The Chemical and Product Categories database (CPCat) catalogs the use of over 40,000 chemicals and their presence in different consumer products. The chemical use information is compiled from multiple sources while product information is gathered from publicly available Material Safety Data Sheets (MSDS). EPA researchers are evaluating the possibility of expanding the database with additional product and use information.

Alesco's Consumer Database contains demographic information on almost every household in the nation. Nowhere else will you find more complete and accurate information on U.S. households, individuals by name and age, lifestyle interests, hobbies, purchase behavior and ethnicity along with detailed financial-related data including mortgage, wealth and credit attributes. Alesco provides hundreds of selection options to help you target your customers more precisely.

We build the database utilizing hundreds of sources including public records, directories, county recorder and tax assessor files, US Census data, surveys, and purchase transactions. The file is built at both the individual and household levels to provide multiple targeting options. We continuously utilize USPS processing routines to give you the most complete and up-to-date addresses.

Flexible pricing available to meet all your business needs. Data is available on a transactional basis or for yearly licensing with unlimited use cases for marketing and analytics.

NCCDB is a web-based information system for recording and reporting on household goods, safety violation, hazardous material, cargo tank and passenger complaints. NCCDB allows the public and FMCSA staff to submit complaints using an online form. The database contains, among other information, reports on inspection and test of cargo tanks and inventory of tanks. These reports are used in the development and amendment to regulations of cargo security which is the protection of cargo from theft.

On March 11, 2011, the U.S. Consumer Product Safety Commission launched SaferProducts.gov. This site hosts the agency's new Publicly Available Consumer Product Safety Information Database. On SaferProducts.gov, consumers can submit reports of harm or reports of potential harm. After a short amount of time for review by the agency and named manufacturer, these reports go live on SaferProducts.gov and are searchable by the public. The public also can export search results. The Application Protocol Interface (API), to open the published SaferProducts.gov data to developers and businesses so that the information in SaferProducts.gov can be accessed by an even greater number of consumers online and on mobile devices.

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.

Consumer Mailing Lists and Consumer Email Lists. 210+ Million Consumers. Search using detailed Demographic criteria and Geographic information.

The Consumer Complaint Database is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaints referred to other regulators, such as complaints about depository institutions with less than $10 billion in assets, are not published in the Consumer Complaint Database.This database is not a statistical sample of consumers’ experiences in the marketplace. Complaints are not necessarily representative of all consumers’ experiences and complaints do not constitute “information” for purposes of the Information Quality Act . Complaint volume should be considered in the context of company size and/or market share. For example, companies with more customers may have more complaints than companies with fewer customers. We encourage you to pair complaint data with public and private datasets for additional context. The Bureau publishes the consumer’s narrative description of his or her experience if the consumer opts to share it publicly and after the Bureau removes personal information. We don’t verify all the allegations in complaint narratives. Unproven allegations in consumer narratives should be regarded as opinion, not fact. We do not adopt the views expressed and make no representation that consumers’ allegations are accurate, clear, complete, or unbiased in substance or presentation. Users should consider what conclusions may be fairly drawn from complaints alone.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. Each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

Each week we send thousands of consumers’ complaints about financial products and services to companies for response. Those complaints are published here after the company responds or after 15 days, whichever comes first. By adding their voice, consumers help improve the financial marketplace.

Source: Consumer Complaint Database

Acknowlegement

Foto von Anastasiya Badun auf Unsplash

description:

This database links over 4,000 consumer brands to health effects from Material Safety Data Sheets (MSDS) provided by the manufacturers and allows scientists and consumers to research products based on chemical ingredients.

The Household Products Database of the National Library of Medicine is based on the Consumer Product Information Database 2001-2013 by DeLima Associates. All rights reserved.

This database links over 4,000 consumer brands to health effects from Material Safety Data Sheets (MSDS) provided by the manufacturers and allows scientists and consumers to research products based on chemical ingredients.

The Household Products Database of the National Library of Medicine is based on the Consumer Product Information Database 2001-2013 by DeLima Associates. All rights reserved.

The global Customer Database Software Solutions market is experiencing robust growth, driven by the increasing need for businesses to manage and leverage customer data effectively. The market, valued at approximately $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors, including the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the increasing demand for personalized customer experiences, and the growing importance of data analytics for informed business decisions. Furthermore, the expansion of e-commerce and the surge in data generated from various customer touchpoints necessitate sophisticated database management systems capable of handling large volumes of information. The competitive landscape is populated by established players such as Salesforce, NetSuite, and Zoho, along with emerging niche solutions catering to specific industry needs. The market segmentation is evolving, with a noticeable shift towards solutions integrating CRM functionalities with advanced analytics and marketing automation tools. The continued expansion of the customer database software market is anticipated to be driven by the increasing adoption of AI and machine learning technologies for improved customer segmentation and predictive analytics. Furthermore, the growing emphasis on data security and compliance regulations will further propel the demand for robust and secure solutions. While challenges such as integration complexities and the need for skilled personnel to manage these systems persist, the overall market outlook remains positive. The integration of customer database software with other enterprise solutions is expected to further streamline operations and enhance overall business efficiency. This synergistic approach will drive further market growth in the coming years, creating opportunities for both established players and innovative newcomers.

