Of all of the categories of businesses that exist today,
it is almost a no-brainer that online retailers are among the main ones using
web analytics as a routine part of their business. After all, the success or
fail of that business seems to rely solely on online traffic, clicks,
conversions, etc. But a recent survey
suggests that a vast majority of e-commerce sites are failing to make the most
of their web analytics.
The DBD Media survey, conducted in October 2012, includes
responses from 50 e-commerce sites, of which 73 percent are inflating their
traffic in their reports, while 67 percent haven’t integrated social media.
Some other eye-opening stats from the survey include:
--50 percent of e-commerce businesses track main
conversion points
--60 percent of Google Analytics accounts were not
correctly synced with Google AdWords
--33 percent of websites with on-site search function do
not track site search keywords
--73 percent do not track micro conversion goals such as
newsletter signups or account registrations
--30 percent of websites have incorrect e-commerce tracking
implementation
As you can see, although the “bread and butter” of many
e-commerce sites is traffic and conversions, many of them are not tracking this
information at all or not tracking this information properly. To seemingly be
the master of all things online, online retailers certainly leave a lot to be
desired when it comes to their use of web analytics.
One online retailer that does a pretty good job utilizing
web analytics, though, is Amazon. Have you ever been to Amazon.com to shop for
a Christmas gift (or any other occasion) and, while reading up on the specifications
for a particular item, notice that they also list the top three or four items
customers ultimately bought after viewing that particular item, as well as the
top items that customers buy in addition to that particular item? This is one
of the ways Amazon uses web analytics.
This tactic is part of a much larger strategy – to sell
and cross sell through recommendations. Amazon’s recommendation system is based
on a number of things: what someone has bought in the past, which items they
have in their shopping cart, items they have rated and liked and what other
customers have viewed and purchased. All of this analytical data is collected
and pushed back out to the customers in the form of a recommendation, which
customizes the online shopping experience for each consumer. According to an
article on Fortune.com by JP Mangalindan,
this tactic seems to work for Amazon, as the company reported a 29 percent
sales increase.
Since there are Amazon employees that are responsible for
promoting certain purchases, they may think up similar items and make sure that
customers who have viewed those items receive an email encouraging them to
check out the product the employee is responsible for promoting. Mangalindan’s article also discusses how web
analytics are also used in this scenario. If, for example, a customer qualifies
for both an email for book recommendations and video game recommendations, the
email with the higher average revenue per mail sent will win out.
This is pretty cool from a consumer perspective, because
it prevents my email inbox from being flooded and from a marketing perspective
because it maximizes the purchase opportunity, as Amazon’s conversion rate and
efficiency of such emails are “very high” – significantly more than on-site
recommendations.
Something else Amazon does is optimize the use of its
Thank You page. It uses this page as an opportunity to reengage with an already
highly engaged visitor. For many retailers, it is easy to assume that as soon
as a consumer is finished with their purchase that they will leave the site, so
they don’t see a need to reengage. But with Amazon, the Thank You page is a
place for them to thank the customer for their order and allow customers to
track the status of their order, cancel items from their order, edit the
shipping method, see the status of all orders, organize book and music and
video purchases in their Media Library. There are also recommendations there,
including some based on the purchase that brought the consumer to this page as
well as recommendations for items that are frequently bought with the consumer’s
recent purchase. Other retailers use the Thank You page as an opportunity to
tempt the visitor back into the store, having them sign up for a next-time buy
coupon, or have them participate in a survey.
This page is also measured and the analytics can be
pretty mind-blowing. I image that by tracking the clicks post-purchase, Amazon
determines what percentage of customers leave the site and what percentage of
customers do not. And of those that exit the site, Amazon can examine how can
they serve those customers better.
In terms of tools, methods and metrics that can also be
used to improve Amazon’s overall web analytic efforts, I am not sure what other
tactics Amazon can deploy in order to improve. From a consumer’s point of view,
every time I visit the site, I am logged in and my recommendations appear on
the page. I also receive email recommendations. When I visit other sites, I see
advertisements from Amazon for the specific item I was shopping for previously.
When I try to think outside of the box, Amazon has that
covered too. They allow competitors to sell on their site but once they see
high conversion rates, Amazon begins to sell those products as well, this time
at a lower price. Amazon even tracks clicks on its competitors’ advertisements
on its site. Pricing and product placement are tweaked to maximize the use of
this analytical data.
To reiterate my previous point, many online retailers
have begun to use web analytics to better their business, but either there isn’t
enough businesses doing it, or there isn’t enough businesses doing it well.
Amazon seems to be doing quite well on this front, although there is always new
data to collect and new strategies and tactics to deploy.
