Unlock The Predictive Power Of 527 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 102,527,066 visitors. That's a lot of testing to do on your own.)
Company-Wide
$360
/ month
Billed yearly at $4320$5184
Unlimited User Accounts
License to use the data by unlimited users.
Access to 527+ searchable a/b tests
Sort patterns and tests by impact
Access to all templates
1 A/B Test review per month
(1:1 call to offer guidance on test design)
Team
$120
/ month
Billed yearly at $1440$1728
5 User Accounts
License to use the data by up to 5 users.
Access to 527+ searchable a/b tests
Sort patterns and tests by impact
Access to all templates
Solo
$60
/ month
Billed yearly at $720$864
1 User Account
License to use the data by 1 user.
Access to 527+ searchable a/b tests
Sort patterns and tests by impact
Access to all templates
All Plans Come With
Access To All Tests
Get access to all published tests to guide your own design & experiments.
5+ New Tests Each Month
Get 5 new A/B tests each month as we obtain new test results.
Cancel Anytime
If you find that GoodUI Premium isn't for you, you can cancel at any time.
Companies That Trust GoodUI Patterns & Tests For Higher Conversions
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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 their experiments using instinct or gut feelings. That's difficult to do and may lead 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, while prioritizing experiments using actual effects or track records, teams detect more wins, faster.
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 bigger experiments often fail.
With GoodUI High Impact Leap Experiments Are Possible
With access to patterns and transparent track records from winning and losing tests, big leap (compounding) experiments are made possible. 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 input from what worked or didn't work in the past, we're more likely to repeat the 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.
Common Questions With Answers
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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.
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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.
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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.
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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.