The PXL framework: a prioritisation framework for growth marketers

The PXL framework is a prioritisation framework designed to help marketing and growth teams determine the order in which to work on experiment ideas.

Prioritisation frameworks help marketing and growth teams to:

1. Manage internal stakeholders

2. Bring transparency to team priorities

3. Eliminate opinions around what is and isn't important

4. Empower people to share ideas they feel will have impact

What are the key scoring frameworks?

Opportunity evaluation is an important skill for any high impact growth team that will improve over time. Working on the right projects instead of the wrong ones has a huge impact on team results. These scoring help you to determine what to prioritise and the best places to start.

A number of frameworks exist for evaluating opportunities and prioritising your marketing resources. Some of the most popular are shown below.

• RICE: Developed by Sean McBride at Intercom. Factors: Reach, Impact, Confidence, Effort.

• ICE: Developed by Sean Ellis at GrowthHackers. Factors: Impact, Confidence, Effort.

• PIE: Developed by Chris Goward at WiderFunnel. Factors: Potential, Importance, Ease.

• HiPPO: No specific developer. Factor: Highest paid person's opinion.

• BRASS: Developed by David Arnoux at Growth Tribe. Factors: Blink, Relevance, Availability, Scalability, Score.

• HIPE: Developed by Jeff Chang at Pinterest. Factors: Hypothesis, Investment, Precedent, Experience.

• DICET: Developed by Jeff Mignon at Pentalog. Factors: Dollars (or revenue) generated, Impact, Confidence, Ease, Time-to-money.

• PXL: Developed by Peep Laja at CXL. Factors: Above the fold, noticeable within 5 sec, high traffic pages, ease of implementation, and more.

PXL framework history

The PXL framework (developed by Peep Laja and the ConversionXL team) was designed to address some of the perceived shortcomings of the more popular prioritisation methods. Relatively simple frameworks such as ICE and PIE with just 3 scoring variables leave a lot that is open to interpretation and are often very subjective.

The CXL team set out to design a more objective framework for experimentation using a set of 10 specific questions to score a test. It should be noted that whilst the initial template includes the specific questions below, the intention was for the framework to be a template that can be modified based on individual organisations and needs.

How to use the PXL prioritisation framework

The PXL model was initially developed for conversion optimisation (CRO) projects, so whilst it is applicable to wider marketing and growth projects, it particularly lends itself well to identifying and prioritising underperforming pages for CRO activities.

• 1: Is the change above the fold? (1 or 0)

• 2: Is the change noticeable within 5 seconds? (2 or 0)

• 3: Is the change adding or removing an element? (2 or 0)

• 4: Is the change designed to increase user motivation? (1 or 0)

• 5: Is the change running on high traffic page(s)? (1 or 0)

• 6: Is the change addressing an issue discovered via user testing? (1 or 0)

• 7: Is the change addressing an issue discovered via qualitative feedback? (1 or 0)

• 8: Is the change addressing insights found via digital analytics? (1 or 0)

• 9: Is the change supported by mouse tracking, heat maps, or eye tracking? (1 or 0)

• 10: Is the change easy to implement? (<4 hrs = 3, up to 8 hrs = 2, under 2 days = 1, more = 0)

The PXL prioritisation formula

We made this under the assumption of a binary scale – you have to choose one or the other. So for most variables (unless otherwise noted), you choose either a 0 or a 1.

But we also wanted to weight certain variables because of their importance – how noticeable the change is, if something is added/removed, ease of implementation. So on these variables, we specifically say how things change. For instance, on the Noticeability of the Change variable, you either mark it a 2 or a 0.

Ease factor in the framework – potential, importance, and ease – is assigned a score between 1 and 10 which is used to calculate the overall PIE score.

The CXL team have made a PXL Test Prioritisation google sheet available here.

Downsides to the PXL framework

Here are some of the potential downsides to using PXL model as a prioritisation framework:

1. It requires customisation - the metrics in the screenshot above are just suggestions and should be tailored to your individual needs (the principle of PXL is that binary metrics are less subjective).

2. Newer growth teams may find it overly complicated.

3. Implementing prioritisation necessarily introduces some friction, but the more complex the framework, the more friction that is introduced.

4. The additional complexity adds friction to the growth process.

Resources

Recommended additional reading on the PXL scoring framework and prioritisation in general.

Final thoughts

For marketing and growth teams, the specifics of the various different scoring frameworks, and their pros and cons matter far less than picking one and implementing it within your team.

Creativity combined with rapid iteration are the keys to making progress on user growth. Remember that you can get to 10X growth by a combination of 2Xing a few different metrics, hitting one out of the park, or getting 10% increases across the board. They all multiply together to be 10X. If you can brainstorm a lot of ideas, going for quantity over quality, you’ll have a lot of ideas to evaluate for impact versus cost.

Andrew Chen

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