Meeting Title: lmnt quick wholesale sync Date: 2026-04-09 Meeting participants: Advait Nandakumar Menon, Amber Lin
WEBVTT
1 00:00:43.500 ⇒ 00:00:46.070 Amber Lin: Hello, good morning.
2 00:00:46.070 ⇒ 00:00:47.370 Advait Nandakumar Menon: Hey, how’s it going?
3 00:00:47.790 ⇒ 00:00:55.679 Amber Lin: Pretty good. How’s it going on the wholesale mart? Have you started on the topics?
4 00:00:56.280 ⇒ 00:01:00.000 Advait Nandakumar Menon: Yeah, I just started looking into wholesale. I was…
5 00:01:00.100 ⇒ 00:01:03.979 Advait Nandakumar Menon: looking into the spec, and just… I was looking…
6 00:01:04.459 ⇒ 00:01:12.780 Advait Nandakumar Menon: in Snowflake at the data a little, so I’m going to get into the weeds to create the topics and stuff, so I’m just about to start doing that.
7 00:01:14.080 ⇒ 00:01:14.840 Amber Lin: Okay.
8 00:01:15.850 ⇒ 00:01:18.109 Amber Lin: Sounds good.
9 00:01:23.990 ⇒ 00:01:26.330 Amber Lin: I just saw Greg’s comment.
10 00:01:27.670 ⇒ 00:01:39.559 Amber Lin: Meeting with… Okay, so let’s see, there’s a doc that they want my comments on?
11 00:01:41.940 ⇒ 00:01:45.050 Advait Nandakumar Menon: Yeah, that’s the… spec doc…
12 00:01:54.400 ⇒ 00:01:58.490 Amber Lin: Okay, I can’t… I can’t answer those…
13 00:02:02.650 ⇒ 00:02:17.319 Amber Lin: Wait, sorry, one sec, just reading through the comments. But I can walk you through the things that we used when we did the wholesale stuff, and then I can go answer the questions there.
14 00:02:17.810 ⇒ 00:02:19.800 Advait Nandakumar Menon: Yeah, that would be useful context, yeah.
15 00:02:19.800 ⇒ 00:02:26.659 Amber Lin: Yeah. So… One thing is that metrics here have not been
16 00:02:27.000 ⇒ 00:02:36.060 Amber Lin: updated. Like, I updated the retail ones, but the wholesale ones, I think they’re somewhat updated, but, like, I can’t…
17 00:02:36.280 ⇒ 00:02:39.790 Amber Lin: I can’t confirm that they’re, like, 100% up-to-date.
18 00:02:39.980 ⇒ 00:02:40.780 Amber Lin: Fine, I think.
19 00:02:40.780 ⇒ 00:02:41.120 Advait Nandakumar Menon: Okay.
20 00:02:41.440 ⇒ 00:02:45.660 Amber Lin: I don’t know if this is… yeah, I think this is lines total.
21 00:02:46.270 ⇒ 00:02:55.920 Amber Lin: Sales. Anyways, so there’s this page, and then there is this Element Wholesale sheet.
22 00:02:57.220 ⇒ 00:02:58.459 Advait Nandakumar Menon: I mean, this is the reporting, right?
23 00:02:58.460 ⇒ 00:03:03.840 Amber Lin: Yeah, I think you do. So, this, we have weekly.
24 00:03:04.430 ⇒ 00:03:12.840 Amber Lin: Quarterly averages are just calculated based on weekly and monthly. The data… is in…
25 00:03:13.440 ⇒ 00:03:21.499 Amber Lin: Here, so there, here’s the weekly one. This is already modeled, so you have metrics by week, their value.
26 00:03:21.780 ⇒ 00:03:25.520 Amber Lin: And segment is, like, partner status.
27 00:03:25.900 ⇒ 00:03:27.430 Amber Lin: like…
28 00:03:27.580 ⇒ 00:03:39.759 Amber Lin: Depending on what they are. And these are monthly grain, so we can’t just sum up weekly into monthly, because the weeks are not exact months, so that’s why we have a monthly tab.
29 00:03:41.070 ⇒ 00:03:48.410 Amber Lin: So, right here, we have… these are the metrics broken down by the partner segment.
30 00:03:51.280 ⇒ 00:03:59.520 Amber Lin: So, Trust to Health also includes health practitioner, so you can see here, the formulas are just referencing
31 00:04:00.600 ⇒ 00:04:05.880 Amber Lin: There’s just some if formulas referencing Yeah.
