Dataset

This dataset was created by Shivam Mishra

Contents

Snapshot img

The e-commerce technology market share is expected to increase by USD 10.57 billion from 2020 to 2025, and the market’s growth momentum will accelerate at a CAGR of 19.07%.

This e-commerce technology market research report provides valuable insights on the post-COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers e-commerce technology market segmentation by application (B2C and B2B) and geography (North America, APAC, Europe, South America, and MEA). The e-commerce technology market report also offers information on several market vendors, including Adobe Inc., BigCommerce Holdings Inc., commercetools GmbH, HCL Technologies Ltd., Open Text Corp., Oracle Corp., Pitney Bowes Inc., Salesforce.com Inc., SAP SE, and Shopify Inc. among others.

What will the E-Commerce Technology Market Size be During the Forecast Period?

Download Report Sample to Unlock the e-Commerce Technology Market Size for the Forecast Period and Other Important Statistics

E-Commerce Technology Market: Key Drivers, Trends, and Challenges

The increasing e-commerce sales are notably driving the e-commerce technology market growth, although factors such as growing concerns over data privacy and security may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic's impact on the e-commerce technology industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key E-Commerce Technology Market Driver

One of the key factors driving the e-commerce technology market is increasing e-commerce sales. The e-commerce industry is progressing quickly, owing to various factors, such as the growing tech-savvy population, increasing Internet penetration, and the rising use of smartphones. The demand for globally manufactured products is also fueling growth by generating cross-border e-commerce sales. Furthermore, the presence of various multiple payment options, such as credit and debit cards, Internet banking, electronic wallets, and cash-on-delivery (COD), has led to a paradigm shift in the purchasing patterns of people from brick-and-mortar stores to online shopping. Also, e-commerce platforms not only enable consumers to buy goods easily as they do not have the physical barriers involved in offline stores but also help them in making better and more informed decisions, as consumers can view multiple user reviews on the website before purchasing a product. The growth of the e-commerce sector directly impacts the e-commerce technology market. All these factors have increased the demand for e-commerce software and services from end-users. Hence, the growth of the e-commerce industry will boost the growth of the global e-commerce technology market during the forecast period.

Key E-Commerce Technology Market Trend

The rising focus on developing headless CMS is another factor supporting the e-commerce technology market growth in the forecast period. The increasing number of touchpoints for customers, such as IoT devices, smartphones, and progressive web apps, is making it difficult for legacy e-commerce websites to manage demand from customers. Even though most retailers have not embraced the IoT, more customers are exploring new product information through devices, such as IoT-enabled speakers, smart voice assistance, and in-store interfaces. To resolve this issue and provide a more effective user experience, vendors are offering a headless e-commerce architecture. Headless e-commerce architecture is a back-end-only content management system (CMS). Furthermore, vendors are offering headless CMS solutions to simplify e-commerce applications and provide flexible software packaging for their clients. For instance, Magento, a subsidiary of Adobe Inc., offers GraphQL, a flexible and performant application programming interface (API), which allows users to build custom front ends, including headless storefronts, advanced web applications (PWA), and mobile apps. Such developments are expected to provide high growth opportunities for market vendors during the forecast period.

Key E-Commerce Technology Market Challenge

Growing concerns over data privacy and security will be a major challenge for the e-commerce technology market during the forecast period. Data privacy and security risks are the major barriers to the adoption of e-commerce technology. Hackers are constantly trying to search for vulnerabilities and loopholes in e-commerce infrastructure. Although e-commerce players, vendors, and end-user organizations try to adopt proactive prevention plans to counter security breaches within their systems, the rise in the number of e-commerce website hacking and ransomware attacks has resulted in financial and data loss for companies. In addition, public cloud in

In the period from January 2022 to May 2022, online marketplace bol.com, which serves consumers primarily within The Netherlands and Belgium, was the most visited e-commerce website in the Netherlands, registering almost ** million monthly visits on average. This is over three times as much traffic from the country as was received by global online marketplace amazon.com within the same timeframe.

