Meeting Title: [Javvy] Standup and Weekly Sprint Retro-Planning Date: 2025-04-04 Meeting participants: Aakash Tandel, Annie Yu, Robert Tseng, Awaish Kumar, Caio Velasco
WEBVTT
1 00:01:12.960 ⇒ 00:01:13.620 Robert Tseng: Cool.
2 00:01:18.880 ⇒ 00:01:19.840 Caio Velasco: Hey, Robert
3 00:01:21.970 ⇒ 00:01:22.990 Robert Tseng: Oh, hey? Kyle.
4 00:01:25.160 ⇒ 00:01:26.120 Caio Velasco: How are you too.
5 00:01:27.280 ⇒ 00:01:34.447 Robert Tseng: I’m good I traveled to la last night, so it’s pretty early here.
6 00:01:35.120 ⇒ 00:01:38.539 Robert Tseng: but the jet lag helps me get up. I guess
7 00:01:40.340 ⇒ 00:01:42.270 Caio Velasco: Where are you based
8 00:01:43.030 ⇒ 00:01:47.500 Robert Tseng: I’m I’m in Los Angeles right now. But I’m usually in New York.
9 00:01:47.970 ⇒ 00:01:50.100 Robert Tseng: Oh, he’s in there. Okay, cool.
10 00:01:50.800 ⇒ 00:01:51.290 Robert Tseng: Yeah.
11 00:01:51.290 ⇒ 00:01:52.000 Caio Velasco: That’s great.
12 00:01:57.590 ⇒ 00:02:00.180 Caio Velasco: Yeah, Cleveland, it’s a nice place
13 00:02:01.460 ⇒ 00:02:05.950 Robert Tseng: Yeah, I mean you. You went to school here. I’m pretty close to Ucla.
14 00:02:07.110 ⇒ 00:02:09.100 Caio Velasco: Yeah, I was in Westwood.
15 00:02:09.490 ⇒ 00:02:10.100 Robert Tseng: Yep.
16 00:02:10.787 ⇒ 00:02:23.289 Caio Velasco: Yeah, it’s a very interesting area. Because I remember the one time I was just walking around. And there’s like this theater, like right right in the middle of the of that neighborhood.
17 00:02:23.629 ⇒ 00:02:31.489 Caio Velasco: And then suddenly, I just saw, like a lot of people from Hollywood, they’re like, Oh, my God, what is happening here? They usually
18 00:02:32.459 ⇒ 00:02:37.479 Caio Velasco: I don’t know how to call it when when there’s openings for new movies or something, they usually start there
19 00:02:38.450 ⇒ 00:02:42.510 Robert Tseng: Yeah, yeah, probably a film set like, yeah, they
20 00:02:43.000 ⇒ 00:02:45.739 Robert Tseng: yeah, a lot of people record stuff around there.
21 00:02:46.110 ⇒ 00:02:46.670 Caio Velasco: Yeah.
22 00:02:47.910 ⇒ 00:02:48.420 Robert Tseng: Okay.
23 00:02:49.250 ⇒ 00:02:50.509 Aakash Tandel: Hello! Good morning!
24 00:02:50.860 ⇒ 00:02:51.640 Robert Tseng: We’re in and
25 00:02:52.030 ⇒ 00:02:52.690 Caio Velasco: No need.
26 00:02:52.950 ⇒ 00:02:54.410 Aakash Tandel: How’s everyone doing good?
27 00:02:56.350 ⇒ 00:02:57.460 Aakash Tandel: The weekend
28 00:02:57.460 ⇒ 00:03:00.510 Robert Tseng: Yeah. Excited to get through the day
29 00:03:00.680 ⇒ 00:03:08.610 Aakash Tandel: Yeah, nice alrighty. Let’s go ahead and get started. Let’s start with. Let’s start with a wish.
30 00:03:10.600 ⇒ 00:03:15.249 Aakash Tandel: What are you working on? What’s what’s getting wrapped up?
31 00:03:15.820 ⇒ 00:03:16.700 Aakash Tandel: See? Live.
32 00:03:17.122 ⇒ 00:03:19.659 Awaish Kumar: Yeah. So I’m working on this.
33 00:03:20.590 ⇒ 00:03:29.230 Awaish Kumar: I was working on monthly code summary model. And it is in review. So it is kind of done where you just have to merge the Pr.
34 00:03:30.690 ⇒ 00:03:37.550 Awaish Kumar: But it is available in staging. If any wants to like review and
35 00:03:37.950 ⇒ 00:03:41.256 Awaish Kumar: see the data source like she can like
36 00:03:41.840 ⇒ 00:03:45.719 Awaish Kumar: see the table in staging. But yeah, it should be merged soon.
37 00:03:51.510 ⇒ 00:03:53.600 Annie Yu: Cool. Thank you. Aish.
38 00:03:55.860 ⇒ 00:04:02.177 Aakash Tandel: Awesome. And Kyle, are you planning on reviewing that pr, or does? We don’t need to do that
39 00:04:04.350 ⇒ 00:04:05.400 Caio Velasco: Which one
40 00:04:06.010 ⇒ 00:04:07.990 Awaish Kumar: Yeah, this is assigned to them.
41 00:04:08.310 ⇒ 00:04:10.170 Aakash Tandel: This is admin, okay, I will
42 00:04:10.170 ⇒ 00:04:10.475 Caio Velasco: Okay.
