Meeting Title: [Javvy] Daily Standup Date: 2025-04-15 Meeting participants: Aakash Tandel, Annie Yu, Awaish Kumar, Caio Velasco
WEBVTT
1 00:00:14.090 ⇒ 00:00:15.469 Aakash Tandel: Hey, rich, how’s it going?
2 00:00:43.150 ⇒ 00:00:49.620 Aakash Tandel: Give folks another minute or so.
3 00:00:50.480 ⇒ 00:00:53.540 Aakash Tandel: I know Robert is not gonna join us. So that’s okay.
4 00:00:54.160 ⇒ 00:00:55.690 Aakash Tandel: Here’s Annie
5 00:01:28.670 ⇒ 00:01:29.830 Aakash Tandel: with Kyo.
6 00:01:30.230 ⇒ 00:01:33.030 Aakash Tandel: Another minute or so. Let me see.
7 00:01:34.230 ⇒ 00:01:35.780 Aakash Tandel: No, Robert.
8 00:01:36.160 ⇒ 00:01:37.270 Aakash Tandel: Okay. Cool.
9 00:01:41.890 ⇒ 00:01:46.719 Aakash Tandel: Thursday. Awesome. Alrighty. Let’s go ahead and get started.
10 00:01:47.480 ⇒ 00:01:52.190 Aakash Tandel: Let me start with. I’ll start with away.
11 00:01:53.980 ⇒ 00:01:59.980 Aakash Tandel: I know. I had a question for you. I think you responded on one of the tickets that are finished.
12 00:02:00.612 ⇒ 00:02:04.068 Aakash Tandel: With the response that Ethan had on the
13 00:02:04.900 ⇒ 00:02:10.130 Aakash Tandel: incremental pro cost on portable was that was I correct in thinking that was Ethan.
14 00:02:12.449 ⇒ 00:02:13.489 Awaish Kumar: I’m sorry.
15 00:02:15.070 ⇒ 00:02:17.170 Awaish Kumar: Got it about the cost. Yeah.
16 00:02:17.918 ⇒ 00:02:21.059 Awaish Kumar: he said. I don’t know.
17 00:02:22.579 ⇒ 00:02:23.599 Aakash Tandel: It was.
18 00:02:31.580 ⇒ 00:02:32.320 Aakash Tandel: yeah.
19 00:02:32.320 ⇒ 00:02:34.940 Awaish Kumar: Said about the cost that.
20 00:02:37.190 ⇒ 00:02:39.190 Aakash Tandel: Was this was this Ethan’s response.
21 00:02:42.790 ⇒ 00:02:43.790 Awaish Kumar: Yeah.
22 00:02:44.780 ⇒ 00:02:52.849 Aakash Tandel: Okay? So it sounds like they have this fixed cost, for up to 25 flows. Do we have 25 existing flows or no?
23 00:02:55.644 ⇒ 00:02:59.770 Awaish Kumar: We. I think we do have 12, I think 1213. Something like that.
24 00:02:59.950 ⇒ 00:03:21.949 Aakash Tandel: Okay, so so it won’t go up based off of this. Okay, I will pass this along to Aman. That’s helpful. And that’s that sounds good, and then I’ll get them. I’ll get you unblocked and finished on this guy once they I’m assuming the answer will be, Yes, and we can go ahead and move all those to a quicker refresh. So that sounds good.
25 00:03:23.540 ⇒ 00:03:25.110 Aakash Tandel: Any update here.
26 00:03:25.560 ⇒ 00:03:28.110 Awaish Kumar: Yeah, north beam data is fixed.
27 00:03:28.260 ⇒ 00:03:31.097 Awaish Kumar: and we should have the correct
28 00:03:31.810 ⇒ 00:03:35.310 Awaish Kumar: data for both. The concentrated protein.
29 00:03:36.830 ⇒ 00:03:39.975 Aakash Tandel: Awesome is this? Was there a pr for this?
30 00:03:40.880 ⇒ 00:03:46.039 Awaish Kumar: No, there was not a like. It was a pro issue with the connector and the
31 00:03:46.758 ⇒ 00:03:51.550 Awaish Kumar: and the configuration in the connector. So yeah, there is no Pr required.
32 00:03:57.150 ⇒ 00:03:57.710 Aakash Tandel: Cool.
33 00:03:58.100 ⇒ 00:03:59.999 Aakash Tandel: Thanks. Awesome. Okay.
34 00:04:00.000 ⇒ 00:04:08.330 Annie Yu: Wanna add to this one? I I just took a quick look at the north main dashboard. And the numbers look great. They look. Yeah, yeah.
35 00:04:08.480 ⇒ 00:04:18.399 Aakash Tandel: Perfect. That sounds great, awesome thanks for doing that, Weish thanks for looking into that. Okay, this is still blocked. Add text field.
36 00:04:20.089 ⇒ 00:04:22.940 Awaish Kumar: So these are new tickets, like Error today by Robert.
37 00:04:23.770 ⇒ 00:04:34.180 Aakash Tandel: Oh, okay. So that’s why he canceled that other one. Okay, that makes sense. So yeah, on a call yesterday with Aman, he was asking for this. And I wrote a ticket, but sounds like
38 00:04:34.870 ⇒ 00:04:47.494 Aakash Tandel: rather also wrote tickets. That’s why he canceled my other ticket. So that’s good. Okay, yeah, basically, the Tldr is they need they need a tax information in
39 00:04:48.120 ⇒ 00:04:59.730 Aakash Tandel: meta base. So I think Robert gave you a lot of context. Feel free to kind of read through this, and then DM, or ask Robert in the Channel if you have questions on the fact, order tax field.
