Meeting Title: [Javvy] Standup and Weekly Sprint Retro-Planning Date: 2025-04-11 Meeting participants: Aakash Tandel, Annie Yu, Robert Tseng, Awaish Kumar, Caio Velasco
WEBVTT
1 00:00:26.670 ⇒ 00:00:28.280 Aakash Tandel: Hello! How’s it going.
2 00:00:29.520 ⇒ 00:00:31.609 Caio Velasco: Hey, Akash! Good! How are you?
3 00:00:32.650 ⇒ 00:00:36.609 Aakash Tandel: Not too bad. Let me put on board.
4 00:00:38.580 ⇒ 00:00:39.850 Caio Velasco: No! Any.
5 00:00:44.020 ⇒ 00:00:44.420 Aakash Tandel: Well.
6 00:00:45.660 ⇒ 00:00:46.500 Annie Yu: Akash!
7 00:00:46.950 ⇒ 00:00:48.509 Aakash Tandel: Hey? Good morning.
8 00:01:18.530 ⇒ 00:01:19.939 Aakash Tandel: Why is this glitching up?
9 00:01:22.450 ⇒ 00:01:24.344 Aakash Tandel: Did not click on the thing.
10 00:01:25.720 ⇒ 00:01:26.720 Aakash Tandel: Okay.
11 00:01:36.730 ⇒ 00:01:41.109 Aakash Tandel: we’ll give Robert and wish another minute or 2, and we get started.
12 00:02:01.410 ⇒ 00:02:02.200 Aakash Tandel: Alright.
13 00:02:44.460 ⇒ 00:02:49.774 Aakash Tandel: Let’s go ahead and get started, and then Robert hops on. We can talk through his stuff.
14 00:02:50.290 ⇒ 00:03:03.140 Aakash Tandel: Let’s start with Annie. I saw that we’re waiting on some client feedback from a wait. Not wish I’m on I saw. I don’t think he’s responded, or he has.
15 00:03:03.340 ⇒ 00:03:08.080 Aakash Tandel: This thread is ongoing, so we’ll just wait on kind of some.
16 00:03:09.030 ⇒ 00:03:12.820 Aakash Tandel: Follow up conclusions from, let me see.
17 00:03:17.360 ⇒ 00:03:22.801 Aakash Tandel: Yeah, sounds like there’s some ongoing discussions for this one. So that’s fine.
18 00:03:27.620 ⇒ 00:03:30.359 Aakash Tandel: you got a couple of things in progress. Which one do you want to start with?
19 00:03:31.568 ⇒ 00:03:37.270 Annie Yu: We can do the lifetimely first.st for this one. I
20 00:03:37.450 ⇒ 00:03:41.170 Annie Yu: I was able to build some views, but I think
21 00:03:41.930 ⇒ 00:03:45.539 Annie Yu: I should have linked the dashboard here. But
22 00:03:45.740 ⇒ 00:03:48.559 Annie Yu: I think I have a question is just.
23 00:03:48.820 ⇒ 00:03:52.730 Annie Yu: we have that very, I think, really the main thing we are trying to
24 00:03:53.700 ⇒ 00:04:01.130 Annie Yu: replicate from lifetimely. But there are just some like smaller supplement metrics
25 00:04:01.570 ⇒ 00:04:08.110 Annie Yu: that could be added. But I think my question is, then, should we
26 00:04:08.570 ⇒ 00:04:13.129 Annie Yu: should we like way to ship. Or
27 00:04:13.810 ⇒ 00:04:18.210 Annie Yu: is this kind of what it’s about? Because I think this is
28 00:04:18.339 ⇒ 00:04:21.179 Annie Yu: sorry. I just added a comment there with the link.
29 00:04:21.329 ⇒ 00:04:28.700 Annie Yu: But so this I think this view is really what it’s about to see the
30 00:04:29.250 ⇒ 00:04:36.800 Annie Yu: monthly cohort. And then, months since 1st order and see how they’re kind of accumulative
31 00:04:38.090 ⇒ 00:04:44.399 Annie Yu: sales, revenue and gross profit. Looks like, okay, but yeah.
32 00:04:44.900 ⇒ 00:04:48.180 Aakash Tandel: So this chart here is available currently in in.
33 00:04:48.620 ⇒ 00:04:52.689 Annie Yu: Yeah, but just not with, like you see, like on the left
34 00:04:52.880 ⇒ 00:04:55.650 Annie Yu: bar, between the dates. And then those
35 00:04:55.880 ⇒ 00:05:01.699 Annie Yu: numbers. There are still some columns with some other metrics which I think is
36 00:05:02.350 ⇒ 00:05:07.859 Annie Yu: relatively less important compared to what we’re trying to achieve here. But.
37 00:05:09.872 ⇒ 00:05:16.069 Aakash Tandel: Yeah, lex, I can actually let me check on this computer. I can probably.
38 00:05:16.070 ⇒ 00:05:19.260 Annie Yu: Or I can flash up my screen, if that’s easier.
39 00:05:20.220 ⇒ 00:05:21.700 Robert Tseng: Yeah, can you share your screen?
40 00:05:22.630 ⇒ 00:05:23.310 Annie Yu: Yeah.
41 00:05:31.490 ⇒ 00:05:38.620 Annie Yu: so this is what we have. So the similar view with sales, revenue, gross profit, and gross margin.
42 00:05:40.330 ⇒ 00:05:48.109 Annie Yu: and we also have the distinct customer count but with that other things like repurchase rate.
43 00:05:48.550 ⇒ 00:05:50.979 Annie Yu: we don’t. We currently don’t have.
44 00:05:55.910 ⇒ 00:06:01.759 Aakash Tandel: Okay, do we not have that in the data available? And that’s like, we need to model the data. Or is that.
45 00:06:01.760 ⇒ 00:06:06.689 Annie Yu: Yeah, there are still some adjustments needed to be done.
46 00:06:06.970 ⇒ 00:06:07.700 Aakash Tandel: Okay?
