Meeting Title: Data Team Planning Session Date: 2025-01-27 Meeting participants: Luke Daque, Nicolas Sucari, Uttam Kumaran, Payas Parab, Robert Tseng
WEBVTT
1 00:04:53.090 ⇒ 00:04:53.910 Luke Daque: Hi Robert!
2 00:04:56.990 ⇒ 00:04:59.970 Robert Tseng: Hey, Luke? Hmm!
3 00:05:00.890 ⇒ 00:05:03.879 Robert Tseng: Hold on, my! I guess my headphones are not connected.
4 00:05:05.782 ⇒ 00:05:06.669 Luke Daque: Can you hear me?
5 00:05:09.110 ⇒ 00:05:10.370 Robert Tseng: Yeah.
6 00:05:10.690 ⇒ 00:05:12.840 Robert Tseng: Like, one. Sec.
7 00:05:15.200 ⇒ 00:05:18.529 Luke Daque: Where is my voice? Low, I guess.
8 00:05:19.330 ⇒ 00:05:21.267 Robert Tseng: No, no, I it’s it’s my! It’s my
9 00:05:21.810 ⇒ 00:05:23.460 Robert Tseng: It’s probably my speaker.
10 00:05:31.170 ⇒ 00:05:32.340 Luke Daque: How was your weekend.
11 00:05:34.650 ⇒ 00:05:35.770 Robert Tseng: It was good.
12 00:05:37.890 ⇒ 00:05:38.600 Robert Tseng: I
13 00:05:43.180 ⇒ 00:05:46.959 Robert Tseng: yeah, we we went. My wife and I went to like
14 00:05:47.060 ⇒ 00:05:53.009 Robert Tseng: some snow went upstate, and we did like some sledding and met with
15 00:05:53.140 ⇒ 00:06:00.920 Robert Tseng: my, I have a I have a my family that lives up there from yeah. So
16 00:06:01.030 ⇒ 00:06:05.159 Robert Tseng: it was nice to get away from the city, and just like have a change of pace, but
17 00:06:05.380 ⇒ 00:06:09.818 Robert Tseng: nothing, nothing too exciting. It was just a lot of lot of snow.
18 00:06:10.720 ⇒ 00:06:11.570 Luke Daque: Nice.
19 00:06:12.220 ⇒ 00:06:14.659 Uttam Kumaran: Do you have a house? Is that a house? There is like a cabin.
20 00:06:16.480 ⇒ 00:06:25.189 Robert Tseng: Yeah. Well, so we actually stayed. It was like a few different things. I I have a cousin who lives in Delhi. You know where that is upstate New York.
21 00:06:25.410 ⇒ 00:06:38.219 Robert Tseng: We have a buddy who had a house in Windham. So we stayed there. We have, like big group of people. People just did whatever some went skiing, or whatever we just like hung out there and then did our own thing. So
22 00:06:38.740 ⇒ 00:06:39.510 Robert Tseng: yeah.
23 00:06:45.600 ⇒ 00:06:47.680 Nicolas Sucari: Hey, guys, how are you.
24 00:06:51.380 ⇒ 00:06:53.640 Robert Tseng: Good. How’s your weekend.
25 00:06:55.300 ⇒ 00:06:59.460 Nicolas Sucari: It was good, really hot here in Argentina. This weekend.
26 00:07:01.560 ⇒ 00:07:02.260 Uttam Kumaran: Oh, nice!
27 00:07:02.260 ⇒ 00:07:06.330 Nicolas Sucari: You know. Some summer came. I came here like later. I don’t know.
28 00:07:07.510 ⇒ 00:07:09.829 Nicolas Sucari: But yeah, it’s pretty pretty hot.
29 00:07:10.640 ⇒ 00:07:13.299 Nicolas Sucari: I’m pretty humid, this kind of
30 00:07:14.050 ⇒ 00:07:16.600 Nicolas Sucari: terrible if you’re not with the A/C. On.
31 00:07:20.130 ⇒ 00:07:22.319 Nicolas Sucari: But it was nice, really nice days
32 00:07:26.460 ⇒ 00:07:29.179 Nicolas Sucari: cool. Should we get started.
33 00:07:30.180 ⇒ 00:07:35.690 Uttam Kumaran: Yeah, I said. He’s running late. So let’s just jump into other stuff.
34 00:07:38.860 ⇒ 00:07:39.855 Nicolas Sucari: Okay.
35 00:07:41.300 ⇒ 00:07:47.550 Nicolas Sucari: Robert, I know there’s been some messages there around our art helper. Do you wanna talk about that?
36 00:07:48.140 ⇒ 00:07:55.320 Robert Tseng: Yeah, Sahana was just telling me she was like communicating with Brandon. Seems like there’s still
37 00:07:55.420 ⇒ 00:08:06.869 Robert Tseng: she’s just queuing like some of the events that are coming through kind of weird already, like basically showed her what she needs to do. So I think she’ll be able to close it out like that.
38 00:08:07.060 ⇒ 00:08:20.400 Robert Tseng: Just basic. I basically put 2 tickets for different, like an activation dashboard and retention dashboard. Yeah. Should be pretty straightforward. Once she gets the the data
39 00:08:20.880 ⇒ 00:08:23.189 Robert Tseng: that the events like kind of cleared up.
40 00:08:25.530 ⇒ 00:08:30.760 Robert Tseng: But I guess with meeting Brandon tomorrow I don’t think she’ll be done by then, I think.
41 00:08:30.960 ⇒ 00:08:37.200 Robert Tseng: Seems like there’s gonna be a bit of back and forth, and we’ll probably end up closing it out by the end of the week. So.
42 00:08:37.200 ⇒ 00:08:37.669 Uttam Kumaran: Okay.
43 00:08:37.679 ⇒ 00:08:44.139 Robert Tseng: But if you still wanna kind of just chat through. Okay? Well, yeah, let them know that the scope is wrapping up this week. And then.
44 00:08:44.419 ⇒ 00:08:53.579 Robert Tseng: yeah, like, we wanna see, explore the opportunities. Then I I think that we could. We could talk. We could spend some time talking about that, either in this call or another call.
45 00:08:54.580 ⇒ 00:08:59.430 Uttam Kumaran: Yeah, maybe let’s let’s keep going. And if we have time at the end of this call, let’s do it.
46 00:08:59.710 ⇒ 00:09:01.090 Robert Tseng: Okay. Cool.
47 00:09:06.070 ⇒ 00:09:06.760 Nicolas Sucari: Hey?
48 00:09:09.666 ⇒ 00:09:17.290 Nicolas Sucari: Yes. So moving to Eden, maybe. I know there’s been a lot of things there.
49 00:09:17.430 ⇒ 00:09:26.289 Nicolas Sucari: We still need to work. I think I’ll close out all of the data product mapping stuff. I don’t know what is left there with them.
50 00:09:27.110 ⇒ 00:09:30.840 Uttam Kumaran: Yeah, this one. I’m gonna take this.
51 00:09:31.310 ⇒ 00:09:40.729 Uttam Kumaran: And I currently just have it synced via bigquery to
52 00:09:42.700 ⇒ 00:09:47.860 Uttam Kumaran: from Google sheets to bigquery. I don’t know whether that I don’t think this updates
53 00:09:48.440 ⇒ 00:09:52.670 Uttam Kumaran: like it’s not Etl meaning like it. I don’t think it like gets the latest.
54 00:09:52.890 ⇒ 00:09:53.680 Robert Tseng: Okay.
55 00:09:54.050 ⇒ 00:09:55.080 Uttam Kumaran: So.
56 00:09:55.280 ⇒ 00:10:00.999 Uttam Kumaran: and I don’t know what else. They’re not using any other like. Well, I guess they’re using airbite for some stuff.
57 00:10:02.410 ⇒ 00:10:10.520 Uttam Kumaran: but like I do. You know how often this is? Gonna get updated, or is is it how it is now like sort of like, fixed for a little bit.
58 00:10:10.820 ⇒ 00:10:15.669 Robert Tseng: Yeah, yeah, I don’t think this will probably change like once a month, or once a quarter kind of thing.
59 00:10:15.670 ⇒ 00:10:17.572 Uttam Kumaran: Okay, then then this is no.
60 00:10:18.040 ⇒ 00:10:21.380 Uttam Kumaran: Then I’m gonna just like sort of
61 00:10:21.540 ⇒ 00:10:35.976 Uttam Kumaran: mark. This is done. And then if we could just coordinate on that. Then we can skip sort of like having anything there longer term. I think we’ll there’s some free sort of Etl pipelines that we can do to just move Google sheets and stuff. So we don’t have to talk about bringing on another one.
62 00:10:36.230 ⇒ 00:10:46.419 Robert Tseng: Okay. But the idea of just like keeping it in that sheet. I think just there were a couple of things people didn’t necessarily like the way that we structured the sheet
63 00:10:46.570 ⇒ 00:10:54.300 Robert Tseng: breaking up products with multiple categories. Because there’s like a product that’s like Sema, plus turns up at higher
64 00:10:54.300 ⇒ 00:10:54.669 Robert Tseng: to me.
65 00:10:55.400 ⇒ 00:11:00.769 Robert Tseng: Yeah. So I think we needed to add, like, seems like we just need to add one or one more column or something
66 00:11:01.388 ⇒ 00:11:03.961 Robert Tseng: or one more field. And then,
67 00:11:04.520 ⇒ 00:11:16.369 Robert Tseng: yeah, I think that was it on the mapping side. And then Rebecca, who’s the pharmacy head, is just like she’s gonna her team’s gonna maintain that sheet
68 00:11:20.230 ⇒ 00:11:24.860 Uttam Kumaran: Yeah, so so I think we should do that as part of this like product data design ticket.
69 00:11:25.050 ⇒ 00:11:30.220 Uttam Kumaran: Okay, so one is, yeah, I think basically there was this notion of like
70 00:11:31.570 ⇒ 00:11:40.550 Uttam Kumaran: bundles versus the associated products. Basically, what we can do is I mean.
71 00:11:40.910 ⇒ 00:11:44.689 Uttam Kumaran: I guess it’s it was hard for me to wrap my head around like
72 00:11:45.450 ⇒ 00:11:54.369 Uttam Kumaran: our conversation with them about like, what’s the purpose of that, Doc? Because we are attributing marketing dollars towards the bundles right?
73 00:11:54.580 ⇒ 00:12:00.360 Uttam Kumaran: And they were like, we wanna see this broken up by products. I think.
74 00:12:00.910 ⇒ 00:12:07.939 Uttam Kumaran: I I mean, what we’re what we’ll do is, I think we can add another column. That’s basically like, a
75 00:12:08.330 ⇒ 00:12:11.179 Uttam Kumaran: array of the products that are in the bundle.
