How to scrappily hire for, measure, and unlock growth | Crystal Widjaja, Gojek and Kumu

[00:00] I felt like it was a problem that was very solvable. And we ended up renting a stadium to just hire like 60,000 drivers. [00:07] in a couple of weeks. So, [00:09] I think. [00:10] Looking back, [00:12] It was certainly a risk. When I got there, it was in a house. [00:16] And I realized I probably made a huge mistake. [00:19] But [00:20] We were growing very quickly already. [00:22] even at that small scale of like 4,000 orders per day. [00:29] Crystal Wajaya has been leading product and growth teams at some of the largest consumer businesses in Southeast Asia, including Kumu, where she's currently the chief product officer, and Gojek, where she built and led the growth team through the early years of what is now the largest super app in Southeast Asia. [00:56] Indonesia, and Southeast Asia. In my opinion, American startups have a lot to learn from startups in Asia, and Crystal has been at the ground floor of some of the biggest successes there. In our conversation, we cover the biggest growth unlocks that Crystal has seen across the companies she's worked at, what growth investments usually pay off, and which often don't. We dig into growth models, a bunch of pro tips for accelerating growth, why most analytics efforts [01:26] And we also talk about the nonprofit that Crystal started that aims to help young women get into STEM called Generation Girl. Crystal is such a star, and I hope that you enjoy this episode as much as I did. And with that, I bring you Crystal Wajaya.

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[03:26] flawless data onboarding acts like a catalyst to get them and their customers where they need to go faster. If you'd like to learn more or get started, check out Flatfile at flatfile.com slash lenny. [03:40] Crystal, thank you so much for being here. I've read a bunch of your stuff online. We've exchanged a bunch of emails and tweets, but that's the first time that we're actually chatting. [03:52] For real? [03:53] And I'm really excited to learn from you and for folks to learn about you. We may have like cross paths on Clubhouse and the audio forums or on the Twitterverse. So it's really cool to see. [04:04] Wow, I just remember that. That is so right. I think we were talking about Reforge and Epo. Is that right? Yes, that's right. Good times. Oh my god. Clubhouse days. The days when clubs are also the thing. They have a thing to learn about exponential decay. [04:18] Oh, man. Okay, maybe we'll get to that. And we're going to be chatting a lot about consumer growth and a bunch of stuff. [04:23] Along those lines, [04:24] But before we get into that, you have a fairly unusual path and also geography as compared to many of my other guests. [04:32] And so just to set a little context, could you just kind of walk us through your [04:35] your kind of career path and journey from, I think, as an investment banker initially, and then currently as chief product officer at Kumo and then living in Singapore also. So yeah, tell us all about your path. [04:45] Yeah, I think my path was certainly a non-standard one while I grew up in San Jose, the Bay Area. [04:53] You could see companies like Lyft kind of emerging around the year that I was graduating college.

[04:59] but really it was how do i graduate college as quickly as possible because this is very boring [05:05] So I took a poli sci major. I am not a math or computer science major. [05:12] I didn't know what a consultant was because I was just like, [05:16] trying to get out of college, I didn't realize people start looking for jobs before they graduate. [05:21] So last two weeks of school, I was looking on Craigslist because I was like, Craigslist is how everyone gets a job, right? [05:28] I'm like a first generation American student. So my parents could not help me at all with like, you should look into this company called McKinsey or hear all of the life paths that you have ahead of you. So I ended up taking an investment banking research job. [05:46] And my job there was to figure out how to call startups [05:51] and analyze their potential for VC financing or M&A advisory. [05:57] And I barely knew what those words meant at the time. [06:00] I... [06:01] ended up owning a huge Excel database of like [06:05] 130,000 rows, 60 plus columns, [06:09] and because again i am very impatient i was like this is a terrible [06:13] experience, how would I create a customer database? [06:17] And so I ended up kind of Google-fooing [06:21] all of the work needed to build a MySQL database, I presented a plan, [06:26] And investment banking, surprise, surprise, is not very tech forward.

[06:30] So they looked at my plans and they're like, what is this MySQL thing? Isn't that super expensive? What is open source? [06:36] So I ended up leaving that job because I realized that if I wanted to get into something more tech, [06:43] it would probably not be at an investment bank. [06:47] So I took the investment banking strategies that I had learned there. [06:51] and kind of applied the same pattern matching to [06:53] companies in Southeast Asia. So my family originally is from Indonesia, [06:58] I thought, [06:59] I have a kind of safety net. I must speak Indonesian really well just by birth. So maybe that's a great country for me to look at. [07:07] So I took kind of the... [07:09] approach of [07:11] Let's find a company that makes a lot of sense, that I feel I resonate with. [07:16] And I literally cold called emails and some companies. So Gojek being on that list, I literally emailed someone after Googling HR at Gojek. [07:25] and said, "I'm willing to move to Indonesia." [07:28] take a bet on me and they actually did. [07:31] So I got extremely lucky. [07:32] five years fly by insanely fast. I went through [07:36] building out the data team from scratch, [07:38] When you have all of the data, you know how much fraud you have in the system. So then I ended up building out the fraud and risk team. [07:45] picked up performance marketing and then it was like okay now we're ready to grow so [07:50] You have all of this data, now take on growth. [07:54] Got it. You kind of were very modest about Gojek and the success of that company, and also Kumu, where you work now. So just to kind of set a little...

[08:04] context for folks that aren't familiar with these companies. [08:07] Can you share how big they are and how big of a deal they are in Southeast Asia? [08:10] Yeah, they are pretty massive. So Gojek is now called GoTo. They just merged with the largest e-commerce platform in Indonesia. [08:19] So across Southeast Asia, we had about 170 million users. [08:24] like, [08:25] It's... [08:26] Southeast Asia has scale. If you ever wanted to work at scale, [08:30] you would go to Southeast Asia. We had 20 plus different services from transportation to food, [08:36] shopping, medicine delivery, bill pay, movie tickets, [08:40] So it was like all of the startups in America, [08:44] in one app. [08:45] all being built at the same time with the same user base, [08:48] And so everything was tremendously [08:51] layered because you could fill all of these opportunity gaps in the market. [08:55] where a single app would probably not be as sustainable. [08:59] So, [09:00] Go Jack! [09:01] is massive across Indonesia, Singapore, [09:04] Thailand, Vietnam... [09:07] Then Kumu is a super app for social. [09:11] So Gojek was very transactional. It was like, here's a job to be done. I'm going to pay for something and someone delivers it to me. [09:17] And with Kumu, it's more so of a, I want to do... [09:20] Clubhouse, Zoom, Google Hangouts, Gather Round, all in one app. [09:24] So we cover social feeds, audio, video, multi-seats, [09:29] There's a ton of different use cases that we serve on Kumu, and Kumu is primarily in the Philippines, but ranks top 10 in a bunch of countries as a top grossing mobile app.

