Working From Home: Episode 8 – Building the ecommerce data analysis tool of the future – with John Chao
Nelson is joined by co-founder and CEO of Tresl, John Chao. Described as the “self-driving car” of e-commerce, Tresl provides data science services for e-commerce businesses.
Topics of conversation include the power of data to drive sales, the unprecedented growth of the e-commerce market during COVID, managing a remote team, the ups and downs of working remotely, tracking which investments drive sales, and other topics.
[0:58] – John Chao introduces himself, and shares how his company Tresl is providing powerful new tools for e-commerce businesses.
[12:23] – Data collected is only as good as data used.
[17:03] – The insane boom of e-commerce during COVID.
[19:23] – The power of Shopify, ConversionXL, and other e-commerce tools.
[23:30] – Understanding attribution, marketing, and conversion rate networks.
[33:30] – Starting Tresl, brainstorming ideas, and building the business.
[40:02] – Access to the global markets as a remote freelancer.
[43:00] – The connections that are lost when you are physically isolated from your team.
[46:56] – Using Notion to manage a remote team.
[49:33] – Using process documentation to create SEO rich content for blog posts, videos, etc.
Nelson: Hello there, and welcome to the ‘Working From Home’ podcast with your host me, Nelson Jordan. Today, I’m thrilled to be joined with John Chao, who is the co-founder of Tresl. Hello, John.
John: Hey Nelson, how’s it going?
Nelson: Yeah, not too bad at all. Thank you. And just for our audience’s benefit, obviously, I’ve talked to you several times before. Who are you? What do you do? What should everyone know about you?
John: Sure. So my name is John Chao. I’m the co-founder of Tresl. Right now, we’re building an ecommerce intelligence platform that helps ecommerce brands access the same analytical powers enjoyed by large enterprises…but at a fraction of the cost. We basically become your data team, provide step by step marketing optimizations to help ecommerce brands navigate the complex digital journey.
Nelson: So I the way I kind of understand your product is that it helps people get more from their email list, in terms of an increased revenue, you get access to, you know, high level data analysis that really small to medium ecommerce brands typically can’t afford. Is that right?
John: Yeah, I mean, and much more. So I think we basically started this because what we realised what we were building. So I came from a math and statistics background. I was at LinkedIn before I started my company. We were building a lot of machine learning models for our internal sales and marketing folks. You know, basically account based modelling tools, marketing tools. And it really all comes down to just analysing customer behaviour, users patterns, understanding who’s more likely to buy and who’s more likely to churn. When you’re in LinkedIn, there’s 500 million members, you really can’t email blast everyone, right? I think if you sign up for LinkedIn, you know, we send you a lot of emails already. So, um, it really comes down to this thing, we’re calling a ‘list poll’, let us just prioritise scoring of a new email list. And so, you only want to send it to the people that want to get this email and who is also highly likely to convert. And so that’s basically what we wanted to build, and we realised that Shopify brands, ecommerce merchants just didn’t really have such a tool to help them navigate all the things that they need to do, like be able to monetize data. And so that’s kind of how we started.
Nelson: Cool. And like, the way I understand your product is that you have several different levels, that people can interact with you, whether they are a newbie to Shopify, and not turning over that much at the moment, and still very much in the early days of getting customers and building their email list. But then you also go up to kind of enterprise level, right?
John: That’s right. Yeah. So, we look at it in two ways. Like for the enterprise customers, we offer a lot of deep insight, advanced analytics. And then also, a bit of science consulting; we literally just act as your data scientist. We have a Slack channel with our larger customers, plus merchants, where we answer questions on almost on a daily or weekly basis, that’ll help you analyse the campaigns. With our self-serve plans, with our smaller brands, they leverage the automated segmentation the active email integrations and syncs to Facebook, to leverage those lifecycle marketing tools that we have bought for them, built into the tool. When you have ads, like Facebook ads have been going up crazy, right? Especially after COVID-19, a lot of large brands started spending millions of dollars online, and we’ve been hearing from our customers. Rollout has been going down, cost has been going up. And they need to think about not just blindly spending, you know, ad budget doesn’t go up infinity, right? You have to watch what you’re spending and you’re trying to increase retention. And so this is when data can really help you right, really be able to hone in on things that will work. And help you focus on things and optimise different channels.
Nelson: That’s great. I think that’s a fantastic introduction to what it is you do. I’d like to kind of take a bit of a diversion now and almost work out how you got to this point. And so where are you? Where are you based right now?
John: Right now I’m in Taiwan.
Nelson: How long have you been there?
