Grocery store sales have grown dramatically since the 90’s. Since 1992, sales have more than doubled. The total sales generated by grocery stores in the United States in 2024 amounted to ***** billion U.S. dollars. Top Supermarket Chains The U.S. grocery retail market is dominated by chain supermarkets. In 2018 there were around ****** chain supermarket locations in the United States, compared to only ***** independent supermarkets. The leading American supermarket in terms of sales is the Kroger Company, which owns and operates several smaller supermarket chains across the United States. In 2023, Kroger’s total retail sales reached close to *** billion U.S. dollars. The runner-up, Albertsons, generated some **** billion U.S. dollars in sales that year. Americans at the Grocery Store Going to the grocery store is a familiar and comforting ritual for many Americans. In 2017, a survey of American households found that ** percent of Americans make a weekly trip to the grocery store, while some *** percent went to the grocery store four to seven times in a week. Although many products on the shelves of U.S. supermarkets claim to have various health benefits or that they were produced or sourced ethically, American consumers are most drawn to food products that claim to be fresh or farm-fresh.

Supermarkets have maintained stable volume-driven business strategies amid a pricing environment that has been in the spotlight. Conflict in the Middle East, avian flu outbreaks and other inflationary pressures have driven prices up, with many stores passing on these costs to consumers. While consumers are paying more for groceries and upstream suppliers are seeing their margins shrink, supermarkets Coles and Woolworths have maintained relatively stable profit margins, among the highest in the world. The continued expansion of Aldi and Amazon has forced the two established industry giants to shift gears recently to remain price-competitive on both the physical store and online service fronts, launching short-term price discounting initiatives. These supermarket giants also rely on loyalty programs and promotions. Coles and Woolworths have displayed interest in data analytics, strengthening their relationships with analytics data giants like Palantir to optimise their marketing and operational processes. The ACCC's landmark supermarkets inquiry, while not finding evidence of price gouging, identified 20 key recommendations that would ensure a more sustainable market and avoid oligopolistic exploitation. Supermarket and grocery revenue rose significantly following the COVID-19 outbreak. A combination of panic buying, along with the suspension of many specials and promotions in supermarkets, boosted grocery turnover at the beginning of the period, spiking revenue for the two years through 2020-21. This high benchmark at the start of the period has resulted in an industry correction and an annualised revenue contraction of 0.4% to $144.3 billion over the five years through 2025-26. Revenue is estimated to climb 0.4% in 2025-26, reflecting the price-driven industry growth that falling tobacco sales have offset. Supermarkets and grocery stores are set to perform well, with industry revenue slated to climb at an annualised 1.5% through 2030-31 to $155.6 billion. Population growth will remain a key growth factor that stores rely on, as many continue a volume-driven business approach to generating revenue. Should the transparency-related recommendations from the ACCC's inquiry be implemented, some price-driven growth may be curtailed. Eventually, when inflationary pressures subside and consumer sentiment returns to a positive level, supermarkets and grocers will be well-positioned to take advantage of consumer appetite for value-added and premium goods. Strong growth in online sales is set to continue.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

