Meeting Title: [Javvy] Daily Standup Date: 2025-04-01 Meeting participants: Aakash Tandel, Annie Yu, Robert Tseng, Caio Velasco
WEBVTT
1 00:02:31.630 ⇒ 00:02:32.970 Aakash Tandel: Hey, Robert, how’s it going
2 00:02:49.470 ⇒ 00:02:50.510 Annie Yu: Hello guys.
3 00:02:52.080 ⇒ 00:02:53.459 Aakash Tandel: Hey, Annie, how are you doing
4 00:02:53.800 ⇒ 00:02:55.030 Annie Yu: Good! How are you?
5 00:02:55.260 ⇒ 00:03:02.261 Aakash Tandel: Good need to figure out. Add some light to my face, because it’s I’m very dark right now.
6 00:03:18.610 ⇒ 00:03:26.960 Aakash Tandel: I’m not sure if Aish is joining. I know he’s out sick yesterday, so we’ll give him a minute hopefully. He can join, because I know there’s a couple of things
7 00:03:27.140 ⇒ 00:03:34.490 Aakash Tandel: for him that we wanted to highlight and then also Kyle. But I think voicious.
8 00:03:34.920 ⇒ 00:03:37.640 Aakash Tandel: One pressing thing with the light dash stuff.
9 00:03:46.620 ⇒ 00:03:49.536 Aakash Tandel: Andy. I don’t know if you’re following, but the
10 00:03:50.790 ⇒ 00:03:59.810 Aakash Tandel: idea is for them to do a trial for light dash as an alternative to metabase so we’re gonna have boys set up some
11 00:04:00.190 ⇒ 00:04:02.120 Aakash Tandel: data in that.
12 00:04:02.725 ⇒ 00:04:07.200 Aakash Tandel: Just so they can mess around with light dash and see if it’s something they want to move to
13 00:04:08.470 ⇒ 00:04:09.510 Annie Yu: Got it?
14 00:04:10.230 ⇒ 00:04:14.139 Annie Yu: Do we all have access to light dash already
15 00:04:15.427 ⇒ 00:04:26.129 Aakash Tandel: I think we all do via the credentials in one password. So here’s the credentials
16 00:04:32.230 ⇒ 00:04:33.420 Annie Yu: Hi Kyle.
17 00:04:33.420 ⇒ 00:04:39.560 Caio Velasco: Hello, guys, certainly my headphone just wasn’t working.
18 00:04:39.840 ⇒ 00:04:41.419 Caio Velasco: Have no idea why
19 00:04:43.060 ⇒ 00:04:43.640 Aakash Tandel: All of them.
20 00:04:43.640 ⇒ 00:04:45.930 Caio Velasco: It’s can you guys hear me? Well.
21 00:04:46.570 ⇒ 00:04:48.720 Annie Yu: Okay, perfect. Then thank you.
22 00:04:51.660 ⇒ 00:04:54.320 Aakash Tandel: Okay, let’s get started, and then if
23 00:04:56.230 ⇒ 00:05:03.687 Aakash Tandel: a waste joins, then we can talk through his stuff, but otherwise we’ll sync up. I’ll slack him afterwards.
24 00:05:04.210 ⇒ 00:05:06.090 Aakash Tandel: cool. So,
25 00:05:09.400 ⇒ 00:05:19.619 Aakash Tandel: Annie, it doesn’t seem like we have a lot on your plate at the moment. This is awesome. This is done. Migration. We’re a little block, I think, on a for
26 00:05:20.800 ⇒ 00:05:26.760 Aakash Tandel: this information. Yeah.
27 00:05:27.380 ⇒ 00:05:46.149 Aakash Tandel: that’s fine. I added this task to your plate. And Robert. I don’t know if we it sounds like you’re already doing this. Should I just reassign this to you? It’s trying to figure out if the subscribe and save users are identifiable in the database just without that flag
28 00:05:48.125 ⇒ 00:05:51.309 Robert Tseng: Yeah, I’m I am doing that
29 00:05:52.217 ⇒ 00:05:54.960 Robert Tseng: cause I’m already like doing Amazon
30 00:05:55.210 ⇒ 00:05:59.190 Robert Tseng: stuff right now, so I can. I can just finish finish that out, too.
31 00:05:59.500 ⇒ 00:06:00.299 Aakash Tandel: Okay, cool?
