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...

Our consumer data is meticulously gathered and aggregated from surveys, digital services, and public sources, ensuring the collection of fresh and reliable data points through powerful profiling algorithms. Our comprehensive data enrichment solution spans a variety of datasets, enabling you to address gaps in customer data, gain deeper insights into your customers, and enhance client experiences.

Data Categories and 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

Our dynamic data collection ensures the most updated insights, delivered at intervals best suited to your needs (daily, weekly, or monthly).

Use Cases

Our enriched consumer data supports a 360-degree customer view, data enrichment, fraud detection, and advertising & marketing, providing valuable insights to enhance your business strategies and client interactions.

Techsalerator has access to some of the most qualitative B2C data in the continent.

Thanks to our unique tools and data specialists, we can select the ideal targeted dataset based on unique elements such as the location/ country, gender, age...

Whether you are looking for an entire fill install, an access to one of our API's or if you only need a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

According to a survey carried out in August 2020 in the United Kingdom (UK), ** percent of marketing companies collected customer data through their website. Half did so through social media, while a slightly smaller share said they recorded customer data at organized events. Collection via purchase lists and preference centres were the least used methods.

CCI: Plans to Buy Within 6 Mos: sa: Major Appliances: Vacuum Cleaner data was reported at 5.700 % in Apr 2025. This records a decrease from the previous number of 6.000 % for Mar 2025. CCI: Plans to Buy Within 6 Mos: sa: Major Appliances: Vacuum Cleaner data is updated monthly, averaging 5.400 % from Feb 1967 (Median) to Apr 2025, with 637 observations. The data reached an all-time high of 11.300 % in Jan 2016 and a record low of 2.900 % in Oct 2009. CCI: Plans to Buy Within 6 Mos: sa: Major Appliances: Vacuum Cleaner data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H054: Consumer Confidence Index: Buying Plans & Intended Vacations. [COVID-19-IMPACT]

Dataset

This dataset was created by Joy Chakraborty

Released under Database: Open Database, Contents: Database Contents

Contents

It contains the following files:

CSI: Home Buying Conditions: Good Time to Buy data was reported at 69.000 % in May 2018. This records a decrease from the previous number of 71.000 % for Apr 2018. CSI: Home Buying Conditions: Good Time to Buy data is updated monthly, averaging 73.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 89.000 % in Dec 1998 and a record low of 15.000 % in Jul 1982. CSI: Home Buying Conditions: Good Time to Buy data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house?

About this Dataset

You will get an active email list for real and active buyers who make regular purchases through Amazon and other e-commerce sites. This email list contains 100% original email address. You can also use these emails to increase visits to your website, blog, or YouTube channel. I offer you now, a great treasure to use whenever you want.

So don't waste your time and start boosting your ecommerce business online.

The buyers will be from:

United States of America Canada Europe Union

$ There are no duplicate emails $ No fake IDs $ Audiences ready to buy

Category

Markets

Keywords

market,emails,email ma,list,buyer

Row Count

70150

Price

$90.00

In today’s rapidly evolving digital landscape, understanding consumer behavior has never been more crucial for businesses seeking to thrive. Our Consumer Behavior Data database serves as an essential tool, offering a wealth of comprehensive insights into the current trends and preferences of online consumers across the United States. This robust database is meticulously designed to provide a detailed and nuanced view of consumer activities, preferences, and attitudes, making it an invaluable asset for marketers, researchers, and business strategists.

Extensive Coverage of Consumer Data Our database is packed with thousands of indexes that cover a broad spectrum of consumer-related information. This extensive coverage ensures that users can delve deeply into various facets of consumer behavior, gaining a holistic understanding of what drives online purchasing decisions and how consumers interact with products and brands. The database includes:

Product Consumption: Detailed records of what products consumers are buying, how frequently they purchase these items, and the spending patterns associated with these products. This data allows businesses to identify popular products, emerging trends, and seasonal variations in consumer purchasing behavior. Lifestyle Preferences: Insights into the lifestyles of consumers, including their hobbies, interests, and activities. Understanding lifestyle preferences helps businesses tailor their marketing strategies to resonate with the values and passions of their target audiences. For example, a company selling fitness equipment can use this data to identify consumers who prioritize health and wellness.

Product Ownership: Information on the types of products that consumers already own. This data is crucial for businesses looking to introduce complementary products or upgrades. For instance, a tech company could use product ownership data to target consumers who already own older versions of their gadgets, offering them incentives to upgrade to the latest models.

