Google Website Optimizer, the multivariate testing suite offered with AdWords, has a few little used or known tricks which a practical analyst might find useful. One which I will discuss here is the ability to manipulate the ‘Goal’ script to prevent overloading conversion goal pages with multiple scripts. Its a really simple fix, and a simple and practical idea for running many tests simultaneously, without creating statistical overlap or technical issues for your most important pages.

After recently completing work on a major MVT undertaking of Global Elements on the CableOrganizer.com site, Paul, Juan and myself had built up a significant cache of tests which we wanted to launch right away. Everyday in our morning web initiatives meeting we found ourselves getting anxious to get on new things right away. Juan was looking to put some of our button calls-to-action to the test as well as our process to the cart. Paul wanted to run a follow-up on the headlines portions of our previous test. I got shivers thinking of how much fun its going to be to test a static old version of a major problem page versus a long-researched strategy to implement our targeted content provided by in-house design and the SiteBrand solution.

PROBLEM: Four tests with the same conversion goal running simultaneously.

SOLUTION: Run test with same goal and a single script alluding to several test designations.

Say your site (or client’s site) is based on a conversion objective of online sales. This means that you probably have a shopping cart solution. Most solutions provide a way to manipulate the code as it is presented in HTML by the server scripting. This means that you’d have to potentially put 1 full script for each multivariate test running to identify a goal based on that objective. However, all parts of the script are not necessary to identify as a goal appropriate to a page individually.

Check it out: Here is a screenshot of all our tests currently running using ‘Add to Cart’ as a conversion goal (for reasons we will explain some other time).

All of the tests above are running on one script with 9 different goal codes referred to in the test provided designation function. Implemented in the appropriate place, your script should take on an appearance similar to this:

The victory here is in the ability to not worry about script-heavy pages essential to conversion (i.e. shopping cart). Added benefits are that the script can be altered or exchanged to meet the needs of the test. Instead of adding 9 lines of code, you can get away with 1. Quick, simple and effective from that point on.

If anyone has any interest in, or problems with this, feel free to write me an email or comment here on thoughts.

Early in my training as a ‘web analyst’, Paul (my boss and the COO from CableOrganizer.com) set out some goals for me which included checkpoints to my actually being considered ‘mature’ in my designation. Those steps included tracking and reporting KPIs and insights on a weekly basis, regularly scheduled usability tests, and the successful completion of a multivariate test. I set my sights on those goals while Paul prepared the road for installing a data decision process in the business.

The purpose of this post is to explain the progression from carbon-based report handler to full scale and skill realized web analyst. It will attempt to point to areas which may help new and developing analysts gain momentum in the perfecting of their craft. Lastly, it will outline a few quick experiments which might help prime the process engine. (If you’re looking for ways to improve your value, I’m handing you the recipe)

For the purpose of support, the testing I will be describing includes:

• A/B or Split Testing Using Google Website Optimizer - testing single variant elements per URL which traffic is directed into by a script. Goal success is then attributed to the page.
• Full Factorial Multivariate Testing with GWO - testing multiple elements per single URL, simultaneously, where the items tested are identified within the page and rendered randomly. Goal success is distributed across the table of elements per variation and aggregated statistically to determine relevance of each element to success and compared to all variations and the original.
• Usability Testing -qualitative test to gain insight into a user session experience on a given site where a professional, random, or representative subject is observed performing tasks and navigating a web site. These can be performed by usability experts in labs, by simple observation and analysis, or through a handful of testing services whom use and relay audio/video files back to the customer.

These experimentation methods, in their simplest form, provide an analyst with a set of tools to validate their assertions. The outcome of GWO tests which are set up correctly and run to completion provide invaluable statistical justification for keeping or replacing elements within a website. Usability testing these same areas and elements should augment the data by providing a qualitative perspective to the findings.
Making experimentation the goal forces developing creative hypotheses. Looking back this seems to be the most essential right of passage into the world of active practice of analytics. Where measurement, assertions, and hypotheses are part of the analytical psychosis; knowing there are systems in place to support or diminish statements forces us to think forward. It is by this means that assertions, testing, and ultimately improvements to the site become more innovative and increase the chances of greater success.

Continued success in testing and measuring site improvements for a primary goal (i.e. - conversion) increases the value of an analyst and their merit as authority among colleagues. It has been my personal experience that the more you test and objectively report complete results, the more weight your contribution is given. Sometimes things do not support your hypothesis and it is equally as important that these results are given to the appropriate people. Should the analyst be lucky enough to be surrounded by highly intelligent peers, the resulting discussion from success or failure from each hypothesis should be equally as fruitful in insights on which to base future hypotheses.

GWO Experiments need not be enormous and complicated from the start. Get into testing by making up four or five alternative headlines for a high traffic page. Try each of the testing methodologies. Here’s a quick test to try just to get the mechanics down:

1. Identify and analyze a page for testing with decent traffic and lackluster performance. (*This will help benchmark performance to understand impact more clearly.)
2. Create four or five suitable alternatives (with at least one marginally poor headline to create divergence).
3. Make an appropriate number of copies of pages to match the number of variant headlines
4. Rename pages of variations and supply new URLs to Google
5. Install GWO provided scripts on original page
6. Install GWO provided script on variant pages
7. Install GWO conversion goal script on goal page
8. Test the scripts
9. Execute test

After a few days, or weeks, depending on the level of traffic and the apparent difference in your variations, you should experience some divergence which can begin to allude to validating and supporting, or, possibly diminishing your claim. (again, regardless of the outcome, so long as you have clear data, the test should be considered a success)

(I’ll publish an edit with some photos and some tips on usability here when I have the time.)

Getting a test under your belt can be an enormous benefit. Just knowing you can perform a test makes you think differently. That aspect of perspective is a huge step in getting to where the real analysis takes place.