As of early 2023, approximately ** percent of consumers in the United States said they would prefer to shop mostly online rather than in-store, making it the country with highest online shopping preference. In contrast, more shoppers preferred visiting physical stores in countries such as Austria, Finland, and New Zealand.

This dataset is having data of customers who buys clothes online. The store offers in-store style and clothing advice sessions. Customers come in to the store, have sessions/meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want.

The company is trying to decide whether to focus their efforts on their mobile app experience or their website.

This dataset provides insights into eCommerce shopping preferences and trends among UK adults in 2024. The findings are derived from data collected from a sample of 2,017 UK adults regarding their shopping habits and influencing factors.Furthermore, hundreds of thousands online searches were analysed to collate the most up-to-date statistics.

Deluxe is an online retailer based in UK that deals in a wide range of products in the following categories: 1. Clothing 2. Games 3. Appliances 4. Electronics 5. Books 6. Beauty products 7. Smartphones 8. Outdoors products 9. Accessories 10. Other Basic household products are classified as 'Other' in the category column since they have small value to the business.

Data Description: dates: sale date order_value_EUR : sale price in EUR cost: cost of goods sold in EUR category: item category country: customers' country at the time of purchase customer_name: name of customer device_type: The gadget used by customer to access our online store(PC, mobile, tablet) sales_manager: name of the sales manager for each sale sales_representative: name of the sales rep for each sale order_id: unique identifier of an order

The data was recorded for the period 1/2/2019 and 12/30/2020 with an aim to generate business insights to guide business direction. We would like to see what interesting insights the Kaggle community members can produce from this data.

In 2024, most U.S. shoppers preferred to shop **************** for the holidays. ***************** was only forecast to dominate ****** channels during the weekend after Thanksgiving and after Christmas until the New Year.

Revolutionize Customer Engagement with Our Comprehensive Ecommerce Data

Our Ecommerce Data is designed to elevate your customer engagement strategies, providing you with unparalleled insights and precision targeting capabilities. With over 61 million global contacts, this dataset goes beyond conventional data, offering a unique blend of shopping cart links, business emails, phone numbers, and LinkedIn profiles. This comprehensive approach ensures that your marketing strategies are not just effective but also highly personalized, enabling you to connect with your audience on a deeper level.

What Makes Our Ecommerce Data Stand Out?

• Unique Features for Enhanced Targeting
  Our Ecommerce Data is distinguished by its depth and precision. Unlike many other datasets, it includes shopping cart links—a rare and valuable feature that provides you with direct insights into consumer behavior and purchasing intent. This information allows you to tailor your marketing efforts with unprecedented accuracy. Additionally, the integration of business emails, phone numbers, and LinkedIn profiles adds multiple layers to traditional contact data, enriching your understanding of clients and enabling more personalized engagement.

• Robust and Reliable Data Sourcing
  We pride ourselves on our dual-sourcing strategy that ensures the highest levels of data accuracy and relevance:

  • Real-Time Information from 10 Active Publication Sites: Our databases are continuously updated with the latest information, sourced from ten active publication sites that provide real-time data.
  • Dedicated Contact Discovery Team: Complementing our automated sources, our dedicated Contact Discovery Team conducts thorough research and investigations, ensuring that every piece of data is accurate and reliable. This two-pronged approach guarantees that our Ecommerce Data is both up-to-date and relevant, providing you with a solid foundation for your business strategies.

  Primary Use Cases Across Industries

Our Ecommerce Data is versatile and can be leveraged across various industries for multiple applications: - Precision Targeting in Marketing: Create personalized marketing campaigns based on detailed shopping cart activities, ensuring that your outreach resonates with individual customer preferences. - Sales Enrichment: Sales teams can benefit from enriched client profiles that include comprehensive contact information, enabling them to connect with key decision-makers more effectively. - Market Research and Analytics: Research and analytics departments can use this data for in-depth market studies and trend analyses, gaining valuable insights into consumer behavior and market dynamics.

Global Coverage for Comprehensive Engagement

Our Ecommerce Data spans across the globe, providing you with extensive reach and the ability to engage with customers in diverse regions: - North America: United States, Canada, Mexico - Europe: United Kingdom, Germany, France, Italy, Spain, Netherlands, Sweden, and more - Asia: China, Japan, India, South Korea, Singapore, Malaysia, and more - South America: Brazil, Argentina, Chile, Colombia, and more - Africa: South Africa, Nigeria, Kenya, Egypt, and more - Australia and Oceania: Australia, New Zealand - Middle East: United Arab Emirates, Saudi Arabia, Israel, Qatar, and more

Comprehensive Employee and Revenue Size Information

Our dataset also includes detailed information on: - Employee Size: Whether you’re targeting small businesses or large corporations, our data covers all employee sizes, from startups to global enterprises. - Revenue Size: Gain insights into companies across various revenue brackets, enabling you to segment the market more effectively and target your efforts where they will have the most impact.