John: I’ve been here for about six months now. So I was born in Taiwan. And, you know, grew up to middle school. And then we moved to Canada to BC, British Columbia, where I did my undergrad.
Nelson: Cool. And was that in something related to data or something related to computer science?
John: So my undergrad was in math and statistics. And then I went to California, went to Stanford for a master’s in statistics. And then I basically worked in the Bay Area for about 15 years in data science.
Nelson: Okay, cool. So I mean, like, you laugh that but that’s pretty close to what you were doing. When I went and did my undergrad at University of Birmingham, shout out to them. And I did a course called Specs, which was sports, PE and coaching sciences. It was basically the most looked down on subject there was in the whole university, pretty much. And so yeah, the other subjects kind of love to poke fun at us. And so I’ve gone from sports and kind of coaching, all the way to digital marketing and copywriting, which is what I do now. So, yeah, at least yours was kind of statistics data, and then using that, so awesome.
John: Yeah, when I picked statistics, back in 2004-2005, it was a good subject, but it was not like so front and center of everything, data has not quite exploded yet. I just figured that statistics, we had a lot of predictive modelling and I thought that be able to predict things for people and understand why things happened, using historical data, was going to be useful in general. And so it turns out was pretty right on. So I was lucky there. A lot of things have happened. I went from Stanford to my first job. My first job was all about retail data science, we did a lot of demand forecasting and price optimization, this is when the world is still mostly offline, right? We helped Walmart, Target, Best Buy with their customers, by category, build their pricing, build their optimization, build their displays, promotion, effectiveness, things like that.
Nelson: I can’t remember if it’s Walmart or Target, you might be able to weigh in here, but they’re famous for that story of…I’ll tell everyone in the audience, just so we’re all on the same page. But a well-known story, I’m not sure whether it’s kind of apocryphal or not, or if it really happened. But a man went into the store complaining about some brochures and vouchers that he’d been sent, his daughter been sent, who was at the address, that said, basically, here are all these baby items, here are diapers, here is money off the formula and stuff like that. So he went in to complain and say, like, ‘My daughter’s not pregnant, you’ve got to stop sending me this stuff’. And obviously found it quite intrusive. And then he later found out his daughter was pregnant, but just hadn’t told him yet. And actually, the modelling had found out by the other things that she was she was buying at that time, I think probably vitamins and things like that, that were related, but you know, in a tangential way, they were related, but they weren’t directly baby things. And the computer models had got there, obviously before she’d been able to tell her dad about it. Then I heard later that they changed things up a little bit. And so it was less creepy, and less in your face, they’d also mix in items that weren’t relevant. They wouldn’t just send one email or a brochure with just baby stuff they might send the baby stuff that’s relevant, but they might also include a lawn mower, or a DVD collection, or whatever it is to kind of people would still be able to go through and say, ‘Oh, you know that that applies to me. I’ll buy that’. And it’s almost like one of these fortuitous moments but it’s not it’s obviously. But you can also look at these irrelevant items and say, ‘Well, I don’t need that’. So it just appears less creepy.
John: Correct, yeah.
Nelson: Did you have a part to play in that job?
John: Well, I wish I could claim credit for that. No, that wasn’t us. We were doing stuff that was very similar. We were creating what we call ‘demand groups’ and demand groups were basically highly substitutable products, that when you bought Coke, you’re probably less likely to buy Pepsi, right? So, there’s a lot of cannibalization happening. And now on the flip side, there’s a lot of sort of cross, we call it ‘cross elasticity’, basically relationships between different groups that were more likely to happen when you bought certain items. And this is all a product journey that we’re trying to map. This is something that we’re trying to crack with segments as well, as just to understand purchase patterns between the first, or second to third purchase. You know, what are people buying first? What are they more likely to buy next? What is the timing between? Are they replenishing? Are they switching to different categories? So those are the things that I think is very front and center, for a lot of ecommerce brands. And you can actually prove this out with data. More than you think. You just don’t know where to look and a lot of brands don’t have that time.
Nelson: Or the expertise as well.