40 00:05:00.690 ⇒ 00:05:02.050 Awaish Kumar: Yeah, sure.
41 00:05:02.400 ⇒ 00:05:13.250 Aakash Tandel: And then, yeah. And then, so this is another thing that Aman wanted. So this looks good. I’ll let you go ahead and read this, they basically just wanna
42 00:05:13.880 ⇒ 00:05:23.149 Aakash Tandel: they they in their mind are changing. Or they think about shipping differently than the way we’ve broken it out. So they want to see the data in a different format. So that’s all that is.
43 00:05:24.010 ⇒ 00:05:24.960 Awaish Kumar: Okay.
44 00:05:27.198 ⇒ 00:05:29.579 Aakash Tandel: Anything else for you. I think that’s that’s it. On your plate.
45 00:05:31.320 ⇒ 00:05:31.980 Awaish Kumar: Yep.
46 00:05:32.520 ⇒ 00:05:33.160 Aakash Tandel: Cool.
47 00:05:35.820 ⇒ 00:05:36.540 Awaish Kumar: Oops!
48 00:05:37.380 ⇒ 00:05:38.130 Aakash Tandel: Kind of.
49 00:05:41.369 ⇒ 00:05:45.710 Awaish Kumar: I have one comment here, like Kyle, I saw your message
50 00:05:48.580 ⇒ 00:05:51.129 Awaish Kumar: about this Cox sheet, and
51 00:05:51.770 ⇒ 00:05:59.060 Awaish Kumar: if you want like, I I think the good idea would be to have data in the same sheet
52 00:05:59.866 ⇒ 00:06:10.390 Awaish Kumar: without field differentiating between Amazon and shopify that way. We reduce the connector because the data in these sheets is
53 00:06:10.830 ⇒ 00:06:16.539 Awaish Kumar: is not like a huge, and we can maintain it in in a single sheet.
54 00:06:16.660 ⇒ 00:06:17.560 Awaish Kumar: So yeah.
55 00:06:19.270 ⇒ 00:06:27.720 Caio Velasco: Okay. And do you think that for that? For for this part, it’s okay to just include them in that in that tab.
56 00:06:27.950 ⇒ 00:06:37.629 Caio Velasco: put this marker, as you said, Amazon, and shopify. And how does this impact the how the fact order is.
57 00:06:37.830 ⇒ 00:06:40.469 Awaish Kumar: Yeah, like, what it will affect is that
58 00:06:40.660 ⇒ 00:06:49.029 Awaish Kumar: how it is going? Like you have to add these filters. So right now, the data comes from this sheet, and we just join it.
59 00:06:49.501 ⇒ 00:06:52.820 Awaish Kumar: With the, for example, fact order based on a school level.
60 00:06:53.598 ⇒ 00:07:02.660 Awaish Kumar: But then you have to specify. For example. I’m assume, I assume that school is unique.
61 00:07:03.240 ⇒ 00:07:13.760 Awaish Kumar: Even between the platforms, like there should not be any duplicated school like the in yeah.
62 00:07:13.760 ⇒ 00:07:26.979 Awaish Kumar: between, like shopify and Amazon. So I, by just joining on a school level, should be enough. But still, if if you see that there’s the same school in the
63 00:07:27.240 ⇒ 00:07:30.569 Awaish Kumar: shopify, which which shows some different product.
64 00:07:30.680 ⇒ 00:07:51.990 Awaish Kumar: And the same school in the Amazon represents different product like that should not be the case ideally. But if that is, then we have to add, like we have to like in all the in in tables where we are basically using this sheet directly, if it if it is raw or in fields where we have to make these
65 00:07:52.160 ⇒ 00:07:59.360 Awaish Kumar: and joins in the join, we have to add, like app source is equal to shopify or app sources.
66 00:07:59.550 ⇒ 00:08:00.840 Awaish Kumar: Amazon.
67 00:08:01.080 ⇒ 00:08:05.540 Awaish Kumar: And if like, for if if the intertable is just for shopify.
68 00:08:05.680 ⇒ 00:08:08.500 Awaish Kumar: we might want to just filter out the data
69 00:08:09.072 ⇒ 00:08:14.209 Awaish Kumar: for Amazon, and then make do a join or things like that. So yeah, like
70 00:08:14.370 ⇒ 00:08:17.170 Awaish Kumar: in the sheet level on a connector level.
71 00:08:17.300 ⇒ 00:08:26.529 Awaish Kumar: I don’t think we have to do much. You can just add the data. It will bring it in in the raw table. But from the raw table. How it will go further into
72 00:08:26.800 ⇒ 00:08:29.660 Awaish Kumar: intermediate tables or the Mods.
73 00:08:29.800 ⇒ 00:08:32.979 Awaish Kumar: You have to make those joints based on app source.
74 00:08:33.130 ⇒ 00:08:36.750 Awaish Kumar: So yeah, little bit of modifications will be required.
75 00:08:38.390 ⇒ 00:08:42.679 Caio Velasco: Okay, okay? So adding a column to the tab.
76 00:08:42.840 ⇒ 00:08:46.409 Caio Velasco: it’s also portable. We will understand, and we’ll bring it
77 00:08:47.940 ⇒ 00:08:55.190 Caio Velasco: because they’re rows. Yes, I can add them and then check the duplicates, but adding a column to say, if, like the app source.
78 00:08:55.400 ⇒ 00:08:59.130 Caio Velasco: I’m not sure, like, if portable automatically understands it.
79 00:09:04.130 ⇒ 00:09:04.920 Awaish Kumar: Sorry.
