Datastream Group Insurance Industry Data | Leading Data-as-a-Service Platform
# | LeadQuality |
zip_code |
year |
make |
model |
submodel |
owned_or_leased |
distance |
annual_distance |
deductible |
deductible2 |
Prefered level of coverage |
has_coverage |
former_insurer |
months_insured |
expiration_date |
relationship |
birthdate |
gender |
marital_status |
Military experience |
occupation |
driver_state |
license_status |
sr22 |
incidentpop |
address |
city |
state |
own_or_rent |
credit_rating |
primary_phone |
address2 |
county |
years_at_address |
secondary_phone |
contact_method |
age |
occupation_years |
highest_level |
revoked |
repossessions |
licensed_age |
bankruptcy |
at_home_student |
IsGoodStudent |
days_used |
use |
location_parked |
antitheft |
VinInPolk |
type |
bodilyinjury_person |
bodilyinjury_accident |
propertydamage |
||||
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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 |
2 | 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 | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx |
3 | 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 | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx |
4 | 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 | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx |
5 | 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 | 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 |
6 | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | 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 |
7 | Xxxxxxxxx | xxxxxx | Xxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | 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 |
8 | xxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxx | xxxxxxxx | 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 |
9 | Xxxxxx | Xxxxxx | xxxxxx | xxxxx | xxxxxxx | 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 | Xxxxxx |
10 | Xxxxxx | Xxxxxxx | Xxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxx | xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxxxx | 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 |
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Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
LeadQuality
|
String | PREFERRED | |
zip_code
|
Integer | 98408 | |
year
|
Integer | 2012 | |
make
|
String | VOLKSWAGEN | |
model
|
String | PASSAT | |
submodel
|
String | 1VWBM7A30CC000000*PASSAT-Sedan | |
owned_or_leased
|
String | Owned | |
distance
|
Integer | 9 | |
annual_distance
|
Integer | 12500 | |
deductible
|
Integer | 500 | |
deductible2
|
Integer | 500 | |
Prefered level of coverage
|
String | Standard Coverage | |
has_coverage
|
Boolean | t | |
former_insurer
|
String | Other | |
months_insured
|
Integer | 12 | |
expiration_date
|
String | 2/8/2022 | |
relationship
|
String | Self | |
String | Contact First Name | ||
String | Contact Last Name | ||
birthdate
|
String | 5/1/1968 | |
gender
|
String | ||
marital_status
|
String | Single | |
Military experience
|
Boolean | f | |
occupation
|
String | Unknown | |
driver_state
|
String | WA | |
license_status
|
String | Valid | |
sr22
|
Boolean | f | |
incidentpop
|
Boolean | f | |
address
|
String | 7646 s c st Tacoma wa | |
city
|
String | Tacoma | |
state
|
String | WA | |
own_or_rent
|
String | OWN | |
credit_rating
|
String | Good | |
primary_phone
|
String | 253-228-6062 | |
String | Email Address | ||
address2
|
|||
county
|
|||
years_at_address
|
Integer | 1 | |
secondary_phone
|
String | 253-228-6062 | |
contact_method
|
String | phone | |
age
|
Integer | ||
occupation_years
|
Integer | 3 | |
highest_level
|
String | HighSchoolDiploma | |
revoked
|
Boolean | f | |
repossessions
|
Boolean | f | |
licensed_age
|
Integer | 16 | |
bankruptcy
|
Boolean | f | |
at_home_student
|
Boolean | f | |
IsGoodStudent
|
Integer | 1 | |
days_used
|
Integer | 5 | |
use
|
String | Commute_Work | |
location_parked
|
String | No Cover | |
antitheft
|
String | Alarm | |
VinInPolk
|
Integer | 1 | |
type
|
String | PREFERRED | |
bodilyinjury_person
|
Integer | 100000 | |
bodilyinjury_accident
|
Integer | 300000 | |
propertydamage
|
Integer | 50000 | |
String | IPv4 Address |
Attribute | Type | Example | Mapping |
---|---|---|---|
varies by vertical
|
Description
Country Coverage
History
Volume
40 | million records |
Pricing
License | Starts at |
---|---|
One-off purchase |
$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 Datastream Group Insurance Industry Data Leading Data-as-a-Service Platform?
The Datastream US Consumer Insurance file contains known instances of an insurance policy connected to individuals in our Consumer Database. Verticals include, Home, Health, Auto, and Life.
What is Datastream Group Insurance Industry Data Leading Data-as-a-Service Platform used for?
This product has 3 key use cases. Datasys recommends using the data for Identity-Based Targeting, Cross Device Identity Management, and Insurance marketing. Global businesses and organizations buy Insurance Data from Datasys to fuel their analytics and enrichment.
Who can use Datastream Group Insurance Industry Data Leading Data-as-a-Service Platform?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Insurance 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 Datastream Group Insurance Industry Data Leading Data-as-a-Service Platform go?
This product has 3 years of historical coverage. It can be delivered on a daily, weekly, monthly, quarterly, yearly, real-time, and on-demand basis.
Which countries does Datastream Group Insurance Industry Data Leading Data-as-a-Service Platform cover?
This product includes data covering 1 country like USA. Datasys is headquartered in United States of America.
How much does Datastream Group Insurance Industry Data Leading Data-as-a-Service Platform cost?
Pricing for Datastream Group Insurance Industry Data Leading Data-as-a-Service Platform starts at USD500 per purchase. Connect with Datasys to get a quote and arrange custom pricing models based on your data requirements.
How can I get Datastream Group Insurance Industry Data Leading Data-as-a-Service Platform?
Businesses can buy Insurance Data from Datasys and get the data via SFTP and Email. Depending on your data requirements and subscription budget, Datasys can deliver this product in .csv and .txt format.
What is the data quality of Datastream Group Insurance Industry Data Leading Data-as-a-Service Platform?
Datasys has reported that this product has the following quality and accuracy assurances: 80% match rate. 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 Datastream Group Insurance Industry Data Leading Data-as-a-Service Platform?
This product has 3 related products. These alternatives include McGRAW Home Warranty and Homeowner Data 1.6MM Opt-In Consumer Records, Pixta AI Imagery Data Global 5,000 Stock Images Annotation and Labelling Services Provided Full-body human images for AI & ML, and TagX - 10,000+ Car damage images with annotation Car insurance & inspection Global coverage with custom annotations. You can compare the best Insurance Data providers and products via Datarade’s data marketplace and get the right data for your use case.