Meeting Title: [Javvy] Daily Standup Date: 2025-03-26 Meeting participants: Annie Yu, Aakash Tandel, Robert Tseng, Awaish Kumar, Caio Velasco
WEBVTT
1 00:00:46.130 ⇒ 00:00:47.540 Annie Yu: Morning Akash
2 00:00:47.540 ⇒ 00:00:48.940 Aakash Tandel: Morning, Andy, how are you doing
3 00:00:49.450 ⇒ 00:00:50.649 Annie Yu: Good! How are you?
4 00:00:50.820 ⇒ 00:00:53.095 Aakash Tandel: Not too bad, not too bad at all.
5 00:00:57.090 ⇒ 00:00:59.580 Aakash Tandel: We’ll give everyone else a second. Join
6 00:00:59.760 ⇒ 00:01:06.490 Annie Yu: I I feel like I still haven’t figured out everyone’s role on this project. So you are the project manager. Is that correct?
7 00:01:06.490 ⇒ 00:01:07.110 Aakash Tandel: Yep.
8 00:01:07.470 ⇒ 00:01:09.960 Annie Yu: Okay, okay, are you on other projects
9 00:01:09.980 ⇒ 00:01:11.860 Aakash Tandel: Yeah. I’m also working with Eden
10 00:01:12.130 ⇒ 00:01:16.485 Annie Yu: Okay, okay. So I guess the 2 most busiest.
11 00:01:16.970 ⇒ 00:01:22.795 Aakash Tandel: Yes, yeah, the 2 that are. There’s a lot of swirl
12 00:01:23.646 ⇒ 00:01:24.580 Annie Yu: Going on.
13 00:01:24.770 ⇒ 00:01:25.620 Annie Yu: Gotcha
14 00:01:29.690 ⇒ 00:01:30.460 Aakash Tandel: Okay.
15 00:01:31.060 ⇒ 00:01:42.600 Aakash Tandel: yeah. And I’ll put some time on your calendar just to to meet and chat. I’ll probably do that with some. Everyone on the team. Just to make sure everything’s working for you and stuff like that. So
16 00:01:43.430 ⇒ 00:01:44.620 Annie Yu: Yeah. Awesome.
17 00:01:45.330 ⇒ 00:01:46.090 Aakash Tandel: Hey Kyle.
18 00:01:46.450 ⇒ 00:01:47.430 Caio Velasco: Anyways.
19 00:01:47.590 ⇒ 00:01:48.550 Annie Yu: Hello!
20 00:01:48.920 ⇒ 00:01:49.220 Caio Velasco: No.
21 00:01:51.930 ⇒ 00:01:52.390 Awaish Kumar: Hello!
22 00:01:53.730 ⇒ 00:02:06.780 Aakash Tandel: All right. So let’s go ahead and do stand up. So I pulled up the cycle. We can start with Aish because we started with Annie last time, and then we’ll move reverse order. Where do you want to start
23 00:02:09.509 ⇒ 00:02:11.678 Awaish Kumar: What I want to talk about
24 00:02:13.299 ⇒ 00:02:17.879 Awaish Kumar: So this new bug, the one which is created, it is done.
25 00:02:17.999 ⇒ 00:02:22.885 Awaish Kumar: I have the updated the changes in database. And
26 00:02:23.699 ⇒ 00:02:25.529 Awaish Kumar: it looks good to me. Yeah.
27 00:02:25.900 ⇒ 00:02:26.610 Aakash Tandel: Awesome.
28 00:02:27.030 ⇒ 00:02:29.389 Aakash Tandel: I will mark that as done. Okay.
29 00:02:29.390 ⇒ 00:02:35.549 Caio Velasco: Just a quick question to a wish in this one, just so that I have an understanding of what you do in case
30 00:02:35.550 ⇒ 00:02:36.690 Awaish Kumar: Saw your message
31 00:02:39.960 ⇒ 00:02:44.270 Caio Velasco: Yeah. And what did you do in metabase was just to change in the sequel code over there
32 00:02:46.650 ⇒ 00:02:50.859 Awaish Kumar: No, it’s actually the the report was using total product cost
33 00:02:51.100 ⇒ 00:02:56.560 Awaish Kumar: which it should not know that total protocols is not in the fact order. So we just remove it
34 00:02:57.081 ⇒ 00:03:10.449 Awaish Kumar: and update it with and also for your message. You you send me a message on slack about order line table using the total product cost, and it should not like we should just replace
35 00:03:10.770 ⇒ 00:03:15.250 Awaish Kumar: all the entries for total product cost with Cox product cost so
36 00:03:15.250 ⇒ 00:03:15.860 Caio Velasco: Okay. Cool.
37 00:03:15.860 ⇒ 00:03:27.230 Awaish Kumar: With the we like. We know that total product cost is equal to cox protocols and total protocols was just a duplicate. We removed it, so we will just replace it with the cops, protocols.
38 00:03:29.525 ⇒ 00:03:29.860 Caio Velasco: Okay.
39 00:03:31.450 ⇒ 00:03:31.950 Aakash Tandel: I know
40 00:03:31.950 ⇒ 00:03:32.545 Awaish Kumar: And
41 00:03:33.140 ⇒ 00:03:34.930 Aakash Tandel: Yeah. Otam escalated this one
42 00:03:34.930 ⇒ 00:03:40.010 Awaish Kumar: I don’t know was this was assigned to Carl. Right? Kyle
43 00:03:40.210 ⇒ 00:03:42.840 Aakash Tandel: Yeah, originally assigned to Kyle. Yeah. Tuesday
44 00:03:42.840 ⇒ 00:03:46.410 Aakash Tandel: said that you might be able to take a look at this and see if
45 00:03:46.918 ⇒ 00:03:52.410 Aakash Tandel: you can tackle this I don’t know if you had a chance to look at this, but I just wanted to highlight
46 00:03:52.410 ⇒ 00:03:53.030 Awaish Kumar: I don’t
47 00:03:56.150 ⇒ 00:03:57.050 Aakash Tandel: Yeah, you don’t have to
48 00:03:57.050 ⇒ 00:04:06.900 Awaish Kumar: I haven’t looked at it. I was just following what messages, but if you want I can have a look as well. I’m not sure
49 00:04:06.900 ⇒ 00:04:12.476 Aakash Tandel: With them wanted you to look at this. So let us know after you can do some investigation on that one.