80 00:09:05.740 ⇒ 00:09:12.289 Caio Velasco: For example, like in the in in the spreadsheet, in that tab, the skew, tab. The project cost tab
81 00:09:12.757 ⇒ 00:09:17.530 Caio Velasco: if I were to add the column to differentiate Amazon from from shopify
82 00:09:17.670 ⇒ 00:09:24.349 Caio Velasco: like an app source on, or something would still function. And or do we have to change something?
83 00:09:24.480 ⇒ 00:09:28.649 Caio Velasco: How portable ingest the the data. Because I I really don’t know.
84 00:09:31.096 ⇒ 00:09:33.240 Awaish Kumar: Portable connector with sheet.
85 00:09:35.500 ⇒ 00:09:37.420 Awaish Kumar: I can look into that.
86 00:09:38.720 ⇒ 00:09:39.470 Caio Velasco: Okay.
87 00:09:39.900 ⇒ 00:09:47.500 Awaish Kumar: I can look like. If if you have the sheet ready. Then, please. Maybe Akash, you can create a task for me.
88 00:09:47.670 ⇒ 00:09:53.110 Awaish Kumar: and to ingest the data, I will. I will handle the connector part.
89 00:09:53.890 ⇒ 00:09:55.947 Aakash Tandel: Okay, yeah, I can do that.
90 00:09:57.840 ⇒ 00:10:01.510 Caio Velasco: Okay, and what else?
91 00:10:04.780 ⇒ 00:10:05.569 Caio Velasco: Good though.
92 00:10:14.070 ⇒ 00:10:15.730 Caio Velasco: Connect you to your point.
93 00:10:16.460 ⇒ 00:10:18.212 Aakash Tandel: Will address I will.
94 00:10:20.070 ⇒ 00:10:21.899 Aakash Tandel: I’ll do that after this call.
95 00:10:21.900 ⇒ 00:10:22.889 Caio Velasco: Yeah, cause they can.
96 00:10:23.190 ⇒ 00:10:30.710 Caio Velasco: And also like the the new skew tab. I just got like one
97 00:10:31.080 ⇒ 00:10:41.880 Caio Velasco: skew. And and I tried to filter that one in the shopify Tab. And yes, I found it, but the name was different, so I assume we do have duplicates, as you mentioned.
98 00:10:42.010 ⇒ 00:10:45.529 Caio Velasco: So this is also something else that we have to
99 00:10:45.670 ⇒ 00:10:47.650 Caio Velasco: to take care when we put
100 00:10:47.790 ⇒ 00:10:50.829 Caio Velasco: all of them together in the same tab.
101 00:10:51.260 ⇒ 00:10:56.710 Awaish Kumar: Yeah, yeah. Like, if, like, as I mentioned, if there are duplicate like same school attached to different product
102 00:10:57.429 ⇒ 00:11:10.009 Awaish Kumar: in in different platforms, then, we have to join in the join. We cannot directly just join on a school. We have to add app source as well in our join joining conditions.
103 00:11:10.750 ⇒ 00:11:11.750 Aakash Tandel: Okay. Okay.
104 00:11:12.470 ⇒ 00:11:18.129 Caio Velasco: That makes sense. Okay, yeah. And then I made the other comments. And I think.
105 00:11:19.190 ⇒ 00:11:23.419 Caio Velasco: Do you wanna keep going? It’s it’s me now, right like Akash already.
106 00:11:25.390 ⇒ 00:11:36.219 Caio Velasco: Just go back quickly to that one, just to see another comment like, because I put, I think, 3 or 4 comments starting from the 1st 1. 1st one. Yes.
107 00:11:37.280 ⇒ 00:11:40.720 Caio Velasco: okay. I have like a conclusion at the end of it.
108 00:11:41.160 ⇒ 00:11:42.380 Awaish Kumar: Yes.
109 00:11:43.660 ⇒ 00:11:49.454 Caio Velasco: Okay. So these are just things that I I don’t know if it’s more to Robert or even to I wish
110 00:11:50.460 ⇒ 00:12:02.860 Caio Velasco: because, for example at the end. I understand that this is the equation, the main equation that we have for Cox, for Amazon, like Scba fee plus 15% referral fee plus product cost.
111 00:12:04.630 ⇒ 00:12:07.500 Caio Velasco: We have. Can you go up just a little bit?
112 00:12:07.850 ⇒ 00:12:13.369 Caio Velasco: Yes, so we already have the the Cox protocols.
113 00:12:14.023 ⇒ 00:12:24.940 Caio Velasco: Which I assume is this spreadsheet? Is this tab that we are talking about for the Fba fee? I’m assuming that it’s the one we have in the picture right there, like from the other tab.
114 00:12:25.150 ⇒ 00:12:28.970 Caio Velasco: This is also something that I just need to confirm, even though it seems obvious.
115 00:12:29.398 ⇒ 00:12:38.060 Caio Velasco: And then we also have this 15% refer fee, which I didn’t see in the fact orders. And I didn’t see anywhere else. So this is something new.
116 00:12:38.180 ⇒ 00:12:41.790 Caio Velasco: You I don’t know if you remember about this a wish, or
117 00:12:42.010 ⇒ 00:12:44.670 Caio Velasco: because I assume that if it’s new, we either.
118 00:12:45.260 ⇒ 00:12:46.319 Awaish Kumar: Adds it.
119 00:12:47.650 ⇒ 00:12:51.330 Caio Velasco: In the spreadsheet, or hard code, in the in the
120 00:12:54.400 ⇒ 00:12:55.660 Caio Velasco: well, in the model.
