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 targeting requirements and receive custom pricing for your marketing objectives.

This dataset contains transaction details of users, including their demographics and purchasing behavior. It features information such as User ID, Age, Gender, Country, Purchase Amount, Purchase Date, and Product Category. This data can be useful for analyzing consumer trends, demographic influences on purchasing behavior, and market segmentation.

• User ID: A unique identifier assigned to each user for tracking their transactions.
• Age: The age of the user at the time of purchase, which may influence buying behavior.
• Gender: The gender of the user, allowing for demographic segmentation of purchasing patterns.
• Country: The country of residence for the user, useful for regional market analysis.
• Purchase Amount: The total amount spent by the user during a transaction.
• Purchase Date: The date when the purchase was made, allowing for temporal analysis of buying behavior.
• Product Category: The category of the product purchased, aiding in understanding consumer preferences.

Apple Card owners in the United States in 2023 were typically Millennials (** percent of respondents) who tended to have a relatively high income. This is according to a survey held among Americans who either owned or did not own Apple's credit card. The source adds this demographic was in line with other surveys they held for other Apple products. Statista's Consumer Insights also noted that U.S. Apple iOS users are typically high income. The source of this particular survey, however, does not state how many of its 4,000 respondents owned an Apple Card. All statistics on Apple Pay - and services that rely on it, such as Apple Card and Apple Cash - are estimates, typically based on survey information. Apple Inc. does not share figures on individual services, whereas financial providers who offer Apple Pay, Apple Card, etc. are contractually forbidden to share such information.

Overview:

This dataset provides a comprehensive overview of customer interactions with an online retail store, aiming to predict customer churn based on various behavioral and demographic features. It includes data on customer demographics, spending behavior, satisfaction levels, and engagement with marketing campaigns. The dataset is designed for analysis and development of predictive models to identify customers at risk of churn, enabling targeted customer retention strategies.

Description of Columns:

• Customer_ID: A unique identifier for each customer.
• Age: The customer's age.
• Gender: The customer's gender (Male, Female, Other).
• Annual_Income: The annual income of the customer in thousands of dollars.
• Total_Spend: The total amount spent by the customer in the last year.
• Years_as_Customer: The number of years the individual has been a customer of the store.
• Num_of_Purchases: The number of purchases the customer made in the last year.
• Average_Transaction_Amount: The average amount spent per transaction.
• Num_of_Returns: The number of items the customer returned in the last year.
• Num_of_Support_Contacts: The number of times the customer contacted support in the last year.
• Satisfaction_Score: A score from 1 to 5 indicating the customer's satisfaction with the store.
• Last_Purchase_Days_Ago: The number of days since the customer's last purchase.
• Email_Opt_In: Whether the customer has opted in to receive marketing emails.
• Promotion_Response: The customer's response to the last promotional campaign (Responded, Ignored, Unsubscribed).
• Target_Churn: Indicates whether the customer churned (True or False).

This survey consisted of 4 surveys covering a total of eighteen different services of Wake County. The study attempted to measure resident satisfaction with public services provided by the county. A set of common core questions plus demographics were contain in each survey.

Access high-fidelity consumer data powered by our proprietary modeling technology that provides the most comprehensive consumer intelligence, accurate targeting, first-party data enrichment, and personalization at scale. Our deterministic dataset, anchored in the purchasing habits of over 140 million U.S. consumers, delivers superior targeting performance with proven 70% increase in ROAS.

Core Data Assets Transactional Data Foundation: Real purchasing behavior from over 140 million U.S. consumers with 8.5 billion behavioral signals across 250 million adults. Seven years of daily credit card and debit card purchase data aggregated from all major credit cards sourced from more than 300 national banks, capturing $2+ trillion in annual discretionary spending.

Consumer Demographics & Lifestyle: Comprehensive profiles including age, income, household composition, geographic distribution, education, employment, and lifestyle indicators. Our proprietary taxonomy organizes consumer spending across 8,000+ brands and 2,500+ merchants, from major retailers to emerging direct-to-consumer brands.

