Consumer Spending in the United States increased to 16445.70 USD Billion in the second quarter of 2025 from 16345.80 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Consumer Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level 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 AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

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

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

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

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

The Consumer Expenditure Surveys (CE) program provides data on expenditures, income, and demographic characteristics of consumers in the United States. The CE program provides these data in tables, LABSTAT database, news releases, reports, and public use microdata files. CE data are collected by the Census Bureau for BLS in two surveys, the Interview Survey for major and/or recurring items and the Diary Survey for more minor or frequently purchased items. CE data are primarily used to revise the relative importance of goods and services in the market basket of the Consumer Price Index. The CE is the only Federal household survey to provide information on the complete range of consumers' expenditures and incomes. For more information and data, visit: https://www.bls.gov/cex/

The Consumer Expenditure Survey (CE) consists of two parts: the Quarterly Interview Survey and the Diary Survey. Both surveys provide information on the purchasing habits of American consumers, including data on their expenditures, income, and consumer unit characteristics (e.g., age, education, occupation). The Quarterly Interview Survey (CEQ) includes information on monthly out-of-pocket expenses like housing, apparel, transportation, healthcare, insurance, and entertainment. The Diary Survey (CED) includes information on frequently purchased items like food, beverages, tobacco, personal care products, and nonprescription drugs. Approximately 20,000 independent interview surveys and 11,000 independent diary surveys are completed annually. The United States Bureau of Labor Statistics (BLS) publishes 12-month estimates of consumer expenditures annually, summarized by various income levels and demographic characteristics. Geographic data is available at the national level; for regions, divisions, selected states, and selected metropolitan statistical areas; and by population size of area.

In 2023, housing required the highest amount of consumer expenditure across all races, with Asian individuals spending the most. Additionally, Asian individuals spent more on personal insurance and pensions, as well as education than any other race.

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.

Graph and download economic data for Expenditures: Total Average Annual Expenditures by Age: from Age 25 to 34 (CXUTOTALEXPLB0403M) from 1984 to 2023 about age, 25 years +, average, expenditures, and USA.

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 or CUs) of average expenditures in news releases, reports, 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). These microdata files present detailed expenditure and income data for the Diary component of the CE for 2005. They include weekly expenditure (EXPD), annual income (DTBD) files, and imputed income files (DTID). The data in EXPD, DTBD, and DTID files are categorized by a Universal Classification Code (UCC). The advantage of the EXPD and DTBD files is that with the data classified in a standardized format, the user may perform comparative expenditure (income) analysis with relative ease. The FMLD and MEMD files present data on the characteristics and demographics of CUs and CU members. The summary level expenditure and income information on the FMLD files permits the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files. Estimates of average expenditures in 2005 from the Diary survey, integrated with data from the Interview survey, are published in Consumer Expenditures in 2005. 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, Diary Survey, 2005”.

State Identifier Since the CE is not designed to produce state-level estimates, summing the consumer unit weights by state will not yield state population totals. A CU's basic weight reflects its probability of selection among a group of primary sampling units of similar characteristics. For example, sample units in an urban nonmetropolitan area in California may represent similar areas in Wyoming and Nevada. Among other adjustments, CUs are post-stratified nationally by sex-age-race. For example, the weights of consumer units containing a black male, age 16-24 in Alabama, Colorado, or New York, are all adjusted equivalently. Therefore, weighted population state totals will not match population totals calculated from other surveys that are designed to represent state data. To summarize, the CE sample was not designed to produce precise estimates for individual states. Although state-level estimates that are unbiased in a repeated sampling sense can be calculated for various statistical measures, such as means and aggregates, their estimates will generally be subject to large variances. Additionally, a particular state-population estimate from the CE sample may be far from the true state-population estimate.

Interpreting the data Several factors should be considered when interpreting the expenditure data. The average expenditure for an item may be considerably lower than the expenditure by those CUs that purchased the item. The less frequently an item is purchased, the greater the difference between the average for all consumer units and the average of those purchasing. (See Section V.B. for ESTIMATION OF TOTAL AND MEAN EXPENDITURES). Also, an individual CU may spend more or less than the average, depending on its particular characteristics. Factors such as income, age of family Members, geographic location, taste and personal preference also influence expenditures. Furthermore, even within groups with similar characteristics, the distribution of expenditures varies substantially.
Expenditures reported are the direct out-of-pocket expenditures. Indirect expenditures, which may be significant, may be reflected elsewhere. For example, rental contracts often include utilities. Renters with such contracts would record no direct expense for utilities, and therefore, appear to have no utility expenses. Employers or insurance companies frequently pay other costs.CUs with Members whose employers pay for all or part of their health insurance or life insurance would have lower direct expenses for these items than those who pay the entire amount themselves. These points should be considered when relating reported averages to individual circumstances.

