100+ datasets found
  1. Food purchases by grocery store brand in the U.S. 2023

    • statista.com
    Updated Jul 18, 2023
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    Statista (2023). Food purchases by grocery store brand in the U.S. 2023 [Dataset]. https://www.statista.com/forecasts/1235896/food-purchases-by-grocery-store-brand-in-the-us
    Explore at:
    Dataset updated
    Jul 18, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jun 2, 2023 - Jun 19, 2023
    Area covered
    United States
    Description

    The displayed data on food purchases by grocery store brand shows results of the Consumer Insights Sustainable Consumption survey conducted in the United States in 2023. Some 69 percent of respondents answered the question "Which of the following grocery stores have you shopped at in the last 3 months?" with "Walmart".

  2. Purchase criteria for food in the U.S. 2024

    • statista.com
    Updated Jan 30, 2025
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    Statista (2025). Purchase criteria for food in the U.S. 2024 [Dataset]. https://www.statista.com/forecasts/997208/purchase-criteria-for-food-in-the-us
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    Dataset updated
    Jan 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2024 - Dec 2024
    Area covered
    United States
    Description

    "Good taste / flavor" and "Fresh" are the top two answers among U.S. consumers in our survey on the subject of "Purchase criteria for food".The survey was conducted online among 10,146 respondents in the United States, in 2024.

  3. Data from: Family food datasets

    • gov.uk
    Updated Oct 17, 2024
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    Department for Environment, Food & Rural Affairs (2024). Family food datasets [Dataset]. https://www.gov.uk/government/statistical-data-sets/family-food-datasets
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    Dataset updated
    Oct 17, 2024
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Environment, Food & Rural Affairs
    Description

    These family food datasets contain more detailed information than the ‘Family Food’ report and mainly provide statistics from 2001 onwards. The UK household purchases and the UK household expenditure spreadsheets include statistics from 1974 onwards. These spreadsheets are updated annually when a new edition of the ‘Family Food’ report is published.

    The ‘purchases’ spreadsheets give the average quantity of food and drink purchased per person per week for each food and drink category. The ‘nutrient intake’ spreadsheets give the average nutrient intake (eg energy, carbohydrates, protein, fat, fibre, minerals and vitamins) from food and drink per person per day. The ‘expenditure’ spreadsheets give the average amount spent in pence per person per week on each type of food and drink. Several different breakdowns are provided in addition to the UK averages including figures by region, income, household composition and characteristics of the household reference person.

    UK (updated with new FYE 2023 data)

    countries and regions (CR) (updated with FYE 2022 data)

    equivalised income decile group (EID) (updated with FYE 2022 data)

  4. Food Expenditure Series

    • catalog.data.gov
    • agdatacommons.nal.usda.gov
    Updated Feb 24, 2021
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    Economic Research Service, Department of Agriculture (2021). Food Expenditure Series [Dataset]. https://catalog.data.gov/dataset/food-expenditure-series
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    Dataset updated
    Feb 24, 2021
    Dataset provided by
    Economic Research Servicehttp://www.ers.usda.gov/
    Description

    The ERS Food Expenditure Series annually measures total U.S. food expenditures, including purchases by consumers, governments, businesses, and nonprofit organizations. The ERS Food Expenditure Series contributes to the analysis of U.S. food production and consumption by constructing a comprehensive measure of the total value of all food expenditures by final purchasers. This series annually measures total U.S. food expenditures, including purchases by consumers, governments, businesses, and nonprofit organizations. Because the term expenditure is often associated with household decisionmaking, it is important to recognize that ERS's series also includes nonhousehold purchases. For example, the series includes the dollar value of domestic food purchases by military personnel and their dependents at military commissary stores and exchanges, the value of commodities and food dollars donated by the Federal government to schools, and the value of food purchased by airlines for serving during flights.

