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Ecommerce Data: Best Ecommerce Datasets & Databases

What is Ecommerce Data?

Ecommerce data is used by retail sites for product analytics and online shopping insights. Ecommerce datasets and APIs are used for ecommerce product and customer behavior analysis. Use Datarade's ecommerce data providers to buy and sample online market pricing intelligence. Learn more

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Bright Data | E-commerce Data - Global Coverage - Trending Products, Consumer Sentiment, Inventory-level Product Data

Bright Data has developed a unique eCommerce data collector that allows you to maximize results through ... Bright Data’s eCommerce data collector is designed to help your business drive sales and gain advantages
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Buy eCommerce Leads | eCommerce Leads Database | eCommerce Lead List with Store Owner Contact Details | 100% Real-Time Verified Data

We have millions of eCommerce data ready to go no matter where you are. ... the Shop Owners • 15+ eCommerce Platforms • 20+ Data Points • Lifetime Support Until You 100% Satisfied
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eCommerce Data Scraping & Nutritional Value Data Scraping | Scrape eCommerce Websites

Data AI Solutions offers Pricing & Competitive Intelligence as a Service for retailers globally , allowing ... Collect prices across multiple marketplaces, platforms, and stores • Real Time Brand Positioning • Data
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WooCommerce stores list - 1.6M+ stores worldwide

Get instant access to WooCommerce store data. ... stores) Europe (818k stores) Asia (279k stores) Oceania (94k stores) South America (100k stores) Sample data
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1.6M stores
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E- commerce website listings worldwide.

Most of the work is mined manually to ensure customers receive clean data.
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Bright Data | eCommerce Data - Global Coverage - Pricing Data, Seller Ratings Data, Customer Reviews Data

Bright Data’s retail data collector is enabling you to collect crucial data points for your business ... Bright Data’s retail data collector is uniquely crafted to enable your digital commerce business gain
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Product and Review Datasets from Ecommerce websites | Grepsr

by Grepsr
benefits of e-commerce product and review data is that it supports market analysis. ... Product and review data helps businesses add more agility and scalability to their operations while assisting
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E-commerce data sources & analytics

Maximize your online sales potential with our e-commerce data and analytics solutions. ... Maximize your online sales potential with our e-commerce data and analytics solutions.
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Granular Transactional E-commerce Data for Emerging Markets

Our data is attained with consumer consent from our two consumer apps. ... High-definition SKU level data on a per transactional level for the e-commerce industry across the emerging
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DATAANT | Amazon Data | E-commerce Product Review | Dataset, API | Reviews by keyword, by category, by seller, by product ASIN | 19 countries

by Dataant Request Ecommerce ... Get Amazon Product reviews right from the data extractor without mediators. 19 Amazon Data countries:
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The Ultimate Guide to Ecommerce Data 2023

Learn about ecommerce data analytics, sources, and collection.

Your Map to Ecommerce Data Success: The Ultimate Guide to Location Data in 2021

COVID-19 has been the downfall of more institutions than we can count, but for ecommerce, it’s been a blessing - a blessing that’s raised its share of global retail trade from 14% in 2019 to about 19% in 2021. As physical shops closed, online shops opened, and this boom has been hugely profitable for many ecommerce companies. And this is where ecommerce data subscriptions and datasets come into play. By purchasing ecommerce data, online retailers can keep track of different aspects of their ecommerce, for instance analytics or consumer details. Put simply, an ecommerce dataset will give businesses insights into their online store quickly and clearly so they can improve their profitability.

What is Ecommerce Data?

Ecommerce data is basically data gathered from online retail websites whenever a consumer makes a purchase that tells businesses about their products, customers, stores, sales, purchases, and pricing. Why is this important? So that they can instantly see what their business is getting right, and where they can improve.

There are different kinds of ecommerce datasets

The following datasets can be used for ecommerce data analytics:

  • Ecommerce product data - Information about all the products a business has available to buy online, e.g. details about products, their manufacture and supply, pricing, brand and what category they fit into. This data can also be organised into models which display how frequently a product is visited, and by whom it has been purchased, all information from which businesses can draw conclusions about how to manage a successful ecommerce business.

