Ecommerce data set

This page provides statistics, facts and market data related to electronic commerce e-commerce. This includes information on B2C and B2B e-commerce volume and value, as well as individual sector e-commerce figures and country-specific e-commerce numbers. The worldwide expansion of the internet has considerably contributed to the transformation of trade and store transactions.

E-commerce also makes use of regular technological maintenance to ensure the smooth functioning of online store sites, monetary transactions, as well as everything to do with providing and delivering products. E-commerce statistics confirm the explosive pace at which this industry has developed as worldwide B2C e-commerce sales amounted to more than 1.

Google Analytics Ecommerce Tracking with Google Tag Manager (Part 1)

There are several different types of e-commerce, the most prevalent being B2B business-to-businessB2C business-to-consumer and C2C consumer-to-consumer e-commerce. Furthermore, mobile commerce in the shape of buying and selling goods and content via mobile devices such as smartphones is also on the rise. Current e-commerce statistics state that 40 percent of worldwide internet users have bought products or goods online via desktop, mobile, tablet or other online devices.

Data Sets – Global eCommerce Landscape

This amounts to more than 1 billion online buyers and is projected to continuously grow. According to popular e-commerce market data, US-founded Amazon is one of the leading e-commerce platforms worldwide. Asian competitors such as Rakuten or Alibaba are also constantly expanding their share within the B2C e-commerce market. Online auction website eBay is the most popular example for C2C e-commerce whilst also providing a platform for merchants to sell their goods.

Mobile commerce growth is another exciting trend to watch in terms of e-commerce statistics, considering the popularity and widespread use of smartphones and growing usage of tablets. InUS mobile commerce revenue amounted to more than 38 billion US dollars.

This type of e-commerce includes mobile media and content, retail services, travel purchases and various other services. Digital payments are also closely connected to e-commerce.

Alternative payment methods such as digital wallets or online payment providers have seen increased adoption rates and rapid growth in the past few years.

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Ebay-owned PayPal is one of the current market leaders with more than 14 billion US dollars in mobile payment volume alone. Digital payments are not only convenient for the mobile shopping experience but also for the increasingly available paid digital content like streaming music, online video subscriptions and apps.

For example, global mobile app revenues are projected to surpass 30 billion US dollars in the coming year.Find eCommerce leads at scale or know the intent of the select few that matter.

Find eCommerce merchants to acquire for your lending and insurance products. Insights on DTC brands and Digitally native vertical brands you should be aware of. Find early stage eCommerce startups. Benchmark their business metrics. Global eCommerce Market Size. Playbooks of D2C Brands. D2C Fashion Brands Report. Cannabis Market Size Report. Instagram eCommerce Report. Read More. By Platform. By Location. By Revenue. By Shipping Carriers. By Employees Size. Free Chrome Extension. Retailers - Master list.

Top D2C brands. Top Subscription box companies. Magento Stores. Shopify Stores. Retail Clothing Stores. The best source of insights about mid-market and enterprise eCommerce companies.

Predictions built on top of our insights help in making crucial decisions at world's leading multi-national corporations and fast growing companies. If you are a marketer or a sales operations executive, your primary use of PipeCandy is to generate eCommerce leads. If you are a CMO or a business head or an analyst, your primary use of PipeCandy is to generate insights about eCommerce companies.

Our data sets cater to both. Visualize the above two data sets in different ways using our slice and dice user interface or through our Insights API. PipeCandy does not sell piecemeal lead lists. The below lists are ideas of how you could filter or enrich companies' data within the package you subscribe to. World-class companies use PipeCandy to track global eCommerce landscape. Definition of eCommerce Electronic commerce or eCommerce refers to any form of business transaction facilitated by the internet.

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The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Since I don't have a preference for what app to build, perhaps it may be easier to decide based on freely available sample data for the store. So is there some freely available dataset available that represents a set of products, like groceries, movies, books, cars, apps, electronics, weapons, library, etc?

It doesn't have to be real but as long as it can save me a few hours of entering data, it will be worthwhile. An open data format for the dataset would be useful, a MySQL database would be great. I haven't looked at all the reference in this post but it looks promising: Where can I find sample databases with common formatted data that I can use in multiple database engines?

