Meeting Title: [Javvy] Daily Standup Date: 2025-04-08 Meeting participants: Aakash Tandel, Annie Yu, Awaish Kumar, Caio Velasco
WEBVTT
1 00:00:16.210 ⇒ 00:00:17.949 Caio Velasco: Hey! Wish! How are you?
2 00:00:34.160 ⇒ 00:00:35.289 Aakash Tandel: Hey, guys, how’s it going.
3 00:00:36.490 ⇒ 00:00:38.248 Caio Velasco: Hey, guys, how good! How are you.
4 00:00:39.950 ⇒ 00:00:44.060 Aakash Tandel: Good alright, Robert’s not joining
5 00:00:45.430 ⇒ 00:00:51.210 Aakash Tandel: like, so I can give Andy a second. But let me go ahead and share my screen.
6 00:00:54.460 ⇒ 00:00:56.380 Aakash Tandel: Okay.
7 00:00:57.060 ⇒ 00:01:16.499 Aakash Tandel: I want to run through. Stand up pretty quickly today. Because I want to get to kind of like a refinement of things that are coming down the pipe, or need to be worked on ahead of other things based off of what the clients asking for. Looks like any stuff is.
8 00:01:17.100 ⇒ 00:01:17.989 Aakash Tandel: hey, Annie?
9 00:01:20.060 ⇒ 00:01:20.940 Annie Yu: Hello!
10 00:01:24.420 ⇒ 00:01:29.940 Aakash Tandel: The yeah. So let’s try to run through. Stand up like quickly. And then, yeah, if
11 00:01:30.670 ⇒ 00:01:46.090 Aakash Tandel: we can get to stand refinement, that’d be great. Okay, I’ll start with you, Annie. I think you have. I think, the the lighter stuff. I know that I need to still get you more work for this week. But all the work that you’ve asked for so far has been engineering work so still light on your stuff.
12 00:01:49.170 ⇒ 00:01:50.950 Annie Yu: Yeah. So
13 00:01:54.810 ⇒ 00:01:59.851 Annie Yu: sorry. I just, I’m waiting for my laptop to run. Okay?
14 00:02:00.680 ⇒ 00:02:14.369 Annie Yu: yeah. The monthly cohort. There are just a couple of adjustments. I think a wish already took a look, and we’ll be working on that. And then, once that’s done, I I think we can build that views.
15 00:02:14.670 ⇒ 00:02:15.290 Aakash Tandel: Okay.
16 00:02:15.990 ⇒ 00:02:18.279 Annie Yu: And then, yeah, this is the same thing.
17 00:02:18.930 ⇒ 00:02:24.809 Aakash Tandel: Same thing. Okay, that sounds good, lex pop over to
18 00:02:25.640 ⇒ 00:02:31.420 Aakash Tandel: always. You have a ton of stuff. We’re gonna look at the stuff that’s I guess in
19 00:02:32.570 ⇒ 00:02:44.569 Aakash Tandel: process. I there’s a Pius had a Pr open for his script changes to the shopify matching. Do you want to handle that? Or do you think I should send that to Utham.
20 00:02:45.700 ⇒ 00:02:46.840 Awaish Kumar: Oh.
21 00:02:47.630 ⇒ 00:02:58.480 Awaish Kumar: I can review the code, but the I don’t have much context into what’s what will in terms of angle. So I’m not sure if how much you want to review like.
22 00:02:58.710 ⇒ 00:03:02.099 Awaish Kumar: if Algo needs to be reviewed, then yeah, utham can handle it. But
23 00:03:02.280 ⇒ 00:03:08.240 Awaish Kumar: yeah, in terms of what is in there, and the the code and everything. I can have a look at that.
24 00:03:08.560 ⇒ 00:03:18.727 Aakash Tandel: Okay, let’s just I feel like, I don’t want to use. Honestly, I think you have enough other stuff on your plate, so we’ll see if can pick that up, so I’ll pull that off
25 00:03:20.560 ⇒ 00:03:20.900 Aakash Tandel: so that.
26 00:03:20.900 ⇒ 00:03:32.500 Awaish Kumar: For attentive modeling. So yeah, the Pr is in review. I have created a model which can answer these 3 questions which you posted in the description.
27 00:03:32.980 ⇒ 00:03:33.680 Aakash Tandel: Awesome.
28 00:03:34.880 ⇒ 00:03:39.110 Awaish Kumar: And similarly, for north beam,
29 00:03:41.780 ⇒ 00:03:42.410 Aakash Tandel: Yep.
30 00:03:43.288 ⇒ 00:03:46.399 Awaish Kumar: It’s kind of for the part one
31 00:03:46.976 ⇒ 00:03:50.309 Awaish Kumar: I like the the. It is done like
32 00:03:50.450 ⇒ 00:03:54.022 Awaish Kumar: some of thing which cannot be done using the current
33 00:03:55.060 ⇒ 00:03:58.010 Awaish Kumar: structure. So I have created a Pr.
34 00:03:58.300 ⇒ 00:04:00.759 Awaish Kumar: there’s a model which can be used
35 00:04:00.880 ⇒ 00:04:15.709 Awaish Kumar: to calculate the Cac by like week, month by product for the new customers new subscribers, but for the by, like, we cannot do it country level, because we don’t have a span based on country.
