The US Cell Phone Database: Consumer & Business Contacts is AmeriList’s premier mobile-first dataset, built for marketers, agencies, and enterprises that demand accurate, compliant, and scalable U.S. cell phone data. Covering millions of verified consumer and business mobile numbers, and refreshed weekly for accuracy, this file is one of the most reliable and frequently updated cell phone databases available today.

Why Choose This Database? Today’s marketing success depends on reaching your audience where they are, and that’s on their mobile devices. With this dataset, you gain:

• Nationwide coverage of U.S. consumer and business cell phone contacts.
• Verified mobile numbers that can be DNC-scrubbed to support compliance and responsible outreach.
• Multi-channel readiness with delivery via CSV, API, SFTP, or cloud integrations (AWS, GCP, Azure).

Key Features: - Millions of verified consumer and business mobile phone numbers. - Weekly update cycle to maintain accuracy and compliance.

Schema Preview: First_Name, Last_Name, Phone_Number, DNC_Flag

Use Cases This dataset powers a wide range of mobile-first and cross-channel marketing strategies:

• SMS Campaigns: Deliver time-sensitive promotions and personalized offers.
• Outbound Calling: Connect directly with decision-makers and consumers.
• Mobile-First Advertising: Enhance digital campaigns with compliant mobile targeting.

Industries That Benefit - Retail & E-commerce: Deliver SMS promotions, loyalty program updates, and flash sale alerts. - Healthcare: Share wellness updates, insurance enrollment opportunities, and educational campaigns. - Financial Services & Insurance: Connect with prospects for loan offers, credit card promotions, or new insurance plans. - Real Estate & Home Services: Reach potential buyers, renters, and homeowners with property alerts and service offers.

Why AmeriList? For over 20 years, AmeriList has been a trusted leader in direct marketing data solutions. Our expertise in consumer and business contact databases ensures not only the accuracy of the phone numbers we provide, but also the compliance and strategic value they deliver. With a strong focus on TCPA and CAN-SPAM regulations, data quality, and ROI, AmeriList empowers brands and agencies to unlock the full potential of mobile-first marketing campaigns.

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.

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.

Our highly-targeted consumer healthcare database includes:

🗸 Name 🗸 Postal Address, Email Address, Telephone Number 🗸 Age, Gender 🗸 Most likely to ask a Doctor About an Advertised Prescription Medicine 🗸 Most likely looked for Medical Information on the Web 🗸 Most Likely to Prefer Brand Name Medicines 🗸 Most Likely to Buy Prescriptions through the Mail

The dataset is available for purchase by US region: 🗸 New England (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont) 🗸 Middle Atlantic (New Jersey, New York, and Pennsylvania) 🗸 East North Central (Illinois, Indiana, Michigan, Ohio, and Wisconsin) 🗸 West North Central (Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota) 🗸 South Atlantic (Delaware; Florida; Georgia; Maryland; North Carolina; South Carolina; Virginia; Washington, D.C. and West Virginia) 🗸 East South Central (Alabama, Kentucky, Mississippi, and Tennessee) 🗸 West South Central (Arkansas, Louisiana, Oklahoma, and Texas) 🗸 Mountain (Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, and Wyoming) 🗸 Pacific (Alaska, California, Hawaii, Oregon, and Washington)

Dataset Description

This dataset contains 500 records of customer transactions across five distinct bakeries, providing a rich source of information for analyzing consumer behavior in the bakery industry. Each record is characterized by several key features:

• Bakery_ID: A unique identifier for each bakery, allowing for comparative analysis across different locations.
• Customer_ID: A unique identifier assigned to each customer, facilitating individual transaction tracking without personal identification.
• Items_Purchased: The quantity of items purchased in each transaction, which helps gauge customer buying habits and preferences.
• Amount_Spent: The total expenditure of each customer during their visit, serving as a primary metric for assessing customer spending behavior.
• Payment_Method: The method used by customers to complete their purchases, including options like Card, Cash, and Mobile Payment, which offers insights into payment trends and preferences.
• Loyalty Member: This column indicates whether the customer is a member of the bakery's loyalty program. Analyzing loyalty membership data can provide insights into customer retention and the effectiveness of loyalty initiatives in driving repeat business.
• Age: This column indicates the age of the customer at the time of purchase. It helps analyze spending patterns and preferences across different age groups.
• Gender: This column represents the gender of the customer, providing insights into purchasing behavior and preferences. Analyzing gender data can assist bakeries in tailoring marketing strategies and product offerings.
• Purchase_Date: This column records the date of each transaction, allowing for the identification of seasonal trends and peak shopping periods. It aids in understanding customer buying behavior over time.
• Time_of_Purchase: This column categorizes the time of day when the purchase was made. Analyzing this data helps identify peak hours for customer visits, enabling bakeries to optimize staffing and inventory.

