Meeting Title: [Javvy] Daily Standup Date: 2025-04-09 Meeting participants: Aakash Tandel, Annie Yu, Robert Tseng, Awaish Kumar, Caio Velasco
WEBVTT
1 00:00:13.010 ⇒ 00:00:14.370 Aakash Tandel: Hey, Kai! How’s it going.
2 00:00:15.070 ⇒ 00:00:17.009 Caio Velasco: Hey? Guys, all good. How are you.
3 00:00:17.780 ⇒ 00:00:18.790 Aakash Tandel: Good.
4 00:00:25.220 ⇒ 00:00:26.320 Caio Velasco: Oh, any.
5 00:00:26.790 ⇒ 00:00:28.250 Annie Yu: Morning, everybody.
6 00:00:42.280 ⇒ 00:00:43.200 Aakash Tandel: Hey, Robert.
7 00:00:45.590 ⇒ 00:00:46.870 Robert Tseng: Morning, everyone.
8 00:00:47.800 ⇒ 00:00:48.550 Caio Velasco: Morning.
9 00:00:50.580 ⇒ 00:00:51.790 Annie Yu: Good morning!
10 00:01:00.510 ⇒ 00:01:03.340 Aakash Tandel: We’ll wait another 30 seconds and we can get started.
11 00:01:27.700 ⇒ 00:01:44.509 Aakash Tandel: Alright. Let’s go ahead and get moving. We’ll start with you, Kyle. I know that we’re still waiting on client feedback. I saw your message to Blake in that channel. Sounds like he didn’t make a lot of modifications to that dashboard or the Google sheet. Is that right?
12 00:01:45.020 ⇒ 00:01:58.530 Caio Velasco: Yeah, yeah, I didn’t see any like updates. And whatever is there, even though there is something that seemed that we’re that was paste there like doesn’t seem to be updated, or it’s just a bit of a mess, for now.
13 00:01:59.400 ⇒ 00:02:00.110 Aakash Tandel: Okay.
14 00:02:00.961 ⇒ 00:02:19.979 Aakash Tandel: let’s see if he responds today. If not, I will ping him on just like, Hey, look! That’s not exactly what we need. We need a little bit more data out of Blake or your team. So I’ll give Blake the rest of the day to kind of respond to that. But yeah, if I don’t see anything there, I’ll try and move this along with him on.
15 00:02:21.203 ⇒ 00:02:22.535 Robert Tseng: Mon mentioned that
16 00:02:23.280 ⇒ 00:02:32.830 Robert Tseng: I guess you cause you start with Aaliyah, he said that Aaliyah started it, and then Blake needed to fill in something. I don’t know how true it is. I didn’t actually look like
17 00:02:33.320 ⇒ 00:02:35.909 Robert Tseng: I don’t know if it if we’re like
18 00:02:37.140 ⇒ 00:02:40.790 Robert Tseng: 80% of the way there. I feel like we should just
19 00:02:41.200 ⇒ 00:02:47.439 Robert Tseng: move forward, and then they can fill in the blanks later. But I don’t. I don’t really know what what’s missing. Still.
20 00:02:48.840 ⇒ 00:02:50.797 Aakash Tandel: Yeah, let me.
21 00:02:53.780 ⇒ 00:03:01.450 Caio Velasco: So I think, like the so the dim product is is done but for the cogs part
22 00:03:01.690 ⇒ 00:03:05.390 Caio Velasco: because there are many fields, of of course, and there’s the cogs. One
23 00:03:05.750 ⇒ 00:03:09.790 Caio Velasco: those one. If I were just to take from the fact orders. Some
24 00:03:10.140 ⇒ 00:03:16.440 Caio Velasco: fields are new for for the Amazon parts, because we didn’t have exactly what we need.
25 00:03:17.730 ⇒ 00:03:25.989 Caio Velasco: So yeah. But I mean, the the structure is there. The only thing they have to do is to finish this thing so that we can have all the assumptions and then bring
26 00:03:26.390 ⇒ 00:03:30.170 Caio Velasco: the fields in there. But just basically this.
27 00:03:32.160 ⇒ 00:03:37.099 Caio Velasco: because for the shopify, we have, in fact, orders, for example, but we don’t have for the Amazon.
28 00:03:39.140 ⇒ 00:03:40.120 Aakash Tandel: Okay.
29 00:03:41.910 ⇒ 00:03:46.669 Aakash Tandel: Okay, yeah. Can you? Just maybe tell him.
30 00:03:46.670 ⇒ 00:03:49.559 Robert Tseng: So which tab are we talking about? That needs to be filled in.
31 00:03:49.560 ⇒ 00:03:51.070 Aakash Tandel: Yeah, that’s what I was. Just gonna ask.
32 00:03:51.230 ⇒ 00:03:52.479 Caio Velasco: Yes, those tabs.
33 00:03:52.650 ⇒ 00:03:55.420 Caio Velasco: Yeah, they didn’t make almost anything, really.
34 00:03:55.570 ⇒ 00:04:01.119 Caio Velasco: We have the tabs. And, for example, if you click on the 1st one right after Amazon.