50 00:04:13.860 ⇒ 00:04:18.080 Aakash Tandel: Cool, and then it sounds like the okay, sure progress. Any updates on that
51 00:04:18.089 ⇒ 00:04:25.729 Awaish Kumar: So recharge Api, like I have replied on this one
52 00:04:25.979 ⇒ 00:04:31.509 Awaish Kumar: item, and I need a go ahead with from like someone like
53 00:04:31.769 ⇒ 00:04:41.559 Awaish Kumar: Robert, or or from the client. Right? The the thing we discussed was that for the connect, like the
54 00:04:41.679 ⇒ 00:04:49.889 Awaish Kumar: We have a connector, we we are going with the 5 like we can do incremental load from the
55 00:04:49.989 ⇒ 00:04:57.009 Awaish Kumar: portable team. But I suggested that we should actually
56 00:04:57.707 ⇒ 00:05:02.009 Awaish Kumar: keep a copy of it ourselves, right? Using the
57 00:05:02.628 ⇒ 00:05:06.719 Awaish Kumar: like DVD, or whatever we we want to like.
58 00:05:07.509 ⇒ 00:05:11.849 Awaish Kumar: Keep it otherwise, like even as as
59 00:05:12.249 ⇒ 00:05:33.019 Awaish Kumar: Ethan from Portable like, he mentioned that anytime there’s a change in schema. They run a full refresh, and when they run a full refresh they can load the data whatever it comes from the connector right connectors. Api. So if Api only gives us like 90 days of data, they will have only that. So right now
60 00:05:34.013 ⇒ 00:05:41.119 Awaish Kumar: number one, we should move to the incremental load of for the portable connector.
61 00:05:41.479 ⇒ 00:05:49.527 Awaish Kumar: Right? We can ask Ethan. Right? We we should move to the incremental loading number 2. We should also keep
62 00:05:50.199 ⇒ 00:05:51.569 Awaish Kumar: a copy of it.
63 00:05:51.669 ⇒ 00:05:58.155 Awaish Kumar: So I’m not sure who is the one who who should be giving the green light for for creating a copy
64 00:05:58.849 ⇒ 00:06:03.699 Awaish Kumar: of the data? Right? Is it a client or problem, or what anyone
65 00:06:06.354 ⇒ 00:06:06.819 Caio Velasco: Like
66 00:06:07.410 ⇒ 00:06:10.410 Awaish Kumar: Points like the number one. You you already know, right?
67 00:06:15.550 ⇒ 00:06:16.470 Aakash Tandel: Did it again. I wish
68 00:06:16.470 ⇒ 00:06:19.440 Awaish Kumar: Now keep a copy of the not not the connector, but the
69 00:06:21.400 ⇒ 00:06:23.639 Awaish Kumar: of the data, right of the raw data.
70 00:06:26.980 ⇒ 00:06:29.339 Awaish Kumar: Yeah, raw data from source
71 00:06:30.400 ⇒ 00:06:33.884 Caio Velasco: Just 2 questions to understand. This part
72 00:06:34.690 ⇒ 00:06:43.859 Caio Velasco: so portable does full refresh, and everything gets into raw. But when it gets into raw. We didn’t have any incremental so far
73 00:06:43.970 ⇒ 00:06:44.920 Caio Velasco: correct
74 00:06:50.063 ⇒ 00:06:53.847 Awaish Kumar: Yeah. So when it comes into the raw
75 00:06:54.750 ⇒ 00:06:57.960 Awaish Kumar: we will. We. Our models are right now, are just
76 00:06:58.090 ⇒ 00:07:02.679 Awaish Kumar: replacing, like recalculating everything. We don’t have incremental models
77 00:07:03.110 ⇒ 00:07:05.100 Caio Velasco: Yeah, okay. I didn’t know that
78 00:07:05.100 ⇒ 00:07:05.750 Awaish Kumar: The.
79 00:07:06.050 ⇒ 00:07:07.770 Caio Velasco: Oh, gosh!
80 00:07:07.770 ⇒ 00:07:09.029 Caio Velasco: I assume I assume we have
81 00:07:09.030 ⇒ 00:07:11.580 Awaish Kumar: I don’t know if the from it.
82 00:07:12.910 ⇒ 00:07:17.239 Awaish Kumar: Yeah. But like we can see from the models that
83 00:07:18.190 ⇒ 00:07:21.159 Awaish Kumar: all our models are just recreating everything
84 00:07:23.910 ⇒ 00:07:24.980 Caio Velasco: Okay, cool.
85 00:07:26.440 ⇒ 00:07:29.049 Caio Velasco: And do you think that it could? It can you have in
86 00:07:29.220 ⇒ 00:07:41.210 Caio Velasco: is also portable? They have that their feature to do incremental loading, but we also could do on the Dbt side would be another option just in case correct
87 00:07:45.047 ⇒ 00:07:51.580 Awaish Kumar: Yes, so from like the from 5. What I understand from Ethan’s communication was that
88 00:07:51.830 ⇒ 00:07:53.880 Awaish Kumar: we can run an incremental.
89 00:07:54.280 ⇒ 00:07:56.430 Awaish Kumar: We can like set the
90 00:07:56.630 ⇒ 00:08:08.270 Awaish Kumar: connected to load data incrementally. But there’s a problem. And he says that when there is change in schema or something, they do a kind of full refresh. And when they do this
91 00:08:08.450 ⇒ 00:08:16.440 Awaish Kumar: full refresh thing all the data, whatever comes from Api, they can just keep that. So
92 00:08:16.670 ⇒ 00:08:28.200 Awaish Kumar: there is a change in a recharge Api, which says they will only give us 90 last 90 days of data. Hence, a full refresh will only have 90 days past 90 days of data.
