The Essential Guide to eCommerce Analytics

Whether you are a small mom and pop style eCommerce shop or you have a multi-million dollar operation, the fundamentals of eCommerce analytics are the same. The scale is different, but the challenges are the same – understanding what is working well, what is not, and what needs to be changed to drive more revenue. And eCommerce itself has many flavors. There’s subscription commerce, travel commerce, services commerce, and of course retail eCommerce. Despite the variety, the fundamentals of eCommerce analytics are the same across all these flavors. The terminology may be different, but the concepts are the same. One of those core concepts is web analytics.

And that’s what we’ll focus on in this guide – web analytics for eCommerce. At the end of the guide, you can expect to come away with a framework for measuring and managing core eCommerce web analytics.

There are three key components in eCommerce that should be used to measure web analytics performance:

three_buckets

Let’s review the definitions of these three components:

Channels: These are external sources that drive traffic to the site. e.g. Social media, search, email etc. Each channel has its own set of intricacies that we will get into in later in the guide.

User Experience: The entire experience of the user on the site is generally termed as the user experience. In the context of this guide, the user experience is focused on the conversion funnel that users go through to from entering the site to making a purchase. (note purchase is being loosely used here to refer to an end goal for a transactional site. it could be a booking, a subscription or something else).

Products: This is a generic term used to refer to the service, subscription, booking, or item that a user is able to acquire on the site.

 

So how do we think about eCommerce performance given these three buckets? Here is the framework:

revenue_formula

Each component plays a crucial role in determining the revenue generated through the site. Think of these three components as levers available to you as ways to manage the revenue earning potential of the site. You could either focus on driving more traffic, or optimizing the customer experience, or on adjusting the price/value/margin/availability of products.

So looking at components individually helps. But looking at pairs of these components is also immensely helpful. Let me explain. First let’s look at the value and need for looking at components individually.

 

Channels

A typical set of channels that drive traffic are direct (or typed in), organic search, paid search, social media, email and referrals.

The first thing to know is the distribution of traffic across these channels. This would be a very simple metric that measures sessions or visitors through the channels.

The next thing is the engagement level of visitors for a given channel. This engagement level determines the relevance of the traffic coming from these channels. Why is this important? Let’s consider an example.

Let’s say a site gets 3,000 visits per month. The distribution of traffic across channels is:

sample_ecommerce_analytics_channel_report

 

Based on the table above, one would say that Organic Search is the most important channel. But then look at how engaged the traffic is from each channel. Engagement here is measured as the percent of sessions that enter the top of the conversion funnel. This might measure getting to a product list page or a search results page.

 

Now looking at the table above, it becomes clear that though organic search drives the most traffic, it is email that drives the most engaged traffic. What does this mean then? Here are a few ideas one needs to now investigate:

  1. Are repeat customers responsible for higher engagement in email?
  2. Is organic search traffic landing on non-relevant pages?
  3. Are email landing pages more optimized for engagement?
  4. Can email traffic be scaled up?
  5. How can email success be duplicated in other higher traffic channels?

 

Let’s take this to the next level now. It is simplistic to assume that every channel has the same goal. Perhaps each channel needs to be measured using a unique channel effectiveness goal. That said, these goals need to be highly specific and easily measurable. So channel effectiveness for your site could be a mix of multiple goals such as subscription signups, product engagement, and social shares etc.

 

Defining and measuring channel effectiveness is a cornerstone to great eCommerce analytics. But this is just the beginning. There are several other aspects of traffic that could be investigated. I plan to cover advanced channel analysis in a separate guide in the future.

A few things to avoid when analyzing channel effectiveness:

  1. Using straight traffic numbers to make budget decisions. The days of siloed traffic are long gone. For example, we frequently observe a measurable impact in SEO traffic as a direct result of social media actions
  2. Depending solely on channel attribution for budgeting decisions.
  3. Making traffic behavior assumptions without validation through testing or deeper analysis.

 

 

User Experience

Within the context of eCommerce analytics, user experience is focused on driving conversion on the site. The conversion funnel begins at a top level landing page such as the home page and ends up at the transaction confirmation page. The conversion rate is a simple formula but has several components within it. Let’s take a quick look at it:

ecommerce_conversion_rate

This conversion rate can be broken down into the following components:

ecommerce_rate_components

The definition for each component is:

Engagement Rate or E1: This is percent of site sessions that also saw a product list page or a page where multiple products were displayed.