[09:40] So with Kumu, you joined when they're already doing fairly well? [09:44] With Gojek, as you said, he joined very early. [09:47] What did you see in that company that helped you decide to join such a risky early stage company for folks that are maybe thinking about? [09:54] joining a startup, what kind of things did you take away at what to look for? [09:58] Honestly, it's probably a lot of luck, but also at that age, [10:01] I realized I have very little to lose. [10:04] So with Gojek, I think I felt like it was the right company because I was [10:09] able to really clearly understand the value prop. [10:12] Traffic in Indonesia is crazy. It takes you two hours to go 20 kilometers. [10:17] So of course you want to take a motorcycle taxi to beat that traffic. Of course you don't want to go out [10:24] and get food and then have to come back this [10:27] long pathway of two hours. [10:30] So I think taking that Warren Buffett approach, I knew that the product made sense. [10:34] the market made sense as well. So, [10:37] drivers there were already a thing but it was very hard to connect them to the consumer it was painful to haggle prices [10:44] There were lots of restaurants scattered across Indonesia. [10:48] So the value prop [10:49] and the market made sense and the channel by which you would do it through this mobile app [10:54] Made a little bit less sense at the time because most drivers didn't have a mobile app. [10:59] But I felt like it was a problem that was very solvable. And we ended up renting a stadium to just hire like 60,000 drivers. [11:06] in a couple of weeks. So, [11:09] I think... [11:10] Looking back,

[11:11] It was certainly a risk. Like when I got there, it was in a house. [11:15] And I realized I probably made a huge mistake. [11:18] But... [11:19] We were growing very quickly already. [11:22] even at that small scale of like 4,000 orders per day. [11:26] I want to spend a lot of time talking about what you learned driving growth with these companies, but one quick question. [11:31] So Gojek is kind of the super app where you do a lot of stuff in one app. [11:35] Do you have any insights into why a super app hasn't emerged in the US? [11:40] Yeah, I think the... [11:42] sentimentality of a conglomerate is very different in Southeast Asia. So we've grown up with, you know, [11:48] a specific conglomerate owning not just [11:51] the mall that you go to but also the apartment building that you live in and the school that you go to [11:58] And so they're very well integrated [12:00] And there's a sense of trust in a conglomerate. [12:03] Whereas in America, we already kind of shy away from like, does Google know too much about me? [12:09] There's also, I think, the second aspect of it, which is that [12:12] in asia we've kind of leapfrogged to the computer era so everyone has [12:17] a phone, [12:18] but you may not even have a computer in the entire household. [12:21] And so [12:22] When your phone is full, are you going to delete a photo of your kid or are you going to delete this app? You're probably going to delete the app. [12:29] So for anyone to really survive, it has to be part of this super app concept. [12:34] Oh wow, I've never thought of it that way. That you don't have a lot of space on your phone and so you want one app to do a lot of things. [12:40] That's right. So there's a decision factor that you don't really have in the US because the cloud storage and device capacity there is a little bit bigger.

[12:49] Hmm. So interesting. So in the US, you can have different apps. Basically, the super app doesn't have to be the best at everything. The fact that it does enough and everything good enough. Wow. Fascinating. You just need to get the job done. [13:01] Amazing. Okay, that's super interesting. Okay, so... [13:05] transitioning a bit to growth and things you've learned along the way. [13:09] So you talked about how I think Gojek, you said, hired like tens of thousands of drivers [13:14] really quickly [13:15] Are there things that startups in Asia do that you think companies in the U.S. should do and can learn from? [13:21] In terms of growth. Yeah, so we did kind of crazy things, right? Like, [13:25] If someone told you in the US that they were going to rent out a stadium, [13:29] preload a bunch of mobile devices, [13:32] Mark it that drivers should come here in mass for a job fair. [13:35] They're going to give them a phone and send them on their way. [13:38] Some people would kind of, [13:40] say no. Like that's kind of crazy. [13:43] won't we get in trouble and to an extent maybe that's true so maybe there are some limitations there [13:48] But this... [13:49] concept of doing things that are somewhat crazy but validate a point. [13:53] doing stuff that don't scale, especially, I think is really the bread and butter of what we did at Gojek. Like we were insanely scrappy. [14:02] We would do things as simple as [14:05] wanting to test a subscription feature, which was just released in Singapore a couple of weeks ago. [14:11] We ended up saying we have this voucher system that we can distribute vouchers in the back end. [14:17] We obviously know our driver's phone numbers.

[14:20] why don't we just add them to a WhatsApp group? We'll add 100 drivers randomly to a WhatsApp group. [14:26] will tell them every time you are on a ride, [14:30] with a customer, [14:31] try to sell them this pitch. You are the only driver who can sell, you know, a subscription package, [14:37] have the customer give you $10, [14:39] Text us when they say yes. [14:42] Someone will be sitting by this phone all day every day. [14:46] We'll look up the customer that you were on a ride with in the back end. [14:50] Whoa. [14:51] give them the vouchers in the back end and then we'll deduct ten dollars from your balance. [14:55] Like, [14:56] It works. It's really this Wizard of Oz experience. We don't have to build anything. I coordinated with a bunch of interns. [15:02] and we were able to validate some of the value prop and conversion rates that we would expect in a subscription service. [15:10] when we wanted to do a new onboarding screen but [15:13] Turns out we have lots of engineering work to do. [15:16] We took a screenshot of [15:19] the screen as is, and we just had our designer put [15:22] what the onboarding flow might look like if we had to overlay it on top of the screen and we just sent that as like an in-app [15:29] Message. [15:31] And then... [15:32] Eventually, I think finding stuff that does scale [15:35] intuitively. We knew that we were sending out [15:38] Lots of fake features through things like type form surveys, [15:43] Things like a personality quiz can be very easily done through Typeform. [15:47] And we realized that if we built in the in-app webpage and we made it easier for us to do a website deployment on our backend side, we wouldn't have to wait for a mobile app release.

[15:58] to test some of these new features out that could be done on web. [16:02] So it's really just... [16:04] Like. [16:04] what is the user experience that we want to create? How do we manifest that as quickly as possible? [16:11] Let's just try that first. [16:13] Going back to the stadium example, I knew you said that you hired a stadium full of people. I didn't realize it was actually a stadium. [16:19] It was literally a stadium that we rented, like a football field, a couple of football fields, if I'm not wrong. [16:25] Long lines, boxes of phones and SIM cards. [16:29] So it was a lot of just like, [16:31] doing really hard work. [16:33] to get to that scale. Wow. I know you do a lot of advising to do you advise startups to [16:39] be more scrappy and do things that don't scale. Imagine because in the US the culture is a little different. [16:44] Like, the only thing... [16:46] better than [16:48] know it like [16:49] If you have data of what your customers are doing, that is the best data you could ever get. [16:55] and so if you don't have a tested hypothesis if you can't think of a way [17:00] to run an experiment, [17:02] Then... [17:03] honestly, that idea is pretty useless. Like maybe it makes sense to the market. [17:08] to the model [17:10] But you could have weird consumer sentiments like not everyone is a rational actor. [17:15] So testing the actual [17:17] experience and seeing how people respond to it, that's the best possible data. [17:21] Pulling that thread a little bit, for startups, experiments are often hard because there's just not enough data, not enough users. [17:27] How do you think startups should approach that? Can you run experiments when you're really, really early?

