Meeting Title: Amber x Ryan | Poolparts Catchup Date: 2025-03-13 Meeting participants: Luke Daque, Amber Lin
WEBVTT
1 00:06:23.980 ⇒ 00:06:28.579 Amber Lin: Bye, I hope it’s not too late for you.
2 00:06:30.340 ⇒ 00:06:31.490 Luke Daque: Hello! Hello!
3 00:06:32.560 ⇒ 00:06:33.120 Amber Lin: Yes.
4 00:06:33.120 ⇒ 00:06:33.970 Luke Daque: That’s fine!
5 00:06:34.690 ⇒ 00:06:35.129 Amber Lin: Time, is it.
6 00:06:35.880 ⇒ 00:06:38.130 Luke Daque: It’s still 11.
7 00:06:39.100 ⇒ 00:06:41.871 Luke Daque: I I hope it’s not too early for you.
8 00:06:42.180 ⇒ 00:06:45.390 Amber Lin: Oh, it’s it’s 8 Am.
9 00:06:45.390 ⇒ 00:06:47.098 Luke Daque: It’s a little bit early.
10 00:06:47.950 ⇒ 00:06:57.189 Amber Lin: It’s okay. I cause I wanted to make my 6 30 meeting today. I ran to the gym at 5 30. It was so cold.
11 00:06:57.190 ⇒ 00:06:58.430 Amber Lin: Oh, wow!
12 00:06:58.430 ⇒ 00:07:07.030 Amber Lin: And I was like team. I’m so sorry I am dripping. I am sweaty. I will not turn on my camera.
13 00:07:08.820 ⇒ 00:07:10.080 Luke Daque: That’s funny.
14 00:07:10.480 ⇒ 00:07:12.590 Luke Daque: You go to the gym every day.
15 00:07:13.040 ⇒ 00:07:21.189 Amber Lin: I try. I try to it’s I found that if I don’t go to the gym I get really depressed.
16 00:07:21.550 ⇒ 00:07:28.770 Amber Lin: So to go every day, and also, if I don’t work out, I lose all my gains, so
17 00:07:29.520 ⇒ 00:07:31.169 Amber Lin: have to go. Where were you.
18 00:07:32.590 ⇒ 00:07:41.649 Luke Daque: Yeah, I used to go to the gym all the time, but, like now, I have a like, I joined a table tennis club, something. So
19 00:07:42.580 ⇒ 00:07:50.659 Luke Daque: go there every day. So it’s mostly just cardio. So no, no strength training for like the past few months for me, just because of that. But.
20 00:07:50.660 ⇒ 00:07:52.619 Amber Lin: What do you feel about that.
21 00:07:53.630 ⇒ 00:08:06.050 Luke Daque: Yeah, it’s well, it’s like, I’m losing muscle, for sure, because I’m not like lifting. But yeah, the cardio is great. Still, like, any kind of exercise is fine as long as
22 00:08:06.220 ⇒ 00:08:13.360 Luke Daque: it’s it’s like moving the body just as opposed to just sitting in front of a computer the whole day, like, you know.
23 00:08:13.360 ⇒ 00:08:14.848 Amber Lin: Which is our job.
24 00:08:15.220 ⇒ 00:08:16.600 Luke Daque: Exactly. Yeah.
25 00:08:17.760 ⇒ 00:08:25.192 Amber Lin: I know, and I get on my bed as as you see, I am now. So it’s not only sitting, I am lying down.
26 00:08:26.675 ⇒ 00:08:27.560 Luke Daque: Yeah.
27 00:08:30.860 ⇒ 00:08:33.490 Amber Lin: How long have you been here at the company.
28 00:08:35.186 ⇒ 00:08:38.070 Luke Daque: Let’s see, about a year
29 00:08:38.350 ⇒ 00:08:41.779 Luke Daque: in 3 months, I guess, like I was, Wow, that’s I’m not sure.
30 00:08:41.780 ⇒ 00:08:47.187 Luke Daque: You know. Like I yeah, I I was actually the 1st hire
31 00:08:47.590 ⇒ 00:08:48.110 Amber Lin: Oh!
32 00:08:48.110 ⇒ 00:08:50.159 Luke Daque: Utam’s 1st very 1st hire.
33 00:08:50.160 ⇒ 00:08:51.750 Amber Lin: Whoa!
34 00:08:51.750 ⇒ 00:08:52.790 Luke Daque: Yeah, it is.
35 00:08:52.790 ⇒ 00:08:53.619 Amber Lin: With this.
36 00:08:54.090 ⇒ 00:08:55.250 Luke Daque: I said, data.
37 00:08:55.250 ⇒ 00:08:55.950 Amber Lin: I know him.
38 00:08:55.950 ⇒ 00:08:56.620 Luke Daque: Be the guy.
39 00:08:58.400 ⇒ 00:09:05.019 Luke Daque: Yeah, it’s actually Uttan who contacted me. I can’t remember if it was in Linkedin or
40 00:09:05.200 ⇒ 00:09:10.550 Luke Daque: in some online job website or something. But he did contact me
41 00:09:10.660 ⇒ 00:09:12.588 Luke Daque: like, I think he saw my
42 00:09:13.180 ⇒ 00:09:17.169 Luke Daque: experience, I guess. And then, yeah, he he asked me if, like, I’m I’m up to
43 00:09:17.440 ⇒ 00:09:20.870 Luke Daque: like work for him. And so yeah, we tried for a couple.
44 00:09:20.870 ⇒ 00:09:22.550 Amber Lin: Pull you from the job.
45 00:09:23.170 ⇒ 00:09:24.579 Luke Daque: Yeah, something like that.
46 00:09:24.850 ⇒ 00:09:32.509 Luke Daque: So we we tried like, yeah, we, I can try part time for a couple days or weeks. And let’s see how it goes. And like
47 00:09:32.700 ⇒ 00:09:35.120 Luke Daque: we got like we were in the same like
48 00:09:35.370 ⇒ 00:09:39.059 Luke Daque: mind, frequency based basically like and stuff. So.
49 00:09:39.060 ⇒ 00:09:39.750 Amber Lin: Hold on!
50 00:09:39.750 ⇒ 00:09:40.430 Luke Daque: Yeah.
51 00:09:42.085 ⇒ 00:09:43.020 Amber Lin: Frequency.
52 00:09:43.570 ⇒ 00:09:48.351 Luke Daque: I mean, like, I understood, like what he was doing. And like we, we underst like
53 00:09:48.820 ⇒ 00:10:06.590 Luke Daque: terms of data stuff like he was. He needed help, a lot of help in like, because managing, he was already managing like 3 or 4 clients or something. So he had to. He needed help with the analytics, data, modeling data, engineering and stuff like that. So I basically
54 00:10:07.020 ⇒ 00:10:08.570 Luke Daque: help them on that.
55 00:10:09.230 ⇒ 00:10:15.389 Luke Daque: And then you did hire like other data analysts and data engineers.
56 00:10:15.690 ⇒ 00:10:20.170 Luke Daque: And then, yeah. And then they like, after a couple of months.
57 00:10:20.410 ⇒ 00:10:23.790 Luke Daque: like we were already working with
58 00:10:24.140 ⇒ 00:10:27.999 Luke Daque: Robert and his team. They were a different
59 00:10:28.554 ⇒ 00:10:33.029 Luke Daque: company back then. And then they decided to merge into brain forge. Basically.
60 00:10:33.450 ⇒ 00:10:34.560 Luke Daque: So yeah.
61 00:10:34.900 ⇒ 00:10:36.120 Amber Lin: Oh, cool!
62 00:10:36.230 ⇒ 00:10:44.440 Amber Lin: Wait! What was your experience in it? I know right now you’re doing data engineering for us. What did you do before.
63 00:10:45.640 ⇒ 00:10:49.265 Luke Daque: Yeah, that’s basically data engineering. Still,
64 00:10:50.220 ⇒ 00:10:57.430 Luke Daque: yeah, for a couple of years. Like, I guess it’s my 50 year doing data engineering stuff. But prior to that.
65 00:10:57.820 ⇒ 00:10:59.139 Amber Lin: I worked at.
66 00:10:59.560 ⇒ 00:11:05.609 Luke Daque: Lexmark for like 10 years, 11 years. And I was like wearing different hats.