  • Ecommerce sales data - Businesses can either access sales data, which gives them an overview of their products’ sales performance, or they can buy ecommerce datasets which are broken down by product category. Retailers like Amazon, who have a huge variety of options on their site, may choose the latter, whereas smaller online retailers specializing in specific retail areas may prefer a more general overview of sales. Historical ecommerce data allows online retail platforms to monitor sales trends. With an ecommerce sales dataset, they can develop optimum pricing strategies and make data-driven decisions about stock orders.

  • Ecommerce customer data - Ecommerce customer data provides information about online shoppers and their online purchase activity. The total number of online sales more than tripled in 2020 - that’s $4.2 trillion (!) spent online over the year. With this demand, it’s easy to understand how important quality ecommerce customer data is to online retailers. Ecommerce retailers use these analytics to understand consumer behavior and interests, such as brand loyalty. Using this, they can improve their market intelligence and advertising campaigns to drive up conversion rates.

  • Ecommerce store data - This data tells the user about ecommerce vendors, merchants and platforms - very useful for an ecommerce store to sus out its competiton. The scope for the amount of ecommerce store data you can buy is huge - just think about all the stores that sell products online! A high-quality ecommerce store database will provide firmographic insights from the best online retailers, such as Shopify, Amazon, Bigcommerce, Etsy, eBay, and Walmart.

What are the use cases of Ecommerce Data?

Ecommerce data allows online retailers and marketers to carry out in-depth analysis of the online market, as well as their own platform and customer database. From this, they can accurately predict market trends as well as consumer habits in order to drive up the profitability of their ecommerce store. Ecommerce analytics use cases include market monitoring, demand prediction and retail forecasting:

  • Ecommerce market intelligence - Ecommerce data providers can enable online sellers to understand the ecommerce market in as much depth as possible through a mix of both real-time and historical ecommerce data. More and more online merchants are using third-party ecommerce platforms like Amazon to sell their products. With an ecommerce market dataset, manufacturers and sellers can ensure that their products are listed on websites where online shoppers will find them. This helps merchants to spot new opportunities for growth, and marketers to make in-flight adjustments to campaigns.

  • Ecommerce pricing intelligence - Ecommerce product datasets include information about the price of a certain product. Historical ecommerce data will also show how pricing has changed over time, and what the most effective price point for a product or brand is. For example, through ecommerce data analysis, an Amazon sales dataset would give ecommerce vendors a good indication of how to price their products competitively based on consumer purchase intent.

  • Ecommerce demand prediction - Historical ecommerce databases can be used by online stores to plan for future sales and strategies. Demand prediction uses online shopping data alongside retailer demand data sets to identify trends in supply and demand. Ecommerce demand analytics allow ecommerce sellers to make accurate predictions about which products and brands will be popular in future and base their ecommerce marketing around this. In 2020, there were over 2.05 billion online shoppers worldwide – that’s one quarter of the world’s entire population. Being able to accurately predict what these billions of shoppers are going to be looking at buying can therefore be hugely profitable for online ecommerce retailers.

  • Ecommerce retail forecasting - Online stores can make more accurate sales and supply chain forecasts by using an ecommerce database to monitor consumer behaviour across their ecommerce ecosystem and therefore enrich their internal performance data. These datasets allow management teams at ecommerce retail stores to identify consumer trends and predict how these patterns will affect future sales rates and supply chain logistics. They can use both historical and real-time ecommerce data to then adapt their marketing and supply strategies to suit current and future customer demands.

  • Ecommerce customer service - Information about product delivery speeds and quality of service provided by online companies. Ecommerce data providers also gather reviews left by customers from thousands of online platforms. This allows ecommerce businesses to benchmark their customer satisfaction performance against competitors and optimize CRM.