93 00:08:28.400 ⇒ 00:08:39.720 Awaish Kumar: and when we run, which is also kind of a full, full refresh or full recalculation it also has will have only 90 days of data. Hence, yeah, to to
94 00:08:40.350 ⇒ 00:08:48.280 Awaish Kumar: to handle this situation that we don’t lose the data. So we yeah, we have to do
95 00:08:48.670 ⇒ 00:08:51.140 Awaish Kumar: the 2 points which I just mentioned
96 00:08:52.600 ⇒ 00:08:53.250 Caio Velasco: Got it.
97 00:08:53.350 ⇒ 00:08:53.983 Caio Velasco: Thank you.
98 00:08:56.571 ⇒ 00:09:01.840 Aakash Tandel: Wait! Is this ticket connected to the other ticket? The connecting Amazon, or it’s different
99 00:09:01.840 ⇒ 00:09:04.859 Awaish Kumar: Yeah, for the portable like it’s still in all
100 00:09:05.140 ⇒ 00:09:07.539 Awaish Kumar: this is still blocked right? We don’t have a
101 00:09:11.610 ⇒ 00:09:15.320 Awaish Kumar: in the portable. I don’t think there’s any connected right now
102 00:09:15.760 ⇒ 00:09:16.330 Aakash Tandel: Okay.
103 00:09:23.590 ⇒ 00:09:25.840 Awaish Kumar: Like we have such a job.
104 00:09:26.300 ⇒ 00:09:27.400 Awaish Kumar: Confirm
105 00:09:29.380 ⇒ 00:09:33.500 Aakash Tandel: You can. Yeah, sounds good. Yeah, you can
106 00:09:33.500 ⇒ 00:09:36.169 Awaish Kumar: I can. I can ask, Yeah.
107 00:09:36.470 ⇒ 00:09:37.899 Aakash Tandel: Okay. Sweet.
108 00:09:39.140 ⇒ 00:09:40.459 Aakash Tandel: Anything else.
109 00:09:41.740 ⇒ 00:09:44.480 Awaish Kumar: I have an update on the
110 00:09:45.260 ⇒ 00:09:50.481 Awaish Kumar: I have update on the attentive, which I don’t see a ticket here. But
111 00:09:51.558 ⇒ 00:09:56.220 Awaish Kumar: the Putham said, that I can move ahead using the 5 trail
112 00:09:56.798 ⇒ 00:10:02.049 Awaish Kumar: for the attentive connector. So I will work. I will add. Try to add that today
113 00:10:02.720 ⇒ 00:10:04.200 Aakash Tandel: Okay in the 5 tram.
114 00:10:04.650 ⇒ 00:10:12.210 Aakash Tandel: Let me see if it’s just not being pulled into this sprint oops. That’s you.
115 00:10:12.210 ⇒ 00:10:14.404 Awaish Kumar: And one of the ticket which
116 00:10:19.420 ⇒ 00:10:25.639 Awaish Kumar: which was assigned to me yesterday. By Robert. About this, all right, like
117 00:10:26.150 ⇒ 00:10:28.729 Awaish Kumar: connecting Georgia’s data with a
118 00:10:28.940 ⇒ 00:10:38.880 Awaish Kumar: orders, data? That one. I have some questions on that. And I would like to show a
119 00:10:39.760 ⇒ 00:10:42.029 Awaish Kumar: seriously if I can share the screen
120 00:10:42.450 ⇒ 00:10:47.459 Aakash Tandel: Sure real quick on the attentive one. Is this the ticket, or is there no ticket set right now?
121 00:10:50.230 ⇒ 00:10:51.380 Awaish Kumar: Since.
122 00:10:51.770 ⇒ 00:10:53.579 Awaish Kumar: No, no, not this one.
123 00:10:55.610 ⇒ 00:11:01.790 Awaish Kumar: Like the ingestion of data from attentive like
124 00:11:03.340 ⇒ 00:11:06.779 Aakash Tandel: I don’t see a ticket for that. I might ask you if
125 00:11:06.780 ⇒ 00:11:11.729 Awaish Kumar: Yeah, it was it. It was like, kind of combined ticket with Klavio.
126 00:11:12.280 ⇒ 00:11:15.430 Awaish Kumar: The data sharing thing
127 00:11:16.343 ⇒ 00:11:16.909 Aakash Tandel: This one!
128 00:11:17.740 ⇒ 00:11:21.979 Awaish Kumar: But but the clever one is done
129 00:11:22.350 ⇒ 00:11:30.449 Awaish Kumar: like the send among some samples. Yeah, this for the cloud. It is done for the attentive. We were blocked because there was no connector in portable.
130 00:11:30.610 ⇒ 00:11:36.969 Awaish Kumar: So now I I got to go ahead from Witham that I can connect it using 5 train, so I will do
131 00:11:37.550 ⇒ 00:11:39.060 Awaish Kumar: or do that
132 00:11:46.940 ⇒ 00:11:49.130 Aakash Tandel: Go ahead. You can share your screen.
133 00:12:02.360 ⇒ 00:12:03.490 Awaish Kumar: Can do it.
134 00:12:03.800 ⇒ 00:12:05.059 Aakash Tandel: Yep. We see right now
135 00:12:05.060 ⇒ 00:12:06.919 Awaish Kumar: Can you see my screen?
136 00:12:08.130 ⇒ 00:12:11.939 Awaish Kumar: So it’s more like question for any of the Robert
137 00:12:13.060 ⇒ 00:12:16.940 Awaish Kumar: like the the queries query you shared for
138 00:12:17.160 ⇒ 00:12:32.219 Awaish Kumar: for this connecting Georgia’s with orders. Data basically, this is the format of the table. So if there is only single ticket it might be connected with multiple orders. I’m not like, I’m just trying to understand if that’s the
139 00:12:32.420 ⇒ 00:12:36.409 Awaish Kumar: thing you want, because now I’m creating a model, I want it to be correct.
