Meeting Title: LMNT QA Sync Date: 2026-05-12 Meeting participants: Advait Nandakumar Menon, Jasmin Multani, Shivani Amar
WEBVTT
1 00:00:49.360 ⇒ 00:00:50.290 Advait Nandakumar Menon: Hey.
2 00:00:50.290 ⇒ 00:00:51.160 Jasmin Multani: Hello, Dad.
3 00:00:58.090 ⇒ 00:00:59.140 Shivani Amar: Hello!
4 00:01:00.780 ⇒ 00:01:01.550 Advait Nandakumar Menon: Yeah, surely.
5 00:01:02.190 ⇒ 00:01:03.609 Shivani Amar: How are you guys doing?
6 00:01:04.420 ⇒ 00:01:07.289 Jasmin Multani: Yeah, it’s starting the week off strong.
7 00:01:07.700 ⇒ 00:01:08.820 Jasmin Multani: Very strong.
8 00:01:10.880 ⇒ 00:01:11.879 Shivani Amar: Tell me more.
9 00:01:12.770 ⇒ 00:01:19.250 Jasmin Multani: I’m just, like, restructuring the way I deliver my own output.
10 00:01:19.520 ⇒ 00:01:27.109 Jasmin Multani: So, I even told Abby, like, in the mornings, like, early, early mornings, I’m just gonna push out whatever IC work I have.
11 00:01:27.240 ⇒ 00:01:35.420 Jasmin Multani: And then do my block of meetings, and then in the evening, just do active reviews and replies, and map out the next week, I feel like.
12 00:01:35.750 ⇒ 00:01:39.490 Jasmin Multani: That’s the best way I can… Organize myself right now.
13 00:01:42.620 ⇒ 00:01:43.790 Jasmin Multani: How are you?
14 00:01:44.290 ⇒ 00:01:45.260 Shivani Amar: I’m good.
15 00:01:45.440 ⇒ 00:01:46.500 Shivani Amar: Hmm, like…
16 00:01:46.720 ⇒ 00:01:58.519 Shivani Amar: it’s kind of, at this point, like, hard to… everybody seems very surprised I’m not taking next week fully off, and I was like, is that what people do? They take, like, a full week off before they get married, but I’m realizing it’s, like.
17 00:01:59.590 ⇒ 00:02:02.590 Shivani Amar: My brain is so saturated, it’s like…
18 00:02:03.910 ⇒ 00:02:08.530 Shivani Amar: Just final conversations with vendors, and, like, you just, you know…
19 00:02:08.780 ⇒ 00:02:14.430 Shivani Amar: final timings of things, and getting comms out to people, and I’m like, oh, I’m really tired.
20 00:02:16.500 ⇒ 00:02:18.530 Jasmin Multani: Did you get a wedding planner?
21 00:02:19.020 ⇒ 00:02:25.409 Shivani Amar: Yeah, the day of coordinator, so she’s, like, she’s doing a bunch, but I think it’s, like,
22 00:02:26.740 ⇒ 00:02:34.730 Shivani Amar: And I’m sure the day of she’ll be wonderful, but it was… it’s different than having, like, a full-on wedding planner, which I honestly don’t think I even understood when I booked her. Like…
23 00:02:35.510 ⇒ 00:02:40.510 Shivani Amar: fully got it. So it’s just, like, these final strings of, like,
24 00:02:41.830 ⇒ 00:02:52.820 Shivani Amar: coordinating even with, like, extended family, okay, this is the time we’re gonna do, like, some photos, like, be here by this time, like, she’s kind of helping me, but I’m, like, I have to be the one making the decision for a lot of this stuff, and it’s just… yeah.
25 00:02:52.820 ⇒ 00:02:56.609 Jasmin Multani: Well, I hope you have a sibling who’ll just be like.
26 00:02:56.610 ⇒ 00:03:00.640 Shivani Amar: Yeah, my sister-in-law’s gonna run stuff, I think, so it’s gonna be good.
27 00:03:00.790 ⇒ 00:03:10.919 Jasmin Multani: So you can just, like, start the morning and actually enjoy your day. I think, just watching my cousin get married, because she had two weddings, a Sikh wedding and then a Hindu wedding in the same day.
28 00:03:11.200 ⇒ 00:03:16.210 Jasmin Multani: And… it was an hour out in Canada, so…
29 00:03:16.810 ⇒ 00:03:22.329 Jasmin Multani: It was a lot of coordinating, but her wedding planner thankfully, gave her a bunch of homework to do.
30 00:03:22.480 ⇒ 00:03:23.000 Shivani Amar: Yeah.
31 00:03:23.000 ⇒ 00:03:39.280 Jasmin Multani: to the days, so between work and between the events, she was just doing her wedding planner homework, like, sending a playlist and everything, but I really hope the day of, like, you just get… you tell your sister-in-law, like, you’re in charge of yelling at people.
32 00:03:40.480 ⇒ 00:03:44.090 Jasmin Multani: So that you can just zone out, and she can get you drinks and everything.
33 00:03:44.260 ⇒ 00:03:51.509 Shivani Amar: Yeah, it’s gonna be good. But, on to our dashboarding.
34 00:03:51.510 ⇒ 00:03:52.200 Jasmin Multani: Mmm…
35 00:03:52.290 ⇒ 00:03:55.349 Shivani Amar: Where do you guys want to start?
36 00:03:55.930 ⇒ 00:04:01.700 Jasmin Multani: Yeah, let me pull this up right now. I wanted to start with your dashboard feedback.
37 00:04:01.810 ⇒ 00:04:04.300 Jasmin Multani: I saw that they were trickling through.
38 00:04:04.670 ⇒ 00:04:07.970 Jasmin Multani: I wanted to go pull up the dashboards one by one.
39 00:04:13.070 ⇒ 00:04:19.040 Jasmin Multani: So, let’s start with retail, because that’s just the easiest one to start with.
40 00:04:19.620 ⇒ 00:04:20.260 Shivani Amar: Yeah.
