Meeting Title: LMNT QA Sync Date: 2026-04-21 Meeting participants: Robert Tseng, Advait Nandakumar Menon, Shivani Amar, Jasmin Multani
WEBVTT
1 00:01:48.490 ⇒ 00:01:49.290 Shivani Amar: -
2 00:01:50.850 ⇒ 00:01:52.120 Shivani Amar: It’s interesting.
3 00:01:52.980 ⇒ 00:01:53.660 Robert Tseng: But…
4 00:01:54.070 ⇒ 00:01:54.690 Shivani Amar: like…
5 00:01:55.360 ⇒ 00:02:10.540 Shivani Amar: I was… this is my classic, like, this is the question I keep asking over and over. Show me the point-of-sale data for the last few months for Target at Walmart, split by product type. And, like, this… I’m gonna keep asking this question, like, see what it does. This is, like, the question, and then it says.
6 00:02:10.840 ⇒ 00:02:13.910 Shivani Amar: Oh, there’s now this, like, unknown bucket.
7 00:02:14.710 ⇒ 00:02:17.139 Shivani Amar: I’m like, well, what… what is that?
8 00:02:17.140 ⇒ 00:02:18.270 Jasmin Multani: That…
9 00:02:18.760 ⇒ 00:02:26.670 Shivani Amar: And then I was like, maybe that’s a sparkling 4 counts. Like, maybe it’s, like, somehow there. But no, this is… drink mix.
10 00:02:27.290 ⇒ 00:02:35.730 Shivani Amar: So I don’t know… Like… we were selling these things before, so I don’t know…
11 00:02:37.590 ⇒ 00:02:45.490 Shivani Amar: if a product name got changed, if a new SKU was added, like, I don’t really know what happened, but it’s so interesting that it’s, like.
12 00:02:45.930 ⇒ 00:02:49.600 Shivani Amar: Rather than adding this to drink mix…
13 00:02:49.910 ⇒ 00:02:52.700 Shivani Amar: Bar. It’s, like, pulling it out.
14 00:02:55.990 ⇒ 00:02:58.230 Shivani Amar: So that’s a fun investigation.
15 00:02:59.120 ⇒ 00:03:10.169 Jasmin Multani: Yeah, we have that scoped for ADVIT in the Retail Executive Pulse Check. Cool. So it is seen, but we’re…
16 00:03:10.170 ⇒ 00:03:18.600 Shivani Amar: No, this one is new from the drink… from the sparkling beverage thing, but of it, thanks… thanks for… this is just… I just did this 2 minutes ago, I was like, is… what’s it?
17 00:03:18.820 ⇒ 00:03:22.699 Shivani Amar: I’m just gonna keep querying this every day until I feel like it makes sense to me.
18 00:03:23.340 ⇒ 00:03:27.720 Jasmin Multani: Yeah, yeah, there’s… there are a lot of…
19 00:03:27.950 ⇒ 00:03:39.070 Jasmin Multani: loose ends that we’re trying to backtrack, and we’re trying to be like, okay, was this known 2 months ago, or was it… is it… has there been some sort of change in the back end?
20 00:03:39.070 ⇒ 00:03:40.720 Shivani Amar: Yeah, and, like, the data model.
21 00:03:40.720 ⇒ 00:03:46.749 Jasmin Multani: Yeah, since then, and where in the… in the…
22 00:03:46.880 ⇒ 00:03:52.449 Jasmin Multani: in the chain of commands did this issue happen? So that’s what,
23 00:03:52.820 ⇒ 00:04:08.800 Jasmin Multani: where so much of our time goes into. It’s like investigating where in the chain of commands. Okay, if it’s within our scope, we can change it. But if it’s something that Emerson is providing us, then, then we have to be really heavy-handed with
24 00:04:08.980 ⇒ 00:04:26.260 Jasmin Multani: contacting them, and I’m gonna lean in more there, just because… like, I mean, I fully trust Oish and Utam to drive those conversations, but even as I’m looking through the data, I’m like, okay.
25 00:04:26.420 ⇒ 00:04:37.710 Jasmin Multani: how do I root cause this? So I’m just gonna lean in as a fly on the wall to understand how things are unfolding, so that when we come to you about the dashboards and we see these
26 00:04:38.280 ⇒ 00:04:44.729 Jasmin Multani: edge cases, we’re like, okay, yes, this is a known problem, we’re working on it, or no, this hasn’t been flagged yet, we need to…
27 00:04:44.730 ⇒ 00:04:45.130 Shivani Amar: Yeah.
28 00:04:45.130 ⇒ 00:04:45.890 Jasmin Multani: Triage.
29 00:04:46.060 ⇒ 00:04:46.890 Shivani Amar: Cool.
30 00:04:48.230 ⇒ 00:04:57.100 Jasmin Multani: Cool. Thanks for attending also, Robert. But I can kick off this meeting… And just, like.
31 00:04:58.060 ⇒ 00:05:01.940 Jasmin Multani: do a soft check-in. Is there anything else, Shivani, you wanted to discuss?
32 00:05:03.260 ⇒ 00:05:05.710 Shivani Amar: Oh, sorry, so I,
33 00:05:06.060 ⇒ 00:05:19.160 Shivani Amar: for this meeting, a couple things. For this meeting today, I was like, sh… was I supposed to review something fresh from last week? Okay, because I, like, I click into the Shivani review, and it seems like it’s the same ones, and then…
34 00:05:19.350 ⇒ 00:05:25.179 Shivani Amar: I think, unless you move them out. Like, once I give the round of feedback, do you move them out?
35 00:05:26.320 ⇒ 00:05:28.950 Jasmin Multani: when I cut the tickets, I move them out.
36 00:05:28.950 ⇒ 00:05:29.710 Shivani Amar: Okay, gotcha.
37 00:05:29.710 ⇒ 00:05:36.399 Jasmin Multani: And then, so as of now, like, as of, like, 15 minutes ago,
38 00:05:38.430 ⇒ 00:05:50.499 Jasmin Multani: So let me say this again. Last night, Edvid was able to push out a dashboard. This is the POS velocity. This morning, he and I discussed, what are the open-ended questions we want to ask you, and…
39 00:05:50.630 ⇒ 00:05:55.770 Jasmin Multani: I thought, like, let me ask these live before I need to review, officially.