47 00:06:08.980 ⇒ 00:06:26.200 Aakash Tandel: I think you should. Can you outline what those additional items are in that ticket? And then we will send this over to Aman for review and just be like, Hey, look! These are some of the things that we’re thinking about adding, would you like us to do that next week.
48 00:06:27.010 ⇒ 00:06:29.420 Annie Yu: Okay, okay, no, no, that’s.
49 00:06:30.240 ⇒ 00:06:36.400 Awaish Kumar: I just have one comment here, months since, 1st order of
50 00:06:36.970 ⇒ 00:06:47.699 Awaish Kumar: in the same table. We also have months since 1st order label. If you use that column, it will change minus one to the 1st order.
51 00:06:47.700 ⇒ 00:06:48.650 Annie Yu: Okay. Cool.
52 00:06:49.360 ⇒ 00:06:50.000 Aakash Tandel: Nice.
53 00:06:50.000 ⇒ 00:06:53.259 Annie Yu: Oh, cool! That’s helpful to know. Thank you, Aish.
54 00:06:55.680 ⇒ 00:07:11.800 Aakash Tandel: Okay? Awesome. Yeah. So yeah, if you just message and then tag me in that comment, I can send this over to Aman for initial review. And just let him know that those are some additional items we want to. We could work on. If if they think that data isn’t helpful.
55 00:07:12.190 ⇒ 00:07:13.710 Annie Yu: Yeah, yeah, okay.
56 00:07:14.230 ⇒ 00:07:14.840 Aakash Tandel: Awesome.
57 00:07:15.300 ⇒ 00:07:16.510 Aakash Tandel: All right.
58 00:07:21.010 ⇒ 00:07:21.760 Aakash Tandel: Okay.
59 00:07:23.520 ⇒ 00:07:26.030 Aakash Tandel: Fixed sales, tabs, and gross margin. Dash
60 00:07:29.680 ⇒ 00:07:32.540 Aakash Tandel: Is this still in progress? Or.
61 00:07:33.088 ⇒ 00:07:36.200 Annie Yu: I don’t really know what this is about.
62 00:07:36.620 ⇒ 00:07:45.269 Aakash Tandel: Oh, okay, so maybe, Robert, do you need to sync up with Annie about this one.
63 00:07:48.220 ⇒ 00:07:51.880 Robert Tseng: I just so.
64 00:07:52.770 ⇒ 00:08:05.213 Robert Tseng: This is the the gross margin tab that Annie worked on. I went in and tried to fix some stuff yesterday didn’t have enough time to spend on it. So I think I just created this ticket.
65 00:08:05.880 ⇒ 00:08:11.918 Robert Tseng: yeah, I mean, I may just end up doing it if I if she hasn’t got around to it so.
66 00:08:12.830 ⇒ 00:08:15.007 Aakash Tandel: I’m gonna put that in cycle.
67 00:08:15.610 ⇒ 00:08:19.716 Aakash Tandel: I want to get to the things that are still actively
68 00:08:20.610 ⇒ 00:08:26.500 Robert Tseng: That’s it’s in response to Vlad’s question. And like, yeah, I mean.
69 00:08:27.510 ⇒ 00:08:31.769 Robert Tseng: he’s basically trying to build something similar to what Annie has built before.
70 00:08:32.110 ⇒ 00:08:39.940 Aakash Tandel: Okay, gotcha. Okay? Update Amazon. Dash to filter canceled orders by subscribe and save
71 00:08:42.120 ⇒ 00:08:48.499 Annie Yu: Yeah, for this one I want to clarify. So does that mean we are still blocked right? At least as of now.
72 00:08:50.107 ⇒ 00:08:54.929 Aakash Tandel: I don’t know. We haven’t got to wish I wish did. Were you able to do that ticket?
73 00:08:54.930 ⇒ 00:09:01.159 Awaish Kumar: Yeah, in the spect orders and effect order line in both tables, like we have a
74 00:09:01.270 ⇒ 00:09:04.359 Awaish Kumar: filter called is subscribe and save.
75 00:09:04.570 ⇒ 00:09:10.500 Awaish Kumar: And using that you can find out the.
76 00:09:11.770 ⇒ 00:09:12.619 Aakash Tandel: I’m sorry I was like.
77 00:09:13.040 ⇒ 00:09:20.059 Awaish Kumar: Yeah, subscribe and save orders. But for this ticket I think you will have an issue, because what I see
78 00:09:20.230 ⇒ 00:09:27.580 Awaish Kumar: from the description it says you need to see the cancelled orders, buy, subscribe, and save.
79 00:09:27.990 ⇒ 00:09:36.080 Awaish Kumar: I just want to clarify that our I just saw yesterday that our spect orders table basically filters out canceled orders.
80 00:09:38.060 ⇒ 00:09:38.870 Annie Yu: Oh!
81 00:09:39.520 ⇒ 00:09:41.909 Awaish Kumar: So I’m not sure if that’s
82 00:09:42.360 ⇒ 00:09:45.279 Awaish Kumar: like how we want to handle that
83 00:09:45.510 ⇒ 00:09:50.269 Awaish Kumar: previously, we are just filtering out canceled orders from fat order table.
84 00:09:51.720 ⇒ 00:10:00.600 Aakash Tandel: Okay, sounds like we need to make a different table for canceled orders. Then is that something we should do, Robert? I don’t know how high up on the.
85 00:10:00.810 ⇒ 00:10:22.690 Robert Tseng: Yeah, this is that high anymore. I guess the the ask was like, Hey, look, we don’t know what’s driving Amazon cancel orders. And so we’re trying to add different cuts to like, figure that out, subscribe and save seems to be like one of the buckets that we didn’t have any visibility into. I mean, if we can’t do it, then that we can’t do it. It’s I think.
86 00:10:23.370 ⇒ 00:10:30.359 Robert Tseng: yeah, I mean, I I just. I just needed somebody to to kind of look into it, and and tell us what our capabilities were.