[17:32] You should. I mean, even if you have a sample size of... [17:36] 30. [17:38] The data you get back generally does not change, but its precision will. [17:43] So mathematically speaking, [17:46] you're going to get the same level of trends, [17:48] but the precision at which you understand those trends will become more deep if you have more data. [17:55] But the underlying information that you're getting out of that won't be very different at larger scales. [18:01] So, [18:02] What's better than having 30 data points? Certainly having 100. But what's better than having zero is definitely 30. [18:10] Fascinating. So contrarian. Running experiments at 30 people. [18:14] I love that. You have to. I mean... [18:16] Like every idea is so... [18:18] cheap at that scale, like you could [18:20] Do things that don't scale dramatically. [18:24] Better with 30 people than at 100 if you're testing. [18:28] So when you just to kind of pull out a little bit, when you're running an experiment with 30 people, what do you look for? You're looking for like 20 of them to do something like a large percentage of that group does something, right? [18:39] So everyone wants to go on like retention. They want to see that users are doing this thing. [18:46] and they want to get from step zero to 100 really quickly, [18:49] but they don't realize that like, [18:51] users make decisions based on [18:54] Succeeding events. [18:56] So what's one step before the user makes that decision? What are the things that they have to do, the things that have to be done?

[19:03] So, [19:04] We're always looking for [19:06] What is a specific... [19:08] reason that this user might have converted for things like GoFood, [19:12] It would be things like [19:13] Windows user tried a new merchant [19:16] If. [19:17] what people are ordering right now or just food that they already trust and know. [19:22] If you need to have trust in order to purchase food from a merchant, [19:26] How do we generate that trust? [19:28] So they actually hacked it by... [19:30] connecting people's Facebook connect login. So we had already had permission to look at who they had connected with on Facebook. [19:38] We actually looked at the food that their friends had purchased. [19:42] and used that as [19:44] uh data set of hey here's food that [19:47] Lenny? [19:48] purchased and liked [19:50] Maybe you would like it too. [19:51] And so that was one way to hack the... [19:53] trust factor. [19:55] And we did find that when we told people [19:58] This friend? [19:59] purchased from this merchant you would be twice as likely to purchase from a brand new restaurant [20:04] than users who did not have this feature. [20:06] And that... [20:08] increases GMV that eventually gets you [20:11] to the conversion rate that you wanted. [20:14] but it solved a different problem before. [20:17] how do I convert? It was, how do I solve for trust? How do I break [20:21] the barrier... [20:22] of facilitating that decision-making process, [20:26] That aha moment. [20:28] by fixing the setup moment, which was trust. [20:31] And that's just the general kind of rule of thumb you have. Don't use retention as a goal.

[20:36] I know you wrote about this somewhere. Is that kind of a rough... [20:39] rough rule that I'm using. I think a lot of people thought that I had meant like retention sucks, don't care about it at all but in reality it was really like [20:48] When you think about retention, that's just not specific enough. [20:52] So there is this... [20:53] mental model that I use from [20:56] Made to stick? [20:57] where they'll tell you, Lenny, [20:59] Think of everything in the world that is orange. [21:02] And you're like... [21:03] An orange. [21:05] What else? [21:06] And then if you choose that, [21:08] Structure with Sandbox 2. [21:11] Think of everything orange that's in a construction site. [21:14] then you really start to [21:16] Realize [21:17] and grasp at concrete [21:20] concepts, [21:21] and can actually action on them. [21:24] in real life. [21:25] Got it. Speaking of retention, wherever you found products and companies have the most success increasing retention? [21:32] It's usually the step right before conversion. So [21:36] If they [21:37] aren't sure why the user opens the app, or they aren't sure why the user [21:42] got to this checkout page. [21:44] It's often some like copy or... [21:46] The park. [21:48] has been ineffective in some way, [21:50] I'd like to see founders think about [21:53] the user psych model that the Reyes contractor often talks about, [21:58] So you need some momentum in that user journey. [22:01] to get them over the hump of some of these very [22:04] painful user processes like

[22:07] typing in a credit card. [22:09] That's a lot of work. How do you lower that friction? [22:12] and being able to [22:14] sequence the right steps effectively. [22:16] and just moving around screens actually can do a lot. Going even deeper there. [22:23] So the companies you've worked at, the companies you've advised, you're on the boards of a couple of companies, I noticed. [22:28] What have you found to be really [22:31] good uses of time in terms of growth investments, like things that often work, [22:36] And then kind of a second question, what do you find is really successful where people invest a lot of time and ends up not being really useful for growth? [22:44] Yeah, I think I see a lot of founders like, [22:47] grasping at straws, so there'll be like this brand new feature that does something kind of different from what people are already doing on our app, like this will make things work. [22:58] But they don't have any Wizard of Oz test. They haven't proven that people want to do that. [23:03] They don't have any data of users currently trying to do that. [23:07] And that's a... [23:09] that's a sign of like, [23:10] Why this instead of literally anything else that you could be doing? [23:15] I do find... [23:17] If you have a lot of people landing on a web page or an app, [23:22] and then not doing anything, then it's probably copy. Like they haven't even experienced the product. It's clearly not the product that's wrong. [23:29] So how can you change the copy? [23:31] and resonate with the pain point [23:33] rather than the solution you are offering, so that users understand how to fit themselves into the use case.

