https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Overview:
This dataset contains 1000 rows of synthetic online retail sales data, mimicking transactions from an e-commerce platform. It includes information about customer demographics, product details, purchase history, and (optional) reviews. This dataset is suitable for a variety of data analysis, data visualization and machine learning tasks, including but not limited to: customer segmentation, product recommendation, sales forecasting, market basket analysis, and exploring general e-commerce trends. The data was generated using the Python Faker library, ensuring realistic values and distributions, while maintaining no privacy concerns as it contains no real customer information.
Data Source:
This dataset is entirely synthetic. It was generated using the Python Faker library and does not represent any real individuals or transactions.
Data Content:
Column Name | Data Type | Description |
---|---|---|
customer_id | Integer | Unique customer identifier (ranging from 10000 to 99999) |
order_date | Date | Order date (a random date within the last year) |
product_id | Integer | Product identifier (ranging from 100 to 999) |
category_id | Integer | Product category identifier (10, 20, 30, 40, or 50) |
category_name | String | Product category name (Electronics, Fashion, Home & Living, Books & Stationery, Sports & Outdoors) |
product_name | String | Product name (randomly selected from a list of products within the corresponding category) |
quantity | Integer | Quantity of the product ordered (ranging from 1 to 5) |
price | Float | Unit price of the product (ranging from 10.00 to 500.00, with two decimal places) |
payment_method | String | Payment method used (Credit Card, Bank Transfer, Cash on Delivery) |
city | String | Customer's city (generated using Faker's city() method, so the locations will depend on the Faker locale you used) |
review_score | Integer | Customer's product rating (ranging from 1 to 5, or None with a 20% probability) |
gender | String | Customer's gender (M/F, or None with a 10% probability) |
age | Integer | Customer's age (ranging from 18 to 75) |
Potential Use Cases (Inspiration):
Customer Segmentation: Group customers based on demographics, purchasing behavior, and preferences.
Product Recommendation: Build a recommendation system to suggest products to customers based on their past purchases and browsing history.
Sales Forecasting: Predict future sales based on historical trends.
Market Basket Analysis: Identify products that are frequently purchased together.
Price Optimization: Analyze the relationship between price and demand.
Geographic Analysis: Explore sales patterns across different cities.
Time Series Analysis: Investigate sales trends over time.
Educational Purposes: Great for practicing data cleaning, EDA, feature engineering, and modeling.
• 3M+ Contact Profiles • 5M+ Worldwide eCommerce Brands • Direct Contact Info for Decision Makers • Contact Direct Email and Mobile Number • 15+ eCommerce Platforms • 20+ Data Points • Lifetime Support Until You 100% Satisfied
Buy eCommerce leads from our eCommerce leads database today. Reach out to eCommerce companies to expand your business. Now is the time to buy eCommerce leads and start running a campaign to attract new customers. We provide current and accurate information that will assist you in achieving your goals.
Our database is made up of highly valuable and interested leads who are ready to make online purchases. You can always filter our data and choose the database that best meets your needs if you need eCommerce leads based on industry.
We have millions of eCommerce data ready to go no matter where you are. We’ve acquired hundreds of clients from all over the world over the years and delivered data that they’re happy with.
We were able to do so by obtaining data from various locations around the world. As a result, our database is widely accessible, and anyone can use it from any location on the planet. Please contact us if you want the best eCommerce leads .
We sell eCommerce leads that can be filtered by industry. We know what you’re going through and what you’ll need for your next project. As a result, we’ve compiled a list of eCommerce leads that are exactly what you require. With the most potential data we provide, you can grow your business and achieve your business goals. All of our eCommerce leads are generated professionally, with real people – not bots – entering data.
We’re a leading brand in the industry because we source data from the most well-known platforms, ensuring that the information you receive from us is accurate and reliable. That’s especially true because we verify each and every piece of information in order to provide you with yet another benefit in your life.
The majority of our customers have had success with the information we’ve provided. That is why they keep contacting us for our services. You can count on our business-to-business eCommerce sales leads. Contact us to work with one of the most effective lead generation companies in the industry, which has already helped thousands of potential members achieve success.