87 00:10:32.280 ⇒ 00:10:36.329 Awaish Kumar: Yeah. But I added this in subscribe and say, filter, is that
88 00:10:36.680 ⇒ 00:10:39.139 Awaish Kumar: not helpful like? I don’t know.
89 00:10:39.480 ⇒ 00:10:42.930 Aakash Tandel: No, that is helpful, I think, for the other questions, I think just for this one.
90 00:10:42.930 ⇒ 00:10:43.319 Robert Tseng: Yeah.
91 00:10:43.710 ⇒ 00:10:50.359 Aakash Tandel: This is if if we don’t have the cancel orders and the subscribe save, I don’t think we should.
92 00:10:50.360 ⇒ 00:10:58.150 Robert Tseng: Yeah, we can just move this back into requirements and review or something, because, like, I don’t know, like, it just seems like we can’t do it right now.
93 00:10:58.850 ⇒ 00:11:02.900 Aakash Tandel: Yeah, okay.
94 00:11:05.588 ⇒ 00:11:18.081 Aakash Tandel: Annie, I think this I respond to this morning. I gave you some. The 1st pass. Looks great. Like the 1st basic data. I just asked a couple of different questions of
95 00:11:18.670 ⇒ 00:11:30.930 Aakash Tandel: of different ways. I think that they’re gonna ask for data. So I pose them here, and then you can take time to look at those and let me know if those are not possible, and then we can get away from Kyle on the modeling.
96 00:11:31.260 ⇒ 00:11:35.190 Annie Yu: Okay, thank you so much. I’ll I’ll review it and let you know.
97 00:11:35.590 ⇒ 00:11:56.409 Aakash Tandel: Awesome thanks. And then on this one, I know that. I’m still. I’m gonna dive into the snowflake data and see if I can figure out this just because everyone’s working on stuff. So I think I should be able to figure out the information. But in their spreadsheet that they’re looking at. Aman said. This is coming directly from
98 00:11:56.851 ⇒ 00:12:08.409 Aakash Tandel: north beam. So hopefully, the raw data is from directly from north Beam, and then this one might be coming from Amazon. So I’m gonna figure that out. I’m actually gonna assign this to myself because
99 00:12:08.870 ⇒ 00:12:11.850 Aakash Tandel: I’m working on it all of that off.
100 00:12:12.509 ⇒ 00:12:16.559 Aakash Tandel: Cool. Okay, yeah. So I think the main thing for you is the attentive
101 00:12:16.800 ⇒ 00:12:36.249 Aakash Tandel: data modeling. We’re waiting on that. Amazon. Dash. B, 2. What is this block? Oh, that’s the okay, that’s fine. That’s blocked there. And then these 2, I think, are on. If you finish up answering these questions, and we can ship it to Aman. You can get started on these other 2. But for now.
102 00:12:36.660 ⇒ 00:12:38.269 Aakash Tandel: yeah, this is a top priority.
103 00:12:38.610 ⇒ 00:12:40.070 Annie Yu: Okay. Thanks.
104 00:12:40.880 ⇒ 00:12:42.940 Aakash Tandel: Yep, alright. Anything else, any.
105 00:12:44.426 ⇒ 00:12:45.690 Annie Yu: I think that’s everything.
106 00:12:45.950 ⇒ 00:12:52.155 Aakash Tandel: Okay, Robert, let’s go to you. We always end up with you last. So I thought for you here.
107 00:12:53.000 ⇒ 00:12:53.540 Robert Tseng: Yeah.
108 00:12:55.120 ⇒ 00:13:00.370 Aakash Tandel: Anything I know this is blocked by the client.
109 00:13:01.900 ⇒ 00:13:05.179 Aakash Tandel: I guess these are all in client feedback. Anything else you need
110 00:13:05.460 ⇒ 00:13:07.919 Aakash Tandel: you’ll be working on, or anything you need to deliver.
111 00:13:08.890 ⇒ 00:13:24.269 Robert Tseng: Yeah, no, I think I’m just gonna look at wherever team hasn’t push stuff out. And I’m just gonna help plug in there like, I I like, I think I I’m just gonna reference slack. I was in doing some joby stuff yesterday trying to push a bit.
112 00:13:25.660 ⇒ 00:13:32.699 Robert Tseng: yeah, I think I just want to show something that has used the subscribe and say, Filter, I think
113 00:13:32.940 ⇒ 00:13:36.379 Robert Tseng: that I’m gonna just look for where we can. We can
114 00:13:36.960 ⇒ 00:13:55.959 Robert Tseng: to show that to them. And I mean we owe Amon some videos that I had asked. I had kind of handed off Tom, so I don’t know if he’s gonna do it. He’s about to land in la in an hour, and we’re gonna be doing offside stuff. So I don’t honestly don’t really think I’m gonna put much time into this today.
115 00:13:56.590 ⇒ 00:14:01.229 Aakash Tandel: Okay, so are we gonna not be able to deliver those by today?
116 00:14:03.127 ⇒ 00:14:04.770 Robert Tseng: The videos. I
117 00:14:06.760 ⇒ 00:14:15.400 Robert Tseng: I think we can. I think we we probably still can. Yeah, I’ll try to like, have him set aside some time to to do that, so.
118 00:14:16.060 ⇒ 00:14:20.895 Aakash Tandel: Okay, yeah, I think that would be awesome. I know Oman is like pushing on that one.
119 00:14:21.180 ⇒ 00:14:26.554 Robert Tseng: Yeah, I mean, he makes sense. He’s he feels like it’s an onboarding thing that he needs for him and his.
120 00:14:27.420 ⇒ 00:14:28.030 Aakash Tandel: Yeah.
121 00:14:28.030 ⇒ 00:14:28.610 Robert Tseng: Yeah.
122 00:14:28.810 ⇒ 00:14:35.290 Aakash Tandel: Cool. Alright, that sounds good. Let’s go to wish
123 00:14:37.080 ⇒ 00:14:40.760 Aakash Tandel: Is this in Pr review monthly cohort summary?
124 00:14:42.210 ⇒ 00:14:43.560 Aakash Tandel: Or is this done?