76 00:12:11.708 ⇒ 00:12:16.370 Uttam Kumaran: And that’ll and the and the pro like. I think that’s the thing. When we talk about that
77 00:12:16.620 ⇒ 00:12:23.459 Uttam Kumaran: mapping is, there’s just so many use cases. So it’s probably
78 00:12:24.520 ⇒ 00:12:30.529 Uttam Kumaran: I think it’s fine to keep everything in there. But then we’ll need one more column. That’s sort of like constituent products.
79 00:12:30.860 ⇒ 00:12:37.619 Uttam Kumaran: And then we’ll have an ultimate product table in the database. That’s just a 1 like a primary key of the product. Id
80 00:12:38.336 ⇒ 00:12:43.560 Uttam Kumaran: so you can. Technically, I think the biggest thing to figure out with marketing is like
81 00:12:43.810 ⇒ 00:12:51.960 Uttam Kumaran: if they want to look at product level marketing spend. If if it’s in a bundle like, how do we want to split the cost up? Right?
82 00:12:54.800 ⇒ 00:12:58.649 Uttam Kumaran: So we just have a couple of like question. I I have a couple of questions like that.
83 00:12:59.030 ⇒ 00:12:59.750 Uttam Kumaran: Where.
84 00:13:00.390 ⇒ 00:13:10.869 Uttam Kumaran: If they’re like, Yeah, okay, we don’t want to see this the but like, I think, probably Cutter wants to see this at the bundle level. And then Adam probably is like, I just want to see what we’re spending per product.
85 00:13:11.373 ⇒ 00:13:16.749 Uttam Kumaran: Then we need a decision on like, Okay, if we are spending on a bundle. How do we attribute the cost per product.
86 00:13:19.620 ⇒ 00:13:20.320 Robert Tseng: Yeah.
87 00:13:23.790 ⇒ 00:13:24.320 Uttam Kumaran: So.
88 00:13:24.320 ⇒ 00:13:48.070 Robert Tseng: I think there’s just like misalignment of the entities like the marketing marketing will always want to have the creative liberty to like, go and target like different product variations. So like, that’s why the bundle concept is even there. But like, yeah, Adam doesn’t. He didn’t even really understand the bundle thing which I thought was, I mean, that’s why he sent me. He tried to tell me what to do by sending me his chat, gpt like conversation.
89 00:13:48.430 ⇒ 00:13:50.270 Robert Tseng: and it’s like, Yeah.
90 00:13:50.500 ⇒ 00:13:54.290 Uttam Kumaran: So, but the bundles is like a significant concept for them.
91 00:13:54.450 ⇒ 00:13:55.809 Robert Tseng: It is. It is. Yeah.
92 00:13:55.810 ⇒ 00:14:00.120 Uttam Kumaran: Okay. So they’re like, yeah, I was, gonna say, like, if the bundles aren’t really like
93 00:14:00.720 ⇒ 00:14:05.859 Uttam Kumaran: a thing, then we just do everything at a product level. But if bundles exist, then we should keep it
94 00:14:06.580 ⇒ 00:14:08.070 Uttam Kumaran: like the way it is.
95 00:14:08.370 ⇒ 00:14:11.880 Uttam Kumaran: The only other alternative is is, if if it’s like
96 00:14:11.990 ⇒ 00:14:19.730 Uttam Kumaran: we ditch the concept of bundles. And we source what bundles are getting marketed towards from marketing.
97 00:14:20.890 ⇒ 00:14:25.689 Uttam Kumaran: And then we basically, it’s like if they spend it on a bundle. Then we’ll split up the spend to the right products.
98 00:14:27.670 ⇒ 00:14:33.829 Robert Tseng: Yeah, I mean, I’m okay with getting rid of the bundles. Concept, I do think it over complicates the data model. It’s just like.
99 00:14:34.460 ⇒ 00:14:44.690 Robert Tseng: And yeah, and eventually, when they move off of bask into another like Erp, like they’re not gonna we don’t necessarily we can decide what the data should look like kind of after that. So.
100 00:14:44.690 ⇒ 00:14:51.628 Uttam Kumaran: The reason, the reason why I don’t think it. The reason why I don’t think I don’t think it matters entirely is because
101 00:14:53.020 ⇒ 00:15:02.499 Uttam Kumaran: we can. If if the only thing I would say is, if we can. If we can get the products individually from the marketing campaign, Id. That we don’t need the concept of bundles.
102 00:15:02.730 ⇒ 00:15:08.200 Uttam Kumaran: because I’ll just route the spend to the right product right?
103 00:15:10.360 ⇒ 00:15:19.889 Uttam Kumaran: so like if we’re confident that, like, hey, camp, the campaign names will have the 2 products. Then I will just parse those out and attribute the spend accordingly.
104 00:15:20.210 ⇒ 00:15:22.970 Uttam Kumaran: or we leave that as is.
105 00:15:23.250 ⇒ 00:15:29.329 Uttam Kumaran: and I can handle it. Anyways, it’s just like if you you know, it’s like once you add bundle. There’s now
106 00:15:29.540 ⇒ 00:15:31.960 Uttam Kumaran: like infinite. Now, combinations.
107 00:15:32.210 ⇒ 00:15:32.680 Robert Tseng: Yeah.
108 00:15:32.680 ⇒ 00:15:33.420 Uttam Kumaran: Allow me.
109 00:15:33.780 ⇒ 00:15:34.760 Uttam Kumaran: So
110 00:15:34.860 ⇒ 00:15:45.180 Uttam Kumaran: if marketing is gonna spend on bundles, and if we can, I think the biggest question for me to you or for me to eaten is if they’re spending on a bundle. How do we want to attribute the cost
111 00:15:45.400 ⇒ 00:15:46.550 Uttam Kumaran: for the right product?
112 00:15:47.200 ⇒ 00:15:47.890 Robert Tseng: Okay.
113 00:15:50.020 ⇒ 00:15:51.870 Uttam Kumaran: I mean, I see his point that, like
114 00:15:52.390 ⇒ 00:16:00.839 Uttam Kumaran: he doesn’t probably care that it’s at a bundle level of marketing will care for the data model. If it’s a marketing specific thing, I’d rather just live in marketing
115 00:16:01.900 ⇒ 00:16:05.160 Uttam Kumaran: because the bundles aren’t really gonna yeah.
116 00:16:07.370 ⇒ 00:16:13.570 Robert Tseng: I was thinking that this would also be the one like we call it product sales summary right now. But, like.
117 00:16:13.950 ⇒ 00:16:15.840 Robert Tseng: you know, like I think every.
118 00:16:16.190 ⇒ 00:16:20.919 Robert Tseng: every function, every department, will need to use this mapping like.
119 00:16:20.920 ⇒ 00:16:21.650 Uttam Kumaran: Yes.
120 00:16:22.970 ⇒ 00:16:28.890 Uttam Kumaran: So that’s why it’s like what like apart from marketing, does the bundle concept like appear anywhere.
121 00:16:30.040 ⇒ 00:16:30.890 Robert Tseng: Now.
122 00:16:30.890 ⇒ 00:16:32.030 Uttam Kumaran: Okay, then that’s
123 00:16:32.340 ⇒ 00:16:45.189 Uttam Kumaran: I think that’s like makes it more clear in that marketing spends at a bundle. The only answer I need is, if they, if marketing spends towards a bundle, how do we attribute the cost? Then I’m gonna move that logic out.
124 00:16:46.940 ⇒ 00:16:47.720 Robert Tseng: Okay.
125 00:16:50.450 ⇒ 00:16:54.380 Uttam Kumaran: Because if it is, it’s actually just like plus this plus this, this plus this right.
126 00:16:55.040 ⇒ 00:17:02.809 Robert Tseng: Yeah, yeah, it is like, just bundle equal. It has like 3 products. Or but it could have infinite. It could be a bundle with.
127 00:17:03.980 ⇒ 00:17:10.770 Robert Tseng: I wanna say there are no bundles with only one product. But it’s hypothetically possible. Yeah, everything.
128 00:17:10.770 ⇒ 00:17:23.199 Uttam Kumaran: Yeah, I think I think we can push that all to marketing. Simplify the spreadsheet and then, if if we can, I can, if you want to answer that question, if you want to ask that question.
129 00:17:23.339 ⇒ 00:17:27.390 Uttam Kumaran: and we can get an answer from both of them, and we can get Connor and Adam to like. Agree.
130 00:17:28.170 ⇒ 00:17:29.400 Uttam Kumaran: then I’ll implement.
131 00:17:30.140 ⇒ 00:17:36.049 Robert Tseng: Okay, yeah, I’ll draft. I’ll draft a message and then kind of just make sure it’s asking what you want to want to know.
132 00:17:38.390 ⇒ 00:17:43.719 Uttam Kumaran: Okay, I’m just gonna add that and then cool. So I’m gonna loop. But so I’m gonna
133 00:17:44.190 ⇒ 00:17:47.370 Uttam Kumaran: push all this into this one product data design ticket.
134 00:17:47.947 ⇒ 00:17:57.010 Uttam Kumaran: So one is like, we’re gonna remove the bundle concept from mapping table and understand how to attribute
135 00:17:59.040 ⇒ 00:18:10.850 Uttam Kumaran: ad spend in a bundle cool. And then, yeah, we’re gonna work towards these different primary tables.
136 00:18:16.400 ⇒ 00:18:17.710 Uttam Kumaran: what else?
137 00:18:17.870 ⇒ 00:18:21.569 Uttam Kumaran: Oh, yeah. Okay, we need to do the Cltv.
138 00:18:21.730 ⇒ 00:18:27.290 Uttam Kumaran: and then I need to do the 90 day that 90 day.
139 00:18:27.520 ⇒ 00:18:28.870 Uttam Kumaran: What was that? Again?
140 00:18:29.510 ⇒ 00:18:32.319 Uttam Kumaran: There’s some sort of 90 day, cap thing.
141 00:18:33.170 ⇒ 00:18:34.030 Robert Tseng: Yeah.
142 00:18:34.520 ⇒ 00:18:42.260 Robert Tseng: they have a you know you can look at. They have an existing model already. That’s like 90 K day cap or something. So, being able to just pull it in.
143 00:18:42.630 ⇒ 00:18:46.490 Uttam Kumaran: And then the Cltv is necessary as well.
144 00:18:47.090 ⇒ 00:18:47.900 Robert Tseng: Yeah.
145 00:18:47.900 ⇒ 00:18:48.460 Uttam Kumaran: Okay?
146 00:18:51.290 ⇒ 00:18:54.360 Uttam Kumaran: And you have that calculation, or what was it? Again.
147 00:18:55.414 ⇒ 00:18:59.049 Robert Tseng: It’s just revenue over users. For the
148 00:18:59.280 ⇒ 00:19:03.410 Robert Tseng: for the pro, for the for the specific product. So we have to
149 00:19:06.540 ⇒ 00:19:10.670 Robert Tseng: like. I I would just, you know, within all the or orders, by
150 00:19:11.300 ⇒ 00:19:29.840 Robert Tseng: filtered by products over the total number of users that purchase, that that made those orders, and like that would be the like kind of the Cltv by product. Count, because typically Ltv is calculated at a user level. But they wanted to see it at a product level. So like it, we had to like.