36 00:04:16.180 ⇒ 00:04:16.855 Aakash Tandel: Okay.
37 00:04:18.019 ⇒ 00:04:21.609 Awaish Kumar: So I have a Pr. In the Pr. Description I have.
38 00:04:21.790 ⇒ 00:04:29.809 Awaish Kumar: I have mentioned the the key points, which which is the
39 00:04:30.500 ⇒ 00:04:34.315 Awaish Kumar: which is required for everyone to understand, because, like
40 00:04:34.940 ⇒ 00:04:37.850 Awaish Kumar: how the numbers like it is.
41 00:04:38.120 ⇒ 00:04:42.460 Awaish Kumar: it is important to understand those 3 points, to have a context
42 00:04:43.167 ⇒ 00:04:48.770 Awaish Kumar: in the numbers like how the numbers are being calculated in this model.
43 00:04:48.910 ⇒ 00:05:00.249 Awaish Kumar: So in the Pr. Description, except that there is a Pr. On Github, and I have added a description there, so I think I can paste it here as well in the ticket.
44 00:05:00.690 ⇒ 00:05:01.280 Awaish Kumar: Okay.
45 00:05:01.280 ⇒ 00:05:03.230 Aakash Tandel: Awesome. Yeah, if you can paste that in there.
46 00:05:04.860 ⇒ 00:05:12.689 Awaish Kumar: That is like for everyone who is going to work on it is is important to understand how how data is
47 00:05:13.110 ⇒ 00:05:16.589 Awaish Kumar: data looks in the table, and
48 00:05:16.950 ⇒ 00:05:24.230 Awaish Kumar: we I have asked also contacted portable to see if if there is anything missing.
49 00:05:24.773 ⇒ 00:05:31.319 Awaish Kumar: In our exports, like, for example, we have maybe country information in North Beam, but we are not
50 00:05:31.760 ⇒ 00:05:39.800 Awaish Kumar: bringing it in in our warehouse using the portable, so I have asked them, if there’s any way
51 00:05:40.110 ⇒ 00:05:45.250 Awaish Kumar: how we bring. Bring that in as well, so I will see if if I get an answer
52 00:05:45.470 ⇒ 00:05:52.880 Awaish Kumar: I can. I can like share it with you, but right now, we cannot do on a country level. And yeah, our one is done.
53 00:05:52.880 ⇒ 00:05:53.460 Aakash Tandel: Okay.
54 00:05:54.100 ⇒ 00:06:04.939 Aakash Tandel: awesome and for the stuff you’re talking about for the calculations. I think it’s probably worth the entire team looking at Kyle, you’re gonna I think, can you do the Pr for this one?
55 00:06:09.430 ⇒ 00:06:10.190 Aakash Tandel: Okay.
56 00:06:10.950 ⇒ 00:06:17.550 Caio Velasco: Oh, I was muted well. Review need to review the Pr.
57 00:06:18.690 ⇒ 00:06:19.860 Aakash Tandel: Yes. Yeah.
58 00:06:20.090 ⇒ 00:06:24.799 Caio Velasco: No, I’ll I’ll definitely take a look I was talking to always yesterday about it, and
59 00:06:24.940 ⇒ 00:06:31.489 Caio Velasco: and he would start with this one, and then I would move forward with the other ones, or at least with the ones that I would be comfortable with.
60 00:06:31.630 ⇒ 00:06:37.529 Caio Velasco: And yeah, well, we. I can talk more when it gets my time, but I’ll definitely check it out.
61 00:06:39.520 ⇒ 00:06:40.529 Aakash Tandel: And then, yeah.
62 00:06:40.780 ⇒ 00:06:47.389 Awaish Kumar: The Pr. Is ready. You can review it. That table is in a staging now we can build on top of it.
63 00:06:47.550 ⇒ 00:06:48.330 Awaish Kumar: and
64 00:06:49.310 ⇒ 00:06:57.370 Awaish Kumar: like for the other tickets we need to like investigate first, st that is, is it? Is it possible to do all these tickets?
65 00:06:57.510 ⇒ 00:07:01.900 Awaish Kumar: Because I’m not sure right? The it’s fine.
66 00:07:01.900 ⇒ 00:07:02.460 Aakash Tandel: Yeah.
67 00:07:02.950 ⇒ 00:07:04.340 Awaish Kumar: Like, calculate.
68 00:07:04.340 ⇒ 00:07:04.800 Caio Velasco: Doc, but.
69 00:07:04.800 ⇒ 00:07:05.320 Awaish Kumar: And.
70 00:07:06.370 ⇒ 00:07:22.929 Caio Velasco: Yeah, question I I had when I was looking into the database is because before diving into Snowflake, I thought I understood. The North being has 3 main tables. But then, when I went to the Snowflake, I saw that there was nothing there. So then I started asking myself, Okay, so what are we modeling then? And then there was a
71 00:07:23.760 ⇒ 00:07:25.160 Caio Velasco: has some results.