125 00:14:45.150 ⇒ 00:14:46.209 Awaish Kumar: It is done. Yeah.
126 00:14:46.780 ⇒ 00:14:47.569 Aakash Tandel: Done done.
127 00:14:49.590 ⇒ 00:14:54.240 Robert Tseng: Yeah, can you add Pr, like, links to these stuff like, yeah, this.
128 00:14:54.240 ⇒ 00:15:04.940 Awaish Kumar: So, Akash, is it a way that if I write down the ticket number in the Pr. It automatically connects, or I have to copy paste it.
129 00:15:05.100 ⇒ 00:15:07.410 Aakash Tandel: It should connect
130 00:15:11.355 ⇒ 00:15:26.159 Aakash Tandel: it should connect automatically. But if you can copy and paste that here, I will figure out how to get it connected to Github. Is it supposed to automatically do these things? But I don’t know exactly how to do that. So yeah. So if you can just copy and paste in here. That’d be great for now.
131 00:15:26.280 ⇒ 00:15:27.430 Aakash Tandel: and we can go there.
132 00:15:27.430 ⇒ 00:15:28.120 Awaish Kumar: Okay.
133 00:15:30.070 ⇒ 00:15:35.820 Aakash Tandel: Cool. Okay, any. So I saw this is, I pulled this in progress because you already kind of did a little bit of
134 00:15:36.916 ⇒ 00:15:43.650 Aakash Tandel: you said that you can do multiple refreshes. But it’s a portable thing. So I pulled that into in progress.
135 00:15:43.650 ⇒ 00:15:51.610 Awaish Kumar: No I I can like like. What I’m I wrote is that we can make the sinks.
136 00:15:52.107 ⇒ 00:15:58.070 Awaish Kumar: But I just wanted to ask, like, like, are we really updating Google sheets
137 00:15:58.310 ⇒ 00:16:06.970 Awaish Kumar: like every 6 h like, Do we really want to do it? I don’t know. It might can cost little bit to have it, you know, on a schedule
138 00:16:07.572 ⇒ 00:16:10.710 Awaish Kumar: in the 5 trailer in the portable. So
139 00:16:11.220 ⇒ 00:16:17.570 Awaish Kumar: I’m not sure like, is that really the requirement I just wanted to clarify. I I right.
140 00:16:18.070 ⇒ 00:16:19.180 Awaish Kumar: can do it.
141 00:16:19.660 ⇒ 00:16:44.520 Aakash Tandel: Yeah, I don’t. I don’t see them doing that with this, because I don’t think that’s gonna be updated as frequently. But I think, getting information on like what that would look like. It would be helpful, and then also just pulling it off of the manual sync, I think, is probably good. But yeah, I I agree, it’s very unlikely they’re gonna be updating that Google sheet on A, you know, every 6 h basis to to need that. But yeah.
142 00:16:46.290 ⇒ 00:16:46.620 Awaish Kumar: Okay.
143 00:16:46.620 ⇒ 00:16:51.670 Aakash Tandel: I think information is just helpful for them to make a decision on what? How they want to handle those refreshes
144 00:16:55.430 ⇒ 00:16:59.600 Aakash Tandel: cool. And then is this done? I added this.
145 00:16:59.600 ⇒ 00:17:02.006 Awaish Kumar: It’s a like a it’s a duplicate
146 00:17:02.350 ⇒ 00:17:03.410 Aakash Tandel: Duplicate, ticket.
147 00:17:03.410 ⇒ 00:17:04.430 Awaish Kumar: Ticket. Yeah.
148 00:17:04.660 ⇒ 00:17:09.590 Aakash Tandel: That’s what I thought. Okay, alright. So that’s the subscribe and save stuff.
149 00:17:09.599 ⇒ 00:17:19.449 Awaish Kumar: And incremental data refreshes. I have changed the schedule for all these sources to to run on every 6 h.
150 00:17:19.938 ⇒ 00:17:30.681 Awaish Kumar: I I just put them testing, because I just want to review it after after one day, like how it goes. Then I can put it to done. And
151 00:17:31.603 ⇒ 00:17:38.129 Awaish Kumar: apart from that, I would like that if we we might have to have a single one more subtask.
152 00:17:38.409 ⇒ 00:17:39.499 Awaish Kumar: To like.
153 00:17:40.059 ⇒ 00:17:48.309 Awaish Kumar: What this connect these connections will do is to it. They will just sync the data in raw. Now, we want to have also have to run Dbt
154 00:17:48.549 ⇒ 00:17:52.179 Awaish Kumar: to basically put to bring that into our Mars.
155 00:17:52.419 ⇒ 00:17:55.899 Awaish Kumar: So we have to run our Dbt. March every 6
156 00:17:56.119 ⇒ 00:18:00.979 Awaish Kumar: 6 h hours as well to bring that data into rewards.
157 00:18:01.740 ⇒ 00:18:10.980 Aakash Tandel: So the refreshes will dump the data into raw every 6 h. Booking soon. When you, we
158 00:18:11.330 ⇒ 00:18:18.689 Aakash Tandel: adjust, we need to adjust our dB Dvt models to move the data.
159 00:18:18.800 ⇒ 00:18:24.629 Aakash Tandel: Okay, that sounds good. I’ll create it as a separate ticket.
160 00:18:25.160 ⇒ 00:18:25.800 Awaish Kumar: Okay.
161 00:18:25.980 ⇒ 00:18:37.369 Aakash Tandel: And after we get the go ahead. So I think it’s good that you’re doing like a testing phase. Were there any of these that you cannot run every 4 h? Or could you run every 4 h for all of them.
162 00:18:38.470 ⇒ 00:18:40.869 Awaish Kumar: Yeah, we can run for all of them.
163 00:18:41.120 ⇒ 00:18:50.770 Aakash Tandel: Okay, awesome. Well, that’s that’s good to know. Okay, I will also pass this along to Aman. Just so he’s aware that we it’s possible for all these, and then go from there. Okay.