Supermarkets and grocery stores have endured a transformative journey since 2019, shaped by the pandemic, geopolitical tensions and an ever-changing market landscape. Grocers first encountered unprecedented demand as lockdowns redirected consumers' spending from entertainment to at-home essentials. Sales spiked, but the boom was fleeting; by 2021, factors like declining disposable income and soaring food prices reversed the trend. Even post-pandemic, the industry is evolving—more consumers than ever are embracing online grocery shopping, prompting traditional retailers to bolster their digital presence. Those unable or unwilling to adapt were largely forced out, while the largest supermarket chains maintained dominance through aggressive merger and acquisition activity and by leveraging vertically integrated operations. This momentous period caused heightened revenue volatility that still persists. Revenue has been rising at a CAGR of 0.1% over the past five years and is expected to dip 0.9% in 2024 when revenue will reach $111.9 billion. Amid this transformation, significant profit disparities worsened in an incredibly concentrated industry. Geopolitical instabilities like the war in Ukraine intensified supply chain disruptions, impacting costs for retailers. Rising energy prices compound this issue, squeezing profit as transportation expenses mount. Meanwhile, climate change injects further unpredictability into production costs, forcing grocers to manage these pressures by cautiously adjusting consumer prices. A class-action lawsuit against Loblaw Cos. Ltd. underscores market concentration challenges, spotlighting potential anti-competitive behaviours and their implications. This legal scrutiny, combined with governmental pressure for price transparency, could foster a more equitable marketplace. Should dominant players like Loblaw adjust their pricing strategies, it may level the playing field for smaller competitors, expanding competition and consumer choice. Over the next five years, a stable economic backdrop will support modest revenue growth for supermarkets. As disposable incomes stabilize, a return to preferred brands could uplift grocers' revenue. A more tempered rise in food prices will allow for strategic pricing decisions, providing grocers with a favourable environment for maintaining consumer loyalty. Technological advancements will be pivotal, with retailers expected to deepen investments in e-commerce and in-store technologies like AI-powered inventory management. This investment will be crucial as online grocery shopping and big-box retailers thrive. Governmental regulatory efforts may also reshape industry dynamics, offering smaller grocers a greater chance to compete by enhancing pricing equity. Continued inventory diversification reflecting health-conscious consumer preferences will likely continue, driven by rising interest in plant-based and ethical products. Retailers that navigate these shifts adeptly, leveraging both technology and emerging consumer trends, are poised to gain a competitive edge. Revenue is forecast to climb at a CAGR of 1.7% over the next five years, reaching $122.0 billion in 2029.

Cost of food in the United States increased 2.90 percent in July of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Success.ai’s Retail Data for the Retail Sector in North America offers a comprehensive dataset designed to connect businesses with key players across the diverse retail industry. Covering everything from department stores and supermarkets to specialty shops and e-commerce platforms, this dataset provides verified contact details, business locations, and leadership profiles for retail companies in the United States, Canada, and Mexico.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, marketing, and business development efforts are powered by accurate, continuously updated, and AI-validated data.

Backed by our Best Price Guarantee, this solution empowers businesses to thrive in North America’s competitive retail landscape.

Why Choose Success.ai’s Retail Data for North America?

1. Verified Contact Data for Precision Outreach

   • Access verified phone numbers, work emails, and LinkedIn profiles of retail executives, store managers, and decision-makers.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and efficient campaign execution.

2. Comprehensive Coverage Across Retail Segments

   • Includes profiles of retail businesses across major markets, from large department stores and grocery chains to boutique retailers and online platforms.
   • Gain insights into the operational dynamics of retail hubs in cities such as New York, Los Angeles, Toronto, and Mexico City.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, new store openings, market expansions, and shifts in consumer preferences.
   • Stay aligned with evolving industry trends and emerging opportunities in the North American retail sector.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other privacy regulations, ensuring responsible and lawful use of data in your campaigns.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with executives, marketing directors, and operations managers across the North American retail sector.
• 30M Company Profiles: Access firmographic data, including revenue ranges, store counts, and geographic footprints.
• Store Location Data: Pinpoint retail outlets, regional offices, and distribution centers to refine supply chain and marketing strategies.
• Leadership Contact Details: Connect with CEOs, CMOs, and procurement officers influencing retail operations and vendor selections.

Key Features of the Dataset:

1. Retail Decision-Maker Profiles

   • Identify and engage with store owners, category managers, and marketing directors shaping customer experiences and product strategies.
   • Target professionals responsible for inventory planning, vendor contracts, and store performance.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (luxury, grocery, e-commerce), geographic location, company size, or revenue range.
   • Tailor outreach to align with regional market trends, customer demographics, and operational priorities.

3. Market Trends and Operational Insights

   • Analyze trends such as online shopping growth, sustainability practices, and supply chain optimization.
   • Leverage insights to refine product offerings, identify partnership opportunities, and design effective campaigns.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and enhance engagement outcomes.

Strategic Use Cases:

1. Sales and Lead Generation

   • Present products, services, or technology solutions to retail procurement teams, marketing departments, and operations managers.
   • Build relationships with retailers seeking innovative tools, efficient supply chain solutions, or unique product offerings.