151 00:19:29.840 ⇒ 00:19:30.450 Uttam Kumaran: We have to.
152 00:19:30.450 ⇒ 00:19:32.781 Robert Tseng: Bring in a couple we have to bring in
153 00:19:33.250 ⇒ 00:19:36.600 Robert Tseng: orders somehow into this calculation, otherwise it won’t make sense.
154 00:19:38.860 ⇒ 00:19:43.560 Uttam Kumaran: And then I still haven’t heard back from them on talking to bask folks.
155 00:19:47.510 ⇒ 00:19:48.750 Uttam Kumaran: Can you like
156 00:19:49.240 ⇒ 00:19:54.589 Uttam Kumaran: alright? Can I ask you to press that, or I’m gonna or I’m gonna send another note, and I’ll I’ll have it sent to the Channel.
157 00:19:55.370 ⇒ 00:20:00.780 Uttam Kumaran: I just like if that that’d be mainly for your fast question.
158 00:20:01.530 ⇒ 00:20:04.060 Uttam Kumaran: like, I just need to know how bad
159 00:20:04.820 ⇒ 00:20:07.840 Uttam Kumaran: the situation is from, like the horse’s mouth. Basically.
160 00:20:07.840 ⇒ 00:20:12.769 Robert Tseng: They’re already gonna move off fast. So in a quarter or 2 quarters like, expect them to no longer be on fast.
161 00:20:12.770 ⇒ 00:20:14.940 Uttam Kumaran: But I guess, like, even for our current, like.
162 00:20:15.340 ⇒ 00:20:21.109 Uttam Kumaran: I just even need to know for our current reporting like, if I can get the stuff I need, or if we’re gonna continue with this web book thing.
163 00:20:21.840 ⇒ 00:20:22.550 Robert Tseng: Okay.
164 00:20:23.010 ⇒ 00:20:27.009 Uttam Kumaran: Like, I just need to talk to that guy, cause I will. I’ll be really straight with, like what
165 00:20:27.200 ⇒ 00:20:31.809 Uttam Kumaran: the situation is. And then basically the helpful thing. We’ll tell Adam. Like.
166 00:20:32.380 ⇒ 00:20:38.550 Uttam Kumaran: most likely it’s like, Yo, this is like a huge red alert like our, the data is like, actually really shit right now.
167 00:20:39.010 ⇒ 00:20:41.959 Uttam Kumaran: The data isn’t accurate.
168 00:20:42.280 ⇒ 00:20:49.900 Robert Tseng: I’m gonna bring you into their channel that they have with fast. And then I’ll probably just like at this at the CEO there.
169 00:20:50.230 ⇒ 00:20:51.130 Robert Tseng: So
170 00:20:51.870 ⇒ 00:20:57.530 Robert Tseng: yeah, I don’t. Because making noise in the analytics channel, like the best people aren’t on there, anyway. So I’ll just bring it.
171 00:20:57.530 ⇒ 00:21:02.530 Uttam Kumaran: But I just need to talk to him. And then, basically, like, my goal is to give them like a
172 00:21:02.840 ⇒ 00:21:08.170 Uttam Kumaran: How bad is this? Right. I don’t think Rob really did a good job. He probably was like, I’ll make it happen
173 00:21:08.746 ⇒ 00:21:16.019 Uttam Kumaran: I’ll be really straight with like, how bad is this and like what the actual issues are. And hopefully, when they
174 00:21:16.280 ⇒ 00:21:21.860 Uttam Kumaran: oh, they’re moving to a custom solution. Okay? So then that’s gonna be.
175 00:21:21.860 ⇒ 00:21:22.500 Robert Tseng: Yeah.
176 00:21:22.500 ⇒ 00:21:23.750 Uttam Kumaran: It’d be nightmares, but.
177 00:21:27.050 ⇒ 00:21:37.299 Robert Tseng: Yeah, so we have a bit of Qa to do on the existing model. It seems like we’re gonna restructure it a bit. And then we need to add a couple of metrics that we we were missing in the last one in the last demo
178 00:21:37.782 ⇒ 00:21:45.600 Robert Tseng: the erd and the reporting accuracy thing that’s just like to show impact to the execs because they were just like.
179 00:21:45.840 ⇒ 00:22:04.710 Robert Tseng: I don’t know, because they they just didn’t trust that like we had, like a clear plan for how we’re modeling the data, moving forward with the different entities. And then, like they’re not sure like, well, how much better is our data now that you did it this way, like. So we kind of need to show them that, too.
180 00:22:06.680 ⇒ 00:22:11.652 Uttam Kumaran: So this product profitability, I’m gonna close out. And then
181 00:22:14.270 ⇒ 00:22:14.980 Robert Tseng: Okay.
182 00:22:15.330 ⇒ 00:22:19.229 Uttam Kumaran: And then that’s all. All. Anything else remaining is covered in the other one. So.
183 00:22:19.640 ⇒ 00:22:20.280 Robert Tseng: Yeah.
184 00:22:25.800 ⇒ 00:22:29.436 Robert Tseng: cool the other things just quickly before we move on next one.
185 00:22:30.070 ⇒ 00:22:45.785 Robert Tseng: yeah. So yeah, the marketing campaign labeling strategy. That’s just like, okay, once we have our questions with cutter kind of like sorted out on the on the bundle stuff. We also need to advise him on like, how to like label the campaigns moving forward. So,
186 00:22:46.570 ⇒ 00:22:50.229 Robert Tseng: yeah, I think that’s that’s the next step, because that’s what that’s what he’s
187 00:22:50.480 ⇒ 00:22:59.079 Robert Tseng: with, with any new campaign moving forward. We want to make sure that we we’re not like we’re able to attribute it to the new structure that we’ve created.
188 00:22:59.717 ⇒ 00:23:07.469 Robert Tseng: And then I basically had Sahana kick off with the Cx team today. So she’s working on that.
189 00:23:07.890 ⇒ 00:23:18.499 Robert Tseng: So hopefully, after the data quality stuff is done, the models are updated, they’re ready to flow into. The looker reports. Then Sahana can close out her her database. Tickets.
190 00:23:19.630 ⇒ 00:23:21.210 Robert Tseng: That’s the block. Yeah.
191 00:23:22.190 ⇒ 00:23:23.190 Uttam Kumaran: Okay. Great.
192 00:23:25.830 ⇒ 00:23:35.563 Nicolas Sucari: Okay? And for you know, we have depos python app stuff. I don’t know you, you message that you want to do something about that, so that we don’t leave him hanging there.
193 00:23:36.120 ⇒ 00:23:36.820 Nicolas Sucari: yeah.
194 00:23:37.240 ⇒ 00:23:40.359 Uttam Kumaran: Yeah, I’m not. Gonna that’s just gonna stay in backlog
195 00:23:43.050 ⇒ 00:23:46.650 Uttam Kumaran: And then how about this mix panel thing? Robert.
196 00:23:50.130 ⇒ 00:23:54.750 Robert Tseng: Mixed panel. Oh, yeah. Well, so I I gotta move that back.
197 00:23:55.110 ⇒ 00:24:04.602 Robert Tseng: Well, yeah, I think this is kind of more probably tied to the cost optimization, doc, that I was kinda pinging Nico to kind of get on. Get on?
198 00:24:05.660 ⇒ 00:24:15.929 Robert Tseng: yeah, I mean, they’re they don’t. They’re just gonna keep asking us random questions about tools and stuff until we really get a handle of like these are all the tools that we have. This is how much we pay for them
199 00:24:16.120 ⇒ 00:24:28.839 Robert Tseng: like they’ve asked me multiple times. Can you get us a better mixed panel deal, can you? Whatever like? And so I’ve just kind of been kicking all of those conversations down. But eventually we’ll need to deal with that.
200 00:24:30.070 ⇒ 00:24:38.090 Nicolas Sucari: I I sent you the that kind of tools list of tools and use cases that I’ve put together with everything that we kind of
201 00:24:38.220 ⇒ 00:24:46.149 Nicolas Sucari: have for every client, and they did a specific one for Eden, adding thorough pass and strut. And yeah, the other one, that is
202 00:24:46.900 ⇒ 00:24:59.040 Nicolas Sucari: segment, I think. Take a look I don’t have like the actual cost for segment on tourpass, but if you can, if you let me know, I can just pull together. What are the costs we are paying for each of the tools. And we can yeah, share that with them. Maybe.
203 00:25:00.120 ⇒ 00:25:18.130 Robert Tseng: Yeah, we could do that. I mean, I think we should be doing this in a Google sheet, just because, like, then, we can track monthlies and like, if we negotiated a day a deal like I don’t know, like having it in a static notion. Table like doesn’t really like reflect the changes like I want to be able to show them like a quarter from now.
204 00:25:18.130 ⇒ 00:25:33.430 Robert Tseng: Hey, this you were paying like this much for your tools. We’ve Consolidated like you can see, like where, at what point we’ve we’ve like optimized some of these costs. And now this is like where you’re at. So I think that’s the better way to show it, in my opinion. But.
205 00:25:33.740 ⇒ 00:25:34.370 Uttam Kumaran: Okay.
206 00:25:34.370 ⇒ 00:25:36.269 Nicolas Sucari: I’ll I’ll put together a spreadsheet.
207 00:25:36.270 ⇒ 00:25:36.670 Uttam Kumaran: Check.
208 00:25:36.670 ⇒ 00:25:41.850 Nicolas Sucari: Like month, month to month costs of each tool. And we can yeah, show it like that. Maybe.
209 00:25:45.480 ⇒ 00:25:47.219 Uttam Kumaran: Yeah, I think that might be best, too.
210 00:25:48.970 ⇒ 00:26:09.200 Nicolas Sucari: Cool, but apart from that, it will be good for you to check that that notion, page. Let me know if you want something more added, there, it’s kind of a quick access to take a look on. What are the tools that we’re working on? And maybe we yeah, we just I. I just leave it that as like a standard documentation for all of the product projects, and then we can tweak it for every client.
211 00:26:11.910 ⇒ 00:26:16.989 Uttam Kumaran: And then, is this parallel data still a priority this week.
212 00:26:16.990 ⇒ 00:26:18.810 Robert Tseng: No.
213 00:26:22.540 ⇒ 00:26:27.951 Robert Tseng: yeah, I would say anything that was like ready to go. I kind of put in planned
214 00:26:28.450 ⇒ 00:26:35.620 Robert Tseng: requirements in review, is like not ready to go yet. Yeah, or like, I, I like deprioritize it. I guess. So.
215 00:26:37.020 ⇒ 00:26:37.640 Robert Tseng: yeah.