[23:40] So copy is a big one. If I see conversion rates aren't landing between [23:46] app launch to some first action, [23:50] But if there is conversion and they're just not as frequent, [23:53] I try to look at what the most [23:56] painfully long conversion. [23:59] Events are: [24:00] So users who eventually check out or eventually [24:04] completed [24:05] The aha moment? [24:06] What are the user paths and what is the longest one that seems like it's the most painful? [24:11] Are there enough people trying to do that? And how do we shorten that cycle? [24:15] So for Kumu, it was things like, [24:17] Users wanted to sign up. [24:20] and find their friends on Kumu. And so they were using search [24:24] frequently search was underutilized API, it was kind of slow. [24:29] We sped that up. Conversion rates go from 60% to 90%, like over the course of a few weeks of just optimizing that and putting more content there. [24:38] So, [24:39] looking at like where are people doing things and then failing like you already know this percent of people would convert if you fix to this [24:47] That's a definite potential win. [24:49] So we try to layer these definite wins with like, [24:52] crazy bets of like brand new feature with no data. [24:56] At least run an experiment if you can. [24:58] But I always try to layer in these sure wins. [25:01] When you talk about conversion being good and bad, do you have a rule of thumb or kind of a mental model of like, here's a rough range of like, this is good. [25:09] And we should not really spend a lot of time on this. And this is bad. And,

[25:12] We should optimize... [25:13] So assuming that the frequency [25:15] is correct so you have a weekly frequency [25:18] If users are coming back, [25:20] If it's a free product, 60%, right? It has to be at least 60%. [25:23] If it's a free product, we go every week. [25:25] If it's a paid product, I usually look at that more as like maybe 20 to 30 percent. [25:31] And this is retention, people coming back the next week. Exactly. [25:34] coming back in the second week or month or whenever your frequency ratio is, [25:39] And this is at scale. So if you are much smaller, like your friends and family, that better be near close to 80% no matter what, because if you can't even convince [25:49] the people who care about you, [25:51] to use the product, it probably isn't going to solve the job for anyone else. [25:56] Very handy, very concrete numbers. [25:58] And then your point is that when you're a startup, it's only going to go down because you're kind of early adopters that are more excited and [26:04] And they'll be more excited about coming back. And so, yeah, so you want to start really high. [26:09] I mean, don't make the same mistake that Netflix and Spotify have made. [26:14] which I guess is when they've launched, they've started international expansion, and they see this very small percentage of users [26:21] Start. [26:22] to sign up for Spotify or Netflix. [26:25] There are very few people, though, in Southeast Asia or internationally that have the types of credit cards that Spotify or Netflix would accept. [26:32] And so when they launch in these markets and they see a ton of uptick in the first week, they're like, this is only going to get better. [26:39] When in reality, it's like you just pulled forward everyone who could have possibly

[26:43] subscribed to you. Now you're going to have to work a lot harder to get everyone else. [26:47] The 60% number, so you're saying it's like every week, 60% of the previous week come back, roughly, as just a rule of thumb. Exactly. Is that kind of how you think about it versus, say, cohort retention? Is that just because it's easier as just a... [27:00] simple rule of thumb. I am actually thinking of it as cohorts. So 60% should be your week one. [27:05] And then it should flatten. [27:07] I think I usually give teams like two to three weeks or frequency periods to see things flatten. [27:13] but it better flatten around 60% for a free product. [27:18] that's kind of that's actually what we saw at gojek early days it was like 60 70 retention rates because people [27:26] We're using [27:27] this product that really solved a huge problem for them. And I think that's when I knew we were going to be fine. If people keep coming back, [27:35] The product just needs to work. Wow. So week one, 40% of people drop off. Week two and beyond, basically nobody drops off is kind of what you look for. Yep. [27:43] Wow, what a high bar. [27:45] But I like that because... Well, Gojek is a definite one. [27:49] Okay, there we go. [27:51] If you want to be a Decacorn, there's your new benchmark. [27:54] Exactly. Amazing. Okay. There's a bunch of other stuff I want to dig into. [27:59] One is just data modeling and thinking about growth strategy as a founder. [28:03] So say a startup is just trying to think about how do we drive growth? [28:07] Where do we invest? Do you have kind of a... [28:09] framework or a process? I know this might be a really big question, but just for founders to think about how their growth works, what their drivers might be, how would a founder approach that problem?

[28:20] For sure. So I thought that this... [28:24] was not an obvious process. It wasn't like an explicit process. [28:29] Until I worked with Reforge to build my Data for PM's program, [28:33] Gotta get that plug there. Go reforged. [28:35] I basically talked with the Reforged folks about like, here's what I would do in all of these scenarios. And they're like, oh, so you mean you're doing this step one, step two. And I was like, [28:45] Yes, actually. How did you figure that out? [28:47] so i don't really think in frameworks like this is just [28:51] a logical process to me but i think what i've figured out is it's step one like [28:56] you have constraints, right? Similar to our sandbox example of like everything in the world that's orange versus [29:02] everything in a construction site, [29:04] You have to think about the physics of the current market, the product, the model, and the channels that you're using. [29:11] So to use Gojek as an example, it would be [29:14] market of Indonesia, [29:15] Here are the consumers in this market, the driver side, supply side in this market, [29:21] Here is the product, mobile app, [29:23] We're able to connect drivers and consumers. There is an allocation. [29:27] that we create. [29:28] Model. [29:29] We charge per order. [29:31] channel, [29:32] We are able to do this through push notifications or in acquiring new users. It might be through Facebook ads or [29:39] And this was a really big insight for us. [29:41] It's the real world. There's a physical... [29:44] conception of a driver in a jacket driving around the city who is marketing Gojek for us. [29:50] And word of mouth actually was

[29:52] primarily driven by [29:54] I saw a driver on the street, so I knew Gojek was here. [29:57] And that actually was a huge driver of all of Gojek's growth as it expanded. [30:03] to new cities. [30:05] So step one is what are the physics? [30:07] Step two is [30:09] When you think about loops, [30:11] and growth funnels and [30:13] the [30:15] quantitative inputs, [30:17] to each loop, [30:19] does that fit into these physics or do you have to change like four or five different things [30:25] So we were very careful about changing too many parameters and making too many bets. [30:30] on too many variables going our way, [30:33] So we would always change like one small thing at a time and make sure that it fit into the model. [31:03] or trying to run your experiments through a clunky marketing tool. When I was at Airbnb, one of the things that I loved about our experimentation platform was being able to easily slice results by device, by country, and by user stage. Epo does all that and more, delivering results quickly, avoiding annoying prolonged analytics cycles, and helping you easily get to the root cause of any issue you discover. Epo lets you go beyond basic click-through metrics, and instead use your North Star metrics, like activation, retention,

[31:33] subscriptions, and payments. And EPPO supports tests on the front end, the back end, email marketing, and even machine learning clients. Check out EPPO at getepo.com, get EPPO.com, and 10x your experiment velocity. So step one, just to cover this, is figure out how you're growing. [31:52] In Gojek's case, it was partly real world people just seeing Gojek riding around. I think it's both... [31:59] Figure out how you're growing and also the elements that you have at your disposal. Like what are the levers that you have that maybe you've never tried using? [32:09] When we looked at our model this way, we actually realized we had underutilized [32:13] the driver's capacity to drive our growth. [32:17] Pun definitely intended. [32:19] So, yeah. [32:21] in looking at the model this way we had thought through like what is our goal we want go pig to be [32:27] much bigger than it really is. [32:29] It's an e-wallet service. [32:32] users are able to get access to this digital balance. [32:36] How do we drive adoption? [32:38] And so when we looked at the lever, we have a driver. [32:42] we actually created an incentive model. [32:45] We built a very small service. [32:47] that would check when a driver got allocated to a customer, again, the product. [32:52] and the model. [32:53] We would then check in the database, has this customer ever used our GoPay product before? Did they have a digital balance? [33:01] And if the answer was no, we would message the driver immediately