2. Market Research and Consumer Insights

   • Analyze retail trends, customer behaviors, and seasonal demands to inform marketing strategies and product launches.
   • Benchmark against competitors to identify gaps, emerging niches, and growth opportunities.

3. E-Commerce and Digital Strategy Development

   • Target e-commerce managers and digital transformation teams driving online retail initiatives and omnichannel integration.
   • Offer solutions to enhance online shopping experiences, logistics, and customer loyalty programs.

4. Recruitment and Workforce Solutions

   • Engage HR professionals and hiring managers in recruiting talent for store operations, customer service, or marketing roles.
   • Provide workforce optimization tools, training platforms, or staffing services tailored to retail environments.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality retail data at competitive prices, ensuring strong ROI for your marketing and outreach efforts in North America.

2. Seamless Integration
   ...

This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly

This project aims to use and extend a novel and remarkably comprehensive dataset on food retailing to analyse new questions regarding firms' pricing behaviour at a highly disaggregated level. The analysis will be grounded in existing theoretical and empirical work but will extend beyond it to tackle policy-relevant issues in competition between firms and in the development of inflationary processes. The central research questions to be examined include the following: What was the impact of the major structural change resulting from the takeover of Safeway by Morrison on pricing behaviour and outcomes in the GB food retail market? Is there evidence of prices having a leader-follower pattern amongst GB supermarkets? If so, what are its causes and likely consequences? Finally, and more broadly, how if at all do price movements in times when cost pressures are inflationary differ from those when cost pressures are relatively relaxed? If so, what are the implications? The approach used will involve model building followed by econometric (statistical) analysis of five years' worth of weekly pricing data across four major supermarket chains for a significant number of products, alongside other information on the products and the firms involved. Manual collection from weekly website entries Population is set of products sold by major supermarkets Dataset consists of prices for 370 products (i.e. 370 cases), weekly frequency for seven years across three supermarkets Product category classification data added by researchers

Measure and Map Access to Grocery StoresFrom the perspective of the people living in each neighborhood How do people in your city get to the grocery store? The answer to that question depends on the person and where they live. This collection of layers, maps and apps help answer the question.Some live in cities and stop by a grocery store within a short walk or bike ride of home or work. Others live in areas where car ownership is more prevalent, and so they drive to a store. Some do not own a vehicle, and rely on a friend or public transit. Others rely on grocery delivery for their needs. And, many live in rural areas far from town, so a trip to a grocery store is an infrequent event involving a long drive.This map from Esri shows which areas are within a ten minute walk (in green) or ten minute drive (in blue) of a grocery store in the United States and Puerto Rico. Darker color indicates access to more stores. Summarizing this data shows that 20% of U.S. population live within a 10 minute walk of a grocery store, and 90% of the population live within a 10 minute drive of a grocery store. Click on the map to see a summary for each state.Every census block is scored with a count of walkable and drivable stores nearby, making this a map suitable for a dashboard for any city, or any of the 50 states, DC and Puerto Rico. Two colorful layers visualize this definition of access, one for walkable access (suitable for looking at a city neighborhood by neighborhood) and one for drivable access (suitable for looking across a city, county, region or state).On the walkable layer, shades of green define areas within a ten minute walk of one or more grocery stores. The colors become more intense and trend to a blue-green color for the busiest neighborhoods, such as downtown San Francisco. As you zoom in, a layer of Census block points visualizes the local population with or without walkable access. As you zoom out to see the entire city, the map adds a light blue - to dark blue layer, showing which parts of the region fall within ten minutes' drive of one or more grocery stores. As a result, the map is useful at all scales, from national to regional, state and local levels. It becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car? How to Use This MapUse this map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying against their own experiences. The map was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access. There is data behind the map, which can be summarized to show how many people have walkable access to local grocery stores. The map includes a feature layer of population in Census block points, which are visible when you zoom in far enough. This feature layer of Census block centroids can be plugged into an app like this one that summarizes the population with/without walkable or drivable access. Lastly, this map can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples). The map is a useful visual and analytic resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved. Data sourcesPopulation data is from the 2020 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer. Grocery store locations are from SafeGraph, reflecting what was in the data as of September 2024. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters. The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis. The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels. The SafeGraph grocery store locations were provided by SafeGraph. The source data included NAICS code 445110 and 452311 as an initial screening. The CSV file was imported using the Data Interoperability geoprocessing tools in ArcGIS Pro, where a definition query was applied to the layer to exclude any records that were not grocery stores. The final layer used in the analysis had approximately 63,000 records. In this map, this layer is included as a vector tile layer. MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway. A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in. The results for each analysis were captured in a Lines layer, which shows which origins are within the 10 minute cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle). The Lines layer is not published but is used to count how many stores each origin has access to over the road network. The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step. Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool used a 100 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect. Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle.