40 00:05:55.770 ⇒ 00:06:01.470 Shivani Amar: Perfect. Okay, so is this the… will we go through more than this one today, or just this one?
41 00:06:01.690 ⇒ 00:06:17.680 Jasmin Multani: Given the Slack escalations, were… I reprioritized, for Advivith to, only focus on this, completing two more dashboards by end of week for retail, and,
42 00:06:17.810 ⇒ 00:06:23.120 Jasmin Multani: Make the changes necessary for those blobby questions that you asked.
43 00:06:23.120 ⇒ 00:06:24.050 Shivani Amar: Gotcha.
44 00:06:24.050 ⇒ 00:06:27.419 Jasmin Multani: So that’s how priority’s gonna look like.
45 00:06:27.860 ⇒ 00:06:35.649 Jasmin Multani: And that’s also going to be here, right? So, as of right now, the POS velocity is ready to go, by geography.
46 00:06:35.770 ⇒ 00:06:43.499 Jasmin Multani: Tonight, I think by Thursday, ideally, Adviv will have already touched base with…
47 00:06:44.380 ⇒ 00:06:48.420 Jasmin Multani: Retail and executive, and then we can go from there.
48 00:06:51.780 ⇒ 00:06:59.740 Jasmin Multani: We need to know… wait one second… Oh, sorry. Let me actually… Update this.
49 00:07:00.470 ⇒ 00:07:06.380 Advait Nandakumar Menon: I’m sorry to derail you guys, but I was just checking in Snowflake as well, Shivani, about the unknown.
50 00:07:06.740 ⇒ 00:07:10.049 Advait Nandakumar Menon: Category, and it looks like it started coming…
51 00:07:10.390 ⇒ 00:07:12.949 Advait Nandakumar Menon: like, the business date is 10th April.
52 00:07:13.190 ⇒ 00:07:16.790 Advait Nandakumar Menon: This started coming in since then, April, for some reason.
53 00:07:17.230 ⇒ 00:07:20.980 Advait Nandakumar Menon: And it’s just Walmart. This is happening under…
54 00:07:24.090 ⇒ 00:07:29.009 Shivani Amar: Like, it’s, like, brand new SKUs, or is it, like, suddenly the mapping just stopped?
55 00:07:30.310 ⇒ 00:07:33.759 Advait Nandakumar Menon: That’s, we have to check that, but…
56 00:07:34.840 ⇒ 00:07:42.510 Advait Nandakumar Menon: the whole new mapping of category, like, unknown category is coming in since then tables, what I’m seeing here. I just…
57 00:07:42.660 ⇒ 00:07:47.670 Advait Nandakumar Menon: Ran a simple query right now to see why this is coming, or since when this is coming, so…
58 00:07:48.310 ⇒ 00:07:49.190 Advait Nandakumar Menon: Yeah.
59 00:07:52.800 ⇒ 00:07:59.209 Jasmin Multani: So, Shivani, how often, do new schemes get launched?
60 00:07:59.330 ⇒ 00:08:01.980 Jasmin Multani: At a… like, is it once per quarter?
61 00:08:03.270 ⇒ 00:08:10.140 Shivani Amar: No, it’s probably more than that, because, like, a new SKU isn’t necessarily just a new flavor.
62 00:08:10.800 ⇒ 00:08:15.549 Shivani Amar: a new SKU can also be, like, a 3-count of something, or, like, like, last…
63 00:08:16.030 ⇒ 00:08:21.270 Shivani Amar: quarter, I remember we were, like, discontinuing 30 counts at Amazon to…
64 00:08:21.730 ⇒ 00:08:37.410 Shivani Amar: We were discontinuing, like, 12 counts and introducing 18 counts, so it’s, like, the count of the thing might change, and we’re, like, phasing out one thing. So, just because we launch a new flavor doesn’t mean that that’s the only time that we change the SKUs.
65 00:08:38.250 ⇒ 00:08:46.420 Jasmin Multani: Okay, that’s good to know. And I wanted to ask, as a follow-up, is someone…
66 00:08:47.320 ⇒ 00:08:52.500 Jasmin Multani: tracking? Is someone in Element creating that master SKU list, or…
67 00:08:52.670 ⇒ 00:09:08.519 Shivani Amar: So, I think if you wanted to, like, make the template for it, Dan from Element could probably own it, but, like… or he might even have something, like, in his financial document, but I think if we make, like, a… this is the template that we kind of recommend,
68 00:09:09.020 ⇒ 00:09:15.040 Shivani Amar: like, how would… what would be the best process? That could be, like, a follow-up with him.
69 00:09:15.420 ⇒ 00:09:17.809 Jasmin Multani: Okay, okay. Yeah.
70 00:09:17.810 ⇒ 00:09:25.920 Shivani Amar: could be like, date introduced, date discontinued, if we did discontinue it. Like, it’s tricky because it’s like, let’s say a SKU…
71 00:09:26.240 ⇒ 00:09:32.439 Shivani Amar: was sold on Amazon and Target, and it was only discontinued on Amazon.
72 00:09:32.680 ⇒ 00:09:33.350 Jasmin Multani: Like.
73 00:09:33.350 ⇒ 00:09:40.900 Shivani Amar: Then would a SKU have… Start and stop columns for each potential seller, which is a long…
74 00:09:41.160 ⇒ 00:09:56.850 Shivani Amar: It’s like, Amazon start and stop NA, NA, if it was never at Amazon, and then if it was like, oh, the SKU is sold at Target, okay, when did we start selling it at Target? When did we discontinue it at Target? That might have to be… the set of columns might be start dates for…
75 00:09:57.440 ⇒ 00:10:02.060 Shivani Amar: each… potential… channel. Retailer.
76 00:10:02.430 ⇒ 00:10:09.809 Jasmin Multani: Yeah, we could… we could have something static like that, or…
77 00:10:10.130 ⇒ 00:10:12.920 Jasmin Multani: There’s another way where we just, like, pull
78 00:10:13.150 ⇒ 00:10:16.449 Jasmin Multani: Every week, and then add on to it and see, like, okay.
79 00:10:16.450 ⇒ 00:10:17.380 Shivani Amar: Snapshots.
80 00:10:17.380 ⇒ 00:10:22.190 Jasmin Multani: Yes, and be like, okay, at what point did this information change?
