eCommerce web data collection is the practice of keeping track of user behavior on websites and gathering pertinent information about how users engage with businesses and stores online. Then, this information can be used in a variety of applications, like as:
- evaluation and optimization of digital marketing channels and performance
- user-behavior evaluation on the website and eCommerce performance
- marketing platforms performance evaluation and optimization (e.g. data in Google Ads paid search campaigns).
Extraction and processing of metrics is the process of data analytics. It is very useful for understanding online consumer behavior. The data aids eCommerce businesses in maintaining a competitive edge in their specialized sectors. Businesses can detect bottlenecks in their selling processes with the help of these crucial insights, opening them opportunities to improve strategies.
For gathering and analyzing eCommerce data, there are various tools—eCommerce data analytics software—available. The most effective method for tracking and analyzing eCommerce data is to set up a number of tools and platforms in order to be able to receive, analyze, and assess the data from various sources and with the use of various types of analytical solutions. Typically, the process does not boil down to one single tool or platform.
|Data Tracking Platforms
|Description & Purpose
|Google Analytics (GA)
|Google Analytics is a web analytics service offered by Google that tracks and reports website traffic and user behavior on a website. Google Analytics helps to understand user behavior patterns and use the data to improve the website and digital marketing efforts.
|Facebook Pixel & Events
|The Facebook Pixel is a piece of code that you place on your website, allowing you to monitor conversions from Facebook ads, optimize ads, build targeted audiences for advertising campaigns and retarget people who have previously interacted with your website
|Google Ads conversion tracking
|This is a tool that allows to track which campaigns and ads are driving meaningful actions from the users after they have interacted with the ads. Examples of conversion events that can be tracked: user purchased a product, signed up for your newsletter, called your business, or downloaded your app
|Google Ads Dynamic Remarketing
|Dynamic remarketing lets the company show its previous visitors ads that contain products and services visitors viewed on the company’s website. With messages tailored to your audience, dynamic remarketing helps you build leads and sales by bringing previous visitors back to your site or app to complete what they started
|Floodlight is the conversion tracking system for Google Marketing Platform. Like other conversion tracking systems, it consists of tags that track the activity on your site, along with reporting features for adding conversion data to your reports. It uses a cookie to recognize repeat visits from a specific browser.
|Hotjar is a product experience insights platform that gives you behavior analytics and feedback data to help you empathize with and understand your customers through tools like Heatmaps, Session Recordings, Surveys, and an Incoming Feedback widget
|Universal Event Tracking (UET) is a tool that records what customers do on your website. By creating one UET tag and placing it across your website, Microsoft Advertising will collect data that allows you to track conversion goals and target audiences with remarketing lists.
|The LinkedIn Insight Tag powers conversion tracking, website audiences, and website demographics when using LinkedIn as a marketing channel
Data analysis in an eCommerce project is not one-dimensional, it unfolds in many steps.
1. Data Requirements Specification
During this stage, data is grouped. Once your audience visits your website, they may be divided by age, education, income, relationship status, etc. These details help you to know your customers inside and out. Consumer behavior is vital for conversation and income: the better you know the reasons why your customer buys something, the better chance you get on repeating the sale.
2. Data Collection
At this stage, you are ready to dive into further analysis of the user’s data. It’s up to your company to decide what information to collect. Browser cookies, web databases and ad interactions are some of the most common ways further details are gathered. Data analysis for eCommerce allows you to predict your customer’s behavior.
3. Data Processing
Modern data analytics in eCommerce software organizes information through an automated process. On the back end, information is organized into rows and columns that become structured into graphs and charts. Therefore, it will be easy for your team to analyze the information, choose what is best needed, structure and process it.
4. Data Cleaning
This follow-up audit eliminates duplications and corrects errors before the data is ready to be analyzed. This step is especially crucial when working with financial data in the eCommerce field. Without precision processing of data analysis for an eCommerce project there could be losses and other risks for the business.
5. Data Analysis
This is the step where clean data is presented and ready to be analyzed. Looking at the data sets can help you draw conclusions that will help you make more informed business decisions. At this stage, you need AI systems or manpower to help you transmit the information. As a result, you will get the full data of your current business situation and ways to improve it.
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In general, we follow these steps when setting up a data-tracking ecosystem on an eCommerce website.
Analytics discovery: The analytics team identifies the current tracking setup and prepares the scope for the new implementation, including solution development.
Onboarding: The analytics team is onboarded and is granted the tools and the information they need for executing the project.
Implementation: The dev team sets up all the configurations and ensures that the correct data from the eCommerce platform is passed to the analytics platforms’ configurations.
Go-live: The analytics team joins the go-live and post-go-live stages in order to analyze how the website is functioning after the launch.