43 00:04:11.180 ⇒ 00:04:23.170 Aakash Tandel: Yeah, I’ll see if Lutheran has time. Otherwise we might try to do it within the project team. So we’ll try to get Kyle to review it, but I will sync up with Luthan about that
44 00:04:24.363 ⇒ 00:04:25.046 Aakash Tandel: cool
45 00:04:25.730 ⇒ 00:04:28.789 Awaish Kumar: 5, 14, we. I have added some.
46 00:04:29.240 ⇒ 00:04:36.099 Awaish Kumar: I I created this ticket. Basically, we have data, and I have added some comments there
47 00:04:36.310 ⇒ 00:04:40.210 Awaish Kumar: for Robert and the Annie to review.
48 00:04:40.460 ⇒ 00:04:44.639 Awaish Kumar: and then I would need a little bit more information from
49 00:04:45.230 ⇒ 00:04:49.219 Awaish Kumar: from both of you to like build what kind of model we want to build here
50 00:04:50.420 ⇒ 00:04:59.110 Aakash Tandel: Yeah, do we have clear requirements from the client on what they want to, I know was
51 00:04:59.320 ⇒ 00:05:03.420 Aakash Tandel: like, did they have like a list of questions they want to ask from North being. I don’t know if that’s
52 00:05:04.490 ⇒ 00:05:07.210 Aakash Tandel: been clarified, or if we have a ticket about that right
53 00:05:07.210 ⇒ 00:05:18.809 Robert Tseng: Yeah, there’s there’s a ticket. Annie, already kind of like, wrote an outline which is great of like what they wanted to pull out of there. So yeah, I think it should be ready to build
54 00:05:19.180 ⇒ 00:05:24.820 Annie Yu: Question, though. Cause there is kind of a long list. And do we want
55 00:05:25.020 ⇒ 00:05:27.339 Annie Yu: to make sure we have everything here
56 00:05:28.020 ⇒ 00:05:29.099 Aakash Tandel: This is the list.
57 00:05:29.470 ⇒ 00:05:30.310 Annie Yu: Yeah.
58 00:05:31.703 ⇒ 00:05:37.309 Aakash Tandel: Yeah. So what I’ll do is I will break these
59 00:05:37.600 ⇒ 00:05:55.219 Aakash Tandel: into multiple tickets, because I think this is probably too much for one ticket. This would be like a massive ticket that will take a while, so I’ll break these up away. If you have a specific way, you want to break this data up? Let me know, and I can do that. But I will turn this into multiple tickets for you.
60 00:05:55.220 ⇒ 00:05:55.820 Awaish Kumar: And
61 00:05:55.850 ⇒ 00:05:57.870 Aakash Tandel: And start working on
62 00:05:59.770 ⇒ 00:06:00.380 Awaish Kumar: Okay.
63 00:06:00.380 ⇒ 00:06:04.608 Robert Tseng: Yeah, I mean, I think 1 1 thing I’ll call out, here is
64 00:06:05.610 ⇒ 00:06:30.929 Robert Tseng: I think the top, the top 3 are probably the most important. Obvious, like, I think opt in stuff is by traffic source. Like I, I would imagine. And you may run into issues with that. And then, obviously, we don’t have subscription like I mean, we have subscription stuff, but we don’t have subscribe and save, and whatever down there. So it’s like, I mean, I just look at that list. And in my mind I can see.
65 00:06:31.200 ⇒ 00:06:47.340 Robert Tseng: yeah, there’s like a few that we’ll be able to get done right away, and then there’s a few others that I foresee there being more trouble. I mean, I don’t know like that, I guess to Akash’s point we should probably break it up into like 3 tickets, but I see one where I I see a way to kind of break this down
66 00:06:48.680 ⇒ 00:06:49.155 Annie Yu: Hey?
67 00:06:49.630 ⇒ 00:07:00.190 Annie Yu: just to clarify. So do we have that data for the top 3 already, or it’s working progress.
68 00:07:03.581 ⇒ 00:07:08.680 Aakash Tandel: I guess we I we have the data from North Beam, so I don’t know if a way she can answer that
69 00:07:08.680 ⇒ 00:07:15.040 Awaish Kumar: Like, we have a data from Earthbeam like, if you open the ticket, the one I created
70 00:07:15.842 ⇒ 00:07:19.620 Awaish Kumar: it shows what we have. Right? So if, again.
71 00:07:25.090 ⇒ 00:07:25.450 Aakash Tandel: To this.
72 00:07:25.450 ⇒ 00:07:29.189 Awaish Kumar: So this is what we have on a
73 00:07:30.440 ⇒ 00:07:36.029 Awaish Kumar: broader level. We have a date. We have a product type
74 00:07:36.180 ⇒ 00:07:42.770 Awaish Kumar: product name which we can classify as a concentrator protein. And then we have a platform.
75 00:07:43.330 ⇒ 00:07:47.320 Awaish Kumar: And then we have a spend data and using the product
76 00:07:48.129 ⇒ 00:07:51.720 Awaish Kumar: name, we can map it with the orders. Data
77 00:07:52.972 ⇒ 00:07:57.959 Awaish Kumar: so from the orders, fact order table, we get the funnel type. We get the
78 00:07:58.540 ⇒ 00:08:00.710 Awaish Kumar: total price like revenue.