Analysis of ‘Big Mart Sales’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/akashdeepkuila/big-mart-sales on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

The data scientists at Big Mart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and predict the sales of each product at a particular outlet.

Using this model, Big Mart will try to understand the properties of products and outlets which play a key role in increasing sales.

Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.

Content

The dataset provides the product details and the outlet information of the products purchased with their sales value split into a train set (8523) and a test (5681) set. Train file: CSV containing the item outlet information with sales value Test file: CSV containing item outlet combinations for which sales need to be forecasted

Variable Description

• ProductID : unique product ID
• Weight : weight of products
• FatContent : specifies whether the product is low on fat or not
• Visibility : percentage of total display area of all products in a store allocated to the particular product
• ProductType : the category to which the product belongs
• MRP : Maximum Retail Price (listed price) of the products
• OutletID : unique store ID
• EstablishmentYear : year of establishment of the outlets
• OutletSize : the size of the store in terms of ground area covered
• LocationType : the type of city in which the store is located
• OutletType : specifies whether the outlet is just a grocery store or some sort of supermarket
• OutletSales : (target variable) sales of the product in the particular store

Inspiration

Sales of a given product at a retail store can depend both on store attributes as well as product attributes. The dataset is ideal to explore and build a data science model to predict the future sales.

--- Original source retains full ownership of the source dataset ---

This layer shows the market opportunity for grocery stores in the U.S. in 2017 in a multiscale map (by country, state, county, ZIP Code, tract, and block group). The map uses the Leakage/Surplus Factor, an indexed value that represents opportunity (leakage), saturation (surplus), or balance within a market. This map focuses on the opportunity for grocery stores (NAICS 4451). The pop-up is configured to include the following information for each geography level:Count of grocery stores - NAICS 4451Total annual NAICS 4451 sales (supply)Total annual NAICS 4451 sales potential (demand)Market Opportunity for NAICS 4451 (expressed as an index)Total annual supply and demand for various food industriesFood and Beverage Stores - NAICS 445Specialty Food Stores - NAICS 4452Beer/Wine/Liquor Stores - NAICS 4453Esri's Leakage/Surplus Factor measures the balance between the volume of retail sales (supply) generated by retail businesses and the volume of retail potential (demand) produced by household spending on retail goods within the same industry. The factor enables a one-step comparison of supply against demand, and a simple way to identify business opportunity. Leakage implies that potential sales are "leaking" from an area, while surplus implies a saturation within a given area. The values range from -100 to +100, with a value of 0 representing a balanced market. See the Leakage/Surplus Factor Data Note for more information. Esri's 2017 Retail MarketPlace (RMP) database provides a direct comparison between retail sales and consumer spending by industry and measures the gap between supply and demand. This database includes retail sales by industry to households and retail potential or spending by households. The Retail MarketPlace data helps organizations accurately measure retail activity by trade area and compare retail sales to consumer spending by NAICS industry classification. See Retail MarketPlace Database to view the methodology statement, supported geography levels, and complete variable list. Additional Esri Resources:Esri DemographicsU.S. 2017/2022 Esri Updated DemographicsEssential demographic vocabularyEsri's arcgis.com demographic map layers