216 00:26:38.460 ⇒ 00:26:50.119 Robert Tseng: I mean, I’m gonna connect with a few folks today. So I may like, I said Mondays, Wednesdays, like I may shuffle it a bit, but I don’t think like the immediate things are still kind of gonna stay the same.
217 00:26:50.290 ⇒ 00:26:51.750 Robert Tseng: Bye-bye. Yeah.
218 00:26:54.690 ⇒ 00:26:55.270 Uttam Kumaran: Great.
219 00:26:58.880 ⇒ 00:27:02.770 Nicolas Sucari: Okay, let me add a comment here, just to remember about creating.
220 00:27:02.770 ⇒ 00:27:12.159 Robert Tseng: Yeah, they want me to like prioritize getting them hippa compliance now. So if it ends up being like a lot of work, I may break that ticket up a bit more, but.
221 00:27:12.160 ⇒ 00:27:14.010 Uttam Kumaran: I know someone at Thorough Pass.
222 00:27:14.900 ⇒ 00:27:22.659 Robert Tseng: Yeah, we’re all we’re already on throw pass. And like, it’s just, it’s just admin stuff. So I’m just like, I don’t really think I want to do it, but.
223 00:27:22.780 ⇒ 00:27:26.970 Uttam Kumaran: I know. Well, I know the head of sales engineering. Maybe we can just
224 00:27:29.113 ⇒ 00:27:34.110 Uttam Kumaran: get them to help us with something or like Can would, knowing her help with anything here.
225 00:27:34.960 ⇒ 00:27:35.840 Robert Tseng: Oh, I.
226 00:27:35.840 ⇒ 00:27:39.720 Uttam Kumaran: She. She wanted to help me. She wanted to help us with like compliance.
227 00:27:39.840 ⇒ 00:27:43.301 Uttam Kumaran: I’m like dude. That’s the last fucking thing I care about. But
228 00:27:43.930 ⇒ 00:27:47.750 Uttam Kumaran: I don’t know if it’s interesting to you. I can connect.
229 00:27:48.320 ⇒ 00:27:51.220 Robert Tseng: Well, I think it’s important. A lot of our clients will need to know it.
230 00:27:51.493 ⇒ 00:27:53.949 Uttam Kumaran: Know it’s important. I know it’s important, meaning like.
231 00:27:53.950 ⇒ 00:27:59.599 Robert Tseng: Yeah, yeah, no. I’m saying, like, yeah, we’re gonna do this once for them and but like, I’m sure.
232 00:27:59.600 ⇒ 00:28:01.289 Uttam Kumaran: Oh! This is for us.
233 00:28:02.060 ⇒ 00:28:04.500 Robert Tseng: No, no, it’s it’s it’s for it’s for Eden.
234 00:28:04.500 ⇒ 00:28:05.870 Uttam Kumaran: Oh, okay. Okay. Okay.
235 00:28:05.870 ⇒ 00:28:19.100 Robert Tseng: Yeah, it’s basically just like a checklist of things that you have to go through. You have to coordinate with people inside. Make sure your documents, say Xyz things, and then you can. Then you submit it for review, and then you get hipaa certified pretty much.
236 00:28:20.250 ⇒ 00:28:25.720 Uttam Kumaran: Do you think like we can? If I message her like, she can get someone to help us on their side with this.
237 00:28:25.720 ⇒ 00:28:33.019 Robert Tseng: Oh, I mean, they already have an account manager like they they yeah, yeah, like, we, we pay even pays like 20 KA year for this.
238 00:28:33.840 ⇒ 00:28:37.350 Uttam Kumaran: Why aren’t? Why aren’t those account managers doing this fucking work like.
239 00:28:38.470 ⇒ 00:28:40.199 Robert Tseng: Cause it’s on, it’s on. It’s they need
240 00:28:40.920 ⇒ 00:28:47.605 Robert Tseng: to like, get get things in order, like your Hr. Docs have to like, say certain things or whatever. But
241 00:28:48.200 ⇒ 00:28:53.629 Robert Tseng: yeah, ideally, like, someone on our team would like, learn how to do this. And then we can just like offer it.
242 00:28:53.630 ⇒ 00:28:55.250 Uttam Kumaran: As like a service.
243 00:28:55.250 ⇒ 00:29:07.950 Robert Tseng: To all of our clients and be like, Hey, notice that you’re not compliant. Now we can do a hipaa sock to all this stuff very quickly and so I think it’s a good learning for us to do. But it’s just gonna be tedious and.
244 00:29:07.950 ⇒ 00:29:10.700 Uttam Kumaran: Do you know who the account manager is? Do you have the name.
245 00:29:12.670 ⇒ 00:29:16.180 Robert Tseng: I forgot. I spoke with him on Thursday last week, but I don’t remember.
246 00:29:18.970 ⇒ 00:29:19.990 Uttam Kumaran: Okay. Cool.
247 00:29:22.080 ⇒ 00:29:25.460 Nicolas Sucari: Cool. Okay, anything else I need in.
248 00:29:25.570 ⇒ 00:29:27.440 Nicolas Sucari: Let’s move to Javi. Maybe.
249 00:29:32.580 ⇒ 00:29:38.930 Nicolas Sucari: Okay. So for Javi, we, I know Uten and Luke are still working on the shopify
250 00:29:39.362 ⇒ 00:29:59.980 Nicolas Sucari: to portable migration all of the data is coming in correctly from portable. I know that. We set up manual things for every connector. I think we should set up something. I don’t know if daily it’s gonna work, maybe. Yes, but we will need to be on the scale plan. I think we are on that plan already on portable right.
251 00:30:02.400 ⇒ 00:30:06.650 Uttam Kumaran: Yeah, I don’t think we’re paying or yeah, not yet.
252 00:30:06.900 ⇒ 00:30:12.060 Uttam Kumaran: So I haven’t turned anything on until this is all done. So I’m not gonna turn it on until this is done.
253 00:30:13.280 ⇒ 00:30:13.940 Nicolas Sucari: Okay.
254 00:30:14.090 ⇒ 00:30:20.629 Uttam Kumaran: Cause. Like I I’m again like I don’t care what the vendors say at all. If it doesn’t work.
255 00:30:20.830 ⇒ 00:30:24.050 Uttam Kumaran: then it doesn’t work. And so some of this stuff
256 00:30:24.190 ⇒ 00:30:27.110 Uttam Kumaran: is already different than what I expected from portable.
257 00:30:28.810 ⇒ 00:30:40.359 Uttam Kumaran: so it’s taking us a bit longer to do this, so I don’t really care what they say. Like, I’m gonna wait to see that we get accurate data before moving forward. We can’t screw this up for them. So
258 00:30:41.090 ⇒ 00:30:44.879 Uttam Kumaran: that’s like today, we’re gonna move. We’re gonna do a variance analysis.
259 00:30:45.070 ⇒ 00:30:47.540 Uttam Kumaran: And then, if it’s good, we’ll push the Pr.
260 00:30:47.810 ⇒ 00:30:52.270 Uttam Kumaran: And then we’ll have to migrate the other ones as well.
261 00:30:52.400 ⇒ 00:30:54.727 Uttam Kumaran: and then, as soon as that’s all done, I’ll I’ll
262 00:30:55.100 ⇒ 00:30:59.090 Uttam Kumaran: I’m I’m down to sign with portable, so we’ll see.
263 00:31:00.160 ⇒ 00:31:02.787 Uttam Kumaran: But I’ll I’m coordinating. So let’s see, I think,
264 00:31:03.640 ⇒ 00:31:07.769 Uttam Kumaran: I just wanna make sure that we have all these migration tickets that those are the ones that need to get done.
265 00:31:09.430 ⇒ 00:31:24.149 Luke Daque: Yeah, I I can work on because the shopify one should be pretty much done except for the variance analysis. Yeah. And then I’m I’m planning to start like all the the rest of the models like Okendo recharge in gorgeous
266 00:31:24.390 ⇒ 00:31:28.549 Luke Daque: today. Hopefully, yeah, by tomorrow it should be done for all of them.
267 00:31:29.099 ⇒ 00:31:31.789 Uttam Kumaran: Is so probably gorgeous, is like the number one, right.
268 00:31:31.790 ⇒ 00:31:33.740 Luke Daque: Yeah. Gorgeous should be next.
269 00:31:34.280 ⇒ 00:31:34.880 Uttam Kumaran: So can we?
270 00:31:34.880 ⇒ 00:31:39.270 Uttam Kumaran: Can you change the can we stagger the due dates on these so?
271 00:31:39.760 ⇒ 00:31:45.090 Uttam Kumaran: Or what do you think, Ryan like? Do we have any like.
272 00:31:45.300 ⇒ 00:31:49.129 Uttam Kumaran: It’s already. It’s already like halfway done. It’s already halfway today. So.
273 00:31:49.610 ⇒ 00:31:54.920 Luke Daque: Yeah, yeah, let’s it’s probably better to stagger them. Just so we can.
274 00:31:55.590 ⇒ 00:31:58.889 Luke Daque: Yeah, I have a a pessimistic expectation.
275 00:31:59.815 ⇒ 00:32:03.839 Luke Daque: Maybe 2829, 30, I guess. One day each.
276 00:32:03.840 ⇒ 00:32:04.190 Nicolas Sucari: Okay.
277 00:32:04.190 ⇒ 00:32:07.739 Uttam Kumaran: Yeah, maybe move these 2 that aren’t as urgent to Wednesday.
278 00:32:07.880 ⇒ 00:32:11.250 Uttam Kumaran: And then I think if you can get gorgeous done today, that would be huge.
279 00:32:11.730 ⇒ 00:32:12.370 Luke Daque: Cool.
280 00:32:12.370 ⇒ 00:32:14.199 Uttam Kumaran: That’ll give us something to send to him.
281 00:32:15.770 ⇒ 00:32:16.139 Luke Daque: Yeah.
282 00:32:17.990 ⇒ 00:32:26.270 Luke Daque: And the other thing, aside from the migration, is the cogs one the one from Pias. I think, like the late latest
283 00:32:26.430 ⇒ 00:32:32.369 Luke Daque: update there was like for Amazon. There’s a lot of nulls for the product cost
284 00:32:33.210 ⇒ 00:32:37.049 Luke Daque: box costs and stuff, and it looks like it was coming from.
285 00:32:37.240 ⇒ 00:32:45.550 Luke Daque: like most of the skews in Amazon, just do not exist in the spreadsheet. So they’re getting the default values
286 00:32:45.690 ⇒ 00:32:51.329 Luke Daque: and the default value for the product. Quantity, was it? Or like unit quantity
287 00:32:51.590 ⇒ 00:32:56.810 Luke Daque: was like blank. So that’s why it’s like going to nulls to too late.
288 00:32:56.810 ⇒ 00:32:59.539 Payas Parab: And that’s for that’s for the cost, right? The costing.
