This dataset encompasses deterministic consumer demographics, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Included are age, gender, ethnicity, location, employment, education, income, pet ownership, having kids/children, relationship status, military status, number of people in household, car ownership vs lease, small business owner, spanish TV viewership as a proxy for acculturation, and having kids under 18 in the home.

Knowing who your consumers are is essential for businesses, marketers, and researchers. This detailed demographic file offers an in-depth look at American consumers, packed with insights about personal details, household information, financial status, and lifestyle choices. Let's take a closer look at the data:

Personal Identifiers and Basic Demographics At the heart of this dataset are the key details that make up a consumer profile:

Unique IDs (PID, HHID) for individuals and households Full names (First, Middle, Last) and suffixes Gender and age Date of birth Complete location details (address, city, state, ZIP) These identifiers are critical for accurate marketing and form the base for deeper analysis.

Geospatial Intelligence This file goes beyond just listing addresses by including rich geospatial data like:

Latitude and longitude Census tract and block details Codes for Metropolitan Statistical Areas (MSA) and Core-Based Statistical Areas (CBSA) County size codes Geocoding accuracy This allows for precise geographic segmentation and localized marketing.

Housing and Property Data The dataset covers a lot of ground when it comes to housing, providing valuable insights for real estate professionals, lenders, and home service providers:

Homeownership status Dwelling type (single-family, multi-family, etc.) Property values (market, assessed, and appraised) Year built and square footage Room count, amenities like fireplaces or pools, and building quality This data is crucial for targeting homeowners with products and services like refinancing or home improvement offers.

Wealth and Financial Data For a deeper dive into consumer wealth, the file includes:

Estimated household income Wealth scores Credit card usage Mortgage info (loan amounts, rates, terms) Home equity estimates and investment property ownership These indicators are invaluable for financial services, luxury brands, and fundraising organizations looking to reach affluent individuals.

Lifestyle and Interests One of the most useful features of the dataset is its extensive lifestyle segmentation:

Hobbies and interests (e.g., gardening, travel, sports) Book preferences, magazine subscriptions Outdoor activities (camping, fishing, hunting) Pet ownership, tech usage, political views, and religious affiliations This data is perfect for crafting personalized marketing campaigns and developing products that align with specific consumer preferences.

Consumer Behavior and Purchase Habits The file also sheds light on how consumers behave and shop:

Online and catalog shopping preferences Gift-giving tendencies, presence of children, vehicle ownership Media consumption (TV, radio, internet) Retailers and e-commerce businesses will find this behavioral data especially useful for tailoring their outreach.

Demographic Clusters and Segmentation Pre-built segments like:

Household, neighborhood, family, and digital clusters Generational and lifestage groups make it easier to quickly target specific demographics, streamlining the process for market analysis and campaign planning.

Ethnicity and Language Preferences In today's multicultural market, knowing your audience's cultural background is key. The file includes:

Ethnicity codes and language preferences Flags for Hispanic/Spanish-speaking households This helps ensure culturally relevant and sensitive communication.

Education and Occupation Data The dataset also tracks education and career info:

Education level and occupation codes Home-based business indicators This data is essential for B2B marketers, recruitment agencies, and education-focused campaigns.

Digital and Social Media Habits With everyone online, digital behavior insights are a must:

Internet, TV, radio, and magazine usage Social media platform engagement (Facebook, Instagram, LinkedIn) Streaming subscriptions (Netflix, Hulu) This data helps marketers, app developers, and social media managers connect with their audience in the digital space.

Political and Charitable Tendencies For political campaigns or non-profits, this dataset offers:

Political affiliations and outlook Charitable donation history Volunteer activities These insights are perfect for cause-related marketing and targeted political outreach.

Neighborhood Characteristics By incorporating census data, the file provides a bigger picture of the consumer's environment:

Population density, racial composition, and age distribution Housing occupancy and ownership rates This offers important context for understanding the demographic landscape.

Predictive Consumer Indexes The dataset includes forward-looking indicators in categories like:

Fashion, automotive, and beauty products Health, home decor, pet products, sports, and travel These predictive insights help businesses anticipate consumer trends and needs.

Contact Information Finally, the file includes key communication details:

Multiple phone numbers (landline, mobile) and email addresses Do Not Call (DNC) flags...