121 00:12:56.888 ⇒ 00:13:03.419 Awaish Kumar: Like 15% referral fee, 15% of what total price or what.
122 00:13:04.100 ⇒ 00:13:05.830 Caio Velasco: I think it’s from the bright. Yeah.
123 00:13:07.090 ⇒ 00:13:08.009 Aakash Tandel: Is that a thing that.
124 00:13:08.010 ⇒ 00:13:08.490 Awaish Kumar: Right like.
125 00:13:08.490 ⇒ 00:13:09.120 Aakash Tandel: Mentioned.
126 00:13:09.120 ⇒ 00:13:12.044 Awaish Kumar: We need to understand from the robot like, what is that?
127 00:13:12.740 ⇒ 00:13:15.997 Awaish Kumar: If, if like, is it like 15% of
128 00:13:16.670 ⇒ 00:13:23.679 Awaish Kumar: Oh, our total price is a referral fee. And do we have to add this for all the orders? Then we are.
129 00:13:23.840 ⇒ 00:13:28.830 Awaish Kumar: we don’t, we cannot, we don’t. If that is the case, then we don’t have to hard code right?
130 00:13:29.060 ⇒ 00:13:32.289 Awaish Kumar: From a total price. We will calculate the 15%.
131 00:13:33.120 ⇒ 00:13:34.110 Aakash Tandel: Yeah.
132 00:13:34.110 ⇒ 00:13:45.680 Caio Velasco: It’s from the total price. Because of the spreadsheet that Blake shared. I did 15% of the column called Total price or something. And it was exactly the same the right number.
133 00:13:46.070 ⇒ 00:13:47.189 Caio Velasco: But we can offer.
134 00:13:49.800 ⇒ 00:13:56.119 Aakash Tandel: Okay, yeah. What I’ll do is I’ll tag Robert in slack yeah in slack. And just say, Hey, this has
135 00:13:57.570 ⇒ 00:13:58.159 Aakash Tandel: This has a.
136 00:13:58.160 ⇒ 00:14:03.500 Caio Velasco: Those are just final comments. Yeah. And then I will bring them to team products. Because that’s the main idea.
137 00:14:03.950 ⇒ 00:14:08.399 Aakash Tandel: Yeah, okay, so you’re a little blocked until we get some internal feedback from.
138 00:14:08.690 ⇒ 00:14:09.960 Caio Velasco: Yes, enrollment.
139 00:14:10.502 ⇒ 00:14:34.427 Aakash Tandel: That sounds good. I’ll I’ll slack him and just say, Hey, this ticket has some questions for you. He’s at a conference, so I guess. Pause on this, and then if you can get these other 2, I’ll do pull this one into cycle. Where is this? Yeah, okay, these are the 2 kind of up next. So let’s just wait until you get information from Robert on that.
140 00:14:34.940 ⇒ 00:14:51.269 Aakash Tandel: this should be hopefully easy. Basically, Annie needs this column message, id as a var char. So I thought, maybe add another column message, Id underscore Var, or something like that, just to include that data.
141 00:14:52.904 ⇒ 00:15:01.932 Aakash Tandel: And then the other one is I don’t know how much this one might be a little more difficult, but annie was hoping to filter
142 00:15:02.370 ⇒ 00:15:12.379 Awaish Kumar: Yeah, so I can, I can share some con context here. So I added, this model and in the right. Now, I I’m joining
143 00:15:12.650 ⇒ 00:15:13.694 Awaish Kumar: data.
144 00:15:15.250 ⇒ 00:15:23.980 Awaish Kumar: it’s like, I’m hard coded kind of thing. So any. So I have a date when a message was sent
145 00:15:24.080 ⇒ 00:15:26.849 Awaish Kumar: right, and from that date
146 00:15:27.250 ⇒ 00:15:41.239 Awaish Kumar: till 5 days in the period any order made we will. I include that in the drawing. And then I just like that. That is like some, and we sum that to to
147 00:15:41.420 ⇒ 00:15:47.339 Awaish Kumar: get the revenue from these orders. So what any wants, instead of hard coding this
148 00:15:48.085 ⇒ 00:15:53.099 Awaish Kumar: up like just in the join level we can just leave it
149 00:15:55.643 ⇒ 00:16:00.499 Awaish Kumar: like for for any number of days, and just add a new field which says like
150 00:16:01.420 ⇒ 00:16:03.219 Awaish Kumar: days to order, or something like
151 00:16:03.390 ⇒ 00:16:05.890 Awaish Kumar: like we have ordered it. We have a
152 00:16:06.100 ⇒ 00:16:15.109 Awaish Kumar: message sent date, so you can just calculate number of days, difference of days between them. And then, and you can just filter out and then
153 00:16:15.310 ⇒ 00:16:19.680 Awaish Kumar: aggregate the data in the dashboard.
154 00:16:23.220 ⇒ 00:16:37.400 Caio Velasco: Okay, okay, for for this, for this one and the other one seems that I don’t need to understand anything just to put the new field. I think the other one is okay. This one I have. I would have to stop a bit in like what is not being, etc, etc, etc, because I haven’t done this yet.
155 00:16:37.400 ⇒ 00:16:39.019 Awaish Kumar: It’s not about north beam.
156 00:16:39.680 ⇒ 00:16:42.179 Aakash Tandel: Yeah, this is attentive data. So.
157 00:16:42.180 ⇒ 00:16:44.180 Caio Velasco: Oh, yeah. Sorry. Attended. Okay. Yeah.
158 00:16:44.180 ⇒ 00:16:45.130 Aakash Tandel: Yeah, are we.
159 00:16:45.130 ⇒ 00:16:46.292 Caio Velasco: Know about it.