How is Big Ecommerce Data impacting the Ecommerce Industry?

Big data is classed as data which is used for predictive analytics and is too complex to be processed by traditional data handling tools. It can enhance a retailer’s manufacturing, marketing and targeting processes, understanding of the consumer purchase journey and, perhaps most importantly, edge over competitors better than any individual or traditional tool could, and for that reason is becoming increasingly popular.

What type of data is used for Ecommerce growth?

The key uses of big data analytics for ecommerce growth are optimized pricing, customer service, and competitor benchmarking. For these use cases, ecommerce companies buy ecommerce consumer behavior data and purchase history data. ‘Consumer behavior data’ tells users about which items consumers have shown interest in, indicated when they add products to their basket or frequently buy from a specific merchant or brand. ‘Purchase history data’ refers to a consumers’ previous ecommerce purchases. This data indicates that consumers will purchase similar items, allowing ecommerce retailers to create the most effective ads and pricing strategies to attract these prospective customers and beat competition.

Which companies buy Ecommerce Data?

Some of the biggest global ecommerce brands rely on commercial ecommerce datasets and purchase them regularly. To name a few…

  • uses consumer trend data to create the most popular lines of furniture and homeware products based on ecommerce sales season upon season.
  • Amazon and Walmart buy ecommerce data for better retail and marketing strategization and for more accurate performance measurement.
  • Digital product services also rely on ecommerce data to improve their services, from music streaming sites like Spotify, to video and movie platforms like YouTube and Netflix.

But it’s not just for the giants - small business and independent merchants using listing sites like Shopify can invest in an ecommerce data subscription to give them the strongest start as they enter competitive ecommerce markets.

So as you can see, ecommerce data is popular, for good reason, and it’s only becoming more so - according to Statista, ecommerce sales will hit $6.5 trillion (!) by 2023.

What are typical Ecommerce Data attributes?

Ecommerce datasets contain vast amounts of information about products, customers, marketplaces and sales.

Customer Demographics

  • Age
  • Location
  • Gender
  • Product favorites
  • Brand affinity
  • Most recent purchase

Product Discovery KPI
Product discovery KPIs help ecommerce retailers understand how customers find their products and online marketplace, which then gives them the means to boost campaigns and increase online consumer traffic. Here are a few ways a product can be discovered:

  • Online visibility of ecommerce stores
  • Online and offline impressions - how frequently ads are served to the target market.
  • Social media reach metrics - impressions, cost per 1000 impressions (CPM), frequency
  • Video hosting platform impressions
  • Influencers and partner reach
  • TV, media advertising, and podcast reach

Onsite traffic metrics
These different insights give you an overall understanding of the volume and frequency of online visits:

  • Website sessions
  • Number of users visiting the store
  • The average number of pages viewed per session
  • Bounce rate - percentage of single page visits
  • Average session duration
  • First-time visitors

Organic traffic metrics
Ecommerce datasets show how online stores generate organic traffic, and include:

  • Total clicks from Google search results pages (SERPs)
  • Average click-through rate (CTR)
  • Average ranking position of the e-Commerce store

Email engagement
Email engagement metrics indicate customer behavior and intent, based on how they repond to emails from online shopping platforms. People can receive multiple marketing emails daily from different ecommerce platforms and so retailers like this data to determine whether their marketing campaigns are seeing any success. These metrics include:

  • Email list growth rate
  • Email bounce rate
  • Open rate
  • Email conversion rate
  • Email click-through rate
  • Unsubscribes

Social media engagement attributes
Real-time ecommerce analytics will also take into account social media sentiment in order to gauge online shopper intent. Consumers are increasingly preferring online shopping on their mobile devices. For this reason, social media engagement is important as retailer accounts normally include links to their online stores which consumers can click through to make purchases. These attributes include:

  • Likes per post
  • Shares per post
  • Comments per post
  • Clicks per post

Conversion attributes
Metrics about rates of online customer conversion are gathered via ecommerce data scraping, including:

  • Number of online transactions
  • Average order value (AOV) of customers
  • Specific sales data
  • Number of visits to sale
  • Sales conversion rates
  • Shopping cart abandonment rate
  • Cost Per Acquisition (CPA)

Which methods and sources are used for Ecommerce Data collection?

