 360 Customer View, 10 Best Data Providers for Customer Segmentation, and Best Data Providers For Location-Based Marketing.

What are similar products to Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data?

This product has 3 related products. These alternatives include Factori Location Intelligence with Profile POI + People Data , The Data Appeal Point of Interest (POI) Data Location Data Map Data 200 Million + POI Data Mapped Sentiment & Popularity insights, and Global Bar & Restaurant Data Points of Interest (POI). You can compare the best Foot Traffic Data providers and products via Datarade’s data marketplace and get the right data for your use case.

Starts at
$25,000$22,500 / purchase
License Starts at
One-off purchase
$25,000$22,500 / purchase
Monthly License Not available
Yearly License Not available
Usage-based Not available

Factori

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96% Response rate

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