A/B Test Prioritisation: Why I believe in the PXL Framework
In the digital realm, A/B testing is the holy grail for making data-driven decisions. Whether you’re contemplating a new checkout process or tinkering with ad copy, A/B testing offers invaluable insights. However, the conundrum lies in choosing which tests to run first, especially when you’re brimming with ideas. Time, resources, and traffic are all finite, so how do you ensure you’re investing in the most promising tests?
Fear not! This post will walk you through the labyrinth of prioritisation frameworks, dissect their pros and cons, and introduce you to a revolutionary approach: the PXL framework. So, let’s dive in!
The Landscape of Existing Prioritisation Frameworks
The PIE Framework
The PIE framework, crafted by Chris Goward, is perhaps the most well-known. It evaluates test ideas based on three criteria:
- Potential: How much room for improvement exists?
- Importance: What’s the value of the traffic on the page?
- Ease: How challenging is the test to implement?
While popular, the PIE framework suffers from ambiguity. For instance, how do you objectively measure an idea’s potential? The framework leaves too much room for interpretation, making it less reliable.
The ICE Score
Sean Ellis introduced the ICE Score, another triad of variables:
- Impact: What’s the potential impact if the test succeeds?
- Confidence: How certain are you that the idea will work?
- Ease: Again, how easy is it to implement the test?
The ICE Score, while useful, also falls short in objectivity. If you’re already confident that an idea will work, why test it at all? The framework can easily be manipulated to fit personal biases.
Another ICE Score: A Slight Twist
This version of the ICE Score also employs three core factors but with a slight twist:
- Impact: What’s the potential benefit?
- Cost: What’s the financial investment required?
- Effort: What resources and time are needed?
This framework uses a binary scoring system of 1 or 2, depending on whether the answer is “high” or “low.” While this improves accuracy, it still doesn’t solve the problem of multiple ideas scoring the same.
Hotwire’s Prioritisation Model
Hotwire’s model, shared at a CXL Live event, also uses a binary scoring system but considers a plethora of factors. While it eliminates subjectivity to some extent, it’s not a one-size-fits-all solution.
Introducing the PXL Framework: A Game-Changer
It’s designed to be as objective as possible, relying on data to influence scoring. Here’s what sets it apart:
- Visibility: Is the change above the fold? If yes, it’s likely to have a greater impact.
- Traffic: Will the test run on a high-traffic page? More traffic means a potentially bigger payoff.
- Noticeability: Can people spot the change in less than 5 seconds? If not, the impact could be minimal.
- Addition or Deletion: Does the change add or remove elements from the page? This can significantly affect outcomes.
Furthermore, this framework emphasises data-backed ideas. Are your hypotheses supported by user testing, surveys, heatmaps, or digital analytics? If so, they’re more likely to succeed.
How the PXL Grading System Works
The system uses a binary scoring method but also weighs different criteria based on their importance. For instance, the noticeability of a change is scored either as 2 or 0, as opposed to 1 or 0 for less impactful criteria.
Customise the PXL Framework to Your Needs
Every business is unique. That’s why the framework is flexible, allowing you to add criteria that are pertinent to your specific situation. For example, if SEO drives your marketing, you can include it as a criterion.
Conclusion: Start Testing Better Ideas Today
In the world of A/B testing, prioritisation is not just an option; it’s a necessity. The PXL framework offers an objective, data-driven approach to help you make the most of your testing efforts. So why wait? Start prioritising your A/B test ideas effectively today!
Grab your own copy of our PXL framework here and begin your journey towards more successful A/B testing.