41 00:04:22.960 ⇒ 00:04:25.460 Jasmin Multani: And then, by the end of the call, ideally, we…
42 00:04:25.630 ⇒ 00:04:28.950 Jasmin Multani: Can confirm, like, can we push this on to the next folder or not?
43 00:04:29.440 ⇒ 00:04:30.010 Shivani Amar: Yeah.
44 00:04:31.590 ⇒ 00:04:35.079 Jasmin Multani: Okay, so, high level, how’d you think about this one?
45 00:04:50.840 ⇒ 00:04:54.499 Shivani Amar: What is the ranking of velocity by retailer by SKU?
46 00:04:55.070 ⇒ 00:04:56.300 Shivani Amar: What’s the top?
47 00:04:56.620 ⇒ 00:04:59.080 Shivani Amar: 10 or whatever, like, what’s the ordering?
48 00:04:59.410 ⇒ 00:05:03.790 Jasmin Multani: You can adjust the ordering however way you want it to be.
49 00:05:05.210 ⇒ 00:05:06.469 Jasmin Multani: This is all filterable.
50 00:05:06.980 ⇒ 00:05:07.830 Shivani Amar: Hmm.
51 00:05:07.830 ⇒ 00:05:11.790 Jasmin Multani: But the thing is, like, because this is SKU, this is, we only…
52 00:05:11.790 ⇒ 00:05:12.880 Shivani Amar: Youth Specific.
53 00:05:13.270 ⇒ 00:05:16.819 Jasmin Multani: Yeah, so we’re not gonna have a side-by- a true side-by-side.
54 00:05:17.080 ⇒ 00:05:22.510 Jasmin Multani: For… Products that are… exist.
55 00:05:22.810 ⇒ 00:05:24.570 Jasmin Multani: across retail?
56 00:05:24.990 ⇒ 00:05:27.800 Jasmin Multani: Because this is a SKU-specific labeling.
57 00:05:28.380 ⇒ 00:05:28.730 Shivani Amar: And…
58 00:05:28.920 ⇒ 00:05:31.889 Jasmin Multani: with UPC, we can do it side by side.
59 00:05:32.070 ⇒ 00:05:35.080 Shivani Amar: Mmm… Okay.
60 00:05:36.090 ⇒ 00:05:42.699 Shivani Amar: Understood. Is this a specific SKU… is this based off the string of the letters and the name, basically?
61 00:05:43.020 ⇒ 00:05:48.049 Jasmin Multani: Yeah, SKU is just the words, and this is, like, targets…
62 00:05:48.710 ⇒ 00:05:54.860 Jasmin Multani: Target has a very unique, Data cleanup structure, whereas…
63 00:05:55.220 ⇒ 00:05:57.649 Jasmin Multani: Walmart is gonna look like this.
64 00:05:57.760 ⇒ 00:06:02.629 Jasmin Multani: And that’s just… that’s why I really wanted the push, to be like, hey, let’s step away from SKU.
65 00:06:03.180 ⇒ 00:06:12.980 Jasmin Multani: let’s go towards UPCs, and then ideally, when we get the UPC, we can override… we can pick and choose and be like, hey, Target gives us the best
66 00:06:13.360 ⇒ 00:06:19.470 Jasmin Multani: writing structure. Let’s override the UPC name with this structure.
67 00:06:19.600 ⇒ 00:06:21.370 Jasmin Multani: But then have that singular code.
68 00:06:22.840 ⇒ 00:06:28.530 Jasmin Multani: So that way, like, we… ideally, we just look at this, and then Walmart could be over here.
69 00:06:28.750 ⇒ 00:06:33.200 Jasmin Multani: But in the back end, it’s tied together and joined together by one singular EPC.
70 00:06:33.370 ⇒ 00:06:35.919 Shivani Amar: Hmm, okay, that sounds good.
71 00:06:38.720 ⇒ 00:06:40.350 Jasmin Multani: Should we go through here…
72 00:06:45.920 ⇒ 00:06:46.750 Shivani Amar: Okay.
73 00:06:48.880 ⇒ 00:06:50.720 Shivani Amar: Like, this includes sparkling?
74 00:06:53.130 ⇒ 00:07:01.089 Jasmin Multani: Electrolytes, drinks… Variety Pack… yeah, it should include all.
75 00:07:02.580 ⇒ 00:07:03.770 Shivani Amar: I don’t think so.
76 00:07:06.770 ⇒ 00:07:10.230 Shivani Amar: I mean, maybe in this drop-down thing, but I’m not…
77 00:07:11.920 ⇒ 00:07:13.849 Shivani Amar: Okay, I see the word cans.
78 00:07:15.220 ⇒ 00:07:19.020 Shivani Amar: Is it just, like, really low? Is that why it’s not showing up in the chart?
79 00:07:19.020 ⇒ 00:07:20.280 Jasmin Multani: Number 7.
80 00:07:21.770 ⇒ 00:07:23.470 Jasmin Multani: I see sparkling here.
81 00:07:23.950 ⇒ 00:07:30.030 Shivani Amar: So then if you go down, is it just not showing because in this chart, because…
82 00:07:31.240 ⇒ 00:07:32.970 Jasmin Multani: It’s only top 5 SKUs.
83 00:07:33.430 ⇒ 00:07:34.205 Shivani Amar: Oh…
84 00:07:35.140 ⇒ 00:07:37.230 Jasmin Multani: We can expand this, if you’d like.
85 00:07:38.900 ⇒ 00:07:39.940 Shivani Amar: Yeah…
86 00:07:41.590 ⇒ 00:07:46.889 Shivani Amar: I mean, it’s just kind of a boring graph, right? Like, whenever I look at a graph that’s kind of boring, I’m like, is this needed?
87 00:07:48.350 ⇒ 00:07:50.830 Shivani Amar: What do you… what does this tell me? Nothing.
88 00:07:50.990 ⇒ 00:07:55.710 Shivani Amar: Because it tells me the variety pack is top, which the above thing already tells me.
89 00:07:57.200 ⇒ 00:08:04.480 Shivani Amar: So, like, if I see a bunch of flat lines versus I’m seeing, like, a pickup in sparkling, like, should this just have…
90 00:08:04.830 ⇒ 00:08:08.539 Shivani Amar: But I get what you, like, mean, that it would be really busy.