79 00:08:01.520 ⇒ 00:08:02.889 Awaish Kumar: So we can
80 00:08:03.000 ⇒ 00:08:10.530 Awaish Kumar: join on the product name and get the data basically on the date product, name the spend and the
81 00:08:11.248 ⇒ 00:08:14.389 Awaish Kumar: like, the country information you need and the revenue.
82 00:08:15.090 ⇒ 00:08:19.690 Awaish Kumar: and these revenue and the spend can be used to calculate the Cse. I think
83 00:08:20.560 ⇒ 00:08:27.560 Annie Yu: Okay. And so what table is this? I think I probably missed that
84 00:08:31.290 ⇒ 00:08:37.179 Aakash Tandel: This is like the north beam. Is this saved in like a this is the raw export right away
85 00:08:37.380 ⇒ 00:08:38.460 Awaish Kumar: Yeah, yeah.
86 00:08:38.620 ⇒ 00:08:45.839 Aakash Tandel: Yeah. So I think, Annie, so we need so we’ll get this data into like the
87 00:08:46.100 ⇒ 00:08:50.080 Aakash Tandel: like, the final state. I don’t know what that table will be called. Will that go to?
88 00:08:50.280 ⇒ 00:09:03.459 Aakash Tandel: Do, you know? Well, that doesn’t really matter. I think. The the idea right now is that the data has to be modified a bit for us to answer those 1st questions, and then once it’s available for you. There you can pull that into metabase, or wherever
89 00:09:04.520 ⇒ 00:09:06.230 Annie Yu: Okay. Okay. Gotcha.
90 00:09:07.860 ⇒ 00:09:08.890 Awaish Kumar: Cool. Yeah.
91 00:09:09.429 ⇒ 00:09:14.820 Aakash Tandel: Alright so cool. I will write a ticket for a ways for that, I think.
92 00:09:16.500 ⇒ 00:09:18.740 Aakash Tandel: The so this
93 00:09:19.380 ⇒ 00:09:37.289 Aakash Tandel: ticket was more like an investigation ticket. So I’ll close this out. Because or yeah, I’ll I’ll figure out how to modify these. So it makes more sense. But yeah, I understand that we need to model things, and then Annie will have a separate ticket for you to just put that into a dashboard. So that sounds good.
94 00:09:38.760 ⇒ 00:09:42.999 Annie Yu: Okay, just let me know if you need more clarification from my side.
95 00:09:43.220 ⇒ 00:09:45.889 Aakash Tandel: Okay. Yep, I will do that.
96 00:09:46.807 ⇒ 00:09:50.849 Aakash Tandel: A wish. Is there anything else that you currently are working
97 00:09:52.390 ⇒ 00:09:58.679 Awaish Kumar: Yeah, these are 2 things which I worked yesterday. And today I I see that there’s only one ticket
98 00:09:58.960 ⇒ 00:10:01.120 Awaish Kumar: which I can work like this
99 00:10:01.340 ⇒ 00:10:10.700 Awaish Kumar: in which is in to do. I can work on this one today. It should be quick. Yeah, but I I don’t think there’s anything else
100 00:10:11.240 ⇒ 00:10:22.419 Aakash Tandel: So there is one thing that Aman mentioned about the attentive data that I’m trying to find the ticket, for. He linked to a ticket that I can’t find attentive data modeling and reporting. Let’s see.
101 00:10:23.045 ⇒ 00:10:23.410 Aakash Tandel: So
102 00:10:23.410 ⇒ 00:10:27.350 Awaish Kumar: There was this, Claire, what? Don’t tell you
103 00:10:29.100 ⇒ 00:10:30.350 Aakash Tandel: Say that one more time. Sorry.
104 00:10:31.640 ⇒ 00:10:33.490 Awaish Kumar: Yeah, this one was it?
105 00:10:34.020 ⇒ 00:10:36.900 Awaish Kumar: I don’t know. Okay, so you created this new ticket
106 00:10:38.511 ⇒ 00:10:50.559 Aakash Tandel: So I was. I created this like yesterday, but I was trying to figure out, because so let me let me pull up this. So can you guys still see? No, you don’t see slack right? You don’t see slack
107 00:10:50.560 ⇒ 00:10:51.430 Annie Yu: No.
108 00:10:51.660 ⇒ 00:10:59.790 Aakash Tandel: Alright. Let me share the entire screen. Okay, okay, alright.
109 00:11:00.310 ⇒ 00:11:09.569 Aakash Tandel: So I I know you sent over the attentive data to Aman and I was asking, like, what type of data he wants. And he said.
110 00:11:09.953 ⇒ 00:11:33.250 Aakash Tandel: Let’s focus on what we discussed and the this ticket. I can’t find this ticket. That’s why I’m like a little lost but it says, focus on text sent out revenue and orders placed within 5 days. So I was gonna modify this ticket to have that information. But I don’t see this specific ticket. So I’ll just copy this text sent out revenue and orders. So I’ll just put
111 00:11:34.930 ⇒ 00:11:38.990 Aakash Tandel: text sent out.
112 00:11:42.570 ⇒ 00:11:44.100 Aakash Tandel: Oh, revenue
113 00:11:52.840 ⇒ 00:11:55.680 Aakash Tandel: billing orders, place within.
114 00:12:04.010 ⇒ 00:12:10.109 Aakash Tandel: If this is all he wants. I’ll probably assign this to you if you can get this
115 00:12:10.790 ⇒ 00:12:15.200 Aakash Tandel: add it, and I’ll add it to the queue.