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.

In the period from January 2022 to May 2022, the German domain of global online marketplace and retail giant Amazon, amazon.de, was the most visited e-commerce website in Europe with an average of almost *** million monthly visits. Other country-specific domains of Amazon, or websites providing a similar service as an online marketplace in a particular country, such as ozon.ru in Russia, were also some of the most heavily trafficked sites.

Dataset

This dataset was created by Marwan Diab

Contents

Data

This is a faux session-level dataset simulating user behavior on a major ecommerce website. The site includes:

A homepage, multiple Product Listing Pages (PLPs) representing various lines of business (e.g., Apparel, Electronics, Pet Supplies), and their respective Product Detail Pages (PDPs).

Traffic comes from multiple device types and platforms, reflecting a typical omni-channel shopping environment.

The dataset is ideal for web analytics, conversion analysis, and merchandising optimization exercises.

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

📦 Ecommerce Dataset (Products & Sizes Included)

🛍️ Essential Data for Building an Ecommerce Website & Analyzing Online Shopping Trends 📌 Overview This dataset contains 1,000+ ecommerce products, including detailed information on pricing, ratings, product specifications, seller details, and more. It is designed to help data scientists, developers, and analysts build product recommendation systems, price prediction models, and sentiment analysis tools.

🔹 Dataset Features

Column Name Description product_id Unique identifier for the product title Product name/title product_description Detailed product description rating Average customer rating (0-5) ratings_count Number of ratings received initial_price Original product price discount Discount percentage (%) final_price Discounted price currency Currency of the price (e.g., USD, INR) images URL(s) of product images delivery_options Available delivery methods (e.g., standard, express) product_details Additional product attributes breadcrumbs Category path (e.g., Electronics > Smartphones) product_specifications Technical specifications of the product amount_of_stars Distribution of star ratings (1-5 stars) what_customers_said Customer reviews (sentiments) seller_name Name of the product seller sizes Available sizes (for clothing, shoes, etc.) videos Product video links (if available) seller_information Seller details, such as location and rating variations Different variants of the product (e.g., color, size) best_offer Best available deal for the product more_offers Other available deals/offers category Product category

📊 Potential Use Cases

📌 Build an Ecommerce Website: Use this dataset to design a functional online store with product listings, filtering, and sorting. 🔍 Price Prediction Models: Predict product prices based on features like ratings, category, and discount. 🎯 Recommendation Systems: Suggest products based on user preferences, rating trends, and customer feedback. 🗣 Sentiment Analysis: Analyze what_customers_said to understand customer satisfaction and product popularity. 📈 Market & Competitor Analysis: Track pricing trends, popular categories, and seller performance. 🔍 Why Use This Dataset? ✅ Rich Feature Set: Includes all necessary ecommerce attributes. ✅ Realistic Pricing & Rating Data: Useful for price analysis and recommendations. ✅ Multi-Purpose: Suitable for machine learning, web development, and data visualization. ✅ Structured Format: Easy-to-use CSV format for quick integration.

📂 Dataset Format CSV file (ecommerce_dataset.csv) 1000+ samples Multi-category coverage 🔗 How to Use? Download the dataset from Kaggle. Load it in Python using Pandas: python Copy Edit import pandas as pd
df = pd.read_csv("ecommerce_dataset.csv")
df.head() Explore trends & patterns using visualization tools (Seaborn, Matplotlib). Build models & applications based on the dataset!

Over the period observed, the number of registered e-commerce stores increased and reached over ****** in 2024. It is the highest result of the Polish internet sales market for years. Online stores overview In 2022, the most popular online stores by the number of users were allegro.pl and mediaexpert.pl, where the former was used by more than ** million users, and the latter by nearly ** million. Meanwhile, the top-rated online store ranking for customer feedback, speed of delivery, customer service, and quality of packaging was the footwear store eobuwie.com.pl, which scored ** percent, followed by the online pet store zooart.com.pl. Finally, in terms of the most recognized online brands, Polish respondents most often pointed to ******* and *** as the online brands that first come to their minds. E-commerce users In 2022, the most active age group in Poland shopping online included those aged *****, and this trend has already continued since 2016. By contrast, in terms of gender, slightly more women than men have shopped online in recent years. In 2020, Polish respondents cited clear price details and photos of the product, price transparency, and delivery price as the most important aspects when shopping online.