This dataset is designed to facilitate various analyses, including spending patterns, payment method preferences, and overall consumer trends in the bakery sector. By utilizing this dataset, stakeholders can derive actionable insights to enhance customer engagement, optimize product offerings, and inform marketing strategies.

Dataset

This dataset was created by DEBJYOTI SAHA

Contents

Description: This dataset includes detailed demographic and behavioral information about restaurant consumers. It is designed to provide insights into consumer profiles, preferences, and habits, which can be useful for improving customer experience and developing targeted marketing strategies.

Features:

Consumer_ID: A unique identifier assigned to each consumer in the dataset. City: The city where the consumer resides. State: The state or province where the consumer is located. Country: The country where the consumer lives. Latitude: The geographical latitude of the consumer’s location. Longitude: The geographical longitude of the consumer’s location. Smoker: Indicates whether the consumer is a smoker (e.g., Yes/No). Drink_Level: The consumer’s level of alcohol consumption (e.g., None, Light, Moderate, Heavy). Transportation_Method: The mode of transportation the consumer uses to travel to the restaurant (e.g., Car, Public Transit, Walking). Marital_Status: The consumer’s marital status (e.g., Single, Married, Divorced, Widowed). Usage:

Consumer Profiling: Understand the demographics and habits of different consumer segments to tailor marketing strategies and restaurant offerings. Location Analysis: Analyze consumer location data to identify key markets and optimize restaurant placement or delivery areas. Behavioral Insights: Study smoking and drinking habits to adjust menu options and enhance customer experience. Transportation Trends: Assess how consumers travel to the restaurant to improve accessibility and convenience. Source: The data is collected from restaurant surveys, customer profiles, and demographic studies.

Notes:

Ensure that personal data is handled securely and in compliance with privacy regulations. Regular updates may be necessary to reflect changes in consumer behavior and demographics.

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. The database generally updates daily.

Online Retail Sales and Customer Data

Transactional Data with Product and Customer Details in Online Retail

By Marc Szafraniec [source]

About this dataset

The InvoiceNo column holds unique identifiers for each transaction conducted. This numerical code serves a twofold purpose: it facilitates effortless identification of individual sales or purchases while simultaneously enabling treasury management by offering a repository for record keeping.

In concordance with the invoice number is the InvoiceDate column. It provides a date-time stamp associated with every transaction, which can reveal patterns in purchasing behaviour over time and assists with record-keeping requirements.

The StockCode acts as an integral part of this dataset; it encompasses alphanumeric sequences allocated distinctively to every item in stock. Such a system aids unequivocally identifying individual products making inventory records seamless.

The Description field offers brief elucidations about each listed product, adding layers beyond just stock codes to aid potential customers' understanding of products better and make more informed choices.

Detailed logs concerning sold quantities come under the Quantity banner - it lists the units involved per transaction alongside aiding calculations regarding total costs incurred during each sale/purchase offering significant help tracking inventory levels based on products' outflow dynamics within given periods.

Retail isn't merely about what you sell but also at what price you sell- A point acknowledged via our inclusion of unit prices exerted on items sold within transactions inside our dataset's UnitPrice column which puts forth pertinent pricing details serving as pivotal factors driving metrics such as gross revenue calculation etc

Finally yet importantly is our dive into foreign waters - literally! With impressive international outreach we're looking into segmentation bases like geographical locations via documenting countries (under the name Country) where transactions are conducted & consumers reside extending opportunities for businesses to map their customer bases, track regional performance metrics, extend localization efforts and overall contributing to the formulation of efficient segmentation strategies.