32 00:04:06.180 ⇒ 00:04:10.320 Amber Lin: the spreadsheets… And then…
33 00:04:11.350 ⇒ 00:04:20.179 Advait Nandakumar Menon: So the raw data you just showed, it’s from the respective tables in Snowflake, and it’s already modeled out, right?
34 00:04:20.180 ⇒ 00:04:24.740 Amber Lin: Yeah, that… all those metrics are pre-calculated. This is just, like, a lookup.
35 00:04:25.190 ⇒ 00:04:26.120 Amber Lin: Okay. Especially.
36 00:04:26.120 ⇒ 00:04:26.860 Advait Nandakumar Menon: Okay.
37 00:04:26.860 ⇒ 00:04:29.969 Amber Lin: Yeah, a sum lookup. And then…
38 00:04:30.240 ⇒ 00:04:33.230 Amber Lin: These are already modeled out metrics.
39 00:04:33.670 ⇒ 00:04:44.350 Amber Lin: For gross sales, I think these are also modeled out. So you’ll also be able to see, for example, right here, total sales strength mix.
40 00:04:44.490 ⇒ 00:04:57.540 Amber Lin: Right, and then… You would have… And so… International reseller and U.S. reseller.
41 00:04:57.900 ⇒ 00:05:02.999 Amber Lin: is deducted from drink mix, because we have this categorization.
42 00:05:04.320 ⇒ 00:05:17.490 Amber Lin: revenue, essentially, and then the international and realized reseller, like, usually falls under drink mix, but they want to see it in parallel, so, I was just deducting
43 00:05:18.230 ⇒ 00:05:21.370 Amber Lin: Like, these two values from Drink Mix.
44 00:05:22.110 ⇒ 00:05:25.080 Amber Lin: But all of these are… modeled out.
45 00:05:25.690 ⇒ 00:05:26.350 Amber Lin: For wholesale.
46 00:05:26.350 ⇒ 00:05:28.999 Advait Nandakumar Menon: That’s in… okay, that’s in Snowflake as well, okay.
47 00:05:29.270 ⇒ 00:05:33.540 Amber Lin: Yeah, all of these are, like, if you see a metric here, it’s already modeled out.
48 00:05:34.070 ⇒ 00:05:38.790 Amber Lin: Okay, and these are just by partner segments.
49 00:05:38.910 ⇒ 00:05:41.350 Amber Lin: Also, also modeled out.
50 00:05:41.940 ⇒ 00:05:53.259 Amber Lin: So that’s this spreadsheet, and then another thing you’ll come across upon is revenue reconciliation with finance.
51 00:05:53.750 ⇒ 00:05:55.989 Amber Lin: So this is… Yeah.
52 00:05:55.990 ⇒ 00:06:13.160 Advait Nandakumar Menon: Yeah, before we get into this, so real quick, going back to the data platform documentation. So, I’ll be feeding cursor all this context, right, while coming up with the topics, or improving the existing topics you have built over there.
53 00:06:13.160 ⇒ 00:06:21.349 Advait Nandakumar Menon: So, then what do you recommend, like, feeding as context? Like, do I feed in the platform documentation or not? Because you said.
54 00:06:22.070 ⇒ 00:06:23.219 Advait Nandakumar Menon: up to date.
55 00:06:23.220 ⇒ 00:06:28.310 Amber Lin: You can still, the fields might not be, like, the naming of the…
56 00:06:28.680 ⇒ 00:06:32.919 Amber Lin: the naming here may have changed. I’ve already ran one
57 00:06:32.920 ⇒ 00:06:49.749 Amber Lin: like, I’ve already did it once to update this naming, so, like, I’m at 80 slash 90% sure that these are accurate, but there might be small things that… like, we recently changed line sales from line revenue to line sales, so that’s why.
58 00:06:49.910 ⇒ 00:07:09.690 Amber Lin: Okay. That’s there, but, like, there might be small things that need changing, but you can feed this, feed in the models for sure, and then, like, I don’t know if you’re building new ones or going off of old topics, if you want, still need these.
59 00:07:09.900 ⇒ 00:07:19.509 Amber Lin: Like, pre-aggregated measures, or you’re just gonna ask it to, like, just do the formulas on its own, so that’s up to you.
60 00:07:19.940 ⇒ 00:07:20.500 Amber Lin: Oh.
61 00:07:21.080 ⇒ 00:07:29.399 Amber Lin: Like, data is in here. I know you’ve seen the models before, so I might actually skip that.