Every month, we update our eCommerce store sales leads in order to provide our clients with the most accurate data possible. We have a team of professionals who strive for excellence when it comes to gathering the right leads to ensure you get the number of sales you need. Our experts also double-check that all of the sales data we receive is genuine and accurate.
The accuracy of our eCommerce database is why the majority of our clients choose us. Furthermore, we offer round-the-clock support to provide on-demand solutions. We take care of everything so you can spend less time evaluating our product database and more time becoming one of them.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
E-commerce has become a new channel to support businesses development. Through e-commerce, businesses can get access and establish a wider market presence by providing cheaper and more efficient distribution channels for their products or services. E-commerce has also changed the way people shop and consume products and services. Many people are turning to their computers or smart devices to order goods, which can easily be delivered to their homes.
This is a sales transaction data set of UK-based e-commerce (online retail) for one year. This London-based shop has been selling gifts and homewares for adults and children through the website since 2007. Their customers come from all over the world and usually make direct purchases for themselves. There are also small businesses that buy in bulk and sell to other customers through retail outlet channels.
The data set contains 500K rows and 8 columns. The following is the description of each column. 1. TransactionNo (categorical): a six-digit unique number that defines each transaction. The letter “C” in the code indicates a cancellation. 2. Date (numeric): the date when each transaction was generated. 3. ProductNo (categorical): a five or six-digit unique character used to identify a specific product. 4. Product (categorical): product/item name. 5. Price (numeric): the price of each product per unit in pound sterling (£). 6. Quantity (numeric): the quantity of each product per transaction. Negative values related to cancelled transactions. 7. CustomerNo (categorical): a five-digit unique number that defines each customer. 8. Country (categorical): name of the country where the customer resides.
There is a small percentage of order cancellation in the data set. Most of these cancellations were due to out-of-stock conditions on some products. Under this situation, customers tend to cancel an order as they want all products delivered all at once.
Information is a main asset of businesses nowadays. The success of a business in a competitive environment depends on its ability to acquire, store, and utilize information. Data is one of the main sources of information. Therefore, data analysis is an important activity for acquiring new and useful information. Analyze this dataset and try to answer the following questions. 1. How was the sales trend over the months? 2. What are the most frequently purchased products? 3. How many products does the customer purchase in each transaction? 4. What are the most profitable segment customers? 5. Based on your findings, what strategy could you recommend to the business to gain more profit?
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:
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...
Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
License information was derived automatically
The E-commerce Order Dataset provides comprehensive information related to orders, items within orders, customers, payments, and products for an e-commerce platform. This dataset is structured with multiple tables, each containing specific information about various aspects of the e-commerce operations.
Success.ai’s Ecommerce Store Data for the APAC E-commerce Sector provides a reliable and accurate dataset tailored for businesses aiming to connect with e-commerce professionals and organizations across the Asia-Pacific region. Covering roles and businesses involved in online retail, marketplace management, logistics, and digital commerce, this dataset includes verified business profiles, decision-maker contact details, and actionable insights.
With access to continuously updated, AI-validated data and over 700 million global profiles, Success.ai ensures your outreach, market analysis, and partnership strategies are effective and data-driven. Backed by our Best Price Guarantee, this solution helps you excel in one of the world’s fastest-growing e-commerce markets.
Why Choose Success.ai’s Ecommerce Store Data?
Verified Profiles for Precision Engagement
Comprehensive Coverage of the APAC E-commerce Sector
Continuously Updated Datasets
Ethical and Compliant
Data Highlights:
Key Features of the Dataset:
Comprehensive E-commerce Business Profiles
Advanced Filters for Precision Campaigns
Regional and Sector-specific Insights
AI-Driven Enrichment
Strategic Use Cases:
Marketing Campaigns and Outreach
Partnership Development and Vendor Collaboration
Market Research and Competitive Analysis
Recruitment and Talent Acquisition
Why Choose Success.ai?
