Meeting Title: [Javvy] Daily Standup Date: 2025-03-27 Meeting participants: Annie Yu, Aakash Tandel, Robert Tseng, Awaish Kumar, Caio Velasco
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1 00:01:15.260 ⇒ 00:01:16.200 Robert Tseng: Hey! I wish
2 00:01:18.940 ⇒ 00:01:19.700 Awaish Kumar: Hello!
3 00:01:26.890 ⇒ 00:01:27.789 Robert Tseng: How are you doing
4 00:01:29.930 ⇒ 00:01:31.480 Awaish Kumar: I’m good. How about you?
5 00:01:34.160 ⇒ 00:01:35.380 Robert Tseng: Doing. Okay?
6 00:01:40.270 ⇒ 00:01:41.030 Awaish Kumar: Okay.
7 00:01:41.940 ⇒ 00:01:42.550 Robert Tseng: Yeah.
8 00:01:42.850 ⇒ 00:01:45.360 Caio Velasco: There you wish cool.
9 00:01:45.660 ⇒ 00:01:46.440 Awaish Kumar: Hello!
10 00:01:46.440 ⇒ 00:01:47.380 Robert Tseng: Hey, Kyle.
11 00:02:04.170 ⇒ 00:02:11.275 Robert Tseng: we’ll give any a couple of minutes. But I mean actually waste. The most urgent thing that I want to talk to you about is really on the
12 00:02:11.990 ⇒ 00:02:18.120 Robert Tseng: gross margin data quality stuff. I made some call outs on the on the model. I mean, we
13 00:02:18.340 ⇒ 00:02:21.009 Robert Tseng: we can. We can. We just can we talk through that 1st
14 00:02:27.260 ⇒ 00:02:32.220 Awaish Kumar: Yeah, the ones which, like cogs, issue and the duplicate issue right?
15 00:02:32.640 ⇒ 00:02:33.320 Robert Tseng: Yeah.
16 00:02:34.370 ⇒ 00:02:39.170 Awaish Kumar: Yeah, I actually worked on them yesterday, and they both are done
17 00:02:40.520 ⇒ 00:02:42.630 Robert Tseng: Okay, what was the issue?
18 00:02:44.040 ⇒ 00:02:53.059 Awaish Kumar: Yeah, like, you know, like some of the cogs we were calculating. And some data like we when we are doing joins. So we have a
19 00:02:53.240 ⇒ 00:03:04.680 Awaish Kumar: order line table which is coming from shopify, and then we join it with the cog sheet and the like. The cog sheet has duplicate back entries for the school.
20 00:03:04.810 ⇒ 00:03:07.790 Awaish Kumar: right? So for same school, like being
21 00:03:07.790 ⇒ 00:03:08.440 Robert Tseng: Oh!
22 00:03:08.440 ⇒ 00:03:23.839 Awaish Kumar: Like 2 entries for the same issue in the cog sheet, and also for the product mapping sheet. So there was like duplicate entries, and hence, when we join with them, like, it will create the duplicated entries. So I
23 00:03:24.100 ⇒ 00:03:33.180 Awaish Kumar: investigated all the tables figured out where this duplicate was coming from, and then I like as remove those
24 00:03:33.500 ⇒ 00:03:41.690 Awaish Kumar: like in the in the model model. I just try to take the distinct values. So now we should not have the duplicates anymore.
25 00:03:44.400 ⇒ 00:03:51.400 Robert Tseng: I see. Yeah. So I mean, it sounds like just the the cog sheet we were given was not very clean.
26 00:03:53.070 ⇒ 00:03:53.690 Awaish Kumar: Blogs, chat.
27 00:03:53.690 ⇒ 00:03:54.400 Robert Tseng: Now you.
28 00:03:54.400 ⇒ 00:03:56.029 Awaish Kumar: Mapping sheet, but
29 00:03:56.980 ⇒ 00:03:57.720 Robert Tseng: Yeah.
30 00:03:59.780 ⇒ 00:04:11.320 Robert Tseng: well, that’s always the problem. When we have stuff handed to us for via Google sheet that the client gives us, we’re gonna have the same problem on the Eden side. We can’t really go direct
31 00:04:13.610 ⇒ 00:04:17.060 Robert Tseng: because there isn’t really like an Api or something we can get.
32 00:04:17.399 ⇒ 00:04:18.720 Robert Tseng: Oh.
33 00:04:18.899 ⇒ 00:04:23.800 Robert Tseng: from Amazon. I don’t think they would or not even from Amazon, from shopify. I don’t think they would do that for us.
34 00:04:24.400 ⇒ 00:04:26.510 Awaish Kumar: Yeah, but that’s a lot as we have.
35 00:04:26.800 ⇒ 00:04:43.389 Awaish Kumar: Yeah, right now, I’m in the model. I’m trying to get the unique value for a school like, I know that Cox sheet the primary key for the cock sheet is school, so I can take like unique row for per per school. That way we avoid getting duplicates.
36 00:04:44.790 ⇒ 00:04:45.910 Robert Tseng: But you’re sure that they’re due
37 00:04:45.910 ⇒ 00:04:49.270 Awaish Kumar: Even if there are duplicates in the sheet. We we
38 00:04:51.480 ⇒ 00:04:59.279 Awaish Kumar: yes, they were like the one school was the uppercase, and one was in the lowercase, but it was exactly the same school
39 00:05:00.090 ⇒ 00:05:01.150 Robert Tseng: I see.
40 00:05:01.690 ⇒ 00:05:02.370 Awaish Kumar: Yeah.
41 00:05:04.340 ⇒ 00:05:05.120 Robert Tseng: Okay.
42 00:05:06.540 ⇒ 00:05:08.609 Robert Tseng: Oh, okay. Well, hey, Annie.
43 00:05:10.280 ⇒ 00:05:11.230 Annie Yu: Hello!
44 00:05:14.280 ⇒ 00:05:19.678 Robert Tseng: Alright. Well, I’ll jump into it now, or thanks for trying through that.