72 00:07:25.410 ⇒ 00:07:32.860 Caio Velasco: Yeah, there’s another one with some result like, call data export results. They have stuff there exactly.
73 00:07:33.150 ⇒ 00:07:40.329 Caio Velasco: But then, for the, for example, this tickets, like email opt in those things are
74 00:07:40.470 ⇒ 00:07:56.450 Caio Velasco: when we say email opt in or any other things, is it on the north being side, or is it joining with shopify or Amazon whatever, and then getting the email when they opting in option from that? That’s what I’m a bit confused.
75 00:07:58.820 ⇒ 00:08:00.830 Awaish Kumar: Do we have any description in the ticket?
76 00:08:01.840 ⇒ 00:08:03.839 Aakash Tandel: Yeah. Which? Oh, wait, can we?
77 00:08:04.532 ⇒ 00:08:06.609 Awaish Kumar: 1, 7, 7.
78 00:08:08.030 ⇒ 00:08:09.979 Aakash Tandel: 1, 7, 7. Okay, this guy.
79 00:08:14.050 ⇒ 00:08:14.959 Caio Velasco: The description is more.
80 00:08:14.960 ⇒ 00:08:16.560 Caio Velasco: It almost seems like whatever we want.
81 00:08:16.560 ⇒ 00:08:23.460 Awaish Kumar: Byproduct 2 by to today from
82 00:08:23.790 ⇒ 00:08:32.720 Awaish Kumar: yeah, like, this is possible. I’m not sure why we named Email or SMS. New customers acquired
83 00:08:33.150 ⇒ 00:08:38.859 Awaish Kumar: like in the description. We are saying that new customer, acquired by product by day and by platform.
84 00:08:44.150 ⇒ 00:08:44.950 Awaish Kumar: Okay.
85 00:08:47.280 ⇒ 00:08:48.120 Awaish Kumar: Hmm.
86 00:08:49.410 ⇒ 00:08:53.889 Annie Yu: And what’s the question you said the email new customer acquired.
87 00:09:03.480 ⇒ 00:09:12.930 Aakash Tandel: Yeah, so okay, so so I guess the question is, we is
88 00:09:13.630 ⇒ 00:09:25.699 Aakash Tandel: north beam data. It needs to be appended to other data sources. That’s kind of the idea. That all all this is hinging on right? Because if they just wanted north beam data, they could just go to north beam.
89 00:09:25.720 ⇒ 00:09:44.530 Aakash Tandel: So the idea is we are marrying it with existing other data sources. So in this case, we’re talking about attentive we’re talking about klaviyo. I don’t think we have klaviyo model yet, but attentive. Definitely our product information and our order information. So that’s the idea.
90 00:09:45.510 ⇒ 00:09:48.279 Aakash Tandel: I think if you have follow yeah.
91 00:09:49.450 ⇒ 00:09:54.100 Awaish Kumar: Okay. So I I know, like what? What has to be done here?
92 00:09:56.090 ⇒ 00:09:58.649 Awaish Kumar: I will have some questions like
93 00:09:58.790 ⇒ 00:10:06.060 Awaish Kumar: what we mean by new customer by product. For example, a new SMS sent from attentive.
94 00:10:06.280 ⇒ 00:10:10.149 Awaish Kumar: and after that that customer made some order.
95 00:10:10.270 ⇒ 00:10:13.959 Awaish Kumar: So we have a new customer. We can say
96 00:10:14.200 ⇒ 00:10:21.179 Awaish Kumar: this customer has not bought anything before this. SMS. So that means this customer came
97 00:10:21.310 ⇒ 00:10:25.510 Awaish Kumar: from attentive. It’s a new customer for us, and
98 00:10:25.830 ⇒ 00:10:30.946 Awaish Kumar: we can say that like using this 5 days logic, whatever, right? And
99 00:10:31.530 ⇒ 00:10:36.650 Awaish Kumar: but what about byproduct like, for example, when we say byproduct
100 00:10:37.327 ⇒ 00:10:44.189 Awaish Kumar: do we want to adjust for product, like a customer who came after an SMS.
101 00:10:44.470 ⇒ 00:10:47.970 Awaish Kumar: Made 1st time purchase for protein.
102 00:10:48.400 ⇒ 00:10:51.679 Awaish Kumar: but he already made a purchase for concentrate.
103 00:10:51.880 ⇒ 00:10:55.539 Annie Yu: How do we want to handle that? Is it a new customer for us or not?
104 00:10:55.880 ⇒ 00:10:57.070 Awaish Kumar: When you say by product.
105 00:10:57.810 ⇒ 00:11:14.479 Aakash Tandel: Yeah. So here new customer is always going to be. At least this is my understanding. A person who’s never purchased before. So if they’ve purchased concentrate, and then they purchase protein. They’re only a new customer for that 1st purchase with concentrate not the protein one.
106 00:11:15.500 ⇒ 00:11:20.839 Awaish Kumar: Okay. So you’re saying that a customer who came after
107 00:11:21.020 ⇒ 00:11:32.049 Awaish Kumar: an SMS. Sent from Klavio within 5 days. And then it was the 1st purchase of this, that customer. Then it’s a new customer.
