Ecommerce Data

What is Ecommerce Data?

Ecommerce data is data gathered from online retail websites whenever a consumer makes a purchase. Ecommerce datasets include information about products, customers, stores, sales, purchases, and pricing. Ecommerce data collection involves gathering these data points from global ecommerce platforms like Amazon. Data providers offer ecommerce datasets so online retailers and marketers can carry out ecommerce analysis in order to better understand customer behavior and improve their online shopping experience.

There are different kinds of ecommerce datasets

The following datasets can be used for ecommerce data analytics:

• Ecommerce product data - An ecommerce product database includes information about products available to buy online. Ecommerce product information includes manufacture and supply details, pricing, brand and category. Ecommerce product data models display which products are frequently viewed and purchased.

• Ecommerce sales data - Ecommerce data providers usually sell ecommerce sales data by category. 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. Ecommerce retailers use ecommerce customer analytics to understand consumer behavior and interests, such as brand loyalty. Using this, they can improve their market intelligence and targeting campaigns to drive up conversion.

• Ecommerce store data - This data tells the user about ecommerce vendors, merchants and platforms. Ecommerce store data provides information about an ecommerce seller’s competitors. 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 big 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. 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. 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 spot new opportunities for growth, and marketers make in-flight adjustments to campaigns.

• Ecommerce pricing intelligence - Ecommerce product datasets include information about how much a certain product costs. 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, 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.

• Ecommerce retail forecasting - Online stores can make more accurate sales and supply chain forecasts by using an ecommerce database to enrich their internal performance data. Ecommerce data providers carry out retail and customer behavior monitoring across the online shopping ecosystem. 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.

• Ecommerce customer service - Ecommerce data providers gather reviews left by customers from thousands of online platforms. Ecommerce datasets also include information about product delivery speeds and quality of service provided by online companies. 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.

• 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
Traffic metrics include the volume and frequency of online visits to an ecommerce website.

• 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.

• 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.

• 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.

• 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

• 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. Alterantive methods of ecommerce data collection include information gathered from customer reviews, surveys, sign-up and email preference forms. These ecommerce big data points enable ecommerce analytics, like customers’ online shopping habits and ecommerce marketplace developments.

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.

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 the ecommerce data has the correct coverage, attributes and integrations for your requirements.

How is Ecommerce Data typically priced?

Pricing models for ecommerce data varies depending on your use case, desried geographical coverage and historical requirements. There is also a price variation between ecommerce data APIs and ecommerce databases. 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.

What are the common challenges when buying Ecommerce Data?

• The complexity of managing data quality - Use Datarade’s marketplace to find the right ecommerce data provider for you.
• Security holes - 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?