Ecommerce data providers build their ecommerce data models using a range of methods and sources, from startup retailers to Amazon ecommerce datasets. Automated and AI methods include web scraping, cookie tracking, social media analytics, and natural language processing. Alternative methods of ecommerce data collection include information gathered from customer reviews, surveys, sign-up and email preference forms. A combination of the two methods of data collection can be used to complete historical ecommerce datasets. These ecommerce big data points enable ecommerce analytics, like customers’ online shopping habits and ecommerce marketplace developments.

How to extract or scrape Ecommerce Data from an Ecommerce website?

To scrape data from any website, you need a web scraping tool which harvests information about users’ online behaviour. Ecommerce data vendors use web scraping technology to extract information about products, customer reviews, and pricing from thousands of online shops - on-demand or at regular intervals.

This information is then usually displayed in structured spreadsheet format. Web scraping tools inspect a given domain or URL for the ecommerce platform in question and differentiate between the different data attributes, such as ‘merchant’, ‘shopper’, ‘product’, and ‘price’.

Before you purchase ecommerce data, it’s worth asking the data provider about the coverage of their web scraping tool, and how they verify the quality of the information extracted using web scraping tools. From this, you can verify you are getting the best quality ecommerce data to match your business’s needs.

How to analyze Ecommerce Data?

The first part of any ecommerce data analytics is to define sensible metrics. In other words, what insights do you want to extract from your commercial ecommerce dataset? It could be that you want to benchmark your ecommerce platform’s performance against competitors, or that you want to identify weak points in your marketing and sales strategies, or that you simply want to know how much consumers spend on products from online shops.

There are different types of ecommerce data analysis: descriptive, diagnostic, and predictive. Descriptive analysis will give you basic details about ecommerce metrics, such as revenue reports, KPIs, and sales and leads statistics. Diagnostic analytics allows a user to unlock insights which aren’t immediately obvious, such as consumer purchase trends and basket compositions (ie the products that consumers put in their baskets and whether they ultimately follow through with these purchases). Predictive analytics uses the data to make forecasts about demand, supply chains, and returns.

When shopping for ecommerce data, make sure to choose the type of information that suits your business’s needs. The variety of information provided by an ecommerce dataset means you should ensure that the data is catering to your needs.

How to assess the quality of Ecommerce Data?

A useful ecommerce dataset must be reliable, relevant, and consistent. An ecommerce data platform will usually offer sample ecommerce datasets which are available to download in CSV format or via an API. This means users can be sure that they’re getting the most useful and powerful data models before they agree to buy ecommerce big data and ecommerce datasets. Depending on your ecommerce data needs, it is worth researching whether a specific dataset provides historical or real-time ecommerce data and taking this into account when making purchase decisions.

Additionally, we always suggest you ask the data provider for a sample before making any purchases to ensure their data matches your business’s needs.

1: Understand data sources

Ecommerce data should come from reliable sources only. Ecommerce data providers should use a range of high-quality sources and carry out verification procedures for each source they use. Look for certificates and audits which prove that an ecommerce data source is reliable, and that it complies with legal and ethical guidelines.

2: Ask for a sample set

Request a sample or dummy ecommerce dataset from a data provider before you buy an ecommerce data product. This way, you can test whether the ecommerce data has the correct coverage, attributes and integrations for your requirements more thoroughly than by simply reading the dataset description.

How to minimze risk when buying Ecommerce Data?

To buy ecommerce data online, the first step to make sure you get the data you need is to ask for an ecommerce data sample. This way, you can check whether the data provides the intelligence you need and that it suits your business and use case before committing to any ecommerce data subscription. Other important steps which any savvy data buyer should keep in mind are looking out for customer reviews and success stories left for data providers - you can find these on Datarade’s ecommerce data marketplace. Lastly, clients who shop for ecommerce data reduce risk by discussing their data requirements at length and in detail with data providers. This way, buyers can be sure they’re getting the exact data they need and can be sure in their ecommerce data subscription decisions.