108 00:11:33.240 ⇒ 00:11:34.149 Aakash Tandel: Yep, exactly.
109 00:11:34.150 ⇒ 00:11:39.775 Awaish Kumar: Whatever it purchased is is its product type, whatever it is, it purchased right? And
110 00:11:41.840 ⇒ 00:11:45.970 Awaish Kumar: and one and okay for the new customer. And whatever.
111 00:11:46.990 ⇒ 00:11:55.259 Awaish Kumar: Okay, then we we can say if it it was a second purchase or 3rd purchase, then it was. It is a returning customer.
112 00:11:57.540 ⇒ 00:11:58.170 Aakash Tandel: Yep.
113 00:11:59.880 ⇒ 00:12:18.139 Aakash Tandel: I think that should be across the board. I think we should always define a new customer, someone that we’ve never had purchased an item right? Hopefully, that logic is separate from just north beam. And and it’s across the board, because that’s what the clients gonna look for. They’re gonna look for, you know, new customers never purchased a thing before, even if.
114 00:12:18.140 ⇒ 00:12:25.150 Awaish Kumar: But this does not seem to anything to do with Northweam. Northweam is spam data.
115 00:12:25.280 ⇒ 00:12:28.330 Awaish Kumar: right? And we we don’t. We are not.
116 00:12:28.330 ⇒ 00:12:28.910 Aakash Tandel: Gotcha.
117 00:12:28.910 ⇒ 00:12:33.779 Awaish Kumar: Taking care of any spend data here. So this is this is like
118 00:12:33.930 ⇒ 00:12:53.599 Awaish Kumar: we have to use attentive. I have built some basic, attentive model fact table which is in the Pr Review. It can be used, and for Klavio I don’t think we have right now, but we might have to include Klaviyo model and plus for attentive. I used only SMS because that ticket was
119 00:12:54.030 ⇒ 00:12:56.359 Awaish Kumar: only wanted some.
120 00:12:56.680 ⇒ 00:13:00.539 Awaish Kumar: And they’re interested in the SMS sent right?
121 00:13:00.870 ⇒ 00:13:03.180 Awaish Kumar: So we
122 00:13:03.320 ⇒ 00:13:14.490 Awaish Kumar: so yeah, we have to build some modeling, modeling, more modeling work for attentive. And the Klavio include SMS sent email sent things like that and then build and try to join it with the
123 00:13:15.110 ⇒ 00:13:23.309 Awaish Kumar: with it, orders data, and find out new customers and the repeat customers. And that’s all this ticket is. So it’s it’s not about Northway.
124 00:13:24.120 ⇒ 00:13:25.610 Aakash Tandel: So it’s not.
125 00:13:26.150 ⇒ 00:13:28.780 Aakash Tandel: Is there a strikethrough option? There’s not a strikethrough option.
126 00:13:32.250 ⇒ 00:13:40.840 Caio Velasco: I think also the other ones might not be related to North, being, because at the end it was when that was exactly why I was a bit confused
127 00:13:40.970 ⇒ 00:13:43.619 Caio Velasco: because North Bing is just. Basically
128 00:13:43.880 ⇒ 00:13:47.759 Caio Velasco: they are building statistical models to understand.
129 00:13:48.340 ⇒ 00:13:54.170 Caio Velasco: What if if everything that you’ve spent in that was
130 00:13:56.850 ⇒ 00:14:09.489 Caio Velasco: but if you did it right, or which channel was the best one. So anything that is ready to email opt in, or those things I I don’t see it being connected. But I I’m also not sure, because I I don’t know why a lot about it.
131 00:14:10.500 ⇒ 00:14:14.350 Aakash Tandel: Yeah, that makes sense. Okay. So for North beam.
132 00:14:15.020 ⇒ 00:14:21.160 Aakash Tandel: Cp, what’s do you, Annie? Do you remember what Cpa is.
133 00:14:23.230 ⇒ 00:14:24.220 Aakash Tandel: Acquisition.
134 00:14:27.370 ⇒ 00:14:32.990 Annie Yu: I let me think about it. I think it’s cost per action, marketing.
135 00:14:33.530 ⇒ 00:14:34.250 Aakash Tandel: Okay.
136 00:14:34.660 ⇒ 00:14:36.730 Caio Velasco: Of course, per acquisitions.
137 00:14:37.640 ⇒ 00:14:37.960 Annie Yu: Oh!
138 00:14:37.960 ⇒ 00:14:44.330 Aakash Tandel: Okay. So okay, so the only thing that’s like.
139 00:14:44.440 ⇒ 00:14:57.380 Aakash Tandel: so I guess the the ask was to replicate the amplitude. Dashboard and amplitude. Dashboard is not just ad spend data. So this is the primary ad spend. Data is kind of what we’re what we’re looking at.
140 00:14:58.930 ⇒ 00:14:59.540 Awaish Kumar: Yes.
141 00:15:00.000 ⇒ 00:15:01.100 Aakash Tandel: Okay, so.
142 00:15:01.100 ⇒ 00:15:12.190 Awaish Kumar: But other tickets like there, there’s 1 more ticket I saw. It’s about talking about Cac. That’s also maybe related to add Spam. I don’t know it. Which one it was.