32 00:06:00.948 ⇒ 00:06:07.570 Aakash Tandel: Then I’ll pull that off. Okay, any anything else you’re working on for for this
33 00:06:09.644 ⇒ 00:06:13.780 Annie Yu: I know that I’m still kind of waiting for a way to
34 00:06:14.970 ⇒ 00:06:20.470 Annie Yu: to help with the model for the monthly cohort. But I think I’m gonna
35 00:06:20.830 ⇒ 00:06:31.570 Annie Yu: try build something with what I have currently cause. I think there’s some metrics that I can just build with the fact orders without the new model
36 00:06:32.569 ⇒ 00:06:42.450 Aakash Tandel: Okay, there was a 1 ticket that I made for you that I didn’t see in this, maybe I didn’t pull it in the sprint. It was the training. Yeah. Okay.
37 00:06:42.450 ⇒ 00:06:42.910 Annie Yu: Oh! This is
38 00:06:45.700 ⇒ 00:06:49.060 Aakash Tandel: Just added to you yesterday. So
39 00:06:50.370 ⇒ 00:06:57.480 Aakash Tandel: Let me go back to the cycle and we’ll see that now with you would
40 00:06:58.420 ⇒ 00:07:25.597 Aakash Tandel: okay, cool. So the idea is that we’re gonna have some sort of training with the new analyst on the job team. So I wanted to put together some materials, or just prep you for that conversation. I sent an invite out. I don’t know if let’s see. Yeah. So I added you to it. If this is possible, let me know if it’s not possible we can. We can reorganize. But
41 00:07:26.363 ⇒ 00:07:52.500 Aakash Tandel: basically, we’ll do a hour walkthrough of the meta based dashboards. And I think we can start with kind of the Amazon dash. And this shopify dash like those are the 1st 2 that we should go through and we’ll just do. I I just kind of outlined a couple of different things like the general walkthrough the business context, like, what answers are we, or what questions are we answering for the business? A lot of those are, gonna be fairly straightforward.
42 00:07:52.939 ⇒ 00:08:03.929 Aakash Tandel: The general sources behind the data set. So for that, I can pull up the one of these diagrams once we’re is this
43 00:08:04.020 ⇒ 00:08:05.440 Aakash Tandel: goggy?
44 00:08:11.460 ⇒ 00:08:21.309 Aakash Tandel: This I can’t tell which one this is this job? Yeah, I can follow up this just to kind of explain that the the general layout of kind of how things are moving along, and then
45 00:08:23.670 ⇒ 00:08:47.399 Aakash Tandel: and then just some underlying, maybe expose a couple of the underlying queries that you’re using just to give them a little bit more information, but this is kind of what I wanted to go through. It’s only an hour, so I don’t know how much we’ll get through. We’ll get through as much as we can. But I wanted to give you some prep. So this is kind of my idea. And also if you have anything else you want to share with them. That’s also welcome
46 00:08:47.650 ⇒ 00:08:58.350 Annie Yu: Got it, and for the exploring, underlying queries. Do you mean if we use any queries when building the visuals in Meta Base
47 00:08:58.550 ⇒ 00:09:10.210 Aakash Tandel: Yeah, that was you know this. These are just suggestions. I don’t know exactly if these are helpful. These are the things I was thinking of when I was like, hey, what do I need to know? To ramp up on a dashboard? So that’s just an idea.
48 00:09:10.590 ⇒ 00:09:11.770 Annie Yu: Okay. Cool.
49 00:09:12.340 ⇒ 00:09:19.179 Aakash Tandel: Sweet. So yeah, that should be the main thing for Joby, just because the meetings tomorrow at 9 am. For you.
50 00:09:20.430 ⇒ 00:09:25.830 Annie Yu: Alright. I might shoot some questions today, if anything
51 00:09:26.260 ⇒ 00:09:31.709 Aakash Tandel: Yeah. Sounds good. Yeah. Feel free to tag me or Robert, or both of us in that. And then we can answer those
52 00:09:31.710 ⇒ 00:09:32.880 Annie Yu: Yeah, sounds good.
53 00:09:33.380 ⇒ 00:09:33.960 Aakash Tandel: Cool.
