Context

Typically e-commerce datasets are proprietary and consequently hard to find among publicly available data. However, The UCI Machine Learning Repository has made this dataset containing actual transactions from 2010 and 2011. The dataset is maintained on their site, where it can be found by the title "Online Retail".

Content

"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."

Acknowledgements

Per the UCI Machine Learning Repository, this data was made available by Dr Daqing Chen, Director: Public Analytics group. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Image from stocksnap.io.

Inspiration

Analyses for this dataset could include time series, clustering, classification and more.

I imported the two Olist Kaggle datasets into an SQLite database. I modified the original table names to make them shorter and easier to understand. Here's the Entity-Relationship Diagram of the resulting SQLite database:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2473556%2F23a7d4d8cd99e36e32e57303eb804fff%2Fdb-schema.png?generation=1714391550829633&alt=media" alt="Database Schema">

Data sources:

https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce

https://www.kaggle.com/datasets/olistbr/marketing-funnel-olist

I used this database as a data source for my notebook:

SQL Challenge: E-commerce data analysis

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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.

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.

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.

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   • Leverage data on APAC’s fast-growing e-commerce markets, consumer purchasing trends, and regional challenges.
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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.

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CONTEXT

E-commerce (electronic commerce) is the buying and selling of goods and services, or the transmitting of funds or data, over an electronic network, primarily the internet. These business transactions occur either as business-to-business (B2B), business-to-consumer (B2C), consumer-to-consumer or consumer-to-business

CONTENT

This is simple data set of US online_store from 2020.

INSPIRATION

So, the data cames with some questions !!

What was the highest Sale in 2020? What is average discount rate of charis? What are the highest selling months in 2020? What is the Profit Margin for each sales record? How much profit is gained for each product? What is the total Profit & Sales by Sub-Category? People from city/state shop the most? Develop a function, to return a dataframe which is grouped by a particular column (as an input)

If you have wonderful idea about this dataset, welcome to contribute !!! Happy Kaggling, please up-vote if you find this dataset helpful!🖤!

Comprehensive database of mergers and acquisitions in the E-Commerce industry

Brazil E-Commerce Transactions: Average Order Value (AOV) data was reported at 244.807 USD in 10 May 2025. This records an increase from the previous number of 243.539 USD for 09 May 2025. Brazil E-Commerce Transactions: Average Order Value (AOV) data is updated daily, averaging 158.782 USD from Dec 2018 (Median) to 10 May 2025, with 2324 observations. The data reached an all-time high of 718.941 USD in 30 Apr 2022 and a record low of 56.929 USD in 31 Mar 2020. Brazil E-Commerce Transactions: Average Order Value (AOV) data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Brazil – Table BR.GI.EC: E-Commerce Transactions: by Category.

Discover Retail Store Data for Asia’s retail and e-commerce industries. Includes verified contact data, business histories, and market insights from 70M+ businesses. Best price guaranteed.

E-Commerce Data Integration Software Market size was valued at USD 1,500.75 million in 2024 and the revenue is expected to grow at a CAGR of 12.5% from 2025 to 2032

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.

Our team sources, validates, and lists Product Data based on requirements and requested data attributes. We track global and local Online Marketplaces, eCommerce Platforms, Social Media Platforms and Online Stores to deliver relevant information about product pricing, and its positioning on the market.

Our team extracts, validates, and delivers consumer and product data based on provided requirements and data fields. Sources: Amazon, Walmart, eBay, and others. Exemplary categories: Household Products, Beauty, Fashion, Food, Beverages, Pets, Electronics. Main markets: US, UK, Australia

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Exploring E-commerce Trends: A Guide to Leveraging Dummy Dataset

Introduction: In the world of e-commerce, data is a powerful asset that can be leveraged to understand customer behavior, improve sales strategies, and enhance overall business performance. This guide explores how to effectively utilize a dummy dataset generated to simulate various aspects of an e-commerce platform. By analyzing this dataset, businesses can gain valuable insights into product trends, customer preferences, and market dynamics.