67 00:11:06.376 ⇒ 00:11:07.670 Luke Daque: My latest
68 00:11:07.920 ⇒ 00:11:14.579 Luke Daque: yeah, it’s it’s funny, like I I was. I was not a data engineer at Lexmark. But
69 00:11:15.342 ⇒ 00:11:16.789 Luke Daque: I did work.
70 00:11:16.940 ⇒ 00:11:21.479 Luke Daque: I did. That’s where I fell in love. Fell in love with data, basically.
71 00:11:22.150 ⇒ 00:11:26.900 Luke Daque: because, like my late, my last role, there was as a process analyst.
72 00:11:27.000 ⇒ 00:11:32.039 Luke Daque: So I did like continuous improvement projects and stuff like that. So I got, I got to
73 00:11:32.760 ⇒ 00:11:36.710 Luke Daque: like make data driven decisions. So I need, I needed like.
74 00:11:37.510 ⇒ 00:11:40.310 Luke Daque: Work with data, basically. And then that’s where I.
75 00:11:40.310 ⇒ 00:11:44.510 Amber Lin: You learn on the job. And you’re like, okay, this is what I’m going to do.
76 00:11:44.510 ⇒ 00:11:48.180 Luke Daque: Yeah, something like that. And then the pandemic happened and like.
77 00:11:48.570 ⇒ 00:11:52.710 Luke Daque: and that was like, very like the the timing was impeccable, like
78 00:11:53.321 ⇒ 00:11:58.380 Luke Daque: my, I had my my wife was pregnant during that time.
79 00:11:58.380 ⇒ 00:11:58.859 Amber Lin: I’m in that.
80 00:11:58.860 ⇒ 00:12:04.259 Luke Daque: We had the our son born during the the pandemic, when it was really.
81 00:12:04.260 ⇒ 00:12:05.320 Amber Lin: No.
82 00:12:06.589 ⇒ 00:12:25.380 Luke Daque: Like the very 1st few months of the pandemic, and then, like No, no, nobody knew what was going on, so I decided to stop working at Lexmark at the office and then started to try working online. And then, yeah, that’s that’s where I I started working as like a data person. So online.
83 00:12:25.670 ⇒ 00:12:34.239 Amber Lin: Oh, actually, my brother was also born. Then it I think he was born 2020 August.
84 00:12:34.910 ⇒ 00:12:35.820 Luke Daque: Oh, yeah, cool.
85 00:12:35.820 ⇒ 00:12:42.920 Amber Lin: He is, he is in. He is a Covid baby, and it’s very grow up.
86 00:12:43.680 ⇒ 00:12:49.350 Luke Daque: Yeah. My son was born April. So that was like just when the pandemic started.
87 00:12:49.350 ⇒ 00:12:58.460 Amber Lin: I know it’s so tough. And it’s it’s hard. Because then the kids can’t really interact with other kids. And.
88 00:12:58.460 ⇒ 00:12:59.880 Luke Daque: Yeah, it was difficult. Like.
89 00:12:59.880 ⇒ 00:13:02.529 Amber Lin: All concerned about his social abilities.
90 00:13:02.780 ⇒ 00:13:13.840 Luke Daque: Exactly just like the 1st few years that we were just at home, and he only knew us nobody else. He had no social interaction aside from like face Facebook or whatever like.
91 00:13:13.960 ⇒ 00:13:16.589 Luke Daque: But that’s different from really, yeah.
92 00:13:17.440 ⇒ 00:13:23.969 Amber Lin: Yeah, they don’t really learn how people are. And cause I’m only one version of people. So
93 00:13:24.500 ⇒ 00:13:25.759 Amber Lin: the kids are.
94 00:13:27.040 ⇒ 00:13:27.710 Luke Daque: Exactly.
95 00:13:27.710 ⇒ 00:13:28.880 Amber Lin: I think it’s getting better.
96 00:13:30.210 ⇒ 00:13:32.530 Luke Daque: Yeah, it’s it’s fine. Now, like.
97 00:13:34.870 ⇒ 00:13:37.399 Luke Daque: Okay, how? How about you? Like.
98 00:13:38.060 ⇒ 00:13:38.720 Amber Lin: I.
99 00:13:38.720 ⇒ 00:13:42.179 Luke Daque: How long have you been working as like a project manager?
100 00:13:42.420 ⇒ 00:13:45.709 Amber Lin: I have worked here for a week now.
101 00:13:45.910 ⇒ 00:13:47.149 Luke Daque: Yeah, I know.
102 00:13:48.055 ⇒ 00:13:48.960 Amber Lin: And.
103 00:13:48.960 ⇒ 00:13:50.710 Luke Daque: One of the newest additions.
104 00:13:50.800 ⇒ 00:14:01.640 Amber Lin: I know. So I’m like, I wanna meet everybody. And and I know you’re kind of the leading data engineer. So I wanted to meet you first, st
105 00:14:01.820 ⇒ 00:14:07.819 Amber Lin: and for me I graduated last December, so I recently graduated.
106 00:14:07.820 ⇒ 00:14:08.280 Luke Daque: If you want.
107 00:14:08.280 ⇒ 00:14:09.089 Amber Lin: It’s called
108 00:14:09.370 ⇒ 00:14:27.240 Amber Lin: and my program. So I’m from China. And then I went to Canada for high school and then for my uni program, it’s a combination of a school in Hong Kong, a school in Los Angeles and a school in Europe. So I spent a year each.
109 00:14:27.240 ⇒ 00:14:28.060 Luke Daque: Wow!
110 00:14:28.220 ⇒ 00:14:33.160 Amber Lin: And actually, before I. Before I came to the Us, I took a
111 00:14:33.750 ⇒ 00:14:37.280 Amber Lin: of relay in the Philippines. I went to Manila.
112 00:14:37.280 ⇒ 00:14:37.720 Luke Daque: Oh!
113 00:14:37.720 ⇒ 00:14:44.480 Amber Lin: And I I booked a relay that’s longer. So it was overnight. So I ran out of the airport.
114 00:14:44.720 ⇒ 00:15:05.129 Amber Lin: stayed at a hostel, talked to some random startup guys that’s doing telecom startups. No, that’s interesting. And I didn’t have money for Uber, because my credit card wouldn’t work, so I couldn’t get from the airport to the Hostel, and I was talking to this guy next to me. He turns out to be, I think.
115 00:15:05.580 ⇒ 00:15:25.680 Amber Lin: Chinese heritage. And he was like, Oh, yeah. He took out his wallet, and he was like, Oh, here’s the cash, and I was like, what do you mean? I was ready to take out cash on an ATM, and he’s like no people have helped me before. I shall help you. I was like, Oh, my God and people! When I was in Manila. People were so nice
116 00:15:25.820 ⇒ 00:15:36.069 Amber Lin: like they’re they’re such nice people. And honestly, it’s my! I was really happy that all my, all my team was in Philippines.
117 00:15:37.000 ⇒ 00:15:46.209 Luke Daque: Yeah, that’s cool. Yeah, I I hear a lot of stories like that. Like, I, yeah, like, they, they say, like, Filipinos are mostly nice. But yeah, that’s that’s pretty cool.
118 00:15:46.210 ⇒ 00:15:48.129 Amber Lin: What do you mean? Mostly nice.
119 00:15:48.130 ⇒ 00:15:50.680 Luke Daque: Yeah, like, I mean.
120 00:15:51.090 ⇒ 00:15:57.530 Luke Daque: like, nicer than the average, I guess. Be of people around the world or something. Yeah.
121 00:15:58.430 ⇒ 00:16:03.497 Amber Lin: When you say mostly nice, there’s always the the occasionally not nice.
122 00:16:03.920 ⇒ 00:16:13.459 Luke Daque: Yeah, like, yeah, that’s that’s normal as well, like, there are not nice people in the world as well, like, even here in the Philippines. Yeah.
123 00:16:13.460 ⇒ 00:16:14.050 Amber Lin: Okay.
124 00:16:15.790 ⇒ 00:16:16.410 Luke Daque: Yeah, that’s cool.
125 00:16:16.410 ⇒ 00:16:17.110 Amber Lin: Maybe
126 00:16:17.270 ⇒ 00:16:42.259 Amber Lin: mostly worked in consulting. I did a little bit investment banking. My experience is all over the place, and that’s why I’m at a startup, I think because I did consulting. Mostly I did. Ib, I did marketing. I I personally, I do content creation. And I did. I do a little bit of tax here and there, and I also did door to door sales.