The Diary survey PUMD are organized into five major data files for each quarter: 1. FMLD - a file with characteristics, income, and summary level expenditures for the household 2. MEMD - a file with characteristics and income for each member in the household
3. EXPD - a detailed weekly expenditure file categorized by UCC 4. DTBD - a detailed annual income file categorized by UCC
5. DTID - a household imputed income file categorized by UCC

Analysis unit

Consumer Unit

Kind of data

Sample survey data [ssd]

Mode of data collection

Computer Assisted Personal Interview [capi]

In a 2022 survey, U.S. consumers cut spending in many categories. It was the most common for consumers to reduce spending for food and groceries. Only about ** percent of consumers reported cutting back their spending for eating out and entertainment purposes.

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.

Consumer spending across India amounted to 27.2 trillion rupees by the end of the first quarter of 2025. It reached an all-time high during the fourth quarter of 2024, with a value of 28.4 trillion rupees. What is consumer spending? Consumer spending refers to the total money spent on final goods and services by individuals and households in an economy. It is an important metric that directly impacts the GDP of a country. Items that qualify as consumer spending include durable and nondurable goods and services. Various factors such as debt held by consumers, wages, supply and demand, taxes, and government-based economic stimulus can impact consumer spending in a country. Positive consumer outlook in India India’s consumer spending reflects a positive outlook with renewed consumer confidence post-COVID. Its consumer market is set to become one of the largest in the world as the number of middle- to high-income households rises with increasing amounts of disposable incomes. The country’s young demographic is also considered a driving force for increased consumer spending.

Analyzing Customer Spending Habits to Improve Sales Performance

A Cross-Country Study

By Vineet Bahl [source]

About this dataset

This Sales Data dataset offers a unique insight into the spending habits of customers from various countries across the globe. With detailed information on customer age, gender, product category, quantity, unit cost and price, as well as revenue generated through sales of products listed in this dataset, you can explore and discover patterns in consumer behavior. Analyze shifts in consumer trends with qualitative data like customer age and gender to know what drives customers’ decisions when shopping online or offline. Compare different markets to analyze pricing strategies for new product launches or promotional campaigns. Also with this dataset you can gain valuable insights about the changes in consumer demand for specific products over time – find out which Products had better margin or however see how different promotions impacted overall sales performance from different categories and sub-categories! Analyzing consumer behavior is key to success when it comes to commerce business models so this Sales Data offers powerful ways into understanding your customer base better!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset presents a great opportunity to actively analyze customer spending habits on products and services to improve sales performance. The data contains information about the date of purchase, year, month, customer age, gender, country, state and product category. Further analysis can reveal insights into different customer segments based on their demographic characteristics such as age and gender as well as location (country & state).

The dataset also includes 3 additional columns at the end: quantity purchased in each transaction, unit cost and unit price for each product or service purchased which can be used to determine if customers are purchasing items in bulk or buying more expensive items than usual. Likewise any discrepancies between the unit cost & price can help establish whether discounts were applied during those transactions which could potentially point towards loyalty or reward programs being put in place for returning customers. Lastly the final column shows total revenue generated from those purchases which we can use to identify any patterns whereby certain groups of customers show higher purchasing power than others based on their spends (unit cost & quantity combination) over various periods/months/years of sales interactions with them.

In summary this dataset allows us to explore numerous dimensions related to ascertaining superior sales performance by studying how its various attributes play out together when it comes down to driving profitability through improved customer acquisition strategies as well increasing purchase rates from existing ones minus any discounts available in-between!

Research Ideas

• Analyzing customer demographics by countries and states to better target future marketing campaigns.

• Tracking changes in customers’ spending habits over time for different product categories.

• Identifying which product categories have the highest average revenue per sale to help prioritize resources for those products or services

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: SalesForCourse_quizz_table.csv | Column name | Description | |:---------------------|:--------------------------------------------------| | Date | Date of the sale. (Date) | | Year | Year of the sale. (Integer) | | Month | Month of the sale. (Integer) | | Customer Age | Age of the c...