54 00:09:36.860 ⇒ 00:09:43.080 Aakash Tandel: Kyle. Let’s talk about. Well, I guess all 3 of these are kind of tied together, so feel free to
55 00:09:46.060 ⇒ 00:09:53.920 Caio Velasco: Okay. So yeah. So yesterday I took some time to understand the whole picture and
56 00:09:54.080 ⇒ 00:09:57.939 Caio Velasco: and think about the things we have to do re-regarding this
57 00:09:58.430 ⇒ 00:10:03.710 Caio Velasco: this Amazon cog sheet and apparently there are
58 00:10:04.070 ⇒ 00:10:17.219 Caio Velasco: 2 things right, like we need to build the Dean products, because at the end of the day. We want to have unique skews for both shopify and Amazon and bring that into a Dean products table. This is one
59 00:10:18.060 ⇒ 00:10:20.709 Caio Velasco: and the other thing is
60 00:10:21.830 ⇒ 00:10:45.470 Caio Velasco: well, we we have to update the Amazon fact order, line, or or something related to that. And then I have to spend time understanding a bit more like, why do we have 3 or 4 different fact order tables? Because we have the fact orders one. We have the fact order line. We have the fact Amazon order or something like that. And then, now we are talking about fact Amazon order line.
61 00:10:45.740 ⇒ 00:10:52.041 Caio Velasco: So I need to also understand like, why do we need all of this, and how we can?
62 00:10:52.930 ⇒ 00:10:55.299 Caio Velasco: well, make sure that we have one
63 00:10:55.520 ⇒ 00:11:03.900 Caio Velasco: that has the all the data from from team products and then from anything else, so that we can
64 00:11:04.520 ⇒ 00:11:16.826 Caio Velasco: get to the point of of, you know, using the sheet to to bring that information to into effect, or the table at the at the end of the day. So I’m still a bit like confused about
65 00:11:17.850 ⇒ 00:11:20.169 Caio Velasco: how things have been set up, but
66 00:11:20.827 ⇒ 00:11:25.090 Caio Velasco: I can spend some time looking into it and and understanding
67 00:11:25.480 ⇒ 00:11:25.830 Robert Tseng: Sure.
68 00:11:25.830 ⇒ 00:11:26.769 Caio Velasco: 5 or 6,
69 00:11:26.770 ⇒ 00:11:27.410 Robert Tseng: Excellent.
70 00:11:28.440 ⇒ 00:11:30.800 Caio Velasco: Okay? So we should have basically.
71 00:11:31.150 ⇒ 00:11:35.619 Caio Velasco: And why? Why don’t we have just one factors? Wouldn’t be that sufficient
72 00:11:37.220 ⇒ 00:11:42.509 Robert Tseng: I I don’t know. I think that would be. Luke was the one that built most of these tables before.
73 00:11:42.760 ⇒ 00:11:55.790 Robert Tseng: I think he ran into some issues because Amazon order data doesn’t look the same as shopify. And I think he just created 2 models because there wasn’t really the initial request was not to blend them together. Amazon was more of an add on, later on.
74 00:11:56.490 ⇒ 00:12:02.310 Caio Velasco: Oh, okay, okay, okay, okay, so yeah. So based on what we already have for
75 00:12:02.780 ⇒ 00:12:11.259 Caio Velasco: or shopify. Although the fact orders tables again, it’s A. C. It’s composed of 2 cities. One of those is Amazon stuff.
76 00:12:11.993 ⇒ 00:12:25.800 Caio Velasco: But maybe some of the columns were initialized as new because we didn’t have anything from Amazon. So yeah, I’m I’m studying it. And and I also would like to understand the urgency for this, because at the end of the day, having 10 h per week
77 00:12:26.323 ⇒ 00:12:33.209 Caio Velasco: with half an hour for meetings, and then, you know, I just wanted to know how to distribute this along my week.
78 00:12:34.630 ⇒ 00:12:37.409 Caio Velasco: And how does it relate to the urgency? Yeah.
79 00:12:37.750 ⇒ 00:12:44.129 Aakash Tandel: Yeah, I. So I think so. Priority on these are high. I think. If we can get
80 00:12:44.230 ⇒ 00:12:50.567 Aakash Tandel: this whole thing wrapped by the end of the week that would be good. But this one
81 00:12:51.580 ⇒ 00:12:59.219 Aakash Tandel: this one, I’m hoping, is sooner because we have to get Aman’s
82 00:12:59.880 ⇒ 00:13:06.359 Aakash Tandel: basic information to make sure that this is this works. So
83 00:13:06.970 ⇒ 00:13:10.959 Aakash Tandel: I would say like this, if you can get done by like
84 00:13:14.630 ⇒ 00:13:23.509 Aakash Tandel: tomorrow end of day, and then the other 2 by end of the week.