127 00:16:42.420 ⇒ 00:16:58.090 Amber Lin: So I essentially did all of the different functions. And I was like, you know what I think? Who will like that experience is start us is gonna like that experience. And right now. So I am inserted to all the different teams because I’m on the AI team. So I have to look at all the
128 00:16:58.640 ⇒ 00:17:05.690 Amber Lin: the different team needs. And so that was really, that’s really fun for me and been. I’ve been doing project management since.
129 00:17:05.940 ⇒ 00:17:09.439 Amber Lin: let’s say, 6 months, half half a year ago. But
130 00:17:10.440 ⇒ 00:17:13.649 Amber Lin: right now it’s like ramping up a product match.
131 00:17:14.460 ⇒ 00:17:16.299 Luke Daque: Nice. That’s cool.
132 00:17:16.530 ⇒ 00:17:17.390 Amber Lin: That’s sweet.
133 00:17:17.390 ⇒ 00:17:21.760 Luke Daque: It’s pretty fun like, project management’s a different challenge. Like.
134 00:17:22.290 ⇒ 00:17:25.080 Luke Daque: yeah, it’s it’s it’s nice to be like
135 00:17:25.960 ⇒ 00:17:31.629 Luke Daque: organizing the whole project from from scratch like, that’s also a different challenge. Right? So yeah.
136 00:17:31.630 ⇒ 00:17:31.990 Amber Lin: Oh!
137 00:17:31.990 ⇒ 00:17:32.670 Luke Daque: That’s cool.
138 00:17:32.670 ⇒ 00:17:37.180 Amber Lin: Yeah, totally. And right. Now, what projects are you working on?
139 00:17:39.310 ⇒ 00:17:48.670 Luke Daque: Currently. Well, I I guess I’ve worked with almost all the clients now. But currently, I’m mainly assigned to pull parts and
140 00:17:48.780 ⇒ 00:17:51.359 Luke Daque: stack bits. So, yeah, that’s like, what.
141 00:17:51.360 ⇒ 00:17:53.176 Amber Lin: We will be working together.
142 00:17:53.540 ⇒ 00:17:54.970 Luke Daque: Yeah, it looks like.
143 00:17:55.170 ⇒ 00:18:08.020 Luke Daque: because I think that’s what Tom decided on. Like, each person like each, each data analyst or like analytics, engineer would be just handling a maximum of 2 clients, because, like, we tried
144 00:18:08.370 ⇒ 00:18:12.409 Luke Daque: doing multiple clients before, I worked like 4 or 5 clients, and then.
145 00:18:12.410 ⇒ 00:18:13.100 Amber Lin: And.
146 00:18:13.868 ⇒ 00:18:29.150 Luke Daque: All of the all of the urgent stuff just happened in one day for all 5 plans. And it was like crazy like we had to. Yeah, like Eden was, everything’s urgent in Eden. And then cool parts had also stuff going on. It’s like, yeah.
147 00:18:30.770 ⇒ 00:18:33.320 Luke Daque: that is, that’s fun. Times.
148 00:18:34.700 ⇒ 00:18:44.430 Luke Daque: I mean, I I’ve seen the the company grow a lot like when it was just me and utam. It was like we were all over the place. We’re just trying to get things done.
149 00:18:44.430 ⇒ 00:18:44.750 Amber Lin: Soon.
150 00:18:44.750 ⇒ 00:18:50.739 Luke Daque: We can like. There was no standard practice, nothing, nothing. We are. We’re doing like.
151 00:18:50.960 ⇒ 00:18:54.790 Luke Daque: from data ingestion to analytics, engineering, to
152 00:18:55.020 ⇒ 00:18:57.660 Luke Daque: data analysis and like data validation.
153 00:18:58.040 ⇒ 00:19:07.400 Luke Daque: like to clients. And like, yeah, we we tried to like, we can’t do this like we need to like segregate all this stuff. And so that’s why, like.
154 00:19:07.940 ⇒ 00:19:09.210 Luke Daque: yeah, it’s pretty fun.
155 00:19:11.120 ⇒ 00:19:14.690 Luke Daque: It’s okay, very different. It’s very different from last.
156 00:19:15.970 ⇒ 00:19:18.849 Amber Lin: Yeah, hearing about the past, how it’s been and
157 00:19:18.960 ⇒ 00:19:29.950 Amber Lin: view, because it’s a very different feel now that I’m getting on. We already have some structure. And we’re building more structure. But it’s so different to go from 0 to one. Because if when you’re all
158 00:19:30.450 ⇒ 00:19:34.689 Amber Lin: it’s like I get, I don’t even have capacity to think of structure.
159 00:19:35.450 ⇒ 00:19:36.930 Luke Daque: Yeah, exactly.
160 00:19:37.700 ⇒ 00:19:44.040 Luke Daque: It’s pretty far like, we’ve grown a lot. And there’s still a lot to to work on. Actually like. But
161 00:19:44.470 ⇒ 00:19:47.380 Luke Daque: yeah, it’s it’s already very different.
162 00:19:48.000 ⇒ 00:19:55.280 Amber Lin: I know, and I just I, personally, even for my own benefit, I need this company. I want need
163 00:19:55.450 ⇒ 00:19:59.619 Amber Lin: would like this company to grow as big as possible, because it all.
164 00:19:59.620 ⇒ 00:19:59.990 Luke Daque: So.
165 00:19:59.990 ⇒ 00:20:06.019 Amber Lin: Enhances my experience, because if people have heard of it, they’re like, Oh, you work there! Oh, that’s cool.
166 00:20:06.670 ⇒ 00:20:27.339 Amber Lin: So I was actually talking with Utam about the ascent incentives plan and all that. I think they’re gonna have an incentive plan for Q. 2. For people to handle more projects or to to be on more than one project, and then they will give bonuses for that. So that’s pretty exciting. This end of June.
167 00:20:27.990 ⇒ 00:20:29.400 Luke Daque: Nice. Yeah.
168 00:20:29.400 ⇒ 00:20:30.650 Amber Lin: Yeah, I
169 00:20:30.800 ⇒ 00:20:39.129 Amber Lin: mostly for our meeting today. I wanted to get to know you because we’ll be working together. And I wanted to know a little bit more about the
170 00:20:40.320 ⇒ 00:20:44.579 Amber Lin: backlog stuff for pool part CI don’t even know
171 00:20:45.178 ⇒ 00:20:49.799 Amber Lin: what it is. Gonna be about. So just dump it on me. I’ll process it later.
172 00:20:50.480 ⇒ 00:20:53.460 Luke Daque: Sure, no problem. Well, actually, for
173 00:20:53.640 ⇒ 00:20:59.430 Luke Daque: from an analytics perspective engineering perspective in pool parts, there’s nothing, really.
174 00:21:00.850 ⇒ 00:21:10.369 Luke Daque: There’s no tasks at the moment, like no data, modeling or no requests from the client or even the data analyst, to create new models and stuff, because.
175 00:21:10.510 ⇒ 00:21:19.789 Luke Daque: like cool parts, is, I think, the very 1st client that Utam had. So we already spent a lot of time there creating the data models. And we have the project already.
176 00:21:20.940 ⇒ 00:21:30.429 Luke Daque: I guess, like figured out, but actually, like, like all the all the main stuff like from sales to data marketing data and stuff like that.
177 00:21:30.540 ⇒ 00:21:31.720 Luke Daque: And
178 00:21:31.930 ⇒ 00:21:39.069 Luke Daque: so I guess most of the backlog at the moment is coming from the data analysts because they are like trying to figure out
179 00:21:39.861 ⇒ 00:21:43.380 Luke Daque: like the top skews or whatnot and and like
180 00:21:43.750 ⇒ 00:21:46.270 Luke Daque: and like what we discussed last
181 00:21:46.540 ⇒ 00:21:48.679 Luke Daque: week? Or was it like the the.
182 00:21:48.680 ⇒ 00:21:49.220 Amber Lin: No.
183 00:21:49.220 ⇒ 00:21:50.800 Luke Daque: Earlier this week related.
184 00:21:50.800 ⇒ 00:21:51.320 Amber Lin: To like.
185 00:21:51.320 ⇒ 00:21:56.329 Luke Daque: Forecasting and forecasting, for example, and other.
