Welcome to Marketing School, the only podcast that provides daily top level marketing tips and strategies from entrepreneurs that practice what they preach and live what they teach. Let's start leveling up your marketing knowledge with your instructors, Neil Patel and Eric Sue. All right, guys, before we start, we got a special message from our sponsor. If you want to rank higher on Google, you gotta look at your page speed time. The faster website loads, the better off you are with Google's Core Vital update. That makes it super super important to optimize your site for low time. And one easy way to do it is use the host that Eric and I use, dream Hosts. So just go to dream host or Google it, find it, check it out, and it's a great way to improve your low time. Welcome to another episode of Marketing School. I'm Eric Sue and I'm Neil Patel, and today we are going to talk about how to run twenty powerful growth experiments per month. So they're not just growth experiments, they're powerful. So let me explain what a growth experiment is first, and then Neil and I can kind of go back and forth on our methodologies around this, and then go from there, I'll talk about you know, we can actually even talk about ourselves and then how our teams do it. I think that'll be helpful for people. But a growth experiment is generally this. If you think about Amazon the way they do things, this might complicate a little bit, but they run a lot of experiments, They test a lot of different things, and they're very rapid. They're a fast moving company. And even if you think about Google back in the mid day when they had a twenty percent time where you can work on anything, they're running experiments. Right Gmail was actually an experiment that came out from the employees over at Google. So when you're running experiment, there's two different decisions, and this actually came from one of Amazon's Jeff Bezos annual letters. There's a type one decision and a type two decision, right, or you can call it an experiment if you want. Type one is something that's irreversible. So if Neil works for my company and I fire him, in general, that's pretty irreversible unless some stroke of luck happens for Neil. Or there's a type two decision. A type two decision is basically something that's reversible, so you can turn it back easily. So an example of this might be you run an experiment on a website, like you change the title. That's generally reversible. Most decisions in the world or at a company, i should say, are reversible. So when you think about the context of running an experiment such as an ab test anything like that, just know that operate off this decision framework first and it's going to be a lot easier. So without further ado, I'm going to turn it over to Neil. Neil, how do you run growth experiments in general? So we have a very simple process. We put people into teams of three from all random departments. Because in marketing, you as marketers may have your own ideas, but people in support may have here the same issues over and over again. They'll be like, wait, if we solve this, we increase conversions or traffic or same with sales and design. Everyone's hearing their own feedback. So we'll take people from different groups put them into groups three. We have quite a few of them. But on Monday, everyone submits their ideas for their group on what they want to run by Wednesday, and technically they should be picking the idea. Then on Monday, as well by Wednesday, the experiment needs to be up and running by Friday, you evaluate the results. Sometimes there's not enough data for two days to figure out if something's working or not. Sometimes it takes a week or two. But by doing that, you have multiple teams running really small experiments. And the reason I structure that way is the moment someone's like, yeah, let's use this experiment. I'll get it done in thirty days. Well, thirty one days will turn to sixty days or ninety days when they have to do something within a day or two, and that means it's small enough where you know they can get it done. And the key is to do a lot of small little things because they all add up. And that's how we do a ton of experiments each and every single month. It's actually not hard at all to run twenty plus. In most cases, we can get over thirty plus a month. So on our end, what we do is I'm talking about the single raining team right now, I'm looking at Asana right now. There's actually a growth experiments board, and we treat it as a combind board. So if you think about a combon board, it's basically like you have different and it's like trello. Right, you have different swim lanes and you can move tasks into the next one. Maybe swim lanes isn't the right word, but you can move tasks around like to do doing done. But what we basically do is we make sure we're running at least we cap ourselves out at five concurrent experiments, because what happens is we can come up with a thousand ideas, but we only want to be working on the five most impactful ones because at the end of the day, we only have so much bandwidth. So what we'll do is we'll try to limit the amount of concurrent tests like I mentioned, but we'll also say, hey, you know what, Let's say Neil comes up with an idea, I come up with idea. We also score each idea too, so we score it based on impact, confidence, and ease. Each one is worth ten points, so impact might be ten points, confidence ten points, ease ten points, so for macs of thirty, that way, we at least have some semblance of a way to see, hey, you know, based on a number, should we be doing this test or not. And I'm not going to say it's the best way to do it, but at least it's a way to do it. And basically what we do is we also check the lever for the growth lever that we're pulling, So we use pirate metrics that come from David McClure, and basically you have acquisition, activation, referral, retention, and revenue. Those we can explain another podcast, and I think we've explained it before, but we're looking at all these different things and we're just making sure that we run five tests at once and then you know, eventually