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

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.

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

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

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

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

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

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

Data Broker Service Market Outlook

The global data broker service market size is projected to grow from USD 250 billion in 2023 to an estimated USD 450 billion by 2032, reflecting a compound annual growth rate (CAGR) of 6.7%. This substantial growth can be attributed to increasing digitalization, the exponential rise of data-driven decision-making across industries, and the growing realization of the value derived from data analytics. As businesses continue to recognize the potential of leveraging consumer, business, financial, and health data, the demand for data brokerage services is poised to expand significantly.

One of the primary growth factors for the data broker service market is the increasing importance of data in driving business strategies and operations. Companies are increasingly relying on consumer and market data to gain insights into market trends, consumer behavior, and competitive landscapes. This surge in data utilization across sectors such as retail, healthcare, and finance is propelling the demand for data brokerage services that can provide accurate and comprehensive data sets. The proliferation of digital platforms and the Internet of Things (IoT) has further amplified the volume of data generated, thus boosting the need for efficient data brokerage services.

Moreover, advancements in artificial intelligence (AI) and machine learning (ML) technologies are significantly contributing to the market's growth. These technologies enable enhanced data analysis, predictive analytics, and real-time decision-making, making data brokerage services more valuable. Businesses are increasingly investing in AI and ML to analyze large datasets more efficiently and extract actionable insights. Data brokers, in turn, are leveraging these technologies to offer more sophisticated and tailored data solutions, thus attracting a broader customer base.

Privacy regulations and data protection laws are also playing a crucial role in shaping the data broker service market. While these regulations pose challenges, they also create opportunities for compliant data brokers to differentiate themselves in the market. Companies are more inclined to partner with data brokers that demonstrate robust data governance practices and adhere to regulatory requirements. This trend is driving the market towards more ethical and transparent data brokerage practices, increasing the trust and credibility of data brokers among businesses and consumers alike.

The regional outlook for the data broker service market highlights North America as a dominant player, primarily due to the high adoption of data-driven strategies among businesses and the presence of major data brokerage firms. Europe follows closely, driven by stringent data protection regulations like GDPR, which necessitate secure and compliant data handling. The Asia Pacific region is expected to witness the fastest growth, fueled by the rapid digital transformation in countries like China and India and the increasing use of data analytics in various industries. Latin America and the Middle East & Africa regions are also showing promising growth, supported by the rising awareness of data's strategic value and increasing investments in data analytics infrastructure.

Data Type Analysis

The data broker service market by data type comprises consumer data, business data, financial data, health data, and other categories. Consumer data is one of the most significant segments within this market. This type of data includes information on consumer behavior, preferences, purchasing patterns, and demographics. Businesses leverage consumer data to tailor their marketing strategies, enhance customer experiences, and drive sales growth. The increasing use of digital platforms for shopping, social interaction, and information consumption is continually generating vast amounts of consumer data, thereby fueling the demand for consumer data brokerage services.

Business data, encompassing company profiles, industry trends, and competitive intelligence, is another vital segment. Organizations require business data to strategize market entry, expansion, and competitive positioning. Data brokers play a crucial role in aggregating and providing actionable business insights that help companies navigate complex market dynamics. The rise of global trade, the need for cross-border business intelligence, and the growing importance of data-driven decision-making in corporate strategies are driving the demand for business data brokerage services.

Financial data is crucial for sectors like banking, fina

Consumer's Buying Behavior Dataset

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Additional Information This dataset can be used to analyze the relationship between age, estimated salary, and purchase behavior in response to the advertisement. The dataset appears to be suitable for binary classification tasks, where the goal might be to predict whether an individual will make a purchase based on age and estimated salary. Exploratory data analysis (EDA) techniques can be applied to understand patterns and correlations within the dataset before building predictive models.

Consumer Mailing Lists and Consumer Email Lists. 40K plus filters. Search using detailed Demographic criteria and Geographic information on ProMarketing Leads.

Connect with consumers more effectively using Success.ai’s Real-Time B2C Contact Data API. This API delivers continuously updated consumer data, allowing you to adapt your marketing strategies with the latest information on demographics, behaviors, and purchasing patterns. Best price guaranteed!