81 00:10:22.190 ⇒ 00:10:22.550 Shivani Amar: Yeah.
82 00:10:22.870 ⇒ 00:10:26.460 Jasmin Multani: So, different ways to do it.
83 00:10:27.270 ⇒ 00:10:33.349 Jasmin Multani: I’ll also ask Awayish on how we would want to create it.
84 00:10:33.350 ⇒ 00:10:34.080 Shivani Amar: Sounds good.
85 00:10:34.080 ⇒ 00:10:41.899 Jasmin Multani: Ideally, a snapshot… a rolling snapshot, table would be best case, in my opinion.
86 00:10:42.000 ⇒ 00:10:45.139 Jasmin Multani: So that we’re also holding ourselves accountable, that, like, okay.
87 00:10:45.450 ⇒ 00:10:49.719 Jasmin Multani: When was the last time it really was refreshed, this data table?
88 00:10:50.140 ⇒ 00:10:55.470 Jasmin Multani: So we can go from there. I’ll ask Dan and ask… sync with.
89 00:10:55.650 ⇒ 00:10:58.380 Jasmin Multani: Same with a wage.
90 00:10:58.850 ⇒ 00:11:03.629 Jasmin Multani: Am I allowed to set up meetings with Dan, or should I just.
91 00:11:03.630 ⇒ 00:11:19.429 Shivani Amar: No, so if you want, like, you should create a template, we should go through it, when we feel ready, we will set up time with Dan. And then it’s also, like, next week is rest and assess, so I declined one of our check-ins for next week. Okay. I’ll do one of them.
92 00:11:19.430 ⇒ 00:11:26.890 Shivani Amar: with you, but just, like, I want to make sure you have that in your calendar, too, just so that we, like, have that going.
93 00:11:27.400 ⇒ 00:11:30.700 Shivani Amar: And… yeah, so I think that’s the best path.
94 00:11:31.240 ⇒ 00:11:34.559 Jasmin Multani: Okay, so I’ll write JASMI template.
95 00:11:34.960 ⇒ 00:11:36.050 Jasmin Multani: end up doing.
96 00:11:36.660 ⇒ 00:11:43.319 Jasmin Multani: Before we look into the retail question, I also wanted to ask, is there something similar we can create
97 00:11:43.480 ⇒ 00:11:45.200 Jasmin Multani: for Master Store List.
98 00:11:48.560 ⇒ 00:11:53.839 Shivani Amar: Like… Which stores we sell in, and then what, like, what else would it have?
99 00:11:54.990 ⇒ 00:12:02.380 Jasmin Multani: Just, just… Expectations, like, Do we have a relationship with the store?
100 00:12:02.690 ⇒ 00:12:08.089 Jasmin Multani: Maybe this will also come out of the supply chain dashboards, but,
101 00:12:11.080 ⇒ 00:12:21.300 Jasmin Multani: did we send any, inventory? When was the last time we sent inventory? Those are the two main things I want to ask. If we don’t have that, no worries, we can build it.
102 00:12:22.060 ⇒ 00:12:34.199 Shivani Amar: Wait, sorry, I’m not… I’m not sure I’m totally understanding the… Can you zoom out again? So, yes, I understand the SKU list, and then the store list, I’m not understanding.
103 00:12:34.800 ⇒ 00:12:37.340 Shivani Amar: Is it, like, are you saying, like, Target, Walmart?
104 00:12:37.740 ⇒ 00:12:39.010 Shivani Amar: I’m not…
105 00:12:39.390 ⇒ 00:12:43.619 Jasmin Multani: Like, at the… maybe at the, like, the actual address level.
106 00:12:49.070 ⇒ 00:12:51.699 Robert Tseng: You saying, like, point of sale, like…
107 00:12:53.820 ⇒ 00:12:54.940 Jasmin Multani: Yes.
108 00:12:54.940 ⇒ 00:12:59.359 Robert Tseng: Because this is that region, right? And then, like, there’s stores within regions. You want, at the store level.
109 00:12:59.680 ⇒ 00:13:00.630 Jasmin Multani: Yes, I do.
110 00:13:00.630 ⇒ 00:13:01.920 Robert Tseng: Point of Sale store.
111 00:13:02.140 ⇒ 00:13:02.950 Jasmin Multani: Yes.
112 00:13:03.410 ⇒ 00:13:05.199 Shivani Amar: Okay, but that’s,
113 00:13:06.470 ⇒ 00:13:26.179 Shivani Amar: that’s, like, if we have access to that data, great, but sometimes we send product, probably, to their distribution centers, like, target distribution center, so I don’t know if we know… like, maybe we know where the point of sale happens, and which… whatever. Zip code data is, like, something that I raised with Emerson, saying, hey, we don’t seem to have it.
114 00:13:26.890 ⇒ 00:13:30.730 Shivani Amar: And Robert, like, in the future, when we go through muffin data, instead of…
115 00:13:31.730 ⇒ 00:13:37.000 Shivani Amar: Emerson or something to, like, get this. I don’t know if it’s gonna be better. I don’t know, right? So…
116 00:13:37.000 ⇒ 00:13:37.579 Robert Tseng: Yeah, we don’t know.
117 00:13:37.580 ⇒ 00:13:52.079 Shivani Amar: Like, Russell on retail was telling me that he just logs into Target’s, like, database thing, and, like, downloads data. That’s how he’s able to get zip code data. He’s like, that might not even be what we get from an Emerson perspective. But, like, I just go into the source.
118 00:13:52.260 ⇒ 00:13:58.440 Robert Tseng: Yeah, but even a job like that, we could set up a recurring, to pull from that. We could write a script to do it.
119 00:13:58.440 ⇒ 00:13:58.960 Shivani Amar: Yeah.
120 00:13:58.960 ⇒ 00:14:02.339 Robert Tseng: Yeah, so… if that ends up being more reliable, we will do that.
121 00:14:02.340 ⇒ 00:14:07.760 Shivani Amar: If that’s, like, the most comprehensive dataset, that’s what we would want being pulled in, right? Yeah. Yeah.
122 00:14:09.020 ⇒ 00:14:13.110 Jasmin Multani: Okay, Robert, I’ll ask you… I’ll follow up with you on that.
123 00:14:29.210 ⇒ 00:14:31.140 Jasmin Multani: Okay, cool.