35 00:04:02.730 ⇒ 00:04:14.199 Caio Velasco: that’s just the shopify one, the second one. It’s mine, because I just put as an no, no, so sorry. The second one. They also based on the same thing from shopify. I don’t know why. The 3rd one.
36 00:04:15.010 ⇒ 00:04:20.539 Caio Velasco: I did it just as an example as I did for the others. The 4th one.
37 00:04:21.675 ⇒ 00:04:32.969 Caio Velasco: I am assuming that, you know, I just took the the 1st row of shopify, because this is the Amazon Fba fee. So okay, for that one we would have if there’s nothing new or nothing extra.
38 00:04:33.120 ⇒ 00:04:42.280 Caio Velasco: and the last one I have no idea what they did. I don’t know if they just copy and paste the shopify one of the if, or if this is new.
39 00:04:42.560 ⇒ 00:04:43.699 Caio Velasco: but I don’t know.
40 00:04:45.140 ⇒ 00:04:52.189 Robert Tseng: Okay? I mean, another question I have is like, Why don’t we just have a single tab that like has all the costs I want.
41 00:04:52.410 ⇒ 00:04:55.270 Robert Tseng: I mean, I know we copied what we did for shopify, but.
42 00:04:56.830 ⇒ 00:05:01.422 Caio Velasco: We could have just one. I I thought exactly about this when I created those.
43 00:05:02.710 ⇒ 00:05:11.310 Caio Velasco: it’s just a matter of like defining, because, since we already have the logic for the shopify would be just a simple copy paste, let’s say, from
44 00:05:12.470 ⇒ 00:05:17.639 Caio Velasco: for for the shopify part of the of the code. I’m sorry for the Amazon part of the code.
45 00:05:18.418 ⇒ 00:05:20.210 Caio Velasco: But yeah, you can see.
46 00:05:20.210 ⇒ 00:05:23.520 Robert Tseng: Really make a difference at this point. Yeah, okay.
47 00:05:24.080 ⇒ 00:05:28.879 Aakash Tandel: Okay. So we basically have all of these tabs from basically all the Amazon tabs.
48 00:05:30.780 ⇒ 00:05:32.200 Caio Velasco: Yeah, that’s what’s missing.
49 00:05:32.770 ⇒ 00:05:33.220 Robert Tseng: Yeah.
50 00:05:33.889 ⇒ 00:05:40.900 Aakash Tandel: Okay, yeah. Just let’s message that in the slack thread. I guess.
51 00:05:41.200 ⇒ 00:05:47.749 Aakash Tandel: I guess that’s exactly what he said. So so yeah, that’s
52 00:05:48.440 ⇒ 00:05:51.109 Aakash Tandel: I almost feel like we should just like, remove these.
53 00:05:52.610 ⇒ 00:05:58.290 Aakash Tandel: So it’s very clear to him that, like these are not real. And these are examples.
54 00:05:58.290 ⇒ 00:06:06.390 Caio Velasco: The the first.st Sorry the 1st 2 ones were not there before, so they posted it there. They pasted it there, so I’m not sure if
55 00:06:06.800 ⇒ 00:06:19.849 Caio Velasco: they were trying anything, or we should 1st confirm with them. If for some reason it’s the same one that’s shopify, because it’s clearly the same. Or if they were just trying something and didn’t finish.
56 00:06:20.660 ⇒ 00:06:24.790 Aakash Tandel: Yeah. So let’s say that in the thread.
57 00:06:25.657 ⇒ 00:06:29.900 Aakash Tandel: See that the 1st 2 tabs
58 00:06:38.070 ⇒ 00:06:52.599 Aakash Tandel: actually, I’ll let you do that. Just respond in the in the thread. Just say, Hey, I noticed that these 1st 2 tabs are copies from the shopify data. Is that true? Does that match identical to shopify. And then, yeah, see what this says. There.
59 00:06:53.260 ⇒ 00:06:54.900 Caio Velasco: Okay, okay, perfect.
60 00:06:55.450 ⇒ 00:07:02.810 Aakash Tandel: Yep. And then, yeah. And then, yeah, maybe just remove the examples that you put in and just get them to clarify.
61 00:07:03.420 ⇒ 00:07:03.735 Caio Velasco: Okay.
62 00:07:04.930 ⇒ 00:07:06.460 Aakash Tandel: Alright. Awesome.
63 00:07:08.015 ⇒ 00:07:10.690 Aakash Tandel: Okay. How’s Klavio data modeling, going.
64 00:07:11.130 ⇒ 00:07:14.561 Caio Velasco: So I’ve started it already. And
65 00:07:15.300 ⇒ 00:07:24.380 Caio Velasco: so, as I understand this data modeling will have some dim effects. I already have an idea of the most simple ones to at least have something done.
66 00:07:24.830 ⇒ 00:07:44.030 Caio Velasco: maybe 2 thems, 2 facts like a dim campaign. A team segments campaign is done. I’m I’m doing like I didn’t push Npr yet, but the the campaign would be done. But then the segments one, it’s a bit tricky, because there is a lot of jzone inside Json inside J Zones.
67 00:07:44.170 ⇒ 00:07:45.960 Caio Velasco: And so I’m still doing that.
