In 2022, the number of registered e-commerce websites and applications in Vietnam reached nearly **** thousand. In the same year, the number of e-commerce sites that were confirmed by the Ministry of Industry and Trade of Vietnam amounted to over ** thousand.

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, 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.

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.

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• 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

In 2019, the number of monthly unique visitors from the Middle East, Africa and South Asia (MEASA) was 429 million visitors to Turkey based websites. Iran based websites came second with 36 million monthly unique visitors from MEASA in 2019.

Both in 2019 and in 2020, almost nine out of ten (***** percent of) e-commerce websites in Brazil had lass than 10,000 visits per month. In 2020, approximately **** percent of online shopping websites in the South American country had more than ******* monthly visits. In July 2020, the e-commerce websites with the highest number of monthly visits in Brazil were Mercado Livre, Americanas.com, and Amazon Brasil, among others.

Dataset

This dataset was created by Marwan Diab

Contents

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!

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.

In 2025, Sea Limited's Shopee, a Singaporean technology company, was the leading e-commerce website in Southeast Asia, with average monthly web sessions of *** million. This was followed by Lazada, which is owned by the Chinese Alibaba Group, and Tokopedia, owned by an Indonesian technology company. The rise of Shopee Online shopping usage in Southeast Asia has been on a steadily increasing during the past few years, partly driven by the COVID-19 pandemic beginning in 2020. Digital services accelerated after the pandemic, which helped Shopee accumulate a gross merchandise value of over ** billion U.S. dollars. While it is a Singaporean company, most web visits to Shopee were generated from users in Indonesia and Vietnam, reflecting the high internet user penetration in these countries and the shifting shopping behavior of a new generation. The new ways of commerce Many consumers already indulge in online shopping on websites, but new shopping experiences are making their way into the region. Live commerce and social commerce have become a trend in Southeast Asia, encouraging consumers to indulge in shopping while browsing social media or watching livestream videos. With current trends emerging, Shopee also expanded its platform for live commerce usage and became the second most popular platform to watch live commerce in Southeast Asia, after TikTok. Consumers in the region liked to watch live commerce content because of the good deals it offered, as well as real-time product reviews. Livestreams allow consumers to interact with streamers or other buyers for a more interactive shopping experience, drawing in a wide array of consumers.

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.

United Kingdom E Commerce: Business With Website data was reported at 82.000 % in 2017. This records a decrease from the previous number of 83.700 % for 2016. United Kingdom E Commerce: Business With Website data is updated yearly, averaging 80.300 % from Dec 2007 (Median) to 2017, with 11 observations. The data reached an all-time high of 83.700 % in 2016 and a record low of 70.000 % in 2007. United Kingdom E Commerce: Business With Website data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.S033: E Commerce: Proportion of Businesses With a Website.

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...

United Kingdom E Commerce: Business: Over Website: 1000+ Employees data was reported at 45.700 % in 2016. This records an increase from the previous number of 43.700 % for 2015. United Kingdom E Commerce: Business: Over Website: 1000+ Employees data is updated yearly, averaging 44.200 % from Dec 2008 (Median) to 2016, with 9 observations. The data reached an all-time high of 46.500 % in 2012 and a record low of 33.000 % in 2008. United Kingdom E Commerce: Business: Over Website: 1000+ Employees data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s UK – Table UK.S031: E Commerce: Proportion of Businesses Making E Commerce Sales.

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.

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 chart provides a detailed overview of the number of Spain online retailers by Monthly Visitors. Most Spain stores' Monthly Visitors are Less than 100, there are 81.15K stores, which is 57.12% of total. In second place, 37.81K stores' Monthly Visitors are 100 to 1K, which is 26.62% of total. Meanwhile, 18.07K stores' Monthly Visitors are 1K to 10K, which is 12.72% of total. This breakdown reveals insights into Spain stores distribution, providing a comprehensive picture of the performance and efficient of online retailer.

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.