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.

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 confidence and insight.

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

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?

1. Verified Profiles for Precision Engagement

   • Access verified profiles, business locations, employee counts, and decision-maker details for e-commerce businesses across APAC.
   • AI-driven validation ensures 99% accuracy, improving engagement rates and reducing outreach inefficiencies.

2. Comprehensive Coverage of the APAC E-commerce Sector

   • Includes businesses from major e-commerce hubs such as China, India, Japan, South Korea, Australia, and Southeast Asia.
   • Gain insights into regional e-commerce trends, digital transformation efforts, and logistics innovations.

3. Continuously Updated Datasets

   • Real-time updates ensure that business profiles, employee roles, and operational insights remain accurate and relevant.
   • Stay aligned with dynamic market conditions and emerging opportunities in the APAC region.

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 business profiles for e-commerce professionals and organizations across APAC.
• Firmographic Insights: Gain detailed information, including business locations, employee counts, and operational details.
• Decision-maker Profiles: Connect with key e-commerce leaders, managers, and strategists driving online retail innovation.
• Industry Trends: Understand emerging e-commerce trends, consumer behavior, and market dynamics in the APAC region.

Key Features of the Dataset:

1. Comprehensive E-commerce Business Profiles

   • Identify and connect with businesses specializing in online retail, marketplace management, and digital commerce logistics.
   • Target decision-makers involved in supply chain optimization, digital marketing, and platform development.

2. Advanced Filters for Precision Campaigns

   • Filter businesses and professionals by industry focus (fashion, electronics, grocery), geographic location, or employee size.
   • Tailor campaigns to address specific goals, such as promoting technology adoption, enhancing customer engagement, or expanding supply chains.

3. Regional and Sector-specific Insights

   • Leverage data on APAC’s fast-growing e-commerce markets, consumer purchasing trends, and regional challenges.
   • Refine your marketing strategies and outreach efforts to align with market priorities.

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 e-commerce solutions, logistics services, or digital commerce tools to businesses and professionals in the APAC region.
   • Use verified contact data for multi-channel outreach, including email, phone, and social media campaigns.

2. Partnership Development and Vendor Collaboration

   • Build relationships with e-commerce marketplaces, logistics providers, and payment solution companies seeking strategic partnerships.
   • Foster collaborations that drive operational efficiency, enhance customer experiences, or expand market reach.

3. Market Research and Competitive Analysis

   • Analyze regional e-commerce trends, consumer preferences, and logistics challenges 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 e-commerce industry recruiting for roles in operations, logistics, and digital marketing.
   • Provide workforce optimization platforms or training solutions tailored to the digital commerce sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality e-commerce store data at competitive prices, ensuring strong ROI for your marketing, sales, and strategic initiatives.

2. Seamless Integration

   • Integrate verified e-commerce data into CRM systems, analytics platforms, or market...

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.

1) Data Introduction • The Clickstream Data for Online Shopping is an e-commerce analysis dataset that summarizes user clickstream, product information, country, price, and other session-specific behavior data from April to August 2008 at an online shopping mall specializing in maternity clothing.

2) Data Utilization (1) Clickstream Data for Online Shopping has characteristics that: • Each row contains 14 key variables: year, month, day, click order, country (by access IP), session ID, main category, product code, color, photo location, model photo type, price, category average price, page number, etc. • Data is configured to enable analysis of various consumer behaviors such as click flows for each session, product attributes, and country-specific access patterns. (2) Clickstream Data for Online Shopping can be used to: • Online Shopping Mall User Behavior Analysis: Using clickstream, session, and product information, you can analyze purchase conversion routes, popular products, and behavioral patterns by country and category. • Improve marketing strategies and UI/UX: analyze the relationship between product photo location, color, price, etc. and click behavior and apply to establish effective marketing strategies and improvement of shopping mall UI/UX.

Dataset Description: Online Retail Transaction Data

This dataset contains transactional data from an online retail store, including customer purchases, product details, invoice information, and country-specific data. The dataset is structured into four main files:

Invoices.csv – Contains invoice-related details such as date and customer information.

Products.csv – Includes product-specific data like stock codes, descriptions, and unit prices.

Invoice_rel_product.csv – Represents the relationship between invoices and products, detailing quantities purchased.

Customers.csv – Provides customer identifiers and their respective countries.

Column Descriptions:

InvoiceNo: Unique identifier for each order (invoices starting with "C" indicate refunds/cancellations).

InvoiceDate: The date and time when the invoice was issued.

StockCode: Unique code assigned to each product.