160 00:16:46.680 ⇒ 00:16:52.050 Aakash Tandel: Do you have a a specific table or a place that kyle should start looking from.
161 00:16:53.697 ⇒ 00:17:02.039 Awaish Kumar: What engagement summary? I think that was the name of the table I created, or something like that.
162 00:17:02.170 ⇒ 00:17:04.270 Awaish Kumar: It’s a it’s a table which
163 00:17:04.991 ⇒ 00:17:08.239 Awaish Kumar: joins data which comes from attentive
164 00:17:09.095 ⇒ 00:17:17.540 Awaish Kumar: with the data, with the the data from fact orders phone and.
165 00:17:17.540 ⇒ 00:17:18.099 Caio Velasco: Cool.
166 00:17:18.685 ⇒ 00:17:23.869 Awaish Kumar: Right? Now, what happens when I join this data in the join? I’ve hard coded
167 00:17:24.259 ⇒ 00:17:26.909 Awaish Kumar: that only I need only data.
168 00:17:27.109 ⇒ 00:17:33.741 Awaish Kumar: In the 5 days period after the message sent. So we just have to remove that drive and
169 00:17:34.659 ⇒ 00:17:37.929 Awaish Kumar: add an add a days to order field instead.
170 00:17:48.059 ⇒ 00:17:49.642 Caio Velasco: But that was
171 00:17:50.459 ⇒ 00:17:54.959 Awaish Kumar: That was, yeah. That was initially in the requirement to hard code until 5 days.
172 00:17:54.960 ⇒ 00:17:55.630 Aakash Tandel: Yeah.
173 00:17:59.310 ⇒ 00:18:02.610 Caio Velasco: Okay, okay. And this is for web.
174 00:18:03.680 ⇒ 00:18:25.240 Aakash Tandel: So the other one is immediate, so the other one will take higher priority over this. And then this one if this is like an item that will do as a nice to have, so I can put it as a low priority item. So if you have hours left, and you’re blocked on this item. Just get this
175 00:18:25.500 ⇒ 00:18:36.490 Aakash Tandel: done as you can, but this is kind of like the. This is the highest priority, but you’re blocked. So this is the next priority. And then this. If you’re still blocked on that guy.
176 00:18:37.280 ⇒ 00:18:44.140 Caio Velasco: Okay. And just 1 1 more thing that I saw any made a comment on the Cloud View
177 00:18:44.680 ⇒ 00:18:46.735 Caio Velasco: ticket that she has
178 00:18:47.470 ⇒ 00:18:53.650 Caio Velasco: And and then I well, I started going back to the fact. I created the notion.
179 00:18:54.678 ⇒ 00:19:03.289 Caio Velasco: So that I can like, understand, because when you build a data model from scratch and without, like any knowledge about the source.
180 00:19:03.620 ⇒ 00:19:30.610 Caio Velasco: it’s like it. It takes at least for me. It takes many layers of interactions that I have to go in, so that I understand, like at the end of the day, the business itself, as if I were doing the data analysis myself. Let’s say so. I’m doing this now to be able to help her. So I’m going again like each one like, what is a campaign? What is a flow. What is an event? What is a metric? What? How they are connected, so that I can, you know, help her in in her question. Hit? She had like, very good questions.
181 00:19:30.720 ⇒ 00:19:35.819 Caio Velasco: I just don’t have like answers on top of my mind. So I was doing that now as well.
182 00:19:36.998 ⇒ 00:19:40.429 Caio Velasco: I yeah. I just made a quick reply.
183 00:19:40.540 ⇒ 00:19:45.639 Caio Velasco: I’m already updating fact to, if you if you want to open, I can quickly show
184 00:19:49.090 ⇒ 00:19:54.460 Caio Velasco: yeah, like in the Klavio part go down and down.
185 00:19:55.420 ⇒ 00:20:10.479 Caio Velasco: Yeah. So I started in this core concepts. So like, really understand what is happening, each one of them. And then, you know, and then, after I’m done, I was like, Okay, I wanna look at her questions and and see like how those things are connected, or or I’ll be more able to help.
186 00:20:11.236 ⇒ 00:20:21.369 Caio Velasco: That’s why starting a data model from scratch is kind of tricky because you have to have this knowledge 1st to start the thing, but it just doesn’t come that way.
187 00:20:21.620 ⇒ 00:20:27.480 Caio Velasco: But I’ll I’ll do my best to help, and if you have any other questions, just feel free to put it there and
188 00:20:27.700 ⇒ 00:20:30.249 Caio Velasco: and then I’ll I’ll continue this.
189 00:20:30.530 ⇒ 00:20:32.950 Caio Velasco: Okay, that’s basically it.
190 00:20:33.770 ⇒ 00:20:43.890 Annie Yu: Yeah, I I wanna say, Kyle, thank you so much for building that. FAQ, because I think honestly for me, this series of data is super hard. I think I spent.
191 00:20:43.890 ⇒ 00:20:44.490 Caio Velasco: Cheers.
192 00:20:44.490 ⇒ 00:20:53.779 Annie Yu: I only have some understanding. But your dog is helpful. And yeah, obviously, I still have more questions. But I think it’s.
193 00:20:53.780 ⇒ 00:20:54.799 Caio Velasco: Perfect. I think you have.
194 00:20:54.800 ⇒ 00:20:57.521 Annie Yu: This this one is super hard, honestly.
195 00:20:57.910 ⇒ 00:21:13.449 Caio Velasco: Yeah, no, it is. It is different, different. And I’ve never touched this like email data before. So I mean, somehow, I think we will meet halfway, and I think that’s good, because you have a more like business mentality. And you know, like how things work. And I’m still like the modeling part. So we’ll we’ll get there.