it gets to twenty growth experiments per month. That's kind of the goal. And we also make sure that we discuss the learning. So the biggest thing is if you're running all these experiments and you're not learning from them, and you start making the same mistakes over and over, you're basically fixing the fixed that's what I call it. So you want to make sure everyone actually gets to learn from that experiment so you don't have to keep repeating things that you've already done. I think that's super important, and a lot of people gloss over that, Neil, they do gloss over that. And one thing that we do is we create an internal wiki that breaks down each of our learnings for previous tests, whether it worked out, even if it didn't work out, what do we learn and how can you avoid this again? And then even when an experiment does work, we put it in an air in our wiki where we know that, hey, this has worked and we made this default or it's part of the SI or the flow or whatever it may be. And the reason we do that too is just because something works right now doesn't mean it's going to work forever. So you want to retest things maybe once a year or twice a year. A good example of this is Google authentication. So log in with Google or log in with Facebook, you know, with the click of a button, and websites can do this years ago that would drastically boost conversions. I'm talking about like almost one hundred percent. Now people are like, what data of mine are you going to take? Is there any privacy issues? Are you going to steal anything? Now? Even though these authentication platforms, or these platforms more so like Google and Facebook are really picky at what data they give out when people authenticate, still users are concerned. So now it doesn't always boost conversions. Sometimes it actually decreases conversions, all right, So that is it for today, but before we go, if you want to learn about the best growth experiments out there, the stuff that the five percent of things that are not shared outside in the wild, check out the Growth Accelerator. I mean, the people we have coming in. They've literally helped companies like Facebook, Twitter, Wealth Front Core scale to where they are now and also pricing experts that are really respected by the best vcs out there. Just amazing speakers, but most importantly amazing attendees, and we're also going to be doing different dinners and events on the side too, just to make it a remarkable event that's really more than just a glorified conference. So go to Marketing School the ioslash Live. That's live. Our next event is in San Francisco. This is March eighth through tenth, so that's coming up very quickly. And that being said, we'll see you tomorrow. We appreciate you joining us for this session of Marketing School. Be sure to rate, review, and subscribe to the show and visit marketingschool dot io for more resources based on today's topic, as well as access to more episodes that will help you find true marketing success. Tax marketing School dot io. Until next time, Class dismissed,

Welcome to Marketing School, the only podcast that provides daily top level marketing tips and strategies from entrepreneurs that practice what they preach and live what they teach. Let's start leveling up your marketing knowledge with your instructors, Neil Patel and Eric Sue. All right, guys, before we start, we got a special message from our sponsor. If you want to rank higher on Google, you gotta look at your page speed time. The faster website loads, the better off you are with Google's Core Vital update. That makes it super super important to optimize your site for low time. And one easy way to do it is use the host that Eric and I use, dream Hosts. So just go to dream host or Google it, find it, check it out, and it's a great way to improve your low time. Welcome to another episode of Marketing School. I'm Eric Sue and I'm Neil Patel, and today we are going to talk about how to run twenty powerful growth experiments per month. So they're not just growth experiments, they're powerful. So let me explain what a growth experiment is first, and then Neil and I can kind of go back and forth on our methodologies around this, and then go from there, I'll talk about you know, we can actually even talk about ourselves and then how our teams do it. I think that'll be helpful for people. But a growth experiment is generally this. If you think about Amazon the way they do things, this might complicate a little bit, but they run a lot of experiments, They test a lot of different things, and they're very rapid. They're a fast moving company. And even if you think about Google back in the mid day when they had a twenty percent time where you can work on anything, they're running experiments. Right Gmail was actually an experiment that came out from the employees over at Google. So when you're running experiment, there's two different decisions, and this actually came from one of Amazon's Jeff Bezos annual letters. There's a type one decision and a type two decision, right, or you can call it an experiment if you want. Type one is something that's irreversible. So if Neil works for my company and I fire him, in general, that's pretty irreversible unless some stroke of luck happens for Neil. Or there's a type two decision. A type two decision is basically something that's reversible, so you can turn it back easily. So an example of this might be you run an experiment on a website, like you change the title. That's generally reversible. Most decisions in the world or at a company, i should say, are reversible. So when you think about the context of running an experiment such as an ab test anything like that, just know that operate off this decision framework first and it's going to be a lot easier. So without further ado, I'm going to turn it over to Neil. Neil, how do you run growth experiments in general? So we have a very simple process. We put people into teams of three from all random departments. Because in marketing, you as marketers may have your own ideas, but people in support may have here the same issues over and over again. They'll