Sourcing accurate and up-to-date demographic data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent demographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium demographics data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Demographic Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

11. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

In 2023, the average consumer unit in the United States spent about 9,985 U.S. dollars on food. Americans spent the most on housing, at 25,436 U.S. dollars, reflecting around one third of annual expenditure. The total average U.S. consumer spending amounted to 77,280 U.S. dollars.

Abstract

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. To meet the needs of users, the Bureau of Labor Statistics (BLS) produces population estimates for consumer units (CUs) of average expenditures in news releases, reports, issues, and articles in the Monthly Labor Review. Tabulated CE data are also available on the Internet and by facsimile transmission (See Section XVI. APPENDIX 5). The microdata are available on CD-ROMs. These microdata files present detailed expenditure and income data from the Interview component of the CE for 2008 and the first quarter of 2009. The Interview survey collects data on up to 95 percent of total household expenditures. In addition to the FMLY, MEMB, MTAB, and ITAB_IMPUTE files, the microdata include files created directly from the expenditure sections of the Interview survey (EXPN files). The EXPN files contain expenditure data and ancillary descriptive information, often not available on the FMLY or MTAB files, in a format similar to the Interview questionnaire. In addition to the extra information available on the EXPN files, users can identify distinct spending categories easily and reduce processing time due to the organization of the files by type of expenditure. Estimates of average expenditures in 2008 from the Interview Survey, integrated with data from the Diary Survey, will be published in the report Consumer Expenditures in 2008 (due out in 2010). A list of recent publications containing data from the CE appears at the end of this documentation. The microdata files are in the public domain and, with appropriate credit, may be reproduced without permission. A suggested citation is: "U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, Interview Survey, 2008."

Analysis unit

Consumer Units

Kind of data

Sample survey data [ssd]

Sampling procedure

Samples for the CE are national probability samples of households designed to be representative of the total U.S. civilian population. Eligible population includes all civilian non-institutional persons. The first step in sampling is the selection of primary sampling units (PSUs), which consist of counties (or parts thereof) or groups of counties. The set of sample PSUs used for the 2008 and 2009 samples is composed of 91 areas. The design classifies the PSUs into four categories: 21 "A" certainty PSUs are Metropolitan Statistical Areas (MSA's) with a population greater than 1.5 million. 38 "X" PSUs, are medium-sized MSA's. 16 "Y" PSUs are nonmetropolitan areas that are included in the CPI. 16 "Z" PSUs are nonmetropolitan areas where only the urban population data will be included in the CPI. The sampling frame (that is, the list from which housing units were chosen) for the 2008 survey is generated from the 2000 Census of Population 100-percent-detail file. The sampling frame is augmented by new construction permits and by techniques used to eliminate recognized deficiencies in census coverage. All Enumeration Districts (EDs) from the Census that fail to meet the criterion for good addresses for new construction, and all EDs in nonpermit-issuing areas are grouped into the area segment frame. Interviewers are then assigned to list these areas before a sample is drawn. To the extent possible, an unclustered sample of units is selected within each PSU. This lack of clustering is desirable because the sample size of the Diary Survey is small relative to other surveys, while the intraclass correlations for expenditure characteristics are relatively large. This suggests that any clustering of the sample units could result in an unacceptable increase in the within-PSU variance and, as a result, the total variance. The Interview Survey is a panel rotation survey. Each panel is interviewed for five consecutive quarters and then dropped from the survey. As one panel leaves the survey, a new panel is introduced. Approximately 20 percent of the addresses are new to the survey each month.

Mode of data collection

Computer Assisted Personal Interview [capi]

In 2023, the average annual expenditures of consumer units in the United States totaled to 77,280 U.S. dollars. This is an increase from the previous year, when the average annual expenditures of consumer units totaled to 72,967 U.S. dollars.

