The Exchanges

Q set of advisors. Now, let's think about the hiring process now, because we know that we want to get these people on board, but bluntly, I've never hired a growth professional before, so I have no idea how to do it, Luke. How would you literally structure the process for hiring this as senior as possible growth person to join the team? What does that process look like for you?

A Uh, this is an area I spend a tremendous amount of time Focusing on. In fact, you know, as a growth leader, what levers to pull knowledge is all important, but if you're building a team and you're not a single person doing growth, the impact of your team will be the most important thing. So hiring and leadership become the most important, um, skills in that case. So it's a craft in and of itself that I certainly spent a lot of time refining. So the process itself can, can vary, but the way I do it is I try to look for, uh, what I call signs of excellence. So past performance is the best predictor of future performance. So what I look for is what signals indicate past behavior and success that would be predictive of future success. So that's like the rough scaffolding. Um, so ideally you want signals that Tell you that they're, they're the best. So one signal, for example, uh, I remember when I long time ago, when I started building teams really sat back and looked at, well, what, what are the signals that somebody who's amazing would, would be highly correlated and one that. That I use is that when I meet with somebody, I'll start at the bottom of their LinkedIn page and kind of get them to walk me to the top. And I just listen and ask questions along the way. And one thing I'm looking for is, you know, motivations for leaving role. What, what did they accomplish at each role? …

AI assessment note: “start at the bottom of their LinkedIn page and kind of get them to walk me to the top”

Q On reflection, where do you think most founders go wrong when assembling their growth teams when they're hiring?

A I mean, the biggest mistake you can make as a founder is hiring the wrong person for the role. And I guess it's related to what we were just talking about. It is very hard to get a sense of if the person is any good. Uh, it can be obviously very costly, both in terms of time and money to make the, the wrong mistake there to hire the wrong candidate. Um, So I think that's, that's the biggest mistake. Now, it's, it's very hard to sniff out top talent, so that makes it exceptionally hard. I'd say some common mistakes would be, and this is something that's unique to growth, is that in growth, some of the signals that may suggest that somebody's amazing in a different discipline might not apply to growth. I'll give you an example. In most professions, if somebody's at a company and they're promoted very quickly, that would be a good sign that this person is, uh, you know, exceptional. And in growth, because there's so few people in the space, It could suggest that they're exceptional, but it could also suggest that maybe they were the only person available, the only person this company could hire. Um, there was a lot of historical knowledge. There's different reasons because there are so few growth people in the market, uh, that, um, there's a few other, you know, there's other reasons with growth, why somebody could be promoted, which, so it can be a bit of a counter signal as well…

AI assessment note: “signals that may suggest that somebody's amazing in a different discipline might not apply”

Q On reflection, where do you think most founders go wrong when assembling their growth teams when they're hiring?

A I mean, the biggest mistake you can make as a founder is hiring the wrong person for the role. And I guess it's related to what we were just talking about. It is very hard to get a sense of if the person is any good. Uh, it can be obviously very costly, both in terms of time and money to make the, the wrong mistake there to hire the wrong candidate. Um, So I think that's, that's the biggest mistake. Now, it's, it's very hard to sniff out top talent, so that makes it exceptionally hard. I'd say some common mistakes would be, and this is something that's unique to growth, is that in growth, some of the signals that may suggest that somebody's amazing in a different discipline might not apply to growth. I'll give you an example. In most professions, if somebody's at a company and they're promoted very quickly, that would be a good sign that this person is, uh, you know, exceptional. And in growth, because there's so few people in the space, It could suggest that they're exceptional, but it could also suggest that maybe they were the only person available, the only person this company could hire. Um, there was a lot of historical knowledge. There's different reasons because there are so few growth people in the market, uh, that, um, there's a few other, you know, there's other reasons with growth, why somebody could be promoted, which, so it can be a bit of a counter signal as well…

AI assessment note: “social media halo, uh, or public speaking halo does not correlate with ability”

Q set of advisors. Now, let's think about the hiring process now, because we know that we want to get these people on board, but bluntly, I've never hired a growth professional before, so I have no idea how to do it, Luke. How would you literally structure the process for hiring this as senior as possible growth person to join the team? What does that process look like for you?

