Prioritize multiple hypotheses using PXL model
1. Add all of your hypotheses to the first column in the PXL framework.
You can easily search and download a copy of the framework. You should open the Googlesheets, make your copy of it and save it in your library for future use.
2. Customize the template and variables to suit your organization's goals and industry, and optimize your optimization program.
All organizations operate differently, For example, maybe you’re operating in tangent with a branding or user experience team, and it’s critical that the hypothesis conforms to brand guidelines. Add it as a variable. Maybe you’re at a startup whose acquisition engine is fueled primarily by SEO. Maybe your funding depends on that stream of customers. So you could add a category like, “doesn’t interfere with SEO,” which might alter some headline or copy tests. Point is, all organizations operate under different assumptions, but by customizing the template, you can account for them, and optimize your optimization program
3. Add a number in each metric column based on the scoring guidelines in the column headers to score each hypothesis.
CXL made this framework 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 CXL 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, CXL specify how things change. For instance, on the Noticeability of the Change variable, you either mark it a 2 or a 0.
4. Sort the hypotheses using the Result column, which contains the total for each row, in descending order.
The Result column might also be titled something like PXL Score or Priority Score.
5. Your highest priority hypotheses are the ones with the highest Result scores.
6. Check the top hypotheses to make sure they make sense based on your goals, KPIs, and other initiatives.
If they don’t make sense, review the scoring. See if anything needs to be adjusted and if additional metrics need to be added. For example, adding a Political Need metric can be helpful if certain hypotheses need to be a higher priority based on upcoming projects, upcoming deadlines, urgent requests from a particular team, etc. You could use a score from zero to three with three meaning it has a high political need, which would move it higher up in the framework.