Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific ta...

The following datasets are based on the children and youth (under age 21) beneficiary population and consist of aggregate Mental Health Service data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

The following datasets are based on the adult (age 21 and over) beneficiary population and consist of aggregate MHS data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

Demographics Analysis with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used to compare demographics breakdown (age and income excluded in this free sample view) for one company vs. a competitor for a set period of time (Ex: How do demographics like wealth, ethnicity, children in the household, homeowner status, and political affiliation differ for Walmart vs. Target shopper?).

Inquire about a CE subscription to perform more complex, near real-time demographics analysis functions on public tickers and private brands like: • Analyze a demographic, like age or income, within a state for a company in 2023 • Compare all of a company’s demographics to all of that company’s competitors through most recent history

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Demographics Analysis

Problem A global retailer wants to understand company performance by age group.

Solution Consumer Edge transaction data can be used to analyze shopper transactions by age group to understand: • Overall sales growth by age group over time • Percentage sales growth by age group over time • Sales by age group vs. competitors

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key demographic drivers of growth for company-wide reporting • Reduce investment in underperforming age groups, both online and offline • Determine retention by age group to refine campaign strategy • Understand how different age groups are performing compared to key competitors

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period ...

This data set includes monthly counts and rates (per 1,000 beneficiaries) of behavioral health services, including emergency department services, inpatient services, intensive outpatient/partial hospitalizations, outpatient services, or services delivered through telehealth, provided to Medicaid and CHIP beneficiaries, by state. Users can filter by either mental health disorder or substance use disorder. These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating behavioral health services measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Diagnosis Code - IP, Diagnosis Code - OT. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of “DQ” indicate that data were suppressed due to unusable data. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

We collect, validate, model, and segment raw data signals from over 900+ sources globally to deliver thousands of mobile audience segments. We then combine that data with other public and private data sources to derive interests, intent, and behavioral attributes. Our proprietary algorithms then clean, enrich, unify and aggregate these data sets for use in our products. We have categorized our audience data into consumable categories such as interest, demographics, behavior, geography, etc. Audience Data Categories:Below mentioned data categories include consumer behavioral data and consumer profiles (available for the US and Australia) divided into various data categories. Brand Shoppers:Methodology: This category has been created based on the high intent of users in terms of their visits to Brand outlets in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Place Category Visitors:Methodology: This category has been created based on the high intent of users visiting specific places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Demographics:This category has been created based on deterministic data that we receive from apps based on the declared gender and age data. Marital Status, Education, Party affiliation, and State residency are available in the US. Geo-Behavioural:This category has been created based on the high intent of users in terms of the frequency of their visits to specific granular places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Interests:This segment is created based on users' interest in a specific subject while browsing the internet when the visited website category is clearly focused on a specific subject such as cars, cooking, traveling, etc. We use a deterministic model to assign a proper profile and time that information is valid. The recency of data can range from 14 to 30 days, depending on the topic. Intent:Factori receives data from many partners to deliver high-quality pieces of information about users’ shopping intent. We collect data from sources connected to the eCommerce sector and we also receive data connected to online transactions from affiliate networks to deliver the most accurate segments with purchase intentions, such as laptops, mobile phones, or cars. The recency of data can range from 7 to 14 days depending on the product category. Events:This category was created based on the high interest of users in terms of content related to specific global events - sports, culture, and gaming. Among the event segments, we also distinguish categories related to the interest in certain lifestyle choices and behaviors. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. App Usage:Mobile category is a branch of the taxonomy that is dedicated only to the data that is based on mobile advertising IDs. It is based on the categorization of the mobile apps that the user has installed on the device. Auto Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of automobile and other automotive attributes via a survey or registration. These audiences are currently available in the USA. Motorcycle Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of motorcycle and other motorcycle-based attributes via a survey or registration. These audiences are currently available for the USA. Household:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on users' declaring their marital status, parental status, and the overall number of children via a survey or registration. These audiences are currently available in the USA. Financial:Consumer Profiles - Available for the US and Australia this audience has been created based on their behavior in different financial services like property ownership, mortgage, investing behavior, and wealth and declaring their estimated net worth via a survey or registration. Purchase/ Spending Behavior:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on their behavior in different spending behaviors in different business verticals available in the USA. Clusters:Consumer Profiles - Available for the US and AustraliaClusters are groups of consumers who exhibit similar demographic, lifestyle, and media consumption characteristics, empowering marketers to understand the unique attributes that comprise their most profitable consumer segments. Armed with this rich data, data scientists can drive analytics and modeling to power their brand’s unique marketing initiatives. B2B Audiences;Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring their employee credentials, designations, and companies they work in, further specifying business verticals, revenue breakdowns, and headquarters locations. Customizable Audiences Data Segment:Brands can choose the appropriate pre-made audience segments or ask our data experts about creating a custom segment that is precisely tailored to your brief in order to reach their target customers and boost the campaign's effectiveness. Location Query Granularity:Minimum area: HEX 8Maximum area: QuadKey 17/City