[33:05] "Hey, this customer hasn't done a GoPay top up before. [33:10] If you get them to give you cash, [33:12] and we deposit it into their virtual wallet, we'll give you extra money. [33:17] So using them as the salesperson, [33:20] You wouldn't believe how great of a salesperson someone can be [33:24] when you were literally trapped in a car with them. [33:26] going somewhere. [33:28] And so you have this captive audience, captive attention. You have someone who has the incentive to cross pay or cross sell someone into go pay. [33:36] and customers were able to feel [33:39] the benefit because the driver was explaining it to them directly. [33:42] So, [33:43] It was one small, there was no change to the physics, it was a lever usage. [33:49] What a devious strategy. It was huge. It was like 60% of acquisition once we released that. Oh my god. [33:55] So if we're thinking through your potential levers and physics of your growth, [34:00] Do you think about it like bottoms up? Here's all the things that are going on. [34:04] and here's areas we can invest? Or do you have kind of like [34:07] a menu of options top down of like here's the 10 things it could be [34:10] It's looking like for Gojek, it's these four... [34:13] And let's focus on that. [34:15] Yeah, I think you always have to start from... [34:18] the fact that like we are not wizards like it's very hard to move the physics of a universe when [34:25] you are trying these new things, [34:27] So, yeah. [34:28] Start with what? [34:30] currently works and currently exists. [34:33] and where you think the biggest constraint is or the best lever is, and then fix that one piece.

[34:39] because the entire universe isn't exploding like you're not [34:43] the world isn't changing so dramatically that your physics changed. [34:46] So I think rooted in reality is very important. [34:49] Got it. Okay, so let's see what's working. [34:52] Find the constraint. And then step two is basically what can you do to the product to optimize the funnel slash loop to make it go even faster. [35:00] Love that. [35:02] Maybe as another example, if something comes to mind with Kumu, [35:05] How do you think of Kumu through this lens? [35:08] Yeah, I'm always very hesitant to talk about Kumu because there's so much competition right now. And we're like on the cusp of some very interesting... [35:16] interesting things. [35:17] But I think for Kumu, it's actually very complex because there's a lot of human [35:23] emotion that is involved like with gojek you knew if you got the job done you made a transaction [35:28] With Kumu, how do you know with a consumer, [35:31] made a friend. [35:33] like felt like they had a genuine friendship so you almost have to create more friction [35:37] to identify users who really got past that barrier. [35:42] and aren't explicit with [35:43] Done. [35:44] activity that they did. [35:46] So we have features that tell us [35:49] If the user is really searching for this [35:52] job to be done if they really want to be part of a community [35:55] How do they fill out this? [35:56] Do they fill out the form? Do they fill out a questionnaire? [35:59] of like many questions. [36:01] Do they go through this friction? [36:04] just to get access to a community. [36:06] So we almost create this... [36:08] artificial friction to help differentiate

[36:11] how deeply a user wants something or needs something, [36:14] And if a user doesn't fill out that questionnaire, [36:17] maybe they're actually looking for something else. They were looking for entertainment, they were looking for content, [36:22] or short form content. [36:24] And so creating... [36:26] almost like hand raiser. [36:28] approaches to [36:30] for a user to say, "I wanted this thing." [36:32] We leave a lot of breadcrumbs in the app. [36:34] to be able to identify those paths. [36:37] Awesome. While we're on the topic of these two companies, [36:41] just maybe for inspiration to founders who are thinking of ways to drive growth. [36:45] What were a couple of the bigger unlocks? [36:47] growth-wise for these two companies or even any other company that you've worked with that's interesting [36:52] Yeah, definitely in the early days it was copy. So, [36:56] I think if your product does something that's [36:58] not super familiar. [37:00] you have to tie it to something that is so i talked about using drivers to sell [37:06] Go pay! [37:07] Before that, one thing that we did was... [37:10] to actually take [37:12] someone's virtual account number and put it onto a picture of a credit card like you know what a credit card is [37:18] That's familiar to you. [37:20] A lot of people didn't know what a digital wallet was. [37:22] And so when they looked at this, like, OK, I have [37:25] this virtual thing that acts like a credit card. It works like my debit account. [37:31] then they understood the concept a lot better and we actually saw top-ups increase [37:35] based on us literally just sending that picture with someone's [37:39] virtual account number there. [37:40] So they could go to an ATM and they would just type in the card number as they would a regular debit account.

[37:47] and they realized that they could top up through that channel. [37:50] Because that was something that was pretty... [37:51] interesting to us with just how do we tie [37:54] the familiarity loop back into the consumer [37:58] mental model of the product. [38:00] and drive acquisition that way. [38:01] And that was a go check. [38:03] Is there anything else, maybe since you don't want to talk too much about Kumu, any other advisorships or companies, examples of something that... [38:11] ended up working really well to help them accelerate growth. [38:14] things that have worked really well. So, [38:17] For one of the companies I work with, AB InBev, [38:21] They run a lot of their D to C brands. [38:24] and [38:25] South America and [38:27] globally. [38:28] So, [38:29] One of the features that we were looking at was how do we ensure that subscriptions [38:33] don't actually become a [38:36] canceling point for a user. [38:38] So, [38:39] in the app you could [38:41] cancel, or you could [38:42] resume your subscription, [38:44] But you couldn't pause it. [38:46] So, [38:47] When we looked at the cancellation reasons and we saw that [38:50] They're... [38:51] Number one reason was I still have too much beer. [38:54] we actually decided, well, [38:56] Let's just... [38:57] add a pause button then because like canceling the subscription [39:01] is a permanent solution [39:02] to having too much beer. [39:04] How do you make a temporary solution that solves the actual problem? [39:08] adding in a pause button actually helped alleviate a lot of the churn that was becoming very hard to [39:14] reacquire back [39:17] So that was when...