Consumer complaints registered with the Oregon Dept. of Justice. The database of consumer complaints is derived from consumer contacts for the years of 2017 - 2019 and is for informational purposes only. This dataset may not offer a completely accurate or comprehensive account of every incident. Several factors, including a company’s size and volume of transactions, may affect the likelihood of a consumer complaint being filed. The number of complaints about a business may not be a reliable measure as to whether it is appropriately conducting business. The information in this dataset is updated as soon as possible. However, recently submitted complaints may not be immediately available. The statements in this dataset do not necessarily reflect the opinion of the DOJ. For more information, see http://www.doj.state.or.us/finfraud/index.shtml

Comprehensive dataset of 11 Consumer advice centers in Ohio, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Consumer Complaints Dataset

Description:

This dataset provides detailed information about consumer complaints spanning various financial products and services.

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It includes data on the date of complaint, product and sub-product details, specific issues raised, company responses, and more. The dataset covers a wide range of time periods, allowing users to analyze trends in consumer complaints over the years.

Key Columns

Date received: Description: The date when the consumer complaint was received. Format: MM/DD/YYYY

Product: Description: Type of financial product or service associated with the complaint. Example: Credit reporting, Debt collection, Mortgage, etc.

Sub-product: Description: A more specific category under the main product. Example: FHA mortgage, Credit card, Personal loan, etc.

Issue: Description: Specific problem or reason for the consumer's complaint. Example: Incorrect information on your report, Improper use of your report, Application denials, etc.

Sub-issue: Description: Further details specifying the issue. Example: Information belongs to someone else, Reporting company used your report improperly, etc.

Consumer complaint narrative: Description: Free-form text where consumers provide detailed complaints.

Company public response: Description: Public response provided by the company addressing the consumer's complaint.

Company: Description: Name of the company against which the complaint is filed.

State: Description: State where the consumer resides.

ZIP code: Description: ZIP code of the consumer's location.

Tags: Description: Additional labels or tags associated with the complaint.

Consumer consent provided?: Description: Indicates whether the consumer provided consent regarding the complaint.

Submitted via: Description: The channel through which the complaint was submitted (e.g., Web, Referral).

Date sent to company: Description: Date when the complaint was forwarded to the company.

Company response to consumer: Description: The company's response to the consumer's complaint.

Timely response: Description: Indicates whether the company responded to the complaint in a timely manner.

Consumer disputed: Description: Indicates whether the consumer disputed the company's response.

Complaint ID: Description: Unique identifier for each consumer complaint.

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Usage Suggestions:

Explore patterns and trends in consumer complaints, analyze the responsiveness of companies, and gain insights into the distribution of complaints across different products and regions.

This is a relational database schema for a sales and order management system, designed to track customers, employees, products, orders, and payments. Below is a detailed breakdown of each table and their relationships:

1. productlines Table (Product Categories)

• Represents different product categories.
• Primary Key: productLine
• Attributes:
  • textDescription: A short description of the product line.
  • htmlDescription: A detailed HTML-based description.
  • image: Associated image (if applicable).
• Relationships:
  • One-to-Many with products: Each product belongs to one productLine.

2. products Table (Product Information)

• Stores details of individual products.
• Primary Key: productCode
• Attributes:
  • productName: Name of the product.
  • productLine: Foreign key linking to productlines.
  • productScale, productVendor, productDescription: Additional product details.
  • quantityInStock: Number of units available.
  • buyPrice: Cost price per unit.
  • MSRP: Manufacturer's Suggested Retail Price.
• Relationships:
  • Many-to-One with productlines (each product belongs to one category).
  • One-to-Many with orderdetails (a product can be part of many orders).

3. orderdetails Table (Line Items in an Order)

• Stores details of each product within an order.
• Composite Primary Key: (orderNumber, productCode)
• Attributes:
  • quantityOrdered: Number of units in the order.
  • priceEach: Price per unit.
  • orderLineNumber: The sequence number in the order.
• Relationships:
  • Many-to-One with orders (each order has multiple products).
  • Many-to-One with products (each product can appear in multiple orders).