85 00:13:26.272 ⇒ 00:13:29.409 Aakash Tandel: But I think with all these.
86 00:13:30.430 ⇒ 00:13:51.380 Aakash Tandel: if if you can move faster, definitely like, move faster, I think it’s always good to get work done early, just because if we have to go back and forth with the client it’ll get done in in the right time. So yeah, I would say, this is probably my assessment of what we need. With this one being kind of the 1st of the buckets
87 00:13:52.750 ⇒ 00:14:03.350 Caio Velasco: Okay, perfect. And this one the one that I had to confirm with with Amal. I already sent him the message in a very structured way. And now, we’re just waiting for his reply.
88 00:14:03.480 ⇒ 00:14:09.030 Caio Velasco: It doesn’t, really it, from from my understanding. Now, it doesn’t really affect
89 00:14:09.300 ⇒ 00:14:15.090 Caio Velasco: starting the other 2, because well, we can still structure the fact or the line table
90 00:14:15.867 ⇒ 00:14:22.219 Caio Velasco: without having, let’s say, data from this source of truth that he has to give us.
91 00:14:22.620 ⇒ 00:14:26.449 Caio Velasco: But still, yeah, at the end of the day we need this to to move forward
92 00:14:27.040 ⇒ 00:14:35.720 Aakash Tandel: Yup, okay, cool. Yeah. Cause they’re going to be using. They’re gonna be updating this Google sheet. And then we’re gonna be porting that in using portable to get over to our
93 00:14:36.253 ⇒ 00:14:41.476 Aakash Tandel: snowflake environment. So yeah, let’s get this. Yeah, try to. If he hasn’t responded.
94 00:14:42.260 ⇒ 00:14:56.099 Aakash Tandel: ping him again. Just give a bump on that thread just at him and say, Hey, any movement on kind of any decisions? Or and do you need clarification on anything? I’ve said that type of thing. Just to get him moving on this guy
95 00:14:56.610 ⇒ 00:14:57.640 Caio Velasco: Okay, perfect. Then
96 00:14:57.640 ⇒ 00:15:07.460 Robert Tseng: Yeah, no, I think the shopify that. The you know, the the shopify Google sheet. It’s called 3 pl cost assumptions whatever, like.
97 00:15:07.650 ⇒ 00:15:09.389 Robert Tseng: we’re not gonna have a second
98 00:15:09.530 ⇒ 00:15:13.869 Robert Tseng: sheet for just Amazon. We just add another tab for Amazon for Amazon.
99 00:15:14.050 ⇒ 00:15:31.189 Robert Tseng: like, we just need to tell Aman this is the tab put the stuff in here like I think it’s that simple. I don’t really know. I mean he. We’ve I’ve talked to him twice since he asked for this last week, and I what? That’s what I told him. So I think by. I think I’m just expecting Kyle to just
100 00:15:31.620 ⇒ 00:15:37.550 Robert Tseng: tell him like these are the fields we need in this sheet, and have your Amazon guy fill it in
101 00:15:38.690 ⇒ 00:15:40.960 Robert Tseng: that that’s like the
102 00:15:41.150 ⇒ 00:15:56.339 Robert Tseng: I think this this should be. This is. This is like a 30 second message like I would. I would just send it if if you don’t. If you don’t, we’re not like ready to do like own it. Then I’ll I’ll make the tab, and I’ll I’ll fill in the the call of names, and I’ll tell them to add it in
103 00:15:58.550 ⇒ 00:16:04.810 Caio Velasco: Okay. Okay, so yeah, because when I send the message, I was also trying to to understand if Amazon has any
104 00:16:04.960 ⇒ 00:16:14.770 Caio Velasco: differences in terms of how they calculate the cost, because if they have, all the other tabs would be different, or at least would have more information using those steps. Because
105 00:16:14.770 ⇒ 00:16:21.729 Robert Tseng: You know they have the sample sheet before. That was just kind of not being used anywhere, so I would just look at it, and just kind of
106 00:16:22.120 ⇒ 00:16:29.899 Robert Tseng: put put the pieces together and figure out like what what the difference is like. I I think I could. I think I could probably figure that out in a few minutes.
107 00:16:30.880 ⇒ 00:16:39.449 Caio Velasco: Okay, yeah, no. I checked the one, the the messy one. And yeah, it was a bit like, well, it’s very unstructured, and it’s not like Per.