[39:18] fix where we looked at the again physics of the model like we're not going to create [39:23] new changes to the product or create one time buys or like reactivation emails. We'll just solve the problem at that small constraint. [39:32] where everyone drops off. [39:34] Wait, so can you order beer a subscription? Is that a thing? Is this a consumer product? That's it. It was a thing. Yeah. Cool. Okay. This also reminds me at Airbnb, this is actually one of the biggest wins is adding a snooze. [39:48] feature to your listing. Exactly the same thing. Yeah. [39:51] All right, there we go. Awesome tip for folks that have churn problems. Snooze slash pause. [39:57] I want to shift a little bit to a post that you wrote that maybe is one of your more popular posts you wrote on the Reforge blog. [40:04] called "Why Most Analytics Efforts Fail" [40:07] And I'd love to hear your broad overview of why do most analytics efforts fail? And then how do how do teams avoid this? Maybe what are like two or four things they get two to three things they can do? [40:17] Yeah, I'm actually pretty surprised at how much noise that has generated because [40:23] I guess it came from a place of frustration where I kept [40:26] telling people, [40:27] Bye. [40:28] you're doing this wrong, here's how you should probably be doing it. [40:31] But I think it resonated a lot with Fultz because [40:34] They recognize all of those symptoms, but they weren't sure why it was happening. So to say like, oh, this is the thing. Instrumentation is what's wrong. [40:42] I think it's a very actionable thing. Like it's probably one of the most solvable problems out there.

[40:48] It just takes some time and. [40:50] mental model shifts to do it well. [40:53] So a lot of people look at [40:55] tracking data as [40:57] How do I track my OKR? How do I know if I'm going up or down? [41:01] but they don't use it to track or identify insights. [41:06] So I... [41:07] We'll use the example of using Twitter, [41:11] for the [41:12] quote unquote news when in reality they're actually using Twitter for entertainment. [41:17] Do not treat metric gathering as entertainment. Like it's not there for you to be like, oh, [41:22] That's interesting. How novel? And then not act on it. [41:26] So real news is information that changes what you do in the real world. [41:32] And if you don't change what you're doing, what you are doing is just getting entertainment. [41:35] So, [41:36] Let's use that as a premise. [41:39] The next step in instrumentation is to look at the fact that measurements do not equate to insights. [41:45] A measurement would be an observation. It's a data point in your database. [41:50] So the example being, [41:52] Power users do four times more bookings [41:55] is an observed fact. [41:58] because your transactional database obviously says that that is the case. [42:02] but it's on an insight because it doesn't have context. [42:05] It doesn't give you information that lets you act on it and better understand the problem. [42:11] So another example, [42:14] would be if I see my girlfriend hanging out with a guy I don't know. That is an observed fact that you see in the real world.

[42:21] Your hypothesis could be that your girlfriend is cheating on you. [42:24] But the insight [42:27] uh the actual fact might be that she's not cheating on you it's her cousin [42:32] And now your insight is I am paranoid. [42:35] and I need to change my behavior to be less crazy. [42:39] So the insight will provide value when you have this why answered. [42:44] Why is this person doing this thing? [42:48] Here's why. And then you are going to act differently. [42:51] So, yeah. [42:52] For our purposes, if we look at [42:56] A GoFood user will transact and is more likely to use a voucher [43:02] That's a fact. That's an observation. [43:05] but it's not an insight. An insight would be something like, [43:09] GoFood users who are power users, [43:13] are more likely [43:14] to use a free shipping discount on a high gmv basket [43:19] versus non-power users. [43:22] And that actually tells you how to change your marketing approach. It tells you [43:27] that in what circumstances does someone do this when it's a high gmd basket [43:32] give power users the ability to get a free discount, [43:35] But do not do this for non-powered users because they won't convert any better than they normally would. [43:41] So that helps you change your marketing spend. [43:44] It helps you understand the decision points of power users versus non-power users, [43:49] The insight is

[43:51] instrumenting properties into an event so that you can segment who is doing what behavior, [43:57] and make some hypotheses [43:59] On that. [44:01] Observation, [44:02] test that hypothesis and then you get some causal representation of whether or not that hypothesis was right. [44:09] So it sounds like a lot of the root of the issue is setting up the wrong metrics, the wrong... [44:15] I guess there's the tracking element of just capturing the right information. [44:19] And then also just not focusing on insights versus just having a bunch of information. Exactly. [44:24] What are signs that [44:26] you're doing this. Like say someone's gonna go load up their dashboard and they're like, [44:30] Am I failing or not? What should they be looking for? [44:33] So, [44:34] I already know if a team is good at instrumentation or not, just by looking at the instrumentation spec. [44:40] The symptom of a bad data tracking [44:44] approach is [44:45] You have a ton of bros with a ton of events. [44:49] But every event has like [44:50] one property or no property being tracked. [44:54] So, I don't know. [44:55] An example with Gojek would be [44:57] When a user lands on the [45:00] Map [45:00] to select a drop-off point [45:05] The event would be... [45:06] Drop off. [45:07] or like map loaded, let's say. [45:09] And the properties there should be things like how many drivers do they see on the screen? [45:15] What is the pickup location? Is it what city is it in? What [45:19] Latitude and longitude is it? [45:21] What is the is there surge pricing?

[45:24] what is the current minimum fare do they have a voucher code [45:28] All of these characteristics of the experience and the context [45:32] that can help you look at [45:33] Hey, when a user only sees two drivers on the screen, they're much less likely to convert than a user who sees five drivers on a screen. [45:42] Now we can look at [45:43] In what cities and in what latitude and longitude do we mostly only see two drivers versus five drivers? [45:50] like being able to do the second layer approach of the why [45:54] and not just stop that hmm that's weird when you have two drivers you [45:58] are less likely to book but then you never ask why like that drives me crazy or [46:03] the inability to even know that there were only two drivers on the screen [46:06] Like you're missing so much context of the user's experience that you're unable to make assumptions about [46:12] why the user didn't convert. [46:15] I love this. [46:16] Is there a resource? Maybe your course is probably going to be the answer, but [46:20] For folks that want to figure out how to do this sort of taxonomy and events, well... [46:24] How do they go about doing that? [46:26] So, [46:28] i think it's important to just like go through examples yes every product is different but [46:33] Everyone has the same sign up flow for the most part. So, [46:36] look at the signup flow examples that I have in the blog post, [46:40] or in, I believe, Amplitude actually has a pretty good long winded documentation on this, on how to do event tracking. [46:49] but it's really a matter of like sitting down and thinking really deeply if i were to press this button

[46:55] Why would I and why would I not? And am I tracking that? Am I... [46:59] in my user properties. [47:01] So, [47:02] It's really just like sitting down and mapping out the experience. Speaking of Amplitude and other data tools, do you have a default recommended [47:09] metric stack for founders just to start with and maybe [47:12] a few other things as they evolve. [47:14] It really depends on how early they are. So if they have a single data warehouse with [47:20] all their transactional data usually i say like you can probably get by with google data studio it's free usually with [47:26] whatever you're using, [47:28] If not, Metabase has a great open source free tool [47:32] If you have someone who can write SQL or if you have multiple databases, then Metabase is great. [47:37] If you need an app, [47:39] mobile device event tracking, I usually recommend Clevertop because [47:43] Ms. Pana has unfortunately failed me a lot. [47:46] And Amplitude doesn't have the CRM components that I would need all in one space. [47:51] If I am much bigger and I need more analytics juice, maybe amplitude makes sense on top of this. [47:57] or something like that. [47:58] That helps me pipe data into more dashboards and do less ETL for me than I would get into segment. [48:05] And then once you get into experimentation, obviously have to shout out to Eppo. [48:09] I think they've [48:10] really re-instrumented a lot of the dashboards that I would have normally had to do in experimentation projects. [48:16] So I usually look at something like EPPO to just automate the decision making flow. Awesome. [48:22] I think we're both small investors in Epo, big fans but a little bit biased. But yeah, it's an XRBMB team that built it, so it's cool.