68 00:07:46.200 ⇒ 00:07:52.812 Caio Velasco: But I I might be able to push something today, and at least we have something to look.
69 00:07:54.530 ⇒ 00:08:17.329 Aakash Tandel: Okay, yeah, if the if there’s too complicated of user segments, just, you know, flag that say, Hey, there’s a lot of work on these specific user segments. So bucket that for later, and just try to get a Pr up by end of your day. So that we have something to to show the client. I want to show the client this data by the end of the week, because it was kind of promised to them by then. So.
70 00:08:17.600 ⇒ 00:08:19.059 Caio Velasco: Yeah, for sure, for sure, for sure.
71 00:08:19.960 ⇒ 00:08:20.610 Aakash Tandel: Awesome.
72 00:08:21.320 ⇒ 00:08:22.110 Aakash Tandel: Okay?
73 00:08:22.540 ⇒ 00:08:25.539 Aakash Tandel: Cool. Okay. And then I’ll probably have utham
74 00:08:25.690 ⇒ 00:08:36.420 Aakash Tandel: pr review just because that worked really well. And he he could do those kind of quickly. So yeah, so just once it’s done, just flag it for me, and then I’ll have utham do the Pr review.
75 00:08:36.710 ⇒ 00:08:37.390 Caio Velasco: Okay.
76 00:08:37.870 ⇒ 00:08:38.600 Aakash Tandel: Quick.
77 00:08:38.940 ⇒ 00:08:39.679 Aakash Tandel: All right.
78 00:08:39.940 ⇒ 00:08:41.040 Aakash Tandel: Anything else. Kyle.
79 00:08:41.390 ⇒ 00:08:42.640 Caio Velasco: No, that’s it for me.
80 00:08:43.940 ⇒ 00:08:46.270 Aakash Tandel: Annie, let’s go to you.
81 00:08:47.512 ⇒ 00:08:54.309 Aakash Tandel: I know you have the Amazon or the the 1st chunk of the
82 00:08:54.990 ⇒ 00:09:00.379 Aakash Tandel: North Beam data. Maybe this this information? How’s that going.
83 00:09:01.633 ⇒ 00:09:11.760 Annie Yu: Yeah, wait, let me just share. I did build some views, but then, for some reason, the numbers just
84 00:09:12.020 ⇒ 00:09:18.869 Annie Yu: don’t align with the north bang, and I think specifically for concentrate.
85 00:09:20.238 ⇒ 00:09:23.650 Annie Yu: Our concentrate. Our numbers for concentrate
86 00:09:24.160 ⇒ 00:09:28.840 Annie Yu: are just like very low, for some reason, and I I just can’t.
87 00:09:29.030 ⇒ 00:09:38.109 Annie Yu: Fine. Okay, what’s if there’s anything wrong with it or it? It’s expected to to be that different.
88 00:09:40.580 ⇒ 00:09:45.230 Aakash Tandel: It should be so. The North Bmd. Doesn’t have have
89 00:09:46.360 ⇒ 00:09:53.540 Aakash Tandel: like revenue or order information. It’s just the ad spend. So are you talking about the amplitude data. That’s where
90 00:09:54.387 ⇒ 00:09:58.599 Aakash Tandel: I think so. The that that dashboard they shared.
91 00:10:00.080 ⇒ 00:10:04.540 Annie Yu: So I think, yeah, it’s the one on amplitude.
92 00:10:04.720 ⇒ 00:10:05.859 Aakash Tandel: Okay. Yeah.
93 00:10:07.170 ⇒ 00:10:11.639 Annie Yu: But I did. Yeah, I just put the link down there
94 00:10:11.830 ⇒ 00:10:15.706 Annie Yu: for my draft. If you wanna take a look at it.
95 00:10:19.990 ⇒ 00:10:21.519 Aakash Tandel: I’ll reshare the screen.
96 00:10:28.520 ⇒ 00:10:29.290 Aakash Tandel: Okay.
97 00:10:32.530 ⇒ 00:10:38.650 Annie Yu: So here we can see that I think the numbers for concentrate are just like very low.
98 00:10:40.220 ⇒ 00:10:42.679 Aakash Tandel: Numbers for okay, hold on.
99 00:10:42.680 ⇒ 00:10:49.219 Annie Yu: And then I linked that the the dashboard they they’ve been using.
100 00:10:49.760 ⇒ 00:10:51.429 Aakash Tandel: Okay. I’ll do.
101 00:10:52.140 ⇒ 00:10:52.810 Aakash Tandel: Cool. Okay.
102 00:10:52.810 ⇒ 00:10:53.410 Robert Tseng: I don’t expect
103 00:10:53.860 ⇒ 00:11:01.369 Robert Tseng: match, because we supposedly should have more accurate data. But yeah, if there’s anything that’s off by a lot, then we should probably pay attention.
104 00:11:03.040 ⇒ 00:11:08.930 Aakash Tandel: Yeah, that makes sense. Okay, I think if
105 00:11:09.510 ⇒ 00:11:16.020 Aakash Tandel: so, so, basically, the 1st version of this is like the visualizations built out. But that we’re we’re still questioning the data right?