4. orders Table (Customer Orders)

• Represents customer orders.
• Primary Key: orderNumber
• Attributes:
  • orderDate: Date when the order was placed.
  • requiredDate: Expected delivery date.
  • shippedDate: Actual shipping date (can be NULL if not shipped).
  • status: Order status (e.g., "Shipped", "In Process", "Cancelled").
  • comments: Additional remarks about the order.
  • customerNumber: Foreign key linking to customers.
• Relationships:
  • One-to-Many with orderdetails (an order contains multiple products).
  • Many-to-One with customers (each order is placed by one customer).

5. customers Table (Customer Details)

• Stores customer information.
• Primary Key: customerNumber
• Attributes:
  • customerName: Name of the customer.
  • contactLastName, contactFirstName: Contact person.
  • phone: Contact number.
  • addressLine1, addressLine2, city, state, postalCode, country: Address details.
  • salesRepEmployeeNumber: Foreign key linking to employees, representing the sales representative.
  • creditLimit: Maximum credit limit assigned to the customer.
• Relationships:
  • One-to-Many with orders (a customer can place multiple orders).
  • One-to-Many with payments (a customer can make multiple payments).
  • Many-to-One with employees (each customer has a sales representative).

6. payments Table (Customer Payments)

• Stores payment transactions.
• Composite Primary Key: (customerNumber, checkNumber)
• Attributes:
  • paymentDate: Date of payment.
  • amount: Payment amount.
• Relationships:
  • Many-to-One with customers (each payment is linked to a customer).

7. employees Table (Employee Information)

• Stores details of employees, including reporting hierarchy.
• Primary Key: employeeNumber
• Attributes:
  • lastName, firstName: Employee's name.
  • extension, email: Contact details.
  • officeCode: Foreign key linking to offices, representing the employee's office.
  • reportsTo: References another employeeNumber, establishing a hierarchy.
  • jobTitle: Employee’s role (e.g., "Sales Rep", "Manager").
• Relationships:
  • Many-to-One with offices (each employee works in one office).
  • One-to-Many with employees (self-referential, representing reporting structure).
  • One-to-Many with customers (each employee manages multiple customers).

8. offices Table (Office Locations)

• Represents company office locations.
• Primary Key: officeCode
• Attributes:
  • city, state, country: Location details.
  • phone: Office contact number.
  • addressLine1, addressLine2, postalCode, territory: Address details.
• Relationships:
  • One-to-Many with employees (each office has multiple employees).

Conclusion

This schema provides a well-structured design for managing a sales and order system, covering: ✅ Product inventory
✅ Order and payment tracking
✅ Customer and employee management
✅ Office locations and hierarchical reporting

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.

The US Consumer IP Address file contains information on the location and observation dates of IP addresses tied to individuals in the Consumer Database.

Comprehensive dataset of 6 Consumer advice centers in Finland as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 1 Consumer advice centers in State of Amapá, Brazil as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Andrew Wharton's US Consumer Email Databases provide over 650 million current and active email address records in our 36-month Production Email Database, and additionally, over 1.4 billion historical records in our Legacy Email Database. These databases offer a comprehensive look-back at the digital and terrestrial identity information associated with a consumer. This Identity Graph Data has been collected from website registrations and is 100% opted-in for Third Party Uses.

The Email Address Data is fully populated with email addresses, HEMS (MD5, Sha1, Sha256), first name, last name, postal address (primary and secondary), IP Address, and Time Stamps for Last Registration, Verification, and First Seen. Additionally, our email address information assets can be linked with our Date-of-Birth and Phone Number databases to provide a powerful solution for consumer identity recognition and verification platforms through Identity Linkage Data.

As an add-on to our current and historical information, we also offer a database of hard-bounce email addresses. These are email addresses that have hard-bounced during our large-scale email campaign deployments or were identified as hard-bounces during our email verification processes. This database provides over 400 million unproductive email addresses useable as a part of suppression or fraud identification applications.

Our Email Information Assets are utilized by major Identity Graph Data and Identity Linkage platforms due to our comprehensive information that links the email address to consumer identity and IP Address information. This Identity Graph Data provides a robust alternative approach when faced with third-party cookie deprecation in the digital ecosystem.

Our digital advertising partners leverage this information to understand where their clients' customers and prospects are online and align media and content with consumer behavior. The additional Email Address Data, mobile phone numbers, and IP Addresses also work to increase the reach of your Digital Audience Data.

This Identity Graph Data has the scale and depth to help drive the creation of new platforms and products and provide significant enhancements to existing platforms. By utilizing our extensive Email Address Data and Identity Linkage Data, you can ensure precise consumer identity recognition and verification, making your marketing campaigns more effective and far-reaching.

Contact us at successdelivered@andrewswharton.com or visit us at www.andrewswharton.com to learn more about how our Identity Graph Data, Email Address Data, Identity Linkage Data, and Digital Audience Data can meet your marketing needs.

Comprehensive dataset of 4 Consumer advice centers in Komi Republic, Russia as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.