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.

Multiple A/B Tests at Once

Have you wondered if you can run multiple A/B Tests at once? If you run more than one split test at once and do it correctly, running multiple tests can maximize your gains. If however, it is done incorrectly your testing program is put at risk.

This video will review the important considerations in making the decision to run multiple tests at once.

  • The strategy of running multiple tests at once
  • How location impacts the decision
  • How traffic levels impact the decision

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

Creating Buy-In to Test – 7 Strategies

Do you feel like the organization you work with isn’t doing as much testing as you could? Are you looking for strategies on how to create buy-in for your testing program? I will share with you 7 ways to get the buy-in you need to do more split testing.

These are strategies that I have employed to take businesses doing no testing to making testing essential to the roadmap and planning of the business.

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

  1. Partner
  2. Teaching and training about A/B Testing
  3. Proactive split testing plans
  4. Evangelizing data and results
  5. Identifying multiple angles, departments, and people
  6. Using all your resources and finding allies
  7. Having patience

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

Data Science and A/B Testing

What is data science? What makes a good data scientist? What does A/B Testing have to do with data science? What is the hierarchy of needs for data science? Learn the answers to all these questions and more as we take a look at how optimization and learning with data is best accomplished with experimentation.

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

  • What data science is
  • How A/B Testing fits into the data science hierarchy of needs
  • The relationship between machine learning & AI and A/B Testing
  • What makes a good data scientist

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

Essential Roles of the Cross-Functional Testing Team

In order to get more value by running more and better tests, you have to do more tests. To do more tests you have to create a cross-functional testing team. With the right roles represented a cross-functional testing team enables valuable testing.

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

  • The key roles that every successful cross-functional testing team has represented
  • What to do when you are missing critical roles
  • How to grow your team when you are short on necessary roles

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

Not All Data is Created Equal – 3 Factors to Evaluate Different Data Types

Not all data is equal, but in decision making, you often hear all data being given equal weight. I am going to show you the 3 factors you should use when you evaluate how good a type of data is. We will also talk about how bias can be part of any data set and how it creeps into different types of data.

3 Factors
• Correlation vs. Causation
• Our own ideas vs. What the actual visitors think
• Bias – Type of data can have bias built into it

Types of Data Sets
• UX & Heuristics – Design best practices, your own evaluation of the visitor experience
• User Research / Usability Studies – People or could be end users, but just a few of them, also easily biased
• Analytics & Heatmaps – actual end-user behavior, Correlative data, no bias in the data just in how it is interpreted
• Customer Feedback / Surveys – Actual voice of the end-user, lots of bias because of vocal minorities
• A/B Testing Data – Actual end-users tested, Causal data, no bias in the test

Usability Testing vs. A/B Testing

There are many testing methodologies to answer business questions, but not all of them are equal. Some prefer one method over another and neglect using multiple methods. This video compares and contrasts Usability Testing vs. A/B Testing.

Having worked extensively with an organization that was very entrenched in doing usability testing, I have found there are good and bad things about each approach. There is also an optimal way to do both to derive the most value of any of your research or optimization efforts.

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

  • The pros and cons of each method
  • The difference in how each method determines success
  • How sample size impacts each method
  • Segmentation considerations with each method
  • How a natural vs. unnatural environment influences visitors
  • How each method has a different view of risk

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

Rapid Iteration The Right Way Using A/B Testing

Rapid iterations sound great in theory, but if it is done wrong it can be devastating for an organization and its customers. Rapidly iterating without the causal data you get from A/B Testing can also be detrimental.

Iterating within a strong optimization program is the surest way to iterate rapidly in the right direction.

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

  • The limitations of rapid design iteration
  • How sample size, moderator bias, and going too quickly can be detrimental
  • How A/B testing solves for the limitations of small group design iteration sessions
  • Four things to do that will increase your ability to iterate rapidly in your optimization program

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

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