Nelson: Because I’ve worked with your product before. And I know how to do a basic level of data analysis, but I don’t know how to take all of these different inputs and turn them into something usable. Because that takes a considerable amount of training and experience and practice to be able to do that. It’s not something that you can just open up an Excel spreadsheet, you know, feed your data in and just be like, ‘Okay, this is what I’m going to do’. That’s not how it works. That’s partly why I’ve used your product several times (which is, by the way, how we know each other for the audience), with those stores, because you actually give something useful on the back end of that, that can actually then go into some action. A lot of people that I’ve encountered before, particularly in like the Google Analytics world, when they’re analysing website data they’ll produce these fantastic looking monthly reports for their clients. ‘A bounce rate has done this I conversions on this product is up. We’ve got less engagement on site, blah, blah, blah’, whatever it might be. But rarely, (I mean, the best ones do, but the average ones will just leave the data there), like that has no purpose whatsoever, right? You actually need to do something with that data. It needs to be actionable. And is that something that you’ve been able to kind of build into the product?
John: Yeah, so I think we took a very different approach, when we wanted to build this product. We didn’t just want to build, like another analytics tool. There’s plenty of dashboards that pack the screen with a lot of charts and numbers, and so on. And, as a data scientist, I even get very confused if I have to look at a screen full of numbers. And so, we wanted to cut, cut, cut away. We wanted to just show something that is intuitive, simple, and help people take action. Fortunately, with Shopify, with ecommerce, we’re able to just kind of drill down and cut out the noise. We didn’t have to be a platform for everyone. We never wanted to be a generalised analytics tools for all the different players, all different industries out there, we just wanted to help Shopify merchants in the beginning. We just wanted to be able to niche down on Shopify, and ecommerce. And so, we build something that is very simple. In the beginning was a very simple idea. Can we build like a q&a tool? Almost like a Quora? Where you ask questions and people answer your questions. But except in this case, it’s answered by expert data scientists. And so, we kind of built this, that was the starting point. And we’ve evolved, obviously, much beyond that. But I think what most people find is that actually these questions or these insights, what we call ‘modules’, these modules are actually really relevant to me. And they make sense. And these are the questions that I care about. And so we have kind of built these modules to target different parts of ecommerce marketing. Whether it’s cohort analysis, whether it’s CLV, whether’s it’s RFM segmentation, looking at churn, looking at life cycle, timing, replenishment, so on, the list goes on. That’s basically what we built. We just wanted to help brands and marketers go in, find the answers that you need to optimise different parts of the funnel.
Nelson: Perfect. I mean, this is this is exactly why I’ve got you on John, to talk about stuff like this. Because I mean, part of why your life is kind of interesting to me is that you work from home, and we’ll get on to how Tresl is growing and how you’re managing to grow a remote team as well. But a large part of our audience is obviously, we’ve got freelancers, we’ve got agency owners, we’ve got remote workers, but a large segment (no pun intended with your products), is online business owners. Now one of the largest areas within online businesses, over the last year, to grow and see an absolutely extraordinary spike, especially during COVID, is ecommerce is small, D to C, ecommerce. And it seems like tools, like Shopify, have made it easier than ever for almost beginning entrepreneurs, I might say. There’s probably a better phrase out there for them, but people that just are passionate about a product or want to try out a particular business model that they’ve heard about, and they’ve seen working for other people. I think Shopify is the best platform for that, in my opinion. It’s the platform I recommend most for when people ask me ecommerce questions, and terms of like, ‘Where should I get started?’ And that’s the other reason that I’ve kind of asked you to come on the show because the growth in ecommerce, so over COVID in particular, just seemed like this hockey stick, just rocket ship takeoff. And you have any kind of thoughts about that?
John: Yeah, no. McKenzie, actually put out this report a few months ago, ‘The Key Insight’, and it showed ecommerce, US ecommerce penetration at 35%, from 15% in 90 days. The title was ‘10 years, moving in 90 days’, or something like that. So, it’s just unfathomable. That is just such an extraordinary growth. But at the same time, not surprising at all right. Shopify just executed really well. And actually, they were already doing great before all of this happened. But with COVID, they excelled, you can see it, almost like you feel it, that they’ve accelerated growth and investment in certain areas. In particular, they did three months free trial right around COVID starting, they did Facebook shop integration backed by Shopify, and then they did Walmart integration. And then the year before that they bought River Six Systems for logistics. It is just basically all happening, like in front of your eyes, like their global commerce, operating system vision is literally happening. It is just crazy the amount of creativity, and entrepreneurship, and just learning that is going on, happening around us. People are just, you’d think, ‘Oh, you know, anything that anybody could sell online, people are already doing it’, but you’re still seeing these successes popping up all the time. And so, it just tells you like, if you find the right audience, you can niche down, if you work hard, there’s still opportunity for growth. And that is what we’re seeing. There’s, there’s no reason why you can’t have a million dollar brand when there are already hundreds or thousands of them.
