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.

Demographic Analysis of Shopping Behavior: Insights and Recommendations

Dataset Information: The Shopping Mall Customer Segmentation Dataset comprises 15,079 unique entries, featuring Customer ID, age, gender, annual income, and spending score. This dataset assists in understanding customer behavior for strategic marketing planning.

Cleaned Data Details: Data cleaned and standardized, 15,079 unique entries with attributes including - Customer ID, age, gender, annual income, and spending score. Can be used by marketing analysts to produce a better strategy for mall specific marketing.

Challenges Faced: 1. Data Cleaning: Overcoming inconsistencies and missing values required meticulous attention. 2. Statistical Analysis: Interpreting demographic data accurately demanded collaborative effort. 3. Visualization: Crafting informative visuals to convey insights effectively posed design challenges.

Research Topics: 1. Consumer Behavior Analysis: Exploring psychological factors driving purchasing decisions. 2. Market Segmentation Strategies: Investigating effective targeting based on demographic characteristics.

Suggestions for Project Expansion: 1. Incorporate External Data: Integrate social media analytics or geographic data to enrich customer insights. 2. Advanced Analytics Techniques: Explore advanced statistical methods and machine learning algorithms for deeper analysis. 3. Real-Time Monitoring: Develop tools for agile decision-making through continuous customer behavior tracking. This summary outlines the demographic analysis of shopping behavior, highlighting key insights, dataset characteristics, team contributions, challenges, research topics, and suggestions for project expansion. Leveraging these insights can enhance marketing strategies and drive business growth in the retail sector.

References OpenAI. (2022). ChatGPT [Computer software]. Retrieved from https://openai.com/chatgpt. Mustafa, Z. (2022). Shopping Mall Customer Segmentation Data [Data set]. Kaggle. Retrieved from https://www.kaggle.com/datasets/zubairmustafa/shopping-mall-customer-segmentation-data Donkeys. (n.d.). Kaggle Python API [Jupyter Notebook]. Kaggle. Retrieved from https://www.kaggle.com/code/donkeys/kaggle-python-api/notebook Pandas-Datareader. (n.d.). Retrieved from https://pypi.org/project/pandas-datareader/

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...

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.

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.

The Consumer Behavior database is derived from an analysis of ‘doublebase’ survey data using geodemographic market segmentation. Each of the approximately 40,000 records in the survey is geocoded then assigned the geodemographic market segment code of the block group. The results are then summarized for each variable over the sixty-eight segments, in effect providing the average value for each market segment. For example, a variable such as “Shopped at Macy’s” is computed by summarizing the records for each segment as a yes/no response, then finding the average percentage of households in each segment who shopped at Macy’s. This is often referred to as a profile.

The profile is then applied to geographic areas by making the assumption that households in demographically similar neighborhoods will tend to have similar consumption patterns as a result of their similar economic means, life stage, and other characteristics. The result is a series of estimates for geographic areas which measure the relative propensity of consumers in each geographic area to shop at particular stores, own various household items, and engage in activities.

Consumer Behavior Categories include; • Apparel • Appliances • Attitudes and Organizations • Advertising • Media Advertising • Media Attitudes • Automobiles • Buying Habits • Consumer Confidence • Financial • Food • Health • Intended Purchases • Political Outlook • Public Activities • Sports • Technology • Vacations • Automotive • Baby • Beverages • Computer • Electronics • Family Restaurants • Fast Food and Drive-In Restaurants • Financial • Groceries • Health & Beauty • Health & Medical • Home Furnishings and Equipment • Insurance • Internet • Leisure • Media Radio • Media Read • Media Television • Pets • Shopping • Sports • Telephone • Travel • Video

In this project, I conducted a comprehensive analysis of customer data using Power BI. The objective was to visualize and gain insights from the data, focusing on customer demographics and product categories.

📈The analysis includes the following key visualizations:

Customer Distribution by Age: illustrates the number of customers across different age groups, providing insights into the demographic distribution.

Customer Distribution by Time: This visualization shows the count of customers segmented by year, quarter, month, and day, helping identify trends over time.

Customer Distribution by Gender: displays the distribution of customers by gender, highlighting any significant differences.

Total Amount by Product Category: depicts the total revenue generated by each product category, allowing for easy comparison.

Quantity by Product Category: shows the total quantity of products sold in each category, helping to identify popular items.

The cards display key metrics:

Average Age: 41.39 Total Customers: 1000 Total Quantity Sold: 2514 Total Amount Sold: 465 000$ Total Transactions: 1000 Additionally, I implemented filters for product category, date, gender, quantity, and age, providing users with the ability to refine their analysis.