124 00:14:31.730 ⇒ 00:14:34.330 Jasmin Multani: We have 15 minutes. We can…
125 00:14:35.250 ⇒ 00:14:41.210 Jasmin Multani: Let’s shift gears and review this this dashboard together.
126 00:14:41.550 ⇒ 00:14:44.829 Jasmin Multani: Do you have… do you have this link? I’ll send this link.
127 00:14:44.830 ⇒ 00:14:47.000 Shivani Amar: Yeah, yeah, yeah, I’m good.
128 00:14:47.000 ⇒ 00:14:57.260 Jasmin Multani: Okay, okay. So yeah, so the changes made, since the last check-in has been, partly functional, partly cosmetic.
129 00:14:58.040 ⇒ 00:15:05.030 Jasmin Multani: So, in terms of cosmetic, we’ve enriched, information as to, like, what does it mean to have sales.
130 00:15:05.240 ⇒ 00:15:15.140 Jasmin Multani: Because I knew that… because Evith and I saw that there were varying, definitions of sales, so Abbeth went ahead and, ensured what does it mean by sales.
131 00:15:15.330 ⇒ 00:15:18.689 Jasmin Multani: Some other cosmetic changes we made was…
132 00:15:19.310 ⇒ 00:15:24.090 Jasmin Multani: Capitalization… consistent capitalizations, at the region and state level.
133 00:15:24.230 ⇒ 00:15:26.450 Jasmin Multani: And even at the retail level.
134 00:15:26.620 ⇒ 00:15:31.260 Jasmin Multani: But one caveat is that right now.
135 00:15:31.610 ⇒ 00:15:38.649 Jasmin Multani: given the, limits to the Emerson data, you cannot.
136 00:15:38.980 ⇒ 00:15:40.430 Shivani Amar: Drill down to the zip code.
137 00:15:40.430 ⇒ 00:15:41.520 Jasmin Multani: Yeah, that’s…
138 00:15:41.520 ⇒ 00:15:51.540 Shivani Amar: Like, I think it’s a good starting off point. This is, like, to me, like, this one’s fine. It’s… I’m not necessarily gonna QA it at the regional level, because I don’t…
139 00:15:51.750 ⇒ 00:15:54.819 Shivani Amar: I’m not gonna, like, divide up the states, but, you know, like, I…
140 00:15:55.160 ⇒ 00:16:02.660 Shivani Amar: I’m not gonna QA this one super hardcore, so I’m like, it’s fine, I think. But…
141 00:16:03.350 ⇒ 00:16:08.229 Shivani Amar: Although, yes, I see you pointing out the unknown, and I’m like, I don’t know what that would be.
142 00:16:08.230 ⇒ 00:16:12.990 Jasmin Multani: Yeah, yeah. So, like, this is pretty,
143 00:16:13.550 ⇒ 00:16:23.899 Jasmin Multani: pretty blind spot for us, but I’ve written… in the overview, we’re also going to be, enriching what the caveats and limits of the data are.
144 00:16:23.900 ⇒ 00:16:24.580 Shivani Amar: Yup.
145 00:16:25.020 ⇒ 00:16:30.489 Jasmin Multani: The date, and also there’s metadata to be like, when was this dashboard last updated, and by who?
146 00:16:30.640 ⇒ 00:16:31.500 Shivani Amar: Okay, cool.
147 00:16:31.500 ⇒ 00:16:33.440 Jasmin Multani: Information is just, like, automatic.
148 00:16:33.560 ⇒ 00:16:34.940 Shivani Amar: Yeah, that’s fine.
149 00:16:35.180 ⇒ 00:16:43.159 Shivani Amar: Yeah, I think this is one that I’m, like… like, you see what I’m drawn to. I’m, like, constantly, like, I want to see point of sales by product type by store.
150 00:16:43.300 ⇒ 00:16:46.090 Shivani Amar: Like, that’s what I’m getting.
151 00:16:46.090 ⇒ 00:16:48.990 Jasmin Multani: Yeah, and we can just…
152 00:16:49.170 ⇒ 00:16:55.099 Jasmin Multani: And this is the level of category product type you want, right? It’s not so much going down to the SKU level.
153 00:17:02.990 ⇒ 00:17:13.669 Shivani Amar: Yeah, so it’s like, some… but I might, like, when I talk to Blobby, I might be like, which SKUs in particular, right? But, like, I think that’ll… hopefully… if… I’m saying I think this…
154 00:17:14.040 ⇒ 00:17:21.400 Shivani Amar: It’s fine, like, this overall dashboard. I’m saying when I think about the exact, like, zoom out on the retail area, you can see what I’m more interested in.
155 00:17:21.400 ⇒ 00:17:21.730 Jasmin Multani: Yeah.
156 00:17:21.730 ⇒ 00:17:27.839 Shivani Amar: Which is being able to see by retailer, like, point of sales, by product type, type of thing.
157 00:17:28.010 ⇒ 00:17:39.200 Shivani Amar: So I feel like we will get to that and figure out why we don’t have sparkling flowing into our data, which feels like an issue.
158 00:17:40.430 ⇒ 00:17:41.820 Shivani Amar: And then…
159 00:17:44.140 ⇒ 00:17:49.320 Shivani Amar: Can you go to the wholesale… one of the wholesale dashboards? I need to look up the names of them again.
160 00:17:50.190 ⇒ 00:17:50.980 Shivani Amar: Goodbye.
161 00:17:58.510 ⇒ 00:18:02.609 Shivani Amar: I know these are, like, in… maybe wholesale partner order patterns?
162 00:18:02.840 ⇒ 00:18:03.180 Jasmin Multani: Yeah.
163 00:18:07.040 ⇒ 00:18:10.020 Shivani Amar: Okay, so if you scroll down.
164 00:18:11.480 ⇒ 00:18:16.009 Shivani Amar: We’ve already talked about, I don’t need to repeat any feedback, because I’m sure you’ve ticketed it.
165 00:18:16.370 ⇒ 00:18:17.650 Shivani Amar: Keep going down.
166 00:18:17.770 ⇒ 00:18:19.200 Shivani Amar: So you’ve got…
167 00:18:19.770 ⇒ 00:18:25.470 Shivani Amar: I don’t know what this graph… I don’t know what that graph is, it’s fine, we can keep going. I have, like, partners.