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.

analyze the consumer expenditure survey (ce) with r the consumer expenditure survey (ce) is the primo data source to understand how americans spend money. participating households keep a running diary about every little purchase over the year. those diaries are then summed up into precise expenditure categories. how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular phones, and $13 (±2) on digital e-readers in 2011? an integral component of the market basket calculation in the consumer price index, this survey recently became available as public-use microdata and they're slowly releasing historical files back to 1996. hooray! for a t aste of what's possible with ce data, look at the quick tables listed on their main page - these tables contain approximately a bazillion different expenditure categories broken down by demographic groups. guess what? i just learned that americans living in households with $5,000 to $9,999 of annual income spent an average of $283 (±90) on pets, toys, hobbies, and playground equipment (pdf page 3). you can often get close to your statistic of interest from these web tables. but say you wanted to look at domestic pet expenditure among only households with children between 12 and 17 years old. another one of the thirteen web tables - the consumer unit composition table - shows a few different breakouts of households with kids, but none matching that exact population of interest. the bureau of labor statistics (bls) (the survey's designers) and the census bureau (the survey's administrators) have provided plenty of the major statistics and breakouts for you, but they're not psychic. if you want to comb through this data for specific expenditure categories broken out by a you-defined segment of the united states' population, then let a little r into your life. fun starts now. fair warning: only analyze t he consumer expenditure survey if you are nerd to the core. the microdata ship with two different survey types (interview and diary), each containing five or six quarterly table formats that need to be stacked, merged, and manipulated prior to a methodologically-correct analysis. the scripts in this repository contain examples to prepare 'em all, just be advised that magnificent data like this will never be no-assembly-required. the folks at bls have posted an excellent summary of what's av ailable - read it before anything else. after that, read the getting started guide. don't skim. a few of the descriptions below refer to sas programs provided by the bureau of labor statistics. you'll find these in the C:\My Directory\CES\2011\docs directory after you run the download program. this new github repository contains three scripts: 2010-2011 - download all microdata.R lo op through every year and download every file hosted on the bls's ce ftp site import each of the comma-separated value files into r with read.csv depending on user-settings, save each table as an r data file (.rda) or stat a-readable file (.dta) 2011 fmly intrvw - analysis examples.R load the r data files (.rda) necessary to create the 'fmly' table shown in the ce macros program documentation.doc file construct that 'fmly' table, using five quarters of interviews (q1 2011 thru q1 2012) initiate a replicate-weighted survey design object perform some lovely li'l analysis examples replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using unimputed variables replicate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t -tests using unimputed variables create an rsqlite database (to minimize ram usage) containing the five imputed variable files, after identifying which variables were imputed based on pdf page 3 of the user's guide to income imputation initiate a replicate-weighted, database-backed, multiply-imputed survey design object perform a few additional analyses that highlight the modified syntax required for multiply-imputed survey designs replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using imputed variables repl icate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t-tests using imputed variables replicate the %proc_reg() and %proc_logistic() macros found in "ce macros.sas" and provide some examples of regressions and logistic regressions using both unimputed and imputed variables replicate integrated mean and se.R match each step in the bls-provided sas program "integr ated mean and se.sas" but with r instead of sas create an rsqlite database when the expenditure table gets too large for older computers to handle in ram export a table "2011 integrated mean and se.csv" that exactly matches the contents of the sas-produced "2011 integrated mean and se.lst" text file click here to view these three scripts for...

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.

description:

In late 2020, Hispanic and African American consumers each accounted for nearly a tenth all Amazon retail spending in the United States. Meanwhile, white consumers led the list, representing over 70 percent of the e-commerce platform's consumer spending share.

Adults in the United States planned on spending an average of almost 92 U.S. dollars on Super Bowl Sunday in 2025, with food and beverage purchases being the most common among those planning on watching the game. NFL fans spend billions The fact that American football and the NFL (National Football League) are a big deal in the United States is not a surprising statement to anyone. To cement this fact, one needs only look at the annual sales generated by the Super Bowl. Total planned Super Bowl spending in the United States was estimated to be almost 19 billion U.S. dollars in 2025. Compared to just the year before, total spending was expected to increase by nearly one billion U.S. dollars. Gambling in the United States The Super Bowl also attracts interest from those wishing to make some money – sportsbooks in the state of Nevada took over 150 million U.S. dollars in Super Bowl wagers in 2025. Until recently, Nevada was the only state to permit a wide variety of legal sports betting, but the Supreme Court overturned a federal law in 2018, and sports betting is now legal in many states.

Sourcing accurate and up-to-date geodemographic data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent geodemographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium geodemographics data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Geodemographic Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

11. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

This statistic shows average annual expenditure on jewelry and valuables per consumer unit in the United States from 2018 to 2023, by category. In 2023, the country's average annual expenditure on jewelry amounted to 108.82 U.S. dollars per consumer unit.