91 00:08:08.740 ⇒ 00:08:10.089 Shivani Amar: with,
92 00:08:12.100 ⇒ 00:08:20.679 Shivani Amar: whatever… if you scroll up to the chart above this, like, could I say I actually want to see a velocity by skew for last week?
93 00:08:20.890 ⇒ 00:08:24.079 Shivani Amar: Like, how do I adjust the time frame there?
94 00:08:24.570 ⇒ 00:08:30.700 Jasmin Multani: You can do it for last week, but I don’t think you can do a side-by-side comparison.
95 00:08:31.010 ⇒ 00:08:33.720 Jasmin Multani: Of, like, last week versus two weeks ago.
96 00:08:36.580 ⇒ 00:08:39.240 Jasmin Multani: With just that, leadership board.
97 00:08:41.850 ⇒ 00:08:45.989 Jasmin Multani: So because, you know, even in that line graph, things were pretty stable.
98 00:08:46.100 ⇒ 00:08:50.929 Jasmin Multani: like, we know that this got updated because the pineapple sparkling moved from 7 to 6, so…
99 00:08:52.190 ⇒ 00:08:53.539 Jasmin Multani: That impacted this.
100 00:08:53.970 ⇒ 00:08:57.620 Jasmin Multani: But at least over here, you’re gonna… this is just like a…
101 00:08:57.730 ⇒ 00:08:59.680 Jasmin Multani: Consider it, like, a break metric.
102 00:08:59.870 ⇒ 00:09:03.210 Jasmin Multani: Type of view, where if you see a crazy spike.
103 00:09:03.780 ⇒ 00:09:09.110 Jasmin Multani: Then that could tell us something interesting, but at this point, Worsen that.
104 00:09:09.110 ⇒ 00:09:18.610 Shivani Amar: It should take up… one, I either think it doesn’t… shouldn’t take up so much real estate as being, like, the whole thing, or… or we cut it. Like, because…
105 00:09:19.630 ⇒ 00:09:24.620 Shivani Amar: Right now, it’s not telling us anything, and then, like, it’s… like, to me, it’s more interesting, like.
106 00:09:25.630 ⇒ 00:09:41.080 Shivani Amar: if a SKU pops up, and you want to say, how… now that Sparkling is new, how is Sparkling performing? But then, I get that the graph gets really busy, but I’m just trying to… I’m trying to figure out, like, how you would visually show that a new SKU appeared on the map.
107 00:09:42.250 ⇒ 00:09:45.210 Shivani Amar: And, like, that, like, is maybe busier?
108 00:09:45.410 ⇒ 00:09:50.079 Shivani Amar: of a graph, like, if you change this graph right now to be all SKUs, should we just see what it would look like?
109 00:09:51.000 ⇒ 00:09:54.409 Jasmin Multani: I think we have to go on the back end, right, okay?
110 00:09:55.870 ⇒ 00:09:56.540 Advait Nandakumar Menon: Yeah.
111 00:09:57.460 ⇒ 00:10:00.559 Jasmin Multani: Can I change it in the graph itself? Let’s see.
112 00:10:03.550 ⇒ 00:10:06.519 Advait Nandakumar Menon: Click on Edit Chart. Edit in Workbook.
113 00:10:14.550 ⇒ 00:10:15.699 Jasmin Multani: Edit in Workbook.
114 00:10:20.910 ⇒ 00:10:23.590 Jasmin Multani: Is there a limit on this?
115 00:10:24.790 ⇒ 00:10:29.879 Advait Nandakumar Menon: If you click on Limits up top, on the right.
116 00:10:30.110 ⇒ 00:10:31.510 Advait Nandakumar Menon: Near the sequel?
117 00:10:33.820 ⇒ 00:10:48.309 Advait Nandakumar Menon: Okay, so the limit is not happening here, it’s actually from another query which filters for just the top 5. So if you click on the SKU filter over there, it says it’s from a query with fails.
118 00:10:49.130 ⇒ 00:10:49.660 Jasmin Multani: No.
119 00:10:49.660 ⇒ 00:10:51.479 Advait Nandakumar Menon: I’m gonna see… yeah, this one, yeah, yeah.
120 00:10:51.480 ⇒ 00:10:53.219 Jasmin Multani: Yeah, so I just edit this out.
121 00:10:53.930 ⇒ 00:11:01.410 Advait Nandakumar Menon: Yeah, you can try. Usually, Omni gives errors, because this is from another query view, but you can try doing it.
122 00:11:01.810 ⇒ 00:11:04.400 Jasmin Multani: Yeah, I think there should only be, like, 70-something, right?
123 00:11:04.590 ⇒ 00:11:05.550 Jasmin Multani: Anyways…
124 00:11:06.080 ⇒ 00:11:10.569 Advait Nandakumar Menon: You can click on Advanced Editor to actually type it.
125 00:11:11.930 ⇒ 00:11:13.190 Jasmin Multani: There’s advancement.
126 00:11:13.550 ⇒ 00:11:14.379 Advait Nandakumar Menon: Yeah, over…
127 00:11:14.380 ⇒ 00:11:15.050 Jasmin Multani: right here.
128 00:11:15.250 ⇒ 00:11:15.860 Advait Nandakumar Menon: Yep.
129 00:11:20.280 ⇒ 00:11:21.070 Jasmin Multani: Cool.
130 00:11:21.610 ⇒ 00:11:22.510 Jasmin Multani: Keep growing.
131 00:11:26.120 ⇒ 00:11:29.529 Jasmin Multani: what this looks like. So yeah, it ends up getting busy.
132 00:11:30.700 ⇒ 00:11:31.250 Jasmin Multani: But…
133 00:11:31.250 ⇒ 00:11:40.019 Shivani Amar: But you can see, like, the new little lines that pop up, I guess, where you’re like, oh, wow, like, green, whatever that green is was brand new. The light green at the bottom.
134 00:11:40.210 ⇒ 00:11:42.300 Jasmin Multani: The very… right here?
