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.

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.

This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method.

Columns:

• Order ID: Unique identifier for each sales order.
• Date:Date of the sales transaction.
• Category:Broad category of the product sold (e.g., Electronics, Home Appliances, Clothing, Books, Beauty Products, Sports).
• Product Name:Specific name or model of the product sold.
• Quantity:Number of units of the product sold in the transaction.
• Unit Price:Price of one unit of the product.
• Total Price: Total revenue generated from the sales transaction (Quantity * Unit Price).
• Region:Geographic region where the transaction occurred (e.g., North America, Europe, Asia).
• Payment Method: Method used for payment (e.g., Credit Card, PayPal, Debit Card).

Insights:

• 1. Analyze sales trends over time to identify seasonal patterns or growth opportunities.
• 2. Explore the popularity of different product categories across regions.
• 3. Investigate the impact of payment methods on sales volume or revenue.
• 4. Identify top-selling products within each category to optimize inventory and marketing strategies.
• 5. Evaluate the performance of specific products or categories in different regions to tailor marketing campaigns accordingly.

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.

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.

Graph and download economic data for E-Commerce Retail Sales as a Percent of Total Sales (ECOMPCTSA) from Q4 1999 to Q2 2025 about e-commerce, retail trade, percent, sales, retail, and USA.

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.

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

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

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

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

1. Verified Contact Data for Precision Outreach

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

2. Comprehensive Coverage Across Retail Segments

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

3. Continuously Updated Datasets

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

4. Ethical and Compliant

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

Data Highlights:

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

Key Features of the Dataset:

1. Retail Decision-Maker Profiles

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

2. Advanced Filters for Precision Targeting

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

3. Market Trends and Operational Insights

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

4. AI-Driven Enrichment

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

Strategic Use Cases:

1. Sales and Lead Generation

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

2. Market Research and Consumer Insights

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

3. E-Commerce and Digital Strategy Development

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

4. Recruitment and Workforce Solutions

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

Why Choose Success.ai?

1. Best Price Guarantee

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

2. Seamless Integration
   ...

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Description:

One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

Acknowledgements

The dataset is taken from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

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

Source:

Dr Daqing Chen, Director: Public Analytics group. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Data Set Information:

This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. 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 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 each 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 each customer resides.

Relevant Papers:

The evolution of direct, data and digital marketing, Richard Webber, Journal of Direct, Data and Digital Marketing Practice (2013) 14, 291–309. Clustering Experiments on Big Transaction Data for Market Segmentation, Ashishkumar Singh, Grace Rumantir, Annie South, Blair Bethwaite, Proceedings of the 2014 International Conference on Big Data Science and Computing. A decision-making framework for precision marketing, Zhen You, Yain-Whar Si, Defu Zhang, XiangXiang Zeng, Stephen C.H. Leung c, Tao Li, Expert Systems with Applications, 42 (2015) 3357–3367.

Citation Request:

Daqing Chen, Sai Liang Sain, and Kun Guo, 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, 2012 (Published online before print: 27 August 2012. doi: 10.1057/dbm.2012.17).

This dataset provides a curated subset of the anonymized Google Analytics event data for three months of the Google Merchandise Store. The full dataset is available as a BigQuery Public Dataset.

The data includes information on items sold in the store and how much money was spent by users over time. It is both comprehensive enough to invite real analysis yet simple enough to facilitate teaching.

Original Data

Acknowledgement

Foto von Arthur Osipyan auf Unsplash

As the number of online stores as well as buyers are increasing rapidly, researchers are working on understanding and improving the performance of online stores by studying customer behavior, interests, engagement etc., along with the technical aspects of online stores. This however requires access to log files of real-world. With this objective in mind, we have prepared and made publicly available high-frequency data-set containing one month of log files from an actual and popular Polish online store. This data-set can provide insights to user behavior as well as performance of the online store.