Attitudes and Beliefs: Insights into consumer attitudes, opinions, and beliefs about various products, brands, and market trends. This qualitative data is vital for understanding the emotional and psychological drivers behind consumer behavior. It helps businesses craft compelling narratives and brand messages that align with the values and beliefs of their target audience.

CSI: Home Buying Conditions: Good Time: Prices Low data was reported at 14.000 % in May 2018. This records a decrease from the previous number of 15.000 % for Apr 2018. CSI: Home Buying Conditions: Good Time: Prices Low data is updated monthly, averaging 21.000 % from Feb 1978 (Median) to May 2018, with 467 observations. The data reached an all-time high of 74.000 % in May 2009 and a record low of 2.000 % in May 1979. CSI: Home Buying Conditions: Good Time: Prices Low data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house? Responses to the query 'Why do you say so?'

One of the reasons behind AI-powered customer service is the preference for conversational AI over phone calls. In 2024, 82 percent of consumers stated they would use a chatbot instead of waiting for a customer representative to take their call. An outstanding 96 percent of surveyed shoppers believed that more companies should opt for chatbots over traditional customer support services.

Consumer Intelligence: Comprehensive demographic, lifestyle & purchase data from 140M+ consumers across 8,000+ brands. Deterministic transaction-based modeling delivers 70% ROAS increase vs traditional targeting through 150+ behavioral segments.

United States CSI: Home Buying Conditions: Bad Time to Buy data was reported at 29.000 % in May 2018. This records an increase from the previous number of 27.000 % for Apr 2018. United States CSI: Home Buying Conditions: Bad Time to Buy data is updated monthly, averaging 24.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 79.000 % in Nov 1981 and a record low of 7.000 % in Dec 1998. United States CSI: Home Buying Conditions: Bad Time to Buy data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house?

Background: Chemicals in consumer products are a major contributor to human chemical co-exposures. Consumers purchase and use a wide variety of products containing potentially thousands of chemicals. There is a need to identify potential real-world chemical co-exposures in order to prioritize in vitro toxicity screening. However, due to the vast number of potential chemical combinations, this has been a major challenge. Objectives: We aim to develop and implement a data-driven procedure for identifying prevalent chemical combinations to which humans are exposed through purchase and use of consumer products. Methods: We applied frequent itemset mining on an integrated dataset linking consumer product chemical ingredient data with product purchasing data from sixty thousand households to identify chemical combinations resulting from co-use of consumer products. Results: We identified co-occurrence patterns of chemicals over all households as well as those specific to demographic groups based on race/ethnicity, income, education, and family composition. We also identified chemicals with the highest potential for aggregate exposure by identifying chemicals occurring in multiple products used by the same household. Lastly, a case study of chemicals active in estrogen and androgen receptor in silico models revealed priority chemical combinations co-targeting receptors involved in important biological signaling pathways. Discussion: Integration and comprehensive analysis of household purchasing data and product-chemical information provided a means to assess human near-field exposure and inform selection of chemical combinations for high-throughput screening in in vitro assays. This dataset is associated with the following publication: Stanfield, Z., C. Addington, K. Dionisio, D. Lyons, R. Tornero-Velez, K. Phillips, T. Buckley, and K. Isaacs. Mining of consumer product and purchasing data to identify potential chemical co-exposures.. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 129(6): N/A, (2021).

Context:

This dataset provides detailed insights into consumer behaviour and shopping patterns across various demographics, locations, and product categories. It contains 3,900 customer records with 18 attributes that describe purchase details, shopping habits, and preferences.

The dataset includes information such as:

• Customer demographics (age, gender, location)
• Product details (item purchased, category, size, color, season)
• Purchase information (amount spent in USD, payment method, shipping type)
• Shopping behaviour (frequency of purchases, previous purchases, subscription status, discount usage, promo codes)
• Customer feedback (review ratings)

This dataset can be used to explore consumer decision-making and market trends, including:

• How age, gender, or location influence shopping preferences.
• The relationship between discounts, promo codes, and purchase amounts.
• Which product categories and colors are most popular in different seasons.
• Patterns in payment method usage (e.g., PayPal vs. Credit Card).
• How subscription and loyalty behaviours affect shopping frequency.

Researchers, data analysts, and students can use this dataset to practice customer segmentation, predictive modelling, recommendation systems, and market basket analysis. It also serves as a valuable resource for learning techniques in exploratory data analysis (EDA), machine learning, and business analytics.

Dataset Glossory(Column wise)

Customer ID: A unique identifier assigned to each customer. It helps distinguish one shopper’s data from another without revealing their personal identity.