164 00:18:51.050 ⇒ 00:18:51.870 Aakash Tandel: awesome.
165 00:18:55.430 ⇒ 00:18:58.199 Aakash Tandel: Anything else. Or maybe this is the next thing in your place.
166 00:18:58.200 ⇒ 00:19:08.619 Awaish Kumar: I, just yeah. And then I just put it in testing for that reason that I just want to see if everything works as smoothly, so we can
167 00:19:08.720 ⇒ 00:19:10.739 Awaish Kumar: then share it with the client.
168 00:19:11.090 ⇒ 00:19:13.440 Aakash Tandel: Awesome. Yep, that sounds perfect.
169 00:19:14.672 ⇒ 00:19:16.330 Aakash Tandel: Cool. Yeah.
170 00:19:16.330 ⇒ 00:19:29.949 Awaish Kumar: Yeah, like this one. I I see that from I I saw the code from Aman. And this is the exact field we are getting in the north screen.
171 00:19:30.080 ⇒ 00:19:33.459 Awaish Kumar: the campaign product filter. I
172 00:19:33.800 ⇒ 00:19:38.279 Awaish Kumar: I did reply in the slack, I think, but not here. Yeah, but I can
173 00:19:38.490 ⇒ 00:19:48.425 Awaish Kumar: put put that comment here as well. So it says that I am using exact same field to get the concentrate spend. And
174 00:19:49.382 ⇒ 00:19:55.880 Awaish Kumar: it is around like for individual days. It is between 500 to 1,000 for concentrate.
175 00:19:56.790 ⇒ 00:20:01.110 Awaish Kumar: and like for weekly, it should be higher, not just 500.
176 00:20:01.250 ⇒ 00:20:02.220 Awaish Kumar: So yeah.
177 00:20:09.010 ⇒ 00:20:09.790 Aakash Tandel: Okay.
178 00:20:09.940 ⇒ 00:20:23.020 Robert Tseng: So did you look through like the code snippet that I got from him like this is what they. That’s how any North main data is supposed to be brought into amplitude. And so
179 00:20:23.220 ⇒ 00:20:24.510 Robert Tseng: I mean, yeah.
180 00:20:24.530 ⇒ 00:20:32.100 Robert Tseng: And then I I singled out like, Hey, this is the one filter they’re using to like, differentiate between concentrate and protein.
181 00:20:32.100 ⇒ 00:20:32.500 Awaish Kumar: And.
182 00:20:32.500 ⇒ 00:20:39.020 Robert Tseng: So, yeah, can you just confirm that like, that’s that’s what you understood from this this ticket.
183 00:20:39.680 ⇒ 00:20:48.800 Awaish Kumar: Yeah, yeah, I wrote, I read the full code. And yeah, I see that this was the one which was they were using to identify the product.
184 00:20:49.450 ⇒ 00:21:02.020 Robert Tseng: Yeah. Cause when I looked at your model like I saw that it was just looking at campaign name and like filtering for like. That’s why I said that message to him. I was trying to support you guys on on this
185 00:21:03.650 ⇒ 00:21:12.230 Awaish Kumar: Yeah, it says, breakdown campaign product filter. And that is a like. The the name in the code also is similar. Right
186 00:21:12.460 ⇒ 00:21:14.870 Awaish Kumar: campaign product, filter breakdown.
187 00:21:14.870 ⇒ 00:21:19.389 Robert Tseng: So if you use this filter, it’s the same thing as what you did. Is that what you’re saying.
188 00:21:19.390 ⇒ 00:21:20.960 Awaish Kumar: Yes, yes.
189 00:21:20.960 ⇒ 00:21:21.700 Robert Tseng: Okay.
190 00:21:24.390 ⇒ 00:21:39.860 Awaish Kumar: So I can see I’m from the I just verified from raw data for protein. The the right now the daily spend is like maybe in 30,000 or 40,000 like that. And for concentrate, I’m just seeing between 501,000
191 00:21:40.652 ⇒ 00:21:51.960 Awaish Kumar: in in raw data. So I’m not sure. Maybe I can go into Northweam, Northweam, dashboard itself and verify that. But yeah, I verified with the raw data we are. We were getting.
192 00:21:53.200 ⇒ 00:21:53.570 Robert Tseng: Okay.
193 00:21:53.570 ⇒ 00:21:54.060 Aakash Tandel: Yeah, okay.
194 00:21:54.060 ⇒ 00:22:02.349 Robert Tseng: Yeah, I mean, I tried going north beam and to to confirm I I don’t think it’s very clear to me. But I mean, yeah, I guess.
195 00:22:03.460 ⇒ 00:22:12.930 Robert Tseng: Anyway, I’m I’m just I. I was just trying to help push, push here. So if if there’s more, if I need to spend more time looking at it just like, let me know. But we we should.
196 00:22:13.070 ⇒ 00:22:16.529 Robert Tseng: we should probably, I mean, we need to give him an answer on this, so.
197 00:22:18.540 ⇒ 00:22:18.970 Robert Tseng: Yeah.
198 00:22:18.970 ⇒ 00:22:20.009 Awaish Kumar: Okay. Thank you.
199 00:22:21.010 ⇒ 00:22:33.490 Robert Tseng: So I’m just yeah. You’re gonna go. You’re gonna north beam. You’re gonna figure out what the discrepancy is. Or if there is one like I just, I think to me I just I find it hard to believe that it’s 500 to a thousand dollars like a day, just
200 00:22:33.950 ⇒ 00:22:34.887 Robert Tseng: way too low.
201 00:22:35.940 ⇒ 00:22:37.580 Awaish Kumar: Yeah, so so what? I’m
202 00:22:37.690 ⇒ 00:22:58.360 Awaish Kumar: yeah, from the raw data like the the data we have in our warehouse raw. And the modeling all all of that data. I I verified that it is this spend, and but I can go in North Beam and investigate and try to verify that as well, so I will let you know my findings. So after that, like, yeah, we can
203 00:22:58.630 ⇒ 00:23:00.240 Awaish Kumar: ask the client.