186 00:21:56.720 ⇒ 00:22:08.180 Luke Daque: And now other stuff. So that’s why I actually asked Utam if like, maybe this is a good time, because it’s it’s practically downtime for the analytics engineering
187 00:22:08.920 ⇒ 00:22:14.729 Luke Daque: in pool parts. So maybe this is a good time to work on like technical depths, or
188 00:22:14.960 ⇒ 00:22:29.506 Luke Daque: like in in improving the code base cause like it was all over the place. It’s pretty much all over the place at the moment, so maybe we can align. How we structure the project to what we
189 00:22:30.490 ⇒ 00:22:37.560 Luke Daque: what we decided was the standard structure for the the project, basically.
190 00:22:37.800 ⇒ 00:22:42.159 Luke Daque: because, like, if you, if you, if you can imagine, if like pool parts, was the very 1st
191 00:22:42.360 ⇒ 00:22:43.500 Luke Daque: client, then.
192 00:22:43.500 ⇒ 00:22:43.830 Amber Lin: There’s no.
193 00:22:43.830 ⇒ 00:22:46.409 Luke Daque: We didn’t believe we have an instruction. Yeah, exactly.
194 00:22:46.410 ⇒ 00:22:47.220 Luke Daque: I see.
195 00:22:47.220 ⇒ 00:22:47.920 Luke Daque: So yeah.
196 00:22:48.721 ⇒ 00:22:56.678 Amber Lin: Let’s dive in a little bit more about that. Do you have something you can share screen that I can look at
197 00:22:57.170 ⇒ 00:22:58.150 Amber Lin: up.
198 00:22:59.640 ⇒ 00:23:05.660 Luke Daque: Do you? Are you familiar with like sequel and stuff like that?
199 00:23:05.660 ⇒ 00:23:15.729 Amber Lin: Yeah, I mostly did analyst stuff, not too much engineering, but I should be able to understand hopefully.
200 00:23:16.358 ⇒ 00:23:18.241 Luke Daque: Yeah, that’s fine.
201 00:23:18.870 ⇒ 00:23:22.340 Amber Lin: I will ask you. You’ll get a lot of questions from me.
202 00:23:22.760 ⇒ 00:23:23.690 Luke Daque: No problem.
203 00:23:24.030 ⇒ 00:23:25.870 Luke Daque: Can you see my screen? By the way.
204 00:23:26.180 ⇒ 00:23:27.290 Amber Lin: Yeah, I can see it.
205 00:23:30.800 ⇒ 00:23:32.839 Luke Daque: Yeah. So this is like, how
206 00:23:34.462 ⇒ 00:23:39.099 Luke Daque: like, the repository for poor parts of the moment, the data.
207 00:23:39.100 ⇒ 00:23:39.640 Amber Lin: Okay.
208 00:23:39.900 ⇒ 00:23:41.530 Luke Daque: Side. So we have
209 00:23:42.480 ⇒ 00:24:09.150 Luke Daque: mostly, I’m working on the Dbp project folder, because this is where our all our data modeling happens, and we still even have evidence which I don’t think we are using anymore. Or, yeah, I’m pretty sure we’re not using. But I’m not sure. Actually, if we are. Still, this is evidence is data visualization, a different data visualization tool.
210 00:24:09.250 ⇒ 00:24:13.079 Luke Daque: We used this before. But I believe currently we’re using rail.
211 00:24:13.640 ⇒ 00:24:16.930 Amber Lin: I see, so that should be archived essentially.
212 00:24:17.390 ⇒ 00:24:22.049 Luke Daque: Exactly. Yeah. So this is probably one of the tech depths. Maybe we can remove this because it might be.
213 00:24:22.050 ⇒ 00:24:22.560 Amber Lin: And yeah.
214 00:24:22.670 ⇒ 00:24:31.819 Luke Daque: Like, if there’s a new analyst or a new analytics engineer looking at this and then like they’ll be looking at this. And it’s not even being used. And stuff like that right?
215 00:24:31.820 ⇒ 00:24:32.440 Amber Lin: Yay!
216 00:24:32.440 ⇒ 00:24:37.669 Luke Daque: So yeah, this one integrations. There’s like a couple of
217 00:24:39.080 ⇒ 00:24:47.040 Luke Daque: integrations that I I don’t even know what these are. But maybe this is what Utahn tried before before.
218 00:24:47.520 ⇒ 00:24:48.270 Amber Lin: This is true.
219 00:24:48.270 ⇒ 00:24:51.212 Luke Daque: Like a year ago. Maybe maybe this was
220 00:24:52.000 ⇒ 00:24:57.959 Luke Daque: before. Probably we decided to use 5 tran to integrate data into Snowflake.
221 00:24:58.580 ⇒ 00:24:58.940 Amber Lin: To me.
222 00:24:58.940 ⇒ 00:25:08.630 Luke Daque: Maybe. Yeah, there’s like some some stuff going on here. But I can. I can check with Tom. So maybe this is also something that we need to archive.
223 00:25:12.120 ⇒ 00:25:15.210 Luke Daque: But the yeah, this is like, Epl.
224 00:25:15.510 ⇒ 00:25:16.390 Amber Lin: Where.
225 00:25:16.570 ⇒ 00:25:21.910 Luke Daque: Detail stuff going on loading data from sources to snowflake.
226 00:25:22.580 ⇒ 00:25:37.319 Amber Lin: Cool. Actually, can you show me a good structure once we’re done with this? Can you show me a good structure? Because right now I haven’t looked at what’s good. So this looks okay to me. But maybe there’s something that’s just other projects that just look really good.
227 00:25:38.590 ⇒ 00:25:40.930 Luke Daque: I think we have a
228 00:25:43.640 ⇒ 00:25:49.840 Luke Daque: I think we have a documentation for that, for, like how we structure our project, let me just
229 00:25:51.020 ⇒ 00:25:55.470 Luke Daque: see if it’s in here somewhere in our notion.
230 00:25:58.620 ⇒ 00:26:07.610 Luke Daque: But we can check a shabby, for instance, because this is where we started or like, let’s do stack bits because we’re gonna be working on statics, anyway.
231 00:26:07.610 ⇒ 00:26:08.850 Amber Lin: Hmm, okay.
232 00:26:11.100 ⇒ 00:26:12.729 Luke Daque: So yeah, for
233 00:26:13.200 ⇒ 00:26:21.359 Luke Daque: we only have, like, DVD project and rail. Well, I guess we we need to update the documentation here every file.
234 00:26:21.720 ⇒ 00:26:25.810 Luke Daque: But in in the Dbt project, for instance, we have.
235 00:26:26.370 ⇒ 00:26:29.920 Luke Daque: we’re using intermediate models and march models.
236 00:26:30.350 ⇒ 00:26:30.980 Amber Lin: Oppo.
237 00:26:31.350 ⇒ 00:26:33.569 Luke Daque: Mostly, and if you look at
238 00:26:34.927 ⇒ 00:26:42.299 Luke Daque: pool parts it’s pretty much all over the place. We we have staging marts, and then we have a lot of stuff in here
239 00:26:42.800 ⇒ 00:26:45.650 Luke Daque: for lights, for example, and there’s
240 00:26:46.010 ⇒ 00:26:50.746 Luke Daque: there’s a lot of redundancy going on like customers. There’s
241 00:26:52.900 ⇒ 00:26:58.260 Luke Daque: yeah, we’re like breaking down customers with multiple orders, and like.
242 00:26:59.300 ⇒ 00:27:00.610 Luke Daque: And it’s pretty.
243 00:27:01.970 ⇒ 00:27:06.660 Luke Daque: Yeah, I guess pretty nasty. The code is like not very.
244 00:27:07.150 ⇒ 00:27:08.460 Amber Lin: And the ghostar.
245 00:27:08.900 ⇒ 00:27:09.810 Amber Lin: Yeah.
246 00:27:09.810 ⇒ 00:27:15.309 Luke Daque: Yeah, stuff like that. Even the naming convention. I guess we can.
247 00:27:15.310 ⇒ 00:27:16.400 Amber Lin: Hmm.
248 00:27:16.400 ⇒ 00:27:17.562 Luke Daque: Can also like,
249 00:27:18.490 ⇒ 00:27:25.940 Luke Daque: what do you call this like? Stand, use the standard there, I can. Yeah, look, I’ll look for the documentation that we have or
250 00:27:26.500 ⇒ 00:27:29.699 Luke Daque: for the Standard project, and then maybe we can. You can
251 00:27:30.480 ⇒ 00:27:32.230 Luke Daque: take a look at that. So.