Nelson: I mean, the tools, the software, and the resources that I recommend most to anybody really, the ones that keep cropping up, and are the ones that open up opportunities for people to do something themselves. And by that, I mean, I recommend Shopify a tonne to people because it’s the best tool out there, the best platform out there, for creating your own ecommerce store. I recommend conversion XL which is now cxl.com to people who want to upskill themselves. Again, it’s just opening up opportunities that that weren’t there before. It always seems like the best tools in my mind are the ones that make it easy for people to do something that was difficult before.
John: Mm hmm. The other side of Shopify, right, which is they’ve made it so easy for you to start a store, but at the same time, they’ve basically enabled this new technology and disruption. Like an example that I love to use is with the Kylie Cosmetics, right. Kylie Cosmetics sold 51% of their company to I think, Cody, for $600 million. Right. So instantly, they were worth like, what $1.2 billion or something like that, and did you know how many full-time employees worked at Kylie Cosmetics?
Nelson: I didn’t but you’re going to tell me a ridiculously low number, right?
John: It’s a ridiculous number. It’s 13 people, and they’re worth a billion…they’re worth a billion dollars.. And so that is just, I mean, like, I don’t even know how to do the math, right. That’s like over $80 million per –
Nelson: I mean, if you don’t know how to do the math then I’m definitely not going to be able to do it.
John: It’s just, it’s just crazy to think about. And so, and the only way they’re able to do that is obviously because the technology that was enabled by Shopify, allowed them to, more or less, outsource all of the engineering, all the hosting of the infrastructure to be handled by Shopify. But at the same time, what is their pain points now? What do they need to spend money on? Marketing, logistics, and is serving their customers. And by foregoing engineering, it’s a different breed of customer, right? Digitally native, different breed of brand. There’s no in-house expertise of engineering let alone, data. It’s going to be very hard pressed to find somebody who can build a very, very intricate machine learning system. And so that I think, is a huge gap, right? To understand your marketing, and then to use data to support your marketing, which is the data mocking flywheel, everybody, audit large tech companies are doing or talking about. I think that is the biggest problem right now. When you can grow really easily, when ROWAS is great when you when you spend anything on Facebook, and you’re getting three to five ROWAS, great. But when things are tough, then what do you do? Where do I focus on? How do I actually crack this digital customer journey, when there’s like, hundreds of touch points? You’ve got email, or SMS, you’ve got messenger, you’ve got your Zendesk support, you’ve got loyalty programmes, you’ve got all these different tools. And then you’re trying to figure out like, ‘Okay, well, everybody’s telling me they’ve got me hundred customers, or 2200 customers, they’re in there, but they’re all not D-duped. Everybody steps on each other’s toes’.
Nelson: So it’s talking about attribution there right?
John: Also, that’s also part of it, right? Everybody’s claiming for all their wins.
Nelson: I should probably just, for some of the newer ecommerce store owners, I should probably just talk a little bit about the concept of attribution. Just so everyone’s on the same page. So essentially, when you use something like marketing or a particular channel, you want to figure out which channel was responsible for the sale, or whichever conversion you’re choosing. For example, did somebody find you on Google? Did somebody come through and see a social media ad and then click through and then buy a product. A lot of the times though, it’s way more complex than that, because you either don’t know where they came from for whatever reason, either the tracking isn’t in there or the other thing is kind of your attribution models. So, there are lots of different types of attribution models: there’s first click, last click, linear, value weighted and things like this. Last click is kind of the default for a lot of people. So, it’s what was the last interaction somebody had with your company, with your marketing, before they bought from you? But obviously, when most of the time somebody buys from you, they’ve been involved in multiple different channels with you. So, they might have originally heard of you from an Instagram post, an organic one. Then you might have gotten onto their site, then you might have been retargeted on an ad on Facebook, and you might have signed up for their newsletter. And then finally, the email comes along, and then you buy from them. And then you’re in a situation where it just shows up that you’ve got one sale from email. Well, okay, great, email definitely played a part, but what about all the other boxing channels that they interacted with as well. They obviously played their part; without the first Instagram post, you never would have heard of the company in the first place. Without the remarketing or retargeting, you wouldn’t have been front of mind, and you wouldn’t have signed up for the email newsletter. So, I hope that kind of clears things up for those people that were new to attribution.
