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.

From adolescence to old age: who do we spend our time with?

To understand how social connections evolve throughout our lives, we can look at survey data on how much time people spend with others and who that time is spent with.

This dataset shows the amount of time people in the US report spending in the company of others, based on their age. The data comes from time-use surveys, where people are asked to list all the activities they perform over a full day and the people who were present during each activity. Currently, there is only data with this granularity for the US – time-use surveys are common across many countries, but what is special about the US is that respondents of the American Time Use Survey are asked to list everyone present for each activity.

The numbers in this chart are based on averages for a cross-section of US society – people are only interviewed once, but the dataset represents a decade of surveys, tabulating the average amount of time survey respondents of different ages report spending with other people.

Source

https://ourworldindata.org/time-with-others-lifetime by Esteban Ortiz-Ospina December 11, 2020

American Time Use Survey: Daily Activities

Americans' Daily Activities: Education, Employment, Gender, and Leisure Time

By Throwback Thursday [source]

About this dataset

The American Time Use Survey dataset provides comprehensive information on how individuals in America allocate their time throughout the day. It includes various aspects of daily activities such as education level, age, employment status, gender, number of children, weekly earnings and hours worked. The dataset also includes data on specific activities individuals engage in like sleeping, grooming, housework, food and drink preparation, caring for children, playing with children, job searching, shopping and eating and drinking. Additionally it captures time spent on leisure activities like socializing and relaxing as well as engaging in specific hobbies such as watching television or golfing. The dataset also records the amount of time spent volunteering or running for exercise purposes.

Each entry is organized based on categorical variables such as education level (ranging from lower levels to higher degrees), age (capturing different age brackets), employment status (including employed full-time or part-time), gender (male or female) and the number of children an individual has. Furthermore it provides information regarding an individual's weekly earnings and hours worked.

This extensive dataset aims to provide insights into how Americans prioritize their time across various aspects of their lives. Whether it be focusing on work-related tasks or indulging in recreational activities,it offers a comprehensive look at the allocation of time among different demographic groups within American society.

This dataset can be used for understanding trends in daily activity patterns across demographics groups over multiple years without directly referencing specific dates

How to use the dataset

How to use this dataset: American Time Use Survey - Daily Activities

Welcome to the American Time Use Survey dataset! This dataset provides valuable information on how Americans spend their time on a daily basis. Here's a guide on how to effectively utilize this dataset for your analysis:

• Familiarize yourself with the columns:

  • Education Level: The level of education attained by the individual.
  • Age: The age of the individual.
  • Age Range: The age range the individual falls into.
  • Employment Status: The employment status of the individual.
  • Gender: The gender of the individual.
  • Children: The number of children that an individual has.
  • Weekly Earnings: The amount of money earned by an individual on a weekly basis.
  • Year: The year in which the data was collected.
  • Weekly Hours Worked: The number of hours worked by an individual on a weekly basis.

• Identify variables related to daily activities: This dataset provides information about various daily activities undertaken by individuals. Some important variables related to daily activities include:

  • Sleeping
  • Grooming
  • Housework
  • Food & Drink Prep
  • Caring for Children
  • Playing with Children
  • Job Searching …and many more!

• Analyze time spent on different activities: This dataset includes numerical values representing time spent in minutes for specific activities such as sleeping, grooming, housework, food and drink preparation, etc. You can use this data to analyze and compare how different groups of individuals allocate their time throughout the day.

• Explore demographic factors: In addition to daily activities, this dataset also includes columns such as education level, age range, employment status, gender, and number of children. You can cross-reference these demographic factors with activity data to gain insights into how different population subgroups spend their time differently.

• Identify trends and patterns: You can use this dataset to identify trends and patterns in how Americans allocate their time over the years. By analyzing data from different years, you may discover changes in certain activities and how they relate to demographic factors or societal shifts.

• Visualize the data: Creating visualizations such as bar graphs, line plots, or pie charts can provide a clear representation of how time is allocated for different activities among various groups of individuals. Visualizations help in understanding the distribution of time spent on different activities and identifying any significant differences or similarities across demographics.