This dataset contains transaction details of users, including their demographics and purchasing behavior. It features information such as User ID, Age, Gender, Country, Purchase Amount, Purchase Date, and Product Category. This data can be useful for analyzing consumer trends, demographic influences on purchasing behavior, and market segmentation.

• User ID: A unique identifier assigned to each user for tracking their transactions.
• Age: The age of the user at the time of purchase, which may influence buying behavior.
• Gender: The gender of the user, allowing for demographic segmentation of purchasing patterns.
• Country: The country of residence for the user, useful for regional market analysis.
• Purchase Amount: The total amount spent by the user during a transaction.
• Purchase Date: The date when the purchase was made, allowing for temporal analysis of buying behavior.
• Product Category: The category of the product purchased, aiding in understanding consumer preferences.

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers, including data on their expenditures, income, and consumer unit (families and single consumers) characteristics. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index.

The CE program is comprised of two separate components (each with its own survey questionnaire and independent sample), the Diary Survey and the quarterly Interview Survey (ICPSR 36237). This data collection contains the Diary Survey component, which was designed to obtain data on frequently purchased smaller items, including food, housing, apparel and services, transportation, entertainment, and out-of-pocket health care costs. Each consumer unit (CU) recorded its expenditures in a diary for two consecutive 1-week periods. Although the diary was designed to collect information on expenditures that could not be easily recalled over time, respondents were asked to report all expenses (except overnight travel) that the CU incurred during the survey week.

The 2013 Diary Survey release contains five sets of data files (FMLD, MEMD, EXPD, DTBD, DTID), and one processing file (DSTUB). The FMLD, MEMD, EXPD, DTBD, and DTID files are organized by the quarter of the calendar year in which the data were collected. There are four quarterly datasets for each of these files.

The FMLD files contain CU characteristics, income, and summary level expenditures; the MEMD files contain member characteristics and income data; the EXPD files contain detailed weekly expenditures at the Universal Classification Code (UCC) level; the DTBD files contain the CU's reported annual income values or the mean of the five imputed income values in the multiple imputation method; and the DTID files contain the five imputed income values. Please note that the summary level expenditure and income information on the FMLD files permit the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files.

The DSTUB file provides the aggregation scheme used in the published consumer expenditure tables. The DSTUB file is further explained in Section III.F.6. 'Processing Files' of the Diary Survey Users' Guide. A second documentation guide, the 'Users' Guide to Income Imputation,' includes information on how to appropriately use the imputed income data.

Demographic and family characteristics data include age, sex, race, marital status, and CU relationships for each CU member. Income information was also collected, such as wage, salary, unemployment compensation, child support, and alimony, as well as information on the employment of each CU member age 14 and over.

The unpublished integrated CE data tables produced by the BLS are available to download through NADAC (click on 'Other' in the Dataset(s) section). The tables show average and percentile expenditures for detailed items, as well as the standard error and coefficient of variation (CV) for each spending estimate. The BLS unpublished integrated CE data tables are provided as an easy-to-use tool for obtaining spending estimates. However, users are cautioned to read the BLS explanatory letter accompanying the tables. The letter explains that estimates of average expenditures on detailed spending items (such as leisure and art-related categories) may be unreliable due to so few reports of expenditures for those items.

A global database of Census Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date census data with population trends for real estate, market research, audience targeting, and sales territory mapping.

Self-hosted commercial demographic dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The global Census Data is standardized, unified, and ready to use.

Use cases for the Global Census Database (Consumer Demographic Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Real Estate Data Estimations

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Census data export methodology

Our consumer demographic data packages are offered in CSV format. All Demographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our demographic databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

Apple Card owners in the United States in 2023 were typically Millennials (** percent of respondents) who tended to have a relatively high income. This is according to a survey held among Americans who either owned or did not own Apple's credit card. The source adds this demographic was in line with other surveys they held for other Apple products. Statista's Consumer Insights also noted that U.S. Apple iOS users are typically high income. The source of this particular survey, however, does not state how many of its 4,000 respondents owned an Apple Card. All statistics on Apple Pay - and services that rely on it, such as Apple Card and Apple Cash - are estimates, typically based on survey information. Apple Inc. does not share figures on individual services, whereas financial providers who offer Apple Pay, Apple Card, etc. are contractually forbidden to share such information.

