Landing Page Optimization, or LPO for short, is best explained as wrenching more output from a site without pulling additional traffic to it. It takes a series of tests to see if you can beat what your site is currently achieving. There are many methodologies, such as A/B and multivariate tests, but it all boils down to leveraging more conversions from your existing traffic.

Landing page optimization bookThis is a review of Landing Page Optimization: the Definitive Guide to Testing and Tuning for Conversions, by Tim Ash, Rich Page & Maura Guinty. In the authors’ words, LPO is “the scientific and systematic discovery of the best audience manipulators available to you.” LPO is a key part of the larger world of Conversion Optimization, which aims at making every stage of prospect engagement as efficient as possible. Regardless of whether prospects came via inbound marketing or direct response campaigns, all agree that the only way to guarantee marketing ROI is to test and tune landing pages. I’ve also reviewed Chris Goward’s “You Should Test That!”

The book starts out explaining that the performance of a web page is elastic; of all the visitors to a page, there are some who would follow your call to action, but something about the page prevented them from doing so. The book aims to help you turn as many of these potential conversions into actual conversions. Be aware though that almost everything about a page can be tested, right down to how you reveal pricing or disclose your privacy statement; the law of optimization applies almost as broadly as the law of gravity.

Getting Onboard the Optimization Bandwagon

Recently released tools have put testing within almost everyone’s reach. A non-coder whose site has a content management system can set up a test using the Content Experiments tool within Google Analytics. Once a certain content/design element on the page is changed, the original or “A” version is pitted against that alternative or “B” version, with the help of some pasted script that splits the traffic in half and tracks how each version performed.

While every element could be optimized by testing, the authors outline a shortcut to the process. They list 7 tactics they see so frequently on sub-optimal webpages, they are considered “7 deadly sins” we should always avoid. These poor design or content choices can be averted by simply anticipating the visitor’s questions and building webpages that help them get what they’re looking for.

Statistics and Actionable Information

For most of us, analysis stops with a time-series graph of historical conversions. The book correctly tells us that we must go beyond, to hypothesize how likely the NEXT visitor is to convert. This use of Inferential Statistics makes us ask tough questions about what’s causing conversions and how we could influence more people to convert. Only when we push ourselves to run a methodical test – which takes time up front – will our optimization efforts make headway. Doing this ensures that at the end of the test, we’ll have results that are statistically significant and we’ll be able to act on that information.

Ash and his co-authors do a good job in Chapters 10-12 of explaining several test plans and methodologies. I found these chapters on statistics, though tough slogging, to be the most helpful. They’re better than my old university textbooks for guiding how to gauge the accuracy of web testing results.

Big Data and Big Companies

A harsh reality here is that you have to have a large enough data set (i.e. enough visitors) to have valid test results. If your traffic volumes are low, you have to wait longer to collect a sufficient amount of data. The authors show their bias here for larger company sites; they offer little help to smaller site owners looking to reach the threshold their tests all require, except for urging them to stick with simple A/B tests.

avinash-kaushikAnother way the book is biased toward larger companies is when it comes to building optimization teams, which is the focus of the book’s last quarter. LPO in most smaller businesses is a side-project done by one or two internal people, perhaps with the help of an outside consultant. Instead of explaining how this approach could work, the book describes how to form a multi-disciplinary group that’s dedicated to site optimization. A “Getting Buy-in” section goes further by detailing how such a team should business-case an optimization test (though if a company has already created a whole team, logic dictates that they’d also lend them enough budgetary slack to do their jobs). So be aware, there’s little in this chapter that the average small/midsized business will find applicable.

I’ll summarize my opinion by comparing the book to Avinash Kaushik’s Web Analytics 2.0, another 450+ page book that acts as a good companion piece. With as much breadth as a university survey-course, Ash’s book certainly tries to be the authoritative word on the subject of LPO. Kaushik’s book is narrower, but it gives step-by-step specifics that a practitioner won’t find in Ash’s book. I will credit Ash for distilling trends he’s gleaned from doing many optimization projects. I’m happier seeing his strong opinions in the book than learning for myself what doesn’t work. There’s so much to test…and so little time.

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Image credit: Sybex Publishing