A Uh, this is an area I spend a tremendous amount of time Focusing on. In fact, you know, as a growth leader, what levers to pull knowledge is all important, but if you're building a team and you're not a single person doing growth, the impact of your team will be the most important thing. So hiring and leadership become the most important, um, skills in that case. So it's a craft in and of itself that I certainly spent a lot of time refining. So the process itself can, can vary, but the way I do it is I try to look for, uh, what I call signs of excellence. So past performance is the best predictor of future performance. So what I look for is what signals indicate past behavior and success that would be predictive of future success. So that's like the rough scaffolding. Um, so ideally you want signals that Tell you that they're, they're the best. So one signal, for example, uh, I remember when I long time ago, when I started building teams really sat back and looked at, well, what, what are the signals that somebody who's amazing would, would be highly correlated and one that. That I use is that when I meet with somebody, I'll start at the bottom of their LinkedIn page and kind of get them to walk me to the top. And I just listen and ask questions along the way. And one thing I'm looking for is, you know, motivations for leaving role. What, what did they accomplish at each role? …

AI assessment note: “when I meet with somebody, I'll start at the bottom of their LinkedIn page”

Q think the humility that comes with that understanding is also just in, in a mutual understanding of what growth actually is, and it means a lot of things to a lot of different people, I think. So if you were to kind of put on like a, you know, like a fridge magnet, what is growth? What, how would you define growth today, just so people have a shared understanding?

A Yeah, it kind of goes back to my comment, um, from my takeaway from Facebook. I, I start with the outcome. What is it that you're trying to do? What is the number one metric or, or activity you're trying to create as an outcome? And then What is then it's all the activities and the, all the different approaches that can lead to that, that growth. So I don't define growth very crisply in terms of it's this one thing. It's basically whatever it takes to move that one, um, metric you're trying to, to increase. So I know it's a, it's kind of a broad answer, but it's, it's the reality and it's, um, it's how I've approached growth in general.

AI assessment note: “It's basically whatever it takes to move that one metric you're trying to increase.”

Q Tell me what tactics have died a death over the last five years?

A Over the last five years. So I think SEO is an area that I've spent a lot of time in, and one that comes to mind is, and you can put it under a wrapper of black hat SEO if you want, but basically SEO that used to work, um, but was essentially against the Google guidelines. That is, it, it's not, it's not worth taking that risk anymore. It, it doesn't really work anymore. The algorithms have gotten so good that in many cases it doesn't work. I know it's still possible, but it's just not worth it because, uh, you can get penalized and your entire business can, um, uh, can be out the window. Frankly, you can entirely ruin an entire domain because of that. So I would highly recommend founders to stay away from that. That's something that I don't know that is broadly known by all founders, but it's a question you should definitely ask anybody doing SEO on your teams. Any of your growth leaders that, uh, is no longer valid and can, um, can destroy businesses. It has destroyed businesses.

AI assessment note: “you can put it under a wrapper of black hat SEO if you want”

Q What are the most prominent viral loops at Shopify?

A It's something we're constantly working on and iterating on. One of our, our best sources is through recommendations. Uh, our merchants love Shopify and they mention Shopify to their friends and other merchants come in and join through that loop. So we're, we're always iterating on making it easier for merchants to do that, but it's because we have such a great product and one of the, uh, highest cited reasons for joining Shopify is that somebody had recommended them in that it's, again, it's kind of part of the, the core product where Uh, it would be hard to do if you didn't have a great product, but so for Shopify, it's, it's, we can start off that great baseline, and it's, it's an area we're constantly iterating on.

AI assessment note: “One of our, our best sources is through recommendations.”

Q What are the most prominent viral loops at Shopify?

A It's something we're constantly working on and iterating on. One of our, our best sources is through recommendations. Uh, our merchants love Shopify and they mention Shopify to their friends and other merchants come in and join through that loop. So we're, we're always iterating on making it easier for merchants to do that, but it's because we have such a great product and one of the, uh, highest cited reasons for joining Shopify is that somebody had recommended them in that it's, again, it's kind of part of the, the core product where Uh, it would be hard to do if you didn't have a great product, but so for Shopify, it's, it's, we can start off that great baseline, and it's, it's an area we're constantly iterating on.

AI assessment note: “One of our, our best sources is through recommendations.”

Q What's been your biggest challenge in scaling the growth org at Shopify, Luke?

A I mean, it always comes down to, to the team, to building an amazing team. I wouldn't say it's, it's, uh, Uh, it's a challenge we're having. We were, we built an amazing team. I would say Shopify currently has, has some, some of the best talent in the world, uh, doing growth. Uh, but it's always something I spend a tremendous amount of time, uh, focusing on. And it's one of those. Spaces growth is one of those things where if you make the right hire, it's not a, you know, .5% increase. It's literally a, a, a, a 10 or a hundred, uh, X increase. So. That's the area. I don't know if I'd call it a challenge, but it's the area I focus a lot of time on and also the area that literally making the right decision can be just a game changer for, for growth in, in all aspects. So if you look at whether it's in paid search or in, um, funnel optimization or an SEO or in product building, or we have a, a product org and an engineering org inside the Shopify growth, uh, org, and, um, just bringing in amazing talent is, The thing that matters the most. So that's what I spend the bulk of my time on when I'm not in reviews and getting my hands dirty with the teams. So that's the most important thing. I don't know if you classify it as a challenge, but it's definitely the area that has the most impact at my scale, at the scale that we're operating at.