Abbreviations: 1: BDI = Beck Depression Inventory, 2: SCL-90-R = Symptom Check List Revised, 3: GSI = Global Severity Index, 4: PANAS = Positive and Negative Affect Schedule.

Based on market research, the source divided U.S. Generation Z consumers (born between January 1, 1997 and December 31, 2007) into five segments based on their behavior and attitude. The largest segment, making up 35 percent of the total, were the so-called Stress Strivers, defined by the source as "high achievers, driven by a fear of not being good enough." Authentic Activists and Secluded Perfectionists followed, with 22 percent and 20 percent, respectively.

A global database of Direct Marketing Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future. Leverage up-to-date audience targeting population trends for market research, audience targeting, and sales territory mapping.

Self-hosted marketing population dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The Demographic Data is standardized, unified, and ready to use.

Use cases for the Global Consumer Behavior Database (Direct Marketing Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Audience targeting

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Demographic data export methodology

Our population data packages are offered in CSV format. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our Consumer databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

This dataset contains statistics regarding the population exposed to tobacco, foods or overweight/obesity for country members and partners of OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The exposure levels to health risk or protection factors statistics cover the period 1960-2018.

Two data sets including the group size analysis (All_Sheep_Groups_GroupSizeAnalysis.csv) and the case study in Wrangell St. Elias National Park and Preserve (WRST_CaseStudy.csv), both of which also have associated metadata files. Both data sets were used in Delisle et al., "Linking behavioral ecology and population monitoring: The importance of group size for spatial population models".

Global Location Market is expanding from US$ 120.13 Billion in 2023 to US$ 596.96 Billion by 2032 with a CAGR of 19.5% during forecast 2024 - 2032

The share contains the behavioral data in MatLab format, as well as a demographic file with age and IQ information. The data are organized per column as follows:

% subject number % session number % conditions
% checktime % choice side % choice correct % factual outcome (depending on the condition) % alternative outcome (depending on the condition) % reaction time (millisecond)

This SAS dataset contains data on salivary concentrations of selected immune and neuroendocrine biomarkers, as well as socioeconomic and demographic data, self-reported health data, and behavioral data. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: This dataset is available upon request. Eligible researchers can contact study investigators Andrey Egorov (egorov.andrey@epa.gov) or Michael Nye (nye.michale@epa.gov). Format: This dataset contains demographic, socioeconomic, behavioral and stress data on residents of two communities in Denver, CO, as well as data on biomarker levels in saliva. This dataset is associated with the following publication: Egorov, A., W. Xue, J. Kobylanski, M. Fuzawa, S. Griffin, T. Wade, and M. Nye. Pilot application of an inflammation and physiological dysregulation index based on noninvasive salivary biomarkers. BMC Research Notes. BioMed Central Ltd, London, UK, 18: 53, (2025).

*Note: data are missing for up to 7 women because of failure of some women to complete specific questions in the questionnaire.

Overall sample includes participants who reported male partners only (n = 2,950, 95%), male and female partners (n = 113, 4%), and male and transgender partners (n = 16, 1%); 13 participants did not report one-on-one sexual encounters and did not have encounter-specific data.

According to a survey of social media users aged between 16 and 34 years old in the United States, as of **********, the majority of respondents reported following social media influencers in which they saw similar demographic characteristics as they had, such as age, gender, or lifestyle. By comparison, approximately ** percent of respondents reported not following influencers within similar demographics.

This study explores the behavioral impact of the One Child Policy in China. Using experimental data it examines whether the One Child Policy affected altruism, trust, trust-worthiness, risk-taking, and competitiveness in individuals. Survey data on personality traits and demographics of the sample are included.