289 00:32:59.830 ⇒ 00:33:01.170 Luke Daque: Yeah, for the
290 00:33:01.400 ⇒ 00:33:08.820 Luke Daque: anything related to unit cost, like box cost is like cost. A box cost times. The unit cost. So it’s
291 00:33:09.160 ⇒ 00:33:10.949 Luke Daque: oh, it’s gonna be null for those.
292 00:33:10.950 ⇒ 00:33:14.129 Payas Parab: Is it because the skews are different in Amazon than shopify.
293 00:33:15.450 ⇒ 00:33:21.540 Luke Daque: I guess so for some cause. Yeah, not all of them are in the, in the, in the Google sheet. So.
294 00:33:21.980 ⇒ 00:33:22.820 Payas Parab: Excellent.
295 00:33:22.990 ⇒ 00:33:23.540 Luke Daque: Yeah.
296 00:33:23.710 ⇒ 00:33:25.889 Payas Parab: Okay. So then, do we need for the default?
297 00:33:26.710 ⇒ 00:33:27.360 Payas Parab: Well, we have.
298 00:33:27.360 ⇒ 00:33:27.730 Luke Daque: Yeah.
299 00:33:28.275 ⇒ 00:33:29.910 Payas Parab: Product, cost, right.
300 00:33:29.910 ⇒ 00:33:35.450 Luke Daque: We do. But we we need like a quantity because it was now maybe a pack out unit.
301 00:33:35.790 ⇒ 00:33:37.279 Luke Daque: Adult units.
302 00:33:37.500 ⇒ 00:33:44.669 Luke Daque: It’s null here for the default value. If we can maybe put this into one or 0. I don’t know what it would be.
303 00:33:45.110 ⇒ 00:33:49.330 Luke Daque: Then we should have a sure data there.
304 00:33:50.430 ⇒ 00:33:50.829 Payas Parab: Yeah, I think.
305 00:33:52.650 ⇒ 00:33:57.370 Payas Parab: could you? Could you send me that? Just so I can like review again? I’m just trying to remember all the different assumptions. So.
306 00:33:57.370 ⇒ 00:33:57.699 Luke Daque: Yeah. Sure.
307 00:33:57.990 ⇒ 00:34:02.049 Luke Daque: Is that is that separate from the the nulls and the order level shipping.
308 00:34:02.350 ⇒ 00:34:07.620 Luke Daque: It’s this it’s the
309 00:34:11.040 ⇒ 00:34:16.529 Luke Daque: It’s it’s the same because the the shipping is using the pack out cost as well.
310 00:34:17.860 ⇒ 00:34:20.550 Luke Daque: Or wait. Let me let me double check that.
311 00:34:28.000 ⇒ 00:34:30.709 Luke Daque: Basically, it’s like I can share my screen.
312 00:34:47.090 ⇒ 00:34:54.210 Luke Daque: So this part, right? Needs to have a value.
313 00:34:54.440 ⇒ 00:34:55.230 Payas Parab: Okay.
314 00:34:55.230 ⇒ 00:34:57.409 Luke Daque: Because, like the.
315 00:35:04.490 ⇒ 00:35:07.150 Payas Parab: For the purposes of simplicity. I’m down to just put in one.
316 00:35:07.950 ⇒ 00:35:09.229 Luke Daque: Yeah, we can do that.
317 00:35:09.900 ⇒ 00:35:13.349 Luke Daque: Yeah, like, pack out units would be null for those
318 00:35:13.530 ⇒ 00:35:18.389 Luke Daque: default values as well as with some of the.
319 00:35:18.620 ⇒ 00:35:25.179 Luke Daque: I think the box cost also needs to type out. So yeah, we can add one to that, or it’s 0.
320 00:35:25.407 ⇒ 00:35:29.042 Payas Parab: One other thing that might be good as well just to like. Keep the engagement going.
321 00:35:29.570 ⇒ 00:35:51.460 Payas Parab: Ryan, if you could, quickly, if you just like at me somewhere in that Pr where? Where that like pack out unit is coming from the raw data. Basically, whatever’s missing in the sheets. If we can flag it to them, then we can say, Hey, we’re gonna just keep moving with like an assumption. But it’s nice to like throw something back to them as well. Do we have the the missing skew units that are causing that that weren’t weren’t in the sheet that we got.
322 00:35:51.920 ⇒ 00:36:00.580 Luke Daque: Yeah, I can provide a list. Maybe I can add a a tab here that’s missing skews, or like I, I can probably just add them here, and then they’ll be like nulls.
323 00:36:02.060 ⇒ 00:36:06.730 Payas Parab: Why don’t you? I wouldn’t mess with the sheet, because this is like flowing into the other stuff right? So I don’t. Wanna.
324 00:36:09.010 ⇒ 00:36:11.839 Payas Parab: This is the. This is the one that’s like connected to portable.
325 00:36:12.010 ⇒ 00:36:13.230 Luke Daque: Yeah, that’s right.
326 00:36:13.230 ⇒ 00:36:14.945 Payas Parab: I wouldn’t edit this sheet just to be.
327 00:36:15.410 ⇒ 00:36:17.759 Luke Daque: Extra different one.
328 00:36:18.550 ⇒ 00:36:25.909 Payas Parab: I think we just throw it in the chat to be like, Hey, we just wanna make sure we we have default assumptions going in. But we just wanted to flag to you that
329 00:36:26.418 ⇒ 00:36:28.569 Payas Parab: before we start building a bunch of stuff.
330 00:36:29.190 ⇒ 00:36:29.850 Luke Daque: Okay.
331 00:36:30.660 ⇒ 00:36:35.219 Robert Tseng: Quick question. So the problem is that, like there are unexpected skews coming in.
332 00:36:37.330 ⇒ 00:36:43.540 Luke Daque: There are skews in Amazon that don’t look like this like in in the example that I provided like these ones.
333 00:36:43.540 ⇒ 00:36:46.600 Robert Tseng: So there’s like unexpected missing skews coming through.
334 00:36:46.600 ⇒ 00:36:46.935 Luke Daque: Yeah.
335 00:36:47.690 ⇒ 00:36:48.360 Robert Tseng: Okay.
336 00:36:49.510 ⇒ 00:36:55.689 Payas Parab: yeah, that list. However, you generated that. Do you think we’d also pull account for like the last year, or something, or even like last 30 days?
337 00:36:56.010 ⇒ 00:36:58.500 Luke Daque: Yeah, I can do that. I’ll I’ll send it in the chat.
338 00:36:58.500 ⇒ 00:37:04.299 Payas Parab: Yeah, if you just send it to me, or like what the logic was, I can also, it’s probably coming from Amazon raw, right? I can quickly do it in Snowflake.
339 00:37:04.710 ⇒ 00:37:06.179 Luke Daque: Yeah, it’s probably there.
340 00:37:06.600 ⇒ 00:37:19.110 Robert Tseng: Every Ecom company struggles with this problem like your your map, your product mapping table like never has everything that comes through. I’m sure, when we do this for Eden, there’s gonna be unexpected stuff that comes through as well.
341 00:37:19.110 ⇒ 00:37:19.710 Luke Daque: Yeah.
342 00:37:21.310 ⇒ 00:37:26.689 Uttam Kumaran: Yeah, I mean, we’re doing this. We’re doing this now across 3 clients.
343 00:37:26.690 ⇒ 00:37:39.340 Payas Parab: Dude. I’m doing the same dude pool parts is also a fucking nightmare. The amount of missing skews, even though we have a giant sheet with a truly stupid amount of shit in there. There’s still, somehow in their top 20 there’s 5 missing skews, and I’m like.
344 00:37:39.340 ⇒ 00:37:46.530 Robert Tseng: Is, is there a way for us just to like? I don’t know. Like, pull that list for them, instead of relying on them to generate the list. I don’t know.
345 00:37:47.410 ⇒ 00:37:48.630 Robert Tseng: I mean, it’s a reactive way.
346 00:37:48.630 ⇒ 00:37:50.289 Uttam Kumaran: We already do like we
347 00:37:50.490 ⇒ 00:37:54.280 Uttam Kumaran: for pool parts, like we went through all the systems and pull all the skews.
348 00:37:54.280 ⇒ 00:37:54.930 Robert Tseng: Oh, you did!
349 00:37:54.930 ⇒ 00:37:57.849 Uttam Kumaran: But they have like. But in their in their accounting.
350 00:37:58.040 ⇒ 00:38:02.209 Uttam Kumaran: that’s where they have like, basically like, actually, what the cost is.
351 00:38:02.750 ⇒ 00:38:04.890 Uttam Kumaran: The problem is, the platforms are out of sync.
352 00:38:05.470 ⇒ 00:38:06.190 Robert Tseng: Yeah.
353 00:38:06.190 ⇒ 00:38:06.890 Uttam Kumaran: So.
354 00:38:07.303 ⇒ 00:38:09.370 Robert Tseng: Every client faces this problems.
355 00:38:10.470 ⇒ 00:38:14.360 Uttam Kumaran: But the big thing is you don’t. You don’t care about this until you have to report.
356 00:38:14.700 ⇒ 00:38:19.210 Uttam Kumaran: So then the lands on us like this is, which is
357 00:38:19.460 ⇒ 00:38:23.150 Uttam Kumaran: good. But like, Yeah, it’s tough, like, I wish they they should take this.
358 00:38:23.790 ⇒ 00:38:26.329 Uttam Kumaran: They should have taken care of this like a long time ago.
359 00:38:28.310 ⇒ 00:38:39.680 Payas Parab: As a Ryan. If you send that list, maybe it’s worth just flagging that as like, hey, something we’re gonna bump. But it’s not like it’s not like top tier. So we like at least have something where we’re like, hey, there’s some some stuff that we’re continually working on.
360 00:38:41.070 ⇒ 00:38:50.409 Payas Parab: I I just cause. I the there’s also the. This is the shipping null, that giant like 97% or something that we were talking about in the Pr. This is related to that same issue. It feels like that’s
361 00:38:50.510 ⇒ 00:38:54.820 Payas Parab: a very large percentage for this to be the only issue driving that
362 00:38:56.610 ⇒ 00:38:57.809 Payas Parab: you know what I’m talking about.
363 00:38:58.530 ⇒ 00:39:06.499 Luke Daque: Yeah. But yeah, all the nulls there are like the pack out units. And oh, yeah, it’s over here.
364 00:39:08.990 ⇒ 00:39:13.070 Payas Parab: So those come null. Then there’s no like coalesce, like, kind of clean it up, basically.
365 00:39:15.220 ⇒ 00:39:17.370 Payas Parab: And the final value for shipping becomes known.
366 00:39:17.810 ⇒ 00:39:24.429 Luke Daque: Yeah, I’m already coalescing it, using the default value. But since the raw data is also null.
367 00:39:24.600 ⇒ 00:39:26.769 Luke Daque: so it’s going to coalesce through now.
