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.
3 Types of A/B Testing Metrics- Use the right ones or fail
The three types of metrics are your business metrics (those that impact your bottom line), your test specific metrics (those that are unique to each test), and your analytics metrics (those that are in all your analytics tools).
We will use a real-life example of what these metrics look like for a business.
In this video, you will learn several things to improve your testing efforts including:
- The differences between each type of metric
- When you should use each type of metric
- How primary metrics should guide test results interpretation
- Which types of metrics can be primary metrics, secondary metrics, or both
- A massive caution at optimizing to certain metrics
- And much more
Testing Theory is where professional testers turn to do better A/B testing and get more conversions.