140 00:12:36.410 ⇒ 00:12:37.270 Robert Tseng: Yeah, so.
141 00:12:37.270 ⇒ 00:12:39.709 Awaish Kumar: Right now, if there’s only one ticket.
142 00:12:40.000 ⇒ 00:12:42.190 Awaish Kumar: if a user created one ticket.
143 00:12:42.470 ⇒ 00:12:43.670 Awaish Kumar: Yep.
144 00:12:44.590 ⇒ 00:12:57.479 Robert Tseng: Yeah. So I think what Annie did in her in her query, was that in yeah. As long as there is an order after the ticket. That would count as like a saved customer.
145 00:12:58.326 ⇒ 00:13:08.079 Robert Tseng: So I think you just use the 1st order that was created after the ticket, we should make sure we should probably make sure that we keep the timestamp. So we can monitor.
146 00:13:08.080 ⇒ 00:13:30.269 Robert Tseng: Hey, Macro, we trigger this macro? Yeah, the ticket Ids created on February 21st tickets created on 21, st and then the next order after that would be something. I guess you can see a lot of 2023 orders created here, so that seems a bit off to me. But I would want to be looking for the next order that was created after the ticket, because that would prove that.
147 00:13:31.187 ⇒ 00:13:36.399 Robert Tseng: Yeah, this customer received the macro, and then they place an order after the ticket.
148 00:13:37.880 ⇒ 00:13:39.959 Robert Tseng: Yeah, I think what Andy found was that
149 00:13:39.960 ⇒ 00:13:41.269 Awaish Kumar: Interested in, like.
150 00:13:43.520 ⇒ 00:13:44.050 Robert Tseng: So.
151 00:13:44.050 ⇒ 00:13:44.680 Awaish Kumar: Like
152 00:13:45.090 ⇒ 00:14:03.129 Awaish Kumar: there is a ticket, and there’s a save attempt, Macro, and then we are combining with all the orders. Now. Are we interested in on all the orders after this ticket, or we are only if there’s only we can keep only single order which was placed after this
153 00:14:03.620 ⇒ 00:14:06.179 Awaish Kumar: ticket was created, that is that.
154 00:14:06.740 ⇒ 00:14:09.210 Awaish Kumar: or we want all the orders in the 10
155 00:14:10.090 ⇒ 00:14:21.179 Robert Tseng: So let me just kind of reason through this. Let’s say a customer gets hit with a macro. They place one order, and then they try to cancel again, they get hit with the same macro again. They place another order.
156 00:14:21.520 ⇒ 00:14:26.050 Robert Tseng: I would want to count that as that customer got saved twice.
157 00:14:27.000 ⇒ 00:14:29.750 Robert Tseng: So I I feel like we should just be looking at
158 00:14:29.970 ⇒ 00:14:34.979 Robert Tseng: the 1st order after the macro was placed. But
159 00:14:35.852 ⇒ 00:14:46.797 Robert Tseng: that way. If a single customer gets targeted with the same macro multiple times, and they keep placing new orders. Then I think that Macro should keep getting credit. That’s that’s how I see it?
160 00:14:47.250 ⇒ 00:14:51.920 Robert Tseng: I don’t know this. Do you? Do you guys agree with that? Or like, yeah.
161 00:14:52.570 ⇒ 00:14:54.979 Robert Tseng: that- that. Yeah, that’s what that’s what I think.
162 00:14:57.600 ⇒ 00:14:58.240 Awaish Kumar: Hang on!
163 00:14:58.690 ⇒ 00:14:59.730 Robert Tseng: Does that make sense
164 00:15:01.300 ⇒ 00:15:02.230 Awaish Kumar: Yeah, yeah.
165 00:15:02.420 ⇒ 00:15:09.050 Awaish Kumar: that makes sense. Yeah, I just want to understand that. Because I know we have all the orders in there.
166 00:15:09.180 ⇒ 00:15:17.855 Awaish Kumar: And so, and also the new 2 columns any requested. They are like aggregated columns, and the table is just like
167 00:15:18.350 ⇒ 00:15:24.860 Awaish Kumar: having every row for effect ticket and order. So click!
168 00:15:25.030 ⇒ 00:15:30.380 Awaish Kumar: Do we want an aggregated table or we want it? It’s in that much granularity
169 00:15:33.830 ⇒ 00:15:48.860 Robert Tseng: Well, so if I’m thinking about it from the head of Cx perspective, if I want to go and look at the macro impact of the macro. I want to be able to drill down to the ticket. And I want to know, like the order like afterwards, that that’s what I would check. I wouldn’t necessarily look at
170 00:15:49.050 ⇒ 00:15:55.209 Robert Tseng: how many orders were placed afterwards. I mean, that could be helpful, but I don’t know if that would complicate the model.
171 00:15:55.690 ⇒ 00:16:07.210 Robert Tseng: because maybe it’d be helpful to know. Okay. Macro was triggered. The the next order was placed 2 days later, and in total there were like 5 more orders that were placed like that would be helpful to know.
172 00:16:08.200 ⇒ 00:16:18.540 Robert Tseng: But I I don’t know how how easy that is to do in the model. So I think if if we’re talking about adding in that aggregation, that’s that’s that’s why I think would be an important one to add
173 00:16:21.420 ⇒ 00:16:39.640 Awaish Kumar: Yeah, like to add, like to have this to answer this question. I have to keep all the orders, because after that ticket, if a ticket is created and there is, there is like 5 orders after that, if I keep all of them, then you can just like some like or count of the odd number of orders
174 00:16:40.090 ⇒ 00:16:40.520 Robert Tseng: Yeah.
175 00:16:40.520 ⇒ 00:16:46.039 Awaish Kumar: One, then, like wrong, you cannot delete it.