Age: The age of the customer in years, which can provide insights into generational shopping habits and how preferences differ across age groups.

Gender: Indicates whether the customer is male or female, allowing analysis of gender-based buying trends and preferences in product categories.

Item Purchased: The specific product that the customer bought, giving a direct view of consumer demand and popular items in the dataset.

Category: The broader classification of the purchased item, such as clothing or footwear, which helps in grouping products and understanding category-level trends.

Purchase Amount (USD): The total money spent on the purchase in U.S. dollars, which reflects customer spending power and the value of each transaction.

Location: The state or region where the customer resides, useful for identifying geographical shopping patterns and regional differences in consumer behaviour.

Size: The size of the purchased item (e.g., S, M, L), which helps reveal customer preferences in apparel and how sizing impacts sales.

Color: The chosen color of the purchased item, offering insights into which colors are more appealing to consumers during different seasons or product categories.

Season: The season (Winter, Spring, etc.) in which the purchase was made, showing how customer demand changes across seasonal trends.

Review Rating: A numerical score reflecting the customer’s satisfaction with the product, valuable for measuring quality perception and post-purchase behaviour.

Subscription Status: Indicates whether the customer has an active subscription with the store, which may influence loyalty, discounts, and purchase frequency.

Shipping Type: The delivery option chosen by the customer, such as free shipping or express, which highlights convenience preferences and urgency of purchase.

Discount Applied: Shows whether a discount was used during the purchase, allowing analysis of how discounts affect buying decisions and sales growth.

Promo Code Used: Specifies if the customer used a promotional code, useful for understanding the impact of marketing strategies on purchase behaviour.

Previous Purchases: The number of items the customer has bought before, reflecting their shopping history and overall loyalty to the store.

Payment Method: The mode of payment used (Credit Card, PayPal, etc.), which sheds light on financial behaviour and preferred transaction methods.

Frequency of Purchases: Indicates how often the customer engages in purchasing activities, a critical metric for assessing customer loyalty and lifetime value.

Acknowledgment

Special thanks to Sir Sourav Banerjee Associate Data Scientist at CogniTensor

Kolkata, West Bengal, India

The US Consumer Phone file contains phone numbers, mobile and landline, tied to an individual in the Consumer Database. The fields available include the phone number, phone type, mobile carrier, and Do Not Call registry status.

All phone numbers can be processed and cleansed using telecom carrier data. The telecom data, including phone and texting activity, porting instances, carrier scoring, spam, and known fraud activity, comprise a proprietary Phone Quality Level (PQL), which is a data-science derived score to ensure the highest levels of deliverability at a fraction of the cost compared to competitors.

We have developed this file to be tied to our Consumer Demographics Database so additional demographics can be applied as needed. Each record is ranked by confidence and only the highest quality data is used.

Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

Ivory Coast whatsapp number list provides you with real contact numbers for active whatsapp users. These users are ready to engage with your business and receive your marketing messages. With this data, you can easily connect with these users, promote your brand, and grow your customer base. Most importantly, the contact numbers in our database are reliable and accurate. You can trust that the contacts are real and up-to-date because our experts check them regularly. You can send special offers, updates, and information directly to interested customers. Buy valuable contact data from List to Data at wholesale prices. Ivory Coast whatsapp phone number data is a valuable tool for your business because it helps you quickly connect with many people. With this data, you can reach customers right on their phones. Ivory Coast whatsapp contact number database will help you find the right audience. You can also share news about your products and services with just a few taps. As a result, this makes it faster than sending emails or using other methods. On the other hand, people in your country like using whatsapp because it is simple and quick. Therefore, when you use this data, you can talk to customers who want to hear from you.

Use our Best Buy products to collect ratings, prices, and descriptions about products from an e-commerce online web. You can purchase either the entire dataset or a customized subset, depending on your requirements. The Best Buy Products Dataset stands as a comprehensive resource for businesses, researchers, and analysts aiming to navigate the vast array of products offered by Best Buy, a leading retailer in consumer electronics and technology. Tailored to provide a deep understanding of Best Buy's e-commerce ecosystem, this dataset facilitates market analysis, pricing optimization, customer behavior comprehension, and competitor assessment. At its core, the dataset encompasses essential attributes such as product ID, title, descriptions, ratings, reviews, pricing details, and seller information. These fundamental data elements empower users to glean insights into product performance, customer sentiment, and seller credibility, thereby facilitating informed decision-making processes. Whether you're a retailer looking to enhance your product portfolio, a researcher investigating trends in consumer electronics, or an analyst seeking to refine e-commerce strategies, the Best Buy Products Dataset offers a valuable resource for uncovering opportunities and driving success in the ever-evolving landscape of retail.

