Eye Tracking, Statistical Analysis and Site Success
By Joel Tanner (c) 2007
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 Ăncrease in the number of page views, number of unique visitors or a jump in bandwidth, indicating an Ăncrease 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 wĂnner. 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.
There are a lot of software packages to help in gathering test data. One, called Crazy Egg provides different GUIs of site activity - an overlay view, a lĂst 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, which provides real-time visitor data to help improve everything from content architecture to link placements.
Click Tale 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 - 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.
About The Author
Joel Tanner is a seasoned internet marketing consultant who has been educating web designers on the best techniques in search engine optimization and conversion rate optimization for nearly a decade.
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