  5. UK consumers food shopping influenced by social media 2021, by generation

    • statista.com
    Updated May 12, 2022
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    Statista (2022). UK consumers food shopping influenced by social media 2021, by generation [Dataset]. https://www.statista.com/statistics/1291538/social-media-influence-on-food-purchases-by-generation-uk/
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    Dataset updated
    May 12, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Oct 15, 2021 - Oct 22, 2021
    Area covered
    United Kingdom
    Description

    In a survey conducted in October 2021 in the United Kingdom, 23 percent of Millennials and 22 percent of Gen Z reported being influenced by social media when purchasing groceries/food for home. In contrast, that figure stood at only 11 percent for Generation X and one percent for Baby Boomers.

  6. Approaches for Promoting Healthy Food Purchases by SNAP Participants Project...

    • catalog.data.gov
    • datadiscoverystudio.org
    • +4more
    Updated Jan 3, 2024
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    Food and Nutrition Service (2024). Approaches for Promoting Healthy Food Purchases by SNAP Participants Project [Dataset]. https://catalog.data.gov/dataset/approaches-for-promoting-healthy-food-purchases-by-snap-participants-project
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    Dataset updated
    Jan 3, 2024
    Dataset provided by
    Food and Nutrition Servicehttps://www.fns.usda.gov/
    Description

    Due to the United States' high rates of obesity and diet-related chronic diseases, this project aims to develop a plan for front of package (FOP) and shelf-labeling systems that identifies healthy choices, develops theory-based approaches that leverage FOP and shelf-labeling systems to promote healthier food purchases by SNAP participants, and identifies further exploration through the implementation and testing of a future pilot study.

  7. F

    Advance Retail Sales: Food and Beverage Stores

    • fred.stlouisfed.org
    json
    Updated Mar 17, 2025
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    (2025). Advance Retail Sales: Food and Beverage Stores [Dataset]. https://fred.stlouisfed.org/series/RSDBS
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    jsonAvailable download formats
    Dataset updated
    Mar 17, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Advance Retail Sales: Food and Beverage Stores (RSDBS) from Jan 1992 to Feb 2025 about beverages, retail trade, food, sales, retail, and USA.

  8. d

    Local Law 50 New York State Food Purchasing FY15

    • catalog.data.gov
    • data.cityofnewyork.us
    • +2more
    Updated Nov 29, 2021
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    data.cityofnewyork.us (2021). Local Law 50 New York State Food Purchasing FY15 [Dataset]. https://catalog.data.gov/dataset/local-law-50-new-york-state-food-purchasing-fy15
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    Dataset updated
    Nov 29, 2021
    Dataset provided by
    data.cityofnewyork.us
    Area covered
    New York
    Description

    Local Law 50 of 2011 required MOCS to establish guidelines for City agencies that assist in increasing the purchase of New York State food through food purchase and food-related services contracts. City agencies use the New York State Department of Agriculture and Markets (NYSDAM) list of food items available from New York State sources.

  9. Total retail and food services sales in the U.S. 1992-2023

    • statista.com
    Updated Mar 19, 2024
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    Statista (2024). Total retail and food services sales in the U.S. 1992-2023 [Dataset]. https://www.statista.com/statistics/197569/annual-retail-and-food-services-sales/
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    Dataset updated
    Mar 19, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2023, total retail and food service sales reached 8.33 trillion U.S. dollars for the first time in the United States. This is more than four times the sales numbers that were generated in 1992, not adjusting for inflation.

    Leading retailers and store types

    In 2022, the leading food and grocery retailer in the United States was by far Walmart, which generated sales numbers of close to 421 billion U.S. dollars that year. The Kroger Co., Costco Wholesale Club, and Ahold Delhaize were also among the top U.S. retailers. With a grocery market share of almost 60 percent, the supermarket was the top store type in 2018. The warehouse clubs and superstores category stood in second place, accounting for almost a quarter of the U.S. market.

    Consumer habits

    The American consumer made an average of a little more than one and a half trips to the grocery store per week in 2022. The average amount of trips has noticeably decreased, compared to a decade earlier. In recent times, online grocery shopping has also become an option for consumers. The concept is projected to grow considerably in the coming years, reaching roughly 188 billion U.S. dollars’ worth of sales numbers in the United States by 2024.

