Beyond selling products: listings and GA’s enhanced e-commerce

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Sell more. More bookings. Leverage the best-sellers. Everywhere. Is it the right tactic? Probably there is something smarter you can do to sell better.
I’m one of those still subscribed to many newsletters. Today I open one of them, from an online retailer, entitled “Check our best-sellers“. I click over it and find, with not-so-big surprise that the list of products (five, and hardly more than five) is listed everywhere. I can imagine the situation: some Manager thought that the best way to proceed was to list the best-selling products everywhere. In this way what we achieve is not a best-sellers strategy but an only-seller strategy (or few-seller strategy).
Here we reach the key point of today’s post: product lists is the new kid on the block to optimize. So far we use to optimize our pricing, communication, and merchandizing strategy based on the sales performance of a given product. Maybe, in a second iteration we include traffic (sessions, users, or even pageviews – or unique pageviews) and conversion rates. A traditional visualization for this is to relate the bookings and the pageviews generated by a product and mark four different areas:
– Cash-cows (low pageviews, high bookings)
– Stars (high pageviews, high bookings)
– Normal (average pageviews, average bookings)
– Problematic (high pageviews, low bookings)
Several variations can be also considered: revenue instead of bookings, pageview-to-unit conversion rate instead of pageviews, etc. But still, we use to consider the product as a silo without considering where it was seen or listed in the website, or even where has it been added to the cart from. So, let’s take for example two products, one falling in the cash-cow category (few pageviews, high bookings) and another one falling in the Problematic category (high pageviews, low bookings). At this point we can formulate some questions regarding those pageviews generated:
– From which devices?
– From which traffic sources?
And, the new one:
– Where in our website or app was this product seen? Was it in the homepage? Was it at as a result of a search? Are there differences on performance depending where on our website we show the product? Does it make sense to promote those products in the same places through our website or app?
It might be the case that product A (the cash-cow) is listed in the homepage and, for example, in a category overview, and that product B (the problematic one) is listed only in the homepage. In this case we should analyze the ratio of sales (or cart entries) and impressions, segmented by the location in the website or app where the product impression took place.
This topic gets more importance as the concept of list is rapidly spreading over the businesses. The traditional concept of category is being replaced by meta-categories, by personalization, by searches and refinements, etc. The concept of category pages and the concept of static product showing is gone: lists are replacing them. Furthermore, lists are dynamic as a result of our own browsing experience and personalization efforts.
Fortunately, tools are very aware of this, and this time we want to go through the basics of Google Analytics’ Enhanced E-commerce. You will find the full information about the reporting capabilities and technical instructions here. To give you a glance of the kind of reports you can find, we can state:
  • Goal funnel analysis: how users flow through your goal funnel, where did they abandoned it, and how many re-entered it.
  • Checkout behavior analysis: how users flow through the different steps of your checkout process, how many abandon it, and in which steps. The interesting part of this is that you can later create segments of users based on their behavior on the checkout. For instance, those who reached step 1 but not step 2!
  • Product performance: these are the reports that relate sales performance (bookings, transactions, quantity, etc) with shopping behavior (product listings views, product details views, product adds to carts, product removals, and their correspondent rates).
This feature of Google Analytics allow us to understand better how a product performs also in terms of intention of purchase by relating some concepts of its merchandizing (such as lists and internal promotions) with its sales performance. This will help us to find the better placement(s) for a product, or a set of products in our website or app.
As always, the power of this tool also relies on the segmentation capabilities. Understanding the lists’ behavior over different devices, user type, traffic source, etc., allow us to serve a better user experience. Placing the right product in the right place is a must-have on your merchadizing strategy, and it’s something that traditional off-line retailers have been doing for a long time.
Just to finish, a plea to e-retailers: stop pushing products all through your website. Stop sending me every single product that can be purchased. More does not necessarily mean better. Every product has its right placement(s). You just need to find it (them). Enhanced e-commerce will allow you to do so.

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