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eCommerce Data

What is eCommerce data?

eCommerce data provides information collected from the eCommerce industry - online retail  sites - about products, pricing, sales performance, and customers. Companies with an online retail presence use the data to gain insights into customer behavior and improve their experience. Online businesses and eCommerce retailers leverage eCommerce data to understand how their business performs, carry out competitor analysis, and drive higher online sales.

What are the different types of eCommerce data?

Typically, eCommerce data analytics leverage the following types of data:

  • eCommerce product data: This dataset contains all the information about different products a company offers for online purchase. It can include the product category, brand information, manufacturing and supplier details, pricing, product reviews, and other relevant information. Companies use these data points to gain insights on which products customers frequently search, view, purchase, and combine with other products. They leverage these insights for supply chain, inventories, pricing, customer retention, and marketing strategy.
  • eCommerce store data: This dataset provides details about eCommerce vendors, sellers, and platforms, creating a comprehensive database of competitors. A high-quality eCommerce store dataset delivers extensive firmographic insights for major online retailers as well as niche players. Companies use this data to power competitor analysis and monitor competitor activities.
  • eCommerce sales data: Companies use their own sales data as well as third-party datasets to monitor sales and forecast trends. Historical eCommerce sales data powers analysis to optimize prices, predict demand, manage stocks, and plan a marketing strategy.
  • eCommerce customer data: This type of eCommerce data offers information about online shoppers and their online purchases. With annual online shopping in the range of $4 trillion and increasing rapidly, eCommerce customer data is critical for understanding customer behavior.  ECommerce companies use this data for analyzing customer interest, preferences, shopping habits, buying patterns, and brand loyalty. They use the insights to improve customer experience, drive conversion rates, and design advertising campaigns.

Where does eCommerce data come from?

ECommerce data for a specific company usually comes from a combination of direct and indirect methods. The direct method includes customer sign-up forms,  email notification or newsletter enrollment, online surveys, and online reviews. The indirect methods of data collection consist of web scraping, cookie tracking, and social media analytics. Sources that use Natural Language Processing (NLP) to track customer sentiments also contribute to eCommerce data.

Several third-party sources provide detailed eCommerce data by categories, industries, and regions.  Governmental, semi-governmental, intergovernmental, and private agencies can also track online purchase activities and use them for analysis. 

A large variety of data categories augment eCommerce data for analytics, such as customer sentiment, keyword search trends, social media presence, and brand sentiment data.  

What types of attributes should I expect when working with eCommerce data?

eCommerce datasets contain information about products, stores or marketplaces, sales, and customers. The data can be further segmented by categories, industries, or regions.

  • Product discovery KPIs: These indicators help eCommerce businesses to understand how customers find their products and online stores. Companies leverage these insights to boost online customer traffic and improve sales. 
    • Online visibility of eCommerce stores
    • Online and offline impressions, indicating the frequency of serving advertisements to the target market.
    • Social media reach metrics: impressions, cost per thousand impressions (CPM), frequency
    • Video hosting platform impressions
    • Influencers and partners reach
    • TV, media advertising, and podcast reach
  • Customer Demographics: 
    • Age
    • Location
    • Gender
    • Product favorites
    • Brand affinity
    • Most recent purchases
  • Onsite traffic metrics include the volume and frequency of online visits to an eCommerce website. This information can from analytics tools like Google Analytics and includes: 
    • Website sessions
    • Number of users visiting the store
    • Pages/Session, or the average number of pages viewed per session
    • Bounce rate: percentage of single page visits
    • Average session duration
    • First-time visitors
  • Organic traffic metrics: 
    • Total clicks from SERPs or Google search results pages
    • Average click-through rate (CTR)
    • Average ranking position of the eCommerce store
  • Email engagement metrics indicate customer behavior and intent, based on how they respond to emails from online shopping platforms. Customers receive several daily marketing emails from various eCommerce platforms, and email engagement data helps determine the success of marketing campaigns in driving retail eCommerce sales. 
    • Email list growth rate
    • Email bounce rate
    • Open rate
    • Email conversion rate
    • Email click-through rate
    • Unsubscribes
  • Social media engagement attributes provide real-time data about social media sentiment. 
    • Likes per post
    • Shares per post
    • Comments per post
    • Clicks per post
  • Conversion attributes: Metrics about conversation rates of online customers collected through 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)

Custom datasets can also include attributes that match your specific requirements.

How should I test the quality of eCommerce data?

While buying data from vendors, it needs to be exhaustively tested for accuracy. When vendors use web scraping tools, the tools may overwhelm eCommerce sites and get blocked or report incorrect data. It is also possible that the NLP tools used by vendors for understanding customer and brand sentiment may not be sophisticated enough to deliver precise results.  Testing data for accuracy is critical to ensure trusted analytical results. Ask for certifications from vendors for data quality and compliance with data privacy regulations.

The staggering volume and variety of the data also necessitate testing it for relevance to your use cases.

