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:
- 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.
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.
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.
Quantify the Qualitative Data and Use the Right Quantitative Data
Too often organizations make decisions based on just a few pieces of qualitative feedback or a limited sampling of the quantitative data available. In this video, you will learn how to quantify qualitative feedback so that it can be measured and understood for what it truly is. You will also learn about using the full set of quantitative data available to you.
By quantifying the qualitative and using more quantitative data with your results you will be able to hone in on what matters to your visitors and your business without being swayed by a few strong opinions. You will also be able to put down the internal nay-sayers.
In this video, you will learn several things to improve your testing efforts including:
- What it means to quantify qualitative feedback
- How using the right quantitative data can help you avoid being trapped
- Why telling the full story with the data is essential to building a testing program
Testing Theory is where professional testers turn to do better A/B testing and get more conversions.
Heatmaps And A/B Testing
Like other correlative data sets, heatmaps are often used incorrectly to make decisions about a visitor experience. This video will review how to strategically use heatmaps with your split testing to get the maximum value for your visitors. We will also cover the 2 things to avoid doing as you use your site’s heatmaps.
By using your heatmaps the right way, you will be able to optimize your site faster and get that valuable and intuitive visual analysis with your testing.
In this video, you will learn several things to improve your testing efforts including:
- How correlative heatmaps can hurt your optimization efforts
- The danger of using click only heatmaps
- The benefit of analyzing mouse movement heatmaps
- The 4 steps to follow to get the most value out of your heatmap and testing data
Testing Theory is where professional testers turn to do better A/B testing and get more conversions.
Zoom Zoom A/B Testing Strategy
The Zoom Zoom A/B testing strategy will help you get better testing results by helping you focus on the things that matter most. Too many split testers test things that are so granular that they don’t even matter. This results in tests that have low lifts or no lift at all.
By following the Zoom-Zoom testing strategy you will be able to identify what matters to your visitors and get bigger gains faster because you will know where the value is.
In this video, you will learn several things to improve your testing efforts including:
- How to avoid testing things that are too granular
- What it means to zoom out with your business question
- What a zoom zoom sandwich is
- The 4 steps to following the Zoom-Zoom Testing Strategy
Testing Theory is where professional testers turn to do better A/B testing and get more conversions.
Creating a Culture of Optimization
Good A/B testing isn’t about just running split tests. Conversion Rate Optimization is about creating a data-driven culture inside the company. This video will cover 6 ways to build a data-driven culture of optimization. As you do each of these things you will create a stronger optimization practice.
Here are the six steps and the things that we will cover.
1. Get Test Results
2. Evangelize Results Broadly
3. Ideation From The Company
4. Create Infrastructure
5. Integrate Testing Widely
6. Cross-Functional Team
Testing Theory is where professional testers turn to do better A/B testing and get more conversions.
WebsiteBox Existence Testing
This A/B testing example shows a case study from WebsiteBox.com. The very first two tests we did with them helped us learn how to prioritize our testing to make future tests even more valuable.
These tests show how to do a back to back existence knockout punch that would be great for the first two tests of any site.
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:
- The efficiency of existence testing current pages and content
- Learning over lift approach
- Learning regardless of loss in lift
- How the relative impact of existence testing helps prioritize efforts
- How to do a reverse existence test
- Combination effect of elements
- Redesign based on the discovered value of elements relative to each other
Testing Theory is where professional testers turn to do better A/B testing and get more conversions.