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Inside Each Monthly Issue You’ll Learn From:
- Successful & Failed Applications Of Various GoodUI Ideas
- Real Optimization Stories From Real Consulting Projects
- Testing Process Insights & Reflections
- Fully Transparent Results Data
- How We Make Decisions To Implement Completed Tests
- How We Tell If The Effects Are Real, Or Not
- Various Testing Strategies
Discover Testing Strategies As We Show Process
Use Datastories To Learn About How We Constantly Improve Our A/B Testing Strategies
A good testing strategy is quite simple on paper. The best tests, we believe, are characteristic of: lowest effort, highest effect, highest certainty, and lowest duration. We are constantly learning and driven to be the most efficient in our testing with the above criteria in mind. As we share our complete testing process from real consulting projects with real clients, you will learn along with us.
Not Everyone Has The Time Or Traffic To Run A Test
Use Datastories To Inspire Your Design Teams
Surely, one of the best ways to optimize your online business is to experiment with A/B tests. After all, you can only improve what you can measure. However, tests may take weeks or months of waiting and thousands of unique visitors to get good data. Some businesses will cut corners and act on misleading or insignificant results which can actually backfire and hurt your numbers. Instead, Datastories come with proper sample sizes, and tests with multiple variations worth precious weeks in design, implementation and analysis efforts alone. Know that the high quality results we share with you are repeatable and can be relied on to guide your business. The GoodUI ideas that we test can be used for inspiration as we see if they work in isolation and in various combinations.
Understand The Strength Of Losing & Winning Tests
Use Datastories To Learn About Both What Works & What Doesn’t
We don’t shy away from being honest in our reporting of test results. Always striving for high reusability, we start off by communicating clearly the primary and secondary goals of each test, including the exact way those are measured. On each experiment we also always share sample sizes, absolute conversion data and ranges for each variation so that you can assess how valid the effect really is. Finally, in our elaborate data tables we also do calculate and color code significance levels with p-values for you.