Archive

Monthly Archives: October 2019

Hypothesis Handicapped A/B Testing

Trying to optimize a digital experience while you are hypothesis handicapped is like trying to live with handcuffs on. This video talks about 4 symptoms of being hypothesis handicapped and 4 ways to be free of it.

Optimizers that handicap their hypothesis don’t get the gains they otherwise would with their testing because they limit the possibilities of things they try.

In this video, we will discuss the makeup of a hypothesis and you will learn what it means to be hypothesis handicapped. We will talk about the symptoms which include:
• Testing only what you think will work
• Testing a small set of alternatives that all “safely” revolved around your main concept
• Test results show exactly what you had hoped would win so you stop testing more options
• Not forming any follow-up questions or testing actions

At the end, I will also give you four self-evaluation questions to free yourself from being hypothesis handicapped.

Testing Theory is where professional testers turn to do better A/B testing and get more conversions.

5 Cultural Quagmires Stopping Your A/B Testing

In order to do successful experience optimization, you have to have the right culture of optimization. There are many things that influence a culture of successful A/B Testing and there are even more elements of your culture that hurt your split tests.

In this video, I will cover 5 things that company cultures tend to develop that can hurt your conversion rate optimization efforts.

In this video, you will learn about some of the cultural challenges companies face including:

  • Fear of change
  • Overuse of other research methods
  • Guessing without data
  • Relying on correlative data
  • The IT Beast

Testing Theory is where professional testers turn to do better A/B testing and get more conversions.

Micro vs. Macro Conversions with A/B Testing

Which metric should you use to call a successful test winner, your macro conversion or the micro conversions? This is one of the most common questions I see. A/B testers need to understand which metrics they should use for evaluating test results.

This video will help you learn about choosing the best split testing metrics.

In this video, you will learn several things to improve your testing efforts including:

  • What a macro conversion is
  • What a micro conversion is
  • How to choose the best metrics for your testing programs
  • When to prioritize micro conversions first

Testing Theory is where professional testers turn to do better A/B testing and get more conversions.

Monetizing A/B Test Results

Split test results that don’t show an annualized impact aren’t as good for a number of reasons. Get more buy-in for testing by annualizing your A/B testing results.

This video will help you learn a ton about foundational tests to jumpstart your split testing.

In this video, you will learn several things to improve your testing efforts including:

  • 4 benefits of monetizing your test results
  • 4 inputs into the annualizing formula
  • The exact annualized monetization formula I use on my test results

Testing Theory is where professional testers turn to do better A/B testing and get more conversions.

Perfect Amount of Traffic Per Test Variation

How much traffic should you have per test? What about the ability to choose the percent of the audience per experience? How should you split the traffic among your different variations?

There is a perfect amount of traffic to include with each test and each tested variation.

In this video, you will learn about 4 strategic reasons for including more traffic in each test:

  • Segmentation implications
  • Statistical confidence implications
  • Population representation implications
  • Speed and timing of results implications

Testing Theory is where professional conversion rate optimizers turn to do better A/B testing and get more conversions.

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