John: Yeah, no, thanks for that, Nelson. I think this is the part where Shopify has a really good data API. And I think a lot of brands or merchants don’t actually know what is available to you on Shopify. This information is actually public, whether you guys should go to Shopify, look up their API or their order API. If you just type in Shopify order API they tell you, all this data that is available to you. It’s just that it’s not within access, not accessible today. It’s not something that you think about. Obviously, don’t have the time when the mostly, probably not the expertise to crack this. But for data scientists, we love it. This is my passion. We can do so much with this data. And so, for our brands, it’s so easy. It’s one click installation, and you get all these data insights. You don’t have to move your data around and we already know what’s there. That familiarity, it just being native on Shopify, and these are actually happening things, the things that have happened, purchases that happened with your store, not just sessions or cookies. So, you could figure out like: Where these users are from? What do they buy? How much they’re buying every time? How frequently they buy? How much time has gone by since their last time? You know, creating customer lifetime value, I think that’s really the first part to understanding before you bet on that next ad, shouldn’t you know how much you’re going to get in return? What is the user cost? And then how are you going to make that money back in like 90 days or 120 days? And most brands lose money on the first sale because of the promotions. How are you going to get them to the second one? No more ‘one and dones’, how do we get into the second one? How do we get into the third one? How do we get them to stick? These are the questions that I think everyone should be focusing on.
Nelson: And your product obviously makes that a lot easier. That’s kind of your aim, right?
John: Yeah, exactly. That’s exactly our aim.
Nelson: Fantastic. Cool. Well, I’d like to switch gears a little bit if I may. And I wanted to talk about how you formed this company, because obviously, we’re working at LinkedIn at the time, and then how did it come about? You obviously have this idea in your head?
John: It’s a bit convoluted. I met my cofounder, Tony, at LinkedIn. We were both data scientists are the same broader team. We met playing basketball, kind of shooting some ideas around just like just naturally clicked. We talked about, joked about, starting a company before, together. And you know, we just realised like –
Nelson: I think by the way, sorry to jump in, I think that’s like the new the new conversation with our generation and maybe younger these days. The old conversation used to be ‘Hey, man, like, we should totally start a band, or we should open up our own bar’. And these days it’s, ‘I think we should totally run a company together’.
John: Yeah. Yeah. I think for me, it was just always it was something air or water. That’s what we joke, right? Like when you live in, in the Silicon Valley. And your friends work at startup or started companies before you just figured like someday maybe it’s your turn, right? And so, when data science and data has literally exploded, I mean, across the scene, we figured like it was more or less the right time. But it was kind of too early before, because you know, not a lot people have data. And that was always going to be the first problem: how you can actually get access to a lot of data, so you can actually meet these models will make sense. And so we formed a company, we kind of started kicking some ideas around and while I was on paternity leave, I started talking to some customers. And it just kind of helping them with some just ideas or like just the modelling. And so one thing led to another, right. And so we basically, we quit in 2018. And then we started a company together. Initially, we thought, we’re going to be, like Uber for data science, right? We thought we could be just on demand, like we would just be a ninja team who just come in, and we help you solve your problems, like Palantir, or like Uber, where it’s just plug and play data science. And then what happened was, we talked to a lot of companies, there was actually a decent amount of interest in what we what we could do. But we went up against a lot of politics, a lot of internal politics. And what I mean by that is like, usually the competition would go like this: we get an introduction, and we talked to the CEO, or like, the salesperson, the marketing person and they’re like, ‘Yes, yes, yes, we need this right now. Like, we needed this yesterday, it’d be great! Sign the contract of this model or something to help us grow’. And then inevitably, the competence which is to like a VP of engineering or like a CTO person, and we hit a wall every time and then the the concern is really about, ‘Oh, our data is too sensitive. We can’t give it to anyone else’. Or like, ‘Oh, you know, machine learning, or AI is our core, we can’t give it to anyone else’. And, but they’re really saying, I think is that, ‘You know, 1) my CEO should just give me more resources to hire more people so I can do it. Yeah. 2), like, how’s that going to reflect on me if like, you solve this problem, and I didn’t solve it’. I think that was a little bit of job security, but I think mostly about just they didn’t want to look bad. These guys, who is the third party outside, to come in and solve a problem, then…You know, it happen a lot. It happened quite a few times, where then we figured, ‘Okay, so this is probably why, you know, this doesn’t quite exist yet, even for smaller companies.
Nelson: What sort of companies were they in terms of industry?
John: Oh, there was like, finance company, financial companies, manufacturing companies, startups, like tech startups. So, it’s actually across the board, that there’s some data, there’s some problems I need help with. And usually we got in through some kind of a warm intro or like a friend. But yeah, it’s so it wasn’t it wasn’t just in particular sector. It was kind of all around.
