Your CX testing lives or dies on the quality of your data. You can’t form valid, testable hypotheses using questionable data. And you can’t trust the outcomes of your tests if you don’t know you’re looking at accurate metrics.
That’s why you need to build your testing program around a Single Source of Truth (SSOT) dataset. If you can’t, even the simplest A/B test will lack value. This article explores why establishing an SSOT is so important and shares some of the field-tested best practices we’ve developed for doing that here at Kameleoon.
A/B testing splits traffic 50/50 between a control and a variation. A/B split testing is a new term for an old technique—controlled experimentation.
Yet for all the content out there about it, people still test the wrong things and run A/B tests incorrectly.
A landing page is the first page that visitors see after clicking on your banner ad, PPC ad, or promotional email. It can be a specific page on your website or a separate page created exclusively for search engines.
Landing pages direct visitors to take a specific action, such as making a purchase, completing a registration, or subscribing to your email list.
Your landing page often determines the success of your ad campaign. A good landing page equals good ROI. A crappy landing page (needlessly) wastes money.
Chances are, you’ve heard of Google Optimize by now. It’s Google’s solution for A/B testing and personalization. Over the years, it has become a popular solution for optimizers around the world who wanted a freemium tool to do A/B testing.
In this post, you will learn what you can really expect from this tool. How do you configure it properly? How do you run your first experiment? Let’s go into details:
Color is an essential part of how we experience the world. But do colors really matter for conversion optimization? Can a button color guarantee better performance for a call to action (CTA)?
No single color is better than another. Ultimately, what matters is how much a button color contrasts with the area around it.
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 doesn’t increase 25%. And 12 months after running all those tests, the conversion rate is still pretty much the same. How come?
Even if your A/B tests are well planned and strategized, when run, they can often lead to non-significant results and erroneous interpretations.
You’re especially prone to errors if incorrect statistical approaches are used.
In this post we’ll illustrate the 10 most important statistical traps to be aware of, and more importantly, how to avoid them.
As a business, your email list is one of the most valuable assets you have. The bigger your list and more engaged your subscribers, the more money you can make.
Having a well-thought-out plan for A/B testing Facebook ad campaigns is essential if you want to improve your performance reliably and consistently.
And the more you test, the better. A study of 37,259 Facebook ads found that “most companies only have one ad, but the best had hundreds.”
A/B testing Facebook ad campaigns can get complicated quickly (and easily produce invalid results). Spending the time upfront to perfect your testing process and structure will go a long way.
When should you use multivariate testing, and when is A/B/n testing best?
The answer is both simple and complex.