Spending Patterns Dataset

Dataset Overview

The Spending Patterns Dataset provides a synthetic representation of consumer spending behavior across various categories. This dataset is ideal for exploratory data analysis, statistical modeling, and machine learning applications related to financial forecasting, customer segmentation, or consumer behavior analysis.

Dataset Features

The dataset contains 10,000 transactions for 200 unique customers. Each transaction is associated with detailed information, including category, item, quantity, price, payment method, and transaction date.

Columns

Categories and Items

The dataset includes the following spending categories with example items:

• Groceries: Milk, Bread, Fruits, Vegetables, Meat, etc.
• Shopping: Clothes, Shoes, Electronics, Car.
• Subscriptions: Streaming Service, Gym Membership (Quantity always 1).
• Housing and Utilities: Rent, Electricity Bill, Gas Bill (Quantity always 1).
• Transportation: Gas, Public Transit, Car Repair (Quantity always 1).
• Food: Restaurant Meal, Fast Food, Coffee.
• Medical/Dental: Doctor Visit, Dentist Visit, Medicine (Quantity always 1).
• Personal Hygiene: Toothpaste, Shampoo, Soap.
• Fitness: Yoga Class, Personal Trainer, Workout Equipment.
• Travel: Plane Ticket, Hotel Stay, Taxi/Uber (Plane Ticket and Hotel Stay have Quantity always 1).
• Hobbies: Books, Art Supplies, Video Games.
• Friend Activities: Movie Tickets, Concert Tickets, Dinner with Friends.
• Gifts: Flowers, Gift Cards, Jewelry.

Usage Examples

Example 1: Total Spending by Category

Example 2: Spending Habits of Each Customer

Example 13 Price Change Over Time

In the third quarter of 2024, consumer spending reached over **** trillion U.S. dollars in the United States. In the same quarter of the previous year, consumer spending was around **** trillion U.S. dollars.

This data collection includes detailed information on the purchasing habits of Americans in 1960-1961, with over 200 types of expenditures coded. For the first time since 1941, the Consumer Expenditure Survey sampled both urban, non-farm and rural, farm households in an attempt to provide a complete picture of consumer expenditures in the United States. Personal interviews were conducted in 1960 and 1961 (and a small number in 1959) with 9,476 urban families, 2,285 rural non-farm families, and 1,967 rural farm families, for a total of 13,728 consumer units interviewed. A complete account of family income and outlays was compiled for a calendar year, as well as household characteristics. The expenditures covered by the survey were those which respondents could recall fairly accurately for three months or longer. In general, these expenditures included relatively large purchases, such as those for property, automobiles, and major appliances, or expenditures that occurred on a fairly regular basis, such as rent, utilities, or insurance premiums. Expenditures incurred while on trips were also covered by the survey. Information to determine net changes in the family's assets and liabilities during the year was also gathered. The estimated value of goods and services received, as gifts or otherwise, without direct expenditures by the family, was requested also. In addition, farm families provided farm receipts, disbursements, changes in farm assets, and value of home-produced food. To supplement the annual data, non-farm families who prepared meals at home provided a detailed seven-day record, during the week prior to the interview, of expenditures for food and related items purchased frequently (e.g., tobacco, personal care, and household supplies). For selected items of clothing, house furnishings, and food, the record of expenditures was supplemented by information on quantities purchased and prices paid. Characteristics of the housing occupied by homeowners and renters and an inventory of the major items of house furnishing they owned also were recorded. Demographic information includes sex, age, years of school completed, occupation, race, and marital status of each family member.

Consumer Spending in Ukraine decreased to 1119910 UAH Million in the second quarter of 2025 from 1140768 UAH Million in the first quarter of 2025. This dataset provides - Ukraine Consumer Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Spending in Hungary decreased to 8891717 HUF Million in the third quarter of 2025 from 9283576 HUF Million in the second quarter of 2025. This dataset provides - Hungary Consumer Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Expenditures: Apparel and Services: All Consumer Units (CXUAPPARELLB0101M) from 1984 to 2023 about consumer unit, apparel, expenditures, services, and USA.

Consumer Spending in Sweden increased to 737567 SEK Million in the third quarter of 2025 from 731795 SEK Million in the second quarter of 2025. This dataset provides - Sweden Consumer Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.