
GeoPersona Interest Segments | US | Available Globally | GDPR-Compliant Audience Segmentation
# | postal_code_name |
region_name |
geopersona_segment |
affinity_index_regional |
affinity_index_national |
quarter_range_start |
quarter_range_end |
population |
households |
purchasing_power_per_capita_in_euros |
purchasing_power_per_household_in_euros |
pop_age_0_to_5_years |
pop_0_to_5_years_male |
pop_0_to_5_years_female |
pop_age_5_to_10_years |
pop_5_to_10_years_male |
pop_5_to_10_years_female |
pop_age_10_to_15_years |
pop_10_to_15_years_male |
pop_10_to_15_years_female |
pop_age_15_to_20_years |
pop_15_to_20_years_male |
pop_15_to_20_years_female |
pop_age_20_to_25_years |
pop_20_to_25_years_male |
pop_20_to_25_years_female |
pop_age_25_to_30_years |
pop_25_to_30_years_male |
pop_25_to_30_years_female |
pop_age_30_to_35_years |
pop_30_to_35_years_male |
pop_30_to_35_years_female |
pop_age_35_to_40_years |
pop_35_to_40_years_male |
pop_35_to_40_years_female |
pop_age_40_to_45_years |
pop_40_to_45_years_male |
pop_40_to_45_years_female |
pop_age_45_to_50_years |
pop_45_to_50_years_male |
pop_45_to_50_years_female |
pop_age_50_to_55_years |
pop_50_to_55_years_male |
pop_50_to_55_years_female |
pop_age_55_to_60_years |
pop_55_to_60_years_male |
pop_55_to_60_years_female |
pop_age_60_to_65_years |
pop_60_to_65_years_male |
pop_60_to_65_years_female |
pop_age_65_to_70_years |
pop_65_to_70_years_male |
pop_65_to_70_years_female |
pop_age_70_to_75_years |
pop_70_to_75_years_male |
pop_70_to_75_years_female |
pop_age_75_years_and_older |
pop_75_years_and_older_male |
pop_75_years_and_older_female |
||
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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 | 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 | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx |
2 | 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 | 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 | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx |
3 | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | 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 | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx |
4 | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | 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 | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx |
5 | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | 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 |
6 | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxx | Xxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx |
7 | Xxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxx | xxxxx | xxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx |
8 | xxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxx | xxxxx | xxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxx | Xxxxxx | xxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxx | xxxxxx | xxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxx | xxxxxxx |
9 | Xxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxx | xxxxxx | Xxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxx | xxxxxx | Xxxxxxxx | Xxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | Xxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxx | xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxxxx |
10 | xxxxxx | Xxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxx | xxxxxxxx | xxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxx | xxxxxxxxxx |
... | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxx | Xxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxxxx | Xxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | xxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxx | xxxxxxx | Xxxxxx | Xxxxxx | xxxxxxx | Xxxxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxx | xxxxxxxxxx | Xxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
Integer | 10021 | Postal Code | |
postal_code_name
|
String | New York | |
region_name
|
String | New York | |
String | US | Country Code Alpha-2 | |
geopersona_segment
|
String | Jewelry Shoppers | |
affinity_index_regional
|
Float | 1.06 | |
affinity_index_national
|
Float | 2.1 | |
quarter_range_start
|
DateTime | 2023-10-01T00:00:00+00:00 | |
quarter_range_end
|
DateTime | 2023-12-31T00:00:00+00:00 | |
population
|
Integer | 44644 | |
households
|
Integer | 22461 | |