AI assessment note: “I don't know if I'd call it a challenge, but it's the area”

Q You said there about kind of getting hands dirty in the data, in the trenches there. Can you talk to me about a decision that you made without data, and how did it go?

A So I think data is very important, and especially when you pair it with Like a pattern matching off of previous experience. So I know, for example, uh, so I focus a lot on, on what is the thesis that this experiment is likely going to work? What are the signals that you have that suggests that this experiment is going to work? Just throwing things on the wall. So you start with the data. What is the opportunity size? Is this worth doing? And then you try to look for signals in a thesis that will increase the likelihood of something working. So I remember one situation. At a past company where somebody on the team wanted to make a change to the website to see if it would impact SEO traffic. And I asked them why. And they said, I just want to throw things against the wall and see if it works. That is a bad answer. A better answer is I did this experiment. It had this result, which would suggest that if we do these two things, because we also saw competitor increase in search rankings based on a similar change that will likely see, um, a positive impact from, from this experiment. So, so lack of data is not good. You certainly want to start with a good data foundation, but equally important is what is the sound logic behind the thesis that will make sure that this works? And that's something that, um, I think probably doesn't get spent enough focus on in terms of validating not ju…

AI assessment note: “So, so lack of data is not good. You certainly want to start with a”

Q Luke, how do you structure growth reviews? How, who sets the agenda? Who's invited? How long's the meeting? How often are they? Help me understand what a gross review looks like and what a good one looks like.

A This is something we're always iterating on. We tried a variety different. It depends on the size of the team as well. I mean, a growth review is, is, is not really needed when you're a, uh, you know, maybe a two person growth team, but ultimately you want to look at, and it also depends on what stage. If you're at an early stage, you want to look at what, just get buy in on. Hey, here's, here's the kind of work we want to do, uh, from a growth, uh, optimization perspective. Does that make sense? Uh, we call that the proposal stage. And then when you, everybody's aligned, then we do a review around the, The metrics, the opportunity size, and make sure that, um, we all believe that, you know, this is building the best thing possible. And then, so there's, there's kind of a, an evolution of growth reviews. So it's, they're not all made the same way. Ultimately they should be very driven around metrics and, um, opportunity size, and also a strong conviction around what we think will work. Uh, but it really depends on the, on the stage of the review.

AI assessment note: “Ultimately they should be very driven around metrics and, um, opportunity size”

Q Luke, how do you structure growth reviews? How, who sets the agenda? Who's invited? How long's the meeting? How often are they? Help me understand what a gross review looks like and what a good one looks like.

A This is something we're always iterating on. We tried a variety different. It depends on the size of the team as well. I mean, a growth review is, is, is not really needed when you're a, uh, you know, maybe a two person growth team, but ultimately you want to look at, and it also depends on what stage. If you're at an early stage, you want to look at what, just get buy in on. Hey, here's, here's the kind of work we want to do, uh, from a growth, uh, optimization perspective. Does that make sense? Uh, we call that the proposal stage. And then when you, everybody's aligned, then we do a review around the, The metrics, the opportunity size, and make sure that, um, we all believe that, you know, this is building the best thing possible. And then, so there's, there's kind of a, an evolution of growth reviews. So it's, they're not all made the same way. Ultimately they should be very driven around metrics and, um, opportunity size, and also a strong conviction around what we think will work. Uh, but it really depends on the, on the stage of the review.

AI assessment note: “It depends on the size of the team as well.”

Q You said there about kind of getting hands dirty in the data, in the trenches there. Can you talk to me about a decision that you made without data, and how did it go?

A So I think data is very important, and especially when you pair it with Like a pattern matching off of previous experience. So I know, for example, uh, so I focus a lot on, on what is the thesis that this experiment is likely going to work? What are the signals that you have that suggests that this experiment is going to work? Just throwing things on the wall. So you start with the data. What is the opportunity size? Is this worth doing? And then you try to look for signals in a thesis that will increase the likelihood of something working. So I remember one situation. At a past company where somebody on the team wanted to make a change to the website to see if it would impact SEO traffic. And I asked them why. And they said, I just want to throw things against the wall and see if it works. That is a bad answer. A better answer is I did this experiment. It had this result, which would suggest that if we do these two things, because we also saw competitor increase in search rankings based on a similar change that will likely see, um, a positive impact from, from this experiment. So, so lack of data is not good. You certainly want to start with a good data foundation, but equally important is what is the sound logic behind the thesis that will make sure that this works? And that's something that, um, I think probably doesn't get spent enough focus on in terms of validating not ju…

AI assessment note: “So lack of data is not good. You certainly want to start with”