176 00:16:46.040 ⇒ 00:16:47.150 Robert Tseng: In that case, I think this model
177 00:16:47.150 ⇒ 00:16:47.810 Awaish Kumar: 11
178 00:16:47.810 ⇒ 00:17:05.357 Robert Tseng: Is, I think this model should be fine because you can just go into Meta base. You can add a custom column that basically will just use when order created, like min of when order created is greater than created date. That’ll give you the 1st order after the after the the
179 00:17:07.050 ⇒ 00:17:32.409 Robert Tseng: The the macro was triggered. But I guess what we don’t want is we don’t want any order before the macro to be counted, because I think that would break the aggregation. Right? That would say, Hey, maybe a customer already placed 10 orders before the macro, and then afterwards they only place one, and then, if you aggregate it, it looks like they placed 11, but really they only did one order after the macro
180 00:17:33.000 ⇒ 00:17:33.415 Robert Tseng: right
181 00:17:38.140 ⇒ 00:17:43.990 Awaish Kumar: Okay. So I have something like this like which you can also use. To sum.
182 00:17:44.110 ⇒ 00:17:45.370 Awaish Kumar: So it says,
183 00:17:46.730 ⇒ 00:17:59.979 Awaish Kumar: is canceled order like. So if this means that after a micro saved attempt, if there is any subscription which was canceled if yes, then it’s true.
184 00:18:00.150 ⇒ 00:18:02.580 Awaish Kumar: if not, then there is false.
185 00:18:03.650 ⇒ 00:18:10.970 Awaish Kumar: So for for every order you have this value, that if if this order was placed after a
186 00:18:11.110 ⇒ 00:18:18.590 Awaish Kumar: event of test, make like save a time and plus also. This was after the the
187 00:18:19.510 ⇒ 00:18:21.659 Awaish Kumar: after that event in in terms of time.
188 00:18:21.770 ⇒ 00:18:25.670 Awaish Kumar: So if both conditions are true, then it’s true, otherwise it’s false
189 00:18:26.790 ⇒ 00:18:33.389 Robert Tseng: Okay, that makes sense. But I guess maybe the naming of that column is seems off like total canceled orders. That would not be a cancel
190 00:18:33.390 ⇒ 00:18:34.709 Awaish Kumar: Oh, yeah, it was.
191 00:18:36.150 ⇒ 00:18:37.509 Awaish Kumar: Yeah, it it was.
192 00:18:38.350 ⇒ 00:18:41.159 Awaish Kumar: It’s I. I put it like this because
193 00:18:41.710 ⇒ 00:18:46.649 Awaish Kumar: it was like how it was called in the ticket. But yeah, we we can
194 00:18:47.670 ⇒ 00:18:52.710 Awaish Kumar: change the names like, you know, whatever like, which is more understandable
195 00:18:52.710 ⇒ 00:18:53.140 Robert Tseng: Yeah.
196 00:18:53.140 ⇒ 00:18:53.900 Awaish Kumar: I can send
197 00:18:53.900 ⇒ 00:19:00.600 Robert Tseng: So I mean, yeah, well, I mean, this case. So true is not actually a cancel order. It’s an order after the macro. So it’s like it.
198 00:19:01.330 ⇒ 00:19:03.180 Robert Tseng: isn’t it? I guess you have
199 00:19:03.180 ⇒ 00:19:04.370 Awaish Kumar: Canceled one.
200 00:19:04.470 ⇒ 00:19:10.479 Awaish Kumar: So it is like cancelled subscription, and after the
201 00:19:10.780 ⇒ 00:19:14.049 Awaish Kumar: save the time. So this is for both like
202 00:19:14.240 ⇒ 00:19:21.059 Awaish Kumar: this one is actually doing this, so we say, like subscription, is canceled, which which is coming from this column
203 00:19:21.353 ⇒ 00:19:22.819 Robert Tseng: Got it? Got it? Yeah.
204 00:19:22.820 ⇒ 00:19:29.900 Awaish Kumar: And the time is coming from this one. So if it is after that event, and also it is cancelled, then it’s true
205 00:19:30.640 ⇒ 00:19:34.299 Robert Tseng: Okay, got it, and then you have the macro saved order next to it. Sure.
206 00:19:34.960 ⇒ 00:19:49.499 Robert Tseng: But I think what you’re not accounting for is sometimes like it, they’ll be hit with the macro, but then they won’t actually take any action. So they’re not gonna create a subscript. They’re not gonna cancel their subscription. They’re not gonna place the order. They’re just gonna remain inactive.
207 00:19:50.020 ⇒ 00:19:55.479 Robert Tseng: right? So in that case, like, how would how would you find those
208 00:19:55.480 ⇒ 00:20:01.399 Awaish Kumar: Assuming there’s a so do you mean there is a save attempt? But there’s no order after that
209 00:20:01.790 ⇒ 00:20:06.249 Robert Tseng: Yeah, no order after that. And there’s no cancellation. After that. They’re just they’re just inactive.
210 00:20:07.640 ⇒ 00:20:13.829 Awaish Kumar: Yeah, then there is. We don’t have orders because this will join with orders table.
211 00:20:13.830 ⇒ 00:20:14.840 Awaish Kumar: It’ll be only ours.
212 00:20:14.840 ⇒ 00:20:15.270 Awaish Kumar: How does
213 00:20:15.270 ⇒ 00:20:15.760 Robert Tseng: Okay.
214 00:20:16.035 ⇒ 00:20:19.889 Awaish Kumar: It will. Just this will be only the. There will be no orders right
215 00:20:21.810 ⇒ 00:20:29.359 Robert Tseng: Yeah. So that means we would need to use a different table to find a different model to find those those customers that don’t have any orders afterwards. Right
216 00:20:29.690 ⇒ 00:20:35.299 Awaish Kumar: No, we we then the these values will be null like, we will have the ticket. Id, we will have this.
217 00:20:35.730 ⇒ 00:20:39.810 Awaish Kumar: this values which are coming from cool
218 00:20:39.810 ⇒ 00:20:44.769 Robert Tseng: Oh, the order would just be null. There would just be okay. Sure. I mean, we’ll see that. Yeah.
