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\u201cI think I need to see a testing plan.\u00a0 What can we do with the button?\u201d he said.<\/p>\n
What? When did C-level executives start caring about the details of landing pages<\/i>, I wondered.<\/p>\n
\u201cSure thing,\u201d I replied.<\/p>\n
That was the interaction I recently had at a Fortune 50 client meeting.\u00a0 Agencies are always required to prove value, so the point of the request was no surprise. However, a C-level executive wanting to get into the weeds of A\/B testing was a new one.\u00a0 That\u2019s where digital is at the moment though. The digital mystique has started to fade, and parts of the mystery are becoming normal business lexicon. Testing used to be relegated purely to the realm of a small handful of developers, then into web analytics folks, then business analysts, and is now being planned and executed often by general marketers or, gasp, finance people.\u00a0 More and more often as Google Analytics has made complex web analysis seem easy<\/i>, the planning and analysis of results<\/a>\u00a0has become commonplace and really, expected.\u00a0 While thinking that testing is easy may be a mistake, what is more difficult to manage is how commonly the nuances of A\/B testing are dismissed.\u00a0 However, like so much of life, easy is a matter of the subjective.<\/p>\n I don\u2019t mean to dissuade anyone from running A\/B tests by implying that it is difficult. In fact, I strongly encourage testing.\u00a0 Having made the transition in my career from web analytics guy to business analyst to general marketer to strategist, I have had time along the way to develop four guidelines to running an A\/B Test. If you are already following these guidelines, you are ahead of the competition. If you aren\u2019t using these guidelines, I am amazed at your\u2026creativity.<\/p>\n I love that Google Analytics has made web analytics something that is accessible and at the forefront of marketer\u2019s minds. The rise of Google Analytics, due to its deceptive simplicity and excellent price point, has brought web analytics into casual marketing meetings. The issue with casual usage is that interpreting analytics is still complicated, even when those analytics are packaged well. Web analytics offer a data trend not an absolute.\u00a0 Web trends even has \u201ctrend\u201d in its name. In order to plan for a great test, and to choose relevant page elements to test, a fundamental understanding of your analytics package is required.\u00a0 Knowing which pages are actually converting well and not, in context with traffic sources and mediums, as well as time on page and bounce rates and, more importantly, understanding the context of those numbers, are integral to performing a good test.\u00a0 Having a junior analyst or general marketer login to your analytics and try to interpret your data is a recipe for A\/B tests that repeatedly end in no significant difference.\u00a0 Yes, sometimes the test might end in a push with the creative of both performing in a dead heat, but much more often a test ending in a push is the result of poor planning based on faulty data.\u00a0 The data you currently have is a roadmap, it should give you some ideas about what types of tests would make sense. Is your traffic coming from social media? Using images and large calls to action are the norm.\u00a0 Is your visitor coming from a precise query via Search Engine Optimisation<\/a>, perhaps detailed product information or a spiff of a whitepaper or technical docs might get the conversion? An e-commerce value shopper coming in from product feeds needs to see a price comparison, etc.?\u00a0 Of course you don\u2019t want to alienate audience segments, but you do want to certainly serve your biggest audience base to increase conversions. If you don\u2019t know what your data means, you simply can\u2019t get start the journey.<\/p>\n When you have gone through planning, and are setting up your test, you shouldn\u2019t start with an A\/B test.\u00a0 You should start with an A\/A test.\u00a0 If you have never used your software before, you\u00a0really<\/b>\u00a0should start with an A\/A test.\u00a0 That is you should first load the same creative rather than two pieces of creative and essentially test your software. There are three great reasons to start with an A\/A test:<\/p>\n Your tests should achieve statistical relevance.<\/p>\n Pure and simple, the test should run until you understand the meaning of the data and have a clear winner. While that is simple to say, it is nearly impossible to practically account for. The reason being most people are risk-averse. People are especially risk-averse for something that they perceive as a bottom line driver to both their company\u2019s revenue and their compensation-lead volume.\u00a0 Risk aversion might manifest itself by someone feeling a need to claim a winner, stop the bleeding of a bad test (even if the loser is the original element that has been in play for years), rushing to start a new test\u2026you get the picture. Resist people demanding the easy answer, and get the correct answer.\u00a0 Achieve statistical relevance and help yourself by not using time to set your test limitations- instead, use conversions.\u00a0 If you are \u201closing money\u201d by running a test then something more is at work than simply a landing page optimisation test.\u00a0 That something might be reality, but it is most likely perception.\u00a0 Be sure to advocate, educate and carry on.<\/p>\n What determines statistical relevance largely will depend on your traffic, medium, and test scenario. That said- there are a number of\u00a0A\/B split test calculators<\/a>\u00a0to use in your testing that will take the onus of testing off your shoulders. \u00a0Use them and let the test run until you have a relevant test.<\/p>\n \n Get exclusive insights into digital 1.\u00a0<\/b>Understand your current data.<\/b><\/h2>\n
2.\u00a0<\/b>Multivariate test? A\/B Test? No, A\/A test<\/b><\/h2>\n
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3.\u00a0<\/b>What triggers the end of the test?<\/b><\/h2>\n
4. What are you going to do about it?<\/b><\/h2>\n
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