The different types of data analytics in eCommerce & retail
If you’re in the eCommerce business, you already know the importance of data analytics to deliver personalized solutions at reduced costs, boost sales, understand new shopping patterns, onboard new shoppers, and reduce cart abandonment rates. The usual process includes data requirement specification, data collection, data processing, data cleaning, and data analysis. However, marketers and data analytics experts make sense of data for reporting in four ways:
- Descriptive analytics: As a foundation of data analytics, this process considers historical data collected over-time about how a user interacted in the past. eCommerce specialists can predict future outcomes like product demand and customer behavior trends to effectively handle specific situations. For example, when you study a user’s past shopping behavior, it leads to discovering what drives them towards checkout.
- Diagnostic analytics: This process involves using data to determine trends and correlations between processes and behavior patterns. For example, you can determine which marketing activities drive the maximum purchases with diagnostic analytics or drill down website performance to evaluate revenue changes month-by-month.
- Predictive analytics: It involves analyzing past data to help businesses forecast future possibilities unlike descriptive analytics which uses data to describe actions that have already happened. For example, you can use predictive analytics to determine the sales and engagement for products, optimize strategies as per future outcomes, and plan budgets accordingly.
- Prescriptive analytics: This is more of an advanced process that involves deep analytics and specialized software. For example, when you run an email marketing campaign, you analyze the metrics of your first email like clicks on link, opens email, etc., to personalize and deliver more targeted content to potential buyers.
What insights can you achieve from an eCommerce analytics tool?
Once you generate reports via both Adobe Analytics vs Google Analytics 4 tool, the following are the insights you can achieve from it.
#1: Insights into user behavior
The information from this report covers user behavior during the purchasing and checkout process.
- Shopping Behavior Report details the number of sessions at different journey stages. For example, visitors who viewed products, added them to their carts, initiated checkout, and completed their sessions with a transaction. It dives into visitor behavior and how they move in a sales funnel.
- Checkout Behavior Report tracks how a user behaves during checkout, at what checkout stage do they abandon their carts, to assess and optimize checkout experiences.
#2: Insights into product performance
The information from this report covers product popularity, sales, and overall performance.
1. Product Performance Report provides two views:
- Summary View details the sales metrics such as product revenue, average price, unique purchases, average quantity, product refund initiated. It also details Shopping Behavior as in the number of products sold per the number of product views and number of products sold per number of product detail views.
- Shopping Behavior View talks about product engagement, product detail views, product add-to-cart, product removed from cart, product checkouts, etc.
2. Sales Performance Report details information (tracked via transaction ID) about revenue, tax incurred, shipping charges, refunds initiated, and total number of products sold.
3. Product List Performance Report talks about product cross-selling and upselling opportunities, product list views, product list clicks, and product list click-through-rate (CTR).
#3: Insights into marketing
The information from this report covers details around internal and external marketing efforts.
- Internal Promotion Report provides insights around your website modules like promotional banners placed on the header to assess clicks, conversions, and revenue.
- Order Coupon Report shows performance of your order coupons in terms of redeemed ones, total revenue, and average value.
- Product Coupon Report provides details around product coupons for total revenue, unique purchases, and product revenue per coupon.
- Affiliate Code Report provides details on how much affiliate sites contribute to the total success of your online stores.
Adobe Analytics vs Google Analytics 4: How does these tools differ
When it comes to analytics, you obviously want to leverage the best tool that helps you accomplish all your enhanced eCommerce goals. And both Adobe Analytics vs Google Analytics 4 can help you achieve it. However, depending on your business objectives and needs, the capabilities you need might vary from reporting to analytics to both. With urgency to deliver personalization in full throttle, data analytics has taken the centerstage, which is selecting the right tool is highly crucial.