168 00:18:25.800 ⇒ 00:18:27.860 Shivani Amar: is to Second Order Band.
169 00:18:31.840 ⇒ 00:18:43.370 Shivani Amar: I don’t know how useful this is, but okay, carry on. What I was noticing is that there’s a lot of stuff around best customers, like, basket value and total orders, basket composition, like…
170 00:18:43.730 ⇒ 00:18:55.960 Shivani Amar: top-ordered SKUs, and I’m like, okay, maybe that’s helpful, but, like, we also established something called at-risk, right? Which is, like, who are the at-risk wholesale partners that maybe we need to give some TLC to?
171 00:18:56.270 ⇒ 00:18:57.469 Jasmin Multani: So…
172 00:18:57.470 ⇒ 00:19:00.640 Shivani Amar: I don’t… I think, rather than just being like, add.
173 00:19:00.760 ⇒ 00:19:09.019 Shivani Amar: your top at-risk customers, I’m almost like, what could we replace on this dashboard? Because I haven’t internalized,
174 00:19:11.480 ⇒ 00:19:12.050 Shivani Amar: yellow.
175 00:19:12.050 ⇒ 00:19:20.100 Jasmin Multani: Yes, I think Amber and I worked on the at-risk and churn definitions a while back.
176 00:19:20.100 ⇒ 00:19:25.280 Shivani Amar: Yeah, and they exist, like, I see it in the data model, but it’s just like… or, like, I see it…
177 00:19:25.580 ⇒ 00:19:40.999 Shivani Amar: like, I can ask Blobby to do it, and like, show me the at-risk, and it’s like, yeah, you have this, like, defined in your model as this. So I’m like, I know it exists, but which wholesale partners are at risk of churning, right? Like, which partners are growing the fastest?
178 00:19:46.350 ⇒ 00:19:55.050 Shivani Amar: the typical drop-offs between first and second, third orders placed. So you have days, can you go back to what you have, which is, like, days between?
179 00:20:01.530 ⇒ 00:20:04.159 Shivani Amar: Partner by Dason’s Last Order band.
180 00:20:06.460 ⇒ 00:20:08.249 Shivani Amar: What does this tell you?
181 00:20:09.800 ⇒ 00:20:20.299 Jasmin Multani: This would tell me… How long… just, like, how long… how many stores have,
182 00:20:20.750 ⇒ 00:20:24.589 Jasmin Multani: placed an order for Element in the past 30 days. So…
183 00:20:25.060 ⇒ 00:20:39.339 Jasmin Multani: be good… so these would be good, not at risk, and these are our high power users. The people over here in null, it’s like, okay, they placed an order once, and they just never retained for the second order.
184 00:20:39.560 ⇒ 00:20:42.970 Shivani Amar: Is there a better visual that could, like, tell us about this funnel?
185 00:20:42.970 ⇒ 00:20:43.440 Jasmin Multani: Yes.
186 00:20:43.440 ⇒ 00:20:48.569 Shivani Amar: Probably. Yes. Right? So… so then… I’m almost like…
187 00:20:50.590 ⇒ 00:20:57.620 Shivani Amar: Yeah. And then, like, both of these visuals, I’m just kind of like, I don’t totally get what I would glean from this.
188 00:20:58.030 ⇒ 00:20:58.860 Jasmin Multani: Okay, okay.
189 00:20:59.250 ⇒ 00:21:05.760 Robert Tseng: Can you flashback to it, Jasmine, so I could just screenshot real quick? I want to make a comment in our thread. Yeah, thanks. Alright, I’m good.
190 00:21:08.440 ⇒ 00:21:11.760 Jasmin Multani: Okay, so wholesale, etc.
191 00:21:11.760 ⇒ 00:21:30.700 Shivani Amar: I can try to mock something up, so, like, if you’re ever, like, hey, I’m feeling like you want funnel data, and I’m, like, co… like, what, like, let’s brainstorm it, then, like, I can… I can actually just, like, make fake data in Excel and try to come up with a graph that makes sense to me, or, like, a visual, so don’t feel shy about, like.
192 00:21:30.700 ⇒ 00:21:31.110 Jasmin Multani: Muhammad.
193 00:21:31.110 ⇒ 00:21:35.980 Shivani Amar: pushing me on trying to, like, tell you the visual I want, because I can probably get there.
194 00:21:40.220 ⇒ 00:21:42.599 Robert Tseng: never mind, I guess Shivani will tell you.
195 00:21:42.910 ⇒ 00:21:46.360 Shivani Amar: I know, I’m like, like, Robert, if you have an idea, like, if you have something.
196 00:21:46.360 ⇒ 00:21:47.040 Robert Tseng: casting, like.
197 00:21:47.040 ⇒ 00:21:47.470 Shivani Amar: mature.
198 00:21:47.470 ⇒ 00:21:49.750 Robert Tseng: Retention curve,
199 00:21:50.200 ⇒ 00:21:56.379 Robert Tseng: Of, like, and then bucketed by, kind of, or anyway, we can… there’s different… different things that we can brainstorm, yeah.
200 00:21:56.380 ⇒ 00:21:57.350 Shivani Amar: Okay.
201 00:21:57.350 ⇒ 00:22:00.500 Robert Tseng: I would approach it the same way. I would just use synthetic data and then try to just, like…
202 00:22:00.500 ⇒ 00:22:05.620 Shivani Amar: I’d be like, what is the… what is the visual I’m trying to get to? What am I trying to learn here?
203 00:22:05.740 ⇒ 00:22:08.990 Shivani Amar: Cool.
204 00:22:08.990 ⇒ 00:22:11.949 Jasmin Multani: Okay, and I think I’ll make it explicit.
205 00:22:12.420 ⇒ 00:22:19.330 Jasmin Multani: Like, explicit labels… levels of, at risk.
206 00:22:20.000 ⇒ 00:22:21.260 Jasmin Multani: Versus check.
207 00:22:21.990 ⇒ 00:22:27.710 Jasmin Multani: Plus review… the data.
208 00:22:31.990 ⇒ 00:22:34.530 Jasmin Multani: Okay, I think this should be tabular, right?
209 00:22:36.020 ⇒ 00:22:38.850 Jasmin Multani: Tabular and, incorrect.
210 00:22:58.870 ⇒ 00:23:01.900 Jasmin Multani: I’ll go back to wholesale.
