Exploring Sales Funnel Analysis for E-commerce with GA4

"Life is a Struggle". Yes, it is! We all have our battles to fight. Is there any escape? Maybe, yes. It is to enjoy life in the process! They say "Life is a journey, not a destination". What a powerful quote! I believe, if we focus more on the process or path that we're walking to achieve our goals, we'll end up achieving more than what we set out for.

This is no different when it comes to Business & Marketing. Let's start with setting a goal. What was the goal of your last campaign? Suppose it's to increase sales by 10% in Q1. And once the campaign's over, you measured the success rate. Yes, it's exciting to measure the wins, similarly, it is heartbreaking when you see that despite your best efforts, the needle is not moving, and you couldn't achieve the numbers. ???

However, in today's complex ecosystem, simply measuring the final conversion doesn't tell you the entire story.?What're the steps(events) our customers went through? Are they carving their path? Where are our users losing interest? Is it possible to identify the barriers? So, it's crucial to understand your users' behavior in the various stages of the customer journey. Yes, think about Funnel Analysis, This tool helps us manage the process of achieving the goals by tracking down the problems in the conversion path so we can optimize more efficiently.

Once we set our campaign goal, we need to define the funnel to convert the visitors into customers. The most common sales funnel model is AIDA.

In this article, we're going to explore how to view and analyze sales funnel performance in GA4. So let's log into our demo account, Google Merchandise store. Our final destination is sales and the steps in the funnel we'll be tracking for this e-commerce platform:

So, we created a funnel report in GA4 for the users who visited Google Merchandise Store in the last 30 days. Our first step of the funnel is the users who viewed the category page. We selected the default event named View_Item_List.

The second & third steps are the users who viewed the product page and added products to the cart. Before moving to the next step, let's look at what our basic funnel looks like till now:

There are ~43k users who visited the category page and ~29k made it to the product page. The Abandonment Rate reflects the % of users who dropped off between the current step and the next step. In this case, 64% or ~27k users dropped off after viewing the Category Page. So, the number of users who made it to the next page, Viewed Product Page, should be equal to the difference between the viewed Category Page and the number of abandonments? It will be equal if you make it a Closed Funnel, wherein users must enter the funnel in the first step. But here, we made it an Open Funnel, which means users can enter the funnel at any step. In this case, a lot of users entered directly the product page. The completion rate represents the number of users moved to the next step. It's the inverse of the Abandonment Rate. In this case,?36% of users made it to the next step.?

Now let's look at how many users made it to the Add to Cart page. Only 271 users added one or more products to the cart,?the abandonment rate is as high as 99%! Quite alarming! A whopping 28k users dropped off at the product page itself! At this point, I'm curious to know how many users finally made the purchase. Also, we're going to add a new variable, bifurcating by device category.

Out of 271 Add to Cart users only 124 users finally made the purchase,?54% abandonment rate.

In the final step, we're going to add one more variable, traffic source. We're only considering Direct & Paid traffic. The final funnel report looks like this:

Now let's dive into identifying and plugging the leaks with the help of the above funnel analysis.

1) After viewing the Product Page, we see only 1% of users progressed to the Add to Cart. A whopping 99% of users dropped off at the Product Page. At the final stage, only 45% of the Add to Cart users made the purchase, this makes it 0.3% of the total users. These numbers are quite alarming compared to the industry standard, for e-commerce, the average Add to Cart completion rate hovers ~ around 15%. Google Merchandise Store is missing a lot of revenue. Also, the cart abandonment rate is high too. Maybe Google should look into optimizing the checkout page or sending some more discounts to reclaim the lost sales.?

2) Device Category comparison reveals that completion rates for mobile users are strikingly lower than the desktop users. All mobile users abandoned the funnel after viewing the product page and 0% progressed to the Add to Cart. Are the mobile users of Google Merchandise Store facing any accessibility issues??

3) Traffic Source comparisons tell us the quality users came from the Direct source. The paid source, CPC contributed more users in the first 2 stages. However, no users who came from the CPC source had made it to the Add to Cart or Purchase stage. What's Direct Traffic? Direct Traffic in GA4 includes Organic traffic, i.e., the users who typed your URL directly as well as the users who visited your page through some unknown source because the ad doesn't include the proper tracking parameter.

4) After we identified the problematic high abandonment page, we can further take an in-depth look into where these users are disappearing with the Path Exploration option in GA4.

Shopping Cart is our starting point. Only 30% of the shopping cart visitors moved to the log-in page and the rest 70% navigated away to the Home page and product page. This is a bit surprising. At this stage, a well-designed survey & test should be conducted to identify what's stopping the users to continue.?