62 00:07:29.520 ⇒ 00:07:33.389 Amber Lin: Do you want to talk about this? Okay, so this is…
63 00:07:33.530 ⇒ 00:07:47.660 Amber Lin: This is for finance. Let’s go to the data where it comes from, is essentially this report. So this is monthly by SKU and by price.
64 00:07:48.220 ⇒ 00:07:55.360 Amber Lin: Okay. The, why I say bi- I think… By price is…
65 00:07:55.590 ⇒ 00:07:59.569 Amber Lin: Of course, you have the SKU here, but…
66 00:07:59.570 ⇒ 00:08:00.170 Advait Nandakumar Menon: Huh.
67 00:08:00.170 ⇒ 00:08:08.160 Amber Lin: for… for their purposes, some SKUs are used in both B2C,
68 00:08:08.380 ⇒ 00:08:16.829 Amber Lin: just Shopify selling to consumers and wholesale. They’re kind of reused, but they’re at different price points.
69 00:08:16.970 ⇒ 00:08:25.340 Amber Lin: I think when we… in our data, which has the tagging, I think, either from us or from them.
70 00:08:25.340 ⇒ 00:08:39.170 Amber Lin: we tried our best to tag this, but occasionally it gets mistagged, so maybe, like, a… like a D2C price would be tagged wholesale, but they’re very minor, but that can happen.
71 00:08:39.390 ⇒ 00:08:48.480 Amber Lin: So, in this, like, raw data, the wage model, we have month start date, and then your SKU, unit price.
72 00:08:48.710 ⇒ 00:09:03.729 Amber Lin: And then, the channel, product category. So you’ll also have this in Omni. I believe this is already one of the topics in there, and this… I’ve also tried to replicate this report, so this should be in Omni.
73 00:09:03.840 ⇒ 00:09:05.499 Amber Lin: So we have…
74 00:09:05.790 ⇒ 00:09:17.370 Amber Lin: by channel, by product category. So, channel D2C, product category sparkling, these are the SKUs, this is the price, blah blah blah blah.
75 00:09:17.370 ⇒ 00:09:18.050 Advait Nandakumar Menon: Okay.
76 00:09:18.050 ⇒ 00:09:36.900 Amber Lin: Yeah, and sometimes, I think here’s another thing to note, when they add new products, they might not immediately be categorized, as in, like, giving it a channel, or giving it a product category. I think we also want to include
77 00:09:37.130 ⇒ 00:09:38.560 Amber Lin: like a…
78 00:09:39.800 ⇒ 00:09:53.059 Amber Lin: a field that’s uncategorized products. You should be able to see that in the data, so it’ll be a lot easier to do in Omni, because this is a fixed field, but Omni can be updated live, so…
79 00:09:53.790 ⇒ 00:09:57.859 Amber Lin: Like, this is kind of the… the view that they like to see.
80 00:09:58.310 ⇒ 00:10:03.900 Amber Lin: I can also… beer.
81 00:10:04.390 ⇒ 00:10:09.779 Amber Lin: This is something that… this is from finance, so let me share this with you.
82 00:10:11.700 ⇒ 00:10:15.750 Amber Lin: This is what I did for their revenue reconciliation.
83 00:10:16.840 ⇒ 00:10:28.930 Amber Lin: So this is the sheet that they have for December, and this is the sheet they have for January. You probably don’t need to go through this, but just… this is just to give you a sense of what fields they usually look
84 00:10:29.070 ⇒ 00:10:36.170 Amber Lin: You’re… you already have this. Yeah, they have slightly more fields than I do, because I don’t think we have
85 00:10:36.270 ⇒ 00:10:44.369 Amber Lin: Like, taxes yet, but, like, if they ever ask you about that, this is, like, what they’re… what they’re looking at, what they’re referring to.
86 00:10:46.290 ⇒ 00:10:47.030 Amber Lin: Cool.
87 00:10:47.170 ⇒ 00:10:55.690 Amber Lin: I can go answer more on the… on the questions they posted, but do you have any questions I can help answer right now?
88 00:10:56.630 ⇒ 00:11:00.459 Advait Nandakumar Menon: No, this is a good overview. The one thing I do want to understand is, like.
89 00:11:00.790 ⇒ 00:11:03.359 Advait Nandakumar Menon: So you said, pretty much for wholesale, like.
