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Eye Tracking, Statistical Analysis and Site Success
By Joel Tanner (c) 2007

What Is Eye Tracking?

How a subject views a book page, a store display, an
advertisement or other visual stimuli is measured using
sophisticated tools that track eye scan, also called eye
movement. These tools measure which design elements capture
visitors' attention and which don't.

Eye tracking is used in virtually every kind of marketing -
TV ads, billboards, product packaging and web sites - to
determine what works and what doesn't with consumers.

What Does a Visitor See on Your Site?

The layout of a site page is scanned differently by each visitor
based on individual perception, interest, need, age, education
level, computer monitor, browser settings and other variables
that can be tracked in empirical, eye tracking studies.

The results of numerous eye tracking studies have been
quantified, enabling web site designers and owners to optimize
site pages for maximum impact and "stickiness."

Single- and Multi-Variant Testing

Single-variant testing involves changing one site element and
measuring the impact on conversion rate, for instance.
Multi-variant testing employs a series of simple A/B comparisons
conducted simultaneously or sequentially depending on what's
being tested.

Using statistical analysis, and eye tracking data across
broad-spectrum demographics provides numerical sums based on
number of observations and length of observations of different
elements on any site page. That's something you want to know.
What captures the attention of site visitors? What is ignored?

Single-variant testing is the simplest to initiate and track.
However it's time-consuming and may lead to unsubstantiated
conclusions. Multi-variant testing is a more efficient means of
determining which site appearances and features deliver optimum
results, i.e. the highest conversion rate.

However, multi-variant testing is more complex than changing a
single variable and waiting to gather the A/B test results. It
could take months to optimize a site for conversion. Further,
single-variant testing often requires the tester to make certain
assumptions that may or may not be true.

For example, a change in type font shows a boost in conversion
ratio. Is it logical to assume the change in font style is
responsible for the improvement? No. In fact, this fallacy is
called "post hoc ergo propter hoc" in the world of statistical
analysis. Roughly translated, it means "after this therefore
because of this."

Simply because something occurs (an improvement in conversion
rate, for example) after a single-variable change has been made
(the change in font) does not mean that the improvement in
conversion rate is due to the font change. The improvement could
be based on another factor entirely.

Planning Your Test Model

"If you don't know where you're going, any road will take you
there."

If you blindly (or wildly) change design elements without a
thought to site improvements, all you've done is collect a lot
of data. In order to determine which changes to a site improve
conversion rates, it's important to first define what you're
looking for - your test metric. What site element or elements
will be compared?

Next, in order to develop useful data, you must determine how
you'll measure and compare functionality. What methodology or
"conventions" will you employ to determine a reliable
outcome?

And finally, you must be able to develop a strategy that
optimizes site success, however that success is defined by you.
Here's an example.

Let's say you want to determine which checkout software is
better for your bottom line. Before you can conduct your test,
you must first create a test metric - a measurement that
defines the term "better" in your query: which checkout
software is better?

You might determine the test metric to simply be the number of
visitors who convert. That's easy to measure, but it may not
provide the complete picture. Perhaps a more useful measurement
of which checkout software is better is the dollar amount each
visitor spends. Or the number of repeat buyers you see. An
increase in the number of page views, number of unique visitors
or a jump in bandwidth, indicating an increase in downloads from
your site - all of these are reasonable test metrics depending
on your mission.

This leads to the next step in developing accurate statistical
analyses: how will comparisons between the A/B elements be
measured or quantified. What test "conventions" or methods
will be employed? Will you count all site visitors in the study
- even those that bounce - or will you limit the test pool to
those who actually put something in their cart? Or actually
reach the checkout but abandon the shopping cart? Or actually
complete a transaction? Determining the methodology of your
single-variant or multi-variant testing prevents jumping to
unsubstantiated conclusions.

And finally, what steps can be taken based on the test results
you develop? If you can't answer this last question, why are
you going to all the trouble to conduct the test and collate the
data? If you get result Y, what can you do with that information
versus result Z? This is where statistical analysis is turned
into a practical, organized strategy for improving conversion
ratios.

Once the test metric(s) and conventions are established, you run
an A/B comparison test using the two different checkout models.

Checkout A requires two clicks to complete a transaction.
Checkout B requires six clicks to complete the same transaction.
Your test results reveal that the more complicated checkout
model leads to a higher percentage of shopping cart
abandonments. So can you assume that checkout Software A is
better than Software B?

If your test metric was a simple count of software usability,
Software A is the clear winner. But what if your test metric was
to determine which checkout software led to the highest "per
visitor" purchase amounts? And test results reveal that
checkout Software B delivers fewer purchases but purchases of
higher value. In this case, Software B would be the better
choice. That's why it's essential to determine each test's
metrics and conventions.

Measurement Tools

There are a lot of software packages to help in gathering test
data. One, called Crazy Egg (http://crazyegg.com/) provides
different GUIs of site activity - an overlay view, a list
summary and even a heat map showing what's hot and what's not
on your site. Easy and effective analysis.

Another popular conversion rate analysis software is Click
Density (http://www.clickdensity.com/), which provides real-time
visitor data to help improve everything from content architecture
to link placements.

Click Tale (http://clicktale.com/) tracks every movement of
visitors as they move through your site. This data is then
translated into animated graphics to help you understand visitor
behaviors from the time they arrive until they leave.

Finally, consider using Google Analytics
(http://www.google.com/analytics/) - the simplest statistical
analysis tool available. And it's free. Google Analytics
provides snapshot views of your site's activity, allowing you
to perform tests and analyze data in seconds instead of spending
hours poring through report after report.

The point is this: to improve site conversion rates requires an
understanding of eye tracking and statistical analysis to
produce a useful optimization strategy. The hit-or-miss approach
is simply too time consuming. So, if statistical analysis makes
you light-headed, hire a professional who can design and
validate test metrics and translate those findings into
actionable strategies.

That's how you improve site performance systemically and
efficiently.
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Joel Tanner is a seasoned internet marketing consultant who has
been educating web designers (http://www.1234-find-web-designers.org/
web-design.html) on the best techniques in search engine
optimization (http://www.1234-find-web-designers.org/
searchengineoptimization.html) and conversion rate optimization
for nearly a decade.
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