Global online market place and retail giant Amazon was the most visited e-commerce platform in Spain by a large margin in the period January 2022 to May 2022, with its Spanish domain amazon.es receiving over *** million monthly visits on average and its global domain amazon.com receiving a further ** million monthly visits. Comparatively, online marketplace aliexpress.com was the second most visited e-commerce site during this period, and received an average of only ** million monthly visitors.

Dataset Description: E-commerce Customer Behavior

Overview: This dataset provides a comprehensive view of customer behavior within an e-commerce platform. Each entry in the dataset corresponds to a unique customer, offering a detailed breakdown of their interactions and transactions. The information is crafted to facilitate a nuanced analysis of customer preferences, engagement patterns, and satisfaction levels, aiding businesses in making data-driven decisions to enhance the customer experience.

Columns:

1. Customer ID:

   • Type: Numeric
   • Description: A unique identifier assigned to each customer, ensuring distinction across the dataset.

2. Gender:

   • Type: Categorical (Male, Female)
   • Description: Specifies the gender of the customer, allowing for gender-based analytics.

3. Age:

   • Type: Numeric
   • Description: Represents the age of the customer, enabling age-group-specific insights.

4. City:

   • Type: Categorical (City names)
   • Description: Indicates the city of residence for each customer, providing geographic insights.

5. Membership Type:

   • Type: Categorical (Gold, Silver, Bronze)
   • Description: Identifies the type of membership held by the customer, influencing perks and benefits.

6. Total Spend:

   • Type: Numeric
   • Description: Records the total monetary expenditure by the customer on the e-commerce platform.

7. Items Purchased:

   • Type: Numeric
   • Description: Quantifies the total number of items purchased by the customer.

8. Average Rating:

   • Type: Numeric (0 to 5, with decimals)
   • Description: Represents the average rating given by the customer for purchased items, gauging satisfaction.

9. Discount Applied:

   • Type: Boolean (True, False)
   • Description: Indicates whether a discount was applied to the customer's purchase, influencing buying behavior.

10. Days Since Last Purchase:

    • Type: Numeric
    • Description: Reflects the number of days elapsed since the customer's most recent purchase, aiding in retention analysis.

11. Satisfaction Level:

    • Type: Categorical (Satisfied, Neutral, Unsatisfied)
    • Description: Captures the overall satisfaction level of the customer, providing a subjective measure of their experience.

Use Cases:

1. Customer Segmentation:

   • Analyze and categorize customers based on demographics, spending habits, and satisfaction levels.

2. Satisfaction Analysis:

   • Investigate factors influencing customer satisfaction and identify areas for improvement.

3. Promotion Strategy:

   • Assess the impact of discounts on customer spending and tailor promotional strategies accordingly.

4. Retention Strategies:

   • Develop targeted retention strategies by understanding the time gap since the last purchase.

5. City-based Insights:

   • Explore regional variations in customer behavior to optimize marketing efforts based on location-specific trends.

Note: This dataset is synthetically generated for illustrative purposes, and any resemblance to real individuals or scenarios is coincidental.

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...

If you are looking for a challenging data to work on, then good luck with this.

Data Collection

• We have scraped data from different online vendors, of different categories for our project.
• The data includes all the information found on the page for a product.
• Since the data has been scraped it needs some preprocessing.

Purpose

Main goal of this dataset is to bring a search engine and a recommendation system that clusters data from different vendors without any bias towards one vendor. Biasness happens because the data format of products from one vendor are much alike and therefore it becomes difficult to recommend products across different vendors.