252 00:27:33.840 ⇒ 00:27:40.999 Amber Lin: So is that most of the work of updating the structure. What do you mean by tech depths?
253 00:27:42.840 ⇒ 00:27:46.140 Luke Daque: Well, one of the yeah, this is one of them, like updating
254 00:27:46.490 ⇒ 00:27:48.079 Luke Daque: structure, so that it would be like.
255 00:27:48.080 ⇒ 00:27:48.710 Amber Lin: I think.
256 00:27:49.040 ⇒ 00:27:55.812 Luke Daque: In the standard that we have like naming convention would be one like how we are. Ca,
257 00:27:56.370 ⇒ 00:28:00.700 Luke Daque: how we are creating our our code here, and like, maybe even
258 00:28:01.481 ⇒ 00:28:03.739 Luke Daque: removing the redundancy redundant code.
259 00:28:03.740 ⇒ 00:28:04.120 Amber Lin: Yeah.
260 00:28:04.120 ⇒ 00:28:05.409 Luke Daque: And stuff like that.
261 00:28:05.550 ⇒ 00:28:10.450 Luke Daque: And then also adding tests, data tests, and
262 00:28:11.220 ⇒ 00:28:22.209 Luke Daque: which was all we we also discussed like, because, like currently, we don’t really have a lot of data tests. And that’s also one of the reasons why we could have
263 00:28:23.770 ⇒ 00:28:27.340 Luke Daque: inaccurate data in our final models.
264 00:28:27.640 ⇒ 00:28:28.240 Amber Lin: Is there.
265 00:28:28.240 ⇒ 00:28:31.264 Luke Daque: Might be like duplicates, for example, and do duplicate
266 00:28:32.270 ⇒ 00:28:39.159 Luke Daque: data that we have. So ha, having tests would be helpful for that.
267 00:28:39.940 ⇒ 00:28:50.579 Luke Daque: So yeah, basically stuff. And there’s like documentation would be one thing as well, like Kyle’s always been pushing up for documentation, especially for matrices metrics, measures.
268 00:28:52.640 ⇒ 00:29:06.470 Luke Daque: and like fields like monthly, like total. I total price. What what does that mean? Basically, is it like including discounts or not, including discounts and taxes? So like stuff like that. So we would
269 00:29:06.790 ⇒ 00:29:09.499 Luke Daque: having documentation would be great.
270 00:29:10.860 ⇒ 00:29:17.620 Luke Daque: Like, I’m not sure. Yeah, like here in the yaml file, we can actually add a description
271 00:29:17.980 ⇒ 00:29:21.150 Luke Daque: which would have like the documentation in here.
272 00:29:22.223 ⇒ 00:29:23.310 Luke Daque: That way.
273 00:29:23.520 ⇒ 00:29:25.209 Luke Daque: Yeah, that way we can.
274 00:29:26.120 ⇒ 00:29:29.050 Luke Daque: Yeah, we have better documentation.
275 00:29:29.890 ⇒ 00:29:31.310 Amber Lin: Cool. Yeah. So
276 00:29:31.550 ⇒ 00:29:42.753 Amber Lin: I just searched up what our tech depths and what we talked about. We have 3 things of code structure to update it up to our standards. Providing.
277 00:29:43.140 ⇒ 00:29:43.640 Luke Daque: Can.
278 00:29:43.640 ⇒ 00:29:45.800 Amber Lin: And data tests.
279 00:29:45.800 ⇒ 00:29:47.710 Luke Daque: Data tests yeah.
280 00:29:47.920 ⇒ 00:29:48.680 Amber Lin: Yeah.
281 00:29:48.680 ⇒ 00:29:52.160 Luke Daque: I guess those those would be like, probably the top.
282 00:29:52.740 ⇒ 00:29:55.330 Luke Daque: for on top of my head at the moment.
283 00:29:55.330 ⇒ 00:29:55.909 Amber Lin: Open it.
284 00:29:56.060 ⇒ 00:30:01.190 Amber Lin: And probably code re code reviews as well.
285 00:30:02.480 ⇒ 00:30:04.650 Luke Daque: Yeah, exactly called reviews.
286 00:30:07.000 ⇒ 00:30:09.391 Luke Daque: And like archiving, unused.
287 00:30:10.900 ⇒ 00:30:12.259 Luke Daque: Stuff here, folders, I guess.
288 00:30:12.260 ⇒ 00:30:24.740 Amber Lin: Yeah, that will be. I’ll put that in the structure part. Okay? I think something that we could that we could work on. I’m right. Now. I’m writing up the
289 00:30:24.860 ⇒ 00:30:28.500 Amber Lin: document to send to the client, and
290 00:30:29.180 ⇒ 00:30:36.230 Amber Lin: I’ll also include this sort of improving the whole system as one of the things that we’re gonna do.
291 00:30:37.300 ⇒ 00:30:42.519 Luke Daque: Sure. The yeah. I think the only like the cons with tech depths is, of course, like
292 00:30:42.640 ⇒ 00:30:46.360 Luke Daque: doesn’t really do anything because it’s not changing.
293 00:30:46.360 ⇒ 00:30:46.980 Amber Lin: Yeah.
294 00:30:46.980 ⇒ 00:30:54.940 Luke Daque: Our output. So it’s not. It’s really it’s most. It’s really low priority for this. It’s not like, yeah.
295 00:30:55.580 ⇒ 00:31:01.699 Amber Lin: How’s your capacity this week? Because our and how do you estimate this would take.
296 00:31:02.760 ⇒ 00:31:07.279 Luke Daque: That’s a good question for capacity. I’m pretty. I’m pretty like,
297 00:31:07.960 ⇒ 00:31:13.120 Luke Daque: yeah, I have time, because I’m in in. I’m just using most of the time
298 00:31:13.860 ⇒ 00:31:16.129 Luke Daque: for Stack Blitz and for meetings.
299 00:31:16.410 ⇒ 00:31:16.910 Amber Lin: I’m.
300 00:31:16.910 ⇒ 00:31:21.609 Luke Daque: Not been like doing any pull parts to go work at the moment. So yeah.
301 00:31:21.610 ⇒ 00:31:22.850 Amber Lin: Yeah. Okay.
302 00:31:26.520 ⇒ 00:31:33.179 Luke Daque: For like how? How like timeline? I I’m not sure at the moment. Maybe
303 00:31:34.670 ⇒ 00:31:39.065 Luke Daque: if I work on it every day for, like I don’t know. 4 HA day.
304 00:31:39.340 ⇒ 00:31:41.562 Amber Lin: 4 HA day. It’s a lot.
305 00:31:41.880 ⇒ 00:31:43.569 Luke Daque: Like 2 to 4.
306 00:31:43.740 ⇒ 00:31:49.020 Luke Daque: That’s yeah. That’s a lot. Actually, I don’t know. Maybe
307 00:31:50.980 ⇒ 00:31:54.349 Luke Daque: maybe we can allocate like 10 HA week for that. But
308 00:31:54.920 ⇒ 00:31:56.410 Luke Daque: yeah, like I mentioned, it’s like
309 00:31:56.710 ⇒ 00:32:00.099 Luke Daque: we’re spending 10 h. And it’s not like giving any
310 00:32:00.410 ⇒ 00:32:04.759 Luke Daque: anything to the client like from a client’s point of view. Right.
311 00:32:04.760 ⇒ 00:32:05.910 Amber Lin: Yeah. Gotcha.
312 00:32:05.910 ⇒ 00:32:06.939 Luke Daque: Worried since then.
313 00:32:07.800 ⇒ 00:32:13.660 Amber Lin: Yeah, why don’t I confirm with cause they’re not? Gonna look at this. This is just for us.
314 00:32:14.240 ⇒ 00:32:19.660 Amber Lin: I’m gonna confirm with Utam how much how important this is.
315 00:32:19.850 ⇒ 00:32:44.539 Amber Lin: And once he wants to fo focus on future projects versus this, because we can also, just as we work on future projects. Oh, we see that this is bad, and then we just improve it. But I know there will be hopefully. There will be new projects coming on, and we’ll just make this as a side side quest while we do the projects. Cause I think the stack was part is taking up a lot of time.