106 00:11:16.520 ⇒ 00:11:17.020 Annie Yu: Yeah.
107 00:11:17.440 ⇒ 00:11:28.324 Aakash Tandel: Alright. Well, that sounds like a good like 1st step in in doing that. So that’s good. I’m glad that we have, like the basic north beam data there.
108 00:11:29.100 ⇒ 00:11:44.089 Aakash Tandel: can you just highlight your questions about the data in like the ticket? Just say, Hey, I am questioning like XY, and Z data. It doesn’t look right. And it’s fairly off from amplitude. And then we can kind of go. And Qa, the the data there.
109 00:11:44.470 ⇒ 00:11:46.229 Annie Yu: Okay. Alright, I’ll do that.
110 00:11:46.720 ⇒ 00:11:51.300 Aakash Tandel: Okay, cool. Oh, wait! Did you have anything to add to that? Not sure. If you.
111 00:11:53.008 ⇒ 00:12:00.160 Awaish Kumar: I know, like and without exactly like the
112 00:12:00.670 ⇒ 00:12:04.010 Awaish Kumar: question of the date range, or whatever like, I cannot.
113 00:12:04.110 ⇒ 00:12:05.420 Awaish Kumar: Our service.
114 00:12:05.550 ⇒ 00:12:06.359 Aakash Tandel: Makes sense.
115 00:12:06.800 ⇒ 00:12:11.920 Annie Yu: And then I do have one follow up question. So for cac.
116 00:12:12.490 ⇒ 00:12:24.040 Annie Yu: For calculation. I’m I’m just looking at the north beam metric one on one. I just wanna make sure. So the calculation we agree on would be spend over
117 00:12:24.240 ⇒ 00:12:32.539 Annie Yu: transactions right? So spend over, discount, or their account distinct order. Count.
118 00:12:32.870 ⇒ 00:12:34.729 Aakash Tandel: Can you say that one more time you kind of broke up.
119 00:12:34.730 ⇒ 00:12:42.530 Annie Yu: So the formula for Cac, the spend divided by order, Count.
120 00:12:43.920 ⇒ 00:12:49.400 Robert Tseng: Yeah, it should be order, not transaction. North beam puts that. But it’s.
121 00:12:52.700 ⇒ 00:12:56.939 Awaish Kumar: Should it be the orders or the customers.
122 00:12:57.630 ⇒ 00:13:05.960 Annie Yu: Yeah, that’s where I was confused. But then, looking at this one from north being, I use order account from our table.
123 00:13:09.976 ⇒ 00:13:18.230 Robert Tseng: well, we should just be using like 1st order from first, st like from per, like 1st order per customer. Right?
124 00:13:19.920 ⇒ 00:13:25.620 Awaish Kumar: Okay. So like, we are like, it’s kind of ncac, right? New.
125 00:13:25.620 ⇒ 00:13:26.849 Robert Tseng: 10. K, yeah.
126 00:13:27.650 ⇒ 00:13:36.580 Awaish Kumar: Okay? So it’s like, ad spend some of ad spend divided by new customer.
127 00:13:38.032 ⇒ 00:13:43.830 Awaish Kumar: Some of new customer account. Yeah, and new customer count and new order count should be equal, because.
128 00:13:44.550 ⇒ 00:13:48.630 Robert Tseng: Yeah, new customer count and new order count should be the same thing. Yeah.
129 00:13:50.920 ⇒ 00:13:53.180 Aakash Tandel: New customer got a new order kind of the same thing.
130 00:13:53.680 ⇒ 00:13:56.220 Robert Tseng: Yeah, I mean, I think, in general, yeah, thank you.
131 00:13:56.220 ⇒ 00:14:05.894 Robert Tseng: The new versus returning filter. Right? So we’re already labeling. And then for this 1st subscription order field, I mean separate ticket. But we we also added A
132 00:14:06.390 ⇒ 00:14:13.520 Robert Tseng: you know this. This order is the 1st time 1st subscription order, which is good, because then they can start to look at different versions of Cac.
133 00:14:14.613 ⇒ 00:14:21.029 Annie Yu: They do want both new subscriber cat and.
134 00:14:21.030 ⇒ 00:14:22.470 Awaish Kumar: I, yeah. But yeah.
135 00:14:22.700 ⇒ 00:14:39.600 Awaish Kumar: in this model you have both the sub. I have added 3 fields for, like new customer account one is called new customer account, which is like generally a new tagged as new. Then one is called new, but not subscriptions. Right? So.
136 00:14:39.600 ⇒ 00:14:40.120 Annie Yu: Bye.
137 00:14:40.120 ⇒ 00:14:44.130 Awaish Kumar: It’s marked as new, but it is not a subscription order.
138 00:14:44.330 ⇒ 00:14:46.240 Awaish Kumar: And then there’s also count of
139 00:14:46.580 ⇒ 00:14:53.609 Awaish Kumar: new. And but there’s also subscription count, so I call it sub new subscriber, count.
140 00:14:54.170 ⇒ 00:14:56.910 Annie Yu: Yeah, yeah, that’s super helpful.