All this invaluable information can be found in a sortable CSV file titled online_retail.csv. This dataset will prove incredibly advantageous for anyone interested in or researching online sales trends, developing customer profiles, or gaining insights into effective inventory management practices

How to use the dataset

Identifying Products: StockCode is the unique identifier for each product. You can use it to identify individual products, track their sales, or discover patterns related to specific items.

Assessing Sales Volume: Quantity column tells you about the number of units of a product involved in each transaction. Along with InvoiceNo, you can analyze overall sales volume or specific purchases throughout your selected period.

Observing Price Fluctuations: By using the UnitPrice, not only can the total cost per transaction be calculated (by multiplying with Quantity), but also insightful observations like price fluctuations over time or determining most profitable items could be derived.

Analyzing Description Patterns/Trends: The Description field sheds light upon what kind of products are being traded. This could provide some inspiration for text analysis like term frequency-inverse document frequency (TF-IDF), sentiment analysis on descriptions, etc., to figure out popular trends at given times.

Analysing Geographical Trends: With the help of Country column, geographical trends in sales volumes across different nations can easily be analyzed i.e., which location has more customers or which country orders more quantity or expensive units based on unit price and quantity columns respectively.

Keep in mind that proper extraction and transformation methodology should be applied while handling data from different columns as per their datatypes (textual/alphanumeric/numeric) requirements.

This dataset not only allows retailers to gain an immediate understanding into their operations but could also serve as a base dataset for those interested in machine learning regarding predicting future transactions

Research Ideas

• Inventory Management: By tracking the 'Quantity' and 'StockCode' over time, a business could use this data to notice if certain products are frequently purchased together or in specific seasons, allowing them to better stock their inventory.
• Pricing Strategy:...

Abstract

The L2 Consumer Dataset contains information about consumers from all 50 states and the District of Columbia. The data, which is sourced from credit bureaus and other consumer information sources, is generally bought and used by companies for marketing purposes. Updates are expected quarterly.

All tables (except for New Jersey) were last updated on 03-25-2024. New Jersey was updated on 05-11-2024, when about 25,000 records were removed to comply with Daniel's Law.

Methodology

To create this file, L2 processes nationwide consumer data on an ongoing basis for all 50 states and the District of Columbia with refreshes typically at least every ninety days. The data are sourced from credit bureaus and other consumer information sources. Those data are standardized and consist of approximately 240,000,000 records nationwide.

Usage

Each table contains 667 variables. For more information about these variables, see ***2024-04-20-Commercial-Data-Dictionary.xlsx ***(under Supporting files).

The L2 Consumer and L2 Voter and Demographic data can be joined on the Lalvoterid variable.

One can also use the Lalvoterid variable to validate the state. For example, let's look at the Lalvoterid for one row in the CA-Commercial-2024-03-25 table. The characters in the fourth and fifth positions of this identifier, LALCA25840445, are 'CA' (California).

The date appended to each table name represents when the data was last updated. All tables (except for New Jersey) were last updated on 03-25-2024. New Jersey was updated on 05-11-2024 when about 25,000 records were removed to comply with Daniel's Law. For more information about this release, see 2024-03-27-Commercial-Data-Release-Notes.docx* *(under Supporting files).

Bulk Data Access

Data access is required to view this section.

DataMapping Tool

Data access is required to view this section.

The US Consumer Household Database — Weekly Refreshed is AmeriList’s premier consumer dataset, built for marketers, agencies, and enterprises that demand accurate, scalable, and timely U.S. consumer data. Covering over 200 million households nationwide and enriched with 200+ lifestyle, demographic, and behavioral attributes, this file is one of the most complete and frequently updated consumer databases available today.

Why Choose This Database?

Today’s marketing success depends on reaching the right audience at the right time. With this dataset, you gain: - Nationwide coverage of U.S. households (≈95%). - Unmatched attribute depth including age, income, marital status, homeownership, and lifestyle interests. - Freshness you can trust with weekly updates to keep your campaigns aligned with real-world consumer changes. - Multi-channel readiness with delivery via CSV, API, SFTP, or cloud integrations (AWS, GCP, Azure).

