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 behaviour 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 behaviour. 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
totalled 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.
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 web site (www.conversionchronicles.com)