Survey of Household Spending (SHS), average household spending, Canada, regions and provinces.

In 2024, it was found that roughly 36 million Germans aged 14 years and older were highly willing to spend money on a good diet and good food.The Allensbach Market and Advertising Media Analysis (Allensbacher Markt- und Werbeträgeranalyse or AWA in German) determines attitudes, consumer habits and media usage of the population in Germany on a broad statistical basis.

Graph and download economic data for Expenditures: Food by Race: White and All Other Races, Not Including Black or African American (CXUFOODTOTLLB0903M) from 2003 to 2023 about white, expenditures, food, and USA.

Cambodia KH: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data was reported at 4.920 % in 2019. This records an increase from the previous number of 4.110 % for 2014. Cambodia KH: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data is updated yearly, averaging 4.920 % from Dec 2009 (Median) to 2019, with 3 observations. The data reached an all-time high of 5.730 % in 2009 and a record low of 4.110 % in 2014. Cambodia KH: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cambodia – Table KH.World Bank.WDI: Social: Poverty and Inequality. Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).;Global Health Observatory. Geneva: World Health Organization; 2023. (https://www.who.int/data/gho/data/themes/topics/financial-protection);Weighted average;This is the Sustainable Development Goal indicator 3.8.2[https://unstats.un.org/sdgs/metadata/].

This dataset provides annual estimates developed by the U.S. Bureau of Economic Analysis on consumer spending in the State of Iowa beginning in 1998. Personal consumption expenditures (PCE) is the value of the goods and services purchased by, or on the behalf of, Iowa residents. PCE is reported in millions of current dollars. Also provided is per capita PCE which is reported in current dollars. The Census Bureau’s annual midyear (July 1) population estimates are used for per capita variables.

Consumption category indicates the goods or services associated with personal consumption. All includes both goods and services.

Goods include both durable goods and non durable goods. Durable goods include: motor vehicles and parts, furnishings and durable household equipment, recreational goods and vehicles, and other durable goods. Non durable goods include: food and beverages purchased for off-premises consumption, clothing and footwear, gasoline and other energy goods, and other non durable goods.

Services include household consumption expenditures (for services) and final consumption expenditures of nonprofit institutions serving households (NPISHs). Household consumption expenditures include: housing and utilities, health care, transportation services, recreation services, food services and accommodations, financial services and insurance, and other services. NPISH is the gross output of nonprofit institutions less receipts from sales of goods and services by nonprofit institutions.

Consumer spending on smart home products and services is forecast to grow to more than 170 billion U.S. dollars by 2025. At that time the number of households with smart home systems is projected to exceed 400 million globally.

Smart Home Systems

Smart home systems provide a variety of functions that can be managed remotely by a smartphone or computer. Many consumers are likely to purchase smart speakers and smart home entertainment systems. However, use of smart devices goes beyond simply entertainment, as smart home appliances such as security systems or automatic heating and lighting can help improve the safety and comfort of being at home.

Smart speaker: a versatile favorite

Smart speakers are an increasingly popular component of a smart home and will have shipped over 98 million units by the end of 2019. These devices are more than simply music players, as many have smart assistant capabilities, and can respond to verbal prompts or command other smart home devices. Of these speakers, a majority are sold by Amazon, though in recent quarters, Google and Baidu have both grown to be close competitors in terms of unit shipments.

Utilizing Esri Updated Demographics Categories (boundaries from the 2020 U.S. Census Bureau Data). This layer was created using Esri's Enrich tool to display some of the categories below at a Block Group level for Idaho. Esri Updated Demographics categories include the following:PopulationAge—By Generations, Age Dependency RatiosRace and Ethnicity—Diversity IndexSchool-Educational attainmentWork—Labor Force, Economic Dependency RatiosIncome—Total Income, Income by AgeHouseholds—Total Households, Tenure, FamiliesFamiliesHousing and Wealth—Total Housing Units, Housing Affordability Index, Percent of Income for Mortgage, Wealth Index, Contract RentHistorical Time Series—Population, Households, and Housing Units for each year between 2010 and current yearMethodology 2023/2028 Demographics2023-2028 Data Catalog

Graph and download economic data for Expenditures: Total Average Annual Expenditures by Race: White, Asian, and All Other Races, Not Including Black or African American (CXUTOTALEXPLB0902M) from 1984 to 2023 about asian, average, white, expenditures, and USA.