You can try the nopCommerce sample data. During installation, tick the "create sample data" box. You can then use the data within your own application. The schema is pretty easy to understand. Learn more.

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Asked 9 years, 7 months ago. Active 6 years, 3 months ago. Viewed 18k times. Any suggestions eCommerce sample datasets? Elvin Elvin 85 1 1 gold badge 3 3 silver badges 6 6 bronze badges. I was going to suggest NorthWind as an answer, but you already mention it This question would be on-topic at opendata. Active Oldest Votes. Ben Foster Ben Foster Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name.

Email Required, but never shown. The Overflow Blog. Podcast Programming tutorials can be a real drag. Featured on Meta.Machine learning represents a huge growth opportunity for online retailers.

With machine learning, ecommerce companies can potentially boost sales, reduce waste, and increase overall efficiency while actively engaging with consumers. Not only that, ecommerce companies have a lot of data at their fingertips.

The problem for machine learning developers lies in the availability of that data.

Data Sets – Global eCommerce Landscape

Retail datasets typically contain proprietary information and are consequently hard to find on publicly available databases. Luckily for you, we at Lionbridge AI have scoured the internet to gather a list of publicly available ecommerce and retail datasets for machine learning projects.

Fashion-MNIST : A retail dataset consisting of 60, training images and 10, test images of fashion products across 10 classes. It includes product description, price, category, rating and more. Electronic Products and Pricing Data : A list of over 7, electronic products with 10 fields of pricing information. Fashion Products on Amazon. E-commerce Tagging for Clothing : This retail dataset contains images from E-commerce sites with bounding boxes drawn around shirts, jackets, sunglasses etc.

It has items, of which items have been manually labeled. Brazilian E-Commerce Public Dataset : A Brazilian public retail dataset of anonymized orders made at Olist k orders from to made at multiple marketplaces. Retailrocket Recommender System Dataset : Collected from a real-world ecommerce website, this retail dataset contains information on visitor behavior including events like clicks, add to carts, and transactions.

ECommerce Search Relevance : This set contains image URLs, rank on page, a description for each product, the search query that led to each result, and more from five major English-language ecommerce sites.

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Amazon Commerce Reviews Set : This retail dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition. Multidomain Sentiment Analysis Dataset : A slightly older retail dataset that contains product reviews data by product type and rating. Amazon and Best Buy Electronics : A list of over 7, online reviews from 50 electronic products.

Grammar and Online Product Reviews : A list of 71, online reviews from 1, different products.

Data Sets – Global eCommerce Landscape

Annual Retail Trade Survey ARTS : National estimates of total annual sales, e-commerce sales, end-of-year inventories, inventory-to-sales ratios, purchases, total operating expenses, inventories held outside the United States.BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator.

BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights. We have a newly available ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store loaded into a table in BigQuery. In this lab, you use a copy of that dataset. Sample scenarios are provided, from which you look at the data and ways to remove duplicate information. The lab then steps you through further analysis the data.

In this lab, you learn to use BigQuery to find data, query the data-to-insights public dataset, and write and execute queries. If you complete this lab you'll receive credit for it when you enroll in one of these quests. Privacy Terms. What you'll do In this lab, you use BigQuery to: Access an ecommerce dataset Look at the dataset metadata Remove duplicate entries Write and execute queries Join Qwiklabs to read the rest of this lab Get temporary access to the Google Cloud Console.

Over labs from beginner to advanced levels. Bite-sized so you can learn at your own pace. Join to Start This Lab. Welcome to Your First Lab! Got It.Machine learning represents a huge growth opportunity for online retailers. With machine learning, ecommerce companies can potentially boost sales, reduce waste, and increase overall efficiency while actively engaging with consumers.

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Not only that, ecommerce companies have a lot of data at their fingertips. The problem for machine learning developers lies in the availability of that data. Retail datasets typically contain proprietary information and are consequently hard to find on publicly available databases. Luckily for you, we at Lionbridge AI have scoured the internet to gather a list of publicly available ecommerce and retail datasets for machine learning projects.

Fashion-MNIST : A retail dataset consisting of 60, training images and 10, test images of fashion products across 10 classes. It includes product description, price, category, rating and more. Electronic Products and Pricing Data : A list of over 7, electronic products with 10 fields of pricing information. Fashion Products on Amazon.