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Badakhshan, Badghis, Baghlan, Balkh, Bamyan, Daykundi, Farah, Faryab, Paktya, Ghazni, Ghor, Hilmand, Hirat, Nangarhar, Jawzjan, Kabul, Kandahar, Kapisa, Khost, Kunar, Kunduz, Laghman, Logar, Wardak, Nimroz, Nuristan, Paktika, Panjsher, Parwan, Samangan, Sar-e-pul, Takhar, Uruzgan, Zabul, Market Average, Armavir, Ararat, Aragatsotn, Tavush, Gegharkunik, Shirak, Kotayk, Syunik, Lori, Vayotz Dzor, Yerevan, Kayanza, Ruyigi, Bubanza, Karuzi, Bujumbura Mairie, Muramvya, Gitega, Rumonge, Bururi, Kirundo, Cankuzo, Cibitoke, Muyinga, Rutana, Bujumbura Rural, Makamba, Ngozi, Mwaro, SAHEL, CASCADES, SUD-OUEST, EST, BOUCLE DU MOUHOUN, CENTRE-NORD, PLATEAU-CENTRAL, HAUTS-BASSINS, CENTRE, NORD, CENTRE-SUD, CENTRE-OUEST, CENTRE-EST, Khulna, Chittagong, Barisal, Rajshahi, Dhaka, Rangpur, Sylhet, Mymensingh, Ouaka, Mbomou, Bangui, Nana-Mambéré, Ouham, Sangha-Mbaéré, Ombella M'Poko, Mambéré-Kadéï, Vakaga, Ouham Pendé, Lobaye, Haute-Kotto, Kémo, Nana-Gribizi, Bamingui-Bangoran, Haut-Mbomou, Nord, Extrême-Nord, Ouest, Nord-Ouest, Adamaoua, Sud-Ouest, Est, Littoral, Centre, Haut-Uele, Nord-Kivu, Ituri, Tshopo, Kwilu, Kasai, Sud-Kivu, Kongo-Central, Nord-Ubangi, Sud-Ubangi, Kasai-Central, Bas-Uele, Tanganyika, Lualaba, Kasai-Oriental, Kwango, Haut-Lomami, Haut-Katanga, Maniema, Kinshasa, Equateur, Lomami, Likouala, Brazzaville, Point-Noire, Pool, Bouenza, Cuvette, Lekoumou, Nzerekore, Boke, Kindia, Kankan, Faranah, Mamou, Labe, Kanifing Municipal Council, Central River, Upper River, West Coast, North Bank, Lower River, Bafata, Tombali, Cacheu, Sector Autonomo De Bissau, Biombo, Oio, Gabu, Bolama, Quinara, North, South, Artibonite, South-East, Grande'Anse, North-East, West, North-West, SULAWESI UTARA, SUMATERA UTARA, KALIMANTAN UTARA, JAWA BARAT, NUSA TENGGARA BARAT, NUSA TENGGARA TIMUR, SULAWESI SELATAN, JAMBI, JAWA TIMUR, KALIMANTAN SELATAN, BALI, BANTEN, JAWA TENGAH, RIAU, SUMATERA BARAT, KEPULAUAN RIAU, PAPUA, SULAWESI BARAT, BENGKULU, MALUKU UTARA, DAERAH ISTIMEWA YOGYAKARTA, KALIMANTAN BARAT, KALIMANTAN TENGAH, PAPUA BARAT, SUMATERA SELATAN, MALUKU, KEPULAUAN BANGKA BELITUNG, ACEH, DKI JAKARTA, SULAWESI TENGGARA, KALIMANTAN TIMUR, LAMPUNG, GORONTALO, SULAWESI TENGAH, Anbar, Babil, Baghdad, Basrah, Diyala, Dahuk, Erbil, Ninewa, Kerbala, Kirkuk, Missan, Muthanna, Najaf, Qadissiya, Salah al-Din, Sulaymaniyah, Thi-Qar, Wassit, Coast, North Eastern, Nairobi, Rift Valley, , Eastern, Central, Nyanza, Attapeu, Bokeo, Bolikhamxai, Champasack, Houaphan, Khammouan, Louangphabang, Louangnamtha, Oudomxai, Phongsaly, Salavan, Savannakhet, Sekong, Vientiane