116 00:12:16.820 ⇒ 00:12:26.750 Aakash Tandel: Alright. I’ll put this to do in cycle and then add to cycle.
117 00:12:27.310 ⇒ 00:12:36.230 Aakash Tandel: and then attentive data modeling. And this is the data modeling test. Okay? So now, we should see this.
118 00:12:38.700 ⇒ 00:12:40.580 Aakash Tandel: or did I not assign that to myself?
119 00:12:42.030 ⇒ 00:12:45.920 Aakash Tandel: No worries. Yeah, that’s why. Oh, my, okay.
120 00:12:47.491 ⇒ 00:12:57.770 Aakash Tandel: so this is, I guess, probably more high level or more urgent than the other one. So if you have time to work on this ticket. That would be great.
121 00:12:58.700 ⇒ 00:13:00.790 Awaish Kumar: Okay, sure. I’ll I’ll work on it
122 00:13:00.950 ⇒ 00:13:02.120 Aakash Tandel: Awesome thanks.
123 00:13:02.270 ⇒ 00:13:03.870 Aakash Tandel: Anything else for you, Alicia
124 00:13:06.953 ⇒ 00:13:11.999 Awaish Kumar: So like about this North stream ticket. What like?
125 00:13:12.170 ⇒ 00:13:14.100 Awaish Kumar: I will be waiting for the
126 00:13:14.340 ⇒ 00:13:14.860 Aakash Tandel: Yeah.
127 00:13:14.860 ⇒ 00:13:18.179 Awaish Kumar: Do more tickets from you, and how you would like to.
128 00:13:19.200 ⇒ 00:13:20.700 Awaish Kumar: Yeah, thank you.
129 00:13:21.110 ⇒ 00:13:21.800 Awaish Kumar: No problem
130 00:13:22.120 ⇒ 00:13:30.539 Aakash Tandel: Yep, I’ll write them today, and then I’ll it’ll they won’t be due today. There’s just that’s gonna be too tight of a turnaround, so I’ll set them for next week.
131 00:13:31.150 ⇒ 00:13:31.870 Awaish Kumar: Okay.
132 00:13:32.070 ⇒ 00:13:32.870 Awaish Kumar: Thanks.
133 00:13:33.040 ⇒ 00:13:34.390 Aakash Tandel: Alright. Thank you.
134 00:13:34.790 ⇒ 00:13:37.459 Aakash Tandel: Okay, let’s go to coyote.
135 00:13:39.220 ⇒ 00:13:44.669 Aakash Tandel: Alright. So so we have a bunch of stuff pending client feedback.
136 00:13:44.920 ⇒ 00:13:54.610 Aakash Tandel: And so this is still blocked. So we’re blocked from that’s from
137 00:13:54.790 ⇒ 00:13:58.089 Aakash Tandel: Alia. And then there’s a new person that she looped in
138 00:13:58.090 ⇒ 00:13:58.690 Caio Velasco: Yeah.
139 00:13:59.340 ⇒ 00:14:00.480 Aakash Tandel: Is it Steven?
140 00:14:02.280 ⇒ 00:14:05.949 Caio Velasco: It would be, I think, or no, I don’t know. Let me check
141 00:14:08.460 ⇒ 00:14:10.270 Aakash Tandel: Let’s do another client.
142 00:14:10.400 ⇒ 00:14:11.430 Aakash Tandel: That’s fine.
143 00:14:11.430 ⇒ 00:14:22.330 Caio Velasco: Jason Blake, I’d be late, but also I’m on tagged him. But he hasn’t responded so far
144 00:14:22.830 ⇒ 00:14:23.175 Aakash Tandel: Okay.
145 00:14:23.670 ⇒ 00:14:32.220 Aakash Tandel: that’s fine. I’m gonna place as block because we need their input there. And then this isn’t Pr review is this Pr review
146 00:14:32.670 ⇒ 00:14:36.209 Caio Velasco: Just just an update, because both are are connected.
147 00:14:36.704 ⇒ 00:14:41.870 Caio Velasco: Well, at the end of the day, for example, I brought this use from
148 00:14:41.970 ⇒ 00:14:47.010 Caio Velasco: a source table from Amazon. So for the skews part, we
149 00:14:47.320 ⇒ 00:15:00.220 Caio Velasco: should have everything. But of course we have to wait for them, just to be sure that we have everything the assumptions part which makes up the calculation for prices, then yes, we need them to to do whatever they have to do for that spreadsheet
150 00:15:00.672 ⇒ 00:15:25.339 Caio Velasco: but this means that the product is it’s it’s it’s built already. It already did. And I pushed the Pr. Yesterday. You’ve done reviewed and made some suggestions. I already made the the changes, and I re requested the review. So it’s either on his side now, or even if I don’t know if I wish has time. He could also check because it was very minor stuff.
151 00:15:26.103 ⇒ 00:15:35.649 Caio Velasco: The only thing I can update from the Dean product. It’s when I was checking the differences between shopify and Amazon. Shopify has kind of like 2
152 00:15:36.200 ⇒ 00:15:50.400 Caio Velasco: ids for products. One is a general one, let’s say a T-shirt, and another one. It’s it’s like called a variant one variant id, which is like if it’s a t-shirt that is blue, and another one is green or something.
153 00:15:51.105 ⇒ 00:15:58.800 Caio Velasco: But for Amazon, Amazon doesn’t seem to have something like that. So there’s only one field called Sn.