135 00:11:42.300 ⇒ 00:11:43.060 Shivani Amar: Yeah.
136 00:11:43.430 ⇒ 00:11:50.969 Shivani Amar: So I almost wonder if, Jasmine, what we do is we make this graph twice, and you say Target and Walmart side by side.
137 00:11:50.970 ⇒ 00:12:08.000 Shivani Amar: And then I can see when the new SKUs popped up, and it might be busy, but maybe that’s, like, okay for now, and then when we have more retailers, we can figure out how we want to do it. But, like, otherwise, right now, I’m kind of like, it’s a flat graph, it doesn’t show me when the new SKUs, like, arrived, and if they’re moving or not. And, like, the talk of the town is.
138 00:12:08.010 ⇒ 00:12:11.229 Shivani Amar: Man, we rolled out sparkling and the velocity is not high enough.
139 00:12:11.670 ⇒ 00:12:12.370 Jasmin Multani: Okay.
140 00:12:12.370 ⇒ 00:12:17.120 Shivani Amar: And so if you’re only seeing the top 5 SKUs, I think it’s, like, less compelling.
141 00:12:18.930 ⇒ 00:12:23.219 Jasmin Multani: We launched… So should we just…
142 00:12:25.400 ⇒ 00:12:45.430 Shivani Amar: I would just make it show all SKUs, but split it by store right now, since you don’t have UPC. So you can say targets, top SKU, like, or not top SKUs, targets, SKU velocity, Walmart SKU velocity, and then you have the line graphs for both. And maybe if you need to save space, you make it, like, trailing, like, 8 weeks or something instead of 12.
143 00:12:45.950 ⇒ 00:12:46.800 Jasmin Multani: True.
144 00:12:49.400 ⇒ 00:12:51.150 Jasmin Multani: Most interesting.
145 00:12:51.870 ⇒ 00:12:53.370 Jasmin Multani: Interest, interest.
146 00:13:08.580 ⇒ 00:13:11.569 Jasmin Multani: So maybe we can create, like, a flag for this.
147 00:13:12.000 ⇒ 00:13:17.910 Jasmin Multani: on top of… So we can visually see it, across the busyness.
148 00:13:19.680 ⇒ 00:13:21.110 Jasmin Multani: We can workshop this one.
149 00:13:21.520 ⇒ 00:13:30.079 Jasmin Multani: Okay, cool. But after this, then it should be good for us to push into… approval, right?
150 00:13:31.900 ⇒ 00:13:33.490 Shivani Amar: Yeah, I think so.
151 00:13:36.600 ⇒ 00:13:37.150 Jasmin Multani: Thank you.
152 00:13:37.150 ⇒ 00:13:47.840 Shivani Amar: Can you, you know, it’s so funny how, like, my brain is, like… Works in tables rather than…
153 00:13:48.130 ⇒ 00:13:52.500 Shivani Amar: line chart sometimes, but I like seeing things weekly.
154 00:13:52.500 ⇒ 00:13:53.110 Jasmin Multani: So…
155 00:13:53.110 ⇒ 00:13:56.600 Shivani Amar: So, like, I’m wondering if… I’m wondering if, like…
156 00:13:56.850 ⇒ 00:14:00.090 Shivani Amar: You just showed me velocity by SKU.
157 00:14:00.460 ⇒ 00:14:14.669 Shivani Amar: for Target and Velocico SKU for Walmart in a table form instead of a line chart for now. And maybe it has some conditional formatting or what it may be, but, like, it allows me to just be like, what’s going on with this SKU? And then I can just see it.
158 00:14:16.110 ⇒ 00:14:24.180 Shivani Amar: Right? And if that’s an easier way of visualizing it, and then it can be across the page, it could be Target, Walmart, and take up the full…
159 00:14:24.280 ⇒ 00:14:29.330 Shivani Amar: Real estate, but one after the other. I think that’ll be fine.
160 00:14:38.190 ⇒ 00:14:44.950 Jasmin Multani: So we’ll prioritize having this tabular, and then add conditional formatting so it’s loud and in your face.
161 00:14:47.410 ⇒ 00:14:49.539 Jasmin Multani: Then we’ll ping you when we’re done.
162 00:14:49.990 ⇒ 00:14:52.859 Jasmin Multani: Okay, now I’m gonna move on to wholesale, is that good?
163 00:14:55.500 ⇒ 00:14:58.340 Shivani Amar: I sent a bunch of notes on this stuff today, did you see them?
164 00:14:59.150 ⇒ 00:15:06.580 Jasmin Multani: I think I saw the pings, and I saw some of… the visual questions.
165 00:15:07.340 ⇒ 00:15:10.479 Jasmin Multani: Let me pull those up.
166 00:15:11.030 ⇒ 00:15:13.130 Jasmin Multani: It was in the external, right? Or it was in.
167 00:15:13.130 ⇒ 00:15:19.889 Shivani Amar: No, no, I can just tell you, but, like… or you can just… I’ve made it pretty clear, so if you guys want to…
168 00:15:26.960 ⇒ 00:15:31.340 Shivani Amar: Do you want to talk through it? Do you want to spend some time with it? What would be helpful for you?
169 00:15:35.000 ⇒ 00:15:35.570 Jasmin Multani: Should we just.
170 00:15:35.570 ⇒ 00:15:47.839 Shivani Amar: Because, like, if you wanna… I can, like, share a little bit with you live, just so that we’re all aligned, and then go through it quickly, and then if you want, we could do, like, another 20-minute quick touch base tomorrow, just so that…
171 00:15:47.990 ⇒ 00:15:50.309 Shivani Amar: You’ve been able to make the updates and stuff.
172 00:15:50.550 ⇒ 00:15:51.630 Jasmin Multani: Sure, that works.
173 00:15:52.140 ⇒ 00:15:54.999 Shivani Amar: Okay, so let’s go to the executive pulse Check real quick.
174 00:15:55.320 ⇒ 00:16:05.349 Shivani Amar: So, for this, I wanted the color to be more design guide colors, so I sent you guys more colors to choose from, okay? I also said put Trusted Health at the bottom.
