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Stopping A/B Tests: How Many Conversions Do I Need?

A/B testing is great and very easy to do these days. Tools are getting better and better. As a result, people rely more and more on the tools. As a result, critical thinking is much less common.

It’s not fair to just blame the tools of course. It’s very human to try to (over)simplify everything. Now the internet is flooded with A/B testing posts and case studies full of bullshit data, imaginary wins. Be wary when you read any testing case study, or whenever you hear someone say “we tested that”.

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Why A/A Testing is a Waste of Time

The title may seem a bit controversial, but this is a fairly common question I get from large (and small) companies – “Should I run A/A tests to check whether my experiment is working?”

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5 Uncomfortable A/B Testing Questions

AB testing is supposed to be straightforward and extremely transparent. It should be so easy to see the ROI – especially when compared to opaque stuff like SEO. But is it really so transparent as we’d like to think?

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The Greatest Factors Limiting Your Testing Program

As more and more people start to look to testing and conversion optimization as a consistent and meaningful tool for their marketing and other initiatives it is important that people start to realize that optimization as a discipline is not just a false add-on to existing work.   Testing when done correctly can and should be by far the number one driver of revenue for your entire site and organization, and yet according to 3 of the major tools on the market the average testing program only sees 14% of their tests succeed.

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Statistical Significance Does Not Equal Validity (or Why You Get Imaginary Lifts)

A very common scenario: a business runs tens and tens of A/B tests over the course of a year, and many of them “win”. Some tests get you 25% uplift in revenue, or even higher. Yet – when you roll out the change, the revenue does not increase 25%. And 12 months after running all those tests, the conversion rate is still pretty much the same. How come?

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Migrating to Universal Analytics Using Google Tag Manager

As more and more business owners are learning about the benefits of the new version of Google Analytics (referred to as “Universal Analytics”) as well as the utility of Tag Management Systems (made even more popular by the release of the free Google Tag Manager), Peep reached out to me to write an article about moving an inline GA implementation to Google Tag Manager. This is work we do often over at Analytics Ninja, so I feel more than happy to provide this guide for CXL’s readers.

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Pulling Back The Curtain on P-Values (or How I Learned To Love Small Data)

For all of the talk about how awesome (and big, don’t forget big) Big data is, one of the favorite tools in the conversion optimization toolkit, A/B Testing, is decidedly small data.

Optimization, winners and losers, Lean this that or the other thing, at the end of the day, A/B Testing is really just an application of sampling.

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