154 00:15:58.960 ⇒ 00:16:09.840 Caio Velasco: which they kind of use for unique products, and then I assume they would differentiate, be between callers, for example. So just so that Annie or Robert know.
155 00:16:10.090 ⇒ 00:16:12.859 Caio Velasco: So the Dean product contains
156 00:16:13.130 ⇒ 00:16:21.279 Caio Velasco: both columns. But for the shopify one, you have data, and for Amazon one, you won’t have data for variant. For example.
157 00:16:22.030 ⇒ 00:16:25.420 Aakash Tandel: Okay, that sounds good.
158 00:16:26.150 ⇒ 00:16:30.210 Aakash Tandel: I guess, since there’s nothing else on your active working on, do you
159 00:16:30.520 ⇒ 00:16:38.110 Aakash Tandel: have enough context to pick this one exclude phone SMS from gorgeous channel type. This sounds to me and I’m
160 00:16:38.220 ⇒ 00:16:39.289 Aakash Tandel: I don’t. I haven’t.
161 00:16:39.580 ⇒ 00:16:49.540 Aakash Tandel: I’m less familiar with all this stuff than all of you are. So this to me sounds like it’s fairly straightforward of an ask, Is there a way that you can tackle this item and I can pull it off of oasis plate
162 00:16:50.190 ⇒ 00:16:53.499 Caio Velasco: Yeah, yeah, for today or for next week
163 00:16:53.780 ⇒ 00:16:55.100 Aakash Tandel: For today.
164 00:16:56.020 ⇒ 00:16:57.005 Caio Velasco: Okay.
165 00:16:59.030 ⇒ 00:17:12.450 Caio Velasco: okay, I think I can try. I remember more or less what it was about. And at the end of the day we decided just to future directly on the on the what do you call a database? But then I think if we do that
166 00:17:14.660 ⇒ 00:17:19.700 Caio Velasco: directly in the in the model. Then I think it’s just basically a where clause
167 00:17:20.349 ⇒ 00:17:25.330 Caio Velasco: school the phone and SMS, that should be really quick. If I’m I’m correct
168 00:17:26.260 ⇒ 00:17:28.310 Awaish Kumar: Yeah, yeah, that’s that’s it. Right?
169 00:17:29.239 ⇒ 00:17:34.930 Aakash Tandel: Okay, perfect. So I will just assign this to Kyle to
170 00:17:35.310 ⇒ 00:17:42.909 Aakash Tandel: do. And I’ll set this. It sounds like it’s fairly straightforward end of
171 00:17:44.250 ⇒ 00:17:54.350 Caio Velasco: Now, if I should do that directly on the let’s say on the fact table, right? I don’t need to go until we are consuming from sources like in raw models. Or if models?
172 00:17:54.350 ⇒ 00:17:56.820 Caio Velasco: No, no, we we just do it on flat table.
173 00:17:57.090 ⇒ 00:17:57.500 Caio Velasco: Okay.
174 00:17:58.870 ⇒ 00:17:59.270 Aakash Tandel: Cool.
175 00:17:59.270 ⇒ 00:18:05.740 Awaish Kumar: Or or the like. Maybe if there are any intermediary table, we can do there as well, because the
176 00:18:07.150 ⇒ 00:18:15.659 Awaish Kumar: the data for these 2 channels are is is, we are not interested in that. So if
177 00:18:15.890 ⇒ 00:18:21.939 Awaish Kumar: so, we can just filter this out in the intermediate table as well. If there is any, if not, then just on the fact table
178 00:18:22.550 ⇒ 00:18:23.820 Caio Velasco: Okay. Cool. Perfect.
179 00:18:25.834 ⇒ 00:18:38.440 Aakash Tandel: Cool. Okay? And then for the big list of stuff for north beam, I’ll probably split that between Kyle and Aish. Just for next week. So yeah, we don’t need to talk about that now, but that’s what I’ll do for next week.
180 00:18:39.070 ⇒ 00:18:41.830 Aakash Tandel: Cool anything else, Kyle.
181 00:18:43.107 ⇒ 00:18:50.179 Caio Velasco: You know, just to confirm that I saw your message for next week. The expectation is another 10 h right like this week.
182 00:18:50.180 ⇒ 00:18:50.790 Aakash Tandel: Yep.
183 00:18:51.220 ⇒ 00:18:52.980 Caio Velasco: Okay, no, we’re pretty good.
184 00:18:53.270 ⇒ 00:18:57.339 Aakash Tandel: Awesome. Thanks. All right. Let me switch over to Annie
185 00:19:06.595 ⇒ 00:19:09.210 Annie Yu: Yeah, starting with that in progress.
186 00:19:10.412 ⇒ 00:19:16.410 Annie Yu: I was able to build that pivot table. But I think we are
187 00:19:17.020 ⇒ 00:19:21.719 Annie Yu: really just finishing that part, but the not the MMER.
188 00:19:22.050 ⇒ 00:19:22.570 Aakash Tandel: Okay.
189 00:19:22.570 ⇒ 00:19:28.559 Annie Yu: This one to Robert’s notes everything is changed, according to the note
190 00:19:29.620 ⇒ 00:19:36.330 Robert Tseng: Yeah, I’m I’m actually clicking around. And then I added, Yeah, I mean, it looks good. I just added, like another
191 00:19:36.780 ⇒ 00:19:37.960 Robert Tseng: tile to like.