How is Ecommerce Data typically priced?

Due to the wide variety of ecommerce data available, the price can vary depending on your use case, desired geographical coverage and historical requirements. There is also a price variation between ecommerce data APIs and ecommerce databases. Similarly, the size of the ecommerce dataset desired will affect the price. Usually, ecommerce data providers offer the following pricing models:

Subscription-based Pricing
You can subscribe to your ecommerce data provider on a monthly, quarterly or yearly basis to access data streams and feeds at regular intervals.

Pay Per Use
The “Pay Per Use” pricing model offers increased flexibility. You can pay for ecommerce data on a cost per click (CPC) and cost per mile (CPM) basis.

Custom Quotes
Many data providers offer custom price quotes for special cases. In this case, pricing for ecommerce datasets is calculated based on your unique data needs.

How much does Ecommerce Data cost?

The cost of ecommerce data varies between data providers and depends on your unique data requirements. For example, if you require data with global coverage, this is likely to cost more than ecommerce data just for one country. Likewise, a real-time ecommerce data API will tend to cost more than historical ecommerce data.

Generally speaking, aggregated and analytics-primed data is more expensive than raw ecommerce data as it has already been analyzed to highlight trends and key points of interest. Prices for ecommerce datasets are typically upwards of $1000 per database, but buyers can also pay for custom data models tailored to their budget.

Datarade’s data marketplace has ecommerce data products for a range of prices with a variety of different ecommerce data vendors.

What are the common challenges when buying Ecommerce Data?

  • The complexity of managing data quality - Provider reviews and client testimonials can help ensure that you get accurate data from a trusted supplier. Use Datarade’s marketplace to compare data quality and find the right ecommerce data provider for you.
  • Security holes - Check that your ecommerce data provider has the relevant security and privacy certifications, for exmaple that they aggregate PII and follow GDPR and CCPA. Take precautions at every step to tackle data security challenges. This will ensure that your data is safe and secure.
  • Fetching valuable insights from data - Create a proper system of data sources and relevant factors that provide you with the required insights.

    What to ask Ecommerce Data providers?

    If you’re looking to buy ecommerce data, ask the following questions from your data provider:

  • Do they source data from online or offline channels?
  • Do they deliver ecommerce data via an API, CSV, or bulk databases?
  • Do you model your data?
  • How do you verify your data quality?
  • Is your data GDPR compliant?
  • How often do you update your data?

By asking these questions, you can ensure you will get the ecommerce data best suited to your business’s needs.

Where can I buy Ecommerce Data?

Data providers and vendors listed on Datarade sell Ecommerce Data products and samples. Popular Ecommerce Data products and datasets available on our platform are Bright Data | E-commerce Data - Global Coverage - Trending Products, Consumer Sentiment, Inventory-level Product Data by Bright Data, Buy eCommerce Leads | eCommerce Leads Database | eCommerce Lead List with Store Owner Contact Details | 100% Real-Time Verified Data by Lead for Business, and eCommerce Data Scraping & Nutritional Value Data Scraping | Scrape eCommerce Websites by Data AI Solutions.

How can I get Ecommerce Data?

You can get Ecommerce Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Ecommerce Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Ecommerce Data APIs, feeds and streams to download the most up-to-date intelligence.

What are similar data types to Ecommerce Data?

Ecommerce Data is similar to Consumer Review Data, Product Data, Shopper Data, Brand Data, and In-store Data. These data categories are commonly used for Online Marketing and Purchase Intelligence.

What are the most common use cases for Ecommerce Data?

The top use cases for Ecommerce Data are Online Marketing, Purchase Intelligence, and Consumer Trend Analysis.

Translations for this page

Datos comercio electrónico (ES)