Here we discuss the potential differences between Adobe Analytics and Google Analytics 4 and their respective capabilities.
|ATTRIBUTE||GOOGLE ANALYTICS 4||ADOBE ANALYTICS|
|Type ||Google Analytics, throughout its versions, has been a web reporting tool, meaning you can not run actual analytics on it. ||Adobe Analytics is a web analytics and reporting tool that provides you the real opportunity to derive insights on user behavior, sales, marketing, and everything else. |
|Core Tracking Setup ||Google Analytics 4 depends on dimensions, metrics, and its combination to track website interactions. For conversions, you need to implement custom events and related parameters. ||Adobe Analytics includes conversion variables (eVars), success events (events), and traffic variables (props) for tracking interactions, visits, and conversions on your eCommerce store. |
|Data & eCommerce Tracking||You can track eCommerce data via DataLayer push codes using predefined event names like ‘view_item’, ‘add_to_cart’, ‘purchase’, etc. |
You can track eCommerce data by utilizing success events (s.events) that implement interactions like ‘scOpen’, ‘scRemove’, etc.
Adobe Analytics provides advanced eCommerce tracking that breaks down each metric such as discount that can be given per product, total orders on hit and sub-hit products, total visits, revenue generated per product, tc.
Google Analytics does not track users’ email addresses.
|Adobe Analytics tracks a shoppers’ email address that you can use for email marketing campaigns. |
Google Analytics 4 allows you to create custom reports, dashboards, and visualization leveraging eCommerce data. Some capabilities include:
#1: Google Exploration - This is an inbuilt GA4 report that allows marketers to drag and drop combinations of metrics/dimensions, segments, and filters.
#2: Google Data Studio - Post integrating your Google Analytics 4 property to a blank report in Google Data Studio, you can create visual reports using different filter drop-down options.
#3: Custom Reports & Dashboards - You can create custom reports and dashboards within Google Analytics 4.
Adobe Analytics also provides you with reporting capabilities, however, not as diverse as Google Analytics 4.
#1: Similar to Google Exploration, Adobe has an in-built tool called Workspace that allows you to drag-and-drop combinations of eVars, s.props and s.events to create visual reports.
#2: Adobe Analytics plugin tool for MS Excel makes the sharing of reports a very easy and convenient process.
#3: It comes with pre-structured reports that allow for extensive audits around eCommerce clicks, conversions, and shoppers.
|Tag Management |
Google employs Google Tag Manager as its dedicated tag management solution and facilitates a direct connection between them. Some features of GTM are:
1: Tags are used to define what is fired and the events/parameters associated.
2: Variables are custom in nature and recyclable for different tags.
3: Triggers decide when and where to fire tags/rules.
4: Tag configuration in GTM contains all the available apps as per the tags’ condition.
Adobe employs Adobe Launch to manage both first-party and third-party tagging setup for Adobe Analytics. Some features are:
1: Instead of tags, Rules define what is fired and what events it will result in.
2: Instead of variables, Data Elements are custom and repurposed variables.
3: Events (within Rules) decide when/where to fire an associated rule.
4: Extensions in is the store for all Adobe owned and other third-party apps as per the rules’ condition.
|Debugging Solutions||In Google Analytics 4, you can preview your tag setup before you implement it, thus establishing a higher control over which tags and variables are fired in response to a web interaction. ||Adobe Analytics employs Adobe Launch where the latter relies on browser debugging solutions such as Adobe Experience Cloud debugger for debugging (QA) and validation capabilities. |
|Privacy and Consent Management||Google Analytics 4 can be directly integrated with any consent management tool to create pre-built conditions wherein tags are fired only when user consent is receivd.||Adobe Analytics|
|Data Storage ||In Google Analytics 4, you can store data for 24 months and cookies for 1 month.||Adobe Analytics stores customer data for lifetime whereas cookies for 15 years. |
|Testing||Google Analytics employs Google Optimize as its dedicated A/B, multivariate testing tool. ||Adobe Analytics employs Adobe Target as its testing tool, directly integrated with the platform to receive summarized data around clicks, cost, user behavior, best-selling products, etc. |
|Cost||Google Analytics is a free tool.||Adobe Analytics is a paid tool.|
|Attribution Model||Google Analytics employs multi–channel funnels to provide details around online traffic, its source, what visitors search for, the time duration they spent on your online store, etc. You can also generate event reports for page views, recurring orders, etc. ||Adobe Analytics comes loaded with several custom attribution models. For example, you can use the first-touch attribution model to assess the effectiveness of onsite product recommendations. You can use the last-touch model to analyze keywords or track conversions that have a short cycle. |