[48:30] shifting a bit from metrics and data to just growth teams in general. [48:36] Maybe first question is just how do you recommend companies set up a growth team in the early days and then over time? [48:44] Yeah, so I can talk about how growth was set up at Gojek as an example, which [48:48] I think is probably the best practice. [48:50] So we didn't really know what growth was at that time, but we knew there were obvious gaps to fill. [48:56] So because we had grown so quickly, the [48:59] core product team [49:01] was, [49:02] still making the core product features like as simple as like [49:05] phone number masking that wasn't a thing yet like you had access to your driver's phone number it's probably not a great thing it's probably part of the core functionality and we need to fill that gap. [49:15] At the same time, [49:17] Growth was still necessary because you have all of these users trying to use the product [49:23] that aren't quite getting there. [49:25] So, [49:26] Things like figuring out what SMS provider [49:29] we should use to send the OTP [49:32] to this user who is signing up from this telco provider. [49:37] that was a growth objective that like [49:39] isn't necessarily core feature work, [49:41] but was a gap to fill given the onboarding and SMS success delivery rates. [49:47] Things like telling the driver, [49:49] if this was a brand new customer, because at this point in time, [49:52] Drivers had taken thousands of rides. [49:55] and they assumed every single customer knew how Gojek worked when [49:59] Maybe they didn't. And so the

[50:01] We knew that the protocol was that [50:04] a power user would know, they would make an order, and they would just wait. [50:07] They would wait somewhere. They would keep an eye out for a driver. [50:11] And then they would get on the motorcycle and go. [50:13] But for a brand new user, [50:15] Are you supposed to walk to the driver? Are you supposed to find them? It's unclear to his brand new... [50:21] uneducated new user how to use the product. [50:24] And so first time user experience could have been a terrible one where they went and walked off and then the driver came to the pickup point and they couldn't find them. [50:33] So it was all of these like small acquisition, adoption and engagement use cases that growth was filling the gap on. [50:41] and eventually we embedded [50:43] our growth, I would say, product managers at the time into these teams. [50:48] and they ended up kind of synthesizing what growth was. [50:51] as a full-time role. [50:54] eventually becoming PMs who own specific parts of the product stack. [50:58] So in your experience, and I hear this a lot, is your first growth person shouldn't just come in and figure out what to work on. You should understand here's where we need growth help. Let's find somebody to tackle it versus come help us figure out what to do to drive growth. Is that how you've seen it? [51:11] Exactly. I think it's just setting the bar like, [51:15] too high to expect someone to come in and model everything. [51:19] like again there are physics in place it's very hard to move everything [51:23] So it's really about having someone who already has all of this data, [51:27] Nose. [51:28] where the biggest gaps are doesn't have to start from scratch and figure this out.

[51:32] and then just picks some small [51:35] space to work on that they know is workable. [51:38] Do you have strong opinions about growth being integrated the way that you described where Growth PM is basically [51:43] It has a cross-functional team, basically, as the PM. [51:46] versus kind of a separate growth team that's off to the side. [51:50] Yeah, I think it can work as a separate growth team to the side if the company is truly [51:55] like head over heels [51:57] tripping on [51:58] insane product market fit. [52:00] like if there is insane product market fit and you are really scrambling to do core [52:04] Feature, [52:05] Stocks. [52:07] than maybe a growth team to come and be clean up. [52:10] is fine like we really called ourselves like we're the cleanup crew we pick up the pieces that were left behind [52:15] We connect the dots like you forgot to plug this in, we'll plug it in for you. [52:20] But we were a team of like lots of stats heavy people. So a lot of my team [52:24] were like statistics graduates we cared a lot about looking at numbers and odds and probabilities [52:31] because it really is a numbers game at that scale. [52:34] you could work on anything and everything would probably do something. But what was the thing that would make the most impact now? [52:41] and unlock us for the future. [52:43] I was going to ask you folks look for when they're hiring an early growth person. Is that what you find? Just stats data kind of person? [52:48] Yep. [52:49] You have to have someone who knows how to run the numbers, right? [52:54] If you're looking at. [52:56] ratios of conversion rates but you don't realize that this ratio is of a much smaller base

[53:02] size. [53:03] you're going to make the wrong decision. So someone who is intuitively good, [53:07] at statistics, they know how to do sampling appropriately, they know what selection bias is, like, [53:15] The worst possible thing is to have a growth person who thinks they are doing [53:19] the right thing and is measuring things wrong and then focusing on the wrong areas. [53:24] Do you find that it's often easier or better? [53:27] to hire a young up and coming person or find someone that's got a bunch of experience for your first growth hire. [53:34] I would hire someone who is willing to take an Intro to Statistics course [53:39] and it doesn't matter like if they've had the experience like go wild or not i think [53:45] It really is like, can they focus on the right opportunity rather than the most flashy thing? [53:52] And I think both profiles can come under that. Got it. And then what do you do in a hiring process for someone like this? What kind of things do you suggest founders look for? [54:00] Yep. [54:01] I actually looked for that first principle [54:04] bias so i'll give people case studies of like [54:07] Here's what we see. [54:08] How do you know that this is true? [54:10] And then I have them set up an experiment design. I want to see that they are sampling randomly [54:16] Not that they're like, I'm going to build this feature and launch it. And of course, it's going to work. [54:20] I want to see that they're taking a measured, deliberate approach. [54:23] to considering like why someone might do this or [54:28] what tools are available so a growth team can go terribly wrong when they just try to like onboard a bunch of brand new tools that don't integrate well and it takes six months to integrate fully

[54:38] And then they get nothing done for six months. Like, [54:41] Everything in growth is an opportunity cost of time. [54:44] trade-offed with [54:46] what you could have been [54:47] doing to the product in that time. [54:50] So we bias towards like [54:52] really quick hacky things like in the early days of go-jet growth [54:56] I think our first real growth experiment, we were actually still the data team at this time. [55:02] was, [55:03] to connect the [55:04] a quick python script to the Twilio API that we had access to. [55:09] And we SMSed [55:10] a bunch of drivers through a CSV that we uploaded, [55:13] that said like, "Hey, you're [55:16] acceptance rate is really low. [55:18] You're not supposed to do that. Please accept all the rides that you are getting. [55:21] And that actually increased acceptance rates by 2% across the board. [55:25] And when we look deeper into that data, it did even more so for brand new drivers. And so we then worked with the data driver onboarding team. [55:34] so they could better facilitate the onboarding experience for their drivers. [55:38] For the interview question that you described, like an experiment design question, [55:42] Do you give that as a project where they kind of have time to work on it? Or is it a live thing? Yes. Okay. [55:47] Yeah, I don't think live works really well for these case studies. [55:52] I want to see people put in the time and the work [55:55] to do something to the best of their ability. [55:58] And of course, we asked them like, hey, you have five days. [56:02] expect you to spend probably like [56:04] four hours on this. [56:06] So if you don't have four hours within these five days, let us know.