Product Engagement Rate or E2: This is percent of sessions with product list impressions that saw a product detail view. In other words, of those sessions that did see products in a list, this is the percent that saw a product detail page.

Shopping Rate or S3: This is the percent of sessions with product detail page view that added a product to the cart. In other words, of those sessions that did see the product detail page, this is the percent that added a product to cart.

Checkout Rate or C4: This is the percent of sessions with products added to cart that proceeded to the checkout page.

Purchase Rate or P5: Finally, the conversion from checkout into purchase!

 

At the basic level, one can analyze the component level conversion rate for the entire site. For example, a conversion report would track these rates by day in the following manner:

sample_ecommerce_conversion_report

This approach provides a macro level oversight into site performance. If any of these rates change, then it would warrant further investigation into the root cause. It is very common to publish and review a daily and weekly conversion report by eCommerce analytics teams.

 

These rates can be analyzed at the page level, at the channel level, or at the product level. A detailed explanation of pairing up components (channels + user experience or products + user experience) is beyond the scope of this guide. Be sure to read our guide on product conversion analytics for more on this topic.

A few things to avoid when analyzing site conversion:

  1. Making decisions without segmenting data (more on this at the end of the guide)
  2. Using pageviews or Google Analytics’ goals to measure conversion (conversion is a session level metric, not page level)
  3. Not auditing site’s pages regularly to check analytics tags haven’t fallen off

 

Products

The variability in analytics across eCommerce flavors and sizes is here in the products realm. If a site has more than a handful of products, then they will likely be organized in categories. Products could be relatively static as in a retail scenario or highly dynamic as in a travel commerce situation. Since this is an introduction to eCommerce analytics, let’s focus on a simpler retail scenario.

The goal of analyzing product performance is to understand the top level sales trend in context of product categories, time, and the product price. Below is a sample representation of what a basic product report should look like.

sample ecommerce product report

Given the unique aspects of your website, think about how a product report should need to look in order to be able to catch top level negative and positive trends. Once there is an indication of a change in the trend, then further ad hoc analysis should be undertaken to find the root cause.

A few things to avoid when analyzing product performance:

  1. Making decisions without understanding the relationship between product level conversion and product performance.
  2. Ignoring the impact of promotions and discount codes on product performance.
  3. Glossing over the influence of aspects unique (e.g. repeat purchase patterns, email responsiveness) to your business.

This is a great segue into the next section of this guide. Knowing how to segment analytics data is possibly the most important skill to master when looking to glean actionable insights out of analytics data. So let’s take a look at a few ideas on how to segment data.

 

Segmenting Analytics Data

Everything we have looked at so far has been at a macro level. For example, the conversion report tracks conversion at the site level. And this is good for reviewing site performance regularly.

However when a change in trend is detected in these reports, the root cause is often found by slicing the data using a variety of user attributes. Examples of these slices include technology used, repeat vs new visitors, and channel used. These slices are commonly called segments. Segments help see the data in smaller bites by isolating site traffic for specific attributes. For example, browser type is a segment commonly used to narrow down root cause of site conversion issues.

Such segments can be applied to existing reports without much change in the report itself. The key here is knowing what segments to create and knowing how to create the segments. Here are a few commonly used segments in eCommerce analytics:

  1. Entries through a specific channel
  2. Entries through a specific landing page
  3. Usage of a particular type of browser
  4. Repeat or new visitors
  5. Members or non-members
  6. Past customers vs non-customers
  7. Product category shopped
  8. Viewing of a specific product
  9. Specific internal search terms used
  10. Usage of a mobile or desktop computer

shopping icons 2

Next Steps

Now that you have a handle on how to evaluate each component (channels, user experience, products) of the eCommerce analytics framework separately, the next step would be to tweak the reports slightly and apply segments that are relevant to your site to drive actionable insights.

For example, here are some real world questions we have answered for clients using this approach:

  1. What is the conversion rate for members that enter the site through email?
  2. What are the key areas for improvement in the conversion funnel for SEO traffic?
  3. What are the factors that influence a visitor’s decision to proceed into checkout?

 

Summary

There are three basic components in the eCommerce analytics framework – channels, user experience, and products. Knowing how to pull analytics data for these three components and segmenting that data are the two core skills that are required for excelling in eCommerce web analytics.

For advanced reading, check out our guide on eCommerce product analytics.

 

Looking for web analytics consultants to improve your analytics setup? Call us at 877-694-2495 or email us at info@bayleafdigital.com