Gain exclusive access to verified Shopify store owners with our premium Shopify Users Email List. This database includes essential data fields such as Store Name, Website, Contact Name, Email Address, Phone Number, Physical Address, Revenue Size, Employee Size, and more on demand. Leverage real-time, accurate data to enhance your marketing efforts and connect with high-value Shopify merchants. Whether you're targeting small businesses or enterprise-level Shopify stores, our database ensures precision and reliability for optimized lead generation and outreach strategies. Key Highlights: ✅ 3.9M+ Shopify Stores ✅ Direct Contact Info of Shopify Store Owners ✅ 40+ Data Points ✅ Lifetime Access ✅ 10+ Data Segmentations ✅ FREE Sample Data

Dataset Description

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.

Dataset Features

Orders Table:

• order_id: Unique identifier for an order, acting as the primary key.
• customer_id: Unique identifier for a customer. This table may not be unique at this level.
• order_status: Indicates the status of an order (e.g., delivered, cancelled, processing, etc.).
• order_purchase_timestamp: Timestamp when the order was made by the customer.
• order_approved_at: Timestamp when the order was approved from the seller's side.
• order_delivered_timestamp: Timestamp when the order was delivered at the customer's location.
• order_estimated_delivery_date: Estimated date of delivery shared with the customer while placing the order.

Order Items Table

• order_id: Unique identifier for an order.
• order_item_id: Item number in each order, acting as part of the primary key along with the order_id.
• product_id: Unique identifier for a product.
• seller_id: Unique identifier for the seller.
• price: Selling price of the product.
• shipping_charges: Charges associated with the shipping of the product.

Customers Table

• customer_id: Unique identifier for a customer, acting as the primary key.
• customer_zip_code_prefix: Customer's Zip code.
• customer_city: Customer's city.
• customer_state: Customer's state.

Payments Table

• order_id: Unique identifier for an order.
• payment_sequential: Provides information about the sequence of payments for the given order.
• payment_type: Type of payment (e.g., credit_card, debit_card, etc.).
• payment_installments: Payment installment number in case of credit cards.
• payment_value: Transaction value.

Products Table

• product_id: Unique identifier for each product, acting as the primary key.
• product_category_name: Name of the category the product belongs to.
• product_weight_g: Product weight in grams.
• product_length_cm: Product length in centimeters.
• product_height_cm: Product height in centimeters.
• product_width_cm: Product width in centimeters.

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"

The Forager.ai Global Shopify Store Owner Dataset is backed by advanced AI and offering the highest refresh rate in the industry.

| Volume and Stats |

• Every company record refreshed twice a month, offering an unparalleled update frequency.
• Delivery is made every hour, ensuring you have the latest data at your fingertips.
• Each record is the result of an advanced AI-driven process, ensuring high-quality, accurate data.

| Use Cases |

Sales Platforms, ABM and Intent Data Platforms, Identity Platforms, Data Vendors:

| Delivery Options |

• Flat files via S3 or GCP
• PostgreSQL Shared Database
• PostgreSQL Managed Database
• API
• Other options available upon request, depending on the scale required

Our dataset provides a unique blend of volume, freshness, and detail that is perfect for Sales Platforms, B2B Tech, VCs & PE firms, Marketing Automation, ABM & Intent. It stands as a cornerstone in our broader data offering, ensuring you have the information you need to drive decision-making and growth.

Tags: Company Data, Company Profiles, Employee Data, Firmographic Data, AI-Driven Data, High Refresh Rate, Company Classification, Private Market Intelligence, Workforce Intelligence, Public Companies.