219 00:20:44.890 ⇒ 00:20:46.150 Awaish Kumar: Please will be there.
220 00:20:46.320 ⇒ 00:20:48.110 Awaish Kumar: The orders value will be null
221 00:20:48.680 ⇒ 00:20:52.799 Robert Tseng: Okay. Okay. Alright. I think this makes sense. I think this is, I think this works
222 00:20:54.150 ⇒ 00:20:55.690 Awaish Kumar: Okay. Thanks.
223 00:20:59.420 ⇒ 00:21:03.549 Aakash Tandel: Awesome. Alright. Let’s hop back into stand up.
224 00:21:03.940 ⇒ 00:21:08.839 Aakash Tandel: Okay. Oish, I think that was everything for use. Right? I think we’re good
225 00:21:08.840 ⇒ 00:21:09.680 Awaish Kumar: Yeah.
226 00:21:10.780 ⇒ 00:21:14.160 Aakash Tandel: Alright. I pulled up Annie. Annie.
227 00:21:14.530 ⇒ 00:21:20.560 Aakash Tandel: I see this is done, which is awesome. I see you’re working on this any anything you need from the team
228 00:21:20.989 ⇒ 00:21:33.439 Annie Yu: Yeah, I do have questions on that. Investigate how to recreate monthly cohort. Because I think that’s probably the one I’m gonna prioritize. Now, just one question for
229 00:21:33.720 ⇒ 00:21:39.939 Annie Yu: Robert, or everyone who knows, like the right landing page to get in them lifetimely
230 00:21:40.900 ⇒ 00:21:43.940 Annie Yu: cause I can’t find the right place to get in
231 00:21:45.143 ⇒ 00:21:47.409 Aakash Tandel: Robert, is that available? In the one password?
232 00:21:54.240 ⇒ 00:21:55.610 Aakash Tandel: Robert might have had to step away.
233 00:21:55.610 ⇒ 00:22:03.659 Aakash Tandel: probably stepped away. Yeah, yeah, this one would be the one I’m trying to prioritize today. Given the urgency.
234 00:22:03.660 ⇒ 00:22:04.260 Aakash Tandel: Okay.
235 00:22:07.020 ⇒ 00:22:10.289 Annie Yu: Yeah, we can wait or Rob come back.
236 00:22:10.290 ⇒ 00:22:12.589 Annie Yu: Yeah, sorry. I’m back. I’m back
237 00:22:13.200 ⇒ 00:22:25.590 Robert Tseng: Yeah, I, okay, I’m sorry. I thought I, oh, yeah, I gave you the login. It’s in one pass. Or I guess, yeah, if you go to one pass I’m assuming you logged in, but you just didn’t know how to get to these screenshots, so I need to like send you how to get in there
238 00:22:25.890 ⇒ 00:22:30.680 Annie Yu: And I don’t know the right landing page for this one.
239 00:22:31.140 ⇒ 00:22:36.330 Annie Yu: do I? Is that the right? Is that link? The correct way to get in
240 00:22:38.690 ⇒ 00:22:40.259 Robert Tseng: I I think so.
241 00:22:40.480 ⇒ 00:22:42.580 Robert Tseng: Hey? You go in and
242 00:22:43.730 ⇒ 00:22:54.789 Robert Tseng: yeah. So all you do is type in the shopify URL in one pass. I believe I put it there. It’s not even a real login you just put in like a URL. I think it’s just drink dash, Javi, or something
243 00:22:55.230 ⇒ 00:22:55.930 Annie Yu: Oh!
244 00:22:55.930 ⇒ 00:22:56.500 Robert Tseng: Wow!
245 00:22:56.500 ⇒ 00:22:59.520 Annie Yu: What do you mean by one pass so
246 00:22:59.520 ⇒ 00:23:02.310 Robert Tseng: Oh, are you not in one pack?
247 00:23:02.310 ⇒ 00:23:07.539 Annie Yu: I think I am, but there’s no pop up when I come to this page
248 00:23:07.810 ⇒ 00:23:13.110 Robert Tseng: Oh, yeah, I I don’t really use the pop up to be honest. But I guess
249 00:23:13.210 ⇒ 00:23:15.169 Robert Tseng: if I could just hijack this real quick
250 00:23:16.203 ⇒ 00:23:17.550 Annie Yu: Thank you.
251 00:23:18.710 ⇒ 00:23:21.310 Robert Tseng: So I’ll just share my screen.
252 00:23:21.520 ⇒ 00:23:27.330 Robert Tseng: Yeah, like, I get to this website. And then I look at one pass and I type in like lifetimely
253 00:23:27.652 ⇒ 00:23:28.620 Aakash Tandel: We don’t wait.
254 00:23:29.120 ⇒ 00:23:31.089 Aakash Tandel: Okay, yeah, I see it now. Never mind.
255 00:23:31.790 ⇒ 00:23:36.690 Robert Tseng: Yeah, there’s no real login here, because it’s just drink coffee
256 00:23:36.940 ⇒ 00:23:37.720 Annie Yu: Oh, I thought
257 00:23:37.720 ⇒ 00:23:45.410 Robert Tseng: In and yeah, you’ll log in here, and then you’ll be able to go to lifetime cohorts.
258 00:23:45.540 ⇒ 00:23:53.219 Robert Tseng: It’s a really slow page like this will take like a minute to load, probably, but you would be able to find
259 00:23:54.550 ⇒ 00:23:56.556 Robert Tseng: the specific
260 00:24:03.600 ⇒ 00:24:04.080 Aakash Tandel: Yeah, it’s 1
261 00:24:04.080 ⇒ 00:24:04.750 Robert Tseng: Now
262 00:24:04.750 ⇒ 00:24:05.470 Aakash Tandel: Yep.
263 00:24:05.470 ⇒ 00:24:11.740 Robert Tseng: Yeah, this metric mark gross margin per customer net sales per customer. I think these are the 2 that we should probably check.