108 00:16:40.550 ⇒ 00:16:43.910 Caio Velasco: I mean, I didn’t understand it, so I have to check again, but
109 00:16:44.567 ⇒ 00:16:49.790 Caio Velasco: because it was just a lot of columns, so as if it was like a table, and not like a spreadsheet with
110 00:16:50.620 ⇒ 00:16:53.210 Caio Velasco: basic assumptions per product.
111 00:16:53.450 ⇒ 00:16:56.550 Caio Velasco: But I can take another look and and see what I can find from them.
112 00:16:56.820 ⇒ 00:17:02.660 Robert Tseng: Yeah, if you have any questions, and you just keep hitting them all with questions like, put the onus on him like, Don’t don’t stay confused
113 00:17:03.250 ⇒ 00:17:04.490 Caio Velasco: Okay, perfect.
114 00:17:05.480 ⇒ 00:17:08.530 Aakash Tandel: Cool. So, Robbie, you’re gonna add the tab and
115 00:17:08.530 ⇒ 00:17:09.879 Robert Tseng: Yeah, I’ll just add the tab
116 00:17:10.140 ⇒ 00:17:10.829 Aakash Tandel: Okay?
117 00:17:11.010 ⇒ 00:17:17.300 Aakash Tandel: And then do you want to ping Aman? Say, Hey, can we get the thing in, or do you want Kyle to do that?
118 00:17:18.109 ⇒ 00:17:20.079 Robert Tseng: Yeah, I mean at this point I’ll do it.
119 00:17:20.079 ⇒ 00:17:22.489 Aakash Tandel: Okay. So I’m just gonna reassign this to
120 00:17:22.490 ⇒ 00:17:22.950 Robert Tseng: Yeah.
121 00:17:23.800 ⇒ 00:17:26.349 Aakash Tandel: Okay. Alright. Cool.
122 00:17:27.211 ⇒ 00:17:32.439 Aakash Tandel: Okay, cool. That sounds good. Let’s anything else. Cal.
123 00:17:33.854 ⇒ 00:17:35.500 Caio Velasco: No, not not on my end.
124 00:17:36.840 ⇒ 00:17:41.410 Aakash Tandel: Let’s go to Robert. Like. See where you want to start
125 00:17:43.333 ⇒ 00:17:44.559 Robert Tseng: I think
126 00:17:48.410 ⇒ 00:17:55.819 Robert Tseng: I think there was a investigation of the Amazon cancellation rate. So I mean, I literally just started that this morning. So I mean, I’m
127 00:17:57.030 ⇒ 00:17:58.450 Robert Tseng: in in testing
128 00:17:59.400 ⇒ 00:18:01.760 Aakash Tandel: Okay.
129 00:18:01.760 ⇒ 00:18:13.164 Robert Tseng: Yeah. So I mean, I think I’ve I mean, it’s just like a. This is like actual analysis, like, I had to run a bunch of queries. And like, I have some. I have some recommendations for him, I think.
130 00:18:15.170 ⇒ 00:18:27.200 Robert Tseng: yeah, I mean, like, 18% of the orders are like, they’re not like, I think this is a data quality issue from our side. Like, why are, why, why are like 20% of orders like not having real
131 00:18:29.010 ⇒ 00:18:36.589 Robert Tseng: values coming through like. So I think the Amazon data just looks dirty, in my opinion, like, it is not like.
132 00:18:37.300 ⇒ 00:18:38.550 Robert Tseng: yeah, we’re right there.
133 00:18:38.700 ⇒ 00:18:41.709 Robert Tseng: 20% of the orders don’t have actually don’t have any.
134 00:18:42.560 ⇒ 00:18:50.790 Robert Tseng: don’t have values to them. And then I think there are some clear cuts that are like driving cancellation rates. So
135 00:18:50.990 ⇒ 00:18:57.450 Robert Tseng: one is like, you know, prime customers versus non prime customers. The other one is like
136 00:18:57.630 ⇒ 00:19:06.289 Robert Tseng: Amazon fulfillment network versus, like their their merchant fulfilled network. So there’s there are a few recommendations I provide. But I think for us like
137 00:19:06.460 ⇒ 00:19:07.500 Robert Tseng: it’s to
138 00:19:08.720 ⇒ 00:19:17.989 Robert Tseng: like I don’t. I don’t know who’s the Amazon data owner on our team. I feel like people just kind of deflected. And they’re just like, I don’t know like. And we’re just
139 00:19:18.160 ⇒ 00:19:20.899 Robert Tseng: how can we be so like unclear on
140 00:19:21.140 ⇒ 00:19:27.220 Robert Tseng: on Amazon, or data like shopify. We we know it very well. I just don’t understand why we don’t have any.
