Sold by: AnalyticsIQ
Deployed on AWS
Automotive Attributes – Offline contains individual or household level data variables that provide and predict a variety of automotive preferences and behaviors ranging from the number of vehicles in their garage to those in market for specific auto makes and more.
Overview
Automotive Attributes – Offline (Individual or Household) Data Product Overview
Automotive Attributes – Offline contains individual or household level data variables that provide and predict a variety of automotive preferences and behaviors ranging from the number of vehicles in their garage to those in market for specific auto makes and more.
Use Cases
Automotive Attributes – Offline can be used for personalized and targeted marketing communications by allowing marketers to optimize messaging, creative, and offer based on specific consumer characteristics, behaviors, and preferences. This facilitates better customer retention, customer acquisition, cross-sell or up-sell, and engagement rates.
Automotive Attributes – Offline can be used to enrich CRM data by appending attributes to existing individuals in order to learn more about your customers.
Automotive Attributes – Offline can be used for your own modeling processes.
Metadata
| Description | Value |
|---|---|
| Geographic coverage | United States of America |
| Data Population Level | Individual or Household |
| Number of individuals/households covered | 242.5+ million individuals & 117+ million households |
| Raw or scraped data | Raw Data |
| Key Fields | Average Annual Mileage, Age of Auto, Maintenance Preferences, Auto Make Propensity, In Market for Auto, Make of Auto Owned |
| Data Source(s) | Our data is created in an offline process but leverage offline and online data and behaviors. The vast majority of our database is proprietary although a few publicly available data sources are leveraged in its development AnalyticsIQ sources data from over 100 sources. These are predominantly public sources including: Core Demographic data from multiple sources; Census Block and Block Group level data; Econometric data from the US government; Summarized credit data from multiple credit bureaus; Property and mortgage information from county courthouses; Occupation information from state licensing boards; Past purchase behavior from catalogers and retailers that contribute their data at a category level. AnalyticsIQ is not an original compiler as the data above is readily available for purchase out in the market. However, AnalyticsIQ uses superior analytics to make our data best-in-class. One tool that is completely unique to AnalyticsIQ’s product development process is our proprietary survey data. This is where our Cognitive Sciences Department carefully crafts questions that we serve to a panel of consumers. Survey responses are not directly published on our file, but rather the answers are then modeled across our entire consumer file to create truly unique data points not available anywhere. |
| Update Frequency | AnalyticsIQ data is updated on a quarterly basis |
| Key Words | Automotive, Autos, OEM, Cars, Vehicles, Drivers |
Key Data Points
Key data points include:
- Auto_Avg_Mileage: Average mileage put on car per year
- Number_of_Autos: Number of autos owned by the household
- AutoProp_Acura_MostLikely: Propensity to drive an Acura
- Auto_Family_Use: Likelihood of using vehicle for family use
- Auto_InMkt_v2: Likelihood of being in market for a vehicle
- Auto_Buyer_Methodical: Likelihood to use vehicle for family use
- Auto_Maint_Dealership: Likelihood of preferring dealership for vehicle maintenance
Additional Information
Automotive Audiences – Digital Data Dictionary and AnalyticsIQ Full Data Dictionary are available for download. Please contact aws-support@analytics-iq.com for access.
Pricing Information
For pricing information on our 12-month licenses please reach out to Margo Hock at margoh@analytics-iq.com .
Regulatory and Compliance Information
All AnalyticsIQ audiences are HIPAA compliant, and many audiences are Regulation B / FLA friendly or have alternative Regulation B / FLA friendly versions. To learn more about AnalyticsIQ’s Regulation B / FLA Friendly data.
For users who wish to avoid PII, all AnalyticsIQ data can be anonymized through tokenization thanks to our strong partnership with Datavant, a leading providers of data de-identification services.
For information, please reach out to aws-support@analytics-iq.com .
Need Help?
If you have any questions about AnalyticsIQ or our data, please reach out to aws-support@analytics-iq.com .
About Your Company
Founded in 2007 and based in Atlanta, AnalyticsIQ is vastly made up of data scientists, analysts, researchers, and cognitive psychologists with over 100 years of collective analytical experience. AnalyticsIQ is the first data company to consistently blend cognitive psychology with data science to understand the heart of individuals and their motivations – both as a consumer and as a professional. By linking an individual’s consumer profile at home to their professional profile at work, AnalyticsIQ strives to help companies know their customers and prospects like they know their friends, and its mission is for data to fuel every brand experience. AnalyticsIQ’s data consistently outperforms other data providers, and the company’s flexible approach means that they will do whatever it takes to work with clients to solve the challenges they may be facing. To learn more about AnalyticsIQ, please visit https://analytics-iq.com/ .
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Additional details
You will receive access to the following data sets.
Data set name | Type | Historical revisions | Future revisions | Sensitive information | Data dictionaries | Data samples |
|---|---|---|---|---|---|---|
AnalyticsIQ Offline Sample Data sets | All historical revisions | All future revisions | Not included | Not included |
Resources
Vendor resources
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