45 00:05:22.330 ⇒ 00:05:28.240 Robert Tseng: no Akash this morning. So apologies if I’m a bit less efficient than he is. But
46 00:05:29.920 ⇒ 00:05:32.160 Robert Tseng: kind of the way that I
47 00:05:32.280 ⇒ 00:05:40.379 Robert Tseng: typically like to go through. This is just to go to our most urgent projects. And then we’ll kind of talk through the stuff within the current cycle.
48 00:05:42.000 ⇒ 00:05:48.574 Robert Tseng: so yeah, I think what we just talked about here, which I’ll push, push and update here. So
49 00:05:49.740 ⇒ 00:06:03.140 Robert Tseng: fix you could get cog product cogs issue in orders quality from mappings.
50 00:06:04.780 ⇒ 00:06:05.710 Robert Tseng: Jeez.
51 00:06:08.960 ⇒ 00:06:12.970 Robert Tseng: so it might be helpful to
52 00:06:15.950 ⇒ 00:06:22.200 Robert Tseng: yeah, just for specifically Kyle’s context here. Since you also look at Amazon and shopify data.
53 00:06:22.310 ⇒ 00:06:28.360 Robert Tseng: this cogs. 3 pl assumption sheet. This was kind of I mean, this was here before you joined, I guess. But
54 00:06:28.839 ⇒ 00:06:35.660 Robert Tseng: this is how we get product level cogs for joby right now. Most of these are shopify skews
55 00:06:36.490 ⇒ 00:06:42.539 Robert Tseng: and from there we have all the product level assumptions on the cost, the weight.
56 00:06:43.340 ⇒ 00:06:50.589 Robert Tseng: you know, at the skew level we have some platform fee assumptions that are here, although we’ve changed this in the model.
57 00:06:51.180 ⇒ 00:06:57.990 Robert Tseng: So it’s a different issue. And I know I’m jumping around here, but I feel like it fits better with the story that I’m telling.
58 00:06:58.530 ⇒ 00:07:07.379 Robert Tseng: So like a wish I showed you another issue yesterday. We’re like, Hey, in the fact order stable. We have 5 platform fees, and it’s like
59 00:07:07.380 ⇒ 00:07:08.110 Awaish Kumar: I,
60 00:07:08.960 ⇒ 00:07:17.229 Robert Tseng: Yeah. So I think we’re just consolidating to just the cost platform fee, which is a different calculation than what we see here. Right?
61 00:07:17.230 ⇒ 00:07:18.620 Robert Tseng: Right? So
62 00:07:23.000 ⇒ 00:07:24.990 Awaish Kumar: Yeah, Robert, for you.
63 00:07:25.300 ⇒ 00:07:39.270 Awaish Kumar: I have a question here, like I. Recently we had a ticket where we are adding the for the shopify. We are adding the platform fee like 30 cents plus 2 point
64 00:07:39.270 ⇒ 00:07:40.420 Robert Tseng: 2.9% yep.
65 00:07:40.420 ⇒ 00:07:47.970 Awaish Kumar: Of the total order that, yeah. And that’s the one we want to keep as a platform free, right?
66 00:07:48.590 ⇒ 00:07:49.270 Robert Tseng: Correct.
67 00:07:51.800 ⇒ 00:07:57.239 Awaish Kumar: Merchant fee, and from it, so do we. Which one we want to keep in the model
68 00:07:58.873 ⇒ 00:08:11.269 Robert Tseng: I looked at it, and it looks like most of these are the same. But like I think the cause platform fee is the most updated one. That that is the you can check me. But I think that’s the 2.9% plus 30 cent one.
69 00:08:13.880 ⇒ 00:08:14.950 Robert Tseng: Oh, wait! No, no, no.
70 00:08:14.950 ⇒ 00:08:15.860 Awaish Kumar: I’m saying that this
71 00:08:15.860 ⇒ 00:08:16.689 Robert Tseng: I get, yeah.
72 00:08:16.690 ⇒ 00:08:19.760 Awaish Kumar: Local fee. Should we keep it, or we remove it
73 00:08:21.850 ⇒ 00:08:23.043 Robert Tseng: I see.
74 00:08:25.290 ⇒ 00:08:41.089 Robert Tseng: yeah. Sorry. I I conflated that. So yeah, last time there was a ticket that I asked you to add a platform fee with the 2.9% plus 30 cents. This is a merchant fee, which is different. So actually, yeah, we do need to keep a merchant fee and a platform fee. So
75 00:08:41.230 ⇒ 00:08:48.079 Robert Tseng: that’s my bad, I think. Yeah. So the shopify merchant fee. We need to keep that. And then of these platform fees. We just keep one right
76 00:08:50.630 ⇒ 00:08:58.249 Awaish Kumar: Okay? So yeah, for for the platform fees, we keep the one field. But we keep this merchant fee as a separate field
77 00:08:58.250 ⇒ 00:08:58.960 Robert Tseng: Yes.
78 00:08:59.200 ⇒ 00:08:59.540 Awaish Kumar: Okay.
79 00:09:00.340 ⇒ 00:09:06.309 Robert Tseng: Yeah. So the merchant fee is not going to be reflected in here. For those of you that are looking at this for the 1st time.
80 00:09:06.850 ⇒ 00:09:09.601 Robert Tseng: Yeah, that’s just something that the
81 00:09:10.370 ⇒ 00:09:14.170 Robert Tseng: that- that- that the CEO told us to add.
82 00:09:14.330 ⇒ 00:09:21.260 Robert Tseng: Yeah, it’s just like a hard coded 2.9% of of the of the subtotal plus 30 cents.
83 00:09:22.380 ⇒ 00:09:31.539 Robert Tseng: Yeah. And then the other thing we were trying to handle was with the pick, the shipping assumptions. So obviously, you see, we have these rounded numbers on like number of units, or
84 00:09:32.270 ⇒ 00:09:38.599 Robert Tseng: like the pounds. So we we ran into an issue, and that is also the okay. Great.
85 00:09:38.920 ⇒ 00:09:45.430 Robert Tseng: Yeah. So previously, the shipping costs, we were rounding this as well. So we weren’t showing like a exact 6.2 2.