1. Dataset Overview: The dummy dataset contains information on 1000 products across different categories such as electronics, clothing, home & kitchen, books, toys & games, and more. Each product is associated with attributes such as price, rating, number of reviews, stock quantity, discounts, sales, and date added to inventory. This comprehensive dataset provides a rich source of information for analysis and exploration.

2. Data Analysis: Using tools like Pandas, NumPy, and visualization libraries like Matplotlib or Seaborn, businesses can perform in-depth analysis of the dataset. Key insights such as top-selling products, popular product categories, pricing trends, and seasonal variations can be extracted through exploratory data analysis (EDA). Visualization techniques can be employed to create intuitive graphs and charts for better understanding and communication of findings.

3. Machine Learning Applications: The dataset can be used to train machine learning models for various e-commerce tasks such as product recommendation, sales prediction, customer segmentation, and sentiment analysis. By applying algorithms like linear regression, decision trees, or neural networks, businesses can develop predictive models to optimize inventory management, personalize customer experiences, and drive sales growth.

4. Testing and Prototyping: Businesses can utilize the dummy dataset to test new algorithms, prototype new features, or conduct A/B testing experiments without impacting real user data. This enables rapid iteration and experimentation to validate hypotheses and refine strategies before implementation in a live environment.

5. Educational Resources: The dummy dataset serves as an invaluable educational resource for students, researchers, and professionals interested in learning about e-commerce data analysis and machine learning. Tutorials, workshops, and online courses can be developed using the dataset to teach concepts such as data manipulation, statistical analysis, and model training in the context of e-commerce.

6. Decision Support and Strategy Development: Insights derived from the dataset can inform strategic decision-making processes and guide business strategy development. By understanding customer preferences, market trends, and competitor behavior, businesses can make informed decisions regarding product assortment, pricing strategies, marketing campaigns, and resource allocation.

Conclusion: In conclusion, the dummy dataset provides a versatile and valuable resource for exploring e-commerce trends, understanding customer behavior, and driving business growth. By leveraging this dataset effectively, businesses can unlock actionable insights, optimize operations, and stay ahead in today's competitive e-commerce landscape

E-Commerce Transactions: AOV: Food & Drink: Cooking & Recipes data was reported at 18.021 USD in 20 Sep 2024. This records a decrease from the previous number of 18.544 USD for 18 Sep 2024. E-Commerce Transactions: AOV: Food & Drink: Cooking & Recipes data is updated daily, averaging 23.281 USD from May 2019 (Median) to 20 Sep 2024, with 188 observations. The data reached an all-time high of 83.472 USD in 23 Jul 2023 and a record low of 0.207 USD in 12 Sep 2024. E-Commerce Transactions: AOV: Food & Drink: Cooking & Recipes data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Indonesia – Table ID.GI.EC: E-Commerce Transactions: by Category.

E-Commerce Transactions: AOV: Hobbies & Leisure: Photography data was reported at 12.858 USD in 15 Dec 2024. This records a decrease from the previous number of 65.946 USD for 12 Dec 2024. E-Commerce Transactions: AOV: Hobbies & Leisure: Photography data is updated daily, averaging 45.577 USD from Jan 2019 (Median) to 15 Dec 2024, with 484 observations. The data reached an all-time high of 3,664.496 USD in 13 Jun 2019 and a record low of 1.131 USD in 25 Nov 2023. E-Commerce Transactions: AOV: Hobbies & Leisure: Photography data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Indonesia – Table ID.GI.EC: E-Commerce Transactions: by Category.

Explore the dynamic Cloud Database MySQL market, driven by cloud adoption and digital transformation. Discover key insights, growth drivers, restraints, and future trends shaping the global landscape.

The size of the E-Commerce Data Integration Tool market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

Objectives:

• I leveraged advanced data visualization techniques to extract valuable insights from a comprehensive dataset. By visualizing sales patterns, customer behavior, and product trends, I identified key growth opportunities and provided actionable recommendations to optimize business strategies and enhance overall performance. you can find the GitHub repo here Link to GitHub Repository.