Findings:

The analysis of customer distribution by age reveals no specific relationship between age and the quantity of products sold. This indicates that purchasing behavior may not be strongly influenced by the customer’s age. There are notable peaks in the quantity sold on May 20, 2023, and again in July, suggesting higher purchasing activity during these periods. The customer distribution by gender shows that 49% of customers are female, while 51% are male. In terms of total amount sold by product category, electronics is the top category, generating the highest revenue, followed by clothing, with beauty ranking last. Similarly, when looking at quantity sold by product category, electronics makes up 33.77%, clothing is slightly higher at 35.56%, and beauty is the smallest category at 3.67%. This project demonstrates the power of Power BI in analyzing customer data and deriving actionable insights. The visualizations created provide a clear understanding of customer behavior and preferences, which can help businesses make informed decisions.

There are more male LinkedIn users than females – although it is pretty balanced.

The most significant cohorts of users on Instagram are aged 18 – 24.

As of June 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.

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.

About Dataset:

Domain : Marketing Project: User Profiling and Segmentation Datasets: user_profile_for_ads Dataset Type: Excel Data Dataset Size: 16k+ record

KPI's: 1. Distribution of Key Demographic Variables like: a. Count of Age b. Count of Gender c. Count of Education Level d. Count of Income Level e. Count of Device Usage

1. Understanding Online Behavior like: a. Count of Time Spent Online (hrs/Weekday) b. Count of Time Spent Online (hrs/Weekend)

2. Ad Interaction Metrics: a. Count of likes and Reactions b. Count of click through rates (CTR) c. Count of Conversion Rate d. Count of Ad Interaction Time (secs) e. Count of Ad Interaction Time by Top Interests

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains bar chart, horizontal bars, circle, treemap, area chart, square, line chart, dashboard, slicers, navigation button.

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.

This dataset contains detailed survey responses on user adoption, barriers, and satisfaction with emerging technologies, linked to respondent demographics and technology categories. It enables researchers to analyze adoption trends, identify barriers, and assess user satisfaction across diverse populations and technology types. Ideal for academic studies, market analysis, and product development insights.

The table AK-Demographic-2025-06-26 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 579388 rows across 698 variables.

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%.

The Ryan White HIV/AIDS Program provides a comprehensive system of care that includes primary medical care and essential support services for people living with HIV who are uninsured or underinsured. The Ryan White Grants are Federal funds which offer services to HIV clients in the Austin area and surrounding 10 counties. Medical Transportation is a service available to Ryan White eligible clients to use to get to and from medical appointments. This data includes Client Id, Age Range (10 years) , Gender, Education, Insurance, Race , Ethnicity, Primary Language, Living Situation. It also includes the medical transportation type used, Agency that issued the transportation, Grant Type, Grant Dates and Grant Year.

There were 280 007 300 Facebook users in United States of America in April 2024, which accounted for 81.2% of its entire population. The majority of them were women - 53.9%. People aged 25 to 34 were the largest user group (67 900 000). The highest difference between men and women occurs within people aged 65 and above, where women lead by 13 300 000.

In recent times, it has been observed that social media exerts a favorable influence on consumer purchasing behavior. Many organizations are adopting the utilization of social media platforms as a means to promote products and services. Hence, it is crucial for enterprises to understand the consumer buying behavior in order to thrive. This article presents a novel approach that combines the theory of planned behavior (TPB) with machine learning techniques to develop accurate predictive models for consumer purchase behavior. This study examines three distinct factors of the theory of planned behavior (attitude, social norm, and perceived behavioral control) that provide insights into the primary determinants influencing online purchasing behavior. A total of eight machine learning algorithms, namely K-nearest neighbor, Decision Tree, Random Forest, Logistic Regression, Naive Bayes, Support Vector Machine, AdaBoost, and Gradient Boosting, were utilized in order to forecast consumer purchasing behavior. Empirical findings indicate that gradient boosting demonstrates superior performance in predicting customer buying behavior, with an accuracy rate of 0.91 and a macro F1 score of 0.91. This holds true when all factors, namely attitude (ATTD), social norm (SN), and perceived behavioral control (PBC), are included in the analysis. Furthermore, we incorporated Explainable AI (XAI), specifically LIME (Local Interpretable Model-Agnostic Explanations), to elucidate how the best machine learning model (i.e. gradient boosting) makes its prediction. The findings indicate that LIME has demonstrated a high level of confidence in accurately predicting the influence of low and high behavior. The outcome presented in this article has several implications. For instance, this article presents a novel way to combine the theory of planned behavior with machine learning techniques in order to predict consumer purchase behavior. This integration allows for a comprehensive analysis of factors influencing online purchasing decisions. Also, the incorporation of Explainable AI enhances the transparency and interpretability of the model. This feature is valuable for organizations seeking insights into factors driving predictions and the reasons behind certain outcomes. Moreover, these observations have the potential to offer valuable insights for businesses in customizing their marketing strategies to align with these influential factors.

This statistic shows the demographic changes having largest impact according to insurance companies in Africa in 2017. In 2017, ** percent of African insurers said that the growing black middle class would have a large impact on the insurance market in Africa, whereas only ** percent said the same about population growth.