Seamless Integration into Broader Data Offerings

Our Ecommerce Data is not just a standalone product; it is a critical piece of our broader data ecosystem. It seamlessly integrates with our comprehensive suite of business and consumer datasets, offering you a holistic approach to data-driven decision-making: - Tailored Packages: Choose customized data packages that meet your specific business needs, combining Ecommerce Data with other relevant datasets for a complete view of your market. - Holistic Insights: Whether you are looking for industry-specific details or a broader market overview, our integrated data solutions provide you with the insights necessary to stay ahead of the competition and make informed business decisions.

Elevate Your Business Decisions with Our Ecommerce Data

In essence, our Ecommerce Data is more than just a collection of contacts—it’s a strategic tool designed to give you a competitive edge in understanding and engaging your target audience. By leveraging the power of this comprehensive dataset, you can elevate your business decisions, enhance customer interactions, and navigate the digital landscape with confi...

A comprehensive dataset providing key insights into the eCommerce industry, including global retail online sales projections, number of eCommerce stores, digital buyer statistics, revenue growth in the United States, sector-wise revenue details with a focus on consumer electronics, average conversion rates, and mobile commerce sales forecasts.

Source:

Mariusz Šapczyński, Cracow University of Economics, Poland, lapczynm '@' uek.krakow.pl Sylwester Białowąs, Poznan University of Economics and Business, Poland, sylwester.bialowas '@' ue.poznan.pl

Data Set Information:

The dataset contains information on clickstream from online store offering clothing for pregnant women. Data are from five months of 2008 and include, among others, product category, location of the photo on the page, country of origin of the IP address and product price in US dollars.

Attribute Information:

The dataset contains 14 variables described in a separate file (See 'Data set description')

Relevant Papers:

N/A

Citation Request:

If you use this dataset, please cite:

Šapczyński M., Białowąs S. (2013) Discovering Patterns of Users' Behaviour in an E-shop - Comparison of Consumer Buying Behaviours in Poland and Other European Countries, “Studia Ekonomiczne†, nr 151, “La société de l'information : perspective européenne et globale : les usages et les risques d'Internet pour les citoyens et les consommateurs†, p. 144-153

Data description ìe-shop clothing 2008î

Variables:

1. YEAR (2008)

========================================================

2. MONTH -> from April (4) to August (8)

========================================================

3. DAY -> day number of the month

========================================================

4. ORDER -> sequence of clicks during one session

========================================================

5. COUNTRY -> variable indicating the country of origin of the IP address with the

following categories:

1-Australia 2-Austria 3-Belgium 4-British Virgin Islands 5-Cayman Islands 6-Christmas Island 7-Croatia 8-Cyprus 9-Czech Republic 10-Denmark 11-Estonia 12-unidentified 13-Faroe Islands 14-Finland 15-France 16-Germany 17-Greece 18-Hungary 19-Iceland 20-India 21-Ireland 22-Italy 23-Latvia 24-Lithuania 25-Luxembourg 26-Mexico 27-Netherlands 28-Norway 29-Poland 30-Portugal 31-Romania 32-Russia 33-San Marino 34-Slovakia 35-Slovenia 36-Spain 37-Sweden 38-Switzerland 39-Ukraine 40-United Arab Emirates 41-United Kingdom 42-USA 43-biz (.biz) 44-com (.com) 45-int (.int) 46-net (.net) 47-org (*.org)

========================================================

6. SESSION ID -> variable indicating session id (short record)

========================================================

7. PAGE 1 (MAIN CATEGORY) -> concerns the main product category:

1-trousers 2-skirts 3-blouses 4-sale

========================================================

8. PAGE 2 (CLOTHING MODEL) -> contains information about the code for each product

(217 products)

========================================================

9. COLOUR -> colour of product

1-beige 2-black 3-blue 4-brown 5-burgundy 6-gray 7-green 8-navy blue 9-of many colors 10-olive 11-pink 12-red 13-violet 14-white

========================================================

10. LOCATION -> photo location on the page, the screen has been divided into six parts:

1-top left 2-top in the middle 3-top right 4-bottom left 5-bottom in the middle 6-bottom right