196 00:21:13.910 ⇒ 00:21:30.508 Aakash Tandel: Yeah, I think this is where Robert and I didn’t understand. Cause. I think we, Robert and I both have experience working with this type of data. So if if we need to sync up let’s do that. Annie and I can help you get some of this through the hump. But yeah, this is
197 00:21:31.290 ⇒ 00:21:49.730 Aakash Tandel: yeah, I guess if you’ve never seen this data, it doesn’t make sense. But yeah, this is fairly straightforward email marketing style of stuff. So it’s kind of niche. But it’s like, it’s good for you guys in the future when you ever see email information, because it’ll basically be the same exact way. So that’s good, at least going forward.
198 00:21:49.730 ⇒ 00:21:54.209 Caio Velasco: No perfect. Yeah. The tricky thing is that like sometimes you you have like, for example, the
199 00:21:54.380 ⇒ 00:22:13.689 Caio Velasco: I don’t know flows table and like what is what? And in the table there is a J zone field with a hundred fields, and like, Okay, and one of those fields are events. But the events come from the events table. So and then you start like Jesus. This is a universe in itself. So that’s more or less like how it goes. But yeah, no, perfect. Thank you. Ash.
200 00:22:15.279 ⇒ 00:22:24.549 Aakash Tandel: Okay, let’s and let’s start from the beginning, Annie, on your stuff, because I don’t want to start on there. It seems like you are waiting on internal feedback for lifetimely.
201 00:22:24.856 ⇒ 00:22:32.510 Annie Yu: Yeah. And I got some feedback to kind of separate the description into 2 sections, which I did. And I think the next step is
202 00:22:33.212 ⇒ 00:22:40.607 Annie Yu: another pair of eye from you or Robert. To see if that’s understandable or not.
203 00:22:41.499 ⇒ 00:22:48.800 Aakash Tandel: I will take a look at this. Cause. Robert’s a little busy with that conference. So
204 00:22:50.390 ⇒ 00:22:52.930 Aakash Tandel: review lifetime.
205 00:22:53.520 ⇒ 00:23:06.350 Annie Yu: And I think honestly, it’s pretty straightforward now, and it’s so. If if it’s still not easy to understand, let me know and welcome any like suggestion to.
206 00:23:06.350 ⇒ 00:23:13.609 Aakash Tandel: Sure I’m gonna yeah, I’ll review it, and then, if it’s good, I’ll just ship it and then, if I’m on and them have more questions. We can. We can go from there. But yeah.
207 00:23:13.960 ⇒ 00:23:17.710 Aakash Tandel: that sounds good. That’s basically done on my plate. Done on your plate. At least.
208 00:23:18.180 ⇒ 00:23:18.770 Annie Yu: Okay.
209 00:23:19.764 ⇒ 00:23:27.915 Aakash Tandel: Yeah, attentive. I know. I guess this is waiting on a little bit of stuff, the data, conversion and stuff. I’ll say that.
210 00:23:28.670 ⇒ 00:23:39.580 Aakash Tandel: we kinda they. They only really gave us very basic information. So this days to order filtering that is on Kyle’s plate is a nice to have. But if we don’t get that in the
211 00:23:40.455 ⇒ 00:23:41.120 Aakash Tandel: like
212 00:23:41.720 ⇒ 00:23:50.959 Aakash Tandel: we, we were promised to deliver this. So let’s focus on delivering this, and then work on the the other like filtering by days later, I think.
213 00:23:51.280 ⇒ 00:24:05.719 Annie Yu: Yeah. Okay. In the meantime, I already have some very scrappy table, so I can polish that before. I get the bar chart message Id, because to build line charts or bar charts, I need that field to be
214 00:24:06.080 ⇒ 00:24:12.640 Awaish Kumar: Even I just just question, isn’t it possible to convert that in the metase.
215 00:24:12.640 ⇒ 00:24:26.949 Annie Yu: I can if I used SQL, I think I’m not 100% sure. But I think the goal also is to not to try not to write any SQL query using meta base. So anyone who’s not that technical can
216 00:24:27.110 ⇒ 00:24:27.910 Annie Yu: can edit.
217 00:24:27.910 ⇒ 00:24:29.773 Awaish Kumar: No, no, not carry, you know, like
218 00:24:30.510 ⇒ 00:24:35.960 Awaish Kumar: in the database, I think that you can just add in the ui, you can just say.
219 00:24:36.350 ⇒ 00:24:39.310 Awaish Kumar: cast this as a water term that’s like a
220 00:24:40.610 ⇒ 00:24:45.580 Awaish Kumar: Oh, you mean in the database in the Meta base
221 00:24:45.840 ⇒ 00:24:48.329 Awaish Kumar: in the Ui. You can go to the
222 00:24:48.580 ⇒ 00:24:52.110 Awaish Kumar: like the the table fields, and then
223 00:24:52.370 ⇒ 00:24:57.160 Awaish Kumar: you can select the field and then say, cause this column as a bar chart.
224 00:24:57.762 ⇒ 00:25:02.919 Awaish Kumar: Like it. You don’t have to write whole query, but you just have to write the
225 00:25:03.030 ⇒ 00:25:07.219 Awaish Kumar: conversion. Query casting thing in the database.
226 00:25:07.590 ⇒ 00:25:08.330 Annie Yu: Oh!
227 00:25:09.420 ⇒ 00:25:12.419 Awaish Kumar: You can do that. But yeah, I’m not sure if you.