86 00:09:45.640 ⇒ 00:09:51.930 Robert Tseng: So if, like a if a product weighed 1.5 pounds, it should be bumped up to the 2 pound
87 00:09:52.539 ⇒ 00:10:03.509 Robert Tseng: category, because that’s how you would do it. There’s and then it should be charged $6 and 22 cents. So we were a bit off there. But yeah, I think a way should be fix that, then it should be good
88 00:10:04.220 ⇒ 00:10:09.959 Awaish Kumar: Yeah. So now the weight is rounded and and but the shipping cost is exactly as in the sheet
89 00:10:10.490 ⇒ 00:10:13.300 Robert Tseng: Yeah. And just confirm we’re rounding up on the weight right
90 00:10:14.846 ⇒ 00:10:18.110 Awaish Kumar: Okay for everything like, if it is 1.1
91 00:10:21.120 ⇒ 00:10:23.349 Robert Tseng: Yeah. 1.1 should be rounded up to 2
92 00:10:23.690 ⇒ 00:10:25.920 Awaish Kumar: Okay, okay, I will confirm that
93 00:10:26.630 ⇒ 00:10:31.230 Robert Tseng: Yeah, that’s that’s typically how the the 3 pls will work.
94 00:10:34.550 ⇒ 00:10:37.809 Robert Tseng: So unless we’re told differently. Now, that that should be the logic.
95 00:10:39.620 ⇒ 00:10:44.049 Robert Tseng: Okay. So I know this is really just for shopify on the Amazon side.
96 00:10:44.180 ⇒ 00:10:51.410 Robert Tseng: I think we’re still waiting from them to like, kind of get a version of this like skews, and all these assumptions on Amazon side.
97 00:10:52.096 ⇒ 00:10:53.679 Robert Tseng: So I think eventually.
98 00:10:56.350 ⇒ 00:11:06.380 Robert Tseng: actually, we should, you know, on top of your head. Does this include Amazon skews. I know they’re like. Some of the other assumptions are probably different, because they kept saying they wanted a different source for Amazon
99 00:11:11.740 ⇒ 00:11:29.119 Caio Velasco: I’m not sure if it helps. But when I was dealing with the total product costs and the Cox protocols issue the fact orders table. It’s a union all between stuff from Amazon and stuff from shopify. And within the Amazon part.
100 00:11:29.904 ⇒ 00:11:33.540 Caio Velasco: I believe there was a calculation, a calculation happening.
101 00:11:34.025 ⇒ 00:11:45.159 Caio Velasco: Then I don’t know. I would assume that it comes from, and it comes from these tables, but I’m not sure if it’s from a shopify one, or from Amazon one. I I’m sure they come from this one.
102 00:11:45.400 ⇒ 00:11:49.589 Caio Velasco: If it’s all shopify, then. Yeah, then I don’t know
103 00:11:50.500 ⇒ 00:11:51.110 Robert Tseng: Okay.
104 00:11:51.320 ⇒ 00:11:57.429 Robert Tseng: yeah, it makes sense to me that it should come from the same source. But the calculation is probably different.
105 00:11:59.000 ⇒ 00:12:05.930 Robert Tseng: yeah. But okay, I mean, that’s something I want to clarify with. With them on. So
106 00:12:09.590 ⇒ 00:12:13.019 Caio Velasco: And these spreadsheets were were done by them manually
107 00:12:14.040 ⇒ 00:12:22.699 Robert Tseng: Yeah, this is their manually. I mean, they say they don’t update this. I mean, I think it’s we have a process issue where we don’t know how frequently they update this
108 00:12:22.930 ⇒ 00:12:29.373 Robert Tseng: away, should found that there’s a lot of duplicates here, and that’s what was throwing off for calculation as well. So,
109 00:12:29.850 ⇒ 00:12:32.990 Robert Tseng: yeah, I think this is just a reminder to me that we need to.
110 00:12:33.450 ⇒ 00:12:37.110 Robert Tseng: We yeah, we need to kind of set up better process with them.
111 00:12:38.350 ⇒ 00:12:39.200 Caio Velasco: Great.
112 00:12:52.510 ⇒ 00:13:04.770 Caio Velasco: And since these are all assumptions and and and I think things that we are hard coding, maybe somehow I could put this in notion as well as I was doing for Amazon, and I discovered some things
113 00:13:05.199 ⇒ 00:13:17.480 Caio Velasco: maybe like we can take this as a documentation. But at the end of the day. Maybe it helps us avoid inefficiencies when either, like Amy was new wanted to know something, or myself, or a new person in the future
114 00:13:18.288 ⇒ 00:13:26.180 Caio Velasco: or even to make sure that we are. I mean that we know all definitions and and that we are using the correct ones.
115 00:13:26.870 ⇒ 00:13:28.910 Caio Velasco: Not sure if yeah, totally do that.
116 00:13:29.120 ⇒ 00:13:29.740 Caio Velasco: Okay.
117 00:13:29.740 ⇒ 00:13:32.377 Robert Tseng: Yeah, no, that- that we should. We could do.
118 00:13:34.273 ⇒ 00:13:44.619 Robert Tseng: yeah. Cause that impacts the way that we kind of do analysis on this for any anything related to product revenue or profitability. This is the this is one of the key sources.
119 00:13:47.000 ⇒ 00:13:51.670 Robert Tseng: Okay, so I’m gonna post that update. And then let’s jump to.
120 00:13:51.910 ⇒ 00:13:57.679 Robert Tseng: So on the Amazon side, we have a couple of things that are in progress. Yeah. So
121 00:13:57.860 ⇒ 00:14:10.780 Robert Tseng: basically, the takeaway kind of shared some of Kyle’s findings, I think what wants is as a next step. I’ll probably close out these tickets. I’m not really sure if there’s anything left for the folks to
122 00:14:10.880 ⇒ 00:14:13.020 Robert Tseng: for you guys to do here.
123 00:14:14.210 ⇒ 00:14:15.080 Robert Tseng: But
124 00:14:15.280 ⇒ 00:14:23.279 Robert Tseng: we’re gonna put all your findings into a single doc. But then the main value is, we need to give them on some recommendations on how to address Amazon. Cancel orders.
125 00:14:24.064 ⇒ 00:14:27.735 Robert Tseng: So I think that’s that’s kind of on me to kind of figure that out.
126 00:14:28.200 ⇒ 00:14:32.200 Robert Tseng: so I think after that we should be able to close close this out.