192 00:19:38.130 ⇒ 00:19:53.239 Robert Tseng: I mean, I like to just include any key definitions or assumptions we’re making, because the way that we’re presenting total price is a bit different, because it’s post tax or whatever. So anyway, I was just doing some cleanup there. But overall it looks good
193 00:19:53.530 ⇒ 00:19:58.620 Annie Yu: Okay, thanks. So do we keep this ticket, I guess, or
194 00:19:59.160 ⇒ 00:20:01.840 Aakash Tandel: Is it? Gonna go? You’re gonna do some modifications to it.
195 00:20:02.400 ⇒ 00:20:07.949 Robert Tseng: Yeah. Well, I mean the mer part, I mean, that’s gonna be once we have
196 00:20:09.570 ⇒ 00:20:10.940 Aakash Tandel: So we can’t do that part right
197 00:20:10.940 ⇒ 00:20:14.750 Robert Tseng: Yeah, we can’t do that part yet, but that is really the main point of that tab
198 00:20:15.220 ⇒ 00:20:16.090 Annie Yu: Yeah.
199 00:20:16.090 ⇒ 00:20:16.660 Robert Tseng: Yeah.
200 00:20:27.090 ⇒ 00:20:34.240 Aakash Tandel: Okay? And what is that like, what’s the dependency? Why can’t we do that at the moment
201 00:20:35.100 ⇒ 00:20:36.060 Robert Tseng: North beam.
202 00:20:36.340 ⇒ 00:20:38.089 Robert Tseng: Okay, we need ad spend.
203 00:20:40.670 ⇒ 00:20:41.260 Aakash Tandel: Cool.
204 00:20:41.620 ⇒ 00:20:47.299 Aakash Tandel: Okay, okay, so that will be kind of in the follow up ticket for this guy.
205 00:20:47.750 ⇒ 00:20:49.839 Aakash Tandel: or should I write a separate ticket for that
206 00:20:52.930 ⇒ 00:21:03.669 Robert Tseng: it should be. I mean, that should be. That is the like. I don’t think when you said that that is the tick ticket, I mean it’s like a P. And L. Kind of thing, and
207 00:21:04.840 ⇒ 00:21:12.460 Awaish Kumar: So like for this ticket. Also do we need the participant on a product level or more granular
208 00:21:16.260 ⇒ 00:21:20.129 Robert Tseng: Yeah. So the way that Annie has it is at.
209 00:21:20.860 ⇒ 00:21:26.460 Robert Tseng: we have funnel type, which is basically protein versus coffee. So yeah, I mean.
210 00:21:26.460 ⇒ 00:21:27.620 Awaish Kumar: Yeah, that’s we both get
211 00:21:27.620 ⇒ 00:21:38.940 Robert Tseng: Raw, add data. And every campaign has a product name, right? So we probably have to do something some similar mapping to what we do for Eden, and just map it to product
212 00:21:38.940 ⇒ 00:21:39.770 Awaish Kumar: Knock.
213 00:21:39.770 ⇒ 00:21:40.120 Robert Tseng: Yeah.
214 00:21:40.120 ⇒ 00:21:48.279 Awaish Kumar: Like, we cannot exactly go to the individual product like product line item. But we can go to the funnel right? Protein versus coffee
215 00:21:48.280 ⇒ 00:21:48.720 Robert Tseng: Yeah, yeah, yeah.
216 00:21:48.720 ⇒ 00:22:00.630 Awaish Kumar: And the other, like the other 2 values which we have in that spend like instant ladder and the marketplaces. That accounts for only around 2.5% of total spend.
217 00:22:01.010 ⇒ 00:22:06.469 Awaish Kumar: So it’s like, mostly it is for protein and concentrate only the add to spend
218 00:22:06.470 ⇒ 00:22:14.210 Robert Tseng: Yeah, I mean, whatever instant latte is such a small thing, and then I don’t. Yeah, so it will be mostly protein versus coffee. You’re right.
219 00:22:14.720 ⇒ 00:22:15.450 Awaish Kumar: Okay.
220 00:22:17.530 ⇒ 00:22:18.465 Aakash Tandel: Cool.
221 00:22:24.390 ⇒ 00:22:29.720 Aakash Tandel: Oh, this is the one I’m writing a ticket on, and there’s is there anything specific?
222 00:22:30.884 ⇒ 00:22:34.520 Aakash Tandel: So it’s the wait. Which part of that was.
223 00:22:35.160 ⇒ 00:22:38.340 Aakash Tandel: it’s the order level information, this one, this chunk here
224 00:22:39.464 ⇒ 00:22:52.980 Robert Tseng: No, he was talking about the byproduct. So that cut we’re talking about by product. We’re not actually doing like product at the order line level. It’s just concentrate versus protein. Job is pretty inconsistent with the way they talk about product level
225 00:22:53.190 ⇒ 00:23:14.780 Robert Tseng: before us. They were only looking at protein versus coffee because we built them a fact order line model. They’re able to see so many other categories. I don’t think the paradigm has really shifted yet, though. So whenever they’re talking about products, they’re still probably just talking about concentrate versus protein, which is fine. That’s easier for us.
226 00:23:15.810 ⇒ 00:23:32.110 Robert Tseng: but yeah, other than that. I don’t know country like I think it’s more like, I don’t know if we can actually do by country. I mean we, for we have Amazon like Canada versus like us. I know that’s pretty easy to do. But I I mean, anyway, it’ll just be something small like that
227 00:23:42.140 ⇒ 00:23:42.600 Aakash Tandel: Okay.
