The funny thing about web analytics is that you can make it as simple as you want, or as complicated as you have resources or time for it. Similarly, eCommerce analytics measurement can be as simple as a single metric or can be viewed as a combination of metrics. In its simplest form, it is about measuring the percent of sessions on a site that complete a purchase. That percent is called the eCommerce conversion rate for the site.
But to take action on that information, that onion needs to be peeled. And it sure has a lot of layers. Peeling them to truly understand performance and find actionable insights could get daunting. Fortunately at Bay Leaf Digital, we tend to keep things simple and manageable by grouping them into threes – no more, no less.
Our basic formula to managing ecommerce performance is:
Though this formula can be taken literally, the idea behind this concept is that there are three key levers to improving revenue for an eCommerce site. One can focus on increasing relevant traffic. Or on improving the customer experience (and hence the conversion rate). Or on adjusting the product margin, the mix, the discounts offered based on the price elasticity of the products. For further reading, take a look at my post about ecommerce marketing dilemmas people routinely face.
Product Conversion Analytics
For this article, we will focus on the product aspect of the eCommerce analytics equation. Specifically, how should we think about conversion analytics at the product level?
It starts with measuring the interest in a product, and ends with measuring purchase rate of the product. But before we get too far into it, let’s look at the definitions of a few terms we will use – session, impression, product view, add to cart, and checkout.
Session – This is the basic building block of web analytics. A session is defined as the period of activity of a visitor on a site without a break of (typically) more than 30 mins.
Impression – An impression is the passive act of viewing an item on a page without interacting with it. In this case, when a visitor sees a product in a list or a site search result, it is called a product impression.
Product Detail View – This is a view of the page that contains product detail information such as color, size, date attributes. Typically it also contains a call to action such as add to cart.
Add to Cart – The act of adding a product to the shopping cart. While there are other actions such as remove from cart and cross-sell that are relevant, they will complicate the discussion to no avail.
Checkout – We mean the collection of one or more pages to collect buyer information including personal details, the payment method etc.
Okay now that we have the basics out of the way, let’s get into the details of product conversion analytics.
eCommerce product conversion is the percent of sessions with product impressions that end up purchasing the product. Note this is different from a percent of just sessions. The difference is that the denominator here is sessions that have had product impressions. Here is the formula:
This is a simple definition for product conversion. But in order to do anything remotely useful with this data, it needs to be broken down into its components. We can re-write the product conversion rate as the product of engagement rate, shopping rate, checkout conversion rate, and the purchase rate. So production conversion rate can be re-written as:
Here is a quick definition of each of these rates:
Product Engagement Rate or E2:
This is percent of sessions with impressions that had a product detail view. In other words, of those sessions that did see products, 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!
Note: We use the numbering convention as an easy way to remember the position of the component in the overall scheme of things. If you are wondering why the numbering starts from 2 instead of 1, then check out our essential guide to eCommerce analytics.
Understanding the Components of Product Conversion
Now that we have identified the components, let’s review a few guidelines for analyzing these metrics.
As a rule of thumb, the rates closest to purchase should typically be the highest. So P5 should be higher than the C4 which in turn should be higher than S3 and so on. If these rates don’t follow this rule of thumb, it is because visitors have been hustled into the funnel by removing pages or just by landing them straight deep into the funnel.
Here’s the funny thing about conversion funnels. One might think that eliminating pages will push more people down the path. Less stops and less friction right? Not quite. Eliminating pages doesn’t always result in higher conversion. Instead, the fallout of the funnel just gets shifted and the overall conversion rate remains unchanged.
Instead, one should look for real opportunities to enhance the experience on the page to convince more visitors to travel down the funnel. The goal shouldn’t be to trick people. Instead it needs to be focused on selling visitors the value of the product in a manner that is appropriate given the shopping stage the visitors are in. But enough about conversion rate optimization. Let’s get back to measuring the funnel.
Measuring P5 the Purchase Rate
This is the last step in measuring the product conversion funnel. In a simple implementation, this might be as simple as purchases/checkout sessions. However, the multiple methods of payment supported by sites may require this to be looked at in sub-components. For example you may need to create a dimension that is payment type. And this will help determine how various forms of payment actually influence conversion rate. So P5 will end up looking like this:
P5 should be very high. We routinely see P5s of 90% or higher. The only variable in this rate should be the payment type. So if one of your payment types is dragging down P5, you need to test eliminating that payment type to see the contribution impact of removing the payment option.
Measuring C4 the Checkout Rate
This is the rate at which sessions with products in cart proceed to the checkout page. This rate is typically price elastic. Meaning, the higher the total transaction value, the lower the conversion rate. So to truly understand C4, consider looking at segments of products that have similar price points. Given that product price can change based on attributes such as color, consider grouping together similar product-variants into a segment to measure.
One other variable to keep in mind here is the coupon code. Whether there are actually promotions running or not makes a difference in conversion rates. In addition, even the presence of a coupon field can impact checkout rate. So the checkout rate for your site might look like this:
An example product variant segment would be all jeans under $99. An example of a coupon code segment could be sessions using the coupon code ‘Winter 2016’. Each of these segments will likely show a behavior pattern that can be then tweaked using optimization techniques.
Measuring S3 the Shopping Rate
This is the rate at which products are added to cart from the product detail pages. And this is also where things can get really complicated. Each product has its own unique attributes. Without looking at each product individually, it can become hard to identify patterns of products that can be analyzed together. Here are the most common product attributes that could be used to group products for analysis – price, availability, and category. The best practice is to track segmented S3 against the overall S3 for all products. So the shopping rate analysis for your site might look like this:
The meat of your ecommerce conversion analysis should be here in S3. Because this is where your company has the most influence over the action you want your site’s visitors to take. So segment this conversion rate till you find levers that you can leverage to improve S3.
Measuring E2 the Product Engagement Rate
This is the rate at which sessions that see product list pages end up on product detail pages. The product engagement rate is heavily influenced by position of the product in lists. The higher up the product, the better its E2 will be. The other factors that can influence E2 are attributes such as price, rating, and product image. The tricky part for analysts is knowing when the position of the product in a product list page changes, and figuring out some sort of a weighted average to best represent the position for a given product. This is a fairly big challenge in leading web analytics packages such as Adobe Analytics and Google Analytics. The approach for analyzing the product engagement rate is similar to that for S3. Analysts should identify segments of products that have similar E2s and then track the performance of such segments against the overall E2 for the entire product line. So the E2 analysis for your site may look like this:
There is a lot more to product conversion tracking than meets the eye. Measuring simplistic purchase to sessions conversion falls short of providing any actionable insights. To truly master this aspect of eCommerce analytics, one must have a very strong grasp of the various attributes of products, pages, and payment methods that influence visitor behavior. The components of eCommerce analytics tracking such as engagement and shopping rates can be best analyzed by slicing these rates with attribute based segments.
For further reading on this topic, check out our guide to the fundamentals of eCommerce analytics