Remember that each column represents a specific variable, whi...

The American Time Use Survey (ATUS) provides nationally representative estimates of how, where, and with whom Americans spend their time, and is the only federal survey providing data on the full range of nonmarket activities, from childcare to volunteering. For more information visit https://www.bls.gov/tus/

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

VBA HOUSING BENEFITS PROGRAM to provide direct loans to certain veterans who are, or whose spouses are, Native Americans for the purchase or construction of homes on trust lands. Veterans who are, or whose spouses are, recognized by a Federally Recognized Tribal Government as a Native American and who: (a) Served on active duty on or after September 16, 1940, and were discharged or released under conditions other than dishonorable. If service was any time during World War II, the Korean Conflict, the Vietnam-era, or the Persian Gulf War, then the Native American Veteran must have served on active duty for 90 days or more; peacetime service only must have served a minimum of 181 days continuous active duty. If separated from enlisted service which began after September 7, 1980, or service as an officer which began after October 16, 1981, a veteran must also have served at least 24 months of continuous active duty or the full period for which called or ordered to active duty. Veterans of such recent service may qualify with less service time if they have a compensable service-connected disability or were discharged after at least 181 days, under the authority of 10 U.S.C 1171 or 1173. (b) Any veteran in the above classes with less service but discharged with a service-connected disability. (c) If acknowledged as a Native American by a Federally Recognized Tribal Government, unmarried surviving spouses of otherwise eligible veterans who died in service or whose deaths were attributable to service-connected disabilities and spouses of members of the Armed Forces serving on active duty, who are listed as missing in action, or as prisoners of war and who have been so listed 90 days or more. (d) Members of the Selected Reservists who ae, or whose spouses ae, recognized by a Federally Recognized Tribal Government as Native Americans and who are not otherwise eligible for home loan benefits and who have completed a total of 6 years in the Selected Reserves followed by an honorable discharge, placement on the retired list, or continued service.

From the Web site: USAspending.gov is the official source for spending data for the U.S. Government. Its mission is to show the American public what the federal government spends every year and how it spends the money. You can follow the money from the Congressional appropriations to the federal agencies and down to local communities and businesses.

About Dataset: Walmart Sales Forecast

This dataset focuses on predicting weekly store sales at Walmart by examining holiday effects, temporal patterns, and other influential factors. The goal is to enable efficient stock planning, revenue calculations, and strategic decision-making by understanding patterns related to seasonal sales fluctuations. This machine learning model is developed based on resources from : https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/overview/evaluation .

Dataset Overview

1. Test Data Contains 115,064 rows with information: Store, Department, Date, IsHoliday. "IsHoliday" indicates whether the week includes a special holiday. Holidays tend to show higher average sales than non-holiday periods.

2. Train Data Also contains 115,064 rows with Store, Department, Date, IsHoliday, Weekly Sales. Weekly sales are the recorded weekly sales for specific departments at certain stores.

3. Features Data Consists of 8,190 rows with variables such as Temperature, Fuel Price, CPI, Unemployment, Markdown 1-5, IsHoliday * Temperature: Average temperature (Fahrenheit) in a region. * Fuel Price: Can impact consumer spending and sales. * Markdowns 1-5: Promotional markdowns (missing values marked as NA). * CPI: Consumer Price Index (reflects inflation/deflation). * Unemployment: Unemployment rate in a region that affects consumer spending.

4.Store Data Includes details about Walmart stores such as store numbers, store types, and store sizes. Walmart has 45 stores categorized into 3 types: * Type A: Sizes from 39.690 to 219.622 * Type B: Sizes from 34.875 to 140.167 * Type C: Sizes from 39.690 to 42.988 The target variables for prediction are weekly sales, is holiday, and date. The other features are explored to identify patterns and generate insights to build accurate prediction models.

Modeling Objective

The goal is to predict the impact of holidays on weekly store sales. To achieve this, a Time Series modeling approach was applied using variables such as date, weekly sales, is holiday, lag features, rolling averages, and XGBoost. The evaluation metric used was Weighted Mean Absolute Error (WMAE), which emphasizes periods of higher significance, such as holidays.

