A comprehensive dataset providing key insights into the eCommerce industry, including global retail online sales projections, number of eCommerce stores, digital buyer statistics, revenue growth in the United States, sector-wise revenue details with a focus on consumer electronics, average conversion rates, and mobile commerce sales forecasts.

Note:- Only publicly available data can be worked upon

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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.

Sri Lanka E-Commerce Transactions: Value: Mobile data was reported at 211.706 USD in 24 Mar 2025. This records a decrease from the previous number of 335.171 USD for 07 Jan 2025. Sri Lanka E-Commerce Transactions: Value: Mobile data is updated daily, averaging 761.024 USD from Dec 2018 (Median) to 24 Mar 2025, with 1835 observations. The data reached an all-time high of 131,614.320 USD in 07 Oct 2023 and a record low of 1.023 USD in 16 Aug 2022. Sri Lanka E-Commerce Transactions: Value: Mobile data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Sri Lanka – Table LK.GI.EC: E-Commerce Transactions: by Device.

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. .

The number of smartphone users in Morocco was forecast to continuously increase between 2024 and 2029 by in total 9.9 million users (+35.53 percent). After the eighteenth consecutive increasing year, the smartphone user base is estimated to reach 37.71 million users and therefore a new peak in 2029. Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Tunisia and Algeria.

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 this study, 403 Chinese consumers generalizable to the broader population were surveyed on their motivations to shop for fashion apparel in both high street and e-commerce environments. Statistical analysis was undertaken through multiple T-Tests and MANOVA with the assistance of SPSS and G*Power.

To increase the profits of international brands, this paper presents the motivations of Chinese consumers to engage in fashion retail, building upon established theory in hedonic and utilitarian motivations. With China set to capture over 24% of the $212 billion fashion market, international brands need to understand the unique motivations of Chinese consumers in order to capitalise on the market. However, the motivations of Chinese people to engage in fashion retail are as yet undefined, limiting the ability for international fashion retailers to operate with prosperity in the Chinese market.

• 3M+ Contact Profiles • 5M+ Worldwide eCommerce Brands • Direct Contact Info for Decision Makers • Contact Direct Email and Mobile Number • 15+ eCommerce Platforms • 20+ Data Points • Lifetime Support Until You 100% Satisfied

Buy eCommerce leads from our eCommerce leads database today. Reach out to eCommerce companies to expand your business. Now is the time to buy eCommerce leads and start running a campaign to attract new customers. We provide current and accurate information that will assist you in achieving your goals.

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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.

We were able to do so by obtaining data from various locations around the world. As a result, our database is widely accessible, and anyone can use it from any location on the planet. Please contact us if you want the best eCommerce leads .

We sell eCommerce leads that can be filtered by industry. We know what you’re going through and what you’ll need for your next project. As a result, we’ve compiled a list of eCommerce leads that are exactly what you require. With the most potential data we provide, you can grow your business and achieve your business goals. All of our eCommerce leads are generated professionally, with real people – not bots – entering data.

We’re a leading brand in the industry because we source data from the most well-known platforms, ensuring that the information you receive from us is accurate and reliable. That’s especially true because we verify each and every piece of information in order to provide you with yet another benefit in your life.

The majority of our customers have had success with the information we’ve provided. That is why they keep contacting us for our services. You can count on our business-to-business eCommerce sales leads. Contact us to work with one of the most effective lead generation companies in the industry, which has already helped thousands of potential members achieve success.

Every month, we update our eCommerce store sales leads in order to provide our clients with the most accurate data possible. We have a team of professionals who strive for excellence when it comes to gathering the right leads to ensure you get the number of sales you need. Our experts also double-check that all of the sales data we receive is genuine and accurate.

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This dataset contains customer reviews and ratings for the Shopee mobile application from the Google Play Store. Shopee Pte. Ltd. is a Singaporean multinational technology company specialising in e-commerce, operating as a subsidiary of Sea Limited. Launched in 2015 in Singapore, Shopee has since expanded globally and, as of 2021, is recognised as the largest e-commerce platform in Southeast Asia, attracting 343 million monthly visitors. It facilitates online purchasing and selling for consumers and sellers across East Asia and Latin America. This dataset is designed to offer a clear understanding of public perception and sentiment towards the Shopee app over an extended period.