Key Features - 200M+ U.S. households for broad reach. - 200+ attributes spanning demographics, lifestyle, purchase signals, and household composition. - Household-level granularity with linkable fields for segmentation and modeling. - Evaluation samples under NDA to test match rates and validate quality.

Use Cases This dataset powers a wide range of data-driven marketing strategies:

• Direct Mail Lists: Reach targeted households with personalized campaigns.
• CRM Enrichment: Append missing consumer attributes to strengthen customer records.
• Audience Segmentation: Build granular segments for more relevant messaging.
• Look-Alike Modeling: Expand your audience with predictive targeting.
• Digital Marketing: Activate high-value segments across social, programmatic, and CTV campaigns.

Industries That Benefit

• Retail & E-commerce: Personalize offers for higher conversions.
• Financial Services & Insurance: Target households by income, homeownership, or life stage.
• Healthcare & Wellness: Engage consumers with relevant health and lifestyle attributes.
• Automotive: Reach in-market households for new and used vehicles.
• Real Estate & Home Services: Connect with homeowners, renters, and movers.

Licensing & Access

The US Consumer Household Database is offered via 12-month subscription, with continuous weekly updates included. Evaluation samples are available under NDA. Flexible licensing models ensure it fits enterprises of all sizes.

Why AmeriList? For over 20 years, AmeriList has been a trusted leader in direct marketing data solutions. Our expertise in consumer databases, mailing lists, and CRM enrichment ensures not only the accuracy of the data but also the strategic value it delivers. With a focus on quality, compliance, and ROI, AmeriList helps brands and agencies unlock the full potential of consumer marketing.

Success.ai’s B2C Contact Data Real-Time API provides businesses with on-demand access to continuously updated consumer information, ensuring your marketing and engagement strategies always remain current and impactful. By leveraging AI-validated data from over 700 million global profiles, this API empowers you to adapt swiftly to changes in consumer demographics, behaviors, and purchasing patterns.

From personalizing offers to targeting the right audiences at the right time, Success.ai’s real-time consumer data ensures every interaction is more relevant, timely, and effective. Backed by our Best Price Guarantee, this solution helps you stay ahead in a rapidly evolving consumer market.

Why Choose Success.ai’s B2C Contact Data Real-Time API?

1. Continuously Updated Consumer Data

   • Access the most recent consumer profiles, ensuring you’re always engaging with active, relevant audiences.
   • Real-time data refreshes keep pace with shifting consumer trends, enabling agile decision-making.

2. Comprehensive Global Coverage

   • Includes consumer information from key markets worldwide, allowing you to scale campaigns and tap into emerging demographics.
   • Gain insights into purchasing behaviors, brand preferences, and lifestyle indicators across regions and sectors.

3. AI-Validated Accuracy and Reliability

   • AI-driven validation ensures 99% accuracy, reducing wasted outreach and maximizing campaign success rates.
   • Trust that your data is always high-quality, actionable, and ready to inform your marketing strategies.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring your data usage remains responsible and lawful.

Data Highlights:

• 700M+ Global Profiles: Access a vast and diverse pool of consumer data for more informed targeting.
• Real-Time Updates: Continuously updated data ensures timely relevance, supporting dynamic marketing strategies.
• Behavioral and Lifestyle Insights: Understand consumer behaviors, interests, and preferences to tailor campaigns and messaging.
• Demographic and Purchasing Patterns: Leverage key indicators like age, location, and past purchase behaviors to refine targeting.

Key Features of the Real-Time API:

1. Instant Data Enrichment

   • Seamlessly enhance your CRM or marketing platforms with fresh consumer data, eliminating manual updates and data decay.
   • Maintain top-notch data hygiene to support long-term ROI on marketing efforts.

2. Powerful Filtering and Segmentation

   • Query the API using advanced parameters like demographics, interests, or purchase history.
   • Zero in on precise audience segments for higher conversion rates and personalized consumer experiences.

3. Adaptive Marketing Campaigns

   • Respond quickly to evolving market conditions, seasonal trends, or shifting consumer preferences.
   • Dynamically adjust campaigns and content strategies as new data emerges, ensuring ongoing relevance.

4. AI-Driven Validation

   • Rely on an AI-powered validation framework that continuously verifies data accuracy.
   • Improve reliability and reduce the risk of inaccurate targeting or messaging.

