I spent a decade thinking countdown timers were magic, off the back of a business where I never actually tested the timer. So I dug through the research. They work sometimes, and the timer itself has almost nothing to do with it.

Sticky add to cart used to be my go-to fix for every store. Then a test barely moved the needle, and I found out why some sticky bars work and some just add friction.

This quick article explains what statistical significance is, and why it's something you can't shortcut.

Add to cart isn't always the right proxy. Here's how we pick and combine micro-conversions when a test doesn't have enough purchases to trust yet.

A 3-tier system for sorting any A/B test idea. Tier 1 touches price or offer, Tier 2 touches page flow, Tier 3 is surface UI only. Bigger changes need far less traffic to prove: a 50% lift needs roughly 6x fewer visitors than a 20% lift to reach significance.

Most A/B testing advice assumes huge traffic. Here's how to test offers, pricing and bigger changes when your store only gets a few thousand sessions a month.

Running two A/B tests on one page won't wreck your data. Here's the research on concurrent test interference, and how it unlocks 200+ tests a year.

90% of A/B tests produce less than 1.2% effect. After an A/A test revealed our client's store needed 100+ days to reach significance, we built a scoring framework that classifies every experiment idea into three tiers before it burns any runtime.

Running parallel A/B tests on Shopify is harder than it looks. I dug into how Intelligems actually handles template tests, onsite edits, and price tests under the hood, and figured out which ones can safely stack on the same page and which ones can't.