Best Price Guarantee
Seamless Integration
Success.ai delivers unparalleled access to Retail Store Data for Asia’s retail and e-commerce sectors, encompassing subcategories such as ecommerce data, ecommerce merchant data, ecommerce market data, and company data. Whether you’re targeting emerging markets or established players, our solutions provide the tools to connect with decision-makers, analyze market trends, and drive strategic growth. With continuously updated datasets and AI-validated accuracy, Success.ai ensures your data is always relevant and reliable.
Key Features of Success.ai's Retail Store Data for Retail & E-commerce in Asia:
Extensive Business Profiles: Access detailed profiles for 70M+ companies across Asia’s retail and e-commerce sectors. Profiles include firmographic data, revenue insights, employee counts, and operational scope.
Ecommerce Data: Gain insights into online marketplaces, customer demographics, and digital transaction patterns to refine your strategies.
Ecommerce Merchant Data: Understand vendor performance, supply chain metrics, and operational details to optimize partnerships.
Ecommerce Market Data: Analyze purchasing trends, regional preferences, and market demands to identify growth opportunities.
Contact Data for Decision-Makers: Reach key stakeholders, such as CEOs, marketing executives, and procurement managers. Verified contact details include work emails, phone numbers, and business addresses.
Real-Time Accuracy: AI-powered validation ensures a 99% accuracy rate, keeping your outreach efforts efficient and impactful.
Compliance and Ethics: All data is ethically sourced and fully compliant with GDPR and other regional data protection regulations.
Why Choose Success.ai for Retail Store Data?
Best Price Guarantee: We deliver industry-leading value with the most competitive pricing for comprehensive retail store data.
Customizable Solutions: Tailor your data to meet specific needs, such as targeting particular regions, industries, or company sizes.
Scalable Access: Our data solutions are built to grow with your business, supporting small startups to large-scale enterprises.
Seamless Integration: Effortlessly incorporate our data into your existing CRM, marketing, or analytics platforms.
Comprehensive Use Cases for Retail Store Data:
Identify potential partners, distributors, and clients to expand your footprint in Asia’s dynamic retail and e-commerce markets. Use detailed profiles to assess market opportunities and risks.
Leverage ecommerce data and consumer insights to craft highly targeted campaigns. Connect directly with decision-makers for precise and effective communication.
Analyze competitors’ operations, market positioning, and consumer strategies to refine your business plans and gain a competitive edge.
Evaluate potential suppliers or vendors using ecommerce merchant data, including financial health, operational details, and contact data.
Enhance customer loyalty programs and retention strategies by leveraging ecommerce market data and purchasing trends.
APIs to Amplify Your Results:
Enrichment API: Keep your CRM and analytics platforms up-to-date with real-time data enrichment, ensuring accurate and actionable company profiles.
Lead Generation API: Maximize your outreach with verified contact data for retail and e-commerce decision-makers. Ideal for driving targeted marketing and sales efforts.
Tailored Solutions for Industry Professionals:
Retailers: Expand your supply chain, identify new markets, and connect with key partners in the e-commerce ecosystem.
E-commerce Platforms: Optimize your vendor and partner selection with verified profiles and operational insights.
Marketing Agencies: Deliver highly personalized campaigns by leveraging detailed consumer data and decision-maker contacts.
Consultants: Provide data-driven recommendations to clients with access to comprehensive company data and market trends.
What Sets Success.ai Apart?
70M+ Business Profiles: Access an extensive and detailed database of companies across Asia’s retail and e-commerce sectors.
Global Compliance: All data is sourced ethically and adheres to international data privacy standards, including GDPR.
Real-Time Updates: Ensure your data remains accurate and relevant with our continuously updated datasets.
Dedicated Support: Our team of experts is available to help you maximize the value of our data solutions.
Empower Your Business with Success.ai:
Success.ai’s Retail Store Data for the retail and e-commerce sectors in Asia provides the insights and connections needed to thrive in this competitive market. Whether you’re entering a new region, launching a targeted campaign, or analyzing market trends, our data solutions ensure measurable success.
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Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
License information was derived automatically
There are a lot of unknowns when running an E-commerce store, even when you have analytics to guide your decisions.
Users are an important factor in an e-commerce business. This is especially true in a C2C-oriented store, since they are both the suppliers (by uploading their products) AND the customers (by purchasing other user's articles).
