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.
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.
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.
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.
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.
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.
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:
Partner
Teaching and training about A/B Testing
Proactive split testing plans
Evangelizing data and results
Identifying multiple angles, departments, and people
Using all your resources and finding allies
Having patience
Testing Theory is
where professional testers turn to do better A/B testing and get more
conversions.
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.
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
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.