211 00:23:02.500 ⇒ 00:23:08.809 Jasmin Multani: this will be redone. I’ll also provide, like, a table that describes, which…
212 00:23:09.710 ⇒ 00:23:13.820 Jasmin Multani: Which folks are at risk, churn, and the underlying.
213 00:23:13.820 ⇒ 00:23:18.820 Shivani Amar: Active, at risk from new, like, it’s like, new, total active.
214 00:23:18.990 ⇒ 00:23:28.409 Shivani Amar: at risk, churned. Like, that… just four rows tabular is, like, fine for me to get a feel for the shape of the thing, and then eventually we might want to…
215 00:23:33.540 ⇒ 00:23:39.659 Jasmin Multani: And this is just gonna be supplemental to, whatever retention or funnel curve that we make.
216 00:23:39.810 ⇒ 00:23:50.930 Jasmin Multani: It basically would be like, hey, you know, certain, certain partners will make multiple, multiple orders at small, basket ranges.
217 00:23:50.930 ⇒ 00:23:51.990 Shivani Amar: I see.
218 00:23:51.990 ⇒ 00:23:53.330 Jasmin Multani: or.
219 00:23:53.330 ⇒ 00:23:56.350 Shivani Amar: Or they’re, like, massive and… And…
220 00:23:56.350 ⇒ 00:23:56.940 Jasmin Multani: Wow.
221 00:23:57.590 ⇒ 00:23:58.540 Jasmin Multani: And yeah.
222 00:23:58.670 ⇒ 00:24:12.510 Jasmin Multani: And then, I think it’d be helpful to be like, okay, what was their first… like, eventually, down the line, it’d be nice to be like, what was their first order? When was their first order? When was their last order? To be like.
223 00:24:12.690 ⇒ 00:24:14.740 Jasmin Multani: Does this mean 3 in the past?
224 00:24:15.930 ⇒ 00:24:19.340 Jasmin Multani: year of their contract, or was it just 3?
225 00:24:19.440 ⇒ 00:24:20.799 Jasmin Multani: And it was recent.
226 00:24:20.910 ⇒ 00:24:27.870 Jasmin Multani: But I think, this is gonna… this type of, contrasting metrics is going to set
227 00:24:28.270 ⇒ 00:24:36.660 Jasmin Multani: The supply chain up, down the line, as they figure out which partners that they need to work more closely on for allocation.
228 00:24:36.660 ⇒ 00:24:49.450 Shivani Amar: you’re saying, like, you might even want the same eventually with retailers, where, like, Target is ordering, you know, every 90 days on average, or whatever, like, the shape of how some of these retailers are ordering.
229 00:24:49.450 ⇒ 00:24:58.489 Shivani Amar: I think is, like, a good thing to pin for when you make the dashboard around purchase orders. Right now, we’re just focusing on point of sales, but for the retailers, I am curious, like.
230 00:24:58.630 ⇒ 00:25:08.520 Shivani Amar: Does… is it… how lumpy is it? And even if it was, like, a timeline of orders placed for Target versus Walmart, and you.
231 00:25:08.520 ⇒ 00:25:08.980 Jasmin Multani: You can see when.
232 00:25:08.980 ⇒ 00:25:11.110 Shivani Amar: Did they place their orders and how big the orders were?
233 00:25:11.110 ⇒ 00:25:11.860 Jasmin Multani: That could be…
234 00:25:11.860 ⇒ 00:25:12.939 Shivani Amar: helpful to see, like.
235 00:25:12.940 ⇒ 00:25:13.960 Jasmin Multani: Just…
236 00:25:13.960 ⇒ 00:25:21.610 Shivani Amar: the line, like, oh man, like, Target is kind of consistent, or Walmart is, like, really choppy. That could be a nice visual for that one.
237 00:25:21.610 ⇒ 00:25:25.120 Jasmin Multani: Even for, like,
238 00:25:25.120 ⇒ 00:25:42.000 Jasmin Multani: people who are working within Element and managing those relationships, it can be like, hey, we know that this experiment worked for Walmart, here’s a clear increase, here’s also, like, a clear increase in their sentiments. Let’s use this as an upper benchmark and recreate this for target…
239 00:25:42.310 ⇒ 00:25:44.239 Jasmin Multani: Vitamin shop, so on and so forth.
240 00:25:45.200 ⇒ 00:25:46.140 Shivani Amar: Cool.
241 00:25:46.140 ⇒ 00:25:48.809 Jasmin Multani: Okay, cool, cool, cool.
242 00:25:48.810 ⇒ 00:25:58.859 Shivani Amar: Like, I honestly just find the whole… this wholesale dashboard to be a little crowded, because I’m like, let’s just, like, name the top-level questions, and then eventually, to your point, like, there can be, like, second-order things, but…
243 00:25:59.050 ⇒ 00:26:01.840 Shivani Amar: Like, it’s just like… Whoa.
244 00:26:01.950 ⇒ 00:26:07.949 Shivani Amar: What are sales? What are your top cus- what’s your funnel? What are your top customers? Who are your at-risk customers?
245 00:26:08.350 ⇒ 00:26:09.540 Shivani Amar: feels like…
246 00:26:10.650 ⇒ 00:26:17.040 Shivani Amar: pretty good, right? Like, and maybe a retention curve, right, Robert? To say, like, hey, your funnel is actually that you get
247 00:26:17.450 ⇒ 00:26:19.330 Shivani Amar: 80 applications a week.
248 00:26:19.850 ⇒ 00:26:25.850 Shivani Amar: You’re approving 80 applications a week, but only 30 make their first order every week.
249 00:26:26.320 ⇒ 00:26:29.520 Shivani Amar: And it’s like, okay, like, how do we…
250 00:26:29.680 ⇒ 00:26:32.050 Shivani Amar: Get these people we’re approving to, like.
251 00:26:32.810 ⇒ 00:26:33.400 Jasmin Multani: Actually.
252 00:26:33.400 ⇒ 00:26:34.280 Shivani Amar: lisps.
253 00:26:34.280 ⇒ 00:26:34.890 Jasmin Multani: Like…
254 00:26:34.890 ⇒ 00:26:36.359 Shivani Amar: Omit to their first order.