143 00:15:18.910 ⇒ 00:15:20.290 Aakash Tandel: This one subsurference.
144 00:15:20.290 ⇒ 00:15:21.610 Awaish Kumar: You know this one here?
145 00:15:22.310 ⇒ 00:15:34.699 Awaish Kumar: Yeah, like, gag is customer acquisition cost, which is like any spend through marketing new customer acquired. And I.
146 00:15:34.950 ⇒ 00:15:37.029 Aakash Tandel: Alright, yeah, that can be.
147 00:15:37.460 ⇒ 00:15:40.659 Awaish Kumar: Part of like. I don’t know how it can be solved.
148 00:15:40.800 ⇒ 00:15:46.170 Awaish Kumar: But right now. But yeah, it seems like related to some added, some spam.
149 00:15:47.590 ⇒ 00:15:51.950 Aakash Tandel: Okay, I’m gonna pull these out of
150 00:15:52.880 ⇒ 00:15:56.579 Aakash Tandel: ready for development because it sounds like these are not ready for development
151 00:16:00.500 ⇒ 00:16:02.240 Aakash Tandel: started.
152 00:16:02.830 ⇒ 00:16:04.710 Aakash Tandel: Okay? So
153 00:16:05.470 ⇒ 00:16:30.599 Aakash Tandel: let’s go ahead and not worry about those other ones until I figure out exactly what the clients looking for here, because these don’t like you said these are not north beam, and they also are not prioritizing, attentive and klaviyo. So let’s not worry about that at the moment. Let’s get through the North beam metrics that you have here that’s already done in Pr.
154 00:16:30.600 ⇒ 00:16:31.879 Aakash Tandel: Yes, that’s good.
155 00:16:32.677 ⇒ 00:16:44.610 Aakash Tandel: And then so that makes sense. So I think let’s pause on North beam stuff and do the stuff that they want. Now, which is
156 00:16:45.288 ⇒ 00:16:50.760 Aakash Tandel: actually let? Are you working on anything else? I know you sunk some time into these, but anything else.
157 00:16:50.930 ⇒ 00:16:59.959 Awaish Kumar: No like I I just put push the Pr Prs. If there’s any feedback I will work on that. But yeah, not not anything right now.
158 00:17:00.590 ⇒ 00:17:01.270 Aakash Tandel: Okay.
159 00:17:01.270 ⇒ 00:17:01.700 Awaish Kumar: Saw, the.
160 00:17:01.700 ⇒ 00:17:02.279 Aakash Tandel: Just to get.
161 00:17:02.280 ⇒ 00:17:04.759 Awaish Kumar: Came in, but I have not looked at them.
162 00:17:05.849 ⇒ 00:17:12.659 Aakash Tandel: Yup, that makes sense. Okay, let’s go to Kyle. And then we can talk about the stuff to do because there’s that’s the kind of
163 00:17:13.069 ⇒ 00:17:19.409 Aakash Tandel: last vision. Okay, Kyle. I know you’re blocked here still. I I mean
164 00:17:19.579 ⇒ 00:17:29.889 Aakash Tandel: we. I’ll ping them again in that thread, but they have not given us any feedback on that modeling stuff. I don’t know if you’ve seen anything. But yeah, I think we’re still blocked here.
165 00:17:31.300 ⇒ 00:17:33.879 Caio Velasco: No, I haven’t seen anything yet as well.
166 00:17:34.740 ⇒ 00:17:44.559 Aakash Tandel: Okay. Alright, that’s fine. Okay. Are you working on anything else? Then I know you’re doing that investigation stuff with the wish. But sounds like that was blocked, or that’s getting repaired.
167 00:17:46.920 ⇒ 00:17:58.995 Caio Velasco: Yes, so I was taking the North bean stuff. But I was just doing some investigation of like what it is understanding, like the models they do just to get an understanding of whatever I’m gonna see in the database.
168 00:17:59.745 ⇒ 00:18:09.509 Caio Velasco: But then, now that is that we stopped in. I can just check the Pr. From from a wish, and then at least learn a bit of what he did for the future
169 00:18:10.680 ⇒ 00:18:19.399 Caio Velasco: and what else. The other one is blocked. But I think I just. I’m actually just checking the the spreadsheet. And they they have added stuff.
170 00:18:19.920 ⇒ 00:18:27.320 Caio Velasco: I just hope that it’s well correct. But not a lot. They they added something to the last one, the last tab
171 00:18:29.210 ⇒ 00:18:34.169 Caio Velasco: but I have to check, even if they just copy. They paste. Whatever from sports file I don’t know.
172 00:18:34.360 ⇒ 00:18:36.430 Caio Velasco: Doesn’t seem that they did any.
173 00:18:39.006 ⇒ 00:18:45.960 Caio Velasco: Yeah, I think yeah, well, doesn’t seem that they didn’t make any promise. But yeah, I think other than that.
174 00:18:46.464 ⇒ 00:18:52.920 Caio Velasco: I just talked to to Luke today, but it was about 2 parts, nothing else.