be like, wait, if we solve this, we increase conversions or traffic or same with sales and design. Everyone's hearing their own feedback. So we'll take people from different groups put them into groups three. We have quite a few of them. But on Monday, everyone submits their ideas for their group on what they want to run by Wednesday, and technically they should be picking the idea. Then on Monday, as well by Wednesday, the experiment needs to be up and running by Friday, you evaluate the results. Sometimes there's not enough data for two days to figure out if something's working or not. Sometimes it takes a week or two. But by doing that, you have multiple teams running really small experiments. And the reason I structure that way is the moment someone's like, yeah, let's use this experiment. I'll get it done in thirty days. Well, thirty one days will turn to sixty days or ninety days when they have to do something within a day or two, and that means it's small enough where you know they can get it done. And the key is to do a lot of small little things because they all add up. And that's how we do a ton of experiments each and every single month. It's actually not hard at all to run twenty plus. In most cases, we can get over thirty plus a month. So on our end, what we do is I'm talking about the single raining team right now, I'm looking at Asana right now. There's actually a growth experiments board, and we treat it as a combind board. So if you think about a combon board, it's basically like you have different and it's like trello. Right, you have different swim lanes and you can move tasks into the next one. Maybe swim lanes isn't the right word, but you can move tasks around like to do doing done. But what we basically do is we make sure we're running at least we cap ourselves out at five concurrent experiments, because what happens is we can come up with a thousand ideas, but we only want to be working on the five most impactful ones because at the end of the day, we only have so much bandwidth. So what we'll do is we'll try to limit the amount of concurrent tests like I mentioned, but we'll also say, hey, you know what, Let's say Neil comes up with an idea, I come up with idea. We also score each idea too, so we score it based on impact, confidence, and ease. Each one is worth ten points, so impact might be ten points, confidence ten points, ease ten points, so for macs of thirty, that way, we at least have some semblance of a way to see, hey, you know, based on a number, should we be doing this test or not. And I'm not going to say it's the best way to do it, but at least it's a way to do it. And basically what we do is we also check the lever for the growth lever that we're pulling, So we use pirate metrics that come from David McClure, and basically you have acquisition, activation, referral, retention, and revenue. Those we can explain another podcast, and I think we've explained it before, but we're looking at all these different things and we're just making sure that we run five tests at once and then you know, eventually it gets to twenty growth experiments per month. That's kind of the goal. And we also make sure that we discuss the learning. So the biggest thing is if you're running all these experiments and you're not learning from them, and you start making the same mistakes over and over, you're basically fixing the fixed that's what I call it. So you want to make sure everyone actually gets to learn from that experiment so you don't have to keep repeating things that you've already done. I think that's super important, and a lot of people gloss over that, Neil, they do gloss over that. And one thing that we do is we create an internal wiki that breaks down each of our learnings for previous tests, whether it worked out, even if it didn't work out, what do we learn and how can you avoid this again? And then even when an experiment does work, we put it in an air in our wiki where we know that, hey, this has worked and we made this default or it's part of the SI or the flow or whatever it may be. And the reason we do that too is just because something works right now doesn't mean it's going to work forever. So you want to retest things maybe once a year or twice a year. A good example of this is Google authentication. So log in with Google or log in with Facebook, you know, with the click of a button, and websites can do this years ago that would drastically boost conversions. I'm talking about like almost one hundred percent. Now people are like, what data of mine are you going to take? Is there any privacy issues? Are you going to steal anything? Now? Even though these authentication platforms, or these platforms more so like Google and Facebook are really picky at what data they give out when people authenticate, still users are concerned. So now it doesn't always boost conversions. Sometimes it actually decreases conversions, all right, So that is it for today, but before we go, if you want to learn about the best growth experiments out there, the stuff that the five percent of things that are not shared outside in the wild, check out the Growth Accelerator. I mean, the people we have coming in. They've literally helped companies like Facebook, Twitter, Wealth Front Core scale to where they are now and also pricing experts that are really respected by the best vcs out there. Just amazing speakers, but most importantly amazing attendees, and we're also going to be doing different dinners and events on the side too, just to make it a remarkable event that's really more than just a glorified conference. So go to Marketing School the ioslash Live. That's live. Our next event is in San Francisco. This is March eighth through tenth, so that's coming up very quickly. And that being said, we'll see you tomorrow. We appreciate you joining us for this session of Marketing School. Be sure to rate, review, and subscribe to the show and visit marketingschool dot io for more resources based on today's topic, as well as access to more episodes that will help you find true marketing success. Tax marketing School dot io. Until next time, Class dismissed,