Meeting Title: Element Team QA and Planning Date: 2026-04-17 Meeting participants: Jasmin Multani, Advait Nandakumar Menon, Shivani Amar
WEBVTT
1 00:00:17.160 ⇒ 00:00:18.030 Advait Nandakumar Menon: Hey!
2 00:00:19.780 ⇒ 00:00:20.790 Advait Nandakumar Menon: Happy Friday.
3 00:00:21.840 ⇒ 00:00:24.859 Jasmin Multani: Happy Friday to you, too.
4 00:00:30.940 ⇒ 00:00:33.809 Jasmin Multani: I think I’m just gonna show her the tickets that we cut.
5 00:00:34.290 ⇒ 00:00:36.839 Jasmin Multani: And then we’ll