204 00:23:00.920 ⇒ 00:23:01.390 Robert Tseng: Okay.
205 00:23:01.390 ⇒ 00:23:04.439 Aakash Tandel: Yeah, okay, that sounds good.
206 00:23:06.360 ⇒ 00:23:09.150 Aakash Tandel: Okay, cool anything else. Away on your plate.
207 00:23:10.625 ⇒ 00:23:11.510 Awaish Kumar: No.
208 00:23:11.970 ⇒ 00:23:12.500 Aakash Tandel: Okay?
209 00:23:17.270 ⇒ 00:23:18.580 Aakash Tandel: All right.
210 00:23:19.160 ⇒ 00:23:26.239 Aakash Tandel: Kyle, looks like some things are closed, which is good. This is still,
211 00:23:29.090 ⇒ 00:23:35.600 Aakash Tandel: yeah, this is still blocked. And I need to figure out why they are not
212 00:23:35.910 ⇒ 00:23:44.367 Aakash Tandel: giving us information on, or I don’t know if they don’t understand the task or or what, so I will push on that thread
213 00:23:44.720 ⇒ 00:23:55.440 Robert Tseng: Can we just make some assumptions and move forward with that? Like, if they can’t? If yeah, I mean, I think, yeah, we just we just we can’t. We can’t wait on them like forever. I don’t know.
214 00:23:56.480 ⇒ 00:23:57.200 Aakash Tandel: Yeah.
215 00:23:57.200 ⇒ 00:24:06.990 Robert Tseng: Like. I don’t think I don’t think the risk of moving forward is very high, like I. I feel like if it’s wrong, and they’re like, Oh, these numbers are wrong, then that’s fine like. Then.
216 00:24:07.190 ⇒ 00:24:18.709 Robert Tseng: like, I feel like with this client, they don’t really think more than like the day in front of them like they’re just they can’t, they can’t. They need to see something in order to like give any feedback to it? So.
217 00:24:18.710 ⇒ 00:24:21.809 Aakash Tandel: Question, but can we just copy what we’re doing with shopify.
218 00:24:22.160 ⇒ 00:24:28.039 Robert Tseng: Yeah, like anything that they didn’t give us for Amazon. Just we’re gonna write in there. We’re gonna move forward by just using
219 00:24:28.300 ⇒ 00:24:30.640 Robert Tseng: the shopify or whatever. And
220 00:24:31.060 ⇒ 00:24:40.580 Robert Tseng: I mean worst cases, we just changed some fields in Google Sheet. That’s like a quick fix on our side later on. But like I don’t. I don’t think it should really solve the modeling work.
221 00:24:41.270 ⇒ 00:24:42.360 Aakash Tandel: Yeah.
222 00:24:42.740 ⇒ 00:24:51.190 Aakash Tandel: And Kyle correct me if I’m wrong. But they they didn’t fill out anything. They they copied and pasted the stuff from shopify, and there’s nothing.
223 00:24:51.190 ⇒ 00:25:00.209 Caio Velasco: Yeah, they yeah, they they did this couple of days ago. But then Blake messaged and said some other things that we might use
224 00:25:00.659 ⇒ 00:25:03.400 Caio Velasco: not not fine. I’ll have to take a look into it.
225 00:25:03.760 ⇒ 00:25:10.210 Caio Velasco: Only reason why I didn’t. As I mentioned, I was working on Klavio, and my hours are gone
226 00:25:10.916 ⇒ 00:25:22.709 Caio Velasco: but happy to start next week on this, and then bring it in anything we can with any kind of assumptions. As Robert mentioned, I think that would be the good thing to do.
227 00:25:24.650 ⇒ 00:25:31.510 Aakash Tandel: Yeah, if we just move forward with like a copy from
228 00:25:33.360 ⇒ 00:25:36.640 Aakash Tandel: shopify is that gonna take long, or how? How long will that take.
229 00:25:38.740 ⇒ 00:25:46.980 Caio Velasco: No clue. I would have to go into factors, understand whatever is there for cogs, learn the calculation, bring into them product.
230 00:25:47.400 ⇒ 00:25:50.619 Caio Velasco: See what is left for Amazon. See what is different.
231 00:25:50.880 ⇒ 00:25:52.180 Caio Velasco: It’s it’s work.
232 00:25:52.700 ⇒ 00:25:54.470 Caio Velasco: It’s not. Gonna take like an hour.
233 00:25:55.260 ⇒ 00:25:55.950 Aakash Tandel: Okay.
234 00:25:56.240 ⇒ 00:26:01.421 Aakash Tandel: Alright. Let’s I guess that’s fine. We’ll slate that for next week.
235 00:26:02.120 ⇒ 00:26:14.399 Aakash Tandel: I guess we’ll give them, since we’re not gonna work on it till next week. I’ll give them to the end of the day to add stuff to that. Otherwise we’re gonna move forward, making similar assumptions to that. So let me pop that in thing.
236 00:26:15.240 ⇒ 00:26:23.210 Aakash Tandel: Okay, we will move forward on April with days at 1213, 14.
237 00:26:24.830 ⇒ 00:26:28.020 Robert Tseng: I feel like? He answered these questions right?
238 00:26:28.990 ⇒ 00:26:44.499 Caio Velasco: Yeah, they he gave. They gave some of those new new answers in the spread in that spreadsheet. Then I will have to like understand that spreadsheet and see if we already have everything. Seems that if everything is just Fba stuff, it’s there.
239 00:26:44.790 ⇒ 00:26:48.339 Caio Velasco: There’s no other assumptions like in shopify.
240 00:26:49.069 ⇒ 00:26:54.090 Caio Velasco: But yeah, that’s also like an assumption we are making. Now, I will have to take a look into it and
241 00:26:54.340 ⇒ 00:27:01.360 Caio Velasco: and see like, how would that relate to those fields that are already for done for, shopify in.
242 00:27:01.640 ⇒ 00:27:03.510 Caio Velasco: in fact, orders, and see.