  10. C

    China CN: Chain: Fast Food: Purchase

    • ceicdata.com
    Updated Dec 15, 2024
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    CEICdata.com (2024). China CN: Chain: Fast Food: Purchase [Dataset]. https://www.ceicdata.com/en/china/fast-food/cn-chain-fast-food-purchase
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    Dataset updated
    Dec 15, 2024
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2012 - Dec 1, 2023
    Area covered
    China
    Variables measured
    Domestic Trade
    Description

    China Chain: Fast Food: Purchase data was reported at 48.630 RMB bn in 2023. This records an increase from the previous number of 38.302 RMB bn for 2022. China Chain: Fast Food: Purchase data is updated yearly, averaging 33.506 RMB bn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 48.630 RMB bn in 2023 and a record low of 6.928 RMB bn in 2005. China Chain: Fast Food: Purchase data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CCAC: Fast Food.

  11. LoCard Food Classification

    • zenodo.org
    • data.niaid.nih.gov
    bin, csv, pdf
    Updated Jul 12, 2024
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    Satu Kinnunen; Satu Kinnunen; Noora Kanerva; Noora Kanerva; Jaakko Nevalainen; Jaakko Nevalainen; Anna-Stiina Suur-Uski; Anna-Stiina Suur-Uski; Maijaliisa Erkkola; Maijaliisa Erkkola; Mikael Fogelholm; Mikael Fogelholm; Henna Vepsäläinen; Henna Vepsäläinen; Jelena Meinilä; Jelena Meinilä; Hannu Saarijärvi; Hannu Saarijärvi (2024). LoCard Food Classification [Dataset]. http://doi.org/10.5281/zenodo.7781352
    Explore at:
    pdf, csv, binAvailable download formats
    Dataset updated
    Jul 12, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Satu Kinnunen; Satu Kinnunen; Noora Kanerva; Noora Kanerva; Jaakko Nevalainen; Jaakko Nevalainen; Anna-Stiina Suur-Uski; Anna-Stiina Suur-Uski; Maijaliisa Erkkola; Maijaliisa Erkkola; Mikael Fogelholm; Mikael Fogelholm; Henna Vepsäläinen; Henna Vepsäläinen; Jelena Meinilä; Jelena Meinilä; Hannu Saarijärvi; Hannu Saarijärvi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    How the data was compiled:
    The grocery purchase data including 3574 product groups was received from the retailer for research purposes. The data was reclassified into appropriate categories suitable for the use of nutrition and health research.

    How the data has been handled:
    A four-level hierarchical classification of product groups was used. Each class on the broadest level of hierarchy (Class 1) was subsequently divided into a reasonable number of finer sub-classes starting with Class level 2, followed by Class 3, and finally, Class 4, which was the most detailed level of hierarchy. The main ingredient of the product group, type of the food and purpose of use, nutritional content, and carbon footprint were considered in the reclassification process. The classified food groups were linked with Finnish food composition database.

    How the data can be used for research:
    The re-classified data can be used to measure can be used to infer and describe food purchase patterns, monitor of the nutrition composition of food purchases, monitoring and evaluating dietary environmental and economic sustainability, and deriving consumer segments based on the re-classification.

    The authors strongly recommend reading the detailed description of the classification is published in https://doi.org/10.21203/rs.3.rs-2826970/v1.

  12. C

    China CN: Fujian: Chain: Fast Food: Purchase

    • ceicdata.com
    Updated Dec 15, 2024
    + more versions
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    CEICdata.com (2024). China CN: Fujian: Chain: Fast Food: Purchase [Dataset]. https://www.ceicdata.com/en/china/fast-food-fujian/cn-fujian-chain-fast-food-purchase
    Explore at:
    Dataset updated
    Dec 15, 2024
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2008 - Dec 1, 2019
    Area covered
    China
    Variables measured
    Domestic Trade
    Description

    Fujian: Chain: Fast Food: Purchase data was reported at 1.122 RMB bn in 2019. This records an increase from the previous number of 0.919 RMB bn for 2018. Fujian: Chain: Fast Food: Purchase data is updated yearly, averaging 0.946 RMB bn from Dec 2005 (Median) to 2019, with 15 observations. The data reached an all-time high of 1.122 RMB bn in 2019 and a record low of 0.176 RMB bn in 2007. Fujian: Chain: Fast Food: Purchase data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CCAC: Fast Food: Fujian.

