What is Ecommerce Data? Examples, Datasets and Providers in 2024?

Ecommerce Data Explained

Types of ecommerce datasets & databases

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.

Use Cases

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.

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)

How to get Ecommerce Data

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 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.