Description: Name or description of the product.

UnitPrice: Price per unit of the product (in GBP).

Quantity: Number of units purchased per transaction.

CustomerID: Unique identifier for each customer.

Country: The country from which the order was placed.

Preprocessing Notes:

-Refund Flag: Invoices starting with "C" were marked with an additional feature {is_return: True/False} in the graph database to distinguish refunded transactions.

-Data Cleaning: Rows with negative values in UnitPrice or Quantity were removed using Pandas DataFrame for consistency.

The dataset Customer_feedback_bg consists of customer reviews for online stores in Bulgarian. The data are retrieved from otzivi.bg and pazaruvaj.com, and represent user reviews in Bulgarian language about 87 online stores. 906 customer reviews were collected in free text and manually associated with the following categories: compliments, complaints, mixed, suggestions. Наборът от данни Customer_feedback_bg се състои от потребителски отзиви за онлайн магазини на български език. Данните са получени от otzivi.bg и pazaruvaj.com и представляват потребителски отзиви на български език за 87 онлайн магазина. Събрани са 906 потребителски отзива в свободен текст, които са ръчно асоциирани със следните категории: похвали, оплаквания, смесени, препоръки.

This is a dataset obtained from an online survey conducted in August 2020.

In the survey, participants were introduced to the concept of a smartphone-based shopping assistant application with the help of pictures and videos when shopping with and without the application. Participants were presented with three different shopping scenarios. In each scenario, we showed products on a shelf (groceries, luxury chocolate, shoes, books). The first shopping scenario was a regular shopping scenario (RSS), the second was an augmented reality shopping scenario (ARSS), and the third was an augmented reality shopping scenario with explainable AI features (XARSS). For each scenario participants had to answer questions about how they perceived the scenario and how it influenced their overall purchase intention.

The present work was conducted within the Innovative Training Network project PERFORM funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 765395. The EU Research Executive Agency is not responsible for any use that may be made of the information it contains.

Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

This dataset provides comprehensive guidance and strategies for Colorado-based e-commerce businesses to improve their search engine optimization (SEO) and online visibility. It covers optimizing website foundations, conducting keyword research specific to Colorado customers, enhancing product pages, developing an effective content marketing strategy, and building local authority to compete with larger retailers. The dataset includes step-by-step instructions, industry insights, and local data to help Colorado online retailers rank higher on Google, increase online sales, and effectively reach their target audience.

We'll customize a Wildberries dataset to align with your unique requirements, incorporating data on product categories, customer reviews, pricing trends, popular items, demographic insights, sales figures, and other relevant metrics. Leverage our Wildberries datasets for various applications to strengthen strategic planning and market analysis. Examining these datasets enables organizations to understand consumer preferences and online shopping trends, facilitating refined product offerings and marketing campaigns. Tailor your access to the complete dataset or specific subsets according to your business needs. Popular use cases include conducting competitor analysis to understand market positioning, monitoring brand reputation through consumer feedback, and performing consumer market analysis to identify and predict emerging trends in e-commerce and online retail.

ZARA UK Fashion Dataset offers an extensive collection of fashion product data from ZARA's UK online store, providing a detailed overview of available items. This dataset is valuable for analyzing the European fashion retail market, particularly in the UK, and includes fields such as product titles, URLs, SKUs, MPNs, brands, prices, currency, images, breadcrumbs, country, availability, unique IDs, and timestamps for when the data was scraped.

Key Features:

• Product Details: Includes title, URL, SKU (Stock Keeping Unit), MPN (Manufacturer Part Number), and brand for each product, helping to uniquely identify and differentiate items.
• Pricing Information: Features the price of each product along with the currency used (GBP) to understand the pricing strategies of ZARA in the UK market.
• Visual Data: High-quality images of each product, essential for visual merchandising analysis and online consumer behavior studies.
• Categorical Information: Breadcrumbs data provide context on the product's placement within ZARA's website structure, helping to analyze navigation and product hierarchy.
• Geographical Focus: Specific to the UK market, making it relevant for studies on British fashion retail and consumer trends.
• Availability Status: Includes real-time availability data, which is crucial for understanding stock levels, popular products, and restocking practices.
• Unique Identifiers: Each product is tagged with a uniq_id, ensuring data integrity and making it easier to track and analyze over time.
• Data Collection Timestamp: The scraped_at field records the exact time and date when the data was collected, aiding in time-based analysis of inventory and pricing.