Context

The Customer Shopping Preferences Dataset offers valuable insights into consumer behavior and purchasing patterns. Understanding customer preferences and trends is critical for businesses to tailor their products, marketing strategies, and overall customer experience. This dataset captures a wide range of customer attributes including age, gender, purchase history, preferred payment methods, frequency of purchases, and more. Analyzing this data can help businesses make informed decisions, optimize product offerings, and enhance customer satisfaction. The dataset stands as a valuable resource for businesses aiming to align their strategies with customer needs and preferences. It's important to note that this dataset is a Synthetic Dataset Created for Beginners to learn more about Data Analysis and Machine Learning.

Content

This dataset encompasses various features related to customer shopping preferences, gathering essential information for businesses seeking to enhance their understanding of their customer base. The features include customer age, gender, purchase amount, preferred payment methods, frequency of purchases, and feedback ratings. Additionally, data on the type of items purchased, shopping frequency, preferred shopping seasons, and interactions with promotional offers is included. With a collection of 3900 records, this dataset serves as a foundation for businesses looking to apply data-driven insights for better decision-making and customer-centric strategies.

Dataset Glossary (Column-wise)

• Customer ID - Unique identifier for each customer
• Age - Age of the customer
• Gender - Gender of the customer (Male/Female)
• Item Purchased - The item purchased by the customer
• Category - Category of the item purchased
• Purchase Amount (USD) - The amount of the purchase in USD
• Location - Location where the purchase was made
• Size - Size of the purchased item
• Color - Color of the purchased item
• Season - Season during which the purchase was made
• Review Rating - Rating given by the customer for the purchased item
• Subscription Status - Indicates if the customer has a subscription (Yes/No)
• Shipping Type - Type of shipping chosen by the customer
• Discount Applied - Indicates if a discount was applied to the purchase (Yes/No)
• Promo Code Used - Indicates if a promo code was used for the purchase (Yes/No)
• Previous Purchases - The total count of transactions concluded by the customer at the store, excluding the ongoing transaction
• Payment Method - Customer's most preferred payment method
• Frequency of Purchases - Frequency at which the customer makes purchases (e.g., Weekly, Fortnightly, Monthly)

Structure of the Dataset

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Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic customer shopping experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world customer shopping behavior. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation in the context of consumer preferences and retail scenarios.

Cover Photo by: Freepik

Thumbnail by: Clothing icons created by Flat Icons - Flaticon

CCI: Plans to Buy Within 6 Mos: sa: Home: Yes data was reported at 4.500 % in Apr 2025. This records a decrease from the previous number of 5.600 % for Mar 2025. CCI: Plans to Buy Within 6 Mos: sa: Home: Yes data is updated monthly, averaging 3.600 % from Feb 1967 (Median) to Apr 2025, with 637 observations. The data reached an all-time high of 7.700 % in Jul 2020 and a record low of 1.700 % in Dec 2009. CCI: Plans to Buy Within 6 Mos: sa: Home: Yes 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]

With close to 238M records in the United States , Techsalerator has access to some of the most qualitative B2C data in the US.

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, Home Value, Interest, Behavior, Lifestyle...

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.

customer_id: Unique ID of the customer. age: The age of the customer. annual_income: The customer's annual income (in USD). purchase_amount: The total amount of purchases made by the customer (in USD). purchase_frequency: Frequency of customer purchases (number of times per year). region: The region where the customer lives (North, South, East, West). loyalty_score: Customer's loyalty score (a value between 0-100).

This dataset includes information on customer profiles and their purchasing behaviors. The data features columns for user ID, age, annual income, purchase amount, loyalty score (categorized into classes), region, and purchase frequency. It is intended for analyzing customer segmentation and loyalty trends, and can be used for various machine learning and data analysis tasks related to customer behavior and market research.

Explanation: These data are imaginary data. It was created entirely for the purpose of improving users, it has nothing to do with reality.

United States CSI: Home Buying Conditions data was reported at 140.000 1966=100 in May 2018. This records a decrease from the previous number of 144.000 1966=100 for Apr 2018. United States CSI: Home Buying Conditions data is updated monthly, averaging 149.000 1966=100 from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 182.000 1966=100 in Dec 1998 and a record low of 37.000 1966=100 in Nov 1981. United States CSI: Home Buying Conditions 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?