Final Model Metrics: * Weighted Mean Absolute Error = 211 * Error rate relative to average weekly sales = ~1.32%.

The low error percentage highlights the model's accuracy in forecasting weekly sales and assessing seasonal fluctuations.

Insights

• The analysis provides actionable insights by identifying holiday effects on sales trends.
• This supports better stock planning, strategic financial planning, and risk management.

📢 Published on : My LinkedIn

From the website: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DHMZOW

The world has become much more peaceful, and yet, even after adjusting for inflation, global military spending is now three times greater than at the height of the Cold War. These developments have motivated a renewed interest from both policy makers and scholars about the drivers of military spending and the implications that follow. Existing findings on the relationship between threat and arming and arms races and war hinge on the completeness and accuracy of existing military spending data. Moreover, data on military spending is used to measure important concepts from international relations such as the distribution of power, balancing, the severity of states’ military burdens, and arms races. Everything we know about which states are most powerful, whether nations are balancing, and whether military burdens and arms races are growing more or less severe rests on the accuracy of existing military spending estimates.

Data is plural description: Global military spending. How much money has each country spent, each year, on its military? Different datasets have different answers, cover different timeframes, and use different methodologies. Miriam Barnum et al.’s Global Military Spending Dataset attempts to bring them together. By uniting “76 variables from 9 dataset collection projects,” the authors write, “we provide the most comprehensive and complete set of published datasets on military spending ever assembled.” Each of the variables represents one source/methodology, and each observation is a country-year. “Disagreement on the actual expenditure value for a given country-year is common, even between datasets produced by the same project,” they find. Previously: The Stockholm International Peace Research Institute’s Military Expenditure Database (DIP 2017.03.29), one of the sources.

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Retail Sales in the United States increased 0.20 percent in September of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

A dataset summarizing key demographic, identity, economic, and attitudinal measures for the U.S. Black American market, including generational breakdowns, acculturation, values, optimism, and DEI marketing trends.

The Business Plan Quarterly Data Summaries (QDS) are a core part of the transparency agenda. They provide the latest data on indicators included in Departmental Business Plans. The QDS comprises the QDS template and a complementary measurement annex. The QDS template includes metrics in the following three key headline areas: Spending, Results and People. Spending. This section sets out the outturn (actual spending) for each department, along with details of common areas of spend, major projects and some financial indicators relating to performance on financial management. Results. This section sets out each departments input and impact indicators, additional data sets showing progress against key reforms, and progress against the actions in the Department’s Structural Reform Plan. People. This section set out information on each Department’s workforce in terms of its size, composition (including diversity), attendance, and people survey results. The measurement annex provides information on the indicator methodology, coverage and the period the data relates to. September 2012 The QDS has been withdrawn from publication while the style and content are revised. In the meantime the latest data for the Business plan indicators will be published on this site quarterly. December 2012 Quarterly Data Summary (QDS) Under the new QDS framework departments’ spending data is published every quarter; to show the taxpayer how the Government is spending their money. The QDS grew out of commitments made in the 2011 Budget and the Written Ministerial Statement on Business Plans. For the financial year 2012/13 the QDS has been revised and improved in line with Action 9 of the Civil Service Reform Plan to provide a common set of data that will enable comparisons of operational performance across Government so that departments and individuals can be held to account. The QDS breaks down the total spend of the department in three ways: by Budget, by Internal Operation and by Transaction. At the moment this data is published by individual departments in Excel format, however, in the future the intention is to make this data available centrally through an online application. Over time we will be making further improvements to the quality of the data and its timeliness. We expect that with time this process will allow the public to better understand the performance of each department and government operations in a meaningful way. The QDS template is the same for all departments, though the individual detail of grants and policy will differ from department to department. In using this data: 1. People should ensure they take full note of the caveats noted in each Department’s return. 2. As the improvement of the QDS is an ongoing process data quality and completeness will be developed over time and therefore necessary caution should be applied to any comparative analysis undertaken. Departmental Commentary This is the first time that data have been requested in this format, covering just the core Department. Defra is working closely with Cabinet Office to ensure consistency and completeness of its data across the full range of revised requirements. Further improvements will be evident in future QDS publications.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

American Time Use Survey

Average hours spent on activities in the US over a decade

By Throwback Thursday [source]

About this dataset

How to use the dataset

1. Familiarize Yourself with the Columns

To begin, let's familiarize ourselves with the columns in this dataset:

• Year: The year in which the data was collected.
• Series: The name of the series, representing a specific category or topic.
• Sub-Series: Additional details or categorization within the series.
• Type: Specifies the type of activity being measured.
• Average Hours: The average number of hours spent on the activity.