127 00:14:35.830 ⇒ 00:14:41.099 Robert Tseng: Yeah, subscribe and save. Yep. Go ahead.
128 00:14:41.448 ⇒ 00:14:45.250 Annie Yu: Is there any way you can zoom in the
129 00:14:45.250 ⇒ 00:14:46.280 Robert Tseng: Oh, yeah, sure.
130 00:14:46.280 ⇒ 00:14:47.050 Annie Yu: Thank you.
131 00:14:53.040 ⇒ 00:14:59.245 Robert Tseng: So I know we had a couple things due in cycle here. So the Amazon skew cogs.
132 00:15:00.490 ⇒ 00:15:04.090 Robert Tseng: I is this the same 30?
133 00:15:07.476 ⇒ 00:15:14.250 Robert Tseng: Yeah, okay. So, Kyle, I guess to your point, this is a separate Amazon Steve Sheet. So
134 00:15:14.380 ⇒ 00:15:15.440 Caio Velasco: Oh, okay.
135 00:15:16.150 ⇒ 00:15:16.770 Robert Tseng: Yeah.
136 00:15:17.940 ⇒ 00:15:25.969 Robert Tseng: I don’t know if we’re really using this, though, or if they just sent this to me, and we’re not actually using this yet so. Oh, wait! Have you seen this before
137 00:15:30.755 ⇒ 00:15:31.370 Awaish Kumar: No
138 00:15:32.700 ⇒ 00:15:35.818 Robert Tseng: Okay, well, I mean, this looks kind of messy. But
139 00:15:36.550 ⇒ 00:15:42.600 Robert Tseng: yeah. So I think they sent us Amazon skewed data here. So
140 00:15:44.880 ⇒ 00:15:51.109 Robert Tseng: we’ll need to somehow bring this into the same model as what we’re doing here. So
141 00:15:51.470 ⇒ 00:15:59.479 Robert Tseng: I think there’s actually a bit more work beyond this piece before we can really unblock the skew calls reporting.
142 00:16:05.000 ⇒ 00:16:08.599 Robert Tseng: yeah, me make a model. So
143 00:16:26.650 ⇒ 00:16:29.340 Robert Tseng: I’ll be able to assign this one to wish. Probably.
144 00:16:29.710 ⇒ 00:16:30.500 Robert Tseng: Yeah,
145 00:16:37.990 ⇒ 00:16:43.100 Robert Tseng: and yes, I wish I know you’re blocked until I get these questions answered. So we’ll we’ll kind of talk through that.
146 00:16:45.670 ⇒ 00:16:48.635 Robert Tseng: Okay, so that’s that. And then,
147 00:16:50.610 ⇒ 00:17:06.170 Robert Tseng: yeah, I know, Annie, I have this assigned to you as filtering by subscribe and save. Yeah, it seems like we’re running into issues with adding in the subscribe and save Field. I saw the the 5 message so what I’ve asked Amon. And Channel, that you guys can see is
148 00:17:06.440 ⇒ 00:17:23.660 Robert Tseng: we’re just trying to find a different proxy. For to get this feature flag, we we basically Amazon’s not gonna give us like a tag that’s subscribe and say, so. But this is really important to the job team. So I’m I’m kind of putting on our mind to like. Send us the list of subscribe and save orders
149 00:17:23.750 ⇒ 00:17:37.530 Robert Tseng: that we can look for any patterns to see if, like, there’s another way that we can label them, maybe subscribe and save orders have like something specific in their in their order. Id that helps differentiate them. That would be convenient.
150 00:17:38.312 ⇒ 00:17:49.209 Robert Tseng: But if they don’t, then maybe we yeah, we just we have to kind of see if it’s if it’s worth continuing to push on that so that’s what’s remaining on the Amazon side.
151 00:17:55.280 ⇒ 00:17:56.270 Robert Tseng: Okay.
152 00:17:57.720 ⇒ 00:18:05.121 Robert Tseng: so with that I’ll just jump into other stuff that’s in the current cycle. So we will start with stuff that’s been escalated.
153 00:18:06.790 ⇒ 00:18:13.240 Robert Tseng: I think this is good. Now, the the previous post payment question. So I’m gonna just call that done
154 00:18:16.250 ⇒ 00:18:23.570 Robert Tseng: And then fixing the product shipping cost, casting error. I believe you did this already. So I’m gonna
155 00:18:23.840 ⇒ 00:18:25.240 Robert Tseng: call that done
156 00:18:26.230 ⇒ 00:18:36.299 Robert Tseng: the duplicate products for order. You also dealt with that. So we’re good there consolidating platform fees. You’re still kind of in progress there. So I’ll do that for now.
157 00:18:40.740 ⇒ 00:18:51.339 Robert Tseng: yeah. So attentive data, we’re also running to issues with not being able to pull in missing data from portable. And so we were gonna go check 5 tran. Where are we on that
158 00:18:51.970 ⇒ 00:18:55.139 Awaish Kumar: Yeah, I I will check it today
159 00:18:55.590 ⇒ 00:19:02.219 Awaish Kumar: to connect it to using the 5, 10. And if I if there’s data, I will just send it to Oman, as well
160 00:19:02.810 ⇒ 00:19:03.380 Robert Tseng: Yep.
161 00:19:09.790 ⇒ 00:19:21.689 Robert Tseng: okay. And then, yeah, this is on me to close out the Amazon investigation that I think Kyle’s work pretty much got us 80% of the way there, I just need to come up with some specific recommendations.
162 00:19:23.620 ⇒ 00:19:29.270 Robert Tseng: hopefully, Kyle, you know enough about Amazon data. So if I have any questions I will kind of ask you. But
163 00:19:29.610 ⇒ 00:19:53.569 Robert Tseng: I feel like my approach here is gonna be well, we know what the Amazon cancellation order rate is we can’t separate out subscriber save customers. And so I’m just gonna go look at the cancel orders to try to run some analysis on. If there are any other like patterns to this, I’ll probably compare it to the shopify cancel shopify customers and and do some comparative analysis across those platforms.
164 00:19:54.123 ⇒ 00:20:03.200 Robert Tseng: Ideally, I will find something that tells me, hey, Amazon customers or cancel more because.