This dataset aims to serve as a benchmark for an e-commerce fashion store. Using this dataset, you may want to try and understand what you can expect of your users and determine in advance how your grows may be.
If you think this kind of dataset may be useful or if you liked it, don't forget to show your support or appreciation with an upvote/comment. You may even include how you think this dataset might be of use to you. This way, I will be more aware of specific needs and be able to adapt my datasets to suits more your needs.
This dataset is part of a preview of a much larger dataset. Please contact me for more.
What is inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.
The data was scraped from a successful online C2C fashion store with over 9M registered users. The store was first launched in Europe around 2009 then expanded worldwide.
Visitors vs Users: Visitors do not appear in this dataset. Only registered users are included. "Visitors" cannot purchase an article but can view the catalog.
We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.
Questions you might want to answer using this dataset:
For other licensing options, contact me.
Discover the unparalleled potential of our comprehensive eCommerce leads database, featuring essential data fields such as Store Name, Website, Contact First Name, Contact Last Name, Email Address, Physical Address, City, State, Country, Zip Code, Phone Number, Revenue Size, Employee Size, and more on demand.
With a focus on Shopify, Amazon, eBay, and other global retail stores, this database equips you with accurate information for successful marketing campaigns. Supercharge your marketing efforts with our enriched contact and company database, providing real-time, verified data insights for strategic market assessments and effective buyer engagement across digital and traditional channels.
• 4M+ eCommerce Companies • 40M+ Worldwide eCommerce Leads • Direct Contact Info for Shop Owners • 47+ eCommerce Platforms • 40+ Data Points • Lifetime Access • 10+ Data Segmentations • Sample Data"
https://choosealicense.com/licenses/cdla-sharing-1.0/https://choosealicense.com/licenses/cdla-sharing-1.0/
Bitext - Retail (eCommerce) Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail (eCommerce)] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-ecommerce-llm-chatbot-training-dataset.
https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
The dataset comprises over 12,000 chat conversations, each focusing on specific Retail & E-Commerce related topics. Each conversation provides a detailed interaction between a call center agent and a customer, capturing real-life scenarios and language nuances.
The chat dataset covers a wide range of conversations on Retail & E-Commerce topics, ensuring that the dataset is comprehensive and relevant for training and fine-tuning models for various Retail & E-Commerce use cases. It offers diversity in terms of conversation topics, chat types, and outcomes, including both inbound and outbound chats with positive, neutral, and negative outcomes.
The conversations in this dataset capture the diverse language styles and expressions prevalent in Hindi Retail & E-Commerce interactions. This diversity ensures the dataset accurately represents the language used by Hindi speakers in Retail & E-Commerce contexts.
The dataset encompasses a wide array of language elements, including:
This linguistic authenticity ensures that the dataset equips researchers and developers with a comprehensive understanding of the intricate language patterns, cultural references, and communication styles inherent to Hindi Retail & E-Commerce interactions.
The dataset includes a broad range of conversations, from simple inquiries to detailed discussions, capturing the dynamic nature of Retail & E-Commerce customer-agent interactions.
Each of these conversations contains various aspects of conversation flow like:
Success.ai’s Ecommerce Leads Data for Retail, E-commerce & Consumer Goods Executives Worldwide delivers a robust and comprehensive dataset designed to help businesses connect with decision-makers and professionals in the global retail and e-commerce sectors. Covering industry leaders, marketing strategists, product managers, and logistics executives, this dataset offers verified contact details, business locations, and decision-maker insights.
With access to over 700 million verified global profiles and actionable data from retail and consumer goods companies, Success.ai ensures your outreach, market research, and business development initiatives are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution equips you to succeed in the competitive e-commerce landscape.
Why Choose Success.ai’s Ecommerce Leads Data?
Verified Contact Data for Precision Outreach
Comprehensive Global Coverage
Continuously Updated Datasets
Ethical and Compliant
Data Highlights:
Key Features of the Dataset:
Professional Profiles in E-commerce and Retail
Advanced Filters for Precision Campaigns
Industry and Regional Insights
AI-Driven Enrichment
Strategic Use Cases:
Marketing Campaigns and Lead Generation
Product Development and Innovation
Partnership Development and Collaboration
Market Research and Competitive Analysis
Why Choose Success.ai?