Overview

The Market_Basket_Optimisation dataset is a classic transactional dataset often used in association rule mining and market basket analysis.
It consists of multiple transactions where each transaction represents the collection of items purchased together by a customer in a single shopping trip.

• File Name: Market_Basket_Optimisation.csv
• Format: CSV (Comma-Separated Values)
• Structure: Each row corresponds to one shopping basket. Each column in that row contains an item purchased in that basket.
• Nature of Data: Transactional, categorical, sparse.
• Primary Use Case: Discovering frequent itemsets and association rules to understand shopping patterns, product affinities, and to build recommender systems.

Detailed Information

📊 Dataset Composition

• Transactions: 7,501 (each row = one basket).
• Items (unique): Around 120 distinct products (e.g., bread, mineral water, chocolate, etc.).
• Columns per row: Up to 20 possible items (not fixed; some rows have fewer, some more).
• Data Type: Purely categorical (no numerical or continuous features).
• Missing Values: Present in the form of empty cells (since not every basket has all 20 columns).
• Duplicates: Some baskets may appear more than once — this is acceptable in transactional data as multiple customers can buy the same set of items.

🛒 Nature of Transactions

• Basket Definition: Each row captures items bought together during a single visit to the store.
• Variability: Basket size varies from 1 to 20 items. Some customers buy only one product, while others purchase a full set of groceries.
• Sparsity: Since there are ~120 unique items but only a handful appear in each basket, the dataset is sparse. Most entries in the one-hot encoded representation are zeros.

🔎 Examples of Data

Example transaction rows (simplified):

Here, empty cells mean no item was purchased in that slot.

📈 Applications of This Dataset

This dataset is frequently used in data mining, analytics, and recommendation systems. Common applications include:

1. Association Rule Mining (Apriori, FP-Growth):

   • Discover rules like {Bread, Butter} ⇒ {Jam} with high support and confidence.
   • Identify cross-selling opportunities.

2. Product Affinity Analysis:

   • Understand which items tend to be purchased together.
   • Helps with store layout decisions (placing related items near each other).

3. Recommendation Engines:

   • Build systems that suggest "You may also like" products.
   • Example: If a customer buys pasta and tomato sauce, recommend cheese.

4. Marketing Campaigns:

   • Bundle promotions and discounts on frequently co-purchased products.
   • Personalized offers based on buying history.

5. Inventory Management:

   • Anticipate demand for certain product combinations.
   • Prevent stockouts of items that drive the purchase of others.

📌 Key Insights Potentially Hidden in the Dataset

• Popular Items: Some items (like mineral water, eggs, spaghetti) occur far more frequently than others.
• Product Pairs: Frequent pairs and triplets (e.g., pasta + sauce + cheese) reflect natural meal-prep combinations.
• Basket Size Distribution: Most customers buy fewer than 5 items, but a small fraction buy 10+ items, showing long-tail behavior.
• Seasonality (if extended with timestamps): Certain items might show peaks in demand during weekends or holidays (though timestamps are not included in this dataset).

📂 Dataset Limitations

1. No Customer Identifiers:

   • We cannot track repeated purchases by the same customer.
   • Analysis is limited to basket-level insights.

2. No Timestamps:

   • No temporal analysis (trends over time, seasonality) is possible.

3. No Quantities or Prices:

   • We only know whether an item was purchased, not how many units or its cost.

4. Sparse & Noisy:

   • Many baskets are small (1–2 items), which may produce weak or trivial rules.

🔮 Potential Extensions

• Synthetic Timestamps: Assign simulated timestamps to study temporal buying patterns.
• Add Customer IDs: If merged with external data, one can perform personalized recommendations.
• Price Data: Adding cost allows for profit-driven association rules (not just frequency-based).
• Deep Learning Models: Sequence models (RNNs, Transformers) could be applied if temporal ordering of items is introduced.

...

Can you predict how much a customer will spend in our fictional shop? This is a simulated data set. The intention was not to create realistic data but data with interesting/odd patterns.

- Gender - Age - City - Income - Relationship: Boolean feature (True when the person is in a relationship otherwise false) - Children: Boolean feature (True when the person has children otherwise false) - Degree: Highest degree - Review: Review for previous purchases ('No Prior Purchase' when first time purchase, 'No Review' when there was no rating given, otherwise on a scale from 0 to 5) - y: The amount of money spent in our shop