264 00:24:11.940 ⇒ 00:24:18.499 Robert Tseng: Yeah, I, we should be able to calculate this like, I don’t really think you need 5. It’s just more of a check.
265 00:24:20.380 ⇒ 00:24:30.399 Robert Tseng: yeah. So just kind of figuring out how we’re going to visualize this in Meta base like, do we? Is our is our model, that we have sufficient, which I think it is.
266 00:24:30.893 ⇒ 00:24:33.319 Robert Tseng: I think you could recreate something like this.
267 00:24:33.836 ⇒ 00:24:39.039 Robert Tseng: Yeah. But if you need to, you can just run the check here. So there’s a few
268 00:24:39.210 ⇒ 00:24:40.770 Robert Tseng: metrics that you can look at.
269 00:24:41.040 ⇒ 00:25:10.409 Robert Tseng: But really we’re just focusing on gross margin, and then probably net sales net sales will not add up because our gross margin calculate, and I don’t think neither of them will add up completely, because the way we calculate margin includes shipping includes box costs includes pick costs. They probably don’t include any of that. It probably just is product cost and price like that’s pretty much all it is. So I’m not expecting it to match. But I’m curious, like, how close we get. Yeah.
270 00:25:11.240 ⇒ 00:25:16.740 Annie Yu: Alright! Alright! I’ll take a look at this, and if I do have more questions I’ll follow up with you.
271 00:25:17.150 ⇒ 00:25:19.200 Robert Tseng: Okay, cool. Thank you.
272 00:25:21.070 ⇒ 00:25:23.349 Robert Tseng: I’ll turn it back to Vikash.
273 00:25:23.590 ⇒ 00:25:32.589 Aakash Tandel: Cool. I pulled that into in progress because you started that just now. And anything on. I know this is pretty much done. Is this
274 00:25:32.950 ⇒ 00:25:35.083 Annie Yu: I still got 2 dashboards.
275 00:25:35.620 ⇒ 00:25:40.680 Annie Yu: I guess, to move from and I will.
276 00:25:40.850 ⇒ 00:25:44.200 Annie Yu: I’m not sure if I’ll get this done by today.
277 00:25:44.340 ⇒ 00:25:48.669 Annie Yu: but once done, I’ll probably just close that
278 00:25:48.670 ⇒ 00:25:49.900 Aakash Tandel: Cool. Yep. Okay.
279 00:25:50.220 ⇒ 00:25:51.510 Robert Tseng: That’s that’s good.
280 00:25:51.860 ⇒ 00:26:03.020 Aakash Tandel: And then, yeah, we haven’t seen anything from Java address matching stuff in the Channel. So, Robert, if you want to ask Aman if he has that
281 00:26:03.390 ⇒ 00:26:08.379 Annie Yu: Is that something I should kind of wait, or should I like? Ask
282 00:26:09.219 ⇒ 00:26:17.710 Robert Tseng: I’ll follow up. I mean, we’re I’m gonna meet with them on in my 30 min, so I’ll probably just follow up with them see like cause. They had asked for us to just
283 00:26:18.350 ⇒ 00:26:27.160 Robert Tseng: send the script over to them. I also don’t really care if they send it to us once a week if we can run it, so I’ll just get some clarity on him on how he wants to move forward. There
284 00:26:27.160 ⇒ 00:26:29.930 Annie Yu: Alright. And what’s the role of this Oman
285 00:26:30.790 ⇒ 00:26:40.530 Robert Tseng: Aman is the I guess he’s the head of tech, I guess. He’s he’s he’s our main sponsor. So any like questions you have
286 00:26:40.690 ⇒ 00:26:47.030 Robert Tseng: that our team can’t answer we would share with him, and you know he he would connect us to the, to the right people
287 00:26:47.390 ⇒ 00:26:49.139 Annie Yu: Gotcha. Okay. Cool.
288 00:26:52.980 ⇒ 00:26:56.990 Aakash Tandel: Okay? And then this is to do after these things. But any other questions
289 00:26:57.590 ⇒ 00:27:00.010 Annie Yu: I believe that’s everything, for now
290 00:27:01.690 ⇒ 00:27:02.190 Aakash Tandel: Cool.
291 00:27:04.590 ⇒ 00:27:13.780 Aakash Tandel: Let’s go to Kyle. Okay, looks like this is okay. This is the thing that’s interview with oash.
292 00:27:13.940 ⇒ 00:27:14.955 Aakash Tandel: Awesome.
293 00:27:18.217 ⇒ 00:27:19.879 Caio Velasco: Yeah, this is, this is actually done.
294 00:27:20.120 ⇒ 00:27:21.199 Aakash Tandel: This is done. Okay.
295 00:27:22.110 ⇒ 00:27:30.619 Caio Velasco: And then. Now that I have the other ticket that is trying to do the same. But for the fact or the line table. I’m already on it as well
296 00:27:30.930 ⇒ 00:27:35.570 Aakash Tandel: Oh, okay, let’s see, it’s probably just not in this
297 00:27:35.940 ⇒ 00:27:38.150 Aakash Tandel: cycle. You said fact order table
298 00:27:39.510 ⇒ 00:27:41.118 Caio Velasco: Expect, for the line
299 00:27:41.520 ⇒ 00:27:44.060 Aakash Tandel: Order, line.
300 00:27:45.030 ⇒ 00:27:47.859 Caio Velasco: I think it was. I’m not sure if it’s in the backlog, or
301 00:27:48.860 ⇒ 00:27:50.300 Caio Velasco: but I can see on my end.
302 00:27:50.850 ⇒ 00:27:59.200 Caio Velasco: You do see, it should total that one in the on the bottom, in the backlog
303 00:28:00.115 ⇒ 00:28:06.609 Aakash Tandel: Okay, pull this into in progress and cycle one. Okay. Awesome.
304 00:28:09.275 ⇒ 00:28:10.010 Aakash Tandel: Samples.