141 00:14:57.750 ⇒ 00:14:58.619 Robert Tseng: Great. Yeah.
142 00:14:59.720 ⇒ 00:15:00.270 Aakash Tandel: Awesome.
143 00:15:01.390 ⇒ 00:15:02.070 Aakash Tandel: Okay?
144 00:15:02.830 ⇒ 00:15:08.815 Aakash Tandel: Cool. Okay. Yeah. So I guess. Follow up with your questions on like the the
145 00:15:09.740 ⇒ 00:15:13.060 Annie Yu: What about the the numbers? Yeah.
146 00:15:13.060 ⇒ 00:15:19.219 Aakash Tandel: Yep, and then a wish can try to help Qa. Those. And Robert and I can also try to help Qa. That as well.
147 00:15:19.600 ⇒ 00:15:20.600 Annie Yu: Okay. Cool.
148 00:15:21.810 ⇒ 00:15:24.969 Aakash Tandel: All right. How’s a lifetimely going.
149 00:15:26.171 ⇒ 00:15:30.900 Annie Yu: For this one. I do need that monthly.
150 00:15:30.900 ⇒ 00:15:31.480 Aakash Tandel: The Udes.
151 00:15:31.480 ⇒ 00:15:38.349 Annie Yu: Summary I think the the one that I really have to
152 00:15:38.480 ⇒ 00:15:44.799 Annie Yu: be done is that I think the 3rd ticket that you? Yeah, this one, the distinct customers.
153 00:15:44.800 ⇒ 00:15:45.330 Aakash Tandel: Okay.
154 00:15:45.330 ⇒ 00:15:55.660 Annie Yu: This one will have to be right for for me to build abuse, but for the other 2 they are. I’m not like worried. They are more like supplement.
155 00:15:56.050 ⇒ 00:15:56.790 Aakash Tandel: Okay.
156 00:15:59.230 ⇒ 00:15:59.900 Awaish Kumar: Okay.
157 00:16:01.310 ⇒ 00:16:15.779 Aakash Tandel: So I will say, let’s pull this one in a way. I’ll pull this add to your list, and then I’ll put these 2 as I’m gonna add these to next week, if
158 00:16:19.370 ⇒ 00:16:24.370 Aakash Tandel: if if you have bandwidth, I mean, we can always pull it in. But at the moment. Let’s just do that one.
159 00:16:26.480 ⇒ 00:16:28.470 Aakash Tandel: Okay, great.
160 00:16:28.840 ⇒ 00:16:30.040 Aakash Tandel: I like him.
161 00:16:30.040 ⇒ 00:16:33.260 Annie Yu: There are 2 other tickets about the
162 00:16:33.360 ⇒ 00:16:39.779 Annie Yu: dashboard. So one is that they also want that pivot view from the
163 00:16:41.081 ⇒ 00:16:46.169 Annie Yu: Google sheet into that north. So I can do that.
164 00:16:46.920 ⇒ 00:16:52.130 Aakash Tandel: Yeah, see if you can get this pivot table or this. I guess this.
165 00:16:52.130 ⇒ 00:16:56.050 Annie Yu: It’s the same table, right? The product summary
166 00:16:56.290 ⇒ 00:16:59.270 Annie Yu: that oh, built for North Spain.
167 00:17:01.020 ⇒ 00:17:02.120 Aakash Tandel: Say that one more time.
168 00:17:02.670 ⇒ 00:17:06.950 Annie Yu: So I should be using the same table for this part.
169 00:17:10.395 ⇒ 00:17:11.189 Annie Yu: The same as.
170 00:17:11.190 ⇒ 00:17:12.700 Aakash Tandel: Yeah, I believe so.
171 00:17:12.700 ⇒ 00:17:16.249 Annie Yu: The one I’m using for North Spain. Dashboard.
172 00:17:19.136 ⇒ 00:17:24.160 Robert Tseng: Well, 1st spend. Yeah. But I would say, I don’t think there’s.
173 00:17:24.770 ⇒ 00:17:32.600 Robert Tseng: I don’t think has order, data or sales. I mean, we should use the order and sales data we have. That’s not from Northeas.
174 00:17:34.740 ⇒ 00:17:35.410 Aakash Tandel: Yeah.
175 00:17:35.900 ⇒ 00:17:38.030 Annie Yu: Okay, I’ll I’ll look into it.
176 00:17:38.310 ⇒ 00:17:43.450 Aakash Tandel: Okay, yeah, I haven’t. Yeah. I don’t actually know how you compile this data. So I
177 00:17:43.570 ⇒ 00:17:45.830 Aakash Tandel: but we’ll have to do that. Figure that out.
178 00:17:46.000 ⇒ 00:17:47.920 Annie Yu: Yeah, same. So I’ll I’ll do that.
179 00:17:48.140 ⇒ 00:17:51.670 Aakash Tandel: Okay, awesome. And then, yeah, if you can
180 00:17:52.230 ⇒ 00:17:56.519 Aakash Tandel: figure out like, estimate how long this will take you, that’d be helpful.
181 00:18:05.150 ⇒ 00:18:05.940 Aakash Tandel: Yeah, I wish.