Potential Use Cases:

• Market Research: Analyze UK and European fashion trends, consumer preferences, and competitive positioning within the fast fashion sector.
• E-commerce Analysis: Study ZARA's product placement, pricing, and availability to optimize online retail strategies.
• Stock Management: Use SKU and availability data to predict inventory needs and enhance supply chain efficiency.
• Brand Analysis: Examine the impact of brand identity on consumer choices and product performance in the UK market.
• Academic Research: Ideal for research projects focused on fashion retail, marketing strategies, and consumer behavior in Europe.

Data Sources:

The data is meticulously collected from ZARA's official UK website and other reliable retail databases, reflecting the latest product offerings and market dynamics specific to the UK and European fashion markets.

• ZARA US Retail Products Dataset: Explore over 10,000 product records from ZARA's USA online store, including titles, prices, images, and availability.
  
  
• Fashion Products Dataset from GAP.com: Access detailed product information from GAP's online store, featuring over 4,500 fashion items with attributes like price, brand, color, reviews, and images.
  
  
• Myntra Fashion Products Dataset: A comprehensive dataset from Myntra.com, offering over 12,000 fashion products with detailed attributes for in-depth analysis.

This statistic presents a ranking of the most popular online stores in Germany in the electronics and media segment in 2021, sorted by annual net e-commerce sales. For more information please visit ecommerceDB.com.In 2021, market leader amazon.de generated 10.1 billion U.S. dollars via the sale of physical goods in the category electronics and media in Germany. The online store mediamarkt.de was ranked second with a revenue of 1.4 billion U.S. dollars.

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.

Korean Companies’ E-commerce Data provides important information to analyze online shopping behavior and trends. This data includes purchase history, product reviews, and cart behavior. Collected from e-commerce platforms and payment systems, it helps investors identify online market trends, analyze consumer preferences, and optimize marketing strategies, essential for valuing Korean e-commerce companies.

Internet sales in Great Britain by store type, month and year.

This furniture e-commerce dataset includes 140,000+ structured product records collected from online retail sources. Each entry provides detailed product information, categories, and breadcrumb hierarchies, making it ideal for AI, machine learning, and analytics applications.

Key Features:

• 📊 140K+ furniture product records in structured format

• 🏷 Includes categories, subcategories, and breadcrumbs for taxonomy mapping

• 📂 Delivered as a clean CSV file for easy integration

• 🔎 Perfect dataset for AI, NLP, and machine learning model training

Best Use Cases:
✔ LLM training & fine-tuning with domain-specific data
✔ Product classification datasets for AI models
✔ Recommendation engines & personalization in e-commerce
✔ Market research & furniture retail analytics
✔ Search optimization & taxonomy enrichment

Why this dataset?

• Large volume (140K+ furniture records) for robust training

• Real-world e-commerce product data

• Ready-to-use CSV, saving preprocessing time

• Affordable licensing with bulk discounts for enterprise buyers

Note:
Each record in this dataset includes both a url (main product page) and a buy_url (the actual purchase page).
The dataset is structured so that records are based on the buy_url, ensuring you get unique, actionable product-level data instead of just generic landing pages.

This statistic presents a ranking of the most popular online stores in the United Kingdom in the food and personal care segment in 2021, sorted by annual net e-commerce sales. For more information please visit ecommerceDB.com.In 2021, market leader tesco.com generated 6.1 billion U.S. dollars via the sale of products from the foor and personal care segment in the United Kingdom. The online store sainsbury.co.uk was ranked second with a revenue of 4 billion U.S. dollars.

Use our Best Buy products to collect ratings, prices, and descriptions about products from an e-commerce online web. You can purchase either the entire dataset or a customized subset, depending on your requirements. The Best Buy Products Dataset stands as a comprehensive resource for businesses, researchers, and analysts aiming to navigate the vast array of products offered by Best Buy, a leading retailer in consumer electronics and technology. Tailored to provide a deep understanding of Best Buy's e-commerce ecosystem, this dataset facilitates market analysis, pricing optimization, customer behavior comprehension, and competitor assessment. At its core, the dataset encompasses essential attributes such as product ID, title, descriptions, ratings, reviews, pricing details, and seller information. These fundamental data elements empower users to glean insights into product performance, customer sentiment, and seller credibility, thereby facilitating informed decision-making processes. Whether you're a retailer looking to enhance your product portfolio, a researcher investigating trends in consumer electronics, or an analyst seeking to refine e-commerce strategies, the Best Buy Products Dataset offers a valuable resource for uncovering opportunities and driving success in the ever-evolving landscape of retail.