========================================================

11. MODEL PHOTOGRAPHY -> variable with two categories:

1-en face 2-profile

========================================================

12. PRICE -> price in US dollars

========================================================

13. PRICE 2 -> variable informing whether the price of a particular product is higher than

the average price for the entire product category

1-yes 2-no

========================================================

14. PAGE -> page number within the e-store website (from 1 to 5)

++++++++++++++++++++++++++++++++++++++++++++++++++++++++

Dataset

This dataset was created by Ashish

Contents

In 2023, e-commerce comprised over 15.6 percent of total retail sales in the United States. Forecasts suggest that this proportion will continue to rise steadily in the coming years, reaching approximately 20.6 percent by 2027. Fashion fever The digital revolution has significantly changed how retail is done, impacting a wide range of product categories. Out of all e-commerce product categories, apparel and accessories are the most purchased online in the United States. As of February 2023, roughly 18 percent of all fashion retail sales took place online. Furniture and home furnishing, as well as computer and consumer electronics, ranked second, with over 15 percent of each product category purchased via the internet. The product categories that are least purchased online are office equipment and supplies (1.4 percent) and books, music, and video (5.1 percent). Shopping hotspots Amazon dominates the e-commerce industry in the United States, though other competitors still have significant market share. In December 2023, amazon.com was the most-visited e-commerce and shopping site in the United States. That month, around 45 percent of all visits to e-commerce sites were made to Amazon. Other popular shopping sites include ebay.com, walmart.com, etsy.com, and target.com. The staggering proportion of online retail sales in the country attributed to Amazon is quite remarkable. In 2023, Amazon's website accounted for almost half of all online computer and consumer electronics sales. Similarly, nearly one-third of online fashion purchases in the country were made on Amazon.

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

In the fourth quarter 2024, the share of e-commerce in total U.S. retail sales stood at 16.4 percent, up from the previous quarter. From October to December 2024, retail e-commerce sales in the United States hit over 309 billion U.S. dollars, the highest quarterly revenue in history. How e-commerce measures up in total U.S. retail In 2023, the reported total value of retail e-commerce sales in the United States amounted to over one trillion U.S. dollars—impressive, but the figure pales compared to the total annual retail trade value of seven trillion U.S. dollars. E-commerce still accounts for a mere 15.4 percent of total retail sales in the United States. Rising e-commerce segments Online shopping is popular among all age groups, though digital purchases are most common among Millennial internet users. In 2022, around 55 percent of Millennials purchased items via the internet. Mobile commerce is also growing in popularity, as consumers increasingly rely on their smartphones and mobile apps for shopping activities. In the fourth quarter of 2022, m-commerce spending made up 38 percent of the overall online spending in the United States.

The Product Comparison dataset for online shopping is a new, manually annotated dataset with about 15K human generated sentences, which compare related products based on one or more of their attributes (the first such data we know of for product comparison). It covers ∼8K product sets, their selected attributes, and comparison texts.

The dataset contains 1,462 entries and 22 columns, primarily capturing responses from a survey about e-customer relationships in e-commerce. Key demographic information includes age and sex, alongside questions on e-commerce usage patterns, such as daily app usage time and weekly purchase frequency.

The survey assesses factors influencing customer decisions, including the impact of e-commerce promotions (vouchers, coupons, flash sales), app usability, order processing speed, logistics ease, and customer service responsiveness. Further columns explore trust in sellers, the importance of regular order updates, perceived product quality, pricing competitiveness compared to physical stores, and the influence of social media advertisements and famous ambassadors. Additionally, participants rated their confidence in flagship stores, consideration of online shop ratings, and tendency to purchase from well-reviewed stores. Each response is rated on a scale, reflecting the importance of various factors in their e-commerce shopping behaviors.

1) Data Introduction • The Consumer Behavior and Shopping Habits Dataset is a tabular collection of customer demographics, purchase history, product preferences, shopping frequency, and online and offline purchasing behavior.

2) Data Utilization (1) Consumer Behavior and Shopping Habits Dataset has characteristics that: • Each row contains detailed consumer and transaction information such as customer ID, age, gender, purchased goods and categories, purchase amount, region, product attributes (size, color, season), review rating, subscription status, delivery method, discount/promotion usage, payment method, purchase frequency, etc. • Data is organized to cover a variety of variables and purchasing patterns to help segment customers, establish marketing strategies, analyze product preferences, and more. (2) Consumer Behavior and Shopping Habits Dataset can be used to: • Customer Segmentation and Target Marketing: You can analyze demographics and purchasing patterns to define different customer groups and use them to develop customized marketing strategies. • Product and service improvement: Based on purchase history, review ratings, discount/promotional responses, etc., it can be applied to product and service improvements such as identifying popular products, managing inventory, and analyzing promotion effects.

Data Set Information:

This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.

Attribute Information:

InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric.
InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated. UnitPrice: Unit price. Numeric. Product price per unit in sterling (£). CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal. The name of the country where a customer resides.