305 00:28:11.980 ⇒ 00:28:13.829 Aakash Tandel: Okay. Anything else that you
306 00:28:14.330 ⇒ 00:28:18.139 Caio Velasco: Yeah, no. Just for for a week. For example, I know that he
307 00:28:18.661 ⇒ 00:28:30.218 Caio Velasco: it’s gonna take a look at the canceled versus free post payment and I saw that was escalated. And then you change it to him. So yesterday also found another
308 00:28:31.630 ⇒ 00:28:44.589 Caio Velasco: for another information from the Amazon website itself. That kind of confirmed the idea. But then, I mean, if he has to go through all of it, it mostly everything I did is on the comments, but the last one
309 00:28:45.516 ⇒ 00:28:49.669 Caio Velasco: or the one before the last one. Just to start as my.
310 00:28:49.900 ⇒ 00:29:02.580 Caio Velasco: yeah, this one has my snowflake worksheet that I organized all my my logic. And then the last comment is the last thing I found that kind of confirmed that
311 00:29:02.690 ⇒ 00:29:29.659 Caio Velasco: supposedly. Since I think a buyer has, I know, 30 min or something to to cancel an order, and only after everything happens that the the order would go into a financial state which I would assume that it’s like payments happening. So yeah, seems to be that cancellation happens pre payment. If if those things and all the the assumptions are are correct. So then I think it would be
312 00:29:29.930 ⇒ 00:29:32.839 Caio Velasco: he has more experience, so no more than I do
313 00:29:33.630 ⇒ 00:29:44.783 Aakash Tandel: Okay, cool. That sounds good. Yeah. We sounds like you have a lot to work off of here. Feel free to ask Kyle if you have any questions or anything.
314 00:29:46.350 ⇒ 00:29:55.559 Robert Tseng: Yeah, I looked over this ticket as well, I think, like, the yeah. The order statuses make sense. I’m okay with just calling it prepayment cancellation. So I’m gonna tell them on today.
315 00:29:55.710 ⇒ 00:30:08.239 Robert Tseng: I think we’re just kind of stuck on the subscribe and save piece like, now we won’t, wanna we know, like Amazon orders, 20% of them cancel. And I need to tell the team.
316 00:30:08.670 ⇒ 00:30:32.209 Robert Tseng: who are these customers like? Are they subscribe and save customers? Are they single purchase customers when they cancel? Is it because they’re canceling like pre or post payment, like, you know those. That’s where this whole investigation came from. Basically the the core driving question here is like, who are like, what’s driving order cancellations on Amazon. And we’re trying to answer that for for the team
317 00:30:32.760 ⇒ 00:30:33.460 Aakash Tandel: Cool.
318 00:30:33.460 ⇒ 00:30:45.409 Caio Velasco: Okay. Best thing that I will ask from this. So given that this was well, I did a lot of discovery, let’s say behind this. But this isn’t. This is nowhere other than my my worksheet.
319 00:30:45.520 ⇒ 00:30:54.549 Caio Velasco: Should that become something that it’s like a documentation about Amazon, which is what I assumed I would have before even starting, because that that might be helpful
320 00:30:55.660 ⇒ 00:30:58.251 Aakash Tandel: Yeah, Robert, do we have
321 00:30:59.080 ⇒ 00:31:06.030 Aakash Tandel: Should we have, like some sort of documentation around this specific pipeline, basically, or dashboard
322 00:31:06.420 ⇒ 00:31:10.640 Robert Tseng: Yeah, I mean to be honest, I feel like we.
323 00:31:10.790 ⇒ 00:31:28.409 Robert Tseng: yeah, we’ve we’ve like, we’re doing all these interesting integrations for clients. And like, we’ve done some of them here and there, and we’re not really building out like an internal data platform with all this documentation. Yet I it’s like we should do this because this isn’t gonna be our last Amazon customer or whatnot. But
324 00:31:28.560 ⇒ 00:31:30.359 Robert Tseng: I don’t know. I feel like
325 00:31:30.730 ⇒ 00:31:38.640 Robert Tseng: we, you know, that’s not. Gonna I think maybe, Akash, if you could bring that back, Tom, and let him know like.
326 00:31:38.840 ⇒ 00:32:06.810 Robert Tseng: Hey, like we have all this very niche client work that we’re doing and learnings that we’re having. But we’re not really consolidating them into like documentation. So I want him to make the call on like, Okay, who’s gonna because it’s not. That’s not billable time like, do I really want like Tyle spending his time doing that like? It’s it’s gonna be just for our internal use, not really for the client anymore. So I kind of don’t know where to where to draw that line. What we talk to make that call
327 00:32:09.760 ⇒ 00:32:12.840 Aakash Tandel: Okay, that makes sense. Yeah, I will. I’ll bring that up with
328 00:32:13.340 ⇒ 00:32:17.590 Aakash Tandel: Butam. And then, yeah, I’ll follow up with you all here.
329 00:32:18.390 ⇒ 00:32:23.449 Aakash Tandel: Cool. Okay, I think I know we’re at time. I think that’s everything.
330 00:32:23.930 ⇒ 00:32:39.120 Aakash Tandel: If anyone has anything else, you know, feel free to slack or sync up with the people that need need to chat. But yeah, I think this is looking good. And then tomorrow, Robert, you might have a little bit of update for us because you’re meeting with Aman. So that sounds awesome.
331 00:32:40.690 ⇒ 00:32:41.220 Robert Tseng: Yeah, I mean.
332 00:32:43.400 ⇒ 00:32:45.534 Caio Velasco: Quite helpful. Honestly, these last weeks
333 00:32:45.890 ⇒ 00:32:49.019 Aakash Tandel: Great. I’m glad it. Yeah, I’m glad sales are going. Well, nice.
334 00:32:49.230 ⇒ 00:32:49.770 Aakash Tandel: Alright. Y’all
335 00:32:50.070 ⇒ 00:32:50.680 Annie Yu: Thank you.
336 00:32:50.680 ⇒ 00:32:51.020 Annie Yu: Okay.
337 00:32:51.020 ⇒ 00:32:52.049 Aakash Tandel: Talk to you soon. Bye.