Capital, Vientiane, Xaignabouly, Xiengkhouang, Akkar, Mount Lebanon, Baalbek-El Hermel, Beirut, Bekaa, El Nabatieh, Nimba, Grand Kru, Grand Cape Mount, Gbarpolu, Grand Bassa, Rivercess, Montserrado, River Gee, Lofa, Bomi, Bong, Sinoe, Maryland, Margibi, Grand Gedeh, East, North Central, Uva, Western, Sabaragamuwa, Southern, Northern, North Western, Kidal, Gao, Tombouctou, Bamako, Kayes, Koulikoro, Mopti, Segou, Sikasso, Yangon, Rakhine, Shan (North), Kayin, Kachin, Shan (South), Mon, Tanintharyi, Mandalay, Kayah, Shan (East), Chin, Magway, Sagaing, Zambezia, Cabo_Delgado, Tete, Manica, Sofala, Maputo, Gaza, Niassa, Inhambane, Maputo City, Nampula, Hodh Ech Chargi, Hodh El Gharbi, Brakna, Adrar, Assaba, Guidimakha, Gorgol, Trarza, Tagant, Dakhlet-Nouadhibou, Nouakchott, Tiris-Zemmour, Central Region, Southern Region, Northern Region, Tillaberi, Tahoua, Agadez, Zinder, Dosso, Niamey, Maradi, Diffa, Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Adamawa, Cordillera Administrative region, Region XIII, Region VI, Region V, Region III, Autonomous region in Muslim Mindanao, Region IV-A, Region VIII, Region VII, Region X, Region II, Region IV-B, Region XII, Region XI, Region I, National Capital region, Region IX, North Darfur, Blue Nile, Nile, Eastern Darfur, West Kordofan, Gedaref, West Darfur, North Kordofan, South Kordofan, Kassala, Khartoum, White Nile, South Darfur, Red Sea, Sennar, Al Gezira, Central Darfur, Tambacounda, Diourbel, Ziguinchor, Kaffrine, Dakar, Saint Louis, Fatick, Kolda, Louga, Kaolack, Kedougou, Matam, Thies, Sedhiou, Shabelle Hoose, Juba Hoose, Bay, Banadir, Shabelle Dhexe, Gedo, Hiraan, Woqooyi Galbeed, Awdal, Bari, Juba Dhexe, Togdheer, Nugaal, Galgaduud, Bakool, Sanaag, Mudug, Sool, Warrap, Unity, Jonglei, Northern Bahr el Ghazal, Upper Nile, Eastern Equatoria, Central Equatoria, Western Bahr el Ghazal, Western Equatoria, Lakes, Aleppo, Dar'a, Quneitra, Homs, Deir-ez-Zor, Damascus, Ar-Raqqa, Al-Hasakeh, Hama, As-Sweida, Rural Damascus, Tartous, Idleb, Lattakia, Ouaddai, Salamat, Wadi Fira, Sila, Ennedi Est, Batha, Tibesti, Logone Oriental, Logone Occidental, Guera, Hadjer Lamis, Lac, Mayo Kebbi Est, Chari Baguirmi, Ennedi Ouest, Borkou, Tandjile, Mandoul, Moyen Chari, Mayo Kebbi Ouest, Kanem, Barh El Gazal, Ndjaména, Al Dhale'e, Aden, Al Bayda, Al Maharah, Lahj, Al Jawf, Raymah, Al Hudaydah, Hajjah, Amran, Shabwah, Dhamar, Ibb, Sana'a, Al Mahwit, Marib, Hadramaut, Sa'ada, Amanat Al Asimah, Socotra, Taizz, Abyan

