599 Global export shipment records of Grocery Item with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

GroceryList Dataset

  Dataset Summary

The GroceryList dataset consists of grocery items and their corresponding categories. It is designed to assist in tasks such as grocery item classification, shopping list organization, and natural language understanding related to common grocery-related terms. The dataset contains only a training split and is not pre-divided into test or validation sets. It includes two main columns:

Item: Contains the names of various grocery items… See the full description on the dataset page: https://huggingface.co/datasets/AmirMohseni/GroceryList.

Explore this rich dataset capturing daily prices of grocery essentials. From fresh produce to pantry staples, delve into the fluctuations of everyday items to uncover valuable insights for data scientists and analysts. Whether you're tracking market trends or optimizing shopping strategies, this dataset offers a treasure trove of information for informed decision-making.

Here's a short description for each column in your dataset:

Product Name: The name or description of the grocery item.

Category: The category to which the product belongs (e.g., fruits, vegetables, dairy, etc.)

Quantity: The quantity or amount of the product purchased.

Original Price (Rs.): The original price of the product before any discounts, in Indian Rupees.

Discount: The discount offered on the product, usually represented as a percentage.

Discounted Price (Rs.): The price of the product after applying the discount, in Indian Rupees.

Here are a few use cases for this project:

1. Smart Inventory Management: Develop an automated inventory system for grocery stores, where the "Grocery Items" computer vision model identifies and tracks product quantities on shelves, helping store managers optimize restocking and reduce product waste.

2. Assisted Shopping Experience: Implement a user-friendly app for visually impaired users, where the computer vision model recognizes specific grocery items, making it easier for these individuals to identify and locate the products they need while shopping.

3. Autonomous Grocery Robots: Develop a shopping assistant robot that uses the computer vision model to identify and collect specific items from a shopping list for customers, improving shopping efficiency and convenience.

4. Data-driven Marketing Analysis: Leverage the computer vision technology to gather in-store data on product placement and store layout, enabling retailers to make better informed decisions about promotions, discounts, and product placement to maximize sales.

5. Checkout-less Stores: Create a fully automated grocery store where the "Grocery Items" computer vision model tracks picked up and returned items, allowing customers to simply walk out of the store with their selected items while automatically generating their bills, increasing checkout efficiency and reducing wait times.

MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

Explore the Meijer Grocery Store Dataset, a comprehensive collection of data on products available at Meijer, a leading American grocery store chain. This dataset includes detailed information on a wide variety of grocery items such as fresh produce, dairy, meat, beverages, household essentials, and more. Each product entry provides essential details, including product names, categories, prices, brands, descriptions, and availability, offering valuable insights for researchers, data analysts, and retail professionals.

Key Features:

• Extensive Product Range: Contains a wide array of grocery items from Meijer, covering multiple categories like fresh produce, dairy, meat, beverages, household essentials, and more.
• Detailed Product Information: Each entry includes key details such as product name, category, price, brand, description, and availability, allowing for in-depth analysis of retail trends and consumer preferences.
• Ideal for Market Analysis: Perfect for researchers, data scientists, and retail professionals interested in analyzing consumer behavior, studying grocery market trends, or optimizing inventory strategies in the retail sector.
• Rich Source of Retail Data: Provides a comprehensive overview of the grocery market at Meijer, helping professionals stay updated on the latest trends, popular products, and pricing strategies.

Whether you're analyzing market trends in the grocery sector, researching consumer behavior, or developing new retail strategies, the Meijer Grocery Store Dataset is an invaluable resource that provides detailed insights and extensive coverage of products available at Meijer.

Explore our detailed Foodlion groceries dataset, featuring extensive information on a wide array of grocery items available at Foodlion.

This dataset includes product names, categories, descriptions, prices, and availability, providing a thorough view of the grocery offerings.

Ideal for market research, competitive analysis, and business intelligence, this dataset helps analysts and businesses understand pricing trends, inventory levels, and product assortments.

Enhance your insights into the grocery retail sector with this comprehensive collection of Foodlion product data.

Foodlion groceries dataset in CSV format. Crawl feeds team extracted by using in-house tools.

Complexity: Difficult

1) Data Introduction • The Grocery Store Dataset is a tabulated retail dataset of detailed information, including detailed classifications, prices, discounts, ratings, product names, currencies, key features, and detailed descriptions of groceries collected from the Costco online market.

2) Data Utilization (1) Grocery Store Dataset has characteristics that: • Each row contains a variety of attributes needed for grocery analysis, including detailed categories of products, prices, applied discounts, customer ratings, product names, currencies, key features, and detailed descriptions. • The data encompasses a wide range of products and is organized to enable multi-faceted analysis of price policies, promotions, customer evaluations, and product characteristics. (2) Grocery Store Dataset can be used to: • Analysis of pricing and discount strategies: Use price, discount, and rating data to create effective pricing policies and promotion strategies. • Product recommendations and popularity analysis by category: Based on product characteristics, ratings, and detailed descriptions, it can be applied to recommend customized products and derive popular products by category.

221 Global import shipment records of Grocery Item with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Product recognition is a task that receives continuous attention by the computer vision/deep learning community mainly with the scope of providing robust solutions for automatic checkout supermarkets. One of the main challenges is the lack of images that illustrate in realistic conditions a high number of products. Here the product recognition task is perceived slightly differently compared to the automatic checkout paradigm but the challenges encountered are the same. The setting under which this dataset is captured is with the aim to help individuals with visual impairment in doing their daily grocery in order to increase their autonomy. In particular, we propose a large-scale dataset utilized to tackle the product recognition problem in a supermarket environment. The dataset is characterized by (a) large scale in terms of unique products associated with one or more photos from different viewpoints, (b) rich textual descriptions linked to different levels of annotation and, (c) images acquired both in laboratory conditions and in a realistic supermarket scenario portrayed in various clutter and lighting conditions. A direct comparison with existing datasets of this category demonstrates the significantly higher number of the available unique products, as well as the richness of its annotation enabling different recognition scenarios. Finally, the dataset is also benchmarked using various approaches based both on visual and textual descriptors.