Data Description:

there are exactly 6 table and 1 is a fact table and the rest of them are dimension tables: Fact Table:

payment_key:
  Description: An identifier representing the payment transaction associated with the fact.
  Use Case: This key links to a payment dimension table, providing details about the payment method and related information.

customer_key:
  Description: An identifier representing the customer associated with the fact.
  Use Case: This key links to a customer dimension table, providing details about the customer, such as name, address, and other customer-specific information.

time_key:
  Description: An identifier representing the time dimension associated with the fact.
  Use Case: This key links to a time dimension table, providing details about the time of the transaction, such as date, day of the week, and month.

item_key:
  Description: An identifier representing the item or product associated with the fact.
  Use Case: This key links to an item dimension table, providing details about the product, such as category, sub-category, and product name.

store_key:
  Description: An identifier representing the store or location associated with the fact.
  Use Case: This key links to a store dimension table, providing details about the store, such as location, store name, and other store-specific information.

quantity:
  Description: The quantity of items sold or involved in the transaction.
  Use Case: Represents the amount or number of items associated with the transaction.

unit:
  Description: The unit or measurement associated with the quantity (e.g., pieces, kilograms).
  Use Case: Specifies the unit of measurement for the quantity.

unit_price:
  Description: The price per unit of the item.
  Use Case: Represents the cost or price associated with each unit of the item.

total_price:
  Description: The total price of the transaction, calculated as the product of quantity and unit price.
  Use Case: Represents the overall cost or revenue generated by the transaction.

Customer Table: customer_key:

Description: An identifier representing a unique customer.
Use Case: Serves as the primary key to link with the fact table, allowing for easy and efficient retrieval of customer-specific information.

name:

Description: The name of the customer.
Use Case: Captures the personal or business name of the customer for identification and reference purposes.

contact_no:

Description: The contact number associated with the customer.
Use Case: Stores the phone number or contact details for communication or outreach purposes.

nid:

Description: The National ID (NID) or a unique identification number for the customer.

Item Table: item_key:

Description: An identifier representing a unique item or product.
Use Case: Serves as the primary key to link with the fact table, enabling retrieval of detailed information about specific items in transactions.

item_name:

Description: The name or title of the item.
Use Case: Captures the descriptive name of the item, providing a recognizable label for the product.

desc:

Description: A description of the item.
Use Case: Contains additional details about the item, such as features, specifications, or any relevant information.

unit_price:

Description: The price per unit of the item.
Use Case: Represents the cost or price associated with each unit of the item.

man_country:

Description: The country where the item is manufactured.
Use Case: Captures the origin or manufacturing location of the item.

supplier:

Description: The supplier or vendor providing the item.
Use Case: Stores the name or identifier of the supplier, facilitating tracking of item sources.

unit:

Description: The unit of measurement associated with the item (e.g., pieces, kilograms).

Store Table: store_key:

Description: An identifier representing a unique store or location.
Use Case: Serves as the primary key to link with the fact table, allowing for easy retrieval of information about transactions associated with specific stores.

division:

Description: The administrative division or region where the store is located.
Use Case: Captures the broader geographical area in which...

Lebanon E-Commerce Transactions: AOV: E-Commerce & Shopping: E-Commerce & Shopping data was reported at 143.612 USD in 22 Aug 2024. This records a decrease from the previous number of 177.227 USD for 16 Aug 2024. Lebanon E-Commerce Transactions: AOV: E-Commerce & Shopping: E-Commerce & Shopping data is updated daily, averaging 92.574 USD from Jan 2019 (Median) to 22 Aug 2024, with 703 observations. The data reached an all-time high of 1,925.262 USD in 19 Jun 2023 and a record low of 0.547 USD in 23 Jun 2020. Lebanon E-Commerce Transactions: AOV: E-Commerce & Shopping: E-Commerce & Shopping data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Lebanon – Table LB.GI.EC: E-Commerce Transactions: by Category.