255 00:26:37.380 ⇒ 00:26:40.620 Shivani Amar: And then you can see, like, to me, it’s, like, just, like, the funnel view, it’s like…
256 00:26:41.700 ⇒ 00:26:43.820 Shivani Amar: Over time, we’ve had
257 00:26:44.360 ⇒ 00:26:58.929 Shivani Amar: 4,000 applications, like, even a cumulative, I think, is fine for this. Like, over time, we’ve had 20,000 applications, we’ve had 14,000 approved, we’ve had 12,000 ever place an order, we’ve had 10,000 who’ve placed a second order.
258 00:26:58.930 ⇒ 00:27:04.380 Shivani Amar: Like, even just, like, one thing that’s, like, a cumulative view of all the… of the…
259 00:27:04.420 ⇒ 00:27:07.989 Shivani Amar: From application down to…
260 00:27:08.170 ⇒ 00:27:12.849 Shivani Amar: third order, let’s say. That could be interesting to see what is that shape of that funnel.
261 00:27:14.290 ⇒ 00:27:29.650 Jasmin Multani: Okay, so we have that. I know, Adith is gonna work on migrating the Google Sheets to OmniSheets. Once we validate those numbers are accurate, apples to apples, then I think from there, it’s gonna be, like, a very simple.
262 00:27:29.650 ⇒ 00:27:42.940 Shivani Amar: Yeah, and, like, I kind of am like, that doesn’t… a cumulative version of that is just, like, helpful to see, even… even if it’s not, like, a time-based thing that’s like, in the last month, what did your funnel look like? I’m like, over time, what is the shape of this funnel?
263 00:27:43.280 ⇒ 00:27:46.980 Shivani Amar: Helpful for me to be like, Understand.
264 00:27:47.970 ⇒ 00:27:49.459 Shivani Amar: How it’s been working.
265 00:27:54.450 ⇒ 00:27:56.449 Shivani Amar: Ovid, did that make sense to you?
266 00:27:57.100 ⇒ 00:27:58.120 Shivani Amar: Okay, cool.
267 00:28:00.580 ⇒ 00:28:03.329 Jasmin Multani: We have 3 minutes left.
268 00:28:03.720 ⇒ 00:28:12.550 Jasmin Multani: And just as a FYI, the tickets for wholesale are pretty large, so I’m asking Avid to first finish out the retail dashboard.
269 00:28:12.550 ⇒ 00:28:15.140 Shivani Amar: Yeah, that’s fine, that’s fine. Okay. Yeah.
270 00:28:15.300 ⇒ 00:28:16.870 Jasmin Multani: Cool, cool, cool. That’s fine.
271 00:28:17.430 ⇒ 00:28:18.410 Jasmin Multani: here.
272 00:28:18.620 ⇒ 00:28:20.330 Jasmin Multani: And use this thing.
273 00:28:20.590 ⇒ 00:28:21.700 Jasmin Multani: So…
274 00:28:22.160 ⇒ 00:28:33.159 Jasmin Multani: we’re still QAing for retail, but Shivani, I wanted to ask, officially, for that retail geography, can we push that over as a V1?
275 00:28:33.160 ⇒ 00:28:39.059 Shivani Amar: Yeah, I think so. It’s like a funny thing, because, like, the minute we figure out how to get zip code data.
276 00:28:39.060 ⇒ 00:28:59.829 Shivani Amar: I’m gonna be like, okay, like, that’s actually the juice, where the juice is. Everybody knows Texas and Florida and California are top states, also in terms of, like, population, so it’s like, it is not really telling me anything provocative, but if they’re trying to think, should we do self-distribution now in Dallas, just like we’re doing in Austin, or should we do it in Miami?
277 00:29:00.270 ⇒ 00:29:03.010 Shivani Amar: We want things at the zip code level, so…
278 00:29:03.190 ⇒ 00:29:12.160 Shivani Amar: I’m like, this feels totally fine, you can push it, I don’t think I need to revisit it until we actually get the zip code data, and that can be just, like, the open item on this one.
279 00:29:12.430 ⇒ 00:29:15.909 Jasmin Multani: Okay, cool. So, I’ll bucket it as approved for now.
280 00:29:15.910 ⇒ 00:29:16.320 Shivani Amar: Yeah, that’.
281 00:29:16.320 ⇒ 00:29:27.310 Jasmin Multani: And one caveat I wanted to add, the reason why I wanted the full store list, is because, we started seeing…
282 00:29:27.940 ⇒ 00:29:29.629 Jasmin Multani: point of sales?
283 00:29:30.000 ⇒ 00:29:48.870 Jasmin Multani: But it’s not being backtracked to a store. So, if we had… I’m trying to find the upper limit of the expected stores we should be selling at, so that we can figure out who these actually belong to. And because of the back-end calculations, that’s why it’s showing up as a negative.
284 00:29:49.150 ⇒ 00:29:50.730 Jasmin Multani: Instead of a positive.
285 00:29:52.110 ⇒ 00:30:02.250 Shivani Amar: Yeah, we shouldn’t have any negatives on this, I agree. I’m like, no, I haven’t QA’d this one too closely yet. And then I’m also saying, like, why is Delaware an unknown region? Why is Vermont…
286 00:30:02.440 ⇒ 00:30:09.670 Shivani Amar: Why is, you know, like, or like, why is Puerto Rico… what do we want to do about Puerto Rico? What should we do about DC? Like, just add them to a region.
287 00:30:10.130 ⇒ 00:30:10.890 Shivani Amar: Like…
288 00:30:11.300 ⇒ 00:30:13.539 Jasmin Multani: Yeah, okay, we can do case ones here.
289 00:30:13.540 ⇒ 00:30:19.820 Shivani Amar: Yeah, but then the negative is that, yeah, you’re right, I don’t love seeing that, and then, like, on the back end, in terms of, like.
290 00:30:20.680 ⇒ 00:30:28.989 Shivani Amar: what… where these sales are coming from, like, these 5 stores that have a no-state association? I have no idea. Is that, like.
291 00:30:29.480 ⇒ 00:30:36.370 Shivani Amar: my issue to solve, or is that just something in the data model that needs to be cleaner? Like, that’s, like, an away-ish question.
