What is Ecommerce Data & How to Use It

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What is E-Commerce Data?

Content personalization is a major key to success in the e-Commerce industry. It increases conversion rates on product pages and helps companies develop brand loyalty. Personalization depends on effectively collected e-Commerce data.

E-Commerce data refers to data sets that are analyzed to reveal trends and patterns in customer behavior. This data is related to customers’ needs, preferences, interests, shopping patterns, which is important for delivering the intuitive user experience. E-Commerce data also includes insights related to brand on the store, competitors’ data, and technical data as well.

Marketers use this data to improve their campaigns and increase their business. They use various data collection methodologies to gather pertinent e-Commerce data.

Who uses E-Commerce Data, and for what use cases?

When you have the right and complete knowledge of the market, product, and your customers, you can reap significant rewards for your efforts. Combining your understanding of how the industry works within your competence circle, you can better utilize big data to your benefit.

Marketers use e-Commerce data to develop buyer personas that help determine customer preferences. This data may also reveal some surprising shopping behaviors and enable marketers to tweak their strategies accordingly.

With more and more people choosing to sell on e-Commerce stores as third-party sellers, it is important for them to collecting e-Commerce data in an overall context. This gives sellers a good idea of their products’ performance in a particular store. They get insights about the demand for their products. Using competitors’ data, they get to know what they should do to improve their products’ demand for the store.

Online retailers use e-Commerce data to track customer service experiences. This data can also be used to track customer satisfaction level, delivery times, and identify potential problems as well as ways to resolve them.

Predictive analysis through the use of e-Commerce data can help with supply chain issues in terms of trend forecasting and using this data to expedite the shipping process. This is very helpful for the supply management and logistics department.

What are typical E-Commerce Data attributes?

The list of e-Commerce data attributes is very lengthy. Here are the most common and useful e-Commerce datasets:

Customer Demographics
This section includes basic information about the customer, like:

  • Age
  • Location
  • Gender
  • Favorites
  • Last purchase item and date

Product Discovery KPI
Product discovery KPIs are the factors that help you understand how and when your customers find your products. These are:

  • Online visibility of your e-Commerce store
  • Online and offline impressions: It refers to the number of times your ads are served to your target audience.
  • Social media reach metrics: Impressions, cost per 1,000 impressions (CPM), frequency
  • Video hosting platform impressions
  • Influencers and partner reach
  • TV, media advertising, and Podcast reach

Onsite traffic metrics
This includes the factors that reveal the amount and time of traffic to a web store.

  • 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
This category of e-Commerce data attributes refers to the factors revealing organic traffic to the store.

  • 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 reveal the behavior or response of potential recipients towards an email sent to them.

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

Social media engagement attributes
These attributes provide insights into the reaction and action of the target audience to a social media post.

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

Conversion attributes
These attributes provide information related to the conversion of visitors into customers on a particular e-Commerce store.

  • 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 E-Commerce Data is typically collected?

There are many ways to collect e-Commerce data. You can collect data from viewed and purchased products. Tracking products that users viewed help recognize their interest.

The easiest and most effective way of collecting e-Commerce data is to contact reliable external- or third-party data providers. These data providers collect information in large scales through various means like web scraping, social media, cookie tracking, etc.

Though shoppers are strictly against smartphone tracking, with being responsible, taking only what you need, you can collect some useful data.

Some other ways to collect e-Commerce customer data are using sign up forms, surveys, email preference forms, web analytics, and using competitors’ data.

How to assess the quality of E-Commerce Data?

The quality of data equally matters. So make sure the data you have gathered is reliable, effective, and consistent. It should serve the intended purpose. Poor and irrelevant data may not be helpful at all.

In order to ensure that e-Commerce data you have gathered is of high-quality, follow these steps:

Step 1: Understand the data sources

Your data should come from reliable sources only. These sources should be legal and ethical. Thus, make sure you understand all data sources and collection methods used for e-Commerce data you receive from the provider. The data quality is highly dependent on these.

Step 2: Look for reliable data providers

Only trustworthy and reputed data providers can provide reliable data from credible sources. To make sure you have chosen the right data provider, you can ask them for customer references.

Step 3: Ask for a sample set

Request your data provider to provide you with a sample set and test is in its intended environment. If it generates expected results, it means you can confidently choose that data provider.

How is E-Commerce Data typically priced?

As commerce data can come in many forms, so make sure the pricing models your data provider offers accommodate your needs.
Depending on the dataset and its use case, models you will see in the industry are:

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

Pay Per Use
If you are unsure about your data needs, you can choose the “Pay Per Use” pricing model. You can pay on the basis of cost per click (CPC) and cost per mille (CPM).

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

What are the common challenges when buying E-Commerce Data?

  • The complexity of managing data quality - Luckily there are many technologies that help you cleanse your data and filter the best data sets separately.
  • 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 E-Commerce Data providers?

    When buying e-Commerce data, consider asking the following questions from your data provider:

  • Do they source data from online or offline channels?
  • Do you model your data?
  • How do you verify your data quality?
  • Is your data GDPR compliant?
  • How often do you update your data?

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Wult.io

Wult.io is a data provider offering B2B Contact Data, B2B Data, Lead & Address Data, Travel & Tourism Data, Point-of-Interest (POI) Data,...