This dataset provides insights into the UK food-to-go market value in millions, along with percentage growth projections from 2019 to 2025.

This dataset contains key characteristics about the data described in the Data Descriptor Tesco Grocery 1.0, a large-scale dataset of grocery purchases in London. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format 


      Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.

• The dataset consists of Excel (.xlsx) files with data on sales of plant-based food products between 2017 and 2020 in a number of European countries (i.e. Austria, Belgium, Denmark, France, Germany, Italy, the Netherlands, Poland, Romania, Spain and the UK.)

• The data are clearly labelled within each file. The key variables (common across datasets) are Value in Euros, Volume in KG/LIT and Volume in Selling Units for a number of meat and dairy substitute food products specific to the retail region.

• The data were originally collected by Nielsen Market Track. They were analysed on the Smart Protein project in 2021 and used to publish an extensive market data report and to host a public webinar, both entitled Plant-based foods in Europe: how big is the market?

Success.ai’s Retail Data for Retail Professionals in APAC offers a comprehensive and accurate dataset tailored for businesses and organizations aiming to connect with key players in the retail industry across the Asia-Pacific region. Covering roles such as retail managers, merchandisers, supply chain specialists, and executives, this dataset provides verified LinkedIn profiles, work emails, and professional histories.

With access to over 700 million verified global profiles, Success.ai ensures your outreach, marketing, and collaboration strategies are powered by continuously updated, AI-validated data. Backed by our Best Price Guarantee, this solution empowers you to excel in the dynamic and competitive APAC retail market.

Why Choose Success.ai’s Retail Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of retail professionals across APAC.
   • AI-driven validation ensures 99% accuracy, reducing inefficiencies and boosting engagement outcomes.

2. Comprehensive Coverage of APAC’s Retail Sector

   • Includes professionals from key retail hubs such as China, Japan, South Korea, India, Australia, and Southeast Asia.
   • Gain insights into market trends, consumer behavior, and retail innovations unique to the APAC region.

3. Continuously Updated Datasets

   • Real-time updates capture changes in roles, organizations, and industry dynamics.
   • Stay aligned with evolving trends and capitalize on emerging opportunities in the retail sector.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Access detailed retail data for professionals and organizations across the APAC region.
• Verified Contact Details: Gain work emails, phone numbers, and LinkedIn profiles for precise targeting.
• Professional Histories: Understand career trajectories, areas of expertise, and contributions to the retail sector.
• Regional Insights: Leverage actionable data on consumer preferences, supply chain challenges, and market trends.

Key Features of the Dataset:

1. Comprehensive Retail Professional Profiles

   • Identify and connect with professionals managing retail operations, merchandising, supply chains, and customer engagement strategies.
   • Target decision-makers involved in e-commerce, brick-and-mortar retail, and omnichannel strategies.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (fashion, electronics, grocery), geographic location, or job function.
   • Tailor campaigns to align with specific business needs, such as technology adoption, marketing strategies, or vendor partnerships.

3. Regional and Industry-specific Insights

   • Leverage data on APAC’s retail trends, consumer purchasing patterns, and logistics challenges.
   • Refine strategies to align with unique market dynamics and customer expectations.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Outreach

   • Promote retail technology solutions, marketing tools, or supply chain services to retail professionals in the APAC region.
   • Use verified contact data for multi-channel outreach, including email, phone, and LinkedIn campaigns.

2. Partnership Development and Collaboration

   • Build relationships with retail chains, e-commerce platforms, and logistics providers seeking strategic partnerships.
   • Foster collaborations that enhance customer experiences, expand distribution networks, or improve operational efficiencies.

3. Market Research and Competitive Analysis

   • Analyze regional retail trends, consumer behavior, and supply chain innovations to refine product offerings and business strategies.
   • Benchmark against competitors to identify growth opportunities and high-demand solutions.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers in the retail industry recruiting for roles in merchandising, operations, and digital transformation.
   • Provide workforce optimization platforms or training solutions tailored to the retail sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality retail data at competitive prices, ensuring strong ROI for your marketing, sales, and business outreach efforts.

2. Seamless Integration

   • Integrate verified retail data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, streamlining workflows and enhancing productivity.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide data-driven decisions, refine targeting, and boost conv...