368 00:39:26.770 ⇒ 00:39:31.409 Payas Parab: Out units is null. But but okay, so then it’s really just this core like this little pack out unit, is it?
369 00:39:31.790 ⇒ 00:39:36.989 Payas Parab: That means but that tells me if it’s 97%, that means most of it is relying on the default assumption.
370 00:39:36.990 ⇒ 00:39:39.009 Luke Daque: Yeah, exactly. Most of them are.
371 00:39:39.010 ⇒ 00:39:43.350 Payas Parab: Then, are these just like, what are these skews? And they’ve provided us? That’s the part. I’m also then confused.
372 00:39:43.350 ⇒ 00:39:44.999 Uttam Kumaran: Is the join not happening.
373 00:39:46.180 ⇒ 00:39:53.330 Luke Daque: No, it’s it’s joined. But yeah, I think all of these queues here are probably shopify skews
374 00:39:53.490 ⇒ 00:39:57.310 Luke Daque: and the Amazon skews look different. Basically.
375 00:39:57.310 ⇒ 00:39:58.450 Uttam Kumaran: We’ll just run that many.
376 00:39:58.450 ⇒ 00:40:01.099 Payas Parab: The orders coming from Amazon, though that’s the you know.
377 00:40:01.370 ⇒ 00:40:04.419 Payas Parab: Or is that is that percentage just based on the Amazon ones.
378 00:40:04.880 ⇒ 00:40:07.060 Luke Daque: That was just based on the Amazon Ones.
379 00:40:07.060 ⇒ 00:40:07.780 Payas Parab: Okay.
380 00:40:08.030 ⇒ 00:40:14.800 Uttam Kumaran: Just run a query to show which ones have the join happening versus which ones that don’t, and then you’ll see it.
381 00:40:15.880 ⇒ 00:40:21.139 Luke Daque: Yeah, yeah, like this specific skew, for example, that’s like a hundred, 2,000 of them.
382 00:40:21.580 ⇒ 00:40:22.410 Luke Daque: Let’s.
383 00:40:22.850 ⇒ 00:40:25.200 Uttam Kumaran: But look, look at this other ones like what are these other ones.
384 00:40:25.200 ⇒ 00:40:27.099 Luke Daque: Exactly. Yeah. They don’t.
385 00:40:27.100 ⇒ 00:40:31.950 Uttam Kumaran: Yeah, so can you create? Let’s just create like a
386 00:40:33.190 ⇒ 00:40:39.120 Uttam Kumaran: can. You just create a spreadsheet? A little query that’s like the skew and the Count, where there is a match.
387 00:40:39.750 ⇒ 00:40:44.500 Payas Parab: And maybe like the sum of some of some of total price as well. So we just have, like what the revenue.
388 00:40:45.080 ⇒ 00:40:52.810 Uttam Kumaran: Yeah, you can run it here, run it in Snowflake. But then that way, it’s clear like this, join is just isn’t happening on a couple of these. So.
389 00:40:53.450 ⇒ 00:40:54.070 Payas Parab: Yeah.
390 00:40:55.640 ⇒ 00:41:03.920 Uttam Kumaran: And then, yeah, in terms of the null issue, you just have to. You have to Nvl and and change everything to 0. Basically
391 00:41:04.390 ⇒ 00:41:09.089 Uttam Kumaran: so that way comes up as null. It doesn’t like fuck up the calculation.
392 00:41:09.820 ⇒ 00:41:14.080 Uttam Kumaran: or what you can do is in in in
393 00:41:14.210 ⇒ 00:41:16.720 Uttam Kumaran: spreadsheet. You need to default the value of 0.
394 00:41:17.680 ⇒ 00:41:19.630 Luke Daque: Yeah, we can do that. Make make this.
395 00:41:19.630 ⇒ 00:41:24.429 Uttam Kumaran: I would suggest doing that like. Put a validation on this column and make sure that it can’t be.
396 00:41:25.390 ⇒ 00:41:32.270 Uttam Kumaran: It can’t be a non number and.
397 00:41:32.570 ⇒ 00:41:33.370 Luke Daque: Yeah.
398 00:41:33.730 ⇒ 00:41:34.850 Uttam Kumaran: Something like that.
399 00:41:37.220 ⇒ 00:41:39.620 Luke Daque: Yeah, I’ll do that after the call.
400 00:41:40.190 ⇒ 00:41:46.418 Uttam Kumaran: Yeah, and we’ll we’ll have to do this, Robert, on anything where we have spreadsheets coming in, because this will
401 00:41:47.880 ⇒ 00:41:50.960 Uttam Kumaran: this will be the 1st place, I check when something breaks.
402 00:41:51.610 ⇒ 00:42:01.280 Uttam Kumaran: So we’ll just have to be careful on, like when we’re bringing in values from spreadsheets or strings that they’re like a numeric column doesn’t get put in, or something like that. So we’ll add validation and stuff.
403 00:42:03.140 ⇒ 00:42:09.039 Payas Parab: So maybe it’s also possible that Jonathan just did the shopify skews and never even thought about the Amazon skews. That’s also possible.
404 00:42:09.040 ⇒ 00:42:10.849 Uttam Kumaran: These look like there’s no Amazon on here, so.
405 00:42:10.850 ⇒ 00:42:13.340 Payas Parab: Yeah, it looks like the shopify format. So.
406 00:42:14.350 ⇒ 00:42:16.140 Uttam Kumaran: Just paying, and ask.
407 00:42:16.960 ⇒ 00:42:17.590 Payas Parab: Cool.
408 00:42:20.650 ⇒ 00:42:27.660 Payas Parab: Okay, so this was so I I can quickly go through some of the Javi stuff if if needed, just to. Kinda
409 00:42:27.710 ⇒ 00:42:55.039 Payas Parab: I have a good sense of. So the gross margin I do need another day, and maybe we’ll just talk to like I’m on about that I did get the map I prioritized Justin’s ask about the matching, and I was able to. I I don’t know if everyone’s in that channel, Nico. So I just forwarded it this morning to but I was able to on like once we clean up and like standardize the addresses, be able to get them around 7,000 matches, 7,000 8,000, and then I am now. I ran. I built like a partial matching algorithm.
410 00:42:55.396 ⇒ 00:43:03.989 Payas Parab: That found until it crashed. Unfortunately, it’s like the the partial matches, as you guys know, are like, really, computationally expensive, right? Like they were like, really.
411 00:43:04.070 ⇒ 00:43:16.920 Payas Parab: it’s like partial to the point that it’s like, Okay, like, how close is the name? How close is the address? Does the house number match? It’s sort of like I created like a scoring function to try and like figure out the similarity. I’ve been running it in python, but that one
412 00:43:16.920 ⇒ 00:43:34.820 Payas Parab: until it crashed it had yielded like another couple 1,000 matches as well, like partial matches. And it was like picking up really cool things like if someone in Amazon is Ed, and then in the other one. They’re Edward. We’re able to like capture that match. So I need to rerun that code. I want to get that to Justin asap, because that seems like it’s top of mind for him.
413 00:43:35.109 ⇒ 00:43:46.269 Payas Parab: I’m gonna rerun the partial matching. And hopefully, my computer doesn’t crap out this time. But I even like just standardizing the addresses we’re able to get like 7,000, which is like, really not like. It’s already we’re at a good spot.
414 00:43:46.630 ⇒ 00:43:47.160 Robert Tseng: Yes.
415 00:43:47.540 ⇒ 00:43:49.320 Payas Parab: With the partial matching. I think we’re going to.
416 00:43:49.320 ⇒ 00:43:52.550 Uttam Kumaran: Where are you? Where are you running this like? You’re just running as a notebook.
417 00:43:53.180 ⇒ 00:43:54.150 Payas Parab: Yeah, yeah.
418 00:43:54.150 ⇒ 00:43:56.769 Uttam Kumaran: Why don’t you run it? Why don’t you run it in Snowflake.
419 00:43:58.246 ⇒ 00:44:03.220 Payas Parab: The the logic is a little bit complicated, though. There’s like there’s like quite a lot that clean for cleaning it.
420 00:44:03.220 ⇒ 00:44:05.740 Uttam Kumaran: No, no! Why don’t you run in snowflake notebooks?
421 00:44:06.560 ⇒ 00:44:08.740 Payas Parab: I should do that. Yeah. So wait.
422 00:44:09.580 ⇒ 00:44:12.719 Payas Parab: Yeah, I should do that. Let me do that. Well.
423 00:44:12.980 ⇒ 00:44:16.080 Payas Parab: I’m just. I’m just so used to like draw dogging the way I know how it gets done.
424 00:44:16.080 ⇒ 00:44:22.539 Uttam Kumaran: I know. But, dude, it’s not running. It’s like you’re at the point where it’s not running on your laptop. You can’t. It doesn’t matter what you’re gonna try to do.
425 00:44:22.820 ⇒ 00:44:27.617 Uttam Kumaran: I don’t want you working on like RAM optimization like, just run it in
426 00:44:28.820 ⇒ 00:44:30.109 Uttam Kumaran: I’ll just show you where to go.
427 00:44:30.110 ⇒ 00:44:32.269 Payas Parab: I spent a shameful amount of time last night.
428 00:44:32.270 ⇒ 00:44:34.760 Uttam Kumaran: I know I know I know. Cause I oh, my God.
429 00:44:34.810 ⇒ 00:44:38.229 Uttam Kumaran: I’ve done this before. Yeah, that’s what I’m saying. I don’t want you to be like
430 00:44:38.340 ⇒ 00:44:41.620 Uttam Kumaran: trying to work on your like CPU cores, and like.
431 00:44:41.620 ⇒ 00:44:42.910 Payas Parab: That’s literally what I was doing.
432 00:44:43.239 ⇒ 00:44:46.860 Uttam Kumaran: Out every single browser tab that’s like useless like. I’ll just.
433 00:44:46.860 ⇒ 00:44:56.989 Payas Parab: I was like, Okay, the matching algorithm and testing it and getting the cleaning done took like 3 h. The rest of the 3 h, was just trying to convert everything into like optimal.
434 00:44:56.990 ⇒ 00:44:58.570 Uttam Kumaran: Like, if you’re lowering like
435 00:44:59.440 ⇒ 00:45:05.179 Uttam Kumaran: character sizes and stuff. Yeah, we’ll skip that. I’ll see if it works. But let’s just try that in there.
436 00:45:05.370 ⇒ 00:45:09.510 Uttam Kumaran: You can just import it into there, and you should be able to run everything in Snowflake. So.
437 00:45:10.580 ⇒ 00:45:19.199 Payas Parab: Let’s do that. And it’s like pretty seamless like, if I have like an import. Csv, I could basically just turn that into a load into snowflake, and then run the same python script pretty easily.
438 00:45:19.200 ⇒ 00:45:19.730 Uttam Kumaran: Yeah.