165 00:20:03.370 ⇒ 00:20:10.470 Robert Tseng: or on specific products, or at a particular price, or whatever it is. I think that’s kind of what
166 00:20:11.141 ⇒ 00:20:14.858 Robert Tseng: I’m hoping to get out of of my my analysis.
167 00:20:15.630 ⇒ 00:20:27.975 Robert Tseng: yeah. And then maybe there’s something with the order statuses based on kind of what you discovered. Maybe the cancellations are happening post payment
168 00:20:28.690 ⇒ 00:20:51.960 Robert Tseng: or sorry if cancellations are happening prepayment. Then maybe it’s just like a a different way that Amazon is calculating cancellation rates compared to shopify the shopify cancellations are post payment, and if Amazon is prepayment, then that could be a significant discrepancy. And we need to do like a we need to like be looking at it from the same perspective. So
169 00:20:52.850 ⇒ 00:20:53.600 Robert Tseng: like
170 00:20:53.760 ⇒ 00:21:05.369 Robert Tseng: either Amazon cancellations are being inflated or like shopify should be inflated. So I’m just talking through at a high level like, that’s kind of how I’m gonna approach that investigation
171 00:21:05.880 ⇒ 00:21:17.259 Caio Velasco: 1. 1 question that I also thought about this morning is the when we say payment here? Is it on the buyer side, on the seller side, meaning generally.
172 00:21:17.450 ⇒ 00:21:18.149 Caio Velasco: because this is also
173 00:21:18.730 ⇒ 00:21:21.230 Caio Velasco: Because it happens in a different, in different ways.
174 00:21:22.260 ⇒ 00:21:31.079 Robert Tseng: Yeah. Well, I guess your conclusion was on the buyer side, right? So before the paint buyer’s payment goes through, that’s when it has. Yeah.
175 00:21:31.370 ⇒ 00:21:32.560 Caio Velasco: So.
176 00:21:33.280 ⇒ 00:21:39.939 Robert Tseng: Yeah, I don’t. I don’t. I think it’s I think that’s the case. It should always be on the buyer side. We don’t really know the seller side. Yeah.
177 00:21:41.720 ⇒ 00:21:42.350 Caio Velasco: Cool.
178 00:21:42.350 ⇒ 00:21:42.970 Caio Velasco: Okay.
179 00:21:45.190 ⇒ 00:21:46.000 Robert Tseng: Yeah.
180 00:21:46.660 ⇒ 00:21:54.679 Robert Tseng: Move on to that. Okay, Annie, how about the monthly cohort? I don’t. Oh, sorry I didn’t review this comment.
181 00:21:54.680 ⇒ 00:21:59.110 Annie Yu: Oh, no, that’s that’s some notes for myself. I
182 00:21:59.390 ⇒ 00:22:04.820 Annie Yu: I did figure out kind of how they calculate things, and I think we can
183 00:22:04.920 ⇒ 00:22:10.860 Annie Yu: with what we have in the tape, in the, in the models. I think I can
184 00:22:11.260 ⇒ 00:22:15.919 Annie Yu: get something pretty close to that view, even though I probably have
185 00:22:16.080 ⇒ 00:22:18.863 Annie Yu: with them, split them and
186 00:22:20.580 ⇒ 00:22:30.379 Annie Yu: in like a couple more different views. Given that there are like combining different metrics together. But I think we do have everything except one thing.
187 00:22:30.750 ⇒ 00:22:38.600 Annie Yu: that is the Cac. The customer acquisition cost. And is that something?
188 00:22:40.260 ⇒ 00:22:46.180 Annie Yu: We have any formula, or they are like looking to add to their metric
189 00:22:46.180 ⇒ 00:22:57.629 Robert Tseng: Yeah, you won’t be able to do that until we do the north beam reporting it should be already in North Beam. We can calculate tech. I would say, just for your purposes, you should just focus on getting
190 00:22:57.900 ⇒ 00:23:02.910 Robert Tseng: margin per customer and like sales per customer. We don’t have to worry about that
191 00:23:02.910 ⇒ 00:23:07.450 Annie Yu: I think we’re all set, and I think we can probably put that
192 00:23:10.120 ⇒ 00:23:19.331 Annie Yu: when we say tomorrow. Does that mean it? It should be done by today before tomorrow, or that means I’ll get it done within tomorrow.
193 00:23:19.972 ⇒ 00:23:21.880 Robert Tseng: Yeah, it’s usually just by by tomorrow.
194 00:23:22.430 ⇒ 00:23:36.330 Annie Yu: Okay, yeah, I think I can get that and one also, like clarifying question, I know that here gross margin, do we want that margin in percentage, or we want the gross profit
195 00:23:36.910 ⇒ 00:23:37.870 Annie Yu: in dollars
196 00:23:37.870 ⇒ 00:23:46.159 Robert Tseng: That’s that’s a good question. I mean, I, as I’ve been doing both the percentage and the dollar. So maybe it’s just 2 charts dollar and percent
197 00:23:47.440 ⇒ 00:23:50.499 Annie Yu: Okay. So that means we probably would have 3.
198 00:23:50.770 ⇒ 00:23:51.639 Annie Yu: Is that it?
199 00:23:51.640 ⇒ 00:23:55.250 Annie Yu: It’s fine. Yeah, alright, I’ll I’ll do that
200 00:23:56.390 ⇒ 00:23:57.000 Robert Tseng: Great.
201 00:23:58.650 ⇒ 00:24:07.070 Robert Tseng: Okay? And then, yeah, we have migrating dashboards in the database source. I’m assuming. That’s I mean
202 00:24:07.070 ⇒ 00:24:10.889 Annie Yu: Yeah, that one like it just takes time. And I I
203 00:24:10.890 ⇒ 00:24:14.363 Robert Tseng: Yeah, I’ll I’ll just set time for
204 00:24:14.860 ⇒ 00:24:16.562 Annie Yu: It takes time.
205 00:24:17.130 ⇒ 00:24:21.299 Robert Tseng: Yeah, I’ll I’ll set that out for next by next Tuesday or something
206 00:24:21.300 ⇒ 00:24:22.460 Annie Yu: Alright! Alright! Thanks!