175 00:16:07.510 ⇒ 00:16:09.260 Jasmin Multani: At the bottom, okay.
176 00:16:09.500 ⇒ 00:16:09.870 Shivani Amar: Because.
177 00:16:09.870 ⇒ 00:16:11.049 Advait Nandakumar Menon: I feel like real…
178 00:16:11.340 ⇒ 00:16:20.690 Advait Nandakumar Menon: Yeah, real quick about the color, Shivani, it is from the… if you check the screenshot, Jasmine, it is from that. Would you recommend any other colors apart from words?
179 00:16:20.850 ⇒ 00:16:25.520 Advait Nandakumar Menon: represented here, because the… it’s the exact hex codes I have,
180 00:16:26.150 ⇒ 00:16:29.830 Advait Nandakumar Menon: Programmed in as the colors on the bad chart right now.
181 00:16:30.520 ⇒ 00:16:32.919 Shivani Amar: So, let’s see…
182 00:16:32.920 ⇒ 00:16:37.279 Advait Nandakumar Menon: I think watermelon, grapefruit, raw, and chocolate is what’s
183 00:16:38.290 ⇒ 00:16:40.860 Advait Nandakumar Menon: being used right now in the bar chart, but if you…
184 00:16:41.270 ⇒ 00:16:43.740 Advait Nandakumar Menon: Prefer something else, we can do that.
185 00:16:44.250 ⇒ 00:16:48.370 Shivani Amar: Gotcha. Okay, so one second. Sorry, let me look back at the zoom.
186 00:16:48.510 ⇒ 00:16:55.040 Shivani Amar: So… Toggle it… raw… Watermelon.
187 00:16:55.590 ⇒ 00:16:57.680 Shivani Amar: And then… Orange?
188 00:16:57.840 ⇒ 00:17:01.269 Shivani Amar: I don’t know, it just looks, like, messy to me.
189 00:17:01.780 ⇒ 00:17:07.470 Jasmin Multani: Do you want… should we do a scale, and then just pick 1, 2, 3, 4? 4 colors here?
190 00:17:07.960 ⇒ 00:17:11.580 Shivani Amar: It’s okay, I think… I mean, maybe.
191 00:17:12.489 ⇒ 00:17:14.409 Jasmin Multani: Just so the colors are homogenous.
192 00:17:14.410 ⇒ 00:17:16.569 Shivani Amar: Yeah, I think that makes sense.
193 00:17:16.750 ⇒ 00:17:18.000 Shivani Amar: I don’t do that.
194 00:17:18.380 ⇒ 00:17:22.560 Shivani Amar: So, if you choose, like, mango chili or something like that, and then you.
195 00:17:22.560 ⇒ 00:17:23.950 Jasmin Multani: We have Mingo chili.
196 00:17:23.950 ⇒ 00:17:24.900 Shivani Amar: Huh? Yeah.
197 00:17:24.900 ⇒ 00:17:25.750 Jasmin Multani: Yeah.
198 00:17:26.010 ⇒ 00:17:33.380 Advait Nandakumar Menon: Yeah, so this range of colors, what I’ve applied in the other table we just saw, Jasmine, the other retail dashboard, so…
199 00:17:33.380 ⇒ 00:17:34.390 Jasmin Multani: Yep.
200 00:17:34.390 ⇒ 00:17:35.400 Advait Nandakumar Menon: We can do that here.
201 00:17:35.830 ⇒ 00:17:41.379 Shivani Amar: And what I was saying is put Trusted Health at the bottom of the bar, so it’s easier to compare, because it’s the biggest one.
202 00:17:42.170 ⇒ 00:17:42.680 Advait Nandakumar Menon: Okay.
203 00:17:42.680 ⇒ 00:17:43.220 Shivani Amar: Okay.
204 00:17:43.410 ⇒ 00:17:47.960 Shivani Amar: And, okay, then if you scroll down…
205 00:17:48.690 ⇒ 00:18:02.119 Shivani Amar: what was I saying about that applications one? I don’t know if I said anything, but then the funnel, I know we’re gonna clean up what the third order, second order mean, take out zero orders, and then the visuals should just be, like.
206 00:18:02.390 ⇒ 00:18:10.489 Shivani Amar: like, it can also be tabular. It could be, like, monthly, it could also be, like, table, which is, like, monthly, kind of like what you have, honestly.
207 00:18:10.620 ⇒ 00:18:18.350 Shivani Amar: Here, without having the breakdown of trusted health. Not everything, in my mind needs the Trusted Health Bulk Buyer breakdown.
208 00:18:18.750 ⇒ 00:18:21.620 Shivani Amar: Right? So it could just be, like.
209 00:18:21.770 ⇒ 00:18:30.039 Shivani Amar: A monthly, how many applications, how many approved, how many first order, how many second order, how many third order, how many at risk.
210 00:18:30.140 ⇒ 00:18:31.460 Shivani Amar: How many churned?
211 00:18:31.630 ⇒ 00:18:39.459 Shivani Amar: Or whatever, and you just have, like, all of those in a table, kind of like what you had in the spreadsheet, which I think is, like, a fine way of looking at it.
212 00:18:39.890 ⇒ 00:18:40.660 Jasmin Multani: Okay.
213 00:18:40.660 ⇒ 00:18:41.190 Shivani Amar: babe.
214 00:18:41.580 ⇒ 00:18:47.650 Shivani Amar: And then, the graph to the side… With the…
215 00:18:47.870 ⇒ 00:18:48.760 Jasmin Multani: This one.
216 00:18:48.760 ⇒ 00:19:07.530 Shivani Amar: Yeah, it’s just average order value, I don’t like that it’s plummeting in the current month, and so I’m like, whatever math you need to do to understand, like, is it because you’re not dividing by… you’re dividing by 31 and it’s too early, is it that it doesn’t have data in yet, like, or don’t show the current month, or, like, something, because otherwise that looks a little funky.
217 00:19:07.880 ⇒ 00:19:14.739 Jasmin Multani: Okay. Would you prefer, the month to be fully gone, or would you prefer, like, some data?