These columns will be key in understanding and analyzing trends and patterns over time.

2. Focus on Series and Sub-Series

The 'Series' column represents specific categories or topics, while 'Sub-Series' provides additional details or categorization within those categories. Start by exploring these columns to gain an overview of different activities covered in this survey.

For example, you can filter by a particular series such as 'Work', then further narrow it down using sub-series like 'Paid Work' or 'Unpaid Work'. This will help you dive deeper into specific areas of interest.

3. Analyze Types of Activities

The 'Type' column specifies the type of activity being measured. It allows you to identify different types within each series/sub-series combination.

Use this information to segment activities based on their nature or characteristics. For instance, within the Leisure series, you may have sub-series like Socializing, Sports, and Entertainment. Analyzing these types individually can provide unique insights into how people spend their leisure time over a decade.

4. Investigate Average Hours Spent

The 'Average Hours' column quantifies how much time individuals spent on each specified activity on average. Use this numerical data to identify activities that are more time-consuming compared to others.

As you explore different series, sub-series, and types of activities, pay attention to any significant changes in the average hours spent over the years. This will allow you to uncover interesting trends and patterns in time use over the decade covered by this dataset.

5. Combine Filters for Deeper Analysis

To perform more specific analysis, combine multiple filters from different columns simultaneously. For example, you can filter by a particular series like 'Leisure' and then choose a specific sub-series like 'Sports'. Next, further narrow down your analysis by selecting a

Research Ideas

• Analyzing trends in time use: Researchers can use this dataset to analyze how the average hours spent on different activities have changed over a decade. They can identify trends and patterns in time allocation, such as changes in leisure activities, work-related tasks, or household chores.
• Comparing sub-groups: The dataset includes sub-series and types of activities, which allows researchers to compare average hours spent on different activities across various sub-groups of the population. For example, they can analyze if there are any differences between genders in terms of time spent on childcare or leisure activities.
• Understanding societal shifts: By examining the changes in average hours spent on specific series or sub-series over time, researchers can gain insights into societal shifts and changing priorities. This dataset provides an opportunity to understand how behaviors and attitudes towards different activities may have evolved over a decade

Acknowledgements

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

License

See the dataset description for more information.

Columns

Acknowledgements

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

Context

USASpending.gov is the government's official tool for tracking spending, it shows where money goes and who benefits from federal funds.

The Federal Funding Accountability and Transparency Act of 2006 required that federal contract, grant, loan awards over $25k be searchable online to give the American public access to government spending. The data that is collected in USAspending.gov is derived from data gathered at more than a hundred agencies, as well as other government systems. Federal agencies submit contracts, grants, loans and other awards information to be uploaded on USAspending.gov at least twice a month.

Content

The United States spends a lot of money on contracts every year but where does it all go? This data set has information about how much different agencies have spent on awards for the fiscal year 2021. More data can be downloaded, for other years, on USAspending.gov.

Contracts are published to the GSA's Federal Procurement Data System within five days of being awarded, with contract reporting automatically getting posted on USAspending.gov by 9 AM the next day and going live at 8:00 am EST two mornings later

Learn more about the contents here: https://www.usaspending.gov/data-dictionary

The Bureau of the Fiscal Service, United States Department of the Treasury, is dedicated to making government spending data available to everyone.

Data Description

This data starts off separated into smaller files that need to be joined.

Data Overview

The federal government buys a lot of things, like office furniture and aircraft. It also buys services, like telephone and Internet access. The Federal Government and its sub-agencies use contracts to buy these things. They use Product and Service Codes (PSC) to classify the items and services they purchase.