141 00:19:27.670 ⇒ 00:19:31.199 Robert Tseng: Don’t have much like knowledge to share on on the Amazon side.
142 00:19:31.320 ⇒ 00:19:34.649 Robert Tseng: So I’m not. I’m not sure who’s who’s
143 00:19:35.310 ⇒ 00:19:43.659 Robert Tseng: if I do, I need to go and like own the Amazon data source. Like, I hope not. Like I, I need somebody on the team to really be the owner. There
144 00:19:45.030 ⇒ 00:19:47.930 Aakash Tandel: Yeah, that makes sense.
145 00:19:48.420 ⇒ 00:19:50.540 Aakash Tandel: Do we need to?
146 00:19:52.260 ⇒ 00:19:54.490 Aakash Tandel: Yeah, I’ll think about how to
147 00:19:55.650 ⇒ 00:20:02.349 Aakash Tandel: service Amazon information throughout the teams. I feel like all of us should have a general understanding of. Hey, here’s how shopify
148 00:20:02.590 ⇒ 00:20:07.199 Aakash Tandel: orders work. Here’s how Amazon orders work that type of information. I think that would be good to have
149 00:20:07.510 ⇒ 00:20:08.130 Robert Tseng: Yeah.
150 00:20:08.850 ⇒ 00:20:14.254 Aakash Tandel: Okay, that sounds good. Okay.
151 00:20:14.880 ⇒ 00:20:21.440 Robert Tseng: I’m gonna send out this talk to him on hopefully, after you didn’t stand up. I yeah. So
152 00:20:22.760 ⇒ 00:20:23.480 Aakash Tandel: Cool.
153 00:20:24.080 ⇒ 00:20:26.960 Aakash Tandel: Anything on the gross margin. Qa
154 00:20:28.927 ⇒ 00:20:36.680 Robert Tseng: Gross margin. Qa, we’re just waiting, I mean, I think we’re done there, so we don’t have anything to do. He’s just waiting on him to review
155 00:20:37.120 ⇒ 00:20:37.429 Aakash Tandel: Okay.
156 00:20:37.740 ⇒ 00:20:41.509 Robert Tseng: He’ll he’ll probably review it with us tomorrow on on in the check in
157 00:20:42.250 ⇒ 00:20:45.880 Aakash Tandel: Okay, couple pending client feedback.
158 00:20:47.020 ⇒ 00:20:54.960 Aakash Tandel: Okay, confirm with, I’m on for okay, this is what you we just talked about. Amazon dash updates 2.1
159 00:20:56.080 ⇒ 00:20:59.199 Robert Tseng: Yeah, we don’t have subscribe and save. So we that’s just blocked
160 00:20:59.370 ⇒ 00:21:00.000 Aakash Tandel: Yep.
161 00:21:00.720 ⇒ 00:21:17.700 Aakash Tandel: And then, okay. And then you’re working on this thing later. If you need to pass off to Annie, I think this also something. Maybe she could tackle. But I’ll let you make that call. I also was like, Hey, like it’s only time boxes for an hour, because it seems like it’s 1 of those things that
162 00:21:18.640 ⇒ 00:21:22.670 Aakash Tandel: we shouldn’t spend more than an hour, just, you know, digging around and trying to figure out
163 00:21:23.430 ⇒ 00:21:31.540 Robert Tseng: Okay, yeah. I mean, if any’s got time I would prefer her to do it like, obviously, I wanna not be doing. I don’t wanna be knocking out tickets as
164 00:21:32.110 ⇒ 00:21:35.745 Aakash Tandel: Yeah. And let’s, I’ll reassign this one to you.
165 00:21:36.970 ⇒ 00:22:02.969 Aakash Tandel: the basic idea here is aman was wondering if there’s a way, even though we don’t have subscribe and save data, there are still customers in our data that are subscribe and save people. So Robbie sent us over 4 users who have the customer ids that are linked to people with subscribe and save members. So I’m not sure if there’s like a discount field that matches, or something like that
166 00:22:02.970 ⇒ 00:22:13.089 Aakash Tandel: that just says, Hey, look, it’s not obvious. But the data. Can you? From the data you can infer that this person subscribe and save. So does that make sense like direct? Ask.