243 00:27:03.770 ⇒ 00:27:04.340 Robert Tseng: So.
244 00:27:04.340 ⇒ 00:27:08.520 Caio Velasco: It’s just like this doesn’t apply because they’re Fba fees.
245 00:27:08.660 ⇒ 00:27:15.570 Robert Tseng: There are no pick fees, platform fees is 15, and then whatever like column, whatever he like
246 00:27:15.820 ⇒ 00:27:20.080 Robert Tseng: like, I feel like this is this is not like this is pretty simple to figure out right.
247 00:27:22.280 ⇒ 00:27:25.020 Caio Velasco: Yeah, to bring into the model? No.
248 00:27:27.000 ⇒ 00:27:31.949 Aakash Tandel: Maybe, Robert, do you just want to give those assumptions? And then we can move forward next week on modeling that.
249 00:27:32.080 ⇒ 00:27:32.810 Robert Tseng: Okay.
250 00:27:32.930 ⇒ 00:27:41.550 Aakash Tandel: Let me assign this to you. I’m gonna in progress, Robert.
251 00:27:41.750 ⇒ 00:27:55.010 Aakash Tandel: Okay, Roberts, to make the cogs assumptions for Amazon
252 00:27:55.500 ⇒ 00:28:09.580 Aakash Tandel: clients can update these in the future if they disagree with that, assumptions need to move board.
253 00:28:10.060 ⇒ 00:28:12.050 Aakash Tandel: Good modeling. Okay?
254 00:28:12.360 ⇒ 00:28:13.040 Aakash Tandel: Alright.
255 00:28:13.630 ⇒ 00:28:15.820 Aakash Tandel: That sounds good.
256 00:28:17.800 ⇒ 00:28:24.110 Aakash Tandel: And then I I guess the Klavia side was not pulled into sprint, and that’s why I don’t see it. So let me.
257 00:28:25.270 ⇒ 00:28:30.170 Aakash Tandel: So they all the issues. Let me go here.
258 00:28:33.150 ⇒ 00:28:34.779 Aakash Tandel: Why did I not see you.
259 00:28:37.810 ⇒ 00:28:38.336 Aakash Tandel: What’s that?
260 00:28:41.310 ⇒ 00:28:42.979 Aakash Tandel: All the issues?
261 00:28:52.890 ⇒ 00:28:58.010 Aakash Tandel: Okay, wait. Where’s the where’s the Clavia stuff you’re working on?
262 00:28:58.370 ⇒ 00:28:59.760 Aakash Tandel: Oh, that was a ticket.
263 00:29:00.040 ⇒ 00:29:03.179 Caio Velasco: It’s yes, it’s assume it’s done.
264 00:29:04.450 ⇒ 00:29:05.350 Aakash Tandel: Oh, it’s done!
265 00:29:05.510 ⇒ 00:29:10.459 Caio Velasco: I think when I push the Pr. And someone accepted, say, automatic goes to that.
266 00:29:11.080 ⇒ 00:29:12.872 Aakash Tandel: Okay, is it.
267 00:29:14.970 ⇒ 00:29:16.130 Caio Velasco: It’s somewhere in.
268 00:29:27.820 ⇒ 00:29:31.109 Caio Velasco: Look it here, 1, 9, 1, 9, 9 is the number.
269 00:29:31.950 ⇒ 00:29:33.070 Aakash Tandel: 1 9.
270 00:29:37.020 ⇒ 00:29:37.360 Caio Velasco: Yes.
271 00:29:37.700 ⇒ 00:29:43.020 Aakash Tandel: Why is that not pulling up? That’s so weird. Okay, okay. So this is done.
272 00:29:43.260 ⇒ 00:29:46.600 Aakash Tandel: So this data is already modeled and stuff and ready to go.
273 00:29:46.600 ⇒ 00:29:53.930 Caio Velasco: Yes, yes, I added something. I added some things from yesterday to today, because I thought it was gonna even be better to have those
274 00:29:54.180 ⇒ 00:30:02.079 Caio Velasco: sources as well. And then at the end, in the last comment, I added them, what was it?
275 00:30:02.200 ⇒ 00:30:03.690 Caio Velasco: That’s another one, I think.
276 00:30:07.820 ⇒ 00:30:13.023 Aakash Tandel: Okay, Andy, this will be something that we probably pick up next week.
277 00:30:15.510 ⇒ 00:30:27.810 Aakash Tandel: let me add to there. Just so your heads up. This is kind of the Klavio data. And the basic. This is kind of what I’m thinking for, like the v 1, just basic scorecards. And then we can get iterative on that afterwards.
278 00:30:28.610 ⇒ 00:30:33.999 Annie Yu: Okay. And is there any way you can also add which table to use.
279 00:30:35.232 ⇒ 00:30:37.060 Aakash Tandel: Kyle, do you know, off the top of your head.
280 00:30:38.330 ⇒ 00:30:41.220 Caio Velasco: Issue. Can you go back to my to the 1 99?
281 00:30:43.620 ⇒ 00:30:49.600 Caio Velasco: So last comment on the last part. Yes, so, though a bit up.
282 00:30:50.316 ⇒ 00:30:56.040 Caio Velasco: Those are the the new tables, campaigns, events, metrics, profiles, and segments
283 00:30:56.180 ⇒ 00:31:07.400 Caio Velasco: never worked with Claudio before, so everything I learned was by just going into the Api, and just literally spending hours understanding what what is their business
284 00:31:07.740 ⇒ 00:31:08.800 Caio Velasco: about.
285 00:31:08.940 ⇒ 00:31:29.089 Caio Velasco: And I made some comments. Then if you go up it down, and whenever I created a table, I made sure that there is a description for each column, because that’s like stuff that I learned. Which part of it can also be a notion, because I’m maintaining fact. But it’s not complete. Otherwise it would take another couple of hours just to do that.
286 00:31:29.370 ⇒ 00:31:32.770 Caio Velasco: But yeah, I tried to be like as much organized as I could.
