How to prioritize which experiments to launch first

When you can finally launch experiments without depending on development, a new and better problem shows up: you have more ideas than you can run at once. Prioritizing well stops being a luxury and becomes the skill that determines your return.

Prioritizing isn't picking the idea you like the most, it's picking the one that best combines potential impact, confidence in the hypothesis and execution effort. This article gives you a practical framework to order your experiment backlog and start with what pays off most.

Three criteria to order your backlog#

A simple, robust way to prioritize is to score each experiment across three dimensions: the expected impact if it works, your level of confidence that it will work, and the effort it takes to execute. The ones that combine high impact, reasonable confidence and low effort go first.

The key is to be honest when scoring. The usual trap is overestimating the impact of the ideas that excite you most and underestimating the effort of the ones that look easy.

• Impact: how much it would move the key metric if the hypothesis holds.
• Confidence: what evidence or logic backs that it will work.
• Effort: how much it costs to set up, launch and measure the experiment.
• Traffic: whether there's enough volume for a reliable result.
• Reversibility: whether you can easily undo it if it goes wrong.

Start with the funnel that weighs most#

Beyond scoring each idea, it's worth concentrating effort where the most traffic and money are at stake, usually the main conversion funnel. A small uplift there beats a large one on a secondary page.

Prioritizing also means deciding where not to experiment yet. Exhausting the high-impact hypotheses at your hottest point before spreading thin concentrates learning where it pays off most.

When execution cost drops, priorities change#

The impact, confidence and effort framework changes radically when launching an experiment no longer depends on devs. As execution effort drops, many ideas that didn't pay off before become worthwhile.

With Bixlow, describing the goal and publishing the same day cuts effort so much that you can afford low-cost, fast-learning experiments you wouldn't even have considered before. Prioritizing stops being about rationing tickets and becomes about maximizing learning.

Key takeaways#

• Prioritize by combining expected impact, confidence in the hypothesis and execution effort.
• Concentrate your first experiments on the funnel that moves the most traffic and money.
• When execution cost drops without devs, many previously discarded ideas become worthwhile.