An obligation is a promise to spend money. An outlay is when the government spends money. When the government enters into a contract or grant, it promises to spend all of the money. This is so it can pay people who do what they agreed to do. When the government actually pays someone, then it counts as an outlay.

Data Items that Help get Started

There are many different variables in this database, which are spread across multiple files. The most important ones to start learning are:

1. The contractor who won the award - recipient_name
2. The agency issuing the award - awarding_agency_name
3. The product or service code (PSC) - product_or_service_code
4. The industry classification code (NAICS) of the vendor - naics_code
5. How much was obligated - total_dollars_obligated or total_obligated_amount
6. The contract modification number - modification_number
7. The description of the award - award_description
8. The date of award - action_date or award_base_action_date

Data Dictionary and Analyst Guide

To learn more about the data, you can reference the data dictionary. The data dictionary includes information on outlays, which are not included in the data provided here. https://www.usaspending.gov/data-dictionary

Please see the analysts guide for more information: https://datalab.usaspending.gov/analyst-guide/

License

The U.S. Department of the Treasury, Bureau of the Fiscal Service is committed to providing open data to enable effective tracking of federal spending. The data is available to copy, adapt, redistribute, or otherwise use for non-commercial or for commercial purposes, subject to the Limitation on Permissible Use of Dun & Bradstreet, Inc. Data noted on the homepage. https://www.usaspending.gov/db_info

Acknowledgements

USAspending.gov collects data from all over the government to provide information to the public. Special thanks for the Data Transparency Team within the Office of the Chief Data Officer at the Bureau of Fiscal Services.

Inspiration

Can we find any patterns to help the public? How about predicting future spending needs or opportunities? Test out your ideas here!

📨 Direct Mail Marketing Response Dataset

This dataset focuses on predicting which customers are most likely to respond to a direct mail marketing promotion.
It is based on real data from a clothing store chain in New England.

• Rows: 28,799 customers
• Features: 51 fields (customer demographics, purchase history, and marketing interactions)
• Target: RESP (whether a customer responded to a promotion)

📊 Dataset Details

Each row corresponds to a unique customer, with information about spending behavior, product preferences, and marketing exposure.

Key Fields

• HHKEY → Unique encrypted customer ID
• ZIP_CODE → Customer’s ZIP code
• FRE → Number of purchase visits
• MON → Total net sales
• AVRG → Average spend per visit
• AMSPEND, PSSPEND, CSSPEND, AXSPEND → Spend across four different franchise brands
• OMONSPEND, TMONSPEND, SMOSPEND → Spend over past 1, 3, and 6 months
• PREVPD → Spend in the same period last year
• GMP → Gross margin percentage
• PROMOS → Number of marketing promotions on file
• DAYS → Number of days customer has been on file
• FREDAYS, LTFREDAY → Time between purchases (recent & lifetime average)
• CLASSES → Number of different product classes purchased
• STYLES → Number of individual items purchased
• STORES → Number of stores shopped at
• MARKDOWN, COUPONS, MAILED, RESPONDE, RESPONSERATE → Promotion and discount engagement
• HI → Product uniformity (lower = more diverse purchases)
• CLUSTYPE → Lifestyle cluster type (encrypted)
• PERCRET → Percent of returns
• CC_CARD, VALPHON, WEB → Flags for credit card, valid phone number, and web shopper status

🛍 Clothing Category Spend Variables

Variables: PSWEATERS, PKNIT_TOPS, PKNIT_DRES, PBLOUSES,PJACKETS, PCAR_PNTS, PCAS_PNTS, PSHIRTS, PDRESSES, PSUITS, POUTERWEAR, PJEWELRY, PFASHION, PLEGWEAR, PCOLLSPND; AC_CALC20

Percentages of spend across 15 clothing/product categories:

sweaters, knit tops, knit dresses, blouses, jackets, career pants, casual pants, shirts, dresses, suits, outerwear, jewelry, fashion, legwear, collectibles

🎯 Target Variable

• RESP → Binary variable indicating whether the customer responded to a mailed promotion.