316 00:32:45.340 ⇒ 00:32:46.120 Luke Daque: Right?
317 00:32:46.720 ⇒ 00:32:49.819 Luke Daque: I agree. Yeah, I think that makes more sense. Actually.
318 00:32:50.450 ⇒ 00:32:52.750 Luke Daque: because, like, why spent so much time.
319 00:32:53.570 ⇒ 00:32:53.960 Amber Lin: Yeah.
320 00:32:54.505 ⇒ 00:32:58.319 Luke Daque: Especially getting them familiar with it will take a lot of time.
321 00:32:59.250 ⇒ 00:33:02.670 Luke Daque: Yeah, it’s like if it’s not
322 00:33:03.250 ⇒ 00:33:06.599 Luke Daque: broken, don’t fix it, or something like that, something like that.
323 00:33:07.010 ⇒ 00:33:10.710 Amber Lin: Oh, oh, one!
324 00:33:12.430 ⇒ 00:33:16.370 Amber Lin: What about Saclitz? How’s that going? What are you doing for that.
325 00:33:16.790 ⇒ 00:33:20.219 Luke Daque: Stuck. Let’s see, there’s still a lot to be done. So
326 00:33:21.167 ⇒ 00:33:24.442 Luke Daque: one thing that I’m focusing at the moment is the
327 00:33:25.250 ⇒ 00:33:31.049 Luke Daque: Wait. Beta is creating data models that they would
328 00:33:31.520 ⇒ 00:33:36.959 Luke Daque: they currently have for bare metrics. So stack bits is using bare metrics for their
329 00:33:40.150 ⇒ 00:33:44.577 Luke Daque: for their data. So like they have this
330 00:33:46.210 ⇒ 00:33:46.760 Amber Lin: Oh!
331 00:33:46.760 ⇒ 00:34:04.739 Luke Daque: These are just screenshots that I I got because we don’t currently don’t have access to their bare metrics. So like the January like they have like they’re looking at monthly, recurring revenue, other revenue which is like token reloads or or revenue out of the subscriptions coming
332 00:34:05.150 ⇒ 00:34:07.300 Luke Daque: outside the subscriptions.
333 00:34:07.470 ⇒ 00:34:14.269 Luke Daque: Yeah, they’re looking at this annual run rate lifetime value per customer, user churn.
334 00:34:14.850 ⇒ 00:34:22.190 Luke Daque: So customer, retention, revenue churn active customer. So so all these that this is like this.
335 00:34:22.570 ⇒ 00:34:33.659 Luke Daque: It’s what I’ve been working on. And like one of the dilemmas I have like, I already have, like models for most of these, except maybe for this downgrades cube with like
336 00:34:33.960 ⇒ 00:34:39.360 Luke Daque: like this, these other stuff like lower priority once I don’t have these yet, but
337 00:34:39.690 ⇒ 00:34:48.799 Luke Daque: but like I’m trying to do some data validation, comparing what I already did to what they have here. And it’s like, off
338 00:34:49.110 ⇒ 00:34:49.630 Luke Daque: by a.
339 00:34:49.944 ⇒ 00:34:50.249 Amber Lin: Like.
340 00:34:50.250 ⇒ 00:35:10.230 Luke Daque: Some percent. So like, that’s what that’s like, the challenge there, trying to figure out like what’s going on. So we really need to as much as possible like have conversations with with them. Like Mitch, is our point of contact for stack. Let’s because every time we have a meeting, and then we
341 00:35:10.900 ⇒ 00:35:14.459 Luke Daque: I didn’t know that that they were doing stuff like that like.
342 00:35:15.130 ⇒ 00:35:15.660 Amber Lin: A subscription.
343 00:35:15.660 ⇒ 00:35:21.269 Luke Daque: Could be upgraded from one tier to another, and then that would affect the Mrr, because, like.
344 00:35:22.110 ⇒ 00:35:34.749 Luke Daque: if they upgraded twice in a certain month. And what what price do we we use for Mrr, or like? What is their metrics even using at that point? Is it the the latest one, or is it prorated between
345 00:35:35.010 ⇒ 00:35:38.547 Luke Daque: the 1st 2 subscriptions that they had, or something like that? Right? So
346 00:35:39.630 ⇒ 00:35:41.210 Amber Lin: Oh, cool!
347 00:35:41.210 ⇒ 00:35:45.960 Luke Daque: And then there’s like token reloads, which is like outside their subscription.
348 00:35:46.590 ⇒ 00:35:49.479 Luke Daque: So yeah, I guess that’s.
349 00:35:50.450 ⇒ 00:35:59.140 Amber Lin: So there’s a lot of granular things that it’s very specific in their system. Do you think Mitch even knows about that? Because.
350 00:35:59.600 ⇒ 00:36:01.939 Luke Daque: Yeah, that’s that’s also the thing. Yes.
351 00:36:02.420 ⇒ 00:36:15.089 Luke Daque: that’s also the thing like he knows some of it that but not all like there was this one time, like last Friday, when we had the meeting, we were looking at one customer because, they had like.
352 00:36:15.210 ⇒ 00:36:30.370 Luke Daque: From my report, they had 6 active subscriptions, but then, in stripes report they were only having one, and that was like I had 6 active subscriptions, because they upgraded like 6 times in one month, or something like that. So.
353 00:36:30.370 ⇒ 00:36:31.760 Amber Lin: Oh!
354 00:36:31.960 ⇒ 00:36:38.370 Luke Daque: That was like even Mitch was like not UN not aware of that as well.
355 00:36:38.871 ⇒ 00:36:47.610 Luke Daque: So that was like also the 1st time he saw stuff going on something like that going on. So yeah, that’s so. Constant communication would be great. For with.
356 00:36:47.610 ⇒ 00:36:48.010 Amber Lin: Nice.
357 00:36:48.010 ⇒ 00:36:49.519 Luke Daque: For that for stuff like this.
358 00:36:49.520 ⇒ 00:37:02.880 Amber Lin: Okay. I mean that I probably would be the person that I’ll just ask him and talk to him constantly, because there’s a lot for you to handle to both. Do this, and then talk to him and handle that communication as well.
359 00:37:03.760 ⇒ 00:37:06.180 Luke Daque: Yeah, yeah, that’s upgrade.
360 00:37:07.640 ⇒ 00:37:14.840 Luke Daque: yeah. And like, lifetime value, like, currently, they also want to be able to say, categorize this, like, for example.
361 00:37:14.840 ⇒ 00:37:20.770 Luke Daque: monthly or recurring revenue, they would want to be able to drill down.
362 00:37:21.590 ⇒ 00:37:21.910 Amber Lin: Which.
363 00:37:21.910 ⇒ 00:37:23.477 Luke Daque: Like, for, like
364 00:37:24.800 ⇒ 00:37:35.690 Luke Daque: drill down to the subscription level, for example. So what would be the monthly recurring revenue for this type of subscription, or this plan versus a, a higher plan.
365 00:37:35.980 ⇒ 00:37:40.610 Luke Daque: and then also be able to determine which are the high value customers.
366 00:37:43.030 ⇒ 00:37:53.970 Luke Daque: and they have a a different description of high value customer, because initially, I I thought it would be like, is it a customer that has a high monthly recurring revenue or a high lifetime
367 00:37:54.200 ⇒ 00:37:55.710 Luke Daque: value right?
368 00:37:56.000 ⇒ 00:37:59.422 Luke Daque: But actually, what they want as a high
369 00:38:00.370 ⇒ 00:38:08.869 Luke Daque: what do you call it? customer that’s been using tokens a lot.
370 00:38:10.000 ⇒ 00:38:10.590 Luke Daque: Oh.
371 00:38:11.350 ⇒ 00:38:14.709 Amber Lin: It’s very different. Because, yeah, definition.
372 00:38:16.160 ⇒ 00:38:20.619 Luke Daque: You have context on stack. Looks like right like what what they are as a company.
373 00:38:20.620 ⇒ 00:38:23.160 Amber Lin: Yeah. It’s the bolt dot new right? The.
374 00:38:23.160 ⇒ 00:38:24.010 Luke Daque: Yeah, exactly.
375 00:38:24.010 ⇒ 00:38:29.810 Amber Lin: Automate grading codes. Oh, so for them, the high value customer
376 00:38:30.120 ⇒ 00:38:36.720 Amber Lin: they just. They probably just generalized it of people who have the most usage because tokens cost money.