167 00:22:13.280 ⇒ 00:22:22.220 Annie Yu: Yeah. And one question, though, I think these numbers are actually older ids, not customer ids. I just quickly
168 00:22:23.860 ⇒ 00:22:24.195 Aakash Tandel: Okay.
169 00:22:24.530 ⇒ 00:22:25.060 Annie Yu: When, no no
170 00:22:25.060 ⇒ 00:22:26.110 Robert Tseng: Yeah, I think she’s right.
171 00:22:26.440 ⇒ 00:22:33.010 Aakash Tandel: Okay, alright. Let me. Just I copied and pasted this thing. So I’ll say, order of these
172 00:22:33.460 ⇒ 00:22:46.630 Aakash Tandel: cool, okay, yeah. Time boxes for like an hour. Hopefully, there is something. But I it’s totally acceptable. If there’s like, literally nothing that differentiates these. Because the data is not there. So that’s all. A fine answer
173 00:22:47.230 ⇒ 00:22:47.780 Annie Yu: Right.
174 00:22:48.600 ⇒ 00:22:52.650 Aakash Tandel: Sweet does anyone have anything else I know
175 00:22:53.030 ⇒ 00:23:00.949 Aakash Tandel: is maybe still out sick. So this light dash trial might be pushed. I don’t know if we’ll be able to do this by the end of the week, if
176 00:23:01.400 ⇒ 00:23:12.340 Aakash Tandel: or I have it tomorrow. I don’t know if that’s possible. If he’s out still, so I will let him on know that, hey? The person that would be in charge of doing this is out, so
177 00:23:12.530 ⇒ 00:23:14.479 Aakash Tandel: I’ll communicate that cool
178 00:23:15.010 ⇒ 00:23:15.540 Caio Velasco: Okay.
179 00:23:15.920 ⇒ 00:23:16.930 Annie Yu: Thank you.
180 00:23:17.660 ⇒ 00:23:21.139 Aakash Tandel: Let us know if you guys need anything otherwise, have a good rest of your day.
181 00:23:21.140 ⇒ 00:23:26.639 Caio Velasco: I I have just a quick second for Robert, but then I don’t know if you guys want want to drop, feel free
182 00:23:27.070 ⇒ 00:23:28.059 Robert Tseng: Oh, yeah, sure.
183 00:23:28.540 ⇒ 00:23:52.080 Caio Velasco: Okay. Robert. So since since we you mentioned that that we should just well send them on a few columns, or or whatever to add to the other to the other tab. Why would we need a different tab if at the end we are, we’re probably doing the same like product costs shipping costs. And and it’s it’s the same tab right at the end. We we just with more skews
184 00:23:52.830 ⇒ 00:24:04.549 Robert Tseng: Yeah, I mean, it’s really just for the for their team, their shopify guy is different from their Amazon guy. So he’s gonna go in the shopify the Amazon guy is gonna go in the shopify Tab. He’s gonna be like, what is this? And
185 00:24:05.000 ⇒ 00:24:23.679 Robert Tseng: I think the reality is just that most people’s jobs are very siloed. And so if you don’t make it very easy for them, they’re just gonna be confused and not do anything. So I just want to give him another tab. Even if he’s putting the same information as the other tab it will. He will not be confused by looking at shopify data
186 00:24:24.060 ⇒ 00:24:32.370 Caio Velasco: Okay. I didn’t know that. No, now it makes total sense. Now it makes sense. Why, we are starting from from scratch in this and
187 00:24:33.890 ⇒ 00:25:00.270 Robert Tseng: And the shopify. Guy didn’t add his data incorrectly, which is why I always had to update the models like he uploaded a bunch of random duplicate products and that kind of messed up some of the margin reporting because we were basically like we had for single product orders. They were showing up as like 3 product orders, and like we, we missed some of that last time we shared it out with them. So I mean, I think. Just in general.
188 00:25:00.970 ⇒ 00:25:19.819 Robert Tseng: I don’t like that. We have to take data from these stakeholders because they obviously don’t carry their job. It’s a lot easier for us to get it directly from source. But cogs data is like, these are all assumptions. So we kind of have to rely on human. Input and so we just need to think about like.
189 00:25:19.920 ⇒ 00:25:31.209 Robert Tseng: okay, anything we get from them. We expect it to be messy. There will be missing values. There will be duplicates. And so like, we have to kind of treat it as as such when we’re when we’re ingesting it.