The B2C E-commerce Market size was valued at USD 6.23 trillion in 2023 and is projected to reach USD 21.18 trillion by 2032, exhibiting a CAGR of 19.1 % during the forecasts period. The B2C e-commerce can be defined as the sale of commercial products or services through the internet between buyers and sellers. This market pertains to several industries that fall under its fold that includes the area of retail, travelling, electronics and digital products. Some of the most common implementations are in the ecommerce sites, mobile applications, and membership services. Some aspects of the B2C e-commerce market include increased popularity of omnichannel retailing that combines online and offline environments and the shift to the concept of individualization due to the digitalization and data processing using artificial intelligence and machine learning. Also, growth is noted in mobile commerce (m-commerce) as a result of the increase in the number of mobile devices and more effective mobile payments. To this list one should also include the concepts of social commerce and sustainability which also became significant in today’s society due to increasing importance of ethical and convenient shopping. Recent developments include: In March 2024, Blink, an Amazon company, launched the Blink Mini 2 camera. The new compact plug-in camera offers enhanced features such as person detection, a broader field of view, a built-in LED spotlight for night view in color, and improved image quality. The Blink Mini 2 is designed to work indoors and outdoors, with the option to purchase the Blink Weather Resistant Power Adapter for outdoor use. , In October 2023, Flipkart.com introduced the 'Flipkart Commerce Cloud,' a customized suite of AI-driven retail technology solutions for global retailers and e-commerce businesses. This extensive offering includes marketplace technology, retail media solutions, pricing, and inventory management features rigorously assessed by Flipkart.com. The company aims to equip international sellers with reliable and secure tools to enhance business expansion and efficiency within the competitive global market. , In August 2023, Shopify and Amazon.com, Inc. announced a strategic partnership that will allow Shopify merchants to seamlessly implement Amazon's "Buy with Prime" option on their sites. As a result of the agreement, Amazon.com, Inc. Prime customers will enjoy a more efficient checkout process on various platforms. This collaboration allows Amazon Prime members to utilize their existing Amazon payment options, while Shopify will handle the transaction processing through its system, showcasing a partnership between the two leading companies. , In February 2023, eBay acquired 3PM Shield, a developer of AI-powered online retail solutions. 3PM Shield uses machine learning and artificial intelligence to analyze extensive data sets, enhancing marketplace compliance and user experience. This acquisition aligns with eBay's goal to offer a "safe and reliable" platform by boosting its ability to block the sale of counterfeit and prohibited items. By incorporating 3PM Shield's sophisticated monitoring technologies, eBay seeks to enhance its capability to address problematic seller behavior and spot problematic listings, fostering a safer e-commerce space for its worldwide community of sellers and buyers. .

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

Over 2 million sellers of 10 European Amazon marketplaces: (Italy, France, Spain, Germany, Belgium, Greece, Czech, Poland, Croatia and the United Kingdom.

• The data can be customized to your filtering criteria and needs.

Fields available: • Seller URL • Seller ID • Store Name • Main Category • Brand owner • FBA Seller • Experience on Amazon • Orders • Business Name • Full Business Address • Country • State/Province • ZIP Code • City • Owner first name • Owner last name • Owner email • Email - company • Website • Star rating • Percentage of positive reviews • Lifetime number of seller ratings • Number of products listed • Brands listed • Number of brands listed

Pricing (no order minimums): • <500 leads: $0.59 per lead • 501-5000 leads: $0.39 per lead • 5000+ leads: $0.29 per lead

We update our seller database every month to keep the data current.

Shopify Inc. is a Canadian multinational e-commerce company headquartered in Ottawa, Ontario. Shopify is the name of its proprietary e-commerce platform for online stores and retail point-of-sale systems. Wikipedia Stock price: SHOP (NYSE) $34.20 -2.66 (-7.22%) Aug 19, 16:00 EDT - Disclaimer CEO: Tobias Lütke (Apr 2008–) CFO: Amy E. Shapero Founded: 2006 Headquarters: Ottawa, Canada Number of employees: 10,000+ ISIN: CA82509L1076 Founders: Tobias Lütke, Daniel Weinand, Scott Lake

AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites Overview

Unlock the next generation of agentic commerce and automated shopping experiences with this comprehensive dataset of meticulously annotated checkout flows, sourced directly from leading retail, restaurant, and marketplace websites. Designed for developers, researchers, and AI labs building large language models (LLMs) and agentic systems capable of online purchasing, this dataset captures the real-world complexity of digital transactions—from cart initiation to final payment.