175 00:18:53.640 ⇒ 00:19:00.440 Aakash Tandel: Okay. Alright cool. So it sounds like we can put some stuff on your plate. Let me put.
176 00:19:01.037 ⇒ 00:19:06.840 Awaish Kumar: I saw the modeling version, one for Klaviyo, for Kyle.
177 00:19:06.840 ⇒ 00:19:09.200 Caio Velasco: I saw. Yes, yes, it was one of.
178 00:19:11.790 ⇒ 00:19:15.159 Awaish Kumar: I don’t know the other ticket about
179 00:19:15.310 ⇒ 00:19:20.430 Awaish Kumar: like the alternative clavier modeling task like
180 00:19:21.590 ⇒ 00:19:29.570 Awaish Kumar: I don’t know if if it if this modeling work blocks that ticket. I’m not sure how how much
181 00:19:29.670 ⇒ 00:19:37.160 Awaish Kumar: it it links like, how much is required out of that modeling work. But yeah, something might be helpful.
182 00:19:38.750 ⇒ 00:19:41.050 Aakash Tandel: Yeah, let’s talk through.
183 00:19:41.210 ⇒ 00:19:52.159 Aakash Tandel: Let’s talk through some of these things. So the 1st thing I need to the 1st thing that I want to talk through is, I think, for you.
184 00:19:52.600 ⇒ 00:19:57.839 Aakash Tandel: You’re you’re waiting on those 2 Prs, and then that’s basically it, for
185 00:19:58.170 ⇒ 00:20:02.649 Aakash Tandel: on your plate. So let me pull the 1st thing which is a new.
186 00:20:04.040 ⇒ 00:20:13.840 Aakash Tandel: Where is it? I pulled this investigate. Okay, so this one. So they’re back to the subscribe and save thing.
187 00:20:14.740 ⇒ 00:20:33.418 Aakash Tandel: so they want to basically see if we can use this Api, which is called the replenish Api to flag a user as a subscribe and save user. So again, we’re still kind of coming back to this. They think that we can use this Api
188 00:20:34.560 ⇒ 00:20:55.180 Aakash Tandel: This Api does have subscribe and save data in it. It’s right here. But it’s tied to a marketplace. Id. And I don’t know if this id is, you know, joinable to our other data to say, Hey, this user is a subscribe and save person. Or this order came from a subscribe and save person. So
189 00:20:56.650 ⇒ 00:21:01.469 Awaish Kumar: Okay, so do we want to handle this like, like, we want to
190 00:21:01.550 ⇒ 00:21:19.360 Awaish Kumar: hit the Api ourselves. Because right now, what we are doing is, we are using the external connectors. Right? So we have. We have 5 train 5 train further, like, we just connect 5 train Amazon selling partner, Api, right
191 00:21:19.360 ⇒ 00:21:38.209 Awaish Kumar: and Amazon, like the 5 trend does these Api calls for us to the Amazon and gets data into our warehouse, and they are missing. Subscribe and save information. So now are we investigating that? It’s is it possible? And then ask the 5 trend
192 00:21:38.490 ⇒ 00:21:42.200 Awaish Kumar: to do it? Or do we want to make these requests ourselves?
193 00:21:43.080 ⇒ 00:21:47.580 Aakash Tandel: Yeah. So I think that’s so we definitely want to use
194 00:21:48.323 ⇒ 00:21:56.009 Aakash Tandel: the 5 tran using 5 tran Api or connector
195 00:21:57.213 ⇒ 00:22:03.596 Aakash Tandel: because that’s the way that we’re currently handling all of our Amazon Api. So we want to continue to do that.
196 00:22:04.240 ⇒ 00:22:21.090 Aakash Tandel: my question before we do that is, is this data even like mergeable to our other data, like, I don’t want to set up connector and sync a bunch of time into that. And then the answer still be like, Oh, yeah. By the way, this still doesn’t have the like. We still can’t link that. So
197 00:22:21.090 ⇒ 00:22:34.810 Aakash Tandel: I wanted to see if you could investigate this Api through their docs and stuff and determine that. You know, it’s like, it’s definitely possible. It’s, you know, 50% possible. Or it’s definitely not possible that type of thing. And then we can go from there.
198 00:22:35.830 ⇒ 00:22:41.780 Awaish Kumar: Okay. So we just want to see if it is mergeable. Then we make a decision on how to get the data in right.
199 00:22:42.000 ⇒ 00:22:43.130 Aakash Tandel: Exactly. Yep.
200 00:22:43.130 ⇒ 00:22:43.870 Awaish Kumar: Okay.
201 00:22:44.950 ⇒ 00:22:56.020 Aakash Tandel: Awesome. So this will be your I think this is the highest priority task for you. The today. And I also don’t want you to spend more than like 2 h like
202 00:22:56.400 ⇒ 00:23:11.119 Aakash Tandel: our. Our time spent investigating these things should not be more than our time to developing work. So if it’s not possible, that’s a totally fair answer. And we can. We can go back to them and say that. So that’s a totally fine thing for you to you to say as well.
203 00:23:12.649 ⇒ 00:23:19.340 Aakash Tandel: And I will just add a due date of tomorrow. This guy, okay?