90 00:11:03.550 ⇒ 00:11:07.580 Advait Nandakumar Menon: Most of the stuff are modeled out, and it’s ready to use. So…
91 00:11:07.780 ⇒ 00:11:13.249 Advait Nandakumar Menon: Do you think, then, the topic creation or even building the dashboards will be much simpler with Wholesale?
92 00:11:15.490 ⇒ 00:11:22.009 Amber Lin: Here’s the thing, the model right now is only… I think we have… and let me pull up on…
93 00:11:23.130 ⇒ 00:11:31.589 Amber Lin: What are the questions we need to answer? I think the thing we’re debating is, should we model it out
94 00:11:32.210 ⇒ 00:11:33.020 Amber Lin: Blink.
95 00:11:33.410 ⇒ 00:11:35.510 Amber Lin: have pre-aggregated?
96 00:11:35.690 ⇒ 00:11:40.769 Amber Lin: measures, or should we let Omni calculate it? What do you think?
97 00:11:42.600 ⇒ 00:11:50.579 Advait Nandakumar Menon: I think… Since it’s already modeled out, pre-aggregated, I think we can go ahead with that, because
98 00:11:51.180 ⇒ 00:11:54.889 Advait Nandakumar Menon: If we are using the raw…
99 00:11:55.030 ⇒ 00:11:58.650 Advait Nandakumar Menon: data, and then asking Omni to calculate
100 00:11:58.780 ⇒ 00:12:03.970 Advait Nandakumar Menon: I mean, what I’m trying to say is, like, we have this deadline tomorrow that
101 00:12:04.230 ⇒ 00:12:08.180 Advait Nandakumar Menon: to create the topics, as well as deliver the dashboard, so…
102 00:12:09.160 ⇒ 00:12:12.609 Advait Nandakumar Menon: with respect to thinking about time, I think…
103 00:12:13.180 ⇒ 00:12:16.899 Advait Nandakumar Menon: Whichever’s the easiest would make sense at this point.
104 00:12:17.130 ⇒ 00:12:36.800 Amber Lin: I see, okay. I think the easiest way is to create a daily view of this. Should be very easy, just to copy and paste at the models. I actually think I might have a PR in there, but, like, I don’t think we approved it or we moved forward with it. But if we have
105 00:12:37.640 ⇒ 00:12:43.669 Amber Lin: batch… You’ll be able to answer, like, latest, what’s the…
106 00:12:43.860 ⇒ 00:12:55.400 Amber Lin: data for the latest day, or last three days, you might need to change some, like, add some AI context for, oh, for this measure, don’t do sum, do averages, but, like.
107 00:12:55.790 ⇒ 00:13:05.740 Amber Lin: And you’re already familiar with that, so, like, we have monthly, weekly, we have, like, wholesale customers,
108 00:13:06.210 ⇒ 00:13:11.100 Amber Lin: I guess, like, the question I’ll ask you is, should we
109 00:13:11.740 ⇒ 00:13:27.250 Amber Lin: do… base it off of fact orders and order items, or should we use, like, these pre-aggregated models? Because we’re also able to answer, hey, what’s this… what’s the revenue for drink mites based on
110 00:13:27.250 ⇒ 00:13:33.000 Amber Lin: Based on this, because there is, like, a drink mix field, and there is an order total field.
111 00:13:34.830 ⇒ 00:13:35.420 Advait Nandakumar Menon: Yeah.
112 00:13:35.780 ⇒ 00:13:42.380 Amber Lin: So that’s up to you, depending, like, how strict or how predetermined we want the measures to be.
113 00:13:43.020 ⇒ 00:13:44.700 Advait Nandakumar Menon: Okay, okay, got it.
114 00:13:45.110 ⇒ 00:13:45.640 Amber Lin: Yeah.
115 00:13:45.840 ⇒ 00:13:51.320 Amber Lin: Cool. We’re at time. I think I’ll go answer questions in the doc.
116 00:13:51.590 ⇒ 00:13:56.910 Amber Lin: But otherwise, like, sh… Send me emails, send me stack messages.
117 00:13:57.560 ⇒ 00:14:02.960 Advait Nandakumar Menon: Yeah, sounds good. This was helpful, so… I’ll get started, and if anything, I will…
118 00:14:03.070 ⇒ 00:14:04.429 Advait Nandakumar Menon: Yeah, I’ll let you know.
119 00:14:04.750 ⇒ 00:14:07.220 Amber Lin: Cool. Awesome. Alright. Bye-bye.
120 00:14:07.220 ⇒ 00:14:07.890 Advait Nandakumar Menon: Yep, bye-bye.