377 00:38:36.720 ⇒ 00:38:37.190 Luke Daque: 6, 8.
378 00:38:37.190 ⇒ 00:38:40.659 Amber Lin: But if they use more tokens, then they probably use more money, overall.
379 00:38:41.010 ⇒ 00:38:43.300 Amber Lin: more willing to spend more money.
380 00:38:43.560 ⇒ 00:38:47.420 Luke Daque: Yeah. So that’s what how they define like high value customers like
381 00:38:47.590 ⇒ 00:38:52.379 Luke Daque: users that use tokens a lot not necessarily like the annual.
382 00:38:52.760 ⇒ 00:38:53.300 Amber Lin: Okay.
383 00:38:54.540 ⇒ 00:39:03.160 Luke Daque: So, yeah, but we we do have. I can show you like our dashboard with this one.
384 00:39:09.550 ⇒ 00:39:11.040 Luke Daque: yeah. So
385 00:39:12.330 ⇒ 00:39:27.930 Luke Daque: yeah, so for subscriptions, for instance, we already do have this. Although this, this is still like a work in progress. So it’s like, this is the subscriptions dashboard. We have the total number of subscriptions. We should be able to.
386 00:39:29.630 ⇒ 00:39:36.089 Luke Daque: Like we we need to like. I need to add here, like active subscriptions, right versus.
387 00:39:36.090 ⇒ 00:39:36.920 Amber Lin: Oh!
388 00:39:37.360 ⇒ 00:39:43.259 Luke Daque: Because this is just everything, not active. So this we should be able to break this down. How many are still active at the moment.
389 00:39:43.260 ⇒ 00:39:44.600 Amber Lin: Yeah, so, maybe totally.
390 00:39:44.650 ⇒ 00:39:48.670 Luke Daque: If it’s not cancelled, then yeah, this might be the
391 00:39:48.850 ⇒ 00:39:54.310 Luke Daque: total number of active subscriptions that aren’t cancelled. So I guess this would be active.
392 00:39:55.380 ⇒ 00:40:06.820 Luke Daque: Yeah, like, we have average Mrr, arr and annual run rate.
393 00:40:09.740 ⇒ 00:40:12.170 Luke Daque: Yeah, basically, I’ll call all of these.
394 00:40:13.450 ⇒ 00:40:16.470 Luke Daque: And yeah.
395 00:40:17.760 ⇒ 00:40:27.609 Amber Lin: Let’s see. So this is more of right now. It’s still more engineering work, and the data analysts will come a little bit later to look at what they mean right?
396 00:40:27.880 ⇒ 00:40:29.260 Luke Daque: Yeah, exactly.
397 00:40:29.540 ⇒ 00:40:29.929 Amber Lin: He said.
398 00:40:29.930 ⇒ 00:40:34.470 Luke Daque: Data analysts would be able to come in and look at and and like, tell them, like.
399 00:40:34.570 ⇒ 00:40:45.739 Luke Daque: Yeah, the the pro plan is the highest number of subscriptions. Maybe we can look into that and like, see if we can have users upgrade to a higher plan or something like that. Like.
400 00:40:46.430 ⇒ 00:40:51.050 Luke Daque: yeah, like, even look at the customers that have like multiple subscriptions
401 00:40:51.190 ⇒ 00:40:55.629 Luke Daque: like this one over here, specific customer that has, like 19 subscriptions.
402 00:40:55.630 ⇒ 00:40:57.980 Amber Lin: Oh, maybe this is considered a.
403 00:40:59.340 ⇒ 00:41:03.010 Luke Daque: Something that then maybe there’s something we can think about.
404 00:41:03.760 ⇒ 00:41:04.580 Luke Daque: 14.
405 00:41:04.580 ⇒ 00:41:09.449 Amber Lin: It’s more like, maybe, like enterprise. Why would they have 19 subscriptions.
406 00:41:09.450 ⇒ 00:41:14.639 Luke Daque: Yeah, I’m not sure like, like, there’s like role plans here.
407 00:41:18.670 ⇒ 00:41:23.109 Luke Daque: Yeah, maybe this is also, this could be a data issue. Probably maybe this is
408 00:41:23.260 ⇒ 00:41:29.919 Luke Daque: a customer has been like upgrading and downgrading and stuff like that. Maybe they they found a hack to the system to
409 00:41:30.250 ⇒ 00:41:34.219 Luke Daque: I don’t know, or something. Yes.
410 00:41:34.220 ⇒ 00:41:36.260 Amber Lin: They just use a different account.
411 00:41:36.860 ⇒ 00:41:38.529 Luke Daque: Something. Maybe I don’t know.
412 00:41:38.770 ⇒ 00:41:41.020 Luke Daque: Oh, that’s so interesting.
413 00:41:42.730 ⇒ 00:41:43.590 Luke Daque: Yeah.
414 00:41:44.320 ⇒ 00:41:45.350 Amber Lin: Cool stuff.
415 00:41:46.160 ⇒ 00:41:55.160 Amber Lin: Yeah, I mean stockless. Right now. I think they’re having a meeting later today, maybe, are you on the meeting? I’m not sure to talk with to talk with Mitch.
416 00:41:55.160 ⇒ 00:41:57.869 Luke Daque: Oh, yeah, we have a roadmap meeting with.
417 00:41:58.360 ⇒ 00:41:59.530 Amber Lin: Oh, gee!
418 00:42:00.708 ⇒ 00:42:09.759 Amber Lin: I want to be on them, but I don’t think they want to overwhelm them with too much people, so I’ll just look at the recording.
419 00:42:11.540 ⇒ 00:42:13.899 Luke Daque: Yeah, I guess you should be here. Actually, right?
420 00:42:14.570 ⇒ 00:42:15.120 Amber Lin: Yeah.
421 00:42:15.120 ⇒ 00:42:15.519 Luke Daque: Thank you.
422 00:42:15.520 ⇒ 00:42:17.100 Amber Lin: Can you.
423 00:42:17.100 ⇒ 00:42:17.969 Luke Daque: You can ask anytime.
424 00:42:17.970 ⇒ 00:42:20.710 Amber Lin: Yeah, I think I’ll I’ll ask him.
425 00:42:21.040 ⇒ 00:42:21.540 Amber Lin: Let me.
426 00:42:21.540 ⇒ 00:42:22.640 Luke Daque: Yeah. Let’s ask him.
427 00:42:22.640 ⇒ 00:42:23.560 Amber Lin: Panel.
428 00:42:23.760 ⇒ 00:42:24.690 Amber Lin: Yeah.
429 00:42:25.680 ⇒ 00:42:26.659 Amber Lin: One of your names.
430 00:42:26.660 ⇒ 00:42:29.879 Luke Daque: And ask him the channel with clients that channel.
431 00:42:30.410 ⇒ 00:42:34.220 Amber Lin: Okay, do you want me to ask? I can ask, okay?
432 00:42:35.610 ⇒ 00:42:36.005 Amber Lin: Oh.
433 00:42:36.400 ⇒ 00:42:37.050 Luke Daque: Thank you.
434 00:42:38.990 ⇒ 00:42:42.380 Luke Daque: If you if you’re yeah, you can ask.
435 00:42:42.920 ⇒ 00:42:46.640 Amber Lin: And I’d be either today.
436 00:42:50.100 ⇒ 00:42:50.850 Amber Lin: So.
437 00:42:52.510 ⇒ 00:42:53.276 Luke Daque: On, the.
438 00:43:14.690 ⇒ 00:43:18.080 Amber Lin: Ask him, Yeah, when is it?
439 00:43:19.420 ⇒ 00:43:20.130 Amber Lin: Is it.
440 00:43:20.780 ⇒ 00:43:22.359 Amber Lin: Good for you, isn’t it?
441 00:43:23.340 ⇒ 00:43:26.160 Luke Daque: That’s fine that. I it’s like
442 00:43:26.930 ⇒ 00:43:30.180 Luke Daque: one am. So that’s like an hour and a half from now.
443 00:43:31.810 ⇒ 00:43:33.190 Amber Lin: Oh, okay.
444 00:43:33.380 ⇒ 00:43:34.130 Amber Lin: See?
445 00:43:35.350 ⇒ 00:43:39.199 Luke Daque: So I guess that would be 11. Your time or something.