Key Features

Breadth of Coverage: Over 10,000 unique checkout journeys across hundreds of top e-commerce, food delivery, and service platforms, including but not limited to Walmart, Target, Kroger, Whole Foods, Uber Eats, Instacart, Shopify-powered sites, and more.

Actionable Annotation: Every flow is broken down into granular, step-by-step actions, complete with timestamped events, UI context, form field details, validation logic, and response feedback. Each step includes:

Page state (URL, DOM snapshot, and metadata)

User actions (clicks, taps, text input, dropdown selection, checkbox/radio interactions)

System responses (AJAX calls, error/success messages, cart/price updates)

Authentication and account linking steps where applicable

Payment entry (card, wallet, alternative methods)

Order review and confirmation

Multi-Vertical, Real-World Data: Flows sourced from a wide variety of verticals and real consumer environments, not just demo stores or test accounts. Includes complex cases such as multi-item carts, promo codes, loyalty integration, and split payments.

Structured for Machine Learning: Delivered in standard formats (JSONL, CSV, or your preferred schema), with every event mapped to action types, page features, and expected outcomes. Optional HAR files and raw network request logs provide an extra layer of technical fidelity for action modeling and RLHF pipelines.

Rich Context for LLMs and Agents: Every annotation includes both human-readable and model-consumable descriptions:

“What the user did” (natural language)

“What the system did in response”

“What a successful action should look like”

Error/edge case coverage (invalid forms, OOS, address/payment errors)

Privacy-Safe & Compliant: All flows are depersonalized and scrubbed of PII. Sensitive fields (like credit card numbers, user addresses, and login credentials) are replaced with realistic but synthetic data, ensuring compliance with privacy regulations.

Each flow tracks the user journey from cart to payment to confirmation, including:

Adding/removing items

Applying coupons or promo codes

Selecting shipping/delivery options

Account creation, login, or guest checkout

Inputting payment details (card, wallet, Buy Now Pay Later)

Handling validation errors or OOS scenarios

Order review and final placement

Confirmation page capture (including order summary details)

Why This Dataset?

Building LLMs, agentic shopping bots, or e-commerce automation tools demands more than just page screenshots or API logs. You need deeply contextualized, action-oriented data that reflects how real users interact with the complex, ever-changing UIs of digital commerce. Our dataset uniquely captures:

The full intent-action-outcome loop

Dynamic UI changes, modals, validation, and error handling

Nuances of cart modification, bundle pricing, delivery constraints, and multi-vendor checkouts

Mobile vs. desktop variations

Diverse merchant tech stacks (custom, Shopify, Magento, BigCommerce, native apps, etc.)

Use Cases

LLM Fine-Tuning: Teach models to reason through step-by-step transaction flows, infer next-best-actions, and generate robust, context-sensitive prompts for real-world ordering.

Agentic Shopping Bots: Train agents to navigate web/mobile checkouts autonomously, handle edge cases, and complete real purchases on behalf of users.

Action Model & RLHF Training: Provide reinforcement learning pipelines with ground truth “what happens if I do X?” data across hundreds of real merchants.

UI/UX Research & Synthetic User Studies: Identify friction points, bottlenecks, and drop-offs in modern checkout design by replaying flows and testing interventions.

Automated QA & Regression Testing: Use realistic flows as test cases for new features or third-party integrations.

What’s Included

10,000+ annotated checkout flows (retail, restaurant, marketplace)

Step-by-step event logs with metadata, DOM, and network context

Natural language explanations for each step and transition

All flows are depersonalized and privacy-compliant

Example scripts for ingesting, parsing, and analyzing the dataset

Flexible licensing for research or commercial use

Sample Categories Covered

Grocery delivery (Instacart, Walmart, Kroger, Target, etc.)

Restaurant takeout/delivery (Ub...

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SHOP:TSX Shopify Inc.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SHOP Shopify Inc. Class A Subordinate Voting Shares

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data