218 00:19:14.740 ⇒ 00:19:23.130 Shivani Amar: It could be accurate data of, like, based off it being 11 days of data you have so far, this is what the average order value is.
219 00:19:23.320 ⇒ 00:19:27.449 Shivani Amar: That’s fine, but… I don’t like that it plummets.
220 00:19:50.670 ⇒ 00:19:51.590 Jasmin Multani: Sounds good!
221 00:19:52.140 ⇒ 00:19:55.690 Jasmin Multani: And then you mentioned this, but those are the main ones, right?
222 00:19:55.950 ⇒ 00:19:56.630 Shivani Amar: Yeah.
223 00:19:57.860 ⇒ 00:19:58.530 Jasmin Multani: Okay.
224 00:20:08.150 ⇒ 00:20:14.039 Shivani Amar: Okay, next one… What are the top numbers here that are, like…
225 00:20:14.250 ⇒ 00:20:18.790 Shivani Amar: Sales and selected window, like, what is the… what is the selected window?
226 00:20:22.880 ⇒ 00:20:27.850 Jasmin Multani: Abbott, do you remember if it was, like, current period is negative minus 30 days?
227 00:20:28.280 ⇒ 00:20:31.330 Advait Nandakumar Menon: Yeah, if you scroll up, you can, literally just…
228 00:20:31.330 ⇒ 00:20:38.939 Shivani Amar: Previous month, so I don’t know if that’s completed month, or if that’s, like, the last 30 days. Like, I actually don’t know what that means.
229 00:20:39.590 ⇒ 00:20:42.729 Advait Nandakumar Menon: Can you scroll to the filters, adjustment? So…
230 00:20:42.730 ⇒ 00:20:46.829 Shivani Amar: Yeah, it says in the past 1 complete month. Now, is that… does that mean April?
231 00:20:47.280 ⇒ 00:21:01.909 Advait Nandakumar Menon: Yes, it means not including May till end of April, so that’s how one complete month… that’s what complete month means. As you’re comparing that one complete month with the previous period, which is, in this case, will be March.
232 00:21:02.890 ⇒ 00:21:09.630 Advait Nandakumar Menon: So, April will be compared to March, so you can change that one complete month to, there are a lot of selections.
233 00:21:09.630 ⇒ 00:21:19.059 Shivani Amar: Yeah, yeah, yeah. Okay, so that’s also… okay, so then if you scroll down with this, let’s see, like, scissors are just telling me wholesale sales were $3 million in…
234 00:21:20.560 ⇒ 00:21:22.779 Shivani Amar: April. That’s what that’s telling us.
235 00:21:23.240 ⇒ 00:21:30.820 Shivani Amar: So, but then is that, like… Also, summary report.
236 00:21:31.820 ⇒ 00:21:32.800 Shivani Amar: Hold on.
237 00:21:49.590 ⇒ 00:21:52.320 Shivani Amar: Okay, so this matches… can you go back to that number?
238 00:21:52.610 ⇒ 00:22:00.130 Shivani Amar: 3276… okay, so it’s, like, a little bit different from what the spreadsheet says. Okay. And it’s missing manual orders, right?
239 00:22:01.560 ⇒ 00:22:02.630 Jasmin Multani: Yes.
240 00:22:03.070 ⇒ 00:22:11.699 Shivani Amar: So, like, maybe we can also just have a note on this one, or anywhere that we’re talking about wholesale sales, that we don’t have all the orders. It only has Shopify data.
241 00:22:14.010 ⇒ 00:22:15.470 Jasmin Multani: That’s better right now.
242 00:22:20.070 ⇒ 00:22:21.230 Jasmin Multani: Sales or phone.
243 00:22:29.490 ⇒ 00:22:34.810 Advait Nandakumar Menon: You need to right-click, Justin, just… can you scroll up?
244 00:22:36.590 ⇒ 00:22:39.179 Advait Nandakumar Menon: Yeah, click on the three.
245 00:22:39.180 ⇒ 00:22:39.940 Jasmin Multani: Oh, yep.
246 00:22:39.940 ⇒ 00:22:41.580 Advait Nandakumar Menon: Okay, yeah.
247 00:22:42.650 ⇒ 00:22:44.020 Advait Nandakumar Menon: Edit text type.
248 00:22:45.270 ⇒ 00:22:59.230 Jasmin Multani: So from here, we say, full of orders, but… Shopify… No manual orders.
249 00:23:01.890 ⇒ 00:23:02.760 Jasmin Multani: Save…
250 00:23:50.550 ⇒ 00:23:52.500 Shivani Amar: Coverage and recency.
251 00:23:53.000 ⇒ 00:23:55.799 Shivani Amar: Wholesale partner coverage and recency.
252 00:23:56.450 ⇒ 00:24:01.260 Shivani Amar: So this is ranked by your biggest wholesale partners.
253 00:24:01.580 ⇒ 00:24:05.579 Shivani Amar: And it’s just telling you when their most recent order date was?
254 00:24:06.280 ⇒ 00:24:13.050 Jasmin Multani: Yeah, and how many days has it been since the last order? In case you want to, like, double-click and check up on them.
255 00:24:16.410 ⇒ 00:24:17.110 Shivani Amar: Okay.
256 00:24:21.810 ⇒ 00:24:25.350 Shivani Amar: What is this one? Sales and orders by segment.
257 00:24:31.820 ⇒ 00:24:42.659 Advait Nandakumar Menon: So, this, again, you can… the filter above will be useful to help the… what period you’re looking at, comparing with previous month, or however you want to change it, so…
258 00:24:43.300 ⇒ 00:24:44.050 Advait Nandakumar Menon: Yep.
259 00:24:56.830 ⇒ 00:24:57.500 Shivani Amar: Okay.