Applications

This dataset can be used to create - Search Engine: that takes a user query or keywords and finds relevant products. - Search Engine with Filters: you can add filters of different specs. As the are no explicit specs in the dataset, rather they are in JSON formal in a column, it becomes a challenge to filter out with desired specs - Recommendation System: You can use content based filtering for recommendation but again you have to avoid bias towards one vendor, as it happens because of similarity of keywords intra vendors

This dataset includes products from multiple Ecommerce sites like ShopHive, HomeShopping, PriceOye.

Features: 1. Product Name 2. Product Image 3. Product Link 4. Product Ratings 5. Rating Count 6. Product Description 7. Fetch Date 8. Product Price 9. Product Category 10. Product Store

I have used this dataset for content Based Recommendation System, Collaborative Recommendation System, Hybrid Recommendation System

This chart provides a detailed overview of the number of India online retailers by Number of Employee. Most India stores' Number of Employee are Less than 10, there are 46.08K stores, which is 91.67% of total. In second place, 1.11K stores' Number of Employee are 10 to 20, which is 2.21% of total. Meanwhile, 1.11K stores' Number of Employee are 20 to 50, which is 2.20% of total. This breakdown reveals insights into India stores distribution, providing a comprehensive picture of the performance and efficient of online retailer.

In the period from January 2022 to May 2022, amazon.com was the most visited e-commerce website in Canada, registering over ** million monthly visits in Canada on average. This is almost twice as much traffic as was received by homedepot.ca, the second most visited website in the country. For comparison, global e-commerce giants eBay and Ali Express both received less than ** million monthly visits on average during this time period.

E-commerce companies sell various goods and associated services through online portals, either on websites, mobile apps or integrated into social media platforms. Internet access across Europe continues to accelerate, with the vast majority of countries boasting usage rates of over 80% of the population. The spread of fast broadband and mobile data has enabled rising numbers of Europeans to engage in e-shopping. Over the five years through 2025, e-commerce revenue is slated to climb at a compound annual rate of 4% to reach €352.5 billion. E-tailers benefit from lower overhead costs than bricks-and-mortar stores, enabling them to offer highly competitive prices and draw sales away from traditionally popular establishments like department stores. E-tailers have taken off by leveraging these cost advantages to appeal to an increasingly price-conscious consumer base. The expansion of value-added services like buy now, pay later and fast, flexible delivery options have contributed to strong industry growth. However, the industry hasn’t been immune to recent cos-of-living pressures; sky-high inflation across much of Europe severely dented Europeans’ spending power, with drops in sales volumes affecting many online stores in 2023. Despite this, revenue continues on an upwards trajectory as inflation outweighs the drop in volume sales, contributing to forecast revenue growth of 3.9% in 2025. Looking forwards, rising internet penetration will continue to provide a growing market for e-tailers, driving revenue upwards at a projected compound annual rate of 6.3% over the five years through 2030 to reach €478.9 billion. E-tailers will continue to adapt their business practices and product selections to reflect the ever-growing level of environmental awareness. Delivery fleets will become fully electrified for many companies, while increasingly stringent waste regulations will force companies to adopt biodegradable or recyclable packaging in the coming years. Still, online retailers must innovate to compete with rival Asian companies like Temu as these competitors increasingly penetrate European markets. The integration of Gen AI and data analytics will transform business operations, making them more efficient and helping to lower wage costs, supporting profitability.

This is the classification based E-commerce text dataset for 4 categories - "Electronics", "Household", "Books" and "Clothing & Accessories", which almost cover 80% of any E-commerce website.

The dataset is in ".csv" format with two columns - the first column is the class name and the second one is the datapoint of that class. The data point is the product and description from the e-commerce website.

The dataset has the following features :

Data Set Characteristics: Multivariate

Number of Instances: 50425

Number of classes: 4

Area: Computer science

Attribute Characteristics: Real

Number of Attributes: 1

Associated Tasks: Classification

Missing Values? No

Gautam. (2019). E commerce text dataset (version - 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3355823