207 00:24:24.210 ⇒ 00:24:32.839 Robert Tseng: And then oh, I didn’t touch on this recharge Api impact on data ingestion. I assign this to myself. Honestly, don’t remember.
208 00:24:33.030 ⇒ 00:24:38.749 Robert Tseng: Kyle had this investigation. How do you can you remind me? Was, what was the action for this
209 00:24:41.770 ⇒ 00:24:53.729 Caio Velasco: Yeah, I think, the last one to to touch was a week, because we were talking with Ethan to understand. How were they updating the Api, and I believe we are. We want to do some kind of incremental
210 00:24:54.870 ⇒ 00:24:56.999 Robert Tseng: All right. This is incremental, refreshing, right?
211 00:24:57.000 ⇒ 00:24:59.170 Caio Velasco: Yes, exactly on the Portugal side
212 00:25:00.490 ⇒ 00:25:04.710 Robert Tseng: Okay. So not really any action for us. Are are you waiting on his response, or like what?
213 00:25:04.820 ⇒ 00:25:06.350 Robert Tseng: I don’t remember
214 00:25:08.005 ⇒ 00:25:14.019 Caio Velasco: I wish did we make any decision in in about that? Yeah, that’s the one
215 00:25:15.070 ⇒ 00:25:15.610 Robert Tseng: Yeah.
216 00:25:15.610 ⇒ 00:25:27.730 Awaish Kumar: There is a like yesterday, we added a comment there like we need to confirm it with the either like client or that if we we are. We want to call.
217 00:25:28.350 ⇒ 00:25:30.290 Awaish Kumar: If you want to maintain the copy
218 00:25:30.600 ⇒ 00:25:33.420 Awaish Kumar: of the data in our warehouse.
219 00:25:34.300 ⇒ 00:25:34.950 Robert Tseng: Yeah.
220 00:25:36.230 ⇒ 00:25:38.113 Robert Tseng: So you know, I think
221 00:25:39.260 ⇒ 00:25:47.080 Robert Tseng: yeah, may make sure. So I mean, I wish you were on the call with Aman yesterday. This is kind of zooming out a bit. But if we just kind of go to his message here.
222 00:25:47.270 ⇒ 00:25:51.290 Robert Tseng: yeah, he had a comment here about portable sync frequency.
223 00:25:53.430 ⇒ 00:25:54.390 Robert Tseng: I think
224 00:25:55.280 ⇒ 00:26:10.779 Robert Tseng: I don’t think our snowflake costs are gonna go up much like they’re pretty predictable, like they’re not moving. They’re not storing that much data. But he wanted to know, like, how much does it cost for us to to shift to more like to more frequent updates? So if we do, incremental refreshes.
225 00:26:10.920 ⇒ 00:26:13.660 Robert Tseng: you know, that’s more frequent syncing. But
226 00:26:13.810 ⇒ 00:26:24.959 Robert Tseng: obviously, the data that’s moving is is smaller. So I mean, I think, typically who Tom has been the one to take this on. But away. Since you’re kind of like on your kind of taking that role right now.
227 00:26:25.530 ⇒ 00:26:35.910 Robert Tseng: do you feel like you have enough to be able to kind of answer this question, for, like I’ve handled this section for Vermont, because I think this is directly related to his recharge question. Here
228 00:26:36.370 ⇒ 00:26:51.780 Awaish Kumar: Yeah, it’s it’s actually not about the frequency. Right like this ticket is more about creating incremental models like creating a copy of the data in our data warehouse such that we don’t lose data if
229 00:26:52.120 ⇒ 00:26:56.679 Awaish Kumar: if portable, mess up in something right? So
230 00:26:57.160 ⇒ 00:26:59.829 Awaish Kumar: like if they run full refresh on the data.
231 00:27:00.030 ⇒ 00:27:02.399 Awaish Kumar: And and they
232 00:27:02.560 ⇒ 00:27:20.789 Awaish Kumar: and we because of that, we lose some data. So to avoid that situation for recharge, we just want to create a copy of the data kind of you can say it like backup, or whatever so like. We are not deciding on frequency right now. It may be once a day, or it can be
233 00:27:21.880 ⇒ 00:27:26.100 Awaish Kumar: it. It is enough if it’s just one once a day for this ticket.
234 00:27:26.100 ⇒ 00:27:26.680 Robert Tseng: Yeah.
235 00:27:27.370 ⇒ 00:27:53.830 Robert Tseng: okay, no, I I understand. Yeah. So I’ll consolidate this. I’ll I’ll make. I’ll make him on. Give the answer. I think he will, I’ll phrase, as a recommendation, I think he’ll just say, Okay, like doesn’t really understand, anyway. But yeah, I think later on, this will ladder up to. Okay. Well, for refresh or sorry for recharge and all these other sources. How how often do you want to be syncing them? But I understand, for now, we’re just talking about whether we how we handle the incremental and storing another copy.
236 00:27:55.740 ⇒ 00:27:58.380 Robert Tseng: Okay, let’s just kind of keep going through here.
237 00:27:58.380 ⇒ 00:27:58.850 Awaish Kumar: Yes.
238 00:27:58.850 ⇒ 00:27:59.490 Robert Tseng: Other things that
239 00:27:59.490 ⇒ 00:28:02.350 Awaish Kumar: And it’s it’s a issue. Particular.
240 00:28:04.920 ⇒ 00:28:05.770 Awaish Kumar: Sorry?
241 00:28:06.090 ⇒ 00:28:07.819 Awaish Kumar: I was saying, yeah, yeah.
242 00:28:07.820 ⇒ 00:28:28.659 Awaish Kumar: particular to recharge, because recharge now is not sending all of the data through the Api. They are just sending 90 days of data in the past. So if we lose the data and we want to recover all the data we cannot from their Api. So this is like this unique situation for recharge. And we want to handle that
243 00:28:29.740 ⇒ 00:28:32.899 Robert Tseng: Okay? Noted. Yeah, I’ll get a month to sign up on that.
244 00:28:33.773 ⇒ 00:28:44.380 Robert Tseng: Okay, things that are blocked. So this is the same thing, this subscribe and save issue tick, tock still still blocked? Are we just like stuck like? Is there anything we can do? Here?