228 00:23:42.600 ⇒ 00:23:43.330 Awaish Kumar: And
229 00:23:43.870 ⇒ 00:23:46.120 Aakash Tandel: It sounds like. At least you have an understanding of what’s
230 00:23:46.540 ⇒ 00:23:48.350 Aakash Tandel: that request is? So that’s good.
231 00:23:48.350 ⇒ 00:23:55.890 Awaish Kumar: Okay, I’m only I’m I’m only concerned about the orders where it is both right then how do we want to
232 00:23:56.200 ⇒ 00:24:15.099 Awaish Kumar: join the orders like right now, if we join the order data with the spend, we have only one way to do. It is based on the product name and or or the funnel type which we are talking. But then we have in the orders. We have some orders which are assigned to both concentrate and the protein
233 00:24:15.100 ⇒ 00:24:15.420 Robert Tseng: Yep.
234 00:24:15.420 ⇒ 00:24:17.670 Awaish Kumar: How do we want to handle them like
235 00:24:20.780 ⇒ 00:24:21.530 Robert Tseng: So.
236 00:24:21.530 ⇒ 00:24:24.390 Awaish Kumar: Should they be counted in both, or like? How
237 00:24:24.810 ⇒ 00:24:28.960 Robert Tseng: Yeah, we should count them in both, like, I think we do that in Eden, too. So
238 00:24:29.870 ⇒ 00:24:30.760 Awaish Kumar: Okay.
239 00:24:38.400 ⇒ 00:24:47.409 Aakash Tandel: Cool. Alright, let me back up. Okay, so sounds like, Annie. This is like, kind of the main thing. Is there anything else. You’re working on
240 00:24:48.742 ⇒ 00:24:53.240 Annie Yu: I finished that Amazon subscriber and Safety Conference
241 00:24:53.240 ⇒ 00:24:53.800 Aakash Tandel: Yep.
242 00:24:54.499 ⇒ 00:25:00.780 Annie Yu: Spend some time here, and I think honestly, my conclusion is, the discount rate is more consistent.
243 00:25:01.010 ⇒ 00:25:03.780 Annie Yu: Then, like the order dates
244 00:25:03.990 ⇒ 00:25:05.210 Robert Tseng: Oh, yeah, cool.
245 00:25:05.660 ⇒ 00:25:10.680 Annie Yu: Even though it’s still not like always consistent. But
246 00:25:11.300 ⇒ 00:25:17.590 Annie Yu: I also added, like a temp column, to see the order like differences that’s
247 00:25:17.840 ⇒ 00:25:20.520 Annie Yu: like you. You can’t see a pattern there.
248 00:25:20.650 ⇒ 00:25:23.619 Annie Yu: but at least for the discount.
249 00:25:24.660 ⇒ 00:25:25.720 Annie Yu: Low.
250 00:25:26.200 ⇒ 00:25:33.680 Annie Yu: Maybe like for one person, 3 out of 5. It’s ticket.
251 00:25:34.020 ⇒ 00:25:35.930 Annie Yu: It would be like 10%
252 00:25:36.390 ⇒ 00:25:37.070 Robert Tseng: Yeah.
253 00:25:37.520 ⇒ 00:25:46.750 Robert Tseng: I think that makes sense to me like, that’s a close enough proxy. We should probably. Yeah, we should run that by mon and be like, look, this is what we think we can do.
254 00:25:46.980 ⇒ 00:26:03.440 Robert Tseng: I mean, do we have it? He’s gonna then he’s gonna come back and be like, well, how accurate do you think it is? I mean, if they’re okay with Amazon matching being like 50% like, I don’t think the bar is that high we could probably I don’t know. Do do you feel like we have 50% confidence at least, Eddie
255 00:26:03.810 ⇒ 00:26:07.509 Annie Yu: I don’t have answer to that, as of now. I
256 00:26:08.000 ⇒ 00:26:14.009 Annie Yu: I doubt but I can. If we want. I can spend more time and see
257 00:26:16.640 ⇒ 00:26:19.130 Annie Yu: if that’s like 50%
258 00:26:19.330 ⇒ 00:26:24.969 Robert Tseng: Okay, that’s fine. I think we have enough now to at least put the ball in a man’s court. Let him let him know like, well, like.
259 00:26:25.140 ⇒ 00:26:44.079 Robert Tseng: yeah, I mean, if if you want us to spend more time actually doing that. But I think it’s it’ll be good news to him to be like, Hey, look! We can’t pull in the subscribe and save Field from Amazon, but we thought of a different solution like this is a proxy that we can use. That’s based off of like
260 00:26:44.190 ⇒ 00:26:56.341 Robert Tseng: multiple. If a customer had multiple orders with the same discount applied, that’s like 10 or 15%, you know, we think that the signal that it’s probably a subscribe and save customer.
261 00:26:56.920 ⇒ 00:27:02.696 Robert Tseng: yeah, it’s just kind of letting him know that we did do this investigation and subscribe and save. So
262 00:27:03.170 ⇒ 00:27:17.870 Robert Tseng: yeah, Akash, I think this is like good. Another good example that we bring back to him and be like, look dude. If you want us to solve problems like we have to think about it like we, we think about alternatives and stuff. We don’t just like count down on hours spent on stuff. So
263 00:27:18.470 ⇒ 00:27:18.960 Robert Tseng: yeah.
