CXL course GTM advanced Review

Enhanced Ecommerce is certainly one of the finest reporting user interface features that Google Analytics has to offer. Enhanced Ecommerce, as the name implies, is a set of dimensions, metrics, and reports, which combine to provide you with a fairly complete view into how users are interacting with your products in your webstore. The main downside of Enhanced Ecommerce is, as with all good things, that it’s complicated to implement.

With learning from CXL advance GTM course, setting up enhanced e-commerce is quite easy. Sending complete data of all ecommerce transactions with the help of datalayer and tags in google tag manager. Functions like transaction id ,Transaction value, Time stamp, order id, order details are all need to set with Tag manager .

One thing to pay heed to is that you can collect both Standard Ecommerce and Enhanced Ecommerce hits to an Enhanced Ecommerce -enabled Google Analytics view! This means that if you leave your “old” tracking in place and run an Enhanced Ecommerce implementation in parallel, you might end up double-counting transactions. Product Category is collected to a different dimension, depending on which method we are using. So if we populate the category key for a product using either method, the dimension we’re looking for in GA is going to be different. Enhanced Ecommerce requires a specific syntax for the objects pushed to dataLayer or generated with a Custom JavaScript variable. For example, a dataLayer.push(). we will need to populate the keys in the object with relevant values. For example, the value for would need to be a unique identifier of the order itself. We could generate one randomly using JavaScript, but typically we’ll want to use the same order ID that was included in the actual transaction.

The dataLayer.push() must be perfectly formed. There’s no way to modify it on-the-go if we activate the “Use data layer” option. So it’s absolutely vital that we specify and audit the object that is being pushed into dataLayer for Enhanced Ecommerce data collection. When a tag with “Use data layer” fires, it really only accesses the most recent ecommerce push. So, if we’ve split Product Impressions into multiple pushes and then fire the tag when the last one is done, if the tag has “Use data layer” checked, only the last impressions object is sent to Google Analytics.

Using the variable has a very simple logic. All the variable needs to do is return a valid ecommerce object. In other words, the variable needs to return exactly the same thing that the developers would push into dataLayer in a perfect world.

Scraping the DOM refers to the habit of accessing the dynamic HTML representation of the page (the Document Object Model) directly with JavaScript and populating the Enhanced Ecommerce key-value pairs with values found on the page. On a receipt page, for example, we often see the order ID, the total value of the transaction, all products, shipping costs, and so forth. So we could just as well scrape these from the page rather than going through a complicated specification project with front-end developers.

Enhanced Ecommerce in Google Tag Manager allows us to send more than one data type in a single Enhanced Ecommerce hit. This is because all the data types are deconstructed into parameters of the Google Analytics request, and as long as each parameter only has a single value, we can combine them to our heart’s content. With Enhanced Ecommerce, Google Analytics introduced Product-scoped Custom Dimensions and Metrics. These are extra metadata that can be added directly into the objects within a products array. The dataLayer syntax is, again, fairly rigid. They must be named dimensionN and metricN (where N is the index number), and they must be embedded in the product object that is included in the products array.

Product List is a very important attribute in Enhanced Ecommerce tracking. For one, it’s really one of the only places in Enhanced Ecommerce where some sort of attribution takes place. But also, it’s a great way to look at how different parts of the site contribute to the success of your ecommerce efforts.

The attribution works in a very specific way. When we send the list attribute in one of our Enhanced Ecommerce actions or impressions, then after that every single subsequent Enhanced Ecommerce action will be credited to that list as long as the product IDs in those subsequent actions match those of the one where we sent the list.

Actions are Enhanced Ecommerce events that include some type of user-initiated action. This isn’t always a clear definition, and e.g. Product Detail Views could arguably not be user-initiated actions. But that’s the definition that Google uses. The Purchase hit is, arguably, the most important hit in the Enhanced Ecommerce funnel. Without a reliable Purchase hit being collected, all the funnels, attribution models, goal values, conversion calculations, and transaction data become fairly unusable.

An Impression Click is sent when an impression that the user has viewed is clicked. It’s an important metric to follow in case we are measuring Impression Views, because once we start collecting Impression Clicks, too, we can measure things like click-through-rates per product list, position, and individual impression.

Promotions are similar to impressions in that they, too, comprise a View and a Click. However, unlike any of the other Enhanced Ecommerce data types, promotions do not involve products. Rather, we are measuring the promotions themselves. These would typically be banners, product category highlights or something similar.

With so much detail coverage on Enhanced E-commerce and datalayers important for it, CXL course has offer me great in depth knowledge about GTM and Google analytics. Continuing my learning on analytics journey, I would deep dive into other important parts of google analytics including google data studio reports ,Excels and sheets important functions and formulas. CXL is institute I will recommend to all my connections if they are really considering to advancing in digital marketing journey. Learning from course would be much helpful for my present career and upcoming futures.



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MInar Joshi

I am Digital analyst and I love sharing information about analytics domain