Best Price Guarantee
Seamless Integration
Unlock the door to business expansion by investing in our real-time eCommerce leads list. Gain direct access to store owners and make informed decisions with data fields including Store Name, Website, Contact First Name, Contact Last Name, Email Address, Physical Address, City, State, Country, Zip Code, Phone Number, Revenue Size, Employee Size, and more on demand.
Ensure a lifetime of access for continuous growth and tailor your campaigns with accurate and reliable information, initiating targeted efforts that align with your marketing goals. Whether you're targeting specific industries or global locations, our database provides up-to-date and valuable insights to support your business journey.
• 4M+ eCommerce Companies • 40M+ Worldwide eCommerce Leads • Direct Contact Info for Shop Owners • 47+ eCommerce Platforms • 40+ Data Points • Lifetime Access • 10+ Data Segmentations • Sample Data
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
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.
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
License information was derived automatically
This table contains 3 series, with data for years 2016 - 2017 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Sales (3 items: Retail trade; Electronic shopping and mail-order houses; Retail E-commerce sales).
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
Use of information and communication technology (ICT) and e-commerce activity by UK businesses. Annual data on e-commerce sales and how businesses are using the internet.
https://cubig.ai/store/terms-of-servicehttps://cubig.ai/store/terms-of-service
1) Data Introduction • The E-Commerce Data Dataset contains actual transaction records from an online retail company based in the UK. It includes various transaction-related attributes such as customer ID, product information, transaction date, quantity, and country.
2) Data Utilization (1) Characteristics of the E-Commerce Data Dataset: • This dataset is structured as time-series consumer behavior data at the transaction level. It includes attributes such as product category, quantity, unit price, and country, making it suitable for analyzing country-specific consumption patterns and developing region-based classification models.
(2) Applications of the E-Commerce Data Dataset: • Developing country-specific marketing strategies: By analyzing purchasing trends, frequently bought product categories, and transaction frequency by country, the dataset can be used to design regionally tailored marketing strategies.
Note:- Only publicly available data can be worked upon
In today's ever-evolving Ecommerce landscape, success hinges on the ability to harness the power of data. APISCRAPY is your strategic ally, dedicated to providing a comprehensive solution for extracting critical Ecommerce data, including Ecommerce market data, Ecommerce product data, and Ecommerce datasets. With the Ecommerce arena being more competitive than ever, having a data-driven approach is no longer a luxury but a necessity.
APISCRAPY's forte lies in its ability to unearth valuable Ecommerce market data. We recognize that understanding the market dynamics, trends, and fluctuations is essential for making informed decisions.
APISCRAPY's AI-driven ecommerce data scraping service presents several advantages for individuals and businesses seeking comprehensive insights into the ecommerce market. Here are key benefits associated with their advanced data extraction technology:
Ecommerce Product Data: APISCRAPY's AI-driven approach ensures the extraction of detailed Ecommerce Product Data, including product specifications, images, and pricing information. This comprehensive data is valuable for market analysis and strategic decision-making.
Data Customization: APISCRAPY enables users to customize the data extraction process, ensuring that the extracted ecommerce data aligns precisely with their informational needs. This customization option adds versatility to the service.
Efficient Data Extraction: APISCRAPY's technology streamlines the data extraction process, saving users time and effort. The efficiency of the extraction workflow ensures that users can obtain relevant ecommerce data swiftly and consistently.
Realtime Insights: Businesses can gain real-time insights into the dynamic Ecommerce Market by accessing rapidly extracted data. This real-time information is crucial for staying ahead of market trends and making timely adjustments to business strategies.
Scalability: The technology behind APISCRAPY allows scalable extraction of ecommerce data from various sources, accommodating evolving data needs and handling increased volumes effortlessly.
Beyond the broader market, a deeper dive into specific products can provide invaluable insights. APISCRAPY excels in collecting Ecommerce product data, enabling businesses to analyze product performance, pricing strategies, and customer reviews.