287 00:31:33.642 ⇒ 00:31:38.740 Caio Velasco: So that I can help anyone downstream. At least start from somewhere.
288 00:31:38.890 ⇒ 00:31:46.310 Caio Velasco: and and oh, and and last one. Sorry it’s I just made like a simple query, just to see to show that you can.
289 00:31:47.140 ⇒ 00:31:54.690 Caio Velasco: Yeah, last one over there. The last link, snowflake. Just a query to show that you can.
290 00:31:55.720 ⇒ 00:31:58.039 Caio Velasco: How do you say? Join with orders data.
291 00:31:59.080 ⇒ 00:31:59.650 Aakash Tandel: Okay.
292 00:32:00.475 ⇒ 00:32:11.790 Caio Velasco: Via them via dim customers and sectors not only via sectors, because sector just don’t. They don’t have email over there. And usually it’s using email as an Id to join.
293 00:32:12.690 ⇒ 00:32:16.889 Aakash Tandel: That makes total sense. Okay, awesome and a link to that. So.
294 00:32:16.890 ⇒ 00:32:18.470 Annie Yu: Yeah, thank you so much.
295 00:32:20.360 ⇒ 00:32:25.909 Aakash Tandel: Cool. Okay, awesome. Does anyone have anything else? I know
296 00:32:26.860 ⇒ 00:32:42.469 Aakash Tandel: this sprint is closing. I’ll be able to kind of look at what we’ve done. I know. I’m also gonna go through here and make sure that we’re so this is done. We are technically done with adding in subscribe and save more orders. We just don’t know where we’re gonna
297 00:32:42.680 ⇒ 00:32:50.011 Aakash Tandel: visualize that data. So that’s something. Maybe I or Robert can kind of finalize
298 00:32:50.970 ⇒ 00:33:03.333 Aakash Tandel: clavia data is not done. It’s done from the modeling standpoint. But it’s not done. Visualize, attentive basic data is there? But we need to finalize those visuals.
299 00:33:04.400 ⇒ 00:33:22.999 Aakash Tandel: north beam. Same deal. Data analyst hand off. That’s still happening. And then incremental refreshes. This is, I guess, kind of done. Ish almost done. So yeah, if we can close some of these out by the end of today, that’d be great. I don’t know if that’s gonna happen. So we’ll we’ll figure it out
300 00:33:27.970 ⇒ 00:33:28.810 Aakash Tandel: alright.
301 00:33:29.220 ⇒ 00:33:35.520 Aakash Tandel: Let me know if you guys have anything or any blockers. But yeah, thanks for the good work this week. And yeah, I’ll.
302 00:33:35.520 ⇒ 00:34:04.110 Annie Yu: Yeah, yeah, sorry. I have a note to add, I I saw Robert’s note in slack. And I just wanna say, appreciate the reminder and fully on board with what you’ve shared. The framework is helpful, and I think myself definitely need to internalize that more. But also, I do have one note to share is that, and I’m just speaking for myself is that sometimes it’s kind of tricky just looking at the ticket to align on
303 00:34:04.230 ⇒ 00:34:11.590 Annie Yu: the business question or intended outcome from just looking at the ticket. So I want to say it will be really helpful if.
304 00:34:11.780 ⇒ 00:34:13.939 Annie Yu: when a ticket is created.
305 00:34:14.120 ⇒ 00:34:30.419 Annie Yu: it’s not just like instructions like, Do this do that, but also like a quick note on business context, or what the stakeholders should be able to do with the output. I think that would be really helpful in like cutting down some back and forth and help keep things more aligned with the goal.
306 00:34:30.989 ⇒ 00:34:31.540 Aakash Tandel: Yeah, yeah.
307 00:34:31.540 ⇒ 00:34:45.249 Robert Tseng: Yeah, totally. I mean, I think it’s I mean, where it’s kind of both ways. You’re right. I think you should have agency to push back and be like there’s not enough on this ticket. If you if you feel like you want, you want that, I mean, I I think.
308 00:34:45.860 ⇒ 00:34:58.440 Robert Tseng: yeah, sometimes we’re just like creating tickets on the fly from these standups, and then we don’t end up like writing them out fully. So I know, like my. The tickets that I write are pretty inconsistent sometimes it’s just like a
309 00:34:58.440 ⇒ 00:35:17.389 Robert Tseng: I need some small edits on something, and I don’t write out the full thing. But yeah, if you if if there is a time when you feel like you’re doing too much guessing or assumption making, and you need more questions, or you need more context. I think you should. You should push back on on us as well, and and I think that will at least
310 00:35:17.550 ⇒ 00:35:28.883 Robert Tseng: that that at least tells me. Okay, that’s what I need to do to support you on on a ticket. I just need to tell you a bit more what? What like they’re looking for, or if I don’t then like, we need to ask the client.
311 00:35:29.570 ⇒ 00:35:36.740 Annie Yu: Yeah, yeah, okay, thanks. And and also, I hear you. So thank you for speaking up.
312 00:35:37.420 ⇒ 00:35:50.110 Robert Tseng: Yeah, yeah, no. I mean, I I’m I see that I’m the bottleneck for a lot of the stuff where this team is great. We’re pushing stuff out faster now. And I I try. And I’m trying to.
313 00:35:50.290 ⇒ 00:35:55.950 Robert Tseng: I’m just trying to better support the team. So I think that’s kind of a that’s
314 00:35:56.360 ⇒ 00:36:01.849 Robert Tseng: yeah. That was my intention behind kind of thinking through how we how do we do that? Better? Moving forward.
315 00:36:02.630 ⇒ 00:36:04.959 Annie Yu: Sounds good. Appreciate this team.
316 00:36:06.240 ⇒ 00:36:07.600 Robert Tseng: Alright! Thanks everyone.
317 00:36:07.950 ⇒ 00:36:08.660 Aakash Tandel: Thanks guys.
318 00:36:09.060 ⇒ 00:36:10.429 Caio Velasco: Thank you. Have a good day. Bye.