439 00:45:20.170 ⇒ 00:45:20.870 Payas Parab: Okay.
440 00:45:21.488 ⇒ 00:45:32.020 Payas Parab: let’s do that. I can provide an update to Justin as well like we have it running. We know we’re getting a lot of matches. So we know the algorithms working correctly. We just need to like move it somewhere. So it runs more efficiently.
441 00:45:32.020 ⇒ 00:45:36.350 Uttam Kumaran: Worst case, like, I mean, you could do this in Google Colab also.
442 00:45:37.725 ⇒ 00:45:40.380 Payas Parab: Okay, do we need an account or anything for that?
443 00:45:40.380 ⇒ 00:45:44.580 Uttam Kumaran: No, I would just I would just do it on your Brainforge account.
444 00:45:45.730 ⇒ 00:45:46.400 Payas Parab: Okay.
445 00:45:47.170 ⇒ 00:45:49.870 Uttam Kumaran: Honestly, I would actually just do that first.st
446 00:45:50.390 ⇒ 00:45:53.219 Uttam Kumaran: Don’t worry about the snowflake thing. Just do that first.st
447 00:45:54.020 ⇒ 00:45:54.650 Uttam Kumaran: Yeah.
448 00:45:55.050 ⇒ 00:45:57.189 Uttam Kumaran: And just tell me if it works. Yeah.
449 00:45:58.600 ⇒ 00:45:59.220 Payas Parab: Okay.
450 00:46:00.430 ⇒ 00:46:01.220 Uttam Kumaran: You’re aware.
451 00:46:01.220 ⇒ 00:46:07.190 Uttam Kumaran: look into like Google drive or upload, or everything, it’ll be the same thing. And then also that now can live somewhere. So.
452 00:46:07.410 ⇒ 00:46:07.995 Payas Parab: Yeah.
453 00:46:09.463 ⇒ 00:46:21.996 Payas Parab: great. So, nico, I want to prioritize that in the update that like, we’re working on that trying to get back. Because that seems like it’s very more top of mind, like Justin added. A bunch of people from my looks like engineering. I have no idea who these guys are, but that like are like
454 00:46:22.380 ⇒ 00:46:23.320 Payas Parab: what’s up.
455 00:46:23.940 ⇒ 00:46:28.240 Nicolas Sucari: No, that that’s perfect. Let me know. Or maybe the blur had.
456 00:46:28.800 ⇒ 00:46:36.539 Payas Parab: Yeah, I’ll write up a little blurb, and I’ll kind of get I’ll get something for you, Nico, so that we can send that, and then be like that’s kind of why.
457 00:46:36.770 ⇒ 00:46:37.770 Payas Parab: just holding off.
458 00:46:37.770 ⇒ 00:46:49.660 Nicolas Sucari: No, no, that’s fine, that’s fine. The only thing is that I’m not in. Yeah, I’m not on the other channels, so I’m not sure, like, I don’t get that update. So yeah, just let me know how that goes. And we can share something with a man, so that when he’s.
459 00:46:49.660 ⇒ 00:47:11.959 Payas Parab: Yeah, let’s because I think is in that channel. So he’ll be like, Okay, cool something going on there. The other thing is this, Ryan like this Amazon issue. I say, we just flag it to them proactively now. So they’re like, Hey, like, yeah, we are like working our asses off on this thing, and like there will be. This will come up, not today, but it’ll come up when Jay money sees the dashboard is like, why do all the Amazon ones look wrong. Right? So
460 00:47:12.771 ⇒ 00:47:28.000 Payas Parab: so I think that’s the biggest updates on my end is, let’s see if I can get this thing running more optimally. I know the matches are coming in. So we’re in a good spot, because I’ve been printing. That might have also caused the computer crash. Those printing every match to monitor who tells like cringing so hard, who tells like who.
461 00:47:28.000 ⇒ 00:47:29.240 Uttam Kumaran: I’m not. I just like.
462 00:47:29.605 ⇒ 00:47:29.950 Payas Parab: Yeah.
463 00:47:31.650 ⇒ 00:47:33.970 Uttam Kumaran: I just want. I just want you to win.
464 00:47:34.110 ⇒ 00:47:55.409 Payas Parab: No, no, it’s this is what happens when you have a 64 GB RAM computer is, you just basically are like, I can do anything I need locally anyway. So I think, Nico, we’ll send an update to him on asap on that, and then we’ll get this thing running. Get those additional matches, and then we’ll flag the Amazon issue from Ryan, and then I think that’ll be in a good place along with your guys portable updates to him.
465 00:47:55.560 ⇒ 00:48:00.660 Payas Parab: and we can do that like next hour, cause I can just send a blurb about where we’re at. And the preliminary right.
466 00:48:02.880 ⇒ 00:48:03.510 Nicolas Sucari: Right?
467 00:48:05.048 ⇒ 00:48:12.161 Nicolas Sucari: That’s perfect. Okay, also. Robert, I schedule a meeting for tomorrow so that we can go through the planning stuff.
468 00:48:13.163 ⇒ 00:48:13.830 Nicolas Sucari: Time works.
469 00:48:13.830 ⇒ 00:48:16.260 Robert Tseng: Have ideas. Yeah, that’d be good.
470 00:48:17.150 ⇒ 00:48:44.889 Nicolas Sucari: So that we start planning, and then communicate that with a man, and try to see if we are kind of prioritizing the same things. If not, we can obviously change stuff. But just to have things ahead of time and see that everything is planned out. And then, Aman, maybe pay us. It’s this good to schedule some time with the man and go through this stuff that we have on Thursday. Maybe that we can. Yeah. Share a little bit on a on a call with him. Okay.
471 00:48:48.230 ⇒ 00:48:53.570 Nicolas Sucari: perfect cool. And I think we have full parts left.
472 00:48:56.730 ⇒ 00:48:58.170 Nicolas Sucari: Yeah. For pool parts.
473 00:48:58.170 ⇒ 00:48:59.479 Payas Parab: Give an update there as well.
474 00:49:00.550 ⇒ 00:49:01.179 Nicolas Sucari: Yes, please.
475 00:49:01.180 ⇒ 00:49:05.030 Payas Parab: So the skew we have the top 20 skews. We’re still missing some
476 00:49:05.670 ⇒ 00:49:17.809 Payas Parab: like they’re not in our cleaned up list, but there’s also like, I took a look. And it’s not like they’re anywhere. So they’re sort of their own thing. I don’t know if that’s like expected behavior from this like Asia data like the the new skew got introduced.
477 00:49:17.920 ⇒ 00:49:19.470 Payas Parab: But we know which ones
478 00:49:20.120 ⇒ 00:49:33.749 Payas Parab: top 20. Those will be our kind of priority. We’ll try and figure out costing. I’m having some like on the revenue side alone. I was having some issues around, like, what is the correct way to look at it, because it’s like, clearly an accountant’s export, not like a
479 00:49:34.440 ⇒ 00:49:46.450 Payas Parab: data export. So I’m like, do I, you know, is this the right filter? Is this the right reflection of sales. So I sent Ian an email on that on the costing side, trying to do that second part, right? Which is, get that whole profit calc.
480 00:49:46.740 ⇒ 00:49:54.400 Payas Parab: same issue like it’s, it’s kind of like, it’s really ambiguous, like, okay, which ones are the right cost of good like, which ones are the right costing columns and stuff.
481 00:49:54.600 ⇒ 00:50:08.290 Payas Parab: So it might be helpful to chat through with Ian and then to keep it moving. You, Tom. Maybe that’s something with your relationship with it’s Dan, right? Yeah, Dan, like, maybe we just bump it, being like, there’s a lot of accounting nuance in there. And like, we don’t know what
482 00:50:08.370 ⇒ 00:50:29.690 Payas Parab: the relationship is with him. But it just be good to kind of go through those. That’s the main update. I think the Ryan I know you’re super swamped. So I wanna like there’s only one. I think that maybe I’ve made a couple of tickets on all these things. The most important is that for Kim, that 5 tran data load like there’s no other. There’s no other explanation than like other than the raw data being wrong.
483 00:50:30.193 ⇒ 00:50:33.230 Payas Parab: I’ve checked like every other avenue, I think, and I don’t know.
484 00:50:33.230 ⇒ 00:50:33.580 Luke Daque: Yeah.
485 00:50:33.580 ⇒ 00:50:43.579 Payas Parab: Based on my analysis. So something in 5 Tran is not copying. I tried to like. Maybe it’s like a divide by 100, right? Sometimes it comes in and cents, and I don’t think it’s that either. It’s like.
486 00:50:43.580 ⇒ 00:51:08.129 Luke Daque: I, I think it’s like duplicating something because I did look at this, how it’s being set up in 5 Tran, and it’s actually coming from email, right? So I wonder if we are like receiving the same data multiple times for that specific date like the November 14, th maybe we’d we’d receive the post pilot overview report like 10 times or something
487 00:51:09.150 ⇒ 00:51:13.749 Luke Daque: 14. So it’s like appending all those data together.
488 00:51:14.060 ⇒ 00:51:18.689 Luke Daque: And we’re we’re seeing it as just one date instead of like.
489 00:51:19.740 ⇒ 00:51:32.080 Payas Parab: That the email, the email that we get dash like the way that 5 train process is that as like, is that a function we’ve set up? Or is that like 5 trans. Way of doing it like, would that be something we’d have to take it to 5 train? Or is that something we need to like. We have 10.
490 00:51:32.080 ⇒ 00:51:32.640 Luke Daque: Hi.
491 00:51:32.640 ⇒ 00:51:33.760 Payas Parab: Like filter.
492 00:51:33.910 ⇒ 00:51:34.450 Luke Daque: Yeah.
493 00:51:34.450 ⇒ 00:51:35.770 Nicolas Sucari: I think we set it up.
494 00:51:36.450 ⇒ 00:51:37.070 Luke Daque: We did.
495 00:51:37.070 ⇒ 00:51:37.750 Uttam Kumaran: Yeah.
496 00:51:38.150 ⇒ 00:51:42.500 Luke Daque: Let me share my screen. This one, the the post pilot, overview.
497 00:51:43.780 ⇒ 00:51:50.719 Nicolas Sucari: The issue here. The issue here with them is that we are getting like a super high costs on some marketing campaigns that we’re getting
498 00:51:51.070 ⇒ 00:51:51.880 Nicolas Sucari: pilot.
499 00:51:52.180 ⇒ 00:51:56.519 Nicolas Sucari: And we yeah, we received that value from the email, that
500 00:51:56.650 ⇒ 00:51:59.459 Nicolas Sucari: post by your, yeah reports. And us.
501 00:51:59.890 ⇒ 00:52:03.839 Luke Daque: I wonder if, like, we’re getting a thousand emails for this
502 00:52:04.280 ⇒ 00:52:07.740 Luke Daque: specific day, because it’s like 82,000 cents. That’s.