To navigate the complexities of the Ecommerce world, you need access to robust datasets. APISCRAPY's commitment to providing comprehensive Ecommerce datasets ensures businesses have the raw materials required for effective decision-making.
Our primary focus is on Amazon data, offering businesses a wealth of information to optimize their Amazon presence. By doing so, we empower our clients to refine their strategies, enhance their products, and make data-backed decisions.
[Tags: Ecommerce data, Ecommerce Data Sample, Ecommerce Product Data, Ecommerce Datasets, Ecommerce market data, Ecommerce Market Datasets, Ecommerce Sales data, Ecommerce Data API, Amazon Ecommerce API, Ecommerce scraper, Ecommerce Web Scraping, Ecommerce Data Extraction, Ecommerce Crawler, Ecommerce data scraping, Amazon Data, Ecommerce web data]
In 2024, global retail e-commerce sales reached an estimated ************ U.S. dollars. Projections indicate a ** percent growth in this figure over the coming years, with expectations to come close to ************** dollars by 2028. World players Among the key players on the world stage, the American marketplace giant Amazon holds the title of the largest e-commerce player globally, with a gross merchandise value of nearly *********** U.S. dollars in 2024. Amazon was also the most valuable retail brand globally, followed by mostly American competitors such as Walmart and the Home Depot. Leading e-tailing regions E-commerce is a dormant channel globally, but nowhere has it been as successful as in Asia. In 2024, the e-commerce revenue in that continent alone was measured at nearly ************ U.S. dollars, outperforming the Americas and Europe. That year, the up-and-coming e-commerce markets also centered around Asia. The Philippines and India stood out as the swiftest-growing e-commerce markets based on online sales, anticipating a growth rate surpassing ** percent.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Overview:
This dataset contains 1000 rows of synthetic online retail sales data, mimicking transactions from an e-commerce platform. It includes information about customer demographics, product details, purchase history, and (optional) reviews. This dataset is suitable for a variety of data analysis, data visualization and machine learning tasks, including but not limited to: customer segmentation, product recommendation, sales forecasting, market basket analysis, and exploring general e-commerce trends. The data was generated using the Python Faker library, ensuring realistic values and distributions, while maintaining no privacy concerns as it contains no real customer information.
Data Source:
This dataset is entirely synthetic. It was generated using the Python Faker library and does not represent any real individuals or transactions.
Data Content:
Column Name | Data Type | Description |
---|---|---|
customer_id | Integer | Unique customer identifier (ranging from 10000 to 99999) |
order_date | Date | Order date (a random date within the last year) |
product_id | Integer | Product identifier (ranging from 100 to 999) |
category_id | Integer | Product category identifier (10, 20, 30, 40, or 50) |
category_name | String | Product category name (Electronics, Fashion, Home & Living, Books & Stationery, Sports & Outdoors) |
product_name | String | Product name (randomly selected from a list of products within the corresponding category) |
quantity | Integer | Quantity of the product ordered (ranging from 1 to 5) |
price | Float | Unit price of the product (ranging from 10.00 to 500.00, with two decimal places) |
payment_method | String | Payment method used (Credit Card, Bank Transfer, Cash on Delivery) |
city | String | Customer's city (generated using Faker's city() method, so the locations will depend on the Faker locale you used) |
review_score | Integer | Customer's product rating (ranging from 1 to 5, or None with a 20% probability) |
gender | String | Customer's gender (M/F, or None with a 10% probability) |
age | Integer | Customer's age (ranging from 18 to 75) |
Potential Use Cases (Inspiration):
Customer Segmentation: Group customers based on demographics, purchasing behavior, and preferences.
Product Recommendation: Build a recommendation system to suggest products to customers based on their past purchases and browsing history.
Sales Forecasting: Predict future sales based on historical trends.
Market Basket Analysis: Identify products that are frequently purchased together.
Price Optimization: Analyze the relationship between price and demand.
Geographic Analysis: Explore sales patterns across different cities.
Time Series Analysis: Investigate sales trends over time.
Educational Purposes: Great for practicing data cleaning, EDA, feature engineering, and modeling.