Success.ai’s Retail Data for the Retail Sector in Asia enables businesses to navigate dynamic consumer markets, evolving retail landscapes, and rapidly changing consumer behavior across the region. Leveraging over 170 million verified professional profiles and 30 million company profiles, this dataset delivers comprehensive firmographic details, verified contact information, and decision-maker insights for retailers ranging from boutique shops and e-commerce platforms to large department store chains and multinational franchises.

Whether you’re launching new products, entering emerging markets, or optimizing supply chain strategies, Success.ai’s continuously updated and AI-validated data ensures you engage the right stakeholders at the right time, all backed by our Best Price Guarantee.

Why Choose Success.ai’s Retail Data in Asia?

1. Comprehensive Company Information

   • Access verified work emails, phone numbers, and LinkedIn profiles of retail decision-makers, buyers, and merchandising managers across Asia.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and minimizing wasted outreach efforts.

2. Regional Focus on Asian Markets

   • Includes profiles of small specialty retailers, large department stores, convenience chains, online marketplaces, and luxury brands spanning regions like East Asia, Southeast Asia, and South Asia.
   • Understand region-specific consumer preferences, product trends, and competitive dynamics to guide targeted campaigns and market entries.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, store expansions, new franchise agreements, and shifts in inventory sourcing.
   • Stay aligned with evolving market conditions, shopper behaviors, and regulatory environments impacting the Asian retail sector.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and global privacy regulations, ensuring that your data usage remains compliant and your outreach respects personal boundaries.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with executives, buyers, store managers, and e-commerce directors shaping retail landscapes in Asia.
• 30M Company Profiles: Gain insights into brand portfolios, store counts, revenue ranges, and distribution networks.
• Firmographic & Demographic Data: Understand retail categories, merchandising strategies, supply chain partners, and consumer demographics influencing local markets.
• Verified Decision-Maker Contacts: Connect directly with key stakeholders responsible for purchasing decisions, vendor selection, category management, and brand partnerships.

Key Features of the Dataset:

1. Retail Decision-Maker Profiles
   • Identify and connect with CEOs, CFOs, category buyers, inventory planners, marketing directors, and store operations leaders.

2. Target professionals who determine product assortments, vendor negotiations, store layouts, pricing strategies, and promotional campaigns.

3. Advanced Filters for Precision Targeting

   • Filter by retail segment (fashion, electronics, groceries, cosmetics), country of operation, store format, or omnichannel strategies.
   • Tailor campaigns to align with unique cultural preferences, local consumer spending habits, and regulatory frameworks.

4. AI-Driven Enrichment

   • Profiles are enriched with actionable data, enabling personalized messaging, highlighting market-entry value propositions, and improving engagement outcomes in diverse Asian markets.

Strategic Use Cases:

1. Market Entry & Expansion

   • Identify suitable partners, franchisees, or distribution channels when entering new Asian markets.
   • Benchmark against established players, adapt offerings to local tastes, and secure placements in prime retail locations.

2. Supplier and Vendor Relations

3. Connect with procurement managers and inventory planners evaluating new suppliers or seeking innovative products.

4. Present packaging solutions, POS technology, or loyalty programs to retailers aiming to enhance the shopping experience.

5. Omnichannel and E-Commerce Growth

   • Engage e-commerce managers and digital marketing teams embracing online retail, click-and-collect services, and mobile payment integrations.
   • Align technology solutions with growing demand for contactless shopping, personalized recommendations, and seamless customer journeys.

6. Seasonal and Cultural Campaigns

   • Leverage local holidays, shopping festivals, and cultural events by reaching marketing directors and store managers who coordinate merchandise rotations, promotional deals, and experiential activations.
   • Adapt messaging to align with regional festivities and peak shopping periods.

Why Choose Success.ai?

1. Best Price Guarantee

2. Access top-quality verified data at competitive prices, ensuring strong ROI for product launches, brand expansions, and supply chain optimizations.

3. Sea...

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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.

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