purchasing_power_per_capita_in_euros
|
Integer | 177067 | |
purchasing_power_per_household_in_euros
|
Integer | 351943 | |
pop_age_0_to_5_years
|
Integer | 2287 | |
pop_0_to_5_years_male
|
Integer | 1241 | |
pop_0_to_5_years_female
|
Integer | 1046 | |
pop_age_5_to_10_years
|
Integer | 1685 | |
pop_5_to_10_years_male
|
Integer | 865 | |
pop_5_to_10_years_female
|
Integer | 820 | |
pop_age_10_to_15_years
|
Integer | 1971 | |
pop_10_to_15_years_male
|
Integer | 926 | |
pop_10_to_15_years_female
|
Integer | 1045 | |
pop_age_15_to_20_years
|
Integer | 1648 | |
pop_15_to_20_years_male
|
Integer | 656 | |
pop_15_to_20_years_female
|
Integer | 992 | |
pop_age_20_to_25_years
|
Integer | 1281 | |
pop_20_to_25_years_male
|
Integer | 344 | |
pop_20_to_25_years_female
|
Integer | 937 | |
pop_age_25_to_30_years
|
Integer | 4244 | |
pop_25_to_30_years_male
|
Integer | 1338 | |
pop_25_to_30_years_female
|
Integer | 2906 | |
pop_age_30_to_35_years
|
Integer | 4415 | |
pop_30_to_35_years_male
|
Integer | 2082 | |
pop_30_to_35_years_female
|
Integer | 2333 | |
pop_age_35_to_40_years
|
Integer | 2623 | |
pop_35_to_40_years_male
|
Integer | 1190 | |
pop_35_to_40_years_female
|
Integer | 1433 | |
pop_age_40_to_45_years
|
Integer | 3033 | |
pop_40_to_45_years_male
|
Integer | 1517 | |
pop_40_to_45_years_female
|
Integer | 1516 | |
pop_age_45_to_50_years
|
Integer | 2465 | |
pop_45_to_50_years_male
|
Integer | 1261 | |
pop_45_to_50_years_female
|
Integer | 1204 | |
pop_age_50_to_55_years
|
Integer | 2705 | |
pop_50_to_55_years_male
|
Integer | 1359 | |
pop_50_to_55_years_female
|
Integer | 1346 | |
pop_age_55_to_60_years
|
Integer | 2241 | |
pop_55_to_60_years_male
|
Integer | 1238 | |
pop_55_to_60_years_female
|
Integer | 1003 | |
pop_age_60_to_65_years
|
Integer | 2437 | |
pop_60_to_65_years_male
|
Integer | 1321 | |
pop_60_to_65_years_female
|
Integer | 1116 | |
pop_age_65_to_70_years
|
Integer | 2388 | |
pop_65_to_70_years_male
|
Integer | 1209 | |
pop_65_to_70_years_female
|
Integer | 1179 | |
pop_age_70_to_75_years
|
Integer | 2407 | |
pop_70_to_75_years_male
|
Integer | 764 | |
pop_70_to_75_years_female
|
Integer | 1643 | |
pop_age_75_years_and_older
|
Integer | 6814 | |
pop_75_years_and_older_male
|
Integer | 3008 | |
pop_75_years_and_older_female
|
Integer | 3806 |
Attribute | Type | Example | Mapping |
---|---|---|---|
Integer | 10021 | Postal Code | |
postal_code_name
|
String | New York | |
region_name
|
String | New York | |
String | US | Country Code Alpha-2 | |
geopersona_segment
|
String | Jewelry Shoppers | |
affinity_index_regional
|
Float | 1.06 | |
affinity_index_national
|
Float | 2.1 | |
quarter_range_start
|
Date | 2023-10-01 | |
quarter_range_end
|
Date | 2023-12-31 | |
population
|
Integer | 44644 | |
households
|
Integer | 22461 | |
purchasing_power_per_capita_in_euros
|
Integer | 177067 | |
purchasing_power_per_household_in_euros
|
Integer | 351943 | |
pop_age_0_to_5_years
|
Integer | 2287 | |
pop_0_to_5_years_male
|
Integer | 1241 | |
pop_0_to_5_years_female
|
Integer | 1046 | |
pop_age_5_to_10_years
|
Integer | 1685 | |
pop_5_to_10_years_male
|
Integer | 865 | |
pop_5_to_10_years_female
|
Integer | 820 | |
pop_age_10_to_15_years
|
Integer | 1971 | |
pop_10_to_15_years_male
|
Integer | 926 | |
pop_10_to_15_years_female
|
Integer | 1045 | |
pop_age_15_to_20_years
|
Integer | 1648 | |
pop_15_to_20_years_male
|
Integer | 656 | |
pop_15_to_20_years_female
|
Integer | 992 | |
pop_age_20_to_25_years
|
Integer | 1281 | |
pop_20_to_25_years_male
|
Integer | 344 | |
pop_20_to_25_years_female
|
Integer | 937 | |
pop_age_25_to_30_years
|
Integer | 4244 | |
pop_25_to_30_years_male
|
Integer | 1338 | |
pop_25_to_30_years_female
|
Integer | 2906 | |
pop_age_30_to_35_years
|
Integer | 4415 | |
pop_30_to_35_years_male
|
Integer | 2082 | |
pop_30_to_35_years_female
|
Integer | 2333 | |
pop_age_35_to_40_years
|
Integer | 2623 | |
pop_35_to_40_years_male
|
Integer | 1190 | |
pop_35_to_40_years_female
|
Integer | 1433 | |
pop_age_40_to_45_years
|
Integer | 3033 | |
pop_40_to_45_years_male
|
Integer | 1517 | |
pop_40_to_45_years_female
|
Integer | 1516 | |
pop_age_45_to_50_years
|
Integer | 2465 | |
pop_45_to_50_years_male
|
Integer | 1261 | |
pop_45_to_50_years_female
|
Integer | 1204 | |
pop_age_50_to_55_years
|
Integer | 2705 | |
pop_50_to_55_years_male
|
Integer | 1359 | |
pop_50_to_55_years_female
|