182 00:18:07.200 ⇒ 00:18:13.469 Awaish Kumar: Yeah. Actually, I had this for the like North main
183 00:18:14.040 ⇒ 00:18:17.660 Awaish Kumar: dashboard. I had a question we
184 00:18:17.860 ⇒ 00:18:23.279 Awaish Kumar: like with Robert, like we discussed that when the orders which are
185 00:18:23.430 ⇒ 00:18:32.320 Awaish Kumar: assigned, as both should be counted in both protein and concentrate right.
186 00:18:32.320 ⇒ 00:18:32.950 Robert Tseng: Yeah.
187 00:18:34.040 ⇒ 00:18:40.290 Awaish Kumar: So like, like, I just want to make sure that, like, we understand that it can
188 00:18:40.700 ⇒ 00:18:44.726 Awaish Kumar: increase the Ncac numbers, because those same orders.
189 00:18:47.160 ⇒ 00:18:54.270 Aakash Tandel: Yeah, yeah, that might be something we add in this little text, field up here, Annie.
190 00:18:55.069 ⇒ 00:19:04.729 Aakash Tandel: That, you know, if an order contains both protein and concentrate in the splits in these splits it’ll be counted in both.
191 00:19:05.100 ⇒ 00:19:11.749 Aakash Tandel: So you can’t just sum up these 2, because in order that would duplicate an order. Right? Is that correct?
192 00:19:12.870 ⇒ 00:19:13.350 Awaish Kumar: Yeah.
193 00:19:13.980 ⇒ 00:19:17.649 Annie Yu: So do we have that both in here. No right.
194 00:19:19.580 ⇒ 00:19:23.339 Awaish Kumar: So yeah, we don’t have the tagged, as both because
195 00:19:23.530 ⇒ 00:19:32.463 Awaish Kumar: they are now, when joining, they just the the orders which are marked as both gets joined with
196 00:19:33.370 ⇒ 00:19:38.490 Awaish Kumar: protein spend, and also within concentrated spend, and
197 00:19:38.700 ⇒ 00:19:50.649 Awaish Kumar: they come in final counting for both like. So if there are like 2,000 orders in March which were both. Now we, we say, these 2,000 are also
198 00:19:50.920 ⇒ 00:19:56.869 Awaish Kumar: being counted in the protein, and these 2,000 are also counted in concentrate.
199 00:19:57.420 ⇒ 00:20:03.450 Robert Tseng: Yeah, if anything, I feel like it would lower tax, because
200 00:20:03.900 ⇒ 00:20:08.480 Robert Tseng: I mean, as long as it doesn’t change the ad spend number, because that would. That wouldn’t make sense like
201 00:20:08.700 ⇒ 00:20:15.139 Robert Tseng: it’s not like you’re doubling your ad spend. It’s still, if it’s a hundred dollars for 2 orders, it’s still a hundred dollars. But then.
202 00:20:15.660 ⇒ 00:20:28.860 Robert Tseng: yeah, then, it’s like you need to split that between 2 orders. So now there are 2 orders that are tied to that $100 of ad spend. So it ends up being like, I guess we could do an even split and do 50 50 each.
203 00:20:30.330 ⇒ 00:20:33.239 Aakash Tandel: Yeah, cause it would be increasing. The denominator.
204 00:20:33.710 ⇒ 00:20:34.310 Robert Tseng: Yeah.
205 00:20:38.250 ⇒ 00:20:44.669 Aakash Tandel: Yeah, that’s fine. Annie, do you want to write a little caveat in this text field above.
206 00:20:45.200 ⇒ 00:20:54.729 Annie Yu: Yeah, yeah, so wait. So if an order has both concentrate and protein, let’s consider both. And then in this case we
207 00:20:55.890 ⇒ 00:21:05.599 Annie Yu: have them separate, we we kind of duplicate, duplicate them.
208 00:21:07.240 ⇒ 00:21:08.060 Aakash Tandel: Yep.
209 00:21:08.060 ⇒ 00:21:09.030 Annie Yu: Salad.
210 00:21:09.990 ⇒ 00:21:10.670 Annie Yu: Okay.
211 00:21:10.670 ⇒ 00:21:12.609 Robert Tseng: Well like it shouldn’t affect.
212 00:21:13.160 ⇒ 00:21:15.740 Robert Tseng: We’ll spend by product here.
213 00:21:16.490 ⇒ 00:21:23.120 Robert Tseng: It would like the total spend should still be the same as like.
214 00:21:23.120 ⇒ 00:21:23.720 Awaish Kumar: Thank you.
215 00:21:23.730 ⇒ 00:21:31.429 Robert Tseng: I I got. I don’t think it. How would it impact this chart? So you would add one to protein. Okay? I mean.
216 00:21:36.060 ⇒ 00:21:36.760 Robert Tseng: yeah, I mean.
217 00:21:36.760 ⇒ 00:21:37.440 Awaish Kumar: It’s just.
218 00:21:37.440 ⇒ 00:21:43.020 Robert Tseng: That particular. I don’t know what the underlying table of this looks looks like. I can’t imagine what other
219 00:21:43.020 ⇒ 00:21:52.300 Robert Tseng: yeah like for nkec, I don’t think it is going to affect. So like I spend, totally understand is same. What is happening is that like we have.