Columns

• Index: A unique identifier for each review.
• review_text: The full text of the user's review.
• review_rating: The rating given by the user, on a scale of 1 to 5.
• author_id: A unique identifier for the author of the review.
• author_name: The display name of the review's author.
• author_app_version: The version of the Shopee application used by the author at the time of the review.
• review_datetime_utc: The date and time (in UTC) when the review was posted.
• review_likes: The number of likes received by the review.

Distribution

The dataset is typically provided in a CSV file format and is structured as tabular data. It contains approximately 782,000 records. Specific file size details are not available.

Usage

This dataset is invaluable for gaining insight into public opinion regarding the Shopee app over time. It can be used for various analytical purposes, including: * Extracting sentiments and identifying evolving trends in user feedback. * Determining which versions of the app elicited the most positive or negative feedback. * Applying topic modelling techniques to pinpoint specific pain points or common issues reported by users, and many more analytical applications.

Coverage

The dataset primarily covers app reviews from the Google Play Store for the Shopee application. While Shopee operates globally across Southeast Asia, East Asia, and Latin America, the dataset title suggests a focus on reviews from Singaporean users. The time range for the reviews spans from 22 June 2015 to 13 November 2023. The data reflects feedback from mobile app users who submitted reviews during this period.

License

CC-BY-SA

Who Can Use It

This dataset is suitable for a wide range of users, including: * Data Analysts and Market Researchers to understand consumer behaviour and sentiment. * Product Managers and App Developers for identifying user needs, improving app features, and addressing pain points. * Businesses and E-commerce Platforms seeking competitive analysis or insights into customer satisfaction in the online retail sector. * Academics and Students for research in natural language processing (NLP), sentiment analysis, and consumer studies.

Dataset Name Suggestions

• Shopee Google Play App Reviews
• Singapore Shopee App User Feedback
• Shopee Mobile App Ratings Dataset
• Google Play Shopee Review Analysis
• Shopee E-commerce App Reviews

Attributes

Original Data Source: 🇸🇬 Shopee App Reviews from Google Store

E-Commerce Transactions: Volume: Mobile data was reported at 2.000 Unit in 13 Mar 2025. This records a decrease from the previous number of 3.000 Unit for 12 Mar 2025. E-Commerce Transactions: Volume: Mobile data is updated daily, averaging 290.000 Unit from Dec 2018 (Median) to 13 Mar 2025, with 2119 observations. The data reached an all-time high of 1,533.000 Unit in 25 Feb 2021 and a record low of 2.000 Unit in 13 Mar 2025. E-Commerce Transactions: Volume: Mobile data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Malaysia – Table MY.GI.EC: E-Commerce Transactions: by Device.

Context

This is the largest retail e-commerce orders dataset from Pakistan. It contains half a million transaction records from March 2016 to August 2018. The data was collected from various e-commerce merchants as part of a research study. I am releasing this dataset as a capstone project for my data science course at Alnafi (alnafi.com/zusmani).
There is a dire need for such dataset to learn about Pakistan’s emerging e-commerce potential and I hope this will help many startups in many ways.

Content

Geography: Pakistan

Time period: 03/2016 – 08/2018

Unit of analysis: E-Commerce Orders

Dataset: The dataset contains detailed information of half a million e-commerce orders in Pakistan from March 2016 to August 2018. It contains item details, shipping method, payment method like credit card, Easy-Paisa, Jazz-Cash, cash-on-delivery, product categories like fashion, mobile, electronics, appliance etc., date of order, SKU, price, quantity, total and customer ID. This is the most detailed dataset about e-commerce in Pakistan that you can find in the Public domain.

Variables: The dataset contains Item ID, Order Status (Completed, Cancelled, Refund), Date of Order, SKU, Price, Quantity, Grand Total, Category, Payment Method and Customer ID.

Size: 101 MB

File Type: CSV

Acknowledgements

I like to thank all the startups who are trying to make their mark in Pakistan despite the unavailability of research data.

