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A/B Testing

multi-armed bandit tests

When should you use bandit tests, and when is A/B/n testing best?

Though there are some strong proponents (and opponents) of bandit testing, there are certain use cases where bandit testing may be optimal. Question is, when?

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Semrush Signup form

Growing your mailing list and generating leads should be a focus of your marketing. If HubSpot didn’t have 215,000 blog subscribers, they wouldn’t have a business.

Too many businesses don’t give sign-up forms enough attention. They just throw something together—then complain that online lead generation “doesn’t work.”

This post shows you 14 keys to building sign-up forms that convert.

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Best Practices to Establish a Single Source of Truth (SSOT) for A/B Testing

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.

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Orange and apple

A/B testing splits traffic 50/50 between a control and a variation. A/B split testing is a new term for an old techniquecontrolled experimentation.

Yet for all the content out there about it, people still test the wrong things and run A/B tests incorrectly.

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Times Square Timelapse

Your brand’s value proposition is a promise of value to be delivered. It’s the primary reason a prospect should buy from you.

It’s also the #1 thing that determines whether people will bother reading more about your product or hit the back button. On your site, your value proposition is the main thing you need to test—if you get it right, it will be a huge boost.

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Airplane in the sky

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.

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Google Optimize

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:

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Cat tail

One-tailed tests allow for the possibility of an effect in one direction. Two-tailed tests test for the possibility of an effect in two directions—positive and negative.

Simple as that concept may seem, there’s a lot of controversy around one-tailed vs. two-tailed testing. Articles like this one lambaste the shortcomings of one-tailed testing, saying that “unsophisticated users love them.”

On the flip side, some articles and discussions take a more balanced approach and say there’s a time and a place for both.

Let’s set the record straight.

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Which Button Color Converts the Best?

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.

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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 doesn’t 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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