Unlock The Predictive Power Of 504 Tests And Counting To Learn Which Patterns Win, Lose, By How Much And Where

(Each test saves you weeks of testing time and up to thousands of dollars in potential upside from higher conversions. )
(Total sample size of all our tests is 96,539,661 visitors. That's a lot of testing to do on your own.)

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Get access to all published tests to guide your own design & experiments.

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Get 5 new A/B tests each month as we obtain new test results.

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Without GoodUI More Of Your A/B Tests Fall Flat Or Lose

We know the frustration of failing and/or insignificant a/b tests. It's very common for teams to struggle with streaks of failed or insignificant a/b tests one after another with ⅓ or fewer tests generating significant uplifts.

With GoodUI More Of Your A/B Tests Will Win

With access to GoodUI test results, teams increase their odds of detecting more positive and desirable outcomes. When prioritizing your experiments using patterns (actively updated with negative and positive tests), you rely on probabilities and start doing better than chance.

Without GoodUI You Struggle With Subjective Prioritization

Without test data, teams typically prioritize using instinct or gut feelings. That's difficult and leads to random 50/50 results. Not good. Not bad. Just average.

With GoodUI Evidence-Based Prioritization Is Done For You

With access to test data, teams do better than chance while prioritizing using actual effects or track records for greater accuracy and more wins.

Without GoodUI Big Experiments Are Risky

When running bigger experiments without any backing from evidence-based test data, there is an increased risk of multiple changes canceling each other out. This is the reason why often bigger experiments fail.

With GoodUI High Impact Leap Experiments Are Possible

With access to patterns and transparent track records from winning and losing tests, big leap experiments are made possible with lower risk. You can combine positive probability ideas into a single variation with the intent of maximizing gains and shortening your test’s duration.

Without GoodUI Design From Scratch By Re-Inventing The Wheel

Without remembering what worked in the past, we're more likely to repeat the old mistakes - reinventing the wheel.

With GoodUI Design Better Starting From Already Optimized Ideas

With access to GoodUI patterns and templates, you gain an advantage by designing using ideas with higher optimality.

Results Guarantee:

A/B test any GoodUI pattern and if it doesn't get you the predicted results you're happy with, we'll refund you 100% within 30 days.

Let me tell you why I offer this guarantee to you:

I stand by helping teams get positive results using past a/b tests - with probability on our side.

Although a 100% prediction is unrealistic, we’ve seen more positive experiment outcomes than not by using the GoodUI test data ourselves. Because we and the teams we work with have successfully replicated many of the positive patterns, I am also confident that they will also work for you - more likely than not.

It’s simple: use our pattern or test data to estimate your a/b tests. If this doesn’t help your experiment, growth or design team increase your win rate, email me and I’ll refund you 100% of the price within 30 days of purchase.

Jakub Linowski - GoodUI Founder

Common Questions With Answers

  • Are the patterns for desktop or mobile?

    Usually both and we show which ones were tested where. Most of the patterns are highly applicable to various devices. Our tests are also tagged with the device segments that they ran on.

  • What does the repeatability scale mean?

    Everytime a pattern is assigned with a new test, it gains or loses a repeatability point (depending on whether it won or lost). This is one simple way of how we separate the higher probability patterns from the underperformers.

  • Are the patterns guaranteed to work?

    No. It's about probabilities. The more a pattern works (with a higher track record of positive tests), the more likely it will work again. Having said that, we also publish very interesting cases where tests fail. We do this with the intention of helping everyone understand what to avoid and what to replicate.

  • What type of companies share test results on GoodUI?

    We have published quality experiments from a wide range of trusted and leading companies, including: Microsoft, Thomasnet, Backstage, Yummly, Elevate, Designlab, Reverb, VivaReal, ZAP, Volders, Rollbar, Kenhub, Expert Institute, Drip Agency, CXL, etc.

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