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USA Automotive Ownership Data with Consumer Demographics
# | Auto |
---|---|
1 | xxxxxxxxxx |
2 | Xxxxxxxxx |
3 | xxxxxx |
4 | xxxxxxxxxx |
5 | Xxxxx |
6 | Xxxxxx |
7 | Xxxxxxxxxx |
8 | Xxxxxx |
9 | Xxxxxxxxx |
10 | Xxxxxxxxxx |
... | xxxxxxxxx |
# | individual_id |
address_id |
household_id |
first_name |
last_name |
name_suffix |
address |
city |
state |
zip |
zip4 |
dpbc |
carrier_route |
fips_state_code |
fips_county_code |
county_name |
gender |
dob_yr |
dob_mon |
dob_day |
exact_age |
hh_income |
hh_marital_status |
homeowner |
length_of_residence |
email_address |
auto_make |
auto_model |
auto_year |
auto_vin |
auto_tdate |
auto_trim |
auto_style |
auto_vehicle_type |
auto_body_type |
auto_msrp |
auto_fuel_type |
auto_def_engine_cylinders |
auto_def_trans_type |
auto_max_payload |
auto_mfg_code |
auto_gvw_range |
vinflag |
number_of_vehicles_in_hh |
service_date |
service_due |
service_provider |
leasegrantor |
leaseexpirationdate |
warranty |
warrantyexpirationdate |
confidencelevel |
lastseendate |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
2 | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | 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 | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx |
3 | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | 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 | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx |
4 | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | 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 | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx |
5 | 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 | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx |
6 | 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 |
7 | 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 | Xxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxx |
8 | 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 |
9 | Xxxxxxxxx | Xxxxx | Xxxxxxx | 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 |
10 | xxxxxxxxxx | xxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxx | xxxxxxx | 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 |
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Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
Auto
|
String | individual_id |
Attribute | Type | Example | Mapping |
---|---|---|---|
individual_id
|
Integer | 182477072 | |
address_id
|
Integer | 8254840 | |
household_id
|
Integer | 570729404 | |
first_name
|
String | ||
last_name
|
String | ||
name_suffix
|
|||
address
|
String | **** ***** ***** *** | |
city
|
String | ********** | |
state
|
String | OH | |
zip
|
Integer | 45331 | |
zip4
|
Integer | 2422 | |
dpbc
|
Integer | 86 | |
carrier_route
|
String | C007 | |
fips_state_code
|
Integer | 39 | |
fips_county_code
|
Integer | 37 | |
county_name
|
String | Darke | |
gender
|
Boolean | ||
dob_yr
|
Integer | 1979 | |
dob_mon
|
String | * | |
dob_day
|
String | ** | |
exact_age
|
Integer | 44 | |
hh_income
|
String | J | |
hh_marital_status
|
String | S | |
homeowner
|
String | H | |
length_of_residence
|
Integer | 9 | |
email_address
|
|||
auto_make
|
String | FORD | |
auto_model
|
String | Explorer | |
auto_year
|
Integer | 2007 | |
auto_vin
|
String | ***************** | |
auto_tdate
|
String | 6/22/2017 | |
auto_trim
|
String | Xlt | |
auto_style
|
String | XLT 4dr SUV 4WD (V6) | |
auto_vehicle_type
|
String | SUV | |
auto_body_type
|
String | SUV | |
auto_msrp
|
Integer | 28290 | |
auto_fuel_type
|
String | G | |
auto_def_engine_cylinders
|
Integer | 6 | |
auto_def_trans_type
|
String | A | |
auto_max_payload
|
Integer | 1510 | |
auto_mfg_code
|
|||
auto_gvw_range
|
String | 6001-7000 | |
vinflag
|
String | V | |
number_of_vehicles_in_hh
|
Integer | 2 | |
service_date
|
String | 11/11/2023 | |
service_due
|
|||
service_provider
|
String | FIRESTONE EXPRESS LUBE PACK 5 QUART BASIC TO SYNTH 19.99 TO | |
leasegrantor
|
|||
leaseexpirationdate
|
|||
warranty
|
|||
warrantyexpirationdate
|
|||
confidencelevel
|
Integer | 0 | |
lastseendate
|
String | 6/22/2017 |
Attribute | Type | Example | Mapping |
---|---|---|---|
name
|
Joe Smith | ||
String | Address | ||
String | Email Address | ||
String | Phone Number | ||
over 500 demographics
|
mechanic | ||
String | VIN | ||
String | MAID |
Description
Country Coverage
History
Volume
150 million | records |
Pricing
License | Starts at |
---|---|
One-off purchase | Not available |
Monthly License |
$10,000$9,500 / month |
Yearly License |
$96,000$91,200 / year |
Usage-based | Not available |
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is USA Automotive Ownership Data with Consumer Demographics?
Automotive Database: 155M+ U.S. Car Owners, Fully Enhanced & Ready for Licensing or Installs
What is USA Automotive Ownership Data with Consumer Demographics used for?
This product has 5 key use cases. Datasys recommends using the data for Audience Targeting, Contact Validation, Identity-Based Targeting, Behavioral Targeting, and Consumer Intelligence. Global businesses and organizations buy Car Ownership Data from Datasys to fuel their analytics and enrichment.
Who can use USA Automotive Ownership Data with Consumer Demographics?
This product is best suited if you’re a Enterprise or Medium-sized Business looking for Car Ownership Data. Get in touch with Datasys to see what their data can do for your business and find out which integrations they provide.
How far back does the data in USA Automotive Ownership Data with Consumer Demographics go?
This product has 24 months of historical coverage. It can be delivered on a monthly and on-demand basis.
Which countries does USA Automotive Ownership Data with Consumer Demographics cover?
This product includes data covering 1 country like USA. Datasys is headquartered in United States of America.
How much does USA Automotive Ownership Data with Consumer Demographics cost?
Pricing for USA Automotive Ownership Data with Consumer Demographics starts at USD10,000 per month. Datasys offers a 5% discount when you buy data from them through Datarade. Connect with Datasys to get a quote and arrange custom pricing models based on your data requirements.
How can I get USA Automotive Ownership Data with Consumer Demographics?
Businesses can buy Car Ownership Data from Datasys and get the data via S3 Bucket, SFTP, REST API, and Feed API. Depending on your data requirements and subscription budget, Datasys can deliver this product in .csv and .txt format.
What is the data quality of USA Automotive Ownership Data with Consumer Demographics?
Datasys has reported that this product has the following quality and accuracy assurances: 70% match rate to Mobile Devices. You can compare and assess the data quality of Datasys using Datarade’s data marketplace. Datasys appears on selected Datarade top lists ranking the best data providers, including Top 10 Identity & Device Graph Data Providers In the US.
What are similar products to USA Automotive Ownership Data with Consumer Demographics?
This product has 3 related products. These alternatives include Alesco Car Ownership Data - Automotive Data - 275+ Million VIN Data points with 183+ Million Opt-In Emails - US based, licensing available, AGR Automotive Data US VIN Data 165+ million autos with address verification., and Car Spec Data Automotive Data Car Specs, Equip & Price (LATAM) Updated Monthly Product Strategy. You can compare the best Car Ownership Data providers and products via Datarade’s data marketplace and get the right data for your use case.