  13. f

    Table_1_Changes in Food Purchasing Practices of French Households During the...

    • figshare.com
    • frontiersin.figshare.com
    docx
    Updated Jun 4, 2023
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    Daisy Recchia; Pascaline Rollet; Marlène Perignon; Nicolas Bricas; Simon Vonthron; Coline Perrin; Caroline Méjean (2023). Table_1_Changes in Food Purchasing Practices of French Households During the First COVID-19 Lockdown and Associated Individual and Environmental Factors.DOCX [Dataset]. http://doi.org/10.3389/fnut.2022.828550.s001
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    docxAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    Frontiers
    Authors
    Daisy Recchia; Pascaline Rollet; Marlène Perignon; Nicolas Bricas; Simon Vonthron; Coline Perrin; Caroline Méjean
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    French
    Description

    BackgroundTo limit the spread of COVID-19, a strict lockdown was imposed in France between March and May 2020. Mobility limitations and closure of non-essential public places (restaurants, open-air markets, etc.) affected peoples' food environment (FE) and thus their food purchasing practices (FPPs). This study aimed to explore changes in FPPs of French households during lockdown and associations with individual and environmental factors.MethodsIn April of 2020 households from the Mont'Panier cross-sectional study (n = 306), a quota sampling survey conducted in the south of France, were asked to complete an online questionnaire about their FPPs during lockdown and related factors, including perceived FE (distance to closest general food store, perception of increased food prices, etc.). Objective FE (presence, number, proximity, and density of food outlets) was assessed around participant's home using a geographical information system. Multiple correspondence analysis based on changes in frequency of use and quantity of food purchased by food outlet, followed by a hierarchical cluster analysis, resulted in the identification of clusters. Logistic regression models were performed to assess associations between identified clusters and household's sociodemographic characteristics, perceived, and objective FE.ResultsFive clusters were identified. Cluster “Supermarket” (38% of the total sample), in which households reduced frequency of trips, but increased quantity bought in supermarkets during lockdown, was associated with lower incomes and the perception of increased food prices. Cluster “E-supermarket” (12%), in which households increased online food shopping with pickup at supermarket, was associated with higher incomes. Cluster “Diversified” (22%), made up of households who reduced frequency of trips to diverse food outlet types, was associated with the perception of increased food prices. Cluster “Organic Food Store” (20%), in which households did not change frequency of trips, nor quantity purchased in organic food stores, was associated with being older (35–50 y vs.

  14. Groceries & beverages purchased online by category in the U.S. 2024

    • statista.com
    • flwrdeptvarieties.store
    Updated Jan 30, 2025
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    Statista (2025). Groceries & beverages purchased online by category in the U.S. 2024 [Dataset]. https://www.statista.com/forecasts/997137/groceries-and-beverages-purchased-online-by-category-in-the-us
    Explore at:
    Dataset updated
    Jan 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2024 - Dec 2024
    Area covered
    United States
    Description

    "Soft drinks & juices" and "Bottled water" are the top two answers among U.S. consumers in our survey on the subject of "Groceries & beverages purchased online by category".The survey was conducted online among 10,146 respondents in the United States, in 2024. Looking to gain valuable insights about grocery store customers across the globe? Check out our

  15. C

    China CN: Inner Mongolia: Chain: Fast Food: Purchase

    • ceicdata.com
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    China CN: Inner Mongolia: Chain: Fast Food: Purchase [Dataset]. https://www.ceicdata.com/en/china/fast-food-inner-mongolia/cn-inner-mongolia-chain-fast-food-purchase
    Explore at:
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2009 - Dec 1, 2010
    Area covered
    China
    Variables measured
    Domestic Trade
    Description

    Inner Mongolia: Chain: Fast Food: Purchase data was reported at 0.013 RMB bn in 2010. This records an increase from the previous number of 0.012 RMB bn for 2009. Inner Mongolia: Chain: Fast Food: Purchase data is updated yearly, averaging 0.013 RMB bn from Dec 2009 (Median) to 2010, with 2 observations. The data reached an all-time high of 0.013 RMB bn in 2010 and a record low of 0.012 RMB bn in 2009. Inner Mongolia: Chain: Fast Food: Purchase data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CCAC: Fast Food: Inner Mongolia.