Northern bobwhite chick encounter history file for Program MARKINP file associated with encounter histories for northern bobwhite (Colinus virginianus) chick survival. INP file is the standard encounter history file format associated with Program MARK software.NOBOChick.inpScaled quail chick encounter history file for Program MARKINP file associated with encounter histories for scaled quail (Callipepla squamata) chick survival. INP file is the standard encounter history file format associated with Program MARK software.SCQUChick.inpPrincipal components analysis values for weatherPrincipal components analysis values for weather data obtained from local weather stations on Beaver River Wildlife Management Area.PCAValues.xlsxAdult northern bobwhite encounter history file for Program MARKAdult northern bobwhite (Colinus virginianus) encounter history file associated with both brooding and non-brooding individual survival. INP file is the standard encounter history file format associated wi...

Consumer Insurance Experience & Demographic Profile

This dataset provides a detailed view of how individuals engage with insurance products, paired with demographic and lifestyle attributes to enable powerful segmentation, behavioral analysis, and customer journey mapping. By combining real-world insurance experiences with contextual information about each respondent’s background and preferences, this dataset supports a wide range of data-driven decision-making for insurance providers, policy designers, marketing teams, and product strategists.

Value of the Dataset Understanding how consumers perceive and interact with insurance offerings is critical to building products that resonate and services that retain. This dataset offers that visibility across multiple dimensions—capturing not only what type of insurance consumers hold and how they purchased it, but also what drives their satisfaction, loyalty, and likelihood to switch. Paired with demographic details like income, education, family status, and lifestyle, this information becomes a foundation for more personalized outreach, better-designed offerings, and improved customer experiences.

Because the data reflects lived experiences across diverse markets, it is particularly valuable for benchmarking consumer sentiment in emerging economies, identifying service delivery gaps, or evaluating potential uptake of new policy formats such as digital or personalized insurance.

Example Use Cases 1. Targeted Product Design A health insurer looking to launch short-term, digital-first plans could filter this dataset for consumers with low policy tenure, high digital communication preference, and dissatisfaction with current providers. This segment would inform feature design and positioning.

1. Competitive Analysis A provider evaluating churn risk can identify patterns among users who have filed claims but report dissatisfaction—indicating operational areas that may be driving customer loss and where improvements could increase retention.

2. Communication Channel Optimization By analyzing preferred communication methods across different demographic segments, insurers can tailor outreach strategies (e.g., SMS vs. in-app chat) to improve engagement and reduce support costs.

3. Market Expansion & Localization International insurers can explore regional variations in satisfaction drivers, awareness levels, and price sensitivity to refine go-to-market strategies in countries like Senegal, Tanzania, or the UAE.

4. Personalized Policy Offer Design Using data on interest in personalized policies and lifestyle indicators, providers can build customizable offerings for consumers more likely to value flexibility, such as frequent travelers or those with irregular incomes.

Insurance-Specific Fields & Descriptions Current Insurance Type Captures the kind of insurance the individual currently holds, with a focus on health insurance in this dataset.

Purchase Method Indicates how the insurance was obtained—through an agent, online, employer, etc.—to understand acquisition channels.

Policy Length Duration of the current policy, categorized (e.g., less than 1 year, 1–3 years, more than 5 years) to analyze tenure-based behaviors.

Satisfaction Self-reported satisfaction with the current insurance provider, useful for benchmarking sentiment.

Top Factor in Choosing Provider Highlights what influenced the purchase decision most—such as coverage options, customer service, pricing, or brand reputation.

Policy Review Frequency Shows how often individuals revisit their policy details or compare with alternatives, revealing levels of engagement or passive behavior.

Filed Claim A yes/no indicator showing whether the consumer has ever filed a claim, useful for analyzing downstream service experiences.

Claim Satisfaction Measures satisfaction with how past claims were handled, providing insight into operational effectiveness.

Primary Value Sought Captures what consumers value most from their insurance—e.g., peace of mind, financial protection, access to quality care.

Likelihood to Recommend Acts as a proxy for Net Promoter Score (NPS), indicating brand advocacy and potential referral behavior.

Biggest Areas for Improvement Open-ended or multi-select responses identifying where insurers can do better—lower premiums, faster claims, more digital tools, etc.

Preferred Method of Communication Indicates how consumers want to be contacted—via online chat, phone, email, SMS—supporting channel strategy optimization.

Preferred Services Details the types of updates or services consumers want—such as claims status, policy changes, or coverage recommendations.

Insurance Awareness Score Self-reported awareness of how insurance works, including policy options, rights, and terms.

Interest in Personalized Policies Captures whether the individual is open to customized insurance plans, an important indicator for usage-ba...