204 00:23:20.504 ⇒ 00:23:25.839 Aakash Tandel: The other thing on your plate that came up during the
205 00:23:27.810 ⇒ 00:23:41.686 Aakash Tandel: The call with them on yesterday was about data refreshes. So Javi wants to move to 4 incremental data refreshes per day per their data sources.
206 00:23:43.220 ⇒ 00:23:51.509 Aakash Tandel: the. I don’t know if this is possible for all their data sources, and it’s likely it’s not possible for all their data sources. So I think.
207 00:23:52.860 ⇒ 00:24:14.739 Aakash Tandel: the task for you. And this is like, obviously a fair amount of work is to try to turn these into for incremental data sources, data refreshes, and if it’s not possible, just say, Hey, this is impossible, because they only accept like a full refresh or a daily refresh, or whatever like that. So does that request sound? Does that make sense to you?
208 00:24:15.200 ⇒ 00:24:17.000 Awaish Kumar: Yeah, that’s that’s okay.
209 00:24:17.430 ⇒ 00:24:29.439 Aakash Tandel: Okay, cool. So this is gonna be your second priority after the after the investigate Api thing.
210 00:24:31.735 ⇒ 00:24:38.070 Aakash Tandel: And then those data refreshes. Wait. Why is there? Oh, this is cost of goods, assumption.
211 00:24:38.720 ⇒ 00:24:46.789 Aakash Tandel: I don’t know. I’m gonna prioritize this, and I will say, Robert.
212 00:25:02.530 ⇒ 00:25:09.039 Aakash Tandel: pull that guy down. Okay, so that’s incremental. Day refreshes. And then these are all on. Robert. Okay, cool
213 00:25:10.340 ⇒ 00:25:14.890 Aakash Tandel: for Kyle. The
214 00:25:15.480 ⇒ 00:25:22.259 Aakash Tandel: I know that we. Is it true that we have Klavio data in Snowflake? Is that true? At least you might be able to answer that.
215 00:25:22.260 ⇒ 00:25:23.190 Awaish Kumar: Yes.
216 00:25:23.360 ⇒ 00:25:52.650 Aakash Tandel: Awesome. Okay? So, Kyle, I wanted you to. See if you can join the following email metrics with our revenue and order data. So in this scenario, you’d get the email data. So things like sends opens, clicks, conversion rates and marry that with our order and revenue data. So you could say, Hey, from this email campaign. We sent out, you know, 80 emails.
217 00:25:53.775 ⇒ 00:25:54.550 Aakash Tandel: and
218 00:25:54.670 ⇒ 00:26:09.600 Aakash Tandel: we got 5 orders or something like that. That type of information? So that’s the task for you, for that’s like the most pressing. Does this request make sense to you, or is there things I need to add to this for it to make sense.
219 00:26:11.864 ⇒ 00:26:30.060 Caio Velasco: I think it makes sense. I haven’t touched coffee. I don’t even know what it is, but this seems that it’s made of stuff. Then I have to take a look in the tables and see how to connect those 2. When you guys see say revenue and order, I mean, probably it’s dropped by an Amazon order you’re talking about right?
220 00:26:30.670 ⇒ 00:26:32.459 Aakash Tandel: Yep, shopify and Amazon ownership.
221 00:26:33.130 ⇒ 00:26:36.099 Caio Velasco: Okay, so basically sect orders, table.
222 00:26:36.740 ⇒ 00:26:38.030 Aakash Tandel: Yep. Factor, issue. Yep.
223 00:26:38.476 ⇒ 00:26:45.529 Awaish Kumar: Kyle, I have tagged you in a in a select thread, and
224 00:26:45.710 ⇒ 00:26:48.929 Awaish Kumar: I did some investigation on this in the past.
225 00:26:49.080 ⇒ 00:26:54.540 Awaish Kumar: So to join these data like between campaigns
226 00:26:55.883 ⇒ 00:27:06.459 Awaish Kumar: versus events. And like, there are 3 tables, profiles, campaigns, and events. How it can be joined together. So to with like for each user.
227 00:27:06.620 ⇒ 00:27:12.030 Awaish Kumar: which email has been sent to this user kind of thing. So you can look at it. And then.
228 00:27:12.320 ⇒ 00:27:16.510 Awaish Kumar: yeah, the further you have to investigate how to join it with the orders. Data.
229 00:27:18.480 ⇒ 00:27:23.990 Caio Velasco: Okay, perfect. I just posted in in this comment, cool.
230 00:27:23.990 ⇒ 00:27:45.229 Aakash Tandel: Okay, cool. Yeah, it sounds like, yeah. If you have questions, feel free to ping away. And yeah, hopefully, we can help Kyle get this one through. And again, if something is not possible based off of the data. Just flag that just say, Hey, this this one question is not possible because we have limited data, or you know whatever. But yeah, this is kind of the general idea of what they’re looking for.
231 00:27:46.300 ⇒ 00:27:51.219 Caio Velasco: Okay, perfect. So I use the hours I have for this as part of it.