228 00:25:13.080 ⇒ 00:25:13.970 Annie Yu: Okay. Okay.
229 00:25:13.970 ⇒ 00:25:17.220 Awaish Kumar: It will be also straightforward to doing model as well.
230 00:25:17.720 ⇒ 00:25:18.420 Annie Yu: Right.
231 00:25:18.620 ⇒ 00:25:26.360 Annie Yu: Thank you so much. I I will look into that. And it’s yeah. So if that’s doable, I can just do it on my end.
232 00:25:27.780 ⇒ 00:25:33.300 Aakash Tandel: Okay? Yeah, I think, it sounds like.
233 00:25:34.449 ⇒ 00:25:42.759 Aakash Tandel: yeah. Aisha, you are you certain that that’s possible? Because if so, then we can just get if you want to slack Annie. Do you know where that is? In the Ui.
234 00:25:44.610 ⇒ 00:25:56.570 Awaish Kumar: Yeah, like I did. I did try that for Amazon. So I know we can do that. But yeah, I don’t exactly remember where, like it has been little bit
235 00:25:56.830 ⇒ 00:25:59.870 Awaish Kumar: some few weeks ago. So I don’t remember exactly. Yeah.
236 00:25:59.870 ⇒ 00:26:05.550 Aakash Tandel: Okay, but we did it on the Amazon one. Maybe, Annie, we can look at the Amazon one and then see how it’s done. There.
237 00:26:06.350 ⇒ 00:26:07.130 Annie Yu: Okay.
238 00:26:07.810 ⇒ 00:26:17.549 Aakash Tandel: Cool. Okay? But yeah, I think the cleaning this this up a little bit, and then I think I can review it afterwards, and then we can kind of ship this one hopefully soon.
239 00:26:18.086 ⇒ 00:26:27.243 Aakash Tandel: Because, yeah, they did not give us a lot they gave us like, this is all they gave us to work with. So we can, we can move forward on that.
240 00:26:27.930 ⇒ 00:26:33.700 Aakash Tandel: okay, this is still unblocked. Is this still blocked? That’s because we’re waiting on the
241 00:26:35.380 ⇒ 00:26:50.999 Aakash Tandel: no, we’re done now. So okay, so this has been fixed right? The so always push that update. So now I think, is this dashboard? 1st of all, is it unblocked, and is it in progress again, or is it done.
242 00:26:51.160 ⇒ 00:26:55.620 Annie Yu: I? That’s the one I just took a look, and I think
243 00:26:55.760 ⇒ 00:26:58.280 Annie Yu: I’m not gonna change any views.
244 00:26:58.480 ⇒ 00:27:02.169 Annie Yu: So I would say, it’s pretty much done. Unless
245 00:27:03.140 ⇒ 00:27:09.469 Annie Yu: there’s any feedback. And I what I’m doing really, I’m just replicating what they have in that
246 00:27:09.810 ⇒ 00:27:13.479 Annie Yu: amplitude dashboard. So try to have the same view.
247 00:27:13.950 ⇒ 00:27:17.301 Aakash Tandel: Okay, cool. I will also review this one.
248 00:27:19.240 ⇒ 00:27:29.629 Aakash Tandel: I’m gonna put this as internal feedback. I’ll I’ll review this one, and I’ll also let Robert know to if he has time to review that and then we can ship this as well. That sounds good.
249 00:27:30.412 ⇒ 00:27:32.249 Aakash Tandel: What is this?
250 00:27:33.290 ⇒ 00:27:40.149 Aakash Tandel: Okay, that’s that. Yeah, that’s the Kyle. That’s the cog sheet Kyle was working on. Okay, that’s fine. And then.
251 00:27:40.150 ⇒ 00:27:41.299 Annie Yu: Play, meal.
252 00:27:43.290 ⇒ 00:27:45.109 Aakash Tandel: Yeah. So this is.
253 00:27:48.620 ⇒ 00:27:52.099 Aakash Tandel: I want to put Klaviyo as higher priority.
254 00:27:52.550 ⇒ 00:27:54.270 Aakash Tandel: And then this is
255 00:27:59.400 ⇒ 00:28:08.457 Aakash Tandel: created. This Steven created this 4 weeks ago. Okay, that’s really old. All right. Yeah. So clay view is definitely the next one up.
256 00:28:09.590 ⇒ 00:28:14.100 Aakash Tandel: yeah. Is
257 00:28:16.090 ⇒ 00:28:24.729 Aakash Tandel: the data available for you to use to kind of start on getting these, or is it? Do you still need modeling work? I guess that’s question.
258 00:28:24.730 ⇒ 00:28:31.779 Annie Yu: I, I have some progress on the like very simple one and one other question, is that
259 00:28:32.368 ⇒ 00:28:53.919 Annie Yu: revenue orders or aov attributed to email. I’m not sure what logic to use there, and I’m not sure if there’s a standard logic we want to follow, cause I I know Kyle also shared this. We are able to join the tables with fact orders.
260 00:28:54.400 ⇒ 00:28:59.780 Annie Yu: I don’t know what logic to use that there like. Do we want to look at
261 00:29:00.820 ⇒ 00:29:05.380 Annie Yu: 5 days after the email sent and attributed revenue
262 00:29:05.630 ⇒ 00:29:10.029 Annie Yu: to that email. Or I’m not just not sure the window to use. There.
263 00:29:10.030 ⇒ 00:29:22.840 Aakash Tandel: Yeah, I would say, we want to. The client did not give us any exact window. So the thing I would do is I would default to 30 days, and then maybe also do one for 90 days. And just say.
