I'm often asked
how we go about using web analytics to really pinpoint
problems that make the tools worth the investment. Many
people are dubious when asked to fork out 50,000 a year
to have reports about how people visit their website.
What I'm about to describe is a situation where one
of our clients could potentially earn $1 Million per
year because of the analytics tool they have installed.
This article will describe how we used a key performance
indicator to raise the problem and then go onto describe
how we then found out what the issue was on the clients
website.
The Key Performance Indicator or KPI
Ahh the KPI! It's the latest buzzword flying around
in the industry. What key measurements to use is an
important point, but much more important is how you
use them. One KPI I have written about before is page
views per visit. Page views per visit is a KPI I always
use regardless of the site goal. The reason being it's
what I refer to as a tripwire metric. Like a tripwire
it gives you a warning when something is not right.
The way you should use this KPI is to first find out
how many pages it takes to complete the desired action.
In this case the desired action (a purchase) took 7
pages. Then consider what a good browsing experience
might be from your point of view. In this case we figured
if the visitor viewed 5-7 pages and then completed a
purchase (another 7 pages) it would be a good visit
from the businesses point of view. It means that the
visitor finds out that more is on offer than simply
the product they were looking for. Then we add another
7 pages on top of this to flag a too many pages warning.
So we have a bottom limit of 7 pages, a happy medium
of 14 pages and a top limit of 21 pages.
Why is too high a warning?
When an average visit to the website is more than 14
pages in our view it means one of two things, either
the visitor is extremely happy with the website and
is browsing around or the visitor is extremely frustrated
and can't find what they want. A good experience from
any visitors point of view on an e-commerce website
is that they find what they want very quickly and in
this case we figured that should mean browsing less
than 21 pages on average. What we found shows why this
metric is important. The KPI went off the scale showing
that on average a visitor viewed 22 pages per visit.
The next job was to figure out whether this was a good
thing or a bad thing. If an average visit took 22 pages
it meant that either the visitor was happily browsing
around and our client should be very happy, or it meant
that there was a problem and if so we needed to find
out where.
Good or Bad? Happy or Sad?
The KPI had raised the warning signal so we now needed
to find out which visitors this KPI applied to. In HBX
(and many other tools) it s possible to segment the
visitors into groups of people
that follow the same behavior patterns. We wanted to
know if the visitors were flicking through pages very
quickly (a sign that they were unhappy) or if indeed
they were traversing a great many pages each and spending
a normal amount of time on the site (a sign that they
were happy).
Therefore we segmented the visitors into only those
that spent less than 2 minutes on the site and those
that visited the shopping cart. This would enable us
to see if the page views per visit of those visitors
only on the site for a short period of time were racking
up lots of page views or whether it was those that hit
the cart that had trouble finding what they wanted.
Less than 2 minutes showed normal behavior. The people
that spent less than 2 minutes on the website generally
browsed 2 or 3 pages per visit. The people that hit
the shopping cart again went off the scale but this
time it was even more problematic. The average page
views per session was 58 pages. We'd found the people
who were having problems.
58 page views per visit?
Since we'd found the visitors who were having the problems
we now needed to know what they were doing. How on earth
could people be going through 58 page views on average
each? It seemed unlikely we even asked the
developer to check that the tracking code was correctly
installed. However when we checked the path analysis
the problem on the website became crystal clear.
One visitor had traversed 97 pages. We looked through
his visit path and noticed that the path kept referring
to one page a search results error page.
We checked other individual visits and noticed the same
key trend the search results error page.
This lead us to check the failed searches on the website.
When we totaled them up there were over 2000 failed
keyword searches and the big majority were product codes.
The sites internal search engine simply couldn't read
a letter and number combination and most of the product
codes consist of numbers and letters. We'd found the
problem.
The solution therefore is to fix the search engine.
This one fix is a potentially huge find. There were
over 1000 people that keyed in those failed keywords
and didn't complete the purchase. Our customer brings
in over $160,000 per month in online revenue from a
little over 1700 people that did complete a purchase.
That means by doing a little mathematics it s easy to
work out the potential. It s well over $1 million a
year in lost revenue.
Summary
It's easy to worry about the cost of a web analytics
system. They are expensive and with everything that
most businesses have going on they aren't easy to get
the most from. What you really need is an in house expert
looking at the systems to pin point the problems or
outsourcing to a consultancy to get the most from the
systems. However to not use web analytics is like throwing
away money a frustrating and expensive waste
of time.
Author:
Steve Jackson, Editor - Conversion Chronicles
Steve Jackson is CEO of Aboavista, editor of The Conversion
Chronicles and a published writer. You can get a free
copy of his e-book sent to you upon subscription to
the Chronicles website (http://www.conversionchronicles.com/) |