220 00:21:52.840 ⇒ 00:22:08.859 Awaish Kumar: 1,000 orders, which are marked as protein. Now, for example, we have 200 more orders, which are marked as both, we added in protein. So now for protein, we have total of 1,200 orders. And we said, Okay, total spend for protein is
221 00:22:08.890 ⇒ 00:22:30.320 Awaish Kumar: 100,000 in March, so that 100,000 for March is going to be equal to like, we can say like in March, we have 1,200 orders at the 100,000 that is spent. But for Ncag doesn’t make any big difference, because we will only take the new
222 00:22:31.380 ⇒ 00:22:49.009 Awaish Kumar: customers right, only it it they can be only calculated once, so hence it will mitigate the effect for Ncac. But whenever we try to get to the if I add revenue fields in there, then revenue might get duplicated. And we have to
223 00:22:49.590 ⇒ 00:22:51.729 Awaish Kumar: like, have this context in mind.
224 00:22:52.380 ⇒ 00:22:55.690 Robert Tseng: Yeah, okay.
225 00:23:06.550 ⇒ 00:23:06.930 Aakash Tandel: Cool.
226 00:23:06.930 ⇒ 00:23:10.010 Annie Yu: I I will still add that note in here.
227 00:23:15.980 ⇒ 00:23:21.819 Aakash Tandel: Yep, that sounds good. Yeah, I think, adding, that context is is helpful, regardless of the client, is aware of that.
228 00:23:23.520 ⇒ 00:23:37.819 Annie Yu: Okay, cool. And one more question from my side is the Met Meta based dashboard, attentive basic data. So for one, do I also look at the same table I was trying to figure out.
229 00:23:38.640 ⇒ 00:23:49.560 Aakash Tandel: Yeah, I know you finished. Let me add in your card. I know that you. I think the Pr went through for attentive.
230 00:23:50.270 ⇒ 00:23:53.919 Aakash Tandel: Yeah. Where should Annie look for that
231 00:23:54.510 ⇒ 00:23:57.860 Aakash Tandel: in the in Snowflake? Is that a different table.
232 00:23:58.840 ⇒ 00:24:09.000 Awaish Kumar: In the like. The same fraud bots the table name is the engagement summary.
233 00:24:09.280 ⇒ 00:24:11.709 Annie Yu: Okay, engagement, summary.
234 00:24:12.710 ⇒ 00:24:13.280 Awaish Kumar: Yeah.
235 00:24:18.930 ⇒ 00:24:20.859 Annie Yu: Alright. Thank you.
236 00:24:20.860 ⇒ 00:24:21.480 Aakash Tandel: Awesome.
237 00:24:21.940 ⇒ 00:24:33.369 Aakash Tandel: Alright. Let’s go to. I’ll pull Annie’s off oops. Okay, all right away. I know you got some Prs moved yesterday, which is awesome still blocked on portable.
238 00:24:33.490 ⇒ 00:24:41.330 Aakash Tandel: I guess you have nothing in progress. You’re just, or maybe these are in progress now. We just haven’t moved them yet. Which one. Do you want to talk about first? st
239 00:24:42.790 ⇒ 00:24:43.660 Awaish Kumar: So
240 00:24:43.780 ⇒ 00:24:57.470 Awaish Kumar: like for me. Those were in progress for yesterday those pr, it was we get merged. And this is I. I did spend some time on this one the replacement Api.
241 00:24:58.150 ⇒ 00:25:03.709 Awaish Kumar: But I like kind of still like spend
242 00:25:04.600 ⇒ 00:25:12.860 Awaish Kumar: kind of an hour, so I I can spend little bit more to to get a final answer. But yeah, so far, it doesn’t say anything
243 00:25:13.130 ⇒ 00:25:14.499 Awaish Kumar: that we can do that.
244 00:25:15.080 ⇒ 00:25:29.130 Aakash Tandel: Yeah, that’s kind of what I’m expecting. Okay, yeah. It’s been another hour. See if you can figure this out. And then, if not. I mean, we can. Just if not, can you write like a little summary of like, hey? This is why we can’t use the data from this Api for.
245 00:25:30.020 ⇒ 00:25:31.979 Awaish Kumar: Yeah, yeah, sure, I will write it down.
246 00:25:32.550 ⇒ 00:25:37.470 Aakash Tandel: Okay. Awesome. Thank you. And then anything on these incremental refreshes.
247 00:25:38.200 ⇒ 00:25:39.829 Awaish Kumar: I don’t know like it wasn’t.
248 00:25:40.541 ⇒ 00:25:45.110 Awaish Kumar: I didn’t work done. I did not work on them yesterday. So yeah.
249 00:25:45.920 ⇒ 00:25:51.380 Aakash Tandel: Cool. Alright and then what else do we have here? Mind if I we just pull this in.
250 00:25:51.380 ⇒ 00:25:55.320 Awaish Kumar: Yeah, this I can. I can just do it today as well.
251 00:25:55.530 ⇒ 00:25:56.660 Aakash Tandel: Awesome. Okay.