503 00:52:07.740 ⇒ 00:52:14.570 Uttam Kumaran: So the post pilot is not through. It’s actually not like a native connector. We basically built the process.
504 00:52:14.570 ⇒ 00:52:15.290 Payas Parab: Yeah, yeah.
505 00:52:15.290 ⇒ 00:52:28.699 Uttam Kumaran: 5. Tran just takes a Csv. That we get from their team and puts us in the database. I can connect you with both their account manager, who you should just basically hit and say, Go, figure this out, and I’ll add you and I can give you the credentials.
506 00:52:28.870 ⇒ 00:52:32.540 Uttam Kumaran: the post pilot, and you can go in. I would start by just
507 00:52:33.020 ⇒ 00:52:36.780 Uttam Kumaran: putting this on their play and be like, Go, can you guys go check this number.
508 00:52:38.110 ⇒ 00:52:40.020 Payas Parab: Yeah, okay, maybe we can.
509 00:52:40.020 ⇒ 00:52:46.029 Uttam Kumaran: Use the vendor because dude like these guys work for us like, just if there’s a question about the data consistency.
510 00:52:46.940 ⇒ 00:52:57.249 Uttam Kumaran: push it to them. They’ll go find out one way or another, and then in parallel, we can work. We have all the Csv’s of, like all the raw data I can share that with you, too. But
511 00:52:59.010 ⇒ 00:53:03.179 Uttam Kumaran: honestly, that’s probably how I would just move forward is I can connect you with the account manager.
512 00:53:03.590 ⇒ 00:53:04.470 Payas Parab: Okay.
513 00:53:04.470 ⇒ 00:53:10.329 Uttam Kumaran: Or I can just give you your email. And you can just be like, can you guys, can you go confirm this or tell me where on the platform to go. Confirm this.
514 00:53:11.040 ⇒ 00:53:13.720 Payas Parab: Okay, yeah. Ryan, do you agree on on that?
515 00:53:13.720 ⇒ 00:53:15.919 Luke Daque: Yeah, I agree, I agree, this, yeah.
516 00:53:15.920 ⇒ 00:53:26.949 Payas Parab: I, I just wanna like, well, my main thing is like for Kim. It’s like we’ve sort of done multiple iterations on this right where it’s like, okay, here’s the problem. Here’s where we’re at now. It’s like, okay, cool other problem. And now this problem is like
517 00:53:27.380 ⇒ 00:53:34.490 Payas Parab: down to the most root. It’s I. I don’t want it. Also, just be like, Okay, cool. There’s some like core root problem that we’re not solving for, you, you know, like.
518 00:53:34.490 ⇒ 00:53:39.910 Uttam Kumaran: No, no, no! If this is the root problem, then this is it like, and I wouldn’t be surprised that this is it, cause
519 00:53:40.350 ⇒ 00:53:44.790 Uttam Kumaran: it’s this is coming in like a little bit of a jank way. So there’s a possibility.
520 00:53:45.120 ⇒ 00:53:45.740 Luke Daque: Yeah.
521 00:53:46.320 ⇒ 00:53:52.390 Payas Parab: Okay, that that makes sense. Okay, I I sort of like, always assume that all the stuff from 5 trend like looks right. But maybe
522 00:53:52.630 ⇒ 00:53:53.150 Payas Parab: it’s more.
523 00:53:53.150 ⇒ 00:54:07.669 Uttam Kumaran: This will post pilot doesn’t have a native connection. So what we’re having them do is they email it to one of our service email accounts and 5 years picked up in the email. So it’s like, yeah, I would say honestly, you should- you should flip your assumption.
524 00:54:07.970 ⇒ 00:54:12.180 Uttam Kumaran: You should assume everything is like, ductate, basically cool.
525 00:54:12.180 ⇒ 00:54:15.480 Uttam Kumaran: You should start from like there is a chance. It’s wrong.
526 00:54:17.040 ⇒ 00:54:20.171 Uttam Kumaran: Like. Don’t don’t blanket trust because there’s an issue.
527 00:54:20.840 ⇒ 00:54:21.360 Payas Parab: Yeah.
528 00:54:21.360 ⇒ 00:54:29.579 Uttam Kumaran: Even if it’s coming through 5 or whatever these guys like constantly make make mistakes. So just be us to go find it and isolate.
529 00:54:29.580 ⇒ 00:54:38.389 Payas Parab: So why don’t you, Tom? Why don’t you send that account? Manager, email, Nico and Ryan and I can push that forward and just be like, and we have, like an exact, like a day, that something.
530 00:54:38.390 ⇒ 00:54:38.900 Uttam Kumaran: Yeah, exactly.
531 00:54:38.900 ⇒ 00:54:42.776 Payas Parab: Know exactly what it is like. It’s just not right. And so,
532 00:54:43.590 ⇒ 00:54:47.839 Payas Parab: yeah, I think you’re right, Ryan. It’s probably some sort of duplicate issue. If anything.
533 00:54:48.250 ⇒ 00:54:48.660 Luke Daque: Yeah.
534 00:54:48.660 ⇒ 00:54:51.530 Uttam Kumaran: Yeah, maybe it came in twice, or some or a couple of times, or I don’t know.
535 00:54:51.800 ⇒ 00:55:01.080 Payas Parab: That was. It was like a busy campaign day for them, so like relative to the other days. That’s what we were like. Oh, the spike is because you just ran this very busy campaign. But it was like.
536 00:55:01.470 ⇒ 00:55:03.449 Payas Parab: it’s still too big, you know.
537 00:55:03.450 ⇒ 00:55:10.380 Uttam Kumaran: No like, you know, it could be as dumb as like we got the email 3 times like, I’m telling you, it’s like it could be a very dumb explanation.
538 00:55:10.810 ⇒ 00:55:11.270 Uttam Kumaran: Got it?
539 00:55:11.270 ⇒ 00:55:12.819 Uttam Kumaran: Go find out what it is.
540 00:55:12.820 ⇒ 00:55:18.149 Payas Parab: Cool. Let’s let’s do that, then. Cause then I think, and then I would love to just like give Kim that as well, so that we know
541 00:55:18.460 ⇒ 00:55:23.759 Payas Parab: she knows like, okay, we do know the root cause now. And it’s we’re we’re trying to get it fixed there.
542 00:55:26.890 ⇒ 00:55:32.260 Uttam Kumaran: So like, do you want to just send that Pias like to her? You want to just be like, hey? We’re still digging in.
543 00:55:32.500 ⇒ 00:55:33.120 Payas Parab: Yeah.
544 00:55:33.120 ⇒ 00:55:33.779 Uttam Kumaran: Sound, good.
545 00:55:34.370 ⇒ 00:55:35.180 Payas Parab: Sure.
546 00:55:36.850 ⇒ 00:55:41.699 Uttam Kumaran: Yeah, you don’t have to be so descriptive. You can just even just say, like, roughly, what’s going on.
547 00:55:41.700 ⇒ 00:55:42.609 Payas Parab: Yup, yeah.
548 00:55:45.500 ⇒ 00:55:48.399 Nicolas Sucari: We have that external, I think marketing channel with her.
549 00:55:48.628 ⇒ 00:55:49.540 Payas Parab: Seeing it right now.
550 00:55:49.850 ⇒ 00:55:50.300 Uttam Kumaran: Yeah.
551 00:55:50.300 ⇒ 00:55:51.240 Nicolas Sucari: Cool. Okay.
552 00:55:51.470 ⇒ 00:56:01.975 Nicolas Sucari: one more thing on pool parts of this queue analysis. I’ll share it again with you guys. But if you can check that kind of big jam that I created with all the different phases of the project, so that we can
553 00:56:02.290 ⇒ 00:56:29.850 Nicolas Sucari: set up like the next tasks on that one. Just take a look, a quick look about everything that I created about that one, and you let me know if there are things missing there, and we can add and all. And then I scheduled that quarterly planning meeting for tomorrow, too, so that we can go through all of the things that we will have with 4 parts for the next month, so that we can create that kind of backlog and share with them. Okay.
554 00:56:30.320 ⇒ 00:56:33.140 Payas Parab: And that’s for that’s for pool parts or for okay.
555 00:56:33.330 ⇒ 00:56:40.860 Nicolas Sucari: So for Joby, I created one with Robert. I’m gonna go through that with Robert, and for pool party. I created one with you and and.
556 00:56:41.250 ⇒ 00:56:46.009 Payas Parab: Could you add me to the Javi coffee one as well? Just because I think there’ll be somewhat helpful to just have that visibility.
557 00:56:46.010 ⇒ 00:56:47.670 Nicolas Sucari: Yeah, okay.
558 00:56:47.670 ⇒ 00:56:48.210 Payas Parab: Thank you.
559 00:56:48.478 ⇒ 00:56:54.660 Nicolas Sucari: Okay, I’ll I’ll check if you are. If you’re gonna be online at that time. If not, I’m gonna move it. Okay.
560 00:56:54.660 ⇒ 00:56:56.010 Payas Parab: Okay, I appreciate that.
561 00:56:57.720 ⇒ 00:57:02.170 Nicolas Sucari: Cool. And yeah, I think that’s it for pull parts, too.
562 00:57:04.210 ⇒ 00:57:08.719 Nicolas Sucari: And yeah, the only client left is start dates with him. But yeah, I know.
563 00:57:09.161 ⇒ 00:57:13.129 Uttam Kumaran: Is out today, we’re continuing on stuff for stripe.
564 00:57:13.490 ⇒ 00:57:17.839 Uttam Kumaran: And then I’m coordinating with their data and team on like moving in post press data.
565 00:57:18.520 ⇒ 00:57:21.410 Uttam Kumaran: so thankfully. Still, pretty chill.
566 00:57:25.150 ⇒ 00:57:26.930 Nicolas Sucari: Okay, okay.
567 00:57:27.260 ⇒ 00:57:39.199 Nicolas Sucari: perfect guys. Let me know if you need anything else. I’m gonna keep like reviewing all of the tickets. If you need to change any dates or if I’ll need other updates, I will be pinging. You guys. Okay.
568 00:57:41.860 ⇒ 00:57:42.300 Luke Daque: Sounds good.
569 00:57:44.880 ⇒ 00:57:45.560 Uttam Kumaran: Thanks guys.
570 00:57:45.560 ⇒ 00:57:46.010 Nicolas Sucari: Cool.
571 00:57:46.010 ⇒ 00:57:46.670 Robert Tseng: Oh, okay.
572 00:57:46.670 ⇒ 00:57:47.020 Nicolas Sucari: Everyone.
573 00:57:48.040 ⇒ 00:57:48.390 Luke Daque: Thanks.
574 00:57:48.390 ⇒ 00:57:48.930 Nicolas Sucari: Hi, mate!
575 00:57:48.930 ⇒ 00:57:49.640 Luke Daque: Bye-bye.