Behavioral Segmentation: 150+ custom consumer communities including demographic groups (Gen Z, Millennials, Gen X), lifestyle segments (Health & Fitness Enthusiasts, Tech Early Adopters, Luxury Shoppers), and behavioral categories (Deal Seekers, Brand Loyalists, Premium Service Users, Streaming Subscribers). Purchase Intelligence: Deep insights into consumer spending patterns across entertainment, fitness, fashion, technology, travel, dining, and retail categories. Our models identify cross-category purchasing behaviors, seasonal trends, and brand switching patterns to optimize targeting strategies. Advanced Modeling Technology

Our proprietary consumer intelligence engine combines deterministic transaction-based data with Smart Audience Engineering that transforms first-party signals from anonymized website traffic, behavioral indicators, and CRM enrichment into precision-modeled segments. Unlike traditional data providers who sell static lists, our AI-powered predictive modeling continuously learns and optimizes for unprecedented precision and superior conversion outcomes.

Performance Advantages: Audiences built on user-level transactional data deliver 70% increase in ROAS compared to traditional targeting methods. Weekly-optimized audiences with performance narratives eliminate wasted ad spend by 20-30%, while our deterministic AI models analyze hundreds of attributes and conversion-validated signals to identify prospects with genuine purchase intent, not just lookalike behaviors.

This statistic displays the penetration of sharing images and pictures on social media websites in Great Britain in 2013, by demographic. As of April 2013, 28 percent of males reported having shared an image on social media in the month previous to the survey.

Real-Time Offers Market Outlook

According to our latest research, the Global Real-Time Offers market size was valued at $8.7 billion in 2024 and is projected to reach $36.2 billion by 2033, expanding at a robust CAGR of 17.4% during the forecast period of 2025–2033. The primary driver fueling this remarkable growth is the increasing demand for hyper-personalized customer experiences across industries such as retail, BFSI, and telecommunications. Businesses are leveraging real-time offer engines to deliver contextually relevant promotions, discounts, and incentives to consumers at the point of decision, thereby improving conversion rates, customer retention, and overall brand loyalty. This shift towards data-driven, instant engagement is transforming traditional marketing and sales approaches, positioning real-time offers as a cornerstone of modern customer relationship management strategies globally.

Regional Outlook

North America currently commands the largest share of the global Real-Time Offers market, accounting for over 38% of total revenue in 2024. This dominance is attributed to the region’s mature digital infrastructure, high adoption of advanced analytics, and the presence of leading technology providers. The United States, in particular, has seen widespread deployment of real-time offer platforms across retail, BFSI, and telecommunications sectors, driven by the need to enhance customer engagement in a highly competitive landscape. Regulatory frameworks supporting data privacy and digital innovation, coupled with a consumer base that is both digitally savvy and receptive to personalized offers, have accelerated market maturity. Additionally, North American enterprises are early adopters of AI and machine learning, further propelling the capabilities and adoption of real-time offer solutions.

The Asia Pacific region is forecasted to be the fastest-growing market, with a projected CAGR of 21.2% from 2025 to 2033. This rapid expansion is fueled by increasing smartphone penetration, burgeoning e-commerce activity, and significant investments in digital transformation by enterprises across China, India, Japan, and Southeast Asia. Local businesses are leveraging real-time offers to capture the attention of a young, mobile-first consumer demographic, particularly in retail and financial services. Government initiatives supporting digital ecosystems and the proliferation of fintech and super-app platforms have further accelerated market growth. The region’s dynamic startup landscape, coupled with rising foreign direct investment in technology, is expected to sustain this momentum, making Asia Pacific a focal point for innovation and market expansion in the real-time offers domain.

In contrast, Latin America and the Middle East & Africa are emerging markets with immense potential but face unique adoption challenges. While digitalization initiatives are underway, infrastructural constraints, limited access to advanced analytics, and lower consumer trust in digital channels can hinder widespread deployment. However, localized demand is rising, particularly in urban centers where retailers and banks are piloting real-time offer solutions to differentiate their services. Policy reforms aimed at fostering digital inclusion and cross-border e-commerce are gradually improving market conditions. As these regions continue to invest in connectivity and digital literacy, the adoption of real-time offers is expected to accelerate, albeit at a more measured pace compared to their developed counterparts.

