Just wrote this up based on a question I got yesterday and I thought it would be useful for you guys! This is always a fun question because there isn’t a clear answer and there's a lot of nuance. First and foremost, we need to make sure the changes make don’t HARM conversion rate. That will happen about 50% of the time. The trick is we don’t know which times that’s gonna be… so we have to test. Obviously, the more data we have the better. But we don’t want to run tests for months and months. Ask any statistician if you have enough data and they’re always going to say more is better. But we can’t tests run forevermore so we need to compromise and be ok with some level of uncertainty. At the same time, running a test for one single day also doesn’t feel right (for reasons we’ll go over). So the optimal strategy must be somewhere in the middle. Let’s go over some of the competing interests; ✅ Volume of visitors in the test - We don’t want to run a test to 20 visitors and decide the variant is a winner because it has one more conversion than the control. More data is almost certainly better for certainty that a variant is indeed better than the control. ✅ Difference in conversion rate. A control that has 1% CVR and a variant that has 4% CVR requires less data to be certain that we have an improvement in conversion rate. By the same token, if you have a 1% vs. 1.1% conversion rate, you’re going to need a lot of data to be confident that difference isn’t due to random chance. ✅ Product pricing/AOV. Higher ticket products can have a lot more variability day to day. If you have a product that’s more expensive, generally that means there’s a longer buying cycle. If your average buying cycle from click to buy is 7 days, you don’t want to make a decision after 4 days. You haven’t even let one business cycle run through yet. ✅ Getting a representative sample of traffic (days of week) - similar to above, when we are making long term predictions about conversion rate differences, we need to make sure that we have a sample that is close to our long term traffic. Would you want to poll a random set of Americans to make predictions on the Japanese economy? So when running a split test we want to make sure that we are running it during a relatively normal time period AND account for different traffic throughout the week.

Shame to admit it, I hardly know any of you. Would love to find ways to support you all and help y’all win. Grateful for @Tobias Allen for getting us all together 💪🏻

With so many powerful minds in here, I'm sure you're all as driven as I am to continue learning and growing. So, what are you learning? I'm learning a lot about HighLevel, as I'm migrating over to it as my CRM. I've also been learning a lot about sales from a tactical perspective as I'm the only seller as I grow my agency. And I'm learning process development as I'm standardizing client acquisition, onboarding, and management.

I'm thinking of hosting a zoom call for an hour or two later this week if anyone wants to hang and nerd out about marketing. If you'd be interested drop me a line

Forgive my Sunday night 'shower thought' questions haha Last night I asked my wife if she'd prefer a $100 gift card to The Cheesecake Factory (they're building one by our house) or $70 cash. She chose the cash. I tried again. Would she choose a $100 Amazon gift card or $90 cash? She chose the gift card. Interesting. The reason I changed the $70 to $90 is because first, I know that the value of those gift cards is different just in how they're used. You can add Amazon credit to your account and not worry about losing the card, forgetting to use it, or having it expire (which have all happened to us). I also know that my wife is always buying on Amazon, and doesn't go to The Cheesecake Factory that often, so potentially this could be a better proxy for buyer intent, rather than brand strength. But if I could isolate those specific gift card variables and keep them consistent, I think that I could get a good idea of how one brand stacks up against the competition.