Credits: https://iscap.pt/~aazevedo/variaveis.html

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4560787%2F1bf7d8acca3f6ca6adbae87c95df1f33%2F1_MIXrCZ0QAVp6qoElgWea-A.jpg?generation=1697784111548502&alt=media" alt="">

Data is the new oil, and this dataset is a wellspring of knowledge waiting to be tapped😷!

Don't forget to upvote and share your insights with the community. Happy data exploration!🥰

** For more related datasets: ** https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report/data

Description: Welcome to the world of credit card transactions! This dataset provides a treasure trove of insights into customers' spending habits, transactions, and more. Whether you're a data scientist, analyst, or just someone curious about how money moves, this dataset is for you.

Features: - Customer ID: Unique identifiers for every customer. - Name: First name of the customer. - Surname: Last name of the customer. - Gender: The gender of the customer. - Birthdate: Date of birth for each customer. - Transaction Amount: The dollar amount for each transaction. - Date: Date when the transaction occurred. - Merchant Name: The name of the merchant where the transaction took place. - Category: Categorization of the transaction.

Why this dataset matters: Understanding consumer spending patterns is crucial for businesses and financial institutions. This dataset is a goldmine for exploring trends, patterns, and anomalies in financial behavior. It can be used for fraud detection, marketing strategies, and much more.

Acknowledgments: We'd like to express our gratitude to the contributors and data scientists who helped curate this dataset. It's a collaborative effort to promote data-driven decision-making.

Let's Dive In: Explore, analyze, and visualize this data to uncover the hidden stories in the world of credit card transactions. We look forward to seeing your innovative analyses, visualizations, and applications using this dataset.

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!

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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...

How Much Sleep Do Americans Really Get?

The Consequences of Sleep Deprivation

By Makeover Monday [source]

About this dataset

Do you find yourself tossing and turning at night, struggling to fall asleep? You're not alone. A recent study found that the average American adult gets just under seven hours of sleep per night.

But how does this compare to other countries? And what factors contribute to our sleeplessness?

This dataset contains data on the average amount of time Americans spend sleeping, broken down by age group, sex, and activity. The data includes both weekdays and weekends, so you can see how our sleep habits change depending on the day of the week.

So take a look and see if you can find any patterns in the data. Why do you think some groups get more (or less) sleep than others? And what can we do to improve our sleep habits as a nation?

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How to use the dataset

This dataset includes data on the average number of hours per day Americans spend sleeping, broken down by age group, sex, and activity. This can be used to understand patterns in sleep habits among different groups of people, as well as how these patterns may change over time.

To use this dataset effectively, it is important to understand the different variables that are included. The 'Age Group' variable indicates the age group that the data applies to, while the 'Sex' variable indicates whether the data is for male or female respondents. The 'Activity' variable indicates what activity was being undertaken when the respondent was asked about their sleep habits (e.g. 'sleeping', 'working', 'watching TV', etc.), while the 'Type of Days' variable indicates whether the data was collected for weekdays, weekends, or holidays.

Finally, the 'Avg hrs per day sleeping' and 'Standard Error' variables give information on the average amount of time spent sleeping per day, along with a measure of how accurate this estimate is

Research Ideas

• To study the effect of sleep deprivation on health
• To understand the role of sleep in regulating mood and behavior
• To examine the relationship between sleep and cognitive function

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: Time Americans Spend Sleeping.csv | Column name | Description | |:-----------------------------|:---------------------------------------------------------------------------------------------| | Year | The year the data was collected. (Integer) | | Period | The period of the day the data was collected. (String) | | Avg hrs per day sleeping | The average number of hours per day Americans spend sleeping. (Float) | | Standard Error | The standard error for the average number of hours per day Americans spend sleeping. (Float) | | Type of Days | The type of day the data was collected. (String) | | Age Group | The age group of the Americans surveyed. (String) | | Activity | The activity the Americans surveyed were engaged in. (String) | | Sex | The sex of the Americans surveyed. (String) |

Acknowledgements

If you use this dataset in your research, please credit Makeover Monday [source]