446 00:43:39.500 ⇒ 00:43:42.709 Amber Lin: Oh, wait! Are you getting GMT plus 8.
447 00:43:43.110 ⇒ 00:43:43.560 Luke Daque: Yeah.
448 00:43:43.560 ⇒ 00:43:50.719 Amber Lin: Oh, I see I have that on my calendar as well, because my family is in China, and they’re in the same time zone.
449 00:43:50.870 ⇒ 00:43:51.990 Amber Lin: Okay, cool.
450 00:43:51.990 ⇒ 00:43:53.690 Luke Daque: Which part in China are you in.
451 00:43:54.728 ⇒ 00:43:56.030 Amber Lin: In my opinion.
452 00:43:56.030 ⇒ 00:43:56.540 Luke Daque: Look.
453 00:43:56.540 ⇒ 00:43:58.220 Amber Lin: And my family.
454 00:43:58.220 ⇒ 00:44:00.980 Luke Daque: And it’s also GMT, plus 8 there, right.
455 00:44:00.980 ⇒ 00:44:01.460 Amber Lin: Yeah.
456 00:44:01.460 ⇒ 00:44:04.829 Luke Daque: Try it more on the same line.
457 00:44:04.830 ⇒ 00:44:08.520 Luke Daque: Yeah, yeah, cool, awesome.
458 00:44:08.520 ⇒ 00:44:12.060 Luke Daque: I’ve been to Hong Kong only, though, like once.
459 00:44:13.260 ⇒ 00:44:16.790 Amber Lin: Yeah, I was. I was there for school. It’s very.
460 00:44:16.790 ⇒ 00:44:17.390 Luke Daque: Yes.
461 00:44:17.500 ⇒ 00:44:18.430 Amber Lin: Okay.
462 00:44:18.740 ⇒ 00:44:20.289 Luke Daque: Oh, really, you know, it’s like.
463 00:44:20.290 ⇒ 00:44:24.630 Amber Lin: Yeah, it’s still an Asian picture.
464 00:44:24.780 ⇒ 00:44:30.890 Amber Lin: Definitely, you can feel from there to America. But compared to China, there’s a.
465 00:44:32.230 ⇒ 00:44:38.879 Luke Daque: You have good English accent, though, like for for Chinese, you grew up in China.
466 00:44:38.880 ⇒ 00:44:46.530 Amber Lin: I don’t know I was in trying until I was 13. No, not yeah, until I was 13. So I’ve spent
467 00:44:46.710 ⇒ 00:44:50.460 Amber Lin: almost another half of my life outside, so.
468 00:44:51.130 ⇒ 00:44:51.890 Luke Daque: Nice.
469 00:44:52.050 ⇒ 00:44:52.890 Amber Lin: Yeah.
470 00:44:53.490 ⇒ 00:44:55.550 Amber Lin: Do you plan to stay in the Philippines?
471 00:44:57.190 ⇒ 00:45:05.026 Luke Daque: Yeah, I already have a family here, and like we did try to build a house and stuff. So I guess we’re just staying here.
472 00:45:05.890 ⇒ 00:45:08.869 Luke Daque: I mean, we’re fine with, like, it’s it’s, it’s a
473 00:45:09.050 ⇒ 00:45:12.190 Luke Daque: yeah. They say, it’s like, it’s a 3rd world country. But
474 00:45:12.320 ⇒ 00:45:14.499 Luke Daque: like we’re used to it. Basically.
475 00:45:14.970 ⇒ 00:45:19.210 Luke Daque: it’s also like fine, because it’s not that expensive like.
476 00:45:19.210 ⇒ 00:45:23.810 Amber Lin: Yeah. And they’re really nice districts. Are you in Manila?
477 00:45:24.440 ⇒ 00:45:30.580 Luke Daque: No, I’m in the south is pretty like it’s it’s like Hong Kong, like it’s.
478 00:45:30.850 ⇒ 00:45:31.200 Amber Lin: Year.
479 00:45:31.200 ⇒ 00:45:32.910 Luke Daque: Tokyo, in Japan, where it’s.
480 00:45:32.910 ⇒ 00:45:33.340 Amber Lin: I think so.
481 00:45:33.340 ⇒ 00:45:34.630 Luke Daque: And it’s very busy.
482 00:45:35.170 ⇒ 00:45:42.289 Luke Daque: or like New York, like it’s very busy, and it’s very loud, with lots of buildings and stuff. I’m I’m in the South like it’s.
483 00:45:42.490 ⇒ 00:45:46.980 Luke Daque: There’s a lot more nature here, and it’s it’s pretty like.
484 00:45:46.980 ⇒ 00:45:47.520 Amber Lin: That’s right.
485 00:45:47.520 ⇒ 00:45:49.087 Luke Daque: Laid back basically.
486 00:45:49.900 ⇒ 00:45:51.790 Amber Lin: That’s nice. Yeah.
487 00:45:51.790 ⇒ 00:45:53.400 Luke Daque: It’s got, and call.
488 00:45:53.400 ⇒ 00:45:55.149 Amber Lin: D pardon me.
489 00:45:55.810 ⇒ 00:46:06.730 Luke Daque: It’s got its pros and cons like being in a in a laid back city like everybody’s moving so slow like and like you, you don’t. You can’t get anything like I mean.
490 00:46:06.970 ⇒ 00:46:11.279 Luke Daque: like places close at like 8 Pm. 9 pm. There’s nothing.
491 00:46:11.750 ⇒ 00:46:13.210 Luke Daque: Been after.
492 00:46:13.210 ⇒ 00:46:13.515 Amber Lin: And
493 00:46:13.820 ⇒ 00:46:17.990 Luke Daque: And compared to like Manila, like everything’s open. Still.
494 00:46:17.990 ⇒ 00:46:24.749 Amber Lin: Yeah, that’s how I felt when I went to went from Hong Kong to Italy.
495 00:46:25.020 ⇒ 00:46:28.669 Amber Lin: Italy. I was in Milan, and it was so slow and.
496 00:46:29.055 ⇒ 00:46:29.370 Luke Daque: And.
497 00:46:29.370 ⇒ 00:46:33.829 Amber Lin: Wanted to get lunch at 2 Pm. The restaurants are closed from 12
498 00:46:33.950 ⇒ 00:46:46.179 Amber Lin: at like after one they close until 7 or 6, and I wanted to get food, and there was no place to get food, and then the offices take the post office, or whatever they take months.
499 00:46:46.530 ⇒ 00:46:52.060 Amber Lin: and so it’s chill, but if you want to get anything done, it’s a struggle.
500 00:46:52.430 ⇒ 00:46:55.790 Luke Daque: Yeah, same, here, basically.
501 00:46:56.500 ⇒ 00:46:59.720 Luke Daque: Like every almost everybody knows everyone. Yeah.
502 00:46:59.720 ⇒ 00:47:02.600 Amber Lin: I know you can’t really do anything shady.
503 00:47:06.800 ⇒ 00:47:17.060 Amber Lin: Okay, it’s really nice talking to you hopefully. I’ll see you a little bit later. I don’t know how he would respond. No, he said, no need. Okay.
504 00:47:17.180 ⇒ 00:47:18.220 Amber Lin: Sounds good.
505 00:47:18.220 ⇒ 00:47:18.720 Luke Daque: Cool.
506 00:47:19.560 ⇒ 00:47:26.969 Amber Lin: Sounds good, awesome. So I will probably talk to you next week when we get this started.
507 00:47:27.630 ⇒ 00:47:28.730 Amber Lin: Yeah, no, definitely.
508 00:47:28.730 ⇒ 00:47:29.240 Luke Daque: Good.
509 00:47:29.450 ⇒ 00:47:30.639 Amber Lin: About pull parts.
510 00:47:31.750 ⇒ 00:47:33.099 Luke Daque: Thanks, thanks, amber.
511 00:47:33.100 ⇒ 00:47:34.379 Amber Lin: Thanks for hopping on the call.
512 00:47:34.380 ⇒ 00:47:35.070 Luke Daque: Nice meeting, you.
513 00:47:35.070 ⇒ 00:47:36.060 Amber Lin: Meeting you.
514 00:47:36.830 ⇒ 00:47:38.480 Luke Daque: Nice meeting you have a nice day.
515 00:47:38.480 ⇒ 00:47:39.300 Amber Lin: By the way.