E-commerce Tagging for Clothing : This retail dataset contains images from E-commerce sites with bounding boxes drawn around shirts, jackets, sunglasses etc.

ecommerce data set

It has items, of which items have been manually labeled. Brazilian E-Commerce Public Dataset : A Brazilian public retail dataset of anonymized orders made at Olist k orders from to made at multiple marketplaces. Retailrocket Recommender System Dataset : Collected from a real-world ecommerce website, this retail dataset contains information on visitor behavior including events like clicks, add to carts, and transactions.

ECommerce Search Relevance : This set contains image URLs, rank on page, a description for each product, the search query that led to each result, and more from five major English-language ecommerce sites. Amazon Commerce Reviews Set : This retail dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition. Multidomain Sentiment Analysis Dataset : A slightly older retail dataset that contains product reviews data by product type and rating.

Amazon and Best Buy Electronics : A list of over 7, online reviews from 50 electronic products. Grammar and Online Product Reviews : A list of 71, online reviews from 1, different products.

Annual Retail Trade Survey ARTS : National estimates of total annual sales, e-commerce sales, end-of-year inventories, inventory-to-sales ratios, purchases, total operating expenses, inventories held outside the United States. Economic Census : Provides a detailed portrait of business activities in industries and communities once every five years, from the national to the local level.

E-Stats : Surveys used different measures of economic activity such as shipments for manufacturing, sales for wholesale and retail trade, and revenues for service industries. EU External Trade Datasets : The value of imports, exports and trade surplus, volume indices, unadjusted and seasonally adjusted; price and terms of trade indices; imports and exports classified by commodity, and by country of origin or destination.

Exploring Your Ecommerce Dataset with SQL in Google BigQuery

ECommerce Sales by Merchandise Category : Census data showing total ecommerce sales by merchandise line and compound annual growth rate from Liked this article? Lionbridge AI provides custom AI training data in languages for your specific machine learning project needs. Originally from San Francisco but based in Tokyo, she loves all things culture and design.

Sign up to our newsletter for fresh developments from the world of training data. Lionbridge brings you interviews with industry experts, dataset collections and more. Article by Alex Nguyen February 01, Related resources. The article introduces 10 open datasets for linear regression tasks and includes medical data, real estate data, and stock exchange data.

Because finding enough relevant datas in Korean is difficult, we at Lionbridge have put together a comprehensive list of public Korean datasets for machine learning. The lack of public sports data sources has been a major obstacle in the creation of modern, reproducible research and sports analytics. We at Lionbridge AI have created a cheat sheet of publicly available sports machine learning datasets. From sentiment analysis models to content moderation models and other NLP use cases, Twitter data can be used to train various machine learning algorithms.You test them to confirm their truth.

The question…will they increase your conversion rates and profits? We learned visitors do not scroll to the section about Charlotte on the Story page. This section is important as it explains why Charlotte is an authority in the fashion industry and creates credibility around her brand.

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Here is an example video recording. As you can see you are able learn how the visitor interacts with the shop and learn their behaviors.

ecommerce data set

Our next source of data is qualitative and aims to understand reservations visitors have around their purchase. We used Olark live chat to help customers at the point of purchase and then analysed the live chat transcripts for insights.

Customer: Hey guys, Could you please tell me if your sterling silver jewellery will tarnish if worn in salt water? Kind regards, Susanna Scholz. An additional hypothesis here could also revolve around showing store availability and stock levels on the website. Customer: HI! I was wondering how long it would take to ship here?

ecommerce data set

Thanks btw love the website!! Unfortunately the chain where the pearl connects to the necklace has broken. I had only worn the necklace for a day. Can you please let me know about your faulty items policy? Thank you for your help! Customer: Hi there, i have recently purchased a star necklace and it is been sent to the wrong address how do i change it?

This is important as long time customers can skew the data and be overly loyal or pissed off about something. Where as these insights do not always create hypothesis, they can help with suggesting ideas and language for input to your copywriter.

Answer: I appreciate fine jewellery, its obvious that Charlotte does as well. Her pieces are simply beautiful. Answer: I love the lotus collection and was looking for rose gold jewellery. We can also learn that rose gold jewellery could be an SEO term worth ranking for, further research needed, there are always cross over in data sources in CRO.

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