Nelson: So, that was the signal for you that you needed to take a bit of a pivot, right? You’re hitting these walls with CTOs, or Information Officers, or whatever it was, at that point with those different sizes of companies in different industries. What was your next step?
John: We kind of took a deeper look inside; we kicked around some ideas. When we started this company, we jotted down a lot of ideas, we just went through and came up with, like, some crazy ones. And a couple of ideas that we thought could work. And we actually skipped over Shopify, it came up and then we went back and looked at our list our notes, and then we’re like, ‘Hey, why don’t we just actually try to do this? Let’s take a look at Shopify. Take a look at what data is available. Let’s just try to kick a kick a prototype, like Lean Startup style. Like, we could just build something out in a month, ship it to some people get some users and just see their feedback’. And so that’s kind of what we did. Like we did that I would say like late August, September 2018. And then we had something up, I think, somewhere sometime in September, we signed up like 10 users on Facebook, from Shopify, and had them basically give us some feedback and start using it. Some of those users are still using it today. And I think we just felt like we found something. There was definitely a need. And we just had to kind of keep iterating on it. And we launched our app officially on Shopify, April of the next year, so 2019. And then 2020, this year, we got Staff Picks in April. So what right around the one year mark, we had a front page Staff Picks feature and that was great.
Nelson: Staff picks is Shopify’s recommendation.
John: Yeah. And so, we were trending number one for that week. And then I think in August, also they put out this Omni Channel Commerce Guide and we were also featured in about lifecycle marketing, segmentation, and basically product journey stuff. So, we were pretty fortunate, I think, with was just acceleration digit acceleration and ecommerce taking off. And it’s been definitely a good year.
Nelson: Yeah, no. We’ve been talking before about your growth, particularly over the last 12 months or so. And what’s that meant for you personally, and as a company?
John: So big changes are around. We basically left California beginning of this year, because of COVID, mostly because of family reasons. My wife was pregnant with my second child. And so, you know, my son is born in July, and everything is good. But that was a big reason that we had to make some changes to our lives. Basically just like, up and left, and came back to Taiwan where my family lives. And I was fortunate, I think that we were able to do that, because I was mostly working from home. And that is a really key thing for me because we started working from home, right after we quit in 2018. And I had my daughter, who was probably one at a time, at home with me for part of it. And then you know, went to preschool, but I had to learn how to be efficient and find time in odd hours of the day, and just be more efficient. And so that has taught us, like me and Tony both, how to kind of be efficient because we were slacking first, like we’re Slackers. You know, we slack each other all the time. And it’s absolutely being critical, like our mind shares on Slack right now. And so that’s basically helping us to set the tone as like a remote first company. So even though we are in Taiwan now, and we basically started hiring people in Taiwan, we still want to maintain that culture of being just kind of remote first. Embracing that early, I think probably more companies will be doing that going forward, like you see, you know, Twitter was, I think, the first one to institute a permanent work from home. Google, other companies are kind of following suit.
Nelson: Why is remote first important to you as a company?
John: So, when it was just me and Tony, when it was just two people, it was easy to just basically just go along the processes, because we know each other, we had that familiarity and kind of be productive. But we had to scale the company, right? There are some real challenges with that. If we started hiring people in Taiwan, these engineers would need to report to Tony, who’s the CTO, and so how do we, ensure a productive working environment? How do we make sure that they can get, what we call a ‘code review’ or ‘check-in code’ and look at each other’s make sure there’s no bugs? What is a good time to set these company meetings or ‘weekly Scrum’, which is like engineering speak for meetings? Those are become challenges for us and we realised that we have put in a lot of processes to help ensure everybody gets the same kind of timeshare, and also not having this hybrid model of Taiwan office, and then, you know, we know employees in other places, doesn’t interfere with being a remote first company. So those are the things that we obviously will be starting to think about a lot more going forward. But what we’ve learned, fortunately, from some of my friends who have been remote CEOs is you have a lot more documentation, better processes. And by the same time, like being remote, you get access to this global pool of talent like there are some really smart people outside of Silicon Valley, which is kind of a bubble, and will always probably be a bubble. And so, you know, when you tried to hire a Silicon Valley engineer, you could probably hire two or three elsewhere, right? And so, there’s those sort of things you obviously, as a company, you think about?