[56:09] So we're pretty careful about giving them the appropriate amount of time. [56:13] to do it at the level of quality that we would have expected if they were to work here full time. [56:19] So, [56:20] give them those four hours we want to see like do they google if they can't figure it out [56:25] Right now, let's see them Google it. [56:27] We'll ask them like what? [56:29] approaches they took, how do they figure this out? And we like to hear people say that they literally had to Google this and read a bunch of white papers. Like I do that as well. [56:39] For people trying to design one of these for themselves, do you have a question that you've retired that you could share? [56:44] or something that would help somebody design their own kind of prompt. [56:49] Yeah, I can give you a template after this fall. Amazing. We'll include that in the show notes. [56:54] Easy peasy. Amazing. Okay. A last topic that I wanted to cover is a very cool thing that you're involved in. It's a nonprofit. [57:02] that you started called Generation Girl, [57:04] And I think the mission is to help women and young girls get into STEM. [57:07] So I'd love to hear about this program, how you got into it, what it's all about. [57:11] And then also just how listeners can help support what you're doing. [57:14] Absolutely. Yes. Generation Girl is very near and dear to my heart. So I co-founded this with a couple of amazing women who were also at Gojek, but are now full time at Generation Girl. [57:26] So this really stemmed from us repeatedly getting [57:30] annoying comments about working in STEM. [57:33] So things like you can't possibly be the engineer on this project, like, [57:39] You look like you like makeup and stuff.

[57:41] And we were like, yes, I absolutely love makeup, but I also am badass at writing Swift code. So step aside. So... [57:49] Having a... [57:50] experience a lot of the [57:53] kind of misrepresentation of what an engineer should look like or should like. [57:58] I think we really look to... [58:00] Legally Blonde is one of my favorite movies that represents you can take [58:04] the powers that you have, [58:06] whether you like engineering or design or data, and you can be whoever you want and still kick ass at it. [58:14] So, [58:15] a lot of the women that we support [58:18] We're actually happy if they... [58:20] go into one of our classes and they say, "Actually, I don't like engineering." [58:24] That's great. That's agency and empowerment that they got to make that decision for themselves. [58:29] without any... [58:31] cultural biases or social pressure telling them that they should feel this way. [58:36] So we offer free classes for girls 12 to 17. [58:40] We have college classes. [58:42] Partner with teachers about how to teach STEM topics. [58:46] especially in areas where they don't have laptops for every student. Like, how do you teach? How do you use Figma and things like that? [58:53] So, [58:54] people can definitely support us and reach out to us we have a paypal on our website [58:58] Take a look. [58:59] Can you share some of the impact that you've seen from this? [59:02] Are there numbers you can share, anything that you can share around what the organizations have done? So we've already had several thousand students go through Generation Girl summer clubs and programs and classes.

[59:13] So we have an event. [59:15] Every week we have a full summer club that's [59:18] every single day for two weeks, every summer and every winter, [59:22] We have partnerships with some of the biggest tech companies in Indonesia where we partner [59:26] students with engineers and they work on projects together. [59:30] And most recently, we're part of the MIT Solve program with our new initiative, GLASS. [59:36] So plus we're creating a free to use site for teachers. [59:40] So right now we have partnered with a handful of universities in Indonesia [59:44] both in rural and city, [59:47] of Jakarta, [59:48] where teachers can now have the knowledge and material to explain [59:53] newer concepts that maybe they're less familiar with because startup world changes rapidly how you develop changes rapidly [1:00:00] So this is the one thing that we're most excited about because [1:00:03] Every teacher impacts thousands of students a year. [1:00:06] and being able to teach the teachers and give them the resources that they need is something that's really important. It's incredible. [1:00:12] It's currently just in Southeast Asia, is that right? [1:00:15] only in Indonesia because frankly this is where this is where everyone needs the most support I mean [1:00:21] Globally, STEM is not. [1:00:23] well-received or welcoming at all to women. I think it's gotten worse over the past [1:00:30] few decades like below 18 percent. [1:00:32] of college graduates are women in computer science [1:00:36] So, [1:00:37] We're really trying to reach the youngest generation. [1:00:40] because that's when you are told or informed that

[1:00:43] Computer science. [1:00:44] is for specific types of people. It's really sad to hear that it's heading in the wrong direction. What do you think is contributing to that? [1:00:52] I think there is still a lot of [1:00:55] this mental model of what a computer scientist is able to do, [1:00:59] and how much support they're given so it's been [1:01:02] shown in studies that [1:01:04] at the youngest generation middle school high school. [1:01:07] you are more likely to be given introductory STEM classes as a male than as a female. [1:01:14] So women just... [1:01:15] aren't targeted for STEM at that younger age. And so when they enter the high school or college classes for computer science, [1:01:24] They're way behind. [1:01:25] And that's [1:01:27] Does not feel good. No one likes to be like the worst in the class. [1:01:30] And so it's more likely that you'll drop out. [1:01:33] We've seen studies at Carnegie Mellon that actually would [1:01:36] create introductory computer science classes before the college class starts. [1:01:42] And for the women who did join those classes, they actually graduated at similar rates as their male counterparts. [1:01:49] So it's really setting them up for success. If folks want to help, you said that there's a PayPal page. Is there any other sort of... [1:01:55] Action. People can take. [1:01:58] Yes. Enterprise software. We love to teach iOS development, licensed software. We have hundreds of students a year. So let us know. Awesome. And they can reach you on generationgirl.com? [1:02:10] generationgirl.org. [1:02:12] Crystal, thank you so much for being here. I've taken enough of your time. Two last quick questions. Where can folks find you online if they want to reach out?

[1:02:20] And then other than the Generation Girl chat we just had, is there any other way folks can be helpful to you? [1:02:25] Yes, please find me at crystalwijaya.com. You can reach out to me and my email is there. Listeners, please [1:02:32] do instrumentation correctly. Please don't track your KPIs. Please track [1:02:37] your user journeys and experiences. [1:02:40] We'll have much funner things to talk about if you do that. Amazing. PSA. [1:02:44] Thank you so much, Crystal. Thanks, Lenny. This was a blast.