  16. o

    Changes in Food Purchases after Chile's Law of Food Labeling and Advertising...

    • osf.io
    Updated Apr 10, 2020
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    Lindsey Smith Taillie (2020). Changes in Food Purchases after Chile's Law of Food Labeling and Advertising [Dataset]. https://osf.io/mj8az
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    Dataset updated
    Apr 10, 2020
    Dataset provided by
    Center For Open Science
    Authors
    Lindsey Smith Taillie
    Description

    No description was included in this Dataset collected from the OSF

  17. F

    Real personal consumption expenditures: Nondurable goods: Food and beverages...

    • fred.stlouisfed.org
    json
    Updated Feb 27, 2025
    + more versions
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    (2025). Real personal consumption expenditures: Nondurable goods: Food and beverages purchased for off-premises consumption [Dataset]. https://fred.stlouisfed.org/series/DFXARX1Q020SBEA
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Feb 27, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Real personal consumption expenditures: Nondurable goods: Food and beverages purchased for off-premises consumption (DFXARX1Q020SBEA) from Q1 2007 to Q4 2024 about off-premises, beverages, purchase, nondurable goods, PCE, consumption expenditures, food, consumption, personal, goods, real, GDP, and USA.

  18. F

    Contributions to percent change in real gross domestic product: Personal...

    • fred.stlouisfed.org
    json
    Updated Jan 30, 2025
    + more versions
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    (2025). Contributions to percent change in real gross domestic product: Personal consumption expenditures: Nondurable goods: Food and beverages purchased for off-premises consumption [Dataset]. https://fred.stlouisfed.org/series/DFXARY2A224NBEA
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jan 30, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Contributions to percent change in real gross domestic product: Personal consumption expenditures: Nondurable goods: Food and beverages purchased for off-premises consumption (DFXARY2A224NBEA) from 1930 to 2024 about off-premises, contributions, beverages, purchase, nondurable goods, PCE, consumption expenditures, food, consumption, percent, personal, goods, real, GDP, and USA.

  19. Online fresh food sales share in the U.S. 2019-2024

    • statista.com
    Updated Feb 24, 2025
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    Statista (2025). Online fresh food sales share in the U.S. 2019-2024 [Dataset]. https://www.statista.com/statistics/1337386/impact-of-covid-on-online-fresh-food-purchases-in-the-us/
    Explore at:
    Dataset updated
    Feb 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    All fresh food categories saw an increase in online sales during the COVID-19 pandemic compared to before the pandemic. The online sales share of fresh food was about 2.8 percent of the total fresh food sales in 2024.

  20. U.S. Hispanics: how social media influences food purchases in 2023

    • statista.com
    Updated Mar 10, 2025
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    U.S. Hispanics: how social media influences food purchases in 2023 [Dataset]. https://www.statista.com/statistics/1454967/social-media-influencing-food-shopping-among-us-hispanics/
    Explore at:
    Dataset updated
    Mar 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Feb 1, 2023 - Mar 13, 2023
    Area covered
    United States
    Description

    In 2023, Hispanic Americans were more likely to be influenced by social media when buying food. Over a third of U.S. Hispanics surveyed indicated that social media influences their food purchase 'a little', while one in 10 respondents even stated that it influences their decision 'a lot'.

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Statista (2023). Food purchases by grocery store brand in the U.S. 2023 [Dataset]. https://www.statista.com/forecasts/1235896/food-purchases-by-grocery-store-brand-in-the-us
Organization logo

Food purchases by grocery store brand in the U.S. 2023

Explore at:
Dataset updated
Jul 18, 2023
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
Jun 2, 2023 - Jun 19, 2023
Area covered
United States
Description

The displayed data on food purchases by grocery store brand shows results of the Consumer Insights Sustainable Consumption survey conducted in the United States in 2023. Some 69 percent of respondents answered the question "Which of the following grocery stores have you shopped at in the last 3 months?" with "Walmart".

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