Strategic Use Cases:

1. Personalized Marketing Campaigns

   • Tailor your messaging, offers, and content based on real-time consumer insights.
   • Increase engagement, loyalty, and sales by delivering relevant experiences that resonate with target audiences.

2. Audience Expansion and Market Entry

   • Identify new consumer segments and emerging markets supported by the latest consumer profiles.
   • Confidently enter new territories or product categories, backed by high-quality, up-to-date data.

3. Competitive Analysis and Market Insights

   • Monitor changing consumer preferences, compare segments, and spot trends before competitors do.
   • Refine product development, pricing strategies, and promotions to stay ahead of industry shifts.

4. Enhanced Customer Support and Retention

   • Equip support teams with updated consumer data to address inquiries more effectively.
   • Strengthen customer relationships through personalized interactions and timely problem resolution.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality B2C contact data at highly competitive prices, ensuring strong ROI for your marketing, sales, and operational initiatives.

2. Seamless Integration

   • Integrate the Real-Time API into CRM systems, marketing automation tools, or analytics platforms with ease, streamlining workflows and minimizing complexity.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to guide data-driven decisions, refine targeting, and enhance overall engagement outcomes.

4. Customizable and Scalable Solutions

   • Tailor datasets to focus on specific demographics, interests, or regions, adapting as your business needs evolve a...

The global customer database software solutions market is anticipated to reach a value of USD 15.1 billion by 2033, exhibiting a CAGR of 7.2% during the forecast period (2023-2033). The growing need for efficient customer relationship management (CRM) solutions, increasing adoption of cloud-based software, and the rise of big data analytics are driving market expansion. Key market trends include the increasing demand for cloud-based deployment, driven by its cost-effectiveness, flexibility, and scalability. Additionally, the emergence of artificial intelligence (AI) and machine learning (ML) in customer database software is enhancing data analysis capabilities, enabling businesses to gain deeper insights into customer behavior and preferences. The market is witnessing intense competition from established vendors such as Salesforce Customer 360 and Zoho CRM, as well as emerging players offering innovative solutions tailored to specific industry verticals.

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

According to a survey conducted in December 2021, six in ** consumers in the United States stated that companies, more than them, benefit from exchanging consumers' personal data. Only ** percent of respondents thought customers were profiting from trading private information.

Speedeon's Consumer Marketing Prospect Database has incredible depth and coverage. Our database includes: - 217+ Million Individuals - 118+ Million Households - 1,000+ Attributes - Predictive Models at ZIP+4 Level

Users can choose from powerful attributes such as age, gender, marital status, presence of children, income and affluence, housing data, personal interesting, ailment data, investments and insurance needs, ethnicity, occupation.

Our consumer prospect data is sourced from over 120 different sources such as survey and warrantee data, deeds, internet sourced data, various opt-in data, non-private and non-FCRA credit data from credit bureaus.

All our data is multi-sourced, carefully analyzed, and undergoes extensive processing to ensure the highest quality lifestyle data that is compliant with all privacy and security regulations.

Speedeon helps clients utilize this data for CRM enrichment, segmentation, prospecting, and predictive modeling. By working with Speedeon, clients can easily activate this rich audience data for: - Direct mail - Email - Digital & mobile display - Social media marketing like Facebook & Instagram - Advanced TV

Based on a survey conducted in December 2022 among consumers in Vietnam, around ** percent of surveyed respondents expressed their concerns about the privacy of their personal data while interacting with social media companies. Meanwhile, only ** percent of surveyed respondents were anxious about how their personal data is used by health and medical institutions.

Subtitle:

Simulated Dataset of Customer Purchase Behavior

Description:

This dataset contains simulated data representing customer purchase behavior. It includes various features such as age, gender, income, education, region, loyalty status, purchase frequency, purchase amount, product category, promotion usage, and satisfaction score.