264 00:27:18.960 ⇒ 00:27:22.104 Aakash Tandel: Yeah, I agree. Yeah, Annie, if you can write a little
265 00:27:23.120 ⇒ 00:27:38.564 Aakash Tandel: like maybe 2 to 5 sentence explainer of kind of what you found here. That’d be awesome, and then you can send it to us in the channel just in the Javi Channel, and then Rob and I can take a look at it, and then
266 00:27:39.050 ⇒ 00:27:47.950 Aakash Tandel: we can send it over to Mon for for like what he thinks. But yeah, this is awesome, that it’s really cool, that you were able to find that this might be the better proxy
267 00:27:49.150 ⇒ 00:27:51.139 Annie Yu: Yeah, okay, we’ll do that.
268 00:27:51.370 ⇒ 00:27:56.279 Aakash Tandel: Awesome thanks. And yeah, don’t spend too too long on like the write up it, I mean quick, you know
269 00:27:56.380 ⇒ 00:27:57.480 Aakash Tandel: I wouldn’t overthink it.
270 00:27:58.216 ⇒ 00:27:59.749 Aakash Tandel: Robert and I can help
271 00:28:00.060 ⇒ 00:28:03.219 Aakash Tandel: polish it for client if if needed. But yeah, don’t worry about it.
272 00:28:03.220 ⇒ 00:28:04.570 Annie Yu: Great to hear, cool.
273 00:28:05.030 ⇒ 00:28:08.620 Aakash Tandel: Awesome. Okay, we’re almost at time. And.
274 00:28:08.740 ⇒ 00:28:11.877 Aakash Tandel: Robert, you probably have a lot of stuff
275 00:28:12.270 ⇒ 00:28:13.199 Robert Tseng: I actually don’t
276 00:28:13.750 ⇒ 00:28:14.460 Aakash Tandel: Oh, okay.
277 00:28:15.680 ⇒ 00:28:31.116 Robert Tseng: That’s good. Yeah. This 1st subscription order thing. I’m actually working on it right now. So I think I’ve been doing it at the background. I’ll I’ll it’s just another chart on the existing dashboard. So I’m just gonna batch it with the Annie’s updates.
278 00:28:31.630 ⇒ 00:28:37.329 Robert Tseng: and like, yeah, like, you can, you can move that into like in in review at this point, or whatever
279 00:28:38.200 ⇒ 00:28:40.160 Aakash Tandel: Internal or any client, feedback.
280 00:28:40.160 ⇒ 00:28:42.049 Robert Tseng: Pending client feedback. Yeah.
281 00:28:43.070 ⇒ 00:28:50.010 Aakash Tandel: Okay? And then, okay, this update is blocked. But this is was this, this? Yeah, this is a subscriber
282 00:28:50.010 ⇒ 00:28:51.519 Robert Tseng: That’s the Amazon thing. Right? Yeah.
283 00:28:51.790 ⇒ 00:28:57.159 Aakash Tandel: Okay, so don’t remember that. Okay, cool. That’s awesome. Anything else you have to update on
284 00:28:58.310 ⇒ 00:29:02.529 Robert Tseng: No, yeah, I think. That’s kind of where we’re at this week.
285 00:29:02.850 ⇒ 00:29:08.029 Aakash Tandel: The only thing I have left on my plate. Well, actually, let me just see if I have anything
286 00:29:08.030 ⇒ 00:29:12.469 Robert Tseng: The shopify address matching thing. I think we probably need to come back to him, and
287 00:29:13.037 ⇒ 00:29:15.440 Robert Tseng: I don’t know if he gave us like, yeah.
288 00:29:15.440 ⇒ 00:29:18.655 Aakash Tandel: Yeah. So I need to check in with Pius.
289 00:29:19.760 ⇒ 00:29:29.170 Aakash Tandel: yeah, he said. He’d have it by the end of the the week. So I’ll check in and see where he’s at with that. But yeah, the that’s the last thing I think, on our plate for this week.
290 00:29:29.700 ⇒ 00:29:30.280 Robert Tseng: Yep.
291 00:29:31.060 ⇒ 00:29:31.740 Aakash Tandel: Awesome.
292 00:29:32.838 ⇒ 00:29:46.569 Aakash Tandel: I’m we’ve done some pretty cool stuff this week. I’m pretty happy with that. If anyone has any questions I have some ticket follow ups to write up. But yeah, let me know if you have any questions. And yeah, hopefully, we can
293 00:29:47.116 ⇒ 00:29:55.609 Aakash Tandel: change the narrative a little bit on thinking like we’re using too many hours to do too little work. But yeah, that’s a work in progress.
294 00:29:55.610 ⇒ 00:29:56.200 Robert Tseng: Yep.
295 00:29:56.960 ⇒ 00:29:59.330 Aakash Tandel: Sweet. Alright, y’all, thanks so much
296 00:29:59.330 ⇒ 00:29:59.650 Annie Yu: File.
297 00:29:59.650 ⇒ 00:30:01.429 Aakash Tandel: Have a good weekend
298 00:30:01.430 ⇒ 00:30:01.840 Annie Yu: Yep.
299 00:30:01.840 ⇒ 00:30:02.490 Aakash Tandel: See you soon
300 00:30:02.990 ⇒ 00:30:06.200 Caio Velasco: Cool bye for that