Grocery Sales Prediction

This dataset provides a rich resource for researchers and practitioners interested in retail sales prediction and analysis. It contains information about various grocery products, the outlets where they are sold, and their historical sales data.

Product Characteristics:

Item_Identifier: Unique identifier for each product. Item_Weight: Weight of the product item. Item_Fat_Content: Categorical variable indicating the fat content of the product (e.g., low fat, regular). Item_Visibility: Numerical attribute reflecting the visibility of the product in the store (likely a promotional measure). Item_Type: Category of the product (e.g., Snacks, Beverages, Bakery). Item_MRP: Maximum Retail Price of the product. Outlet Information:

Outlet_Identifier: Unique identifier for each outlet (store). Outlet_Establishment_Year: Year the outlet was established. Outlet_Size: Categorical variable indicating the size of the outlet (e.g., Small, Medium, Large). (Note: This data may have missing values) Outlet_Location_Type: Categorical variable indicating the type of location the outlet is in (e.g., Tier 1 City, Tier 2 City, Upstate). Outlet_Type: Categorical variable indicating the type of outlet (e.g., Supermarket, Grocery Store, Convenience Store). Sales Data:

Item_Outlet_Sales: The historical sales data for each product-outlet combination. Profit: The profit margin earned on each product sold. Potential Uses

This dataset can be used for various retail sales analysis and prediction tasks, including:

Demand forecasting: Build models to predict future sales of individual products or product categories at specific outlets. Promotion optimization: Analyze the effectiveness of different promotional strategies (reflected by Item_Visibility) on sales. Assortment planning: Optimize product selection and placement within stores based on sales history and outlet characteristics. Outlet performance analysis: Compare the performance of different outlets based on sales figures and profit margins. Customer segmentation: Identify customer segments with distinct purchasing behavior based on product types and outlet locations. By analyzing these rich data points, retailers can gain valuable insights to improve their sales strategies, optimize inventory management, and maximize profits.

MealMe provides grocery and retail SKU-level product data, including prices, from the top 100 retailers across the USA and Canada. Our real-time data empowers businesses with insights for competitive analysis, pricing strategies, and market research, ensuring accurate and actionable intelligence.

1) Data Introduction • The Grocery Sales Database is a retail dataset of relational tables of grocery store sales transactions, customer information, product details, employee records, geographic information, and more across cities and countries.

2) Data Utilization (1) Grocery Sales Database has characteristics that: • The data consists of seven tables, including product categories, city/country information, customer/employee/product details, and sales details, each of which is interconnected by a unique ID. • Sales data are linked to products, customers, employees, and regions, enabling a variety of business analyses, including monthly sales, popular products, customer behavior, and regional performance. (2) Grocery Sales Database can be used to: • Analysis of sales trends and popular products: It can be used to identify trends and derive best-selling products by analyzing sales by monthly and category and sales by product. • Customer Segmentation and Marketing Strategy: Define customer groups based on customer frequency of purchases, total expenditure, and regional information and apply them to developing customized marketing and promotion strategies.

Context

The data-set is mainly collected by one of the retail store of Kroger in USA. This data was collected during a super-saver weekend to understand more about the customers buying behavior.

Content

The data mainly consist over 9000+ records which is gathered over 3 days of weekend Supersaver deal in one of the kroger retails grocery store.

Inspiration

This data-set may help the retail grocery stores in Up selling and Cross selling of their products.

Discover our comprehensive dataset of groceries and essentials from Kroger, featuring detailed information on a wide range of products available at this major retailer.

The dataset includes product names, categories, descriptions, prices, and availability, providing a thorough view of Kroger’s grocery and essential offerings.

Ideal for market analysis, inventory management, and competitive research, this dataset enables businesses and analysts to track pricing trends, monitor stock levels, and understand consumer preferences.

Gain valuable insights into the grocery retail sector with this extensive collection of Kroger product data.

Kroger groceries and essentials data is extracted by using crawl feeds team in-house tools. Last extracted on Nov 2022.

Contains a list of grocery stores which was used by the city to calculate the estimates of Chicagoans living in food deserts in 2013. Data in this file can be cross-referenced with the city's business license data (http://bit.ly/sMFZdN).

The TESCO UK Grocery Dataset provides comprehensive information about various grocery products available at TESCO in the UK.

This dataset includes product details such as titles, brands, prices, categories, and nutritional information.

Ideal for data analysis, market research, and machine learning projects, this dataset offers valuable insights into the grocery market.

All data is formatted for easy integration into your applications and analysis tools.

The Groceries Market Basket Dataset contains 9835 transactions by customers shopping for groceries. The data contains 169 unique items. Ref: https://github.com/shubhamjha97/association-rule-mining-apriori/tree/master/data

A listing of all retail food stores which are licensed by the Department of Agriculture and Markets.

Dive into our detailed ASDA groceries dataset, offering extensive information on a broad selection of grocery items available at ASDA.

This dataset includes product names, categories, descriptions, prices, and availability, providing a complete view of ASDA’s grocery offerings.

Perfect for market analysis, inventory management, and competitive research, this dataset allows businesses and analysts to track pricing trends, monitor stock levels, and gain insights into consumer preferences.

Enhance your understanding of the grocery retail landscape with this valuable ASDA product data.

ASDA groceries data extracted by crawl feeds team. Last extracted on March 2023.