Inspiration

I’d like to call the attention of my fellow Kagglers to use Machine Learning and Data Sciences to help me explore these ideas:

• What is the best-selling category? • Visualize payment method and order status frequency • Find a correlation between payment method and order status • Find a correlation between order date and item category • Find any hidden patterns that are counter-intuitive for a layman • Can we predict number of orders, or item category or number of customers/amount in advance?

E-Commerce Transactions: AOV: Mobile data was reported at 61.099 USD in 13 Mar 2025. This records a decrease from the previous number of 69.599 USD for 12 Mar 2025. E-Commerce Transactions: AOV: Mobile data is updated daily, averaging 154.707 USD from Dec 2018 (Median) to 13 Mar 2025, with 2119 observations. The data reached an all-time high of 930.058 USD in 09 Mar 2022 and a record low of 40.134 USD in 01 Apr 2021. E-Commerce Transactions: AOV: Mobile data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Malaysia – Table MY.GI.EC: E-Commerce Transactions: by Device.

Explore the POS transactions dataset in Saudi Arabia, including data on mobile transactions, sales using cards, e-commerce transactions, and more. Analyze the No. of Transactions, Total POS, and Sales in Thousand Saudi Riyals to gain insights into the country's payment trends.

POS Using Near Field Communication Technology, No. of Mobile Transactions, Total POS, No. of Transactions, Sales Using Cards in Thousand Saudi Riyals, Sales in Thousand Saudi Riyals, Sales Using Mobile in Thousand Saudi Riyals, E-Commerce Transactions Using Mada Cards, No. of Cards Transactions, No. of Points of Sale Terminals, E-Commerce Transactions Using Mada Cards, Sales, Transactions, POS, Money, Bank, SAMA Monthly

Saudi Arabia Follow data.kapsarc.org for timely data to advance energy economics research..- Sales In Thousand Riyals- End of Period-Including transactions of mada cards through online shopping sites and in-app purchases. It does not include transactions by Visa, MasterCard and other credit cards.

Indonesia E-Commerce Transactions: Value: Mobile data was reported at 3,641.429 USD in 07 Mar 2025. This records a decrease from the previous number of 9,377.408 USD for 06 Mar 2025. Indonesia E-Commerce Transactions: Value: Mobile data is updated daily, averaging 97,971.097 USD from Dec 2018 (Median) to 07 Mar 2025, with 2259 observations. The data reached an all-time high of 2,003,746.833 USD in 11 Apr 2022 and a record low of 675.795 USD in 01 Feb 2025. Indonesia E-Commerce Transactions: Value: Mobile 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 Device.

This synthetic yet realistic dataset offers insights into smartphone features, customer reviews, and sales data. It includes over 90 customer reviews for six popular smartphone models from leading brands such as Apple, Samsung, and Google. The dataset is designed to help understand how various product specifications influence purchasing decisions and overall customer satisfaction. It combines detailed product specifications, customer star ratings, review texts, and verified purchase status with estimated sales figures per model.

Columns

• model_id (Integer): A unique identifier for each distinct phone model.
• brand (String): The manufacturer of the phone (e.g., "Apple", "Samsung", "Google").
• model_name (String): The specific name of the phone model (e.g., "iPhone 15").
• price (Integer): The retail price of the phone in USD.
• screen_size (Float): The diagonal screen size of the phone in inches.
• battery (Integer): The battery capacity of the phone in mAh.
• camera_main (String): The resolution of the phone's main camera (e.g., "48MP").
• ram (Integer): The amount of RAM (Random Access Memory) in GB.
• storage (Integer): The internal storage capacity in GB.
• has_5g (Boolean): Indicates whether the phone model supports 5G connectivity (TRUE/FALSE).
• water_resistant (String): The water resistance rating, if any (e.g., "IP68" or "None").
• units_sold (Integer): An estimated number of units sold for market analysis purposes.
• review_id (Integer): A unique identifier for each customer review.
• user_name (String): A randomly generated name for the reviewer.
• star_rating (Integer): The customer's rating, ranging from 1 (worst) to 5 (best).
• verified_purchase (Boolean): Indicates whether the reviewer's purchase was verified (TRUE/FALSE).
• review_date (Date): The date when the review was submitted, in YYYY-MM-DD format (e.g., "2023-05-10").
• review_text (String): Simulated text of the customer's review, based on features and rating (e.g., "The 48MP camera is amazing!").