260 00:24:57.710 ⇒ 00:25:04.490 Shivani Amar: I’m gonna have to think about that one. Okay, Partners by Day since last order band, it makes sense to me as, like, hey, this is what…
261 00:25:05.010 ⇒ 00:25:07.820 Shivani Amar: I’m gonna scroll down
262 00:25:12.170 ⇒ 00:25:20.050 Shivani Amar: Okay, gotcha. So, Partners by Dayson’s Last Order Band, like, Makes sense to me… But…
263 00:25:23.510 ⇒ 00:25:26.380 Shivani Amar: It’s hard, like, my instinct is, like…
264 00:25:27.070 ⇒ 00:25:33.339 Shivani Amar: as a standalone snapshot, this is not that useful. The only way it is useful is if…
265 00:25:35.200 ⇒ 00:25:37.470 Shivani Amar: People have slowed down their ordering.
266 00:25:38.430 ⇒ 00:25:47.350 Shivani Amar: But as a standalone snapshot, just like, hey, like, I guess it tells a newbie, like, Hey…
267 00:25:50.190 ⇒ 00:25:54.809 Shivani Amar: It tells you, kind of, who’s at risk, but you already see that in the other thing.
268 00:25:55.580 ⇒ 00:25:57.329 Shivani Amar: I realize, hey, it’s been…
269 00:25:59.530 ⇒ 00:26:08.350 Shivani Amar: Like, in the other executive tab, right, where we say at risk is, like, people who are between 180… 180 and…
270 00:26:09.230 ⇒ 00:26:10.990 Shivani Amar: 65, and the fuck?
271 00:26:12.050 ⇒ 00:26:19.339 Shivani Amar: If somebody’s over 365, they’ve turned, quote-unquote, like, I’m seeing that in the other one, so I’m just trying to figure out, like.
272 00:26:20.580 ⇒ 00:26:23.500 Shivani Amar: If I were a regular user of this dashboard.
273 00:26:24.260 ⇒ 00:26:27.449 Shivani Amar: I wouldn’t be able to notice an aberration, like a shift.
274 00:26:28.410 ⇒ 00:26:31.830 Jasmin Multani: Yeah, cause it’s not… by time, like…
275 00:26:32.220 ⇒ 00:26:32.820 Shivani Amar: No.
276 00:26:32.820 ⇒ 00:26:33.699 Jasmin Multani: every snap talk.
277 00:26:35.710 ⇒ 00:26:37.789 Shivani Amar: So I’m like, unless it were…
278 00:26:41.730 ⇒ 00:26:50.880 Shivani Amar: average… not average, average time between orders also doesn’t seem that… that useful. So, like, if I were to go up top and I were to say, I want to actually see…
279 00:26:51.080 ⇒ 00:26:54.010 Shivani Amar: Who…
280 00:26:54.850 ⇒ 00:27:04.099 Shivani Amar: like, if I wanted to say, I want to see who the people are that haven’t ordered in more than 45 days, could they use that dashboard? No, go down, go back down.
281 00:27:04.820 ⇒ 00:27:10.289 Shivani Amar: Okay. Partner who… this one. If I were to say, I want to actually just filter this…
282 00:27:10.410 ⇒ 00:27:20.690 Shivani Amar: one for, like, a certain time range, or a certain whatever. That’s kind of what the spreadsheet allows them to do, right? Right? A bit, where they’re like, okay, I actually want to see who hasn’t ordered since
283 00:27:20.890 ⇒ 00:27:36.469 Shivani Amar: 2025, who hasn’t ordered at all in 2026, and who are in California, or something. So, like, that’s what the beauty of that spreadsheet is. Does this get them there? Because if… I just want them to be able to manipulate this table as they need to.
284 00:27:36.740 ⇒ 00:27:43.560 Shivani Amar: And so is it that they can expand it, export it? Like, what is it that they could do with this wholesale partner coverage and recency?
285 00:27:46.570 ⇒ 00:27:48.119 Jasmin Multani: I don’t think we can,
286 00:27:48.930 ⇒ 00:27:52.590 Jasmin Multani: rank it right now, right? We would have to… No.
287 00:27:53.770 ⇒ 00:27:59.530 Jasmin Multani: Would it be better if we created a filter, or do you…
288 00:27:59.690 ⇒ 00:28:02.149 Jasmin Multani: Want them to just be able to rank it.
289 00:28:03.450 ⇒ 00:28:16.030 Shivani Amar: No, I would want them to be able to export this. Like, if, like, let’s say I’m Madison, and I’m like, okay, I need to do an initiative for people who haven’t ordered at all in 2026, based in New York, because I’m launching in New York.
290 00:28:16.210 ⇒ 00:28:24.210 Shivani Amar: Like, this is the reason that you have the spreadsheet version of the customer table, so that she can, like, filter things and, like, figure out who she needs to do what with.
291 00:28:24.590 ⇒ 00:28:30.729 Shivani Amar: And if we’re replacing the spreadsheet version with, like, with this, then I feel like it should be something that somebody can, like.
292 00:28:31.140 ⇒ 00:28:36.599 Shivani Amar: Get all of the customers and their recency, and then play with it accordingly.
293 00:28:37.780 ⇒ 00:28:42.450 Shivani Amar: And if that’s fine, then you might not need, like… Okay.
294 00:28:42.720 ⇒ 00:28:44.210 Shivani Amar: Let’s see what it looks like.
295 00:28:44.560 ⇒ 00:28:47.859 Jasmin Multani: All possible… okay, let’s download it.
296 00:29:21.450 ⇒ 00:29:24.240 Jasmin Multani: 4,200 folks.
297 00:29:26.540 ⇒ 00:29:27.650 Jasmin Multani: So…
298 00:29:29.160 ⇒ 00:29:30.970 Shivani Amar: It’s not all the customers, right?
299 00:29:31.590 ⇒ 00:29:37.740 Jasmin Multani: It’s gonna be… filtered by… previous month.
300 00:29:39.110 ⇒ 00:29:45.710 Jasmin Multani: Because over here… We also discuss how there are multiple filters on this.
301 00:29:52.060 ⇒ 00:29:54.539 Shivani Amar: I don’t really know what that means, like, so…
302 00:29:57.740 ⇒ 00:29:59.499 Jasmin Multani: So, Athlet, correct me if I’m wrong.