Samsung Pay users in Sweden made up **** percent of respondents in 2025, and were likely to come from a **** income. This is according to questions asked in Statista's Consumer Insights, focusing on what payment services consumers used in the past 12 months. The typical user profile of a Samsung Pay user in Sweden was that they were ****, were ******** years old, and fell in the ******* quantile in terms of income. According to Statista surveys held in 2024, ************** percent of all consumers in Sweden at some point used Samsung Pay in a payment transaction.

The Customer Experience Dataset for AI-Driven Optimization is a simulated dataset designed to help develop and test machine learning models for customer experience optimization. It contains a variety of features representing customer demographics, interactions, and satisfaction levels. This dataset includes 1,000 synthetic records and provides insights into customer behavior, preferences, and retention status.

Key features include:

Customer Demographics: Age, gender, and location of the customer.

Interaction Data: The number of interactions, feedback scores, and products purchased.

Behavioral Data: Information on products viewed and time spent on the website.

Satisfaction & Retention: Satisfaction scores and retention status (whether the customer was retained or churned).

This dataset displays demographic information for all Boulder Parks and Recreation members and visitors. The dataset includes customer age, gender, resident status, location (city, state, and zipcode), entry date, and membership package type(s).

Please note that due to the nature of open-ended data entry for many customer detail fields, some customer data (e.g. city) will need to be cleaned and normalized before analysis.

Imagine reaching individuals actively exploring weight loss and GLP-1 medications right now. We provide Digital Audience Targeting Data that pinpoints these consumers throughout their entire journey. From their very first online searches to making crucial decisions, we capture their intent with remarkable precision.

Our Aliment Data models millions of interest signals, resulting in high-performing audience segments with exceptional accuracy. We offer recency-based targeting, from identifying individuals showing interest in the last 24 hours to those who have been researching for up to a year. This allows you to connect with consumers at every stage of their consideration process.

Furthermore, our geographic targeting covers the entire United States, including all 50 states and over 50 major cities. We also offer sophisticated segmentation options, including:

By leveraging our precise data, you can effectively target your ideal audience with relevant messaging at the right time. Find us on popular demand side platforms (DSPs)

Klarna users in Sweden made up ** percent of respondents in 2025, and were likely to come from a ****** income. This is according to questions asked in Statista's Consumer Insights, focusing on what payment services consumers used in the past 12 months. The typical user profile of a Klarna user in Sweden was that they were ******, were roughly ******** years old or *********************, and fell in the ****** quantile in terms of income. According to Statista surveys held in 2024, ************ percent of all consumers in Sweden at some point used Klarna in a payment transaction.

Google Pay users in Mexico made up *** percent of respondents in 2025 and were likely to come from a **** income. This is according to questions asked in Statista's Consumer Insights, focusing on what payment services consumers used in the past 12 months. The typical user profile of a Google Pay user in Mexico was that they were ****, were ******** years old, and fell in the ******* quantile in terms of income. According to Statista surveys, in 2024, Google Pay in Mexico made up less than 11 percent of payment transactions in Mexico.

There were 256 600 000 Facebook users in United States of America in January 2023, which accounted for 74.2% of its entire population. The majority of them were women - 54.6%. People aged 25 to 34 were the largest user group (60 200 000). The highest difference between men and women occurs within people aged 65 and above, where women lead by 11 100 000.

Klarna users in Austria made up ** percent of respondents in 2025, and were likely to come from a **** income. This is according to questions asked in Statista's Consumer Insights, focusing on what payment services consumers used in the past 12 months. The typical user profile of a Klarna user in Australia was that they were ******, were *********************, and fell in the ******* quantile in terms of income. According to Statista surveys held in 2024, ************ percent of all consumers in Austria at some point used Klarna in a payment transaction.

Introduction

This dataset is a synthetic e-commerce dataset designed to provide a comprehensive view of transaction, customer, product, and advertising data in a dynamic marketplace. It simulates real-world scenarios with seasonal effects, regional variations, advertising metrics, and customer purchasing behaviors. This dataset can serve as a valuable resource for exploring e-commerce analytics, customer segmentation, product performance, and marketing effectiveness.

The dataset includes detailed transaction-level data featuring product categories, customer demographics, discounts, revenue, and advertising metrics such as impressions, clicks, conversion rates, and ad spend. Seasonal trends and regional multipliers are integrated into the data to create realistic patterns that mimic consumer behavior across different times of the year and geographic regions.