Nelson: No, I mean, it’s one of the things that I love about working from home being a remote worker is that I am English, and actually, at the moment, I am living back in the UK. But my wife and I lived in Valencia, in Spain, for the last three years before this. But like, even now, I’m back in the UK, I only have a couple of UK clients, and clients they’re based in England. Vast majority of my clients are based in the US and actually I have a couple of clients in Australia, so I’m working across all zones, basically, at the moment. Which is fun, as you can imagine, but you get to work with, with great people kind of all over the world, which is fantastic. I think it’s open things up massively. But along with that, you have a lot of a lot of problems. That hopefully won’t necessarily be so much of an issue in the future as we kind of get our documentation and our processes and, and kind of just improve them. But you’ve got the age old conundrum for a boss, which I heard from, I was interviewing Luke Szyrmer, who is kind of remote working consultant for people and actually like yourself, who like software based, and companies that are managing lots of people kind of all over the world. He used to run like a 40-60 person team who were based worldwide. And like one of the issues that he’s heard from a lot of bosses is ‘how do I know people are actually doing the work? Even if I’m not there to see them doing it?’ In his answer, he said it’s about execution and it’s about deliverables. If something needs to be done by somebody by a certain date, and it’s not done, then they haven’t done the work, basically. Yeah, I mean, of course, there are exceptions to that things, things crop up. But even if they haven’t completed all of it, you can, like go in and check and see what’s been done. If you have those processes in place, ‘This is going to be submitted by this person, on this day, this person is going to do QA on that process. This person is responsible for auditing and project management’, and things like that. It’s harder than being in the office and putting your head above your computer and the cubicle and yelling at somebody and saying, ‘Hey, have you done that yet?’ You know, it’s more of a process based problem, I suppose, or solution to overcome. So, yeah, it’s very, very interesting. Are there any other things that you found that, like, remotely has been a bit tricky that normally in an office setting, you’d found, an easy solution for?
John: Definitely, I think a lot of people miss small talk. So, I think small talks being just random things that crop up in a water cooler, or like in the kitchen, right? Or even over lunches, people share a lot of information about their life, their kids, sports, but even just like sometimes with work or with other people from different teams, and so it kind of webs people together, right? Like just the connection is stronger, you learn more about people, it’s easier to make connections. And that’s definitely something that is harder. Because we tried to introduce FaceTime and everybody turns on video on the meetings, and at least have a couple of those kind of meetings with your employees with your other team members. But most people are like, you know, you’re obviously very serious and you’re professional, right? It’s kind of hard to be casual or have a lot of those random small talks when you’ve got Zoom or Slack on.
Nelson: I think part of the value of small talk is that it’s unstructured. You know, it’s not like company mandated, it’s not set for a particular time. It’s something that just pops up every now and then and you will have a conversation and there’s no agenda and it might start at something and it might develop into something else, and you know you’ve got the personal side but you’ve also got the work side. And one of the things I talked about with Rob Jones, my very, very first guest in Episode One was, how do you go about putting something in place so that you don’t lose the benefits? And I’m talking not just in terms of the psychological, personal, emotional social benefits that you get from small talk, but also the things that you miss from work, with small talk. So the example that that we used was, you know, because he, he’s in PR, so he might just say to somebody who’s sitting next to him, ‘Hey, do you mind checking over this email for me?’, for example, and somebody will just go, ‘Okay, I changed the second line, you’re coming off a little bit too harsh there, or have you thought about adding in like a link here, or I would men put in something now or whatever that might be, or like, hey, anybody got a word for this? Because I’ve used that word too many times in the previous paragraph’ or something like that. It’s all the stuff that isn’t big enough, or important enough to merit a message, a formal written down message. Nobody is going to send a company-wide email that says, ‘Can anybody think of another word for whatever’, which you might have asked in that, it’s all the things that fall down the cracks. Not that I’m expecting you to have solved this, because it’s early days for you guys. But have you, other than the FaceTime, Is Slack kind of fulfilling that role for you?
John: Yeah, I learned from another, my friend who is the CEO for us be called Co-mentor, now it’s called Ark. And they had a remote summit that they did, and they talked about some of these best practices. And so, he basically talks about, there’s synchronous communications, which is Slack, and then also asynchronous communications, which is more like I guess, your Trello, your Asana or Google Sheets, and Notion. We actually started using Notion and I think that’s been working out really good for us.
John: Can you explain for people that don’t use? I’m familiar with Notion, I’m familiar with Roam, and those sort of things, and Evernote, but can you kind of explain what Notion is.
John: Yeah, so I will say Notion is kind of like a very flexible, a single workflow productivity tool. I would say it’s a very easy webpage builder, almost. Like you could actually plug and play a lot of these templates, and you can start typing, and then you can share it across your teams really easily. And so you have combine styles, views across yourselves, your work customer pipelines, design pipeline, these things you could do meeting notes you could do calendars for, for people. And functions, like Wiki, like a blog post, it could just be writing down meeting notes. But yes, all sorts of things.