Integer | 1346 | |
pop_age_55_to_60_years
|
Integer | 2241 | |
pop_55_to_60_years_male
|
Integer | 1238 | |
pop_55_to_60_years_female
|
Integer | 1003 | |
pop_age_60_to_65_years
|
Integer | 2437 | |
pop_60_to_65_years_male
|
Integer | 1321 | |
pop_60_to_65_years_female
|
Integer | 1116 | |
pop_age_65_to_70_years
|
Integer | 2388 | |
pop_65_to_70_years_male
|
Integer | 1209 | |
pop_65_to_70_years_female
|
Integer | 1179 | |
pop_age_70_to_75_years
|
Integer | 2407 | |
pop_70_to_75_years_male
|
Integer | 764 | |
pop_70_to_75_years_female
|
Integer | 1643 | |
pop_age_75_and_older
|
Integer | 6814 | |
pop_75_and_older_male
|
Integer | 3008 | |
pop_75_and_older_female
|
Integer | 3806 |
Description
Country Coverage
History
Volume
118 | Segments |
Pricing
License | Starts at |
---|---|
One-off purchase | Available |
Monthly License | Available |
Yearly License | Available |
Usage-based | Available |
Suitable Company Sizes
Delivery
Use Cases
Categories
Related Products
Frequently asked questions
What is GeoPersona Interest Segments US Available Globally GDPR-Compliant Audience Segmentation?
GeoPersona identifies postal codes with high affinities for activities like sports, dining, and entertainment via visitation patterns. This non-PII tool enhances marketing, site selection, and customer experience in the US and EU5, using insights from 82M POIs to target locations and boost ROI.
What is GeoPersona Interest Segments US Available Globally GDPR-Compliant Audience Segmentation used for?
This product has 5 key use cases. Echo Analytics recommends using the data for Geotargeting, Location-based Advertising, Audience Targeting, Targeted Advertising, and Location-based Audience Analytics. Global businesses and organizations buy Consumer Behavior Data from Echo Analytics to fuel their analytics and enrichment.
Who can use GeoPersona Interest Segments US Available Globally GDPR-Compliant Audience Segmentation?
This product is best suited if you’re a Medium-sized Business, Enterprise, or Small Business looking for Consumer Behavior Data. Get in touch with Echo Analytics to see what their data can do for your business and find out which integrations they provide.
How far back does the data in GeoPersona Interest Segments US Available Globally GDPR-Compliant Audience Segmentation go?
This product has 20 months of historical coverage. It can be delivered on a monthly, quarterly, yearly, and on-demand basis.
Which countries does GeoPersona Interest Segments US Available Globally GDPR-Compliant Audience Segmentation cover?
This product includes data covering 1 country like USA. Echo Analytics is headquartered in France.
How much does GeoPersona Interest Segments US Available Globally GDPR-Compliant Audience Segmentation cost?
Pricing information for GeoPersona Interest Segments US Available Globally GDPR-Compliant Audience Segmentation is available by getting in contact with Echo Analytics. Connect with Echo Analytics to get a quote and arrange custom pricing models based on your data requirements.
How can I get GeoPersona Interest Segments US Available Globally GDPR-Compliant Audience Segmentation?
Businesses can buy Consumer Behavior Data from Echo Analytics and get the data via S3 Bucket, SFTP, and Email. Depending on your data requirements and subscription budget, Echo Analytics can deliver this product in .csv and .xls format.
What is the data quality of GeoPersona Interest Segments US Available Globally GDPR-Compliant Audience Segmentation?
You can compare and assess the data quality of Echo Analytics using Datarade’s data marketplace. Echo Analytics has received 3 reviews from clients. Echo Analytics appears on selected Datarade top lists ranking the best data providers, including Who’s New on Datarade? August Edition.
What are similar products to GeoPersona Interest Segments US Available Globally GDPR-Compliant Audience Segmentation?
This product has 3 related products. These alternatives include GeoPersona Brand Segments Europe Segmentation Data Available Globally GDPR-Compliant, Digital Audience Data Segments B2C - Increase your match by 3X - Programmatic Audience, and Audience Data API Access 700M+ Profiles Discover Engaged Audiences Worldwide Best Price Guarantee. You can compare the best Consumer Behavior Data providers and products via Datarade’s data marketplace and get the right data for your use case.