Distribution

The dataset is typically provided in a CSV file format. It comprises over 90 customer review records, along with corresponding smartphone product specifications and sales data for 6 distinct phone models. The exact total number of rows or the specific file size in MB/GB is not specified.

Usage

This dataset is ideal for various analytical applications, including: * Feature importance analysis: Determining which smartphone specifications (e.g., battery life, camera quality) most significantly influence customer ratings and purchasing decisions. * Sentiment analysis: Applying Natural Language Processing (NLP) techniques to extract insights and sentiment from customer review texts. * Pricing strategy optimisation: Analysing the correlation between price and customer satisfaction or sales volume. * Market research: Comparing performance and customer perception across different brands (e.g., Apple vs. Samsung vs. Google) and models. * Sales vs. features correlation: Investigating how product features and pricing impact estimated units sold.

Coverage

This dataset has a Global region coverage. It includes data pertaining to six smartphone models from three major brands: Apple (iPhone 14, iPhone 15), Samsung (Galaxy S22, Galaxy S23), and Google (Pixel 7, Pixel 8). The review dates are indicative of data from around 2023. While it includes customer reviews, specific demographic details of the reviewers are not available beyond randomly generated usernames. As a synthetic dataset, it is designed to be realistic for general market analysis.

License

CC0

Who Can Use It

This dataset is suitable for: * Data Analysts and Scientists: For performing regression analysis, sentiment analysis, and predictive modelling. * Marketing Professionals: To understand consumer preferences, optimise product features, and refine marketing strategies. * Product Managers: To inform product development, feature prioritisation, and competitive analysis. * Market Researchers: To study market trends, brand comparisons, and consumer behaviour in the smartphone industry. * Academics and Students: For educational purposes and research projects related to consumer electronics, e-commerce, and data analysis.

Dataset Name Suggestions

• Smartphone Customer Satisfaction Data
• Mobile Phone Market & Reviews Dataset
• Consumer Electronics Feature Analysis
• Smartphone Product Performance
• Mobile Device Sales and Reviews

Attributes

Original Data Source: Smartphone Feature Optimization (Marketing Mix)

Sri Lanka E-Commerce Transactions: Volume: Mobile data was reported at 2.000 Unit in 24 Mar 2025. This stayed constant from the previous number of 2.000 Unit for 07 Jan 2025. Sri Lanka E-Commerce Transactions: Volume: Mobile data is updated daily, averaging 9.000 Unit from Dec 2018 (Median) to 24 Mar 2025, with 1835 observations. The data reached an all-time high of 154.000 Unit in 01 Jan 2023 and a record low of 1.000 Unit in 25 May 2023. Sri Lanka E-Commerce Transactions: Volume: Mobile data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Sri Lanka – Table LK.GI.EC: E-Commerce Transactions: by Device.

Morocco E-Commerce Transactions: Volume: Mobile data was reported at 2.000 Unit in 07 Apr 2025. This stayed constant from the previous number of 2.000 Unit for 02 Dec 2024. Morocco E-Commerce Transactions: Volume: Mobile data is updated daily, averaging 53.000 Unit from Dec 2018 (Median) to 07 Apr 2025, with 2063 observations. The data reached an all-time high of 295.000 Unit in 27 Jun 2023 and a record low of 1.000 Unit in 09 Apr 2020. Morocco E-Commerce Transactions: Volume: Mobile data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Morocco – Table MA.GI.EC: E-Commerce Transactions: by Device.

Philippines E-Commerce Transactions: Value: Mobile data was reported at 998.674 USD in 05 May 2025. This records a decrease from the previous number of 2,711.049 USD for 29 Apr 2025. Philippines E-Commerce Transactions: Value: Mobile data is updated daily, averaging 28,552.384 USD from Dec 2018 (Median) to 05 May 2025, with 2235 observations. The data reached an all-time high of 452,351.984 USD in 27 May 2021 and a record low of 268.954 USD in 09 Nov 2024. Philippines E-Commerce Transactions: Value: Mobile data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Philippines – Table PH.GI.EC: E-Commerce Transactions: by Device.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.