To test the quality of the data:

  • Ensure that it is accurate, and the vendor can provide quality certifications.
  • Assess the data for completeness and consistency.
  • Validate that the data is updated frequently or available in real time.
  • Ascertain that the data is privacy compliant, as it contains personal or personally identifiable information (PII).

Who uses eCommerce data?

Business of all types, from large global eCommerce companies to small startup companies use this data regularly. While smaller companies use their own sales and customer demographic data, large companies tend to buy commercial datasets for various objectives such as:

  • Enhance customer experience
  • Perform competitor analysis
  • Optimize pricing
  • Improve products and services
  • Introduce new products and services
  • Discover potential new markets
  • Design marketing and advertising strategies
  • Achieve better ROI on marketing and advertising spend
  • Optimize supply chain, inventories, and distribution

Governmental, semi-governmental, intergovernmental agencies may use the data to draft or improve their trade and eCommerce policies.

What are the common challenges when buying this type of data?

Vendors usually provide real-time data or regularly updated datasets. But due to the volume and complexity of data, accuracy is a prime concern.  Another common challenge is security and privacy compliance, as the data often contains PII. Data completeness and consistency can also be an issue, as vendors collect data from multiple sources, and there could be overlaps or mismatches.

  • Data accuracy: Large volumes of data collected from diverse sources make data accuracy a critical issue. NLP algorithms derive insights from customer reviews or discussions relevant to eCommerce, and the quality of these insights is also a challenge. 
  • Privacy compliance: The data often includes personally identifiable information (PII), and it must comply with the privacy regulations. The regulations are region-specific and also vary by industry. The vendors must provide compliance certifications for all applicable regions and industries.
  • Data completeness and consistency: Vendors usually provide updated or real-time data. Due to the volume and complexity of data, accuracy is a prime concern.  Another common challenge is security and privacy compliance, as the data often contains PII. Data completeness and consistency can also be an issue, for data from multiple sources can have overlaps or mismatches.

What are similar data types?

eCommerce data is similar to retail data, in-store data, shopper data, brand data, consumer review data, product data, and other related data categories used in eCommerce analytics and marketing.

You can find a variety of examples of B2B and company data in the Explorium Data Gallery.

Sign up for Explorium’s 14-day free trial to access the data available on the platform.               

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What are the most common use cases?

The typical use cases for eCommerce data include competitor analysis, trend analysis, promotion planning, and pricing optimization. This data is also used for eCommerce market intelligence, purchase intelligence, online marketing, demand prediction, retail forecasting, and market share analysis.

  • Competitor Analysis: Retailers constantly monitor competitor sales and market share. This competitor analysis helps them decide on pricing and promotional strategy. It also helps focus on specific customer segments and improve customer experience. eCommerce store data provides detailed information on all types of online retailers, powering competitor analysis.
  • Trend Forecasting: Identifying market trends before they occur provides eCommerce retailers an edge to leverage opportunities. Trend forecasting needs accurate eCommerce sales data, customer sentiment data, social media data, as well as data from other relevant categories. A good trend forecasting model also takes into account weather and events data to predict demand for specific products and services.
  • Promotion planning: eCommerce retailers are always looking for optimizing promotional tools and resources to achieve a better ROI.  They leverage different types of data and augment with other relevant data categories to monitor the success of their promotions.    Retailers also use ML-driven analytical tools to design their promotional strategy and monitor its progress. 
  • Pricing optimization: Retailers perform the analysis to determine the response of potential buyers to different prices for their products and services. Higher prices drive customers away, and lower prices erode the profits. Pricing optimization is the key to maximize profit while growing the customer base. Retailers leverage sales data, industry outlook, and competitor data to arrive at the optimum prices in the best interest of customers and retailers.

Which industries commonly use this type of data?

Industries with an online presence commonly use this data for powering sales and marketing strategies. They include tourism, sports, entertainment, travel, hospitality, leisure, eCommerce, CPG, healthcare, financial service providers, insurance providers, and banking.    

How can you judge the quality of your vendors?

Vendors use diverse sources to deliver eCommerce data that can vary widely from vendor to vendor. You can judge the quality of vendors by leveraging the information available on their websites, using demos, and interacting directly with their reps.

  • Customer reviews and case studies: Most vendor websites showcase customer reviews, ratings, and testimonials. As these come directly from customers, they demonstrate confidence in the vendor. You can also use the detailed reviews and case studies to understand datasets used, project objectives, and industry verticals. This information helps you compare with your own requirements and shortlist suitable vendors.  Case studies also help in assessing vendor commitment and ability to deliver high-quality datasets on time.
  • Demo: Using recorded or live demos is a reliable way of evaluating vendors based on the types of datasets, range of attributes, and ease of integration. In some cases, vendors are happy to arrange a live demo similar to your project, which can quickly help you make a decision.
  • Interacting with vendor reps: The easiest way to shortlist or reject a vendor is by interacting with the vendor reps. You can use your judgment to assess the knowledgeability of the vendors and establish trust in their capabilities. It is an excellent opportunity to discuss your requirements, resolve your queries, and gauge their interest in your project.

 

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