Consumer Data [US]
Our US consumer graph database is a comprehensive data enrichment solution that can help you fill the gaps in your customer data and gain a better knowledge of your consumers. We cover over 300 million profiles with over 100 variables based on location, demographics, lifestyle, hobbies, and behaviors.
What is Consumer Graph?
Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.
Our comprehensive data enrichment solution includes a variety of data sets that can help you in addressing gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.
| Category | Data Attributes |
|---|---|
| Geography | City, State, ZIP, County, CBSA, Census Tract, etc. |
| Demographics | Gender, Age Group, Marital Status, Language etc. |
| Financial | Income Range, Credit Rating Range, Credit Type, Net worth Range, etc |
| Persona | Consumer type, Communication preferences, Family type, etc |
| Interests | Content, Brands, Shopping, Hobbies, Lifestyle etc. |
| Household | Number of Children, Number of Adults, IP Address, etc. |
| Behaviors | Brand Affinity, App Usage, Web Browsing etc. |
| Firmographics | Industry, Company, Occupation, Revenue, etc |
| Retail Purchase | Store, Category, Brand, SKU, Quantity, Price etc. |
| Auto | Car Make, Model, Type, Year, etc. |
| Housing | Home type, Home value, Renter/Owner, Year Built etc |
Data Export Methodology
Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).
Use Cases
360-Degree Customer View
Get a comprehensive image of customers by the means of internal and external data aggregation
Data Enrichment
Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment
Fraud Detection
Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity.
Advertising & Marketing
Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.
Schema
Here's the full combined table presented properly:
| Field Name | Data Type |
|---|---|
| person_id | String |
| first_name | String |
| last_name | String |
| age | Byte |
| gender | String |
| linkedin_url | String |
| twitter_url | String |
| facebook_url | String |
| full_address | String |
| city | String |
| state | String |
| zip | String |
| zip4 | String |
| country | String |
| delivery_point_bar_code | String |
| carrier_route | String |
| walk_sequence_code | Boolean |
| fips_state_code | String |
| fips_country_code | String |
| country_name | String |
| latitude | Float |
| longtiude | Float |
| address_type | Byte |
| metropolitan_statistical_area | String |
| core_based_statistical_area | String |
| census_tract | String |
| census_block_group | Byte |
| census_block | String |
| primary_address | String |
| pre_address | Byte |
| street | String |
| post_address | String |
| address_suffix | Number |
| address_secondline | String |
| address_abrev | String |
| census_median_home_value | Integer |
| home_market_value | Integer |
| property_build_year | Integer |
| property_with_ac | Byte |
| property_with_pool | Boolean |
| property_with_sewer | Byte |
| property_with_water | Byte |
| general_home_value | Integer |
| property_fuel_type | Byte |
| year | Integer |
| month | Integer |
| day | Integer |
| household_id | String |
| census_median_household_income | Integer |
| household_size | Integer |
| occupation_home_office | Boolean |
| dwell_type | Byte |
| household_income | Byte |
| marital_status | Byte |
| length_of_residence | Integer |
| number_of_kids | Integer |
| pre_school_kids | Boolean |
| single_parent | Boolean |
| working_women_in_house_hold | Boolean |
| homeowner | Byte |
| children | String |
| adults | String |
| generations | Byte |
| net_worth | Byte |
| education_level | Byte |
| education_history | String |
| occupation | String |
| occupation_business_owner | Byte |
| credit_lines | Byte |
| credit_card_user | Boolean |
| newly_issues_credit_card_user | Boolean |
| credit_range_new | Byte |
| credit_cards | String |
| loan_to_value | Integer |
| mortgage_loan2_amount | Integer |
| mortgage_loan_type | Byte |
| mortgage_loan2_type | Byte |
| mortgage_lender_code | Integer |
| mortgage_loan2_render_code | Integer |
| mortgage_lender | String |
| mortgage_loan2_lender | String |
| mortgage_loan2_ratetype | Byte |
| mortgage_rate | Integer |
| mortgage_loan2_rate | Integer |
| donor | String |
| investor | String |
| interests | String |
| buyer | String |
| hobby | String |
| personal_email | String |
| work_email | String |
| devices | String |
| phone | String |
| employee_title | String |
| employee_department | String |
| employee_level | Byte |
| employee_job_function | String |
| skills | String |
| recent_job_change | Boolean |
| company_id | String |
| company_name | String |
| company_description | String |
| technologies_used | String |
| office_address | String |
| office_city | String |
| office_county | String |
| office_state | String |
| office_zip5 | String |
| office_zip4 | String |
| office_carrier_route | String |
| office_latitude | Float |
| office_longitude | Float |
| office_cbsa_code | String |
| office_census_block_group | String |
| office_census_tract | String |
| office_county_code | String |
| company_phone | String |
| company_credit_score | Byte |
| company_csa_code | String |
| company_dpbc | String |
| company_franchiseflag | Boolean |
| company_facebookurl | String |
| company_linkedinurl | String |
| company_twitterurl | String |
| company_website | String |
| company_fortune_rank | Integer |
| company_government_type | Byte |
| company_headquarters_branch | Byte |
| company_home_business | Boolean |
| company_industry | String |
| company_num_pcs_used | String |
| company_num_employees | String |
| company_firm_individual | Byte |
| company_msa | String |
| company_msa_name | String |
| company_naics_code | String |
| company_naics_description | String |
| company_naics_code2 | String |
| company_naics_description2 | String |
| company_sic_code2 | String |
| company_sic_code2_description | String |
| company_sic_code4 | String |
| company_sic_code4_description | String |
| company_sic_code6 | String |
| company_sic_code6_description | String |
| company_sic_code8 | String |
| company_sic_code8_description | String |
| company_parent_company | String |
| company_parent_company_location | String |
| company_public_private | Boolean |
| company_subsidiary_company | String |
| company_residential_business_code | Byte |
| company_revenue_at_side_code | String |
| company_revenue_range | String |
| company_revenue | Integer |
| company_sales_volume | String |
| company_small_business | Boolean |
| company_stock_ticker | String |
| company_year_founded | Integer |
| company_minorityowned | Boolean |
| company_female_owned_or_operated | Boolean |
| company_franchise_code | String |
| company_dma | String |
| company_dma_name | String |
| company_hq_address | String |
| company_hq_city | String |
| company_hq_duns | String |
| company_hq_state | String |
| company_hq_zip5 | String |
| company_hq_zip4 | String |
| company_sector | String |
| company_duns_number | Integer |
| company_is_topchain | Boolean |
| work_experience | String |
| motorcycle | String |
| automobile | String |
Reach Statistics
Updated 14 days ago