252 00:25:57.240 ⇒ 00:26:07.800 Aakash Tandel: alright. And then the other one is, I’ll just set that tomorrow. So let’s do okay. And then this one
253 00:26:09.915 ⇒ 00:26:17.330 Aakash Tandel: we can, Robert? I’m not sure what this one is I saw. But we can
254 00:26:18.060 ⇒ 00:26:21.600 Aakash Tandel: add context here. Or is this still a real ticket, Robert? Do you know.
255 00:26:22.420 ⇒ 00:26:28.794 Robert Tseng: Yeah, I mean, this was just what whatever Kyle’s working on, and then
256 00:26:29.380 ⇒ 00:26:32.190 Robert Tseng: I mean, I don’t know if this is duplicated already, but
257 00:26:32.610 ⇒ 00:26:40.210 Robert Tseng: the getting the cogs assumption sheet and then bringing it into them into like dim products, or whatever model we end up storing it in.
258 00:26:40.630 ⇒ 00:26:45.740 Aakash Tandel: Oh, okay, okay, gotcha. Okay. So I’m gonna cancel this ticket and work off the other one. Then.
259 00:26:45.740 ⇒ 00:26:46.350 Robert Tseng: Okay.
260 00:26:47.660 ⇒ 00:26:48.400 Aakash Tandel: Let’s see.
261 00:26:48.750 ⇒ 00:26:50.190 Aakash Tandel: Go ahead
262 00:26:54.766 ⇒ 00:27:00.580 Aakash Tandel: I’ll just say duplicate. Okay, cool. Alright. That sounds good. Let me go back. Anything else. Wish.
263 00:27:03.159 ⇒ 00:27:03.609 Awaish Kumar: Nope.
264 00:27:04.000 ⇒ 00:27:04.620 Aakash Tandel: Awesome.
265 00:27:05.000 ⇒ 00:27:09.270 Aakash Tandel: Alright, Robert, let’s go to you. I know you probably have.
266 00:27:10.902 ⇒ 00:27:13.557 Aakash Tandel: There’s some stuff in client feedback.
267 00:27:14.360 ⇒ 00:27:17.714 Robert Tseng: Yeah, the the training videos. I can record that today.
268 00:27:18.050 ⇒ 00:27:18.620 Aakash Tandel: Awesome.
269 00:27:19.380 ⇒ 00:27:20.030 Robert Tseng: Yeah.
270 00:27:20.650 ⇒ 00:27:27.929 Robert Tseng: I thought I was supposed to do one more investigation on something, but I don’t see it on there and can’t recall, so
271 00:27:29.040 ⇒ 00:27:32.090 Robert Tseng: I guess I can. Just so I’ll I’ll probably
272 00:27:33.230 ⇒ 00:27:36.838 Robert Tseng: be pushing on stuff on stuff today. So
273 00:27:37.450 ⇒ 00:27:44.560 Robert Tseng: yeah, like, yesterday, I was pretty caught up with the other clients. So today I will be. I’ll be pushing more on this side.
274 00:27:44.910 ⇒ 00:27:46.590 Aakash Tandel: Do you know what the investigation was about?
275 00:27:49.100 ⇒ 00:27:58.440 Robert Tseng: If we did, we did. We? We took everything from. Maybe it’s in the backlog. But do we? We put everything? Or maybe we maybe we just decide not to do it in this cycle. So
276 00:27:59.080 ⇒ 00:28:02.730 Aakash Tandel: This is actually not just the cycle. So this should be everything.
277 00:28:03.820 ⇒ 00:28:12.680 Robert Tseng: okay, cause I’m just thinking from our call with Aman and the the sheet. Let me. I guess I can. I can go and dig it up and see see if that’s
278 00:28:13.470 ⇒ 00:28:14.510 Robert Tseng: that’s fine.
279 00:28:16.390 ⇒ 00:28:35.054 Aakash Tandel: Okay, yeah. I checked on this list. I think everything’s either moving or in progress or not doing so. I think everything on this is good. The only thing that I’m not like have no action on is reduce the number of portable connectors. He’s linked up with the portable team in that slack thread or slack channel. So
280 00:28:35.710 ⇒ 00:28:38.079 Aakash Tandel: I’m going to let that one kind of
281 00:28:38.220 ⇒ 00:28:40.369 Aakash Tandel: he. We don’t need to worry about this one.
282 00:28:41.150 ⇒ 00:28:41.820 Robert Tseng: Okay.
283 00:28:42.140 ⇒ 00:28:43.080 Robert Tseng: Sounds good.
284 00:28:44.060 ⇒ 00:28:45.500 Aakash Tandel: Cool. Alright. Y’all
285 00:28:46.435 ⇒ 00:28:58.700 Aakash Tandel: yeah. Things are moving. Let me know if you guys need anything. And yeah, let’s try to get the ball. Yeah, we can push the client a little bit more on some of these things, too. So let’s try to get their help to get these things done.
286 00:28:59.440 ⇒ 00:29:01.190 Annie Yu: Thanks. Everyone.
287 00:29:01.320 ⇒ 00:29:03.660 Aakash Tandel: Alrighty! Have a good day deal.