232 00:27:51.220 ⇒ 00:28:04.320 Aakash Tandel: Yeah, and for the Prs, I’m gonna try to get with them to help us move these along. So that yeah, so that we can get that. Get that moving. So, Kyle, if you could focus on this first, st that would be good.
233 00:28:04.780 ⇒ 00:28:05.786 Caio Velasco: Yes, perfectly.
234 00:28:06.450 ⇒ 00:28:17.580 Aakash Tandel: Cool. Okay? And then, Annie, once we have this attentive data.
235 00:28:17.980 ⇒ 00:28:28.360 Aakash Tandel: Pr go through and north beam, basic data pulled through. I think we should visualize that the basic data in metabase.
236 00:28:29.615 ⇒ 00:28:33.520 Aakash Tandel: So I’m going to create very generic tickets.
237 00:28:36.340 ⇒ 00:28:42.879 Aakash Tandel: Database, dashboard, attentive basic data
238 00:28:46.340 ⇒ 00:28:47.319 Aakash Tandel: not good at
239 00:28:55.858 ⇒ 00:28:57.530 Aakash Tandel: let me create these for you?
240 00:28:58.105 ⇒ 00:29:00.834 Aakash Tandel: And once that data is available,
241 00:29:01.450 ⇒ 00:29:10.029 Aakash Tandel: I’ll kind of leave it up to you to visualize the data and then try to answer the questions. Let’s see.
242 00:29:10.330 ⇒ 00:29:15.449 Aakash Tandel: these are the questions that they’re looking to.
243 00:29:24.950 ⇒ 00:29:28.954 Aakash Tandel: These are very basic. So we’ll we’ll meet, might need to refine these. But
244 00:29:31.980 ⇒ 00:29:34.909 Annie Yu: So that one for attentive it’s
245 00:29:35.230 ⇒ 00:29:38.880 Annie Yu: separate from that North Spain replication is that it.
246 00:29:38.880 ⇒ 00:29:49.500 Aakash Tandel: Yes, that is true. Yep. So for this one it will unblock you on this 1st one. So once that Pr goes through, go ahead and start.
247 00:29:49.750 ⇒ 00:29:53.449 Awaish Kumar: You can see the the data in staging right
248 00:29:54.071 ⇒ 00:30:03.080 Awaish Kumar: and and build on on top of that. And when whenever it is published, then you can switch the source from staging to production for your metaverse dashboard.
249 00:30:05.130 ⇒ 00:30:06.989 Annie Yu: Okay. Okay. Thank you.
250 00:30:20.930 ⇒ 00:30:21.660 Aakash Tandel: Okay.
251 00:30:22.264 ⇒ 00:30:31.049 Aakash Tandel: Okay, cool. Well, it sounds like you can maybe move. Move on this one a little bit sooner. And then after that we can figure out what the attentive dashboard looks like.
252 00:30:31.790 ⇒ 00:30:32.330 Annie Yu: Okay. Cool.
253 00:30:32.330 ⇒ 00:30:35.530 Aakash Tandel: I know. I know we’re over time. If anyone has to drop, feel free to drop.
254 00:30:35.870 ⇒ 00:30:39.669 Aakash Tandel: But this is sounds good in terms of kind of what we’re working on.
255 00:30:40.408 ⇒ 00:30:49.171 Aakash Tandel: Thanks for being flexible and helping go through requirements and stuff like that. Yeah, let’s hold on some of the
256 00:30:49.850 ⇒ 00:30:56.280 Aakash Tandel: 2 to 6 of those north beam things because they’re technically not north beam. I’ll figure that out and then go from there.
257 00:30:58.860 ⇒ 00:31:00.459 Caio Velasco: Alright awesome. Just a quick.
258 00:31:00.460 ⇒ 00:31:22.330 Caio Velasco: just a quick thing. Sorry, just a quick thing that I have. I posted in the I pasted in the in the chat a doc for the Amazon selling partner, Erd from 5 trend. I don’t know if you guys already know it, but I know you guys were talking about. So yeah, it’s exactly subscribe and say, there are subscribe and save things in there. I’m not sure if you know already, but just to share
259 00:31:22.760 ⇒ 00:31:25.489 Caio Velasco: it. It helped me when I was doing stuff in the past.
260 00:31:36.514 ⇒ 00:31:37.089 Awaish Kumar: Okay.
261 00:31:44.770 ⇒ 00:31:45.400 Caio Velasco: Thank you.
262 00:31:49.070 ⇒ 00:31:57.198 Aakash Tandel: Oh, sorry I was muted. I I just put that in the ticket for a wish to look at. Thanks for sending that Kyle?
263 00:31:58.000 ⇒ 00:31:59.270 Aakash Tandel: cool anything else.
264 00:31:59.980 ⇒ 00:32:01.330 Caio Velasco: Yeah, thank you guys.
265 00:32:02.090 ⇒ 00:32:04.369 Aakash Tandel: Awesome. Alright, thanks! All appreciate the help.
266 00:32:04.370 ⇒ 00:32:05.249 Caio Velasco: Thank you. Bye-bye.
267 00:32:05.250 ⇒ 00:32:06.770 Annie Yu: Thanks, bye.