245 00:28:50.900 ⇒ 00:28:57.980 Robert Tseng: Are we just like messaging portable, and we’re just following up with them every like every day, or like kind of what? What’s the what’s the like? The action here
246 00:28:59.020 ⇒ 00:29:05.762 Awaish Kumar: Yeah, right now we are blocked on the portable. I can further discuss with autumn if if we can.
247 00:29:06.390 ⇒ 00:29:14.760 Awaish Kumar: if you want me to like what you say, research, other tools. If any other tool provides us, the connector, we need.
248 00:29:15.120 ⇒ 00:29:15.970 Awaish Kumar: Yeah.
249 00:29:16.240 ⇒ 00:29:16.950 Aakash Tandel: Yeah.
250 00:29:16.950 ⇒ 00:29:32.309 Robert Tseng: I just. I want Ethan to commit to a timeline here like I feel like we asked him for Tiktok like 2 months ago. I have no idea what he’s doing, so we I would like. I would like at least portable to give us an estimate, and if not, then we can go figure out and get it from somewhere else.
251 00:29:33.210 ⇒ 00:29:34.330 Awaish Kumar: Okay. Sure.
252 00:29:35.150 ⇒ 00:29:35.800 Aakash Tandel: Yeah.
253 00:29:35.800 ⇒ 00:29:36.889 Awaish Kumar: I want to look at that
254 00:29:36.890 ⇒ 00:29:49.489 Aakash Tandel: And if it’s like not possible with portable, then we can just communicate to that client and be like, Hey, look! We need to find another tool. Are you cool with us investigating that? That’s probably how we want to handle that just so that they know that we’re using resources to figure out an alternative
255 00:29:50.080 ⇒ 00:29:50.660 Robert Tseng: Yeah.
256 00:29:53.650 ⇒ 00:30:09.810 Robert Tseng: okay? And then the last thing is with the address matching. So I think, Aman kind of reiterated yesterday. I think it’s here that he just wants the python script, and so he will go and build the Ui with his team. So we need to get this ready to hand off. We’re not going to have any run these weekly
257 00:30:13.110 ⇒ 00:30:13.960 Robert Tseng: address.
258 00:30:14.760 ⇒ 00:30:19.390 Aakash Tandel: And those are Pious’s script
259 00:30:19.850 ⇒ 00:30:20.225 Robert Tseng: Yep.
260 00:30:20.970 ⇒ 00:30:26.569 Aakash Tandel: Okay, cool, maybe. Yeah. I’ll just. I can handle that. I’ll sync up with Pius
261 00:30:30.160 ⇒ 00:30:42.450 Robert Tseng: I know, like probably complicate things by saying, oh, he can prove accuracy and stuff still, but just like take it as is and give it and just give it to a month. That’s all they really want. Right now, we don’t need to optimize it anymore for them.
262 00:30:42.880 ⇒ 00:30:46.008 Robert Tseng: Okay, I’m gonna reassign this to
263 00:30:48.670 ⇒ 00:30:49.480 Aakash Tandel: Yeah.
264 00:30:49.480 ⇒ 00:30:50.860 Robert Tseng: Yeah. And
265 00:30:53.110 ⇒ 00:31:02.289 Robert Tseng: alright, well, I know we’re at time. There’s a few things to do in cycle. We haven’t really started these yet, so I think the macro name stream operations. This is
266 00:31:02.730 ⇒ 00:31:05.159 Robert Tseng: done. Question Mark.
267 00:31:09.160 ⇒ 00:31:14.680 Robert Tseng: or I guess we started on this like, I know, yeah, which kind of where are we on this?
268 00:31:17.050 ⇒ 00:31:19.139 Robert Tseng: I think you’re you’re already working on it right
269 00:31:21.270 ⇒ 00:31:22.290 Awaish Kumar: Sorry, who
270 00:31:22.830 ⇒ 00:31:26.139 Robert Tseng: This. Yeah, this is the kind of the
271 00:31:26.910 ⇒ 00:31:32.719 Robert Tseng: the gorgeous field. Kind of specifically for the test. Save attempt.
272 00:31:32.990 ⇒ 00:31:33.810 Robert Tseng: Yeah.
273 00:31:34.730 ⇒ 00:31:46.799 Awaish Kumar: Yeah, I I added a model and it is merged. So yeah, and and I added it the way we discussed it in the call. So maybe if any can have a look, and
274 00:31:47.030 ⇒ 00:31:49.910 Awaish Kumar: when it verifies that it, it looks good.
275 00:31:50.258 ⇒ 00:31:52.089 Awaish Kumar: then we are. We are good on it.
276 00:31:53.390 ⇒ 00:31:57.550 Robert Tseng: Okay, I’m gonna move it to testing. And then I think that kind of
277 00:31:59.570 ⇒ 00:32:04.929 Robert Tseng: yeah, I suppose. Yeah. Anyway, if you can review that for him, and then
278 00:32:05.520 ⇒ 00:32:09.829 Robert Tseng: just see if you can update your your report with that, too.
279 00:32:14.260 ⇒ 00:32:20.170 Robert Tseng: Yeah. So I’m gonna bring this back to in progress.
280 00:32:20.370 ⇒ 00:32:25.430 Robert Tseng: So I’m gonna call this any to update with new model.
281 00:32:29.530 ⇒ 00:32:31.260 Robert Tseng: Yeah, does that sound good
282 00:32:33.490 ⇒ 00:32:34.570 Annie Yu: Yeah.
283 00:32:38.060 ⇒ 00:32:42.219 Robert Tseng: There’s not. Yeah, you can. You can just duplicate it. Don’t don’t like
284 00:32:42.800 ⇒ 00:32:57.259 Robert Tseng: I don’t want the current dashboard to break, like I don’t think they’ve looked into too much, but this is something that we should do because they can’t drill down into tickets right now. So the idea is by adopting oasis model fix like you’ll be able to do it.
285 00:32:57.855 ⇒ 00:33:02.629 Robert Tseng: Not using the custom sequel, and you can just do it with the database, or like the Ui
286 00:33:03.150 ⇒ 00:33:04.139 Annie Yu: Yep. Yeah.