292 00:30:37.590 ⇒ 00:30:40.420 Jasmin Multani: So I was hoping…
293 00:30:42.230 ⇒ 00:30:52.809 Jasmin Multani: to also create alerts on this information. Yeah. So, I feel like the executive decision would be, like, hey, drop anything that’s in a negative.
294 00:30:53.050 ⇒ 00:31:03.370 Jasmin Multani: from this table, but in an additional table down the line below here, surface Any backend data mismatches.
295 00:31:04.130 ⇒ 00:31:06.760 Jasmin Multani: as a meta point.
296 00:31:08.530 ⇒ 00:31:13.429 Shivani Amar: I think, like, what I’m struggling with is, like, this one probably feels like the least high…
297 00:31:13.590 ⇒ 00:31:19.440 Shivani Amar: in terms of priority for us to nail, like, like, it’s like, I want to be able to see point of sales.
298 00:31:19.460 ⇒ 00:31:33.560 Shivani Amar: by product, by retailer. Like, I’ll keep drumming that, you know, I’m like, that, like, well, I can QA that, I can, like, check against my OKRs. I’m less interested in reporting things out at a state level right now, though I think long-term. That’s why I’m like.
299 00:31:33.560 ⇒ 00:31:47.460 Shivani Amar: I’m not even, like, really jumping into QA, this one, at the rigor, I would jump into QA. So I’m like, if you wanted to say it’s semi-approved, like, that’s fine, but you’re already catching some of the QA things, it’s just harder for me to, like…
300 00:31:48.580 ⇒ 00:31:51.389 Shivani Amar: engage at this level, because I’m like, this feels like…
301 00:31:51.780 ⇒ 00:31:54.810 Shivani Amar: On the priority list of dashboards, probably one of the lower ones.
302 00:31:55.030 ⇒ 00:31:58.249 Jasmin Multani: Okay. Okay, okay. In that case, I’ll just, like…
303 00:31:58.310 ⇒ 00:32:13.849 Jasmin Multani: make my own decisions, and then write it in notes. And then, I’d say when the retail executive pulls check, and the wholesale executive pulls check, once those are pushed out, that’s where you have, the final… the final say.
304 00:32:13.850 ⇒ 00:32:24.260 Jasmin Multani: That sounds great. Everything else, I’ll just be like, okay, this is… these are cosmetic questions, we can put this… these are important alert flags to have, but we’ll put this elsewhere.
305 00:32:24.430 ⇒ 00:32:25.350 Shivani Amar: Perfect.
306 00:32:26.080 ⇒ 00:32:27.020 Jasmin Multani: Awesome!
307 00:32:27.020 ⇒ 00:32:28.290 Shivani Amar: Thank you!
308 00:32:28.520 ⇒ 00:32:30.240 Jasmin Multani: I hope this was helpful.
309 00:32:30.240 ⇒ 00:32:34.569 Shivani Amar: So it is, it is helpful. I’m like, I just… I like having the regular beat of us.
310 00:32:36.780 ⇒ 00:32:39.879 Jasmin Multani: White brings it out of those…
311 00:32:40.630 ⇒ 00:32:41.280 Shivani Amar: Okay.
312 00:32:41.910 ⇒ 00:32:42.810 Jasmin Multani: Subtraction.
313 00:32:43.450 ⇒ 00:32:44.260 Shivani Amar: Thank you.
314 00:32:44.960 ⇒ 00:32:52.059 Jasmin Multani: Alright, if there’s anything, please tag us. Otherwise, I’m gonna be more active on the daily,
315 00:32:52.380 ⇒ 00:32:54.250 Jasmin Multani: Progress checks, and .
316 00:32:54.250 ⇒ 00:33:00.599 Shivani Amar: Good. And then, Robert, just a note for you. I was, like, getting frustrated with Muffin Data’s lack of communication with me, so then I…
317 00:33:00.600 ⇒ 00:33:01.120 Robert Tseng: Yeah, I saw that.
318 00:33:01.120 ⇒ 00:33:11.569 Shivani Amar: I scheduled another demo, just like, I went on their website and scheduled another demo, and then Russell told me that he’s doing a training with them on Thursday, and I was like, add me to that, add with them to that, so I’m like…
319 00:33:11.570 ⇒ 00:33:12.380 Robert Tseng: Oh, yeah.
320 00:33:12.380 ⇒ 00:33:17.919 Shivani Amar: Do you want to be in that also, or is it just with them because it’s, like, more like, but how to get the data in?
321 00:33:18.260 ⇒ 00:33:23.749 Robert Tseng: Yeah, I already kind of briefed him on the… what I think the capabilities are. I think he’s gonna ask his questions about what it…
322 00:33:24.180 ⇒ 00:33:26.629 Robert Tseng: and switching to that to get data in, right?
323 00:33:26.630 ⇒ 00:33:34.729 Shivani Amar: Okay, cool. So I asked them to invite him, because I was like, you don’t need to have multiple people at Element trying to, like, get in touch with Muffin Data. It feels duplicative, so…
324 00:33:34.730 ⇒ 00:33:37.400 Robert Tseng: I wonder what Russell’s doing with them, but…
325 00:33:37.400 ⇒ 00:33:47.869 Shivani Amar: No, Russell’s trying to, like, implement Confido. Russell is, like, wanting the data for future… for future retailers also. Like, he’s the one going into the source system at Target and, like, downloading.
326 00:33:47.870 ⇒ 00:33:48.529 Robert Tseng: Oh, okay.
327 00:33:48.530 ⇒ 00:33:49.040 Shivani Amar: So this is a…
328 00:33:49.040 ⇒ 00:33:50.170 Robert Tseng: on his purview.
329 00:33:50.170 ⇒ 00:34:06.119 Shivani Amar: coordinating with Walmart buyers about end caps and things like that, so he’s, like, constantly looking at point-of-sales data and inventory and stuff. He’s just, like, constantly looking at this stuff, so I think he’s excited to get muffin data going so that he can have access to the data that he needs.
330 00:34:06.270 ⇒ 00:34:16.499 Shivani Amar: Yeah. Not keeping in mind the data project at large, right? It’s just, like, his day-to-day operational data that he needs to get efficiently. So, anyway. Okay, thank you guys.
331 00:34:17.010 ⇒ 00:34:17.960 Robert Tseng: Cool. Thanks, Alex.
332 00:34:18.330 ⇒ 00:34:19.100 Advait Nandakumar Menon: Talk soon.