File Information:

• File Format: CSV
• Number of Rows: 100000
• Number of Columns: 12

Column Descriptors:

• age: Age of the customer.
• gender: Gender of the customer (0 for Male, 1 for Female).
• income: Annual income of the customer.
• education: Education level of the customer.
• region: Region where the customer resides.
• loyalty_status: Loyalty status of the customer.
• purchase_frequency: Frequency of purchases made by the customer.
• purchase_amount: Amount spent by the customer in each purchase.
• product_category: Category of the purchased product.
• promotion_usage: Indicates whether the customer used promotional offers (0 for No, 1 for Yes).
• satisfaction_score: Satisfaction score of the customer.

Provenance:

The dataset was simulated using the simstudy package in R. Various distributions and formulas were used to generate synthetic data representing customer purchase behavior. The data is organized to mimic real-world scenarios, but it does not represent actual customer data.

A global survey from Capgemini showed that retail companies were lagging behind consumer products enterprises in the use of data. The gap was significant in the automation of processes and in data collecting: only ** percent of retailers automated data collection, against ** percent of consumer goods companies. However, one in **** organizations in both categories reported to have implemented practices involving data engineering, machine learning, and DevOps.

Customer Data Platforms for Media Market Outlook

According to our latest research, the global market size for Customer Data Platforms (CDP) for Media reached USD 2.08 billion in 2024. The market is registering a robust growth, exhibiting a CAGR of 14.2% during the forecast period. By 2033, the market is expected to reach USD 6.06 billion, driven by the escalating demand for personalized content, the proliferation of digital media channels, and the urgent need for unified customer views across diverse media touchpoints. As per our latest research, the rapid evolution of consumer expectations and digital transformation initiatives within the media industry are key factors catalyzing this growth.

A significant growth factor propelling the Customer Data Platforms for Media Market is the increasing emphasis on personalization in content delivery. Media organizations are under immense pressure to deliver tailored experiences to their audiences, leveraging customer data to curate relevant content, advertisements, and recommendations. The proliferation of streaming platforms, digital publishing, and interactive media has amplified the complexity of audience engagement, necessitating advanced CDP solutions that can aggregate, unify, and analyze first-party data from multiple sources. This drive for personalization not only enhances user satisfaction but also boosts retention rates, advertising effectiveness, and overall revenue streams for media enterprises. As a result, investment in CDP technologies has become a strategic imperative for media companies seeking to gain a competitive edge in a crowded marketplace.

Another pivotal factor influencing market expansion is the convergence of data privacy regulations and consumer trust concerns. With regulations such as GDPR and CCPA shaping data collection and utilization practices, media organizations are increasingly adopting CDPs to ensure compliance while maintaining robust data governance frameworks. These platforms empower companies to centralize consent management, provide transparency, and offer users control over their data preferences. By enabling secure and ethical data usage, CDPs help media enterprises mitigate regulatory risks and foster long-term trust with their audiences. This regulatory-driven adoption, coupled with the growing sophistication of data analytics tools embedded within CDPs, is accelerating market growth by making these platforms indispensable in the modern media landscape.

The surge in omnichannel engagement is also a major driver for the Customer Data Platforms for Media Market. Media consumers today interact with content across a myriad of devices and platforms, from traditional broadcasting to mobile apps and social media. This fragmented landscape creates challenges for media companies seeking a unified view of their customers. CDPs address this by integrating disparate data sources, enabling real-time audience segmentation, and facilitating seamless cross-channel campaign management. The ability to orchestrate cohesive and timely marketing initiatives across platforms not only maximizes audience reach but also optimizes monetization opportunities for publishers, broadcasters, and digital media firms. The relentless pace of digital innovation in the media sector ensures that the demand for agile and scalable CDP solutions will continue to rise.

Regionally, North America continues to dominate the Customer Data Platforms for Media Market, accounting for the largest revenue share in 2024. This leadership is attributed to the early adoption of advanced marketing technologies, a mature media ecosystem, and significant investments in digital transformation by major broadcasters, publishers, and digital media companies. Europe follows closely, fueled by stringent data privacy regulations and a strong focus on audience engagement strategies. The Asia Pacific region is emerging as the fastest-growing market, driven by rapid digitalization, expanding internet penetration, and the proliferation of mobile media consumption. Latin America and the Middle East & Africa are exhibiting steady growth, supported by increasing adoption of digital advertising and content platforms. This regional diversity reflects the global imperative for media companies to harness the power of customer data for competitive differentiation.