287 00:33:04.890 ⇒ 00:33:08.860 Robert Tseng: Okay, okay, cool.
288 00:33:10.020 ⇒ 00:33:14.310 Robert Tseng: No, we’re at time. But if we have another minute.
289 00:33:15.480 ⇒ 00:33:24.600 Robert Tseng: yeah, there’s a couple of things that I added to in cycle. One is on the North Beam side. I think this was kind of brought in as a higher priority. So I I put this on your plate, Annie.
290 00:33:25.590 ⇒ 00:33:31.319 Robert Tseng: I think this is I mean, I realistically, I I whatever you’re working on, we’ll we’ll just set this for in a week.
291 00:33:33.160 ⇒ 00:33:33.900 Robert Tseng: But
292 00:33:35.030 ⇒ 00:33:55.199 Robert Tseng: yeah, I think there’s a there’s, there’s an amplitude dashboard here. You can click and look at it. We just basically want to replicate what they have there, using the north beam data that we supposedly have already. So you might run into some issues where the North beam data we have is not complete. But I think nobody has really built any reporting on it yet, so you’ll kind of be the 1st one to see it
293 00:33:55.790 ⇒ 00:34:02.520 Annie Yu: Okay? And do you have an idea which data table I will be using for this one
294 00:34:03.860 ⇒ 00:34:10.210 Robert Tseng: Yeah. I guess. Who who made the North beam models was it was that? Was that Gutam
295 00:34:11.020 ⇒ 00:34:12.729 Robert Tseng: or Luke a wish
296 00:34:14.699 ⇒ 00:34:19.589 Awaish Kumar: Yeah, I’ve worked with north data for for Javi. So I’m
297 00:34:19.760 ⇒ 00:34:22.500 Robert Tseng: Oh, you did. Okay, yeah. So, and he just
298 00:34:22.500 ⇒ 00:34:23.070 Awaish Kumar: Something
299 00:34:23.070 ⇒ 00:34:24.720 Robert Tseng: Oh, you did not. Oh, okay,
300 00:34:27.739 ⇒ 00:34:32.880 Robert Tseng: yeah. Let’s and if you could connect with Utam on this.
301 00:34:33.100 ⇒ 00:34:35.529 Robert Tseng: I think this is like something that he and
302 00:34:39.760 ⇒ 00:34:40.949 Robert Tseng: Luke did
303 00:34:42.550 ⇒ 00:34:43.239 Annie Yu: Okay.
304 00:34:44.650 ⇒ 00:34:53.634 Robert Tseng: Yeah, okay? And then the last piece?
305 00:34:54.270 ⇒ 00:35:05.150 Robert Tseng: yeah, I mean, I think that’s it. There’s there’s a couple of things that we’re planning for the upcoming projects. But that’s, I guess, for me and Akash to work through. This is pretty much going to be our scope for the remaining month of this contract.
306 00:35:05.518 ⇒ 00:35:11.490 Robert Tseng: I guess the heads up to the team is they did hire another analyst. And so they’re gonna that’s they’re gonna be on the channel.
307 00:35:11.660 ⇒ 00:35:18.367 Robert Tseng: We’re trying to work towards a place where we continue to stay on as a partner, obviously. But
308 00:35:18.860 ⇒ 00:35:41.550 Robert Tseng: I think they just needed somebody who can knock out like small report changes on a daily base on a daily same day turnaround. And that’s not what our team does. So they just hired somebody who’s gonna like change fields make some quick reports and stuff. So we’ll have to do a process of like handing off knowledge to them. So maybe a couple of you will be brought in to do trainings with Aman and the new analyst.
309 00:35:42.330 ⇒ 00:35:49.620 Robert Tseng: And then, yeah, we’re gonna try to figure out what our our role with them. Moving forward at the end of our contract, looks like
310 00:35:51.810 ⇒ 00:36:04.859 Aakash Tandel: Yeah. And you know, it sounds like that person will be able to take on a lot of that like ready ready reporting work. But there’s a lot of other kind of more data modeling, more probably data, heavy things that we do. So I
311 00:36:04.860 ⇒ 00:36:05.220 Robert Tseng: Yeah.
312 00:36:05.390 ⇒ 00:36:07.220 Aakash Tandel: A terrible sign, or anything
313 00:36:07.770 ⇒ 00:36:20.509 Robert Tseng: Yeah, no, I I think that frees our team up to be doing more analysis, like answering the Amazon question, and less like building random reports. So I think, yeah, just giving the team a heads up. But that’s what’s coming
314 00:36:23.370 ⇒ 00:36:37.289 Caio Velasco: Robert. One last thing is that I think I don’t have anything now other than what I mentioned. If you want me to put some time documenting the sources. For example, the the 3 pl assumptions and those things.
315 00:36:37.540 ⇒ 00:36:39.139 Caio Velasco: Or should I do something else
316 00:36:40.040 ⇒ 00:36:42.099 Robert Tseng: Yeah, I think
317 00:36:42.410 ⇒ 00:36:50.549 Robert Tseng: I will have Akash make that call. I know I signed some stuff to a wish, so maybe I think Akash has a better understanding of what’s on a wishes plate. So
318 00:36:50.854 ⇒ 00:36:53.189 Robert Tseng: maybe we’ll we’ll message you if we want to tag you in
319 00:36:53.870 ⇒ 00:36:54.230 Aakash Tandel: Yeah.
320 00:36:54.650 ⇒ 00:37:00.099 Aakash Tandel: I’m kind of swamped in meetings this morning, Kyle, but I I will get you stuff to to work on for the rest of the week, too.
321 00:37:00.660 ⇒ 00:37:02.359 Caio Velasco: No worries. Thank you very much.
322 00:37:02.700 ⇒ 00:37:03.480 Aakash Tandel: Thank you.
323 00:37:03.770 ⇒ 00:37:04.730 Aakash Tandel: Thanks. Y’all.
324 00:37:04.980 ⇒ 00:37:05.540 Annie Yu: Thank you.
325 00:37:05.860 ⇒ 00:37:06.370 Annie Yu: Thanks. Everyone.
326 00:37:06.370 ⇒ 00:37:06.710 Caio Velasco: Thank you.