303 00:29:59.500 ⇒ 00:30:04.889 Advait Nandakumar Menon: If you, if you want it all time, just clear out the one from the… Jasmine.
304 00:30:04.890 ⇒ 00:30:05.480 Jasmin Multani: Okay.
305 00:30:11.300 ⇒ 00:30:12.450 Advait Nandakumar Menon: And woke…
306 00:30:17.050 ⇒ 00:30:21.150 Advait Nandakumar Menon: Is there an option for any time in the past?
307 00:30:21.550 ⇒ 00:30:25.300 Advait Nandakumar Menon: Click on In the past, or up top.
308 00:30:26.440 ⇒ 00:30:26.820 Jasmin Multani: Between…
309 00:30:26.820 ⇒ 00:30:35.199 Advait Nandakumar Menon: Click on more… There was an option for more, like, before this.
310 00:30:40.560 ⇒ 00:30:42.820 Advait Nandakumar Menon: Yeah, and click on Custom.
311 00:30:43.760 ⇒ 00:30:46.930 Advait Nandakumar Menon: And I think if you type out anytime, it should…
312 00:30:48.190 ⇒ 00:30:49.440 Jasmin Multani: Just the word anytime.
313 00:30:49.910 ⇒ 00:30:50.730 Advait Nandakumar Menon: Yeah.
314 00:31:00.160 ⇒ 00:31:00.870 Advait Nandakumar Menon: I see.
315 00:31:01.540 ⇒ 00:31:04.749 Advait Nandakumar Menon: Since it’s using the comparison thing, it’s not, yeah.
316 00:31:04.860 ⇒ 00:31:05.710 Advait Nandakumar Menon: Okay.
317 00:31:08.460 ⇒ 00:31:11.939 Jasmin Multani: I’d kill… Like, it’d be more.
318 00:31:11.940 ⇒ 00:31:15.070 Advait Nandakumar Menon: You can use… yeah, you can use this as well.
319 00:31:15.450 ⇒ 00:31:16.980 Jasmin Multani: Can we adjust?
320 00:31:18.870 ⇒ 00:31:20.400 Jasmin Multani: this table.
321 00:31:20.710 ⇒ 00:31:26.779 Jasmin Multani: The recency table to be between strong boundaries.
322 00:31:32.070 ⇒ 00:31:35.820 Advait Nandakumar Menon: I think, can you click on cancel right now?
323 00:31:36.860 ⇒ 00:31:43.669 Advait Nandakumar Menon: Yeah, now it’s showing between anytime, anytime. I think, yeah, now it should show all the… Data errors, yeah.
324 00:31:51.410 ⇒ 00:31:53.550 Jasmin Multani: Let’s re-download this.
325 00:32:00.540 ⇒ 00:32:02.550 Jasmin Multani: We have 2 more minutes.
326 00:32:02.860 ⇒ 00:32:04.130 Jasmin Multani: Just an FYI.
327 00:32:04.450 ⇒ 00:32:05.260 Shivani Amar: Okay.
328 00:32:43.810 ⇒ 00:32:54.379 Shivani Amar: What would be the reason for some of these things being, like, layered into a dashboard with a bunch of graphs versus one dashboard, one graph kind of thing? Like…
329 00:32:54.660 ⇒ 00:32:58.859 Shivani Amar: Like, this feels like… let’s see how many rows this was, by the way.
330 00:33:01.690 ⇒ 00:33:03.820 Shivani Amar: Okay, so that’s more like it.
331 00:33:06.900 ⇒ 00:33:07.840 Shivani Amar: Right?
332 00:33:10.140 ⇒ 00:33:16.479 Jasmin Multani: Anytime and anytime. I haven’t calculated what the no orders would look like.
333 00:33:18.180 ⇒ 00:33:21.489 Shivani Amar: Yeah, I mean, you can check that later, Jasmine, but what I’m saying is, like.
334 00:33:21.820 ⇒ 00:33:29.199 Shivani Amar: Should this just be a standalone tab in wholesale? So that anybody can… and, like, what we talked about, like.
335 00:33:30.040 ⇒ 00:33:43.999 Shivani Amar: with the customer table being like, when did somebody get an… place their last order? When did they get a fridge? Like, anybody on the wholesale side can, like, download this regularly, and then manipulate it. So, like, you have a table, like.
336 00:33:44.030 ⇒ 00:33:58.090 Shivani Amar: just saying partner coverage and recency gives us one thing, but, like, if anybody’s gonna download this, they’re gonna also want to know, well, do they have a fridge? Do they not have a fridge? So I’m like, why don’t we just do what we have in the spreadsheet as a standalone table, and then they can
337 00:33:58.480 ⇒ 00:34:01.499 Shivani Amar: They can manipulate it by a time range if they need to.
338 00:34:02.450 ⇒ 00:34:06.769 Advait Nandakumar Menon: Yeah, yeah, I can know that we have the topic and the…
339 00:34:06.900 ⇒ 00:34:09.300 Advait Nandakumar Menon: table underneath it for that, so I can…
340 00:34:09.500 ⇒ 00:34:11.890 Advait Nandakumar Menon: Have it as a separate tab on the dashboard.
341 00:34:15.929 ⇒ 00:34:17.689 Jasmin Multani: That’s, that’s stupid.
342 00:34:18.840 ⇒ 00:34:20.030 Jasmin Multani: Recreate.
343 00:34:23.449 ⇒ 00:34:26.279 Shivani Amar: You guys wanna try to link again tomorrow for 20?
344 00:34:28.560 ⇒ 00:34:30.270 Jasmin Multani: Yes.
345 00:34:30.270 ⇒ 00:34:34.879 Shivani Amar: Okay, because some time off next week, so let’s just, like, let’s just meet every day this week.
346 00:34:35.300 ⇒ 00:34:39.230 Jasmin Multani: Okay, yes, and yeah, I’ll message you something else.
347 00:34:39.449 ⇒ 00:34:41.109 Jasmin Multani: Perfect. Thank you.
348 00:34:41.190 ⇒ 00:34:42.130 Shivani Amar: Bye. Bye.