264 00:29:25.300 ⇒ 00:29:29.209 Aakash Tandel: because you have to have a window otherwise indefinitely is not gonna work.
265 00:29:30.230 ⇒ 00:29:33.279 Aakash Tandel: that’s the way I would handle this if that makes sense.
266 00:29:35.390 ⇒ 00:29:40.226 Annie Yu: Okay, yeah, yeah, that makes sense. And one question is,
267 00:29:40.990 ⇒ 00:29:44.859 Annie Yu: And I know, I also asked Kyle this. But I think
268 00:29:45.010 ⇒ 00:29:48.990 Annie Yu: so. That’s the 1st question email sends.
269 00:29:49.850 ⇒ 00:30:01.110 Annie Yu: oh, this is so hard to explain. But basically, there’s message Id, and there’s event. Id. So I think event table is the core table we’re looking at. So
270 00:30:01.976 ⇒ 00:30:24.470 Annie Yu: not every events is tied to a message. So it could be client just clicked into their existing email to unsubscribe, so that one for that one message, Id will be null and so I’m just trying to also have a logic to count if we want to count only the email sent.
271 00:30:25.946 ⇒ 00:30:33.279 Annie Yu: I’m not sure if the best way is to use message Id is not null. That means something is sent
272 00:30:35.280 ⇒ 00:30:37.370 Annie Yu: and then that’s to an event.
273 00:30:37.530 ⇒ 00:30:51.210 Annie Yu: Or there’s a metric name. Metric is a table that shows what action has been taken. So like, I said, clicked into the email to unsubscribe. That will be one metric. And there’s also metric like
274 00:30:51.430 ⇒ 00:31:01.160 Annie Yu: received email that’s also tied to a message. Id. So that only happens when a message. Id is not null. So I think I’m just trying to see.
275 00:31:01.540 ⇒ 00:31:09.529 Annie Yu: So for me, definition, if I want to count, email sends, I would use message name equals
276 00:31:09.760 ⇒ 00:31:17.469 Annie Yu: received email. And I’m I’m I guess I just kind of want a validation from someone that’s
277 00:31:17.990 ⇒ 00:31:19.440 Annie Yu: logical and.
278 00:31:19.440 ⇒ 00:31:20.040 Aakash Tandel: Yeah.
279 00:31:20.040 ⇒ 00:31:20.949 Annie Yu: Use that.
280 00:31:21.713 ⇒ 00:31:39.029 Aakash Tandel: Yeah. Can you record a loom of you pointing that out in the database? And then I can review that, and I’ll try to recreate what you did and then see if that makes sense. I’m I’m assuming that their event table is gonna contain a ton of extra information that’s not relevant to this.
281 00:31:39.030 ⇒ 00:31:53.800 Aakash Tandel: And so we’re gonna have to do some sort of filtering like that. So that makes sense to me. I just don’t know if it’s exactly how we should model it. But yeah, if you can record a quick loom, just even if it’s like 10 to 20 seconds of you just showing exactly what you just talked about. That’d be really helpful.
282 00:31:54.360 ⇒ 00:31:55.080 Annie Yu: Okay.
283 00:31:55.510 ⇒ 00:32:01.030 Aakash Tandel: Awesome, and then you can throw it in this ticket and then tat at me, and then I’ll go from there.
284 00:32:01.750 ⇒ 00:32:02.840 Annie Yu: Okay. Yeah.
285 00:32:03.140 ⇒ 00:32:06.919 Aakash Tandel: Okay, sweet, and then I’ll rew that down to review.
286 00:32:07.910 ⇒ 00:32:13.719 Caio Velasco: And keep looking at the fact, because I’m putting everything I can there, so I think it will be helpful for both of you.
287 00:32:14.440 ⇒ 00:32:25.649 Annie Yu: Okay, yeah. And and yeah, but I do have an opinion. I think if if there’s like not a standard way, I I would like to use metric instead of message. Id is not null.
288 00:32:26.050 ⇒ 00:32:29.949 Aakash Tandel: Okay, yeah, that makes sense. Yeah, I will. Yeah.
289 00:32:30.370 ⇒ 00:32:34.770 Aakash Tandel: Yeah, I can give you a a second set of eyes on that. And then we can go from there.
290 00:32:34.770 ⇒ 00:32:36.550 Annie Yu: Okay, yeah. I’ll do that.
291 00:32:36.690 ⇒ 00:32:37.500 Annie Yu: Thank you.
292 00:32:38.240 ⇒ 00:32:46.930 Aakash Tandel: Alright. I know we’re at time. But let me just check Robert’s check. Roberts issues done.
293 00:32:47.490 ⇒ 00:32:51.769 Aakash Tandel: Oh, yeah, he responded. Into that, and then add.
294 00:32:56.530 ⇒ 00:32:58.480 Aakash Tandel: 3 weeks ago is created.
295 00:32:58.610 ⇒ 00:33:04.819 Aakash Tandel: Okay, I’m not sure about that one. Okay, cool. So I will ping
296 00:33:05.120 ⇒ 00:33:08.988 Aakash Tandel: Robert. The main thing we want him to review is the
297 00:33:10.612 ⇒ 00:33:26.719 Aakash Tandel: the the cogs order sheet. Where was that? Yeah, this guy? Okay, sweet. Thanks. Y’all. Yeah. Let me know if you need a second set of eyes on things and I will talk to you all soon.
298 00:33:27.230 ⇒ 00:33:28.139 Annie Yu: Thank you very much.
299 00:33:28.140 ⇒ 00:33:30.140 Aakash Tandel: Thank you. Bye.
300 00:33:30.140 ⇒ 00:33:30.720 Annie Yu: Okay.