Nelson: Are you using that like as a documentation tool for your internal processes then?
John: I would say mostly documentation, but it’s also we started using it for just as, I guess, a draw for the Wiki. We started using it for meeting notes every week, we started using it as a job board. So there’s just a bunch of different templates, I mean, it’s just got hundreds of different templates that people are used for it that is pre-built, that you can borrow, and then kind of change things up for you. And that’s actually been really good, because you can comment on it, and everybody can see it, and then basically contribute and iterate on different parts, right? So something like, if I wrote this analysis, and I want my teammates to kind of look it over, I could just easily tag them on this section, and then just say, ‘Hey, can you take a look at this part?’ And so that kind of solves some of the things that you were talking about.
Nelson: So take a step back from that line. And let’s, let’s get a little bit more concrete. So when you pick a process that you think, ‘Okay, this is repeatable, this is worthy of documentation’. Where do you start?
John: I guess it’s just like three, like, the magic number is probably like three. If we see like the same question pop up like more than three times, then I think we would want to document it. We’ll make sure like other people are familiar with this. And also, if you run into like a really obscure bug, the chances are probably somebody else might. And so you know, we try to document that process. How do you actually solve this problem? Because you just never know, it might come in handy for the next person. I learned this also, this last part of this next part from another founder. And he basically just, I was curious, I was like, ‘How do you have so many like blog posts? And so many SEO content?’ And he’s like, ‘I just make everyone write, like, every time they take a customer call, every time they solve a bug, and they can write it down. Like, it’s just, you know, if you make them write it down, then those things can turn into blog posts later on, right? Like, you can publish it, you can share it, obviously, you know, not sensitive information. But if you get into the habit of writing down how you solve the parking problem for a customer, I think that those are all really good content later on’.
Nelson: I guess the idea behind that is, if it’s been a problem for me, it’s going to be a problem for somebody else.
John: Yeah. Yeah.
Nelson: So, if I benefited from the solution, so can they?
John: Exactly, yep. And so I would say that will be something that we should be practising more and more going forward, we can remind ourselves that we need to do more of that.
Nelson: No, that makes a lot of sense. And yeah, so what does the future hold for your company?
John: Yeah, so I think right now, we’re, we’re just full scale ahead, like hiring and trying to build on, execute on a product boom that –
Nelson: What are you hiring, for at the moment? Just so anybody that’s listening might be able to reach out?
Nelson: Fantastic so you’re trying to hire for those over the next three, six months or kind of as soon as possible?
John: As soon as possible as possible.
Nelson: I guess a lot of that growth is kind of sparked by your recent kind of new customers and new clients, which is fantastic. I’m sure you’re going to have lots of really interesting stories to tell about hiring and remote working once your team grows. So I’d love to have you back at some point to discuss those. And I think for today that’s a fantastic place to leave it. We’ve talked about how to put together processes, how important they are for remote work and kind of the growth of ecommerce, I think that’s fantastic. And just for everyone who’s interested in finding out more about you, John, or about Tresl where can they go?
John: So I think they can find they can find us on LinkedIn. If you search for Tresl, I think it’s linkedin.com/company/tresl.
Nelson: Okay, and how are you spelling Tresl?
John: It’s TRESL so it’s it’s a play on words. And we’re on Facebook as well. If you’d look at, I think we’re under Tresl, as well. We have a group called Econ Data Science. If you search what you can’t do the science, you will probably find us. And me and Tony, I’m John Chow. Tony, Tony’s last name is Yin, YIN. We’re on LinkedIn on automation platforms. You will find us there.
Nelson: Fantastic. And all of those will be in the show notes as well. So if you want to click through and find john and the rest of his team and find out more about Tresl, Ben, just go to the show notes. And that is it for today. John, thank you so much. been a pleasure having you here and look forward to talking to you again.
John: Yeah, thanks, Nelson. We’ll catch up again soon.
Nelson: Take care. Bye.
Nelson: And that’s it for today. You’ve been listening to the working from home podcast with me, Nelson Jordan. We’ve been talking about the good, the bad and the ugly side of remote work. Thanks so much for listening. And I really hope you’ve enjoyed the time you spent with us today. If you’d like to discuss the podcast, you want to make a new friend, or you’re interested in working with me on a copywriting or digital marketing project, then visit nelson-jordan.com. That’s nelson-jordan.com where you can also sign up to my newsletter to hear about this podcast and other exciting projects. Until next week, goodbye.