190 00:25:31.850 ⇒ 00:25:50.320 Caio Velasco: Okay? And when we ask those columns, are we still opening space for them to say like, Hey, we we also have, whatever Xyz cost here, that that should be added, because, as I see, we have like 4, 4, or 5 for shopping, but maybe they are more for for Amazon, and related
191 00:25:50.690 ⇒ 00:25:52.529 Caio Velasco: that I also found a.
192 00:25:52.640 ⇒ 00:25:55.020 Caio Velasco: A. Another
193 00:25:55.720 ⇒ 00:26:10.450 Caio Velasco: FAQ. From Amazon, that they have this stand like a standardized prices for for those things. So at the end of the day is just basically on their side, is just giving us whatever they
194 00:26:10.620 ⇒ 00:26:12.780 Caio Velasco: the deal they have with Amazon.
195 00:26:13.300 ⇒ 00:26:14.140 Caio Velasco: Right
196 00:26:14.140 ⇒ 00:26:25.460 Robert Tseng: Yeah, I mean, I I mean, at this point you’ve researched the Amazon data more than I have. So that’s why I wanted you to own this. But, like you could just create a tab with just 7 columns, or whatever you think they need like
197 00:26:25.460 ⇒ 00:26:26.030 Caio Velasco: Okay.
198 00:26:26.430 ⇒ 00:26:41.050 Robert Tseng: They’re not gonna think about the problem holistically. They’re just gonna look at the spreadsheet and be like, oh, I need to fill out columns A through C, so whatever boundaries we give them, that’s what they’re going to be on the hook for. So that’s kind of what our role is in this
199 00:26:41.800 ⇒ 00:26:42.789 Caio Velasco: Okay. Thank you.
200 00:26:42.790 ⇒ 00:26:54.310 Robert Tseng: I don’t know the same research that you’ve conducted. So I’m just gonna put the same 4 columns. And if that’s not enough, well, I mean, I kind of need you to to make that call because you’re the one who’s gonna be ingesting the data and modeling it
201 00:26:54.970 ⇒ 00:27:00.669 Caio Velasco: No perfect, and can I have added access to that spreadsheet? Then
202 00:27:05.080 ⇒ 00:27:13.039 Caio Velasco: Because otherwise like if it if it comes and goes and comes and goes, I lose the ownership of the thing, and then that’s why, sometimes I get stuck
203 00:27:15.020 ⇒ 00:27:19.540 Robert Tseng: Yeah, yeah, you should have edit access. I just shared with you
204 00:27:22.620 ⇒ 00:27:30.759 Caio Velasco: Then I can add the right, the other app, the other tab I put like those calls. I see if there’s anything else on the Amazon side that it could be added
205 00:27:31.040 ⇒ 00:27:37.569 Caio Velasco: because I already asked it. Then on the on the question I sent to Aman, and then at least, you can start the this
206 00:27:38.385 ⇒ 00:27:39.090 Caio Velasco: already
207 00:27:39.520 ⇒ 00:27:50.379 Robert Tseng: Yeah. So like, I just created 2 more tabs, that kind of split off sections. So there’s a shopify section. And there’s an Amazon section. So you just kind of add whatever you feel like you need to add to the Amazon section.
208 00:27:50.500 ⇒ 00:27:52.600 Robert Tseng: and then I just tell them on like.
209 00:27:53.530 ⇒ 00:27:56.419 Robert Tseng: have your have your Amazon guy fill out these tabs
210 00:27:57.630 ⇒ 00:27:58.620 Caio Velasco: Okay.
211 00:27:59.080 ⇒ 00:28:04.250 Robert Tseng: Yeah, perfect, perfect. Then start with the same set of tabs from shopify. If that’s like.
212 00:28:04.704 ⇒ 00:28:09.829 Robert Tseng: honestly don’t even know why we have 5 tabs for shopify. It could have just been one. But I mean, that’s
213 00:28:09.830 ⇒ 00:28:11.150 Caio Velasco: Yeah, exactly. Exactly.
214 00:28:11.150 ⇒ 00:28:12.680 Robert Tseng: Just how they wanted it.
215 00:28:14.870 ⇒ 00:28:15.859 Caio Velasco: Okay, thank you.
216 00:28:21.070 ⇒ 00:28:23.320 Caio Velasco: Perfect. Thank you. Thank you. Guys. Thank you. Akash.
217 00:28:23.980 ⇒ 00:28:26.139 Aakash Tandel: Thanks. Guys, talk to you later.