CCI: Plans to Buy Within 6 Mos: sa: Home: Lived In data was reported at 2.100 % in Apr 2025. This records a decrease from the previous number of 2.300 % for Mar 2025. CCI: Plans to Buy Within 6 Mos: sa: Home: Lived In data is updated monthly, averaging 1.700 % from Feb 1967 (Median) to Apr 2025, with 637 observations. The data reached an all-time high of 4.700 % in Feb 2021 and a record low of 0.600 % in Feb 1975. CCI: Plans to Buy Within 6 Mos: sa: Home: Lived In 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]

According to the results of a recent survey conducted in the United States, most respondents across all age groups preferred to buy products directly in stores. The highest share of in-store buyers was among baby boomers, with 83 percent. On the other hand, the same generation did not seem as interested as others in buying products through companies' apps or social media.

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).

Online Shopping Consumer Behavior Dataset

Consumer Buying Patterns in E-Commerce

By Weitong Li [source]

About this dataset

This dataset is a rich compilation of data that thoroughly guides us through consumers' behavior and their buying intentions while engaged in online shopping. It has been constructed with immense care to ensure it effectively examines an array of factors that influence customers' purchasing intentions in the increasingly significant realm of digital commerce.

The dataset is exhaustively composed with careful attention to collecting a diverse set of information, thus allowing a broad view into what affects online shopping behavior. Specific columns included cover customer's existing awareness about the website or source from where they are shopping, their information regarding the products they wish to purchase, and more importantly, their satisfaction level related to previous purchases.

Additionally, the dataset delves deep into investigating both objective and subjective aspects impacting customer behavior online. As such, it includes data on various webpage factors like loading speed, user-friendly interface design, webpage aesthetics, etc., which could significantly persuade the consumer's decision-making process during online shopping. The completion and submission convenience provided by those websites also form part of this database.

In order to fully understand consumer behavior within an online environment from multiple facets', individual consumers' subjective views are also captured in this dataset; it explores how consumers perceive their trust towards an e-commerce site or if they believe it’s convenient for them to shop via these platforms versus traditional methods? Do they feel relaxed when doing so?

In recognizing how crucial products competitiveness within such landscapes influences buyer intention - columns that provide details on critical characteristics like price comparisons against offline stores or similar product competitors across different websites have been included too.

Overall this comprehensive aggregated data collection aims not only at understanding fundamental consumer preferences but also towards predicting future buying behaviors hence forth enabling businesses capitalize on emerging trends within online retail spaces more efficiently & profitably

How to use the dataset

In an online-focused world, understanding consumer behavioral data is crucial. The 'Online Shopping Purchasing Intention Dataset' provides a comprehensive collection of consumer-based insights based on their behavior in virtual shopping environments. This dataset explores various factors that might affect a customer's decision to purchase. Here's how you can harness this dataset:

Defining the Problem

Identify a problem or question this data may answer. This might be: understanding what factors influence buying decisions, predicting whether a visit will result in a purchase based on user behavior, analyzing the impact of the month, operating system or traffic type on online purchasing intention etc.

Data Exploration

Understand the structure of the dataset by getting to know each variable and its meaning: - Administrative: Counting different types of pages visited by the user in that session. - Informational & Product Related: Measures how many informational/product related pages are viewed. - Bounce Rates, Exit Rate, Page Values: Assess these metrics as they provide significant insight about visitor activity. - Special Day: Explore correlation between proximity to special days (like Mother’s day and Valentine’s Day) with transactions. - Operating Systems / Browser / Region / Traffic Type: Uncover behavioral patterns associated with technical specs/geo location/ source of traffic.

Analysis and Visualization

Use appropriate statistical analysis techniques to scrutinize relationships between variables such as correlation analysis or chi-square tests for independence etc.

Visualize your findings using plots like bar graphs for categorical features comparison or scatter plots for multivariate relationships etc.

Model Building

Use machine learning algorithms (like logistic regression or decision tree models) potentially useful if your goal is predicting purchase intention based on given features.

This could also involve feature selection - choosing most relevant predictors; training & testing model and finally assessing model performance through metrics like accuracy score, precision-recall scores etc.

Remember to appropriately handle missing values if any before diving into predictive modeling

The comprehens...

During a consumer survey conducted in the United States in the first quarter of 2021, it was found that the majority of respondents, namely 56 percent, were willing to give out their gender in exchange for receiving personalized ads or offers from companies. Nearly 21 percent of survey participants expressed the will to share their household income for the same purpose.

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

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]