Potential Analyses

1. Customer Insights

• Perform customer segmentation based on demographics, lifetime value, and purchase behavior.
• Analyze trends in customer behavior across regions or product categories.

2. Product Performance

• Identify top-performing products by revenue or units sold.
• Evaluate the impact of discounts and promotions on product sales.

3. Marketing Analytics

• Measure the effectiveness of advertising using CTR, CPC, and conversion rates.
• Assess how ad spend correlates with revenue and impressions.

4. Seasonal Trends

• Analyze seasonality effects on sales volume and revenue.
• Explore spikes in revenue or sales during holiday periods.

5. Regional Analysis

• Investigate regional performance trends using the regional multipliers.
• Examine customer preferences across different regions.

6. Data Science Applications

• Build predictive models for sales forecasting.
• Create clustering models for customer segmentation or product categorization.
• Develop optimization strategies for advertising spend or inventory management.

This dataset provides ample opportunities for data exploration, machine learning, and business analysis. We hope you find it insightful and useful for your projects!

Consumer Packaged Goods (CPG) Market size was valued at USD 21.73 Million in 2024 and is projected to reach USD 26.75 Million by 2032, growing at a CAGR of 2.90% from 2026 to 2032.Global Consumer Packaged Goods (CPG) Market DriversThe market drivers for the Consumer Packaged Goods (CPG) Market can be influenced by various factors. These may include:Changing Customer Preferences: A variety of factors, including changes in lifestyle, demographics, urbanization, and culture, constantly influence consumer preferences and behaviors. CPG companies need to offer products that fit the needs, tastes, and values of their customers in order to adjust to these shifting preferences.Product development and innovation: In the CPG industry, innovation is a major force. Businesses spend money on R&D to produce cutting-edge goods that satisfy changing consumer demands, outperform competitors, improve convenience, and add value. The introduction of new products and strategies for product differentiation propel market expansion and rivalry.Trends in Health and Wellness: Consumers are searching for CPG products that support health, nutrition, and overall well-being as they become more conscious of these issues. Organic, natural, non-GMO, and functional products are becoming more and more in demand as consumers prioritize leading healthier lives. In response, CPG companies provide healthier substitutes and restructure current products to align with consumer inclinations.E-commerce and Digital Transformation: The CPG industry is undergoing a revolution thanks to the spread of digital technologies and e-commerce channels. Because online shopping platforms offer convenience, variety, and personalized experiences, more and more consumers are choosing them. CPG businesses use omnichannel distribution, digital marketing, e-commerce tactics, and data analytics to increase market share, engage customers, and boost revenue.Easy Living and Always-On Lifestyles: The demand for easy-to-consume, portable, portion-controlled CPG products that are portable is driven by time constraints and busy lifestyles. Snacking bars, grab-and-go options, single-serve packaging, and ready-to-eat meals all appeal to customers looking for quick and convenient meal solutions.Sustainability and Environmental Concerns: In the CPG business, consumers' decisions to buy are influenced by their growing awareness of environmental issues and concerns about sustainability. Sustainable sourcing methods, recyclable packaging, and environmentally friendly goods are top priorities for consumers. To meet consumer expectations and improve brand reputation, CPG companies implement sustainable initiatives, minimize waste, lower their carbon footprint, and embrace the principles of the circular economy.Demographic Trends: The dynamics of the CPG market are shaped by demographic variables such as population growth, urbanization, aging populations, and household composition. Businesses customize their product lines, package designs, and advertising tactics to appeal to particular consumer demographics, including millennials, Gen Z, baby boomers, families, and multiculturals.Globalization and Emerging Markets: Globalization gives CPG companies more market opportunities to enter emerging markets and new geographic areas. Consumer spending on CPG products is driven by growing middle-class populations, urbanization, and rising disposable incomes in developing nations. To prosper in a variety of international markets, businesses must modify their marketing tactics, localize their product offerings, and handle regulatory environments.The COVID-19 pandemic has brought to light the significance of resilient and agile supply chains in the consumer packaged goods (CPG) sector. In order to increase flexibility, responsiveness, and continuity during disruptions and volatile market conditions, businesses concentrate on supply chain optimization, inventory management, risk mitigation, and digitalization.

Distribution of Late Baby Boomer home buyers by race and ethnicity.