Most of the latest online purchases from abroad among U.S. shoppers were made in China, according to a 2023 survey. 53 percent of the e-commerce users surveyed in the United States had made their most recent purchase from there. The United Kingdom ranked second, with 10 percent. In turn, the U.S. was the main market where Canadian cross-border shoppers last bought online.

Explore a diverse range of fashion items, home goods, and more, with insights into pricing, availability, ratings, and reviews. Popular use cases include trend forecasting, pricing optimization, and inventory management in the fast-fashion market.

The Shein.com Products dataset provides a detailed overview of the extensive product range available on Shein, offering key insights into the fast-fashion market. This dataset includes essential details such as product names, prices, discounts, descriptions, materials, product images, SKUs (Stock Keeping Units), low-stock indicators, and more.

Ideal for eCommerce professionals, fashion analysts, and market strategists, this dataset supports trend analysis, pricing strategies, and inventory management. Whether you're benchmarking competitors, identifying emerging trends, or optimizing your product offerings, the Shein.com Products dataset delivers valuable insights to stay ahead in the dynamic fashion industry.

Dataset Features

• product_name: The name/title of the product listed.
• description: A brief description of the product, including features or materials.
• initial_price: The original price of the product before any discounts.
• final_price: The actual selling price after applying discounts.
• currency: The currency in which the price is listed (e.g., USD).
• in_stock: Availability status of the product (True if in stock, otherwise False).
• color: Available color(s) for the product.
• size: Size(s) available (e.g., S, M, L, or custom sizes).
• reviews_count: Number of user reviews the product has received.
• main_image: URL to the primary product image.
• category_url: Link to the category page the product belongs to.
• url: Direct link to the product page.
• category_tree: Hierarchical path of the product category.
• country_code: Country code indicating where the product is available.
• domain: The Shein domain where the product was found (e.g., shein.com, shein.uk).
• image_count: Total number of product images.
• image_urls: List/array of URLs for all images related to the product.
• model_number: The product’s model or SKU number.
• offers: Details of promotions or discounts available.
• other_attributes: Miscellaneous product features or labels (e.g., eco-friendly, plus-size).
• product_id: Unique identifier for the product.
• rating: Average user rating (typically on a 5-star scale).
• related_products: List of similar or related products.
• root_category: The broadest category classification (e.g., "Women", "Home").
• top_reviews: Highlighted customer reviews.
• category: Specific product category (e.g., "Bikinis", "T-Shirts").
• brand: Brand name (often "Shein" or sub-brands).
• all_available_sizes: List of all size options for the product.
• category_details: Additional metadata about the product category.
• initial_price_usd: Original price converted to USD.
• final_price_usd: Final price converted to USD.
• discount_price: Price discount amount (initial - final).
• discount_price_usd: Discount amount in USD.
• colors: All color variants of the product.
• store_details: Information about the store or seller.
• shipping_details: Information about shipping costs and delivery time.
• shipping_type: Type of shipping offered (e.g., standard, express).
• product_parent_id: ID representing a grouped product variant.
• tags: Keywords or tags associated with the product.
• model_data: Additional attributes from the product model (could include fit, cut, etc.).

Distribution

• Data Volume: 40 Columns and 42.35 M Rows
• Format: CSV

Usage

This dataset is ideal for a wide range of practical and analytical applications: - Trend Forecasting: Identify emerging fashion trends based on product popularity and review sentiment.
- Pricing Optimization: Analyze discount strategies and dynamic pricing patterns.
- Inventory Management: Monitor stock availability and detect low-stock patterns.
- Recommendation Systems: Build personalized fashion recommendations using product attributes and user ratings.
- Market Benchmarking: Compare Shein's offerings with competitors or across regions.
- Computer Vision: Use product images for training models in visual fashion recognition.

Coverage

• Geographic Coverage: Global
• Time Range: Varies by data collection; generally recent and can be updated periodically.

License

CUSTOM

Please review the respective licenses below:

1. Data Provider's License
   • Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: For training ML models like price predictors, review sentiment classifiers, or image-based search engines.
• Researchers:

Source:

Dr. Daqing Chen, Course Director: MSc Data Science. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Data Set Information:

This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.

Attribute Information:

InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated. UnitPrice: Unit price. Numeric. Product price per unit in sterling (£). CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal. The name of the country where a customer resides.

Relevant Papers:

Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link]. Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link]. Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019. Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019. Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018.

Citation Request:

If you have no special citation requests, please leave this field blank.

Online grocery shopping is being driven by the convergence of several key trends. Immediate growth drivers include time scarcity and digital consumption, while novelty and environmental responsibility are factors that are likely to propel future growth. Read More