Report Scope

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2014-2018). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18

This dataset simulates e-commerce sales data for developing and evaluating forecasting models. It is designed to capture a range of variables that influence online sales in a dynamic and competitive environment. The data includes information on customer behavior, market trends, seasonal patterns, product availability, and other critical factors. By including both internal metrics (e.g., customer engagement, website traffic) and external factors (e.g., economic indicators), the dataset allows for comprehensive analysis and prediction of sales patterns.

Key Features Customer Behavior: A normalized score representing how customers interact with the platform, including engagement levels. Market Trends: Trend indicators representing general market upturns or downturns (e.g., recent demand fluctuations). Seasonal Fluctuations: Categorical data indicating whether sales are affected by high, medium, or low seasonal demand. Product Availability: Count of available units per product, reflecting stock levels. Customer Demographics: Age categories of customers, helping to understand the demographic profile. Website Traffic: Daily visit counts, capturing the volume of potential buyers. Engagement Rate: Percentage of visitors actively interacting with the website, showing engagement quality. Discount Rate: Percentage discount offered on products, affecting customer purchasing behavior. Advertising Spend: Daily spending on digital advertisements, representing marketing efforts. Social Media Engagement: Count of interactions on social media (likes, shares, comments), indicating brand visibility. Returning Customers Rate: Percentage of repeat buyers, showing customer retention. New Customers Count: Number of newly acquired customers per day. Product Categories: The main category of products (e.g., electronics, fashion), enabling analysis by product type. Average Order Value: The average value of orders placed, reflecting purchase amounts. Shipping Speed: Average time for delivery (in days), influencing customer satisfaction. Customer Satisfaction Score: Rating based on customer feedback (1-5 scale). Economic Indicator: Simulated external factor (e.g., consumer confidence) impacting purchasing power. Sales Forecast: Target variable representing the forecasted number of sales.

For the purpose of our partners and the community to find demographic information on individual member of households that applied for services provided by the Office of Social Services. The data is updated anually based on the previous fiscal year and it includes: Client IndexHousehold IndexRaceGenderEthnicityDisability StatusMilitary StatusHealth Insurance (Y/N)Employment StatusEducation StatusHead of Household (Y/N)Age The focus of the Office of Social Services (OSS) is to provide essential services for Louisville residents, especially for low and moderate income populations, including: preventing homelessness; delivering Meals on Wheels; helping families build financial stability and security; operating LIHEAP to help residents stay safe and warm; making microloans to jumpstart small businesses; and supporting eight Neighborhood Places.

As of October 2025, users aged 25 to 34 years made up Facebook's largest audience in the United States, accounting for **** percent of the social network's user base, with **** percent of those users being women. Overall, *** percent of users aged 35 to 44 years were women, and *** percent were men. How many people use Facebook in the United States? ******** is by far the most used social network in the world and finds a huge share of its audience in ****************** Facebook’s U.S. audience size comes second only to India. In 2023, there were over *** million Facebook users in the U.S. By 2028, it is estimated that around *** million people in the U.S. will be signed up for the platform. How do users in the United States view the platform? Although Facebook is widely used and very popular with U.S. consumers, there are issues of trust with its North American audience. As of November 2021, ** percent of respondents reported that they did not trust Facebook with their personal data. Despite having privacy doubts, a May 2022 survey found that ** percent of adults had a very favorable opinion of Facebook, and one-third held a somewhat positive view of the platform.

Distribution of Late Baby Boomer home buyers by race and ethnicity.

Amazon Pay users in Austria made up ***** percent of respondents in 2025, and were likely to come from a **** income. This is according to questions asked in Statista's Consumer Insights, focusing on what payment services consumers used in the past 12 months. The typical user profile of an Amazon Pay user in Austria was that they were ****, were ******** years old, and fell in the ******* quantile in terms of income.

This dataset provides a comprehensive view of insurance policy renewals, combining detailed policy attributes, customer demographics, claims history, payment behavior, and renewal outcomes. It is ideal for predictive modeling, customer retention analysis, and operational optimization in the insurance industry.

The Global Waterless Cosmetics Market Size Was Worth USD 10 Billion in 2023 and Is Expected To Reach USD 20 Billion by 2032, CAGR of 11%.