The food dollar series measures annual expenditures by U.S. consumers on domestically produced food. This data series is composed of three primary series - the marketing bill series, the industry group series, and the primary factor series - that shed light on different aspects of the food supply chain. The three series show three different ways to split up the same food dollar.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Query tool Food Dollar API Food Dollar Series Data Download For complete information, please visit https://data.gov.

📘 ABOUT

The Enhanced Zomato Dataset provides comprehensive information on restaurants, including user ratings, cuisine types, prices, and geographic details. This enhanced version of the popular Zomato dataset includes carefully cleaned data and newly engineered features to support advanced analytics, trend analysis, and machine learning applications.

It is especially valuable for data scientists, analysts, and machine learning practitioners seeking to build recommendation systems, price predictors, or restaurant review models.

✅ Key Features of the Dataset:

• ✅ Cleaned data with no missing values
• ✅ Contains restaurant, cuisine, city, and pricing information
• ✅ Includes user ratings for both dining and delivery experiences
• ✅ Features engineered columns such as popularity scores and price-per-vote ratios
• ✅ Ready for data visualization, machine learning, and business insights

This dataset is an excellent resource for exploring food industry patterns, building ML models, and performing customer behavior analysis.

📂 FILE INFORMATION

The dataset contains structured records of restaurant details, user ratings, pricing, and engineered features. It was compiled from a public Zomato dataset and enhanced through feature engineering and cleaning techniques.

• File Type: CSV
• Data Rows: 123,657
• Data Fields: Restaurant details, cuisine, city, prices, votes, ratings, and additional engineered features

📊 COLUMNS DESCRIPTION

Nothing ever becomes real till it is experienced.

-John Keats

While we don't know the context in which John Keats mentioned this, we are sure about its implication in data science. While you would have enjoyed and gained exposure to real world problems in this challenge, here is another opportunity to get your hand dirty with this practice problem.

Problem Statement :

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.

Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.

Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.

Data :

We have 14204 samples in data set.

Variable Description

Item Identifier: A code provided for the item of sale

Item Weight: Weight of item

Item Fat Content: A categorical column of how much fat is present in the item: ‘Low Fat’, ‘Regular’, ‘low fat’, ‘LF’, ‘reg’

Item Visibility: Numeric value for how visible the item is

Item Type: What category does the item belong to: ‘Dairy’, ‘Soft Drinks’, ‘Meat’, ‘Fruits and Vegetables’, ‘Household’, ‘Baking Goods’, ‘Snack Foods’, ‘Frozen Foods’, ‘Breakfast’, ’Health and Hygiene’, ‘Hard Drinks’, ‘Canned’, ‘Breads’, ‘Starchy Foods’, ‘Others’, ‘Seafood’.

Item MRP: The MRP price of item

Outlet Identifier: Which outlet was the item sold. This will be categorical column

Outlet Establishment Year: Which year was the outlet established

Outlet Size: A categorical column to explain size of outlet: ‘Medium’, ‘High’, ‘Small’.

Outlet Location Type: A categorical column to describe the location of the outlet: ‘Tier 1’, ‘Tier 2’, ‘Tier 3’

Outlet Type: Categorical column for type of outlet: ‘Supermarket Type1’, ‘Supermarket Type2’, ‘Supermarket Type3’, ‘Grocery Store’

Item Outlet Sales: The number of sales for an item.

Evaluation Metric:

We will use the Root Mean Square Error value to judge your response

Since 2003, this office has commissioned the Institute of Food Science and Technology to carry out the promotion and guidance plan for health functional foods. Through activities such as establishing the "Health Food Industry Service Network", setting up a health food consultation window, holding practical technology seminars, and issuing newsletters, the office aims to establish a health food industry service platform. It also combines the research and development capabilities of domestic research institutions to develop health food materials, establish health food specifications and testing methods, conduct expert interviews and technical guidance for health food business operators, and hopes to promote the advancement of the health food industry, increase the competitiveness of domestic health food exports, and effectively increase the output value of the health food industry. In line with the government's promotion of open data measures, this office is now opening the "Health Food Industry Service Network" dataset (http://www.functionalfood.org.tw/), providing basic information of domestic health food manufacturers, including company names, addresses, contact information, and website, for everyone to use.