Evaluating A/B Tests with the Efficiency Equation

If you want to run good split tests and get more wins with every test, the Efficiency Equation can guide your efforts. This simple equation can help you know which tests are efficient to run and which ones you should avoid. It can also help you know where you should focus your future efforts by doing post-test comparisons.

If you want more wins with your conversion rate optimization efforts, then this video is for you.

In this video, you will learn several things to improve your A/B testing including:

  • What the Efficiency Equation is
  • How to use the Efficiency Equation
  • The value of pre-test analysis with the equation
  • Doing post-test comparisons with the equation
  • A real-life example of the Efficiency Equation

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

Guess Who A/B Testing Strategy

The strategy to win at the game of Guess Who is the same strategy that can help you win at A/B Testing. By employing the Guess Who Testing strategy you will be able to get more wins more often.

The strategy says that you should eliminate things that aren’t good, not by trying everything out, but by asking bigger broader questions first. If you get too specific too soon, you will fail at testing just like you will lose at the game.

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

  • What the Guess Who testing strategy is
  • How the Guess Who testing strategy works
  • The benefits of using the Guess Who testing strategy

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

Actionable Rates of Action

A rate of action by itself doesn’t tell you if it is good or bad. Most rates of action are used in correlative data sets and lead to poor recommendations.

In this video, I will show you a common example of how rates of action are used improperly. We will also discuss the two things needed to make your rates of action actionable.

  1. Comparable data sets
  2. Causal data

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

Experience Optimization not CRO

True optimization experts understand this very important principle. We don’t optimize conversion rates and we aren’t A/B testing teams. We aren’t split testers who also run multivariate tests. We are experience optimizers.

Our true success as optimizers in the digital marketing industry is to realize that we are optimizing the visitor experience. We aren’t optimizing rates or running tests. If we aren’t improving the visitor experience we are potentially getting short term gains at the cost of long term profits.

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

  • The difference between experience optimization and conversion rate optimization
  • How you can have one without the other and one that does both
  • The key metrics you need to consider as you become experience optimizers

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

Combining Multiple Testing Strategies

The best conversion rate optimizers use multiple testing strategies to maximize the conversions and learnings with their split tests. In this video, I will show you a few of the strategies we employed to optimize our home page.

A/B Testing is a dynamic process and the strategic approaches used should be dynamic as well. Knowing what strategies to use at what time is essential to maximizing impact.

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

  • The baseline strategy
  • The existence testing strategy
  • Multivariate testing as a strategy
  • Isolation as a strategy
  • Iteration as a strategy

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

Case Study: Order Confirmation Page A/B Test

After a visitor has completed the main action you want them to take, what should you do with them at that point?

Once an order is complete you aren’t done with your visitors. They have a relationship with you and you have a relationship with them. Just because they have completed an order or signed up, that relationship still needs to be nurtured to encourage future opportunities.

This is a quick a/b testing case study of a test on the confirmation page. Our business question was about how much content we should show the visitor and what type of content would be most compelling to get a visitor to further engage.

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

  • How we created variations to offer different content
  • How we reduced some content to learn about the ideal amount of content
  • The things the test helped us learn and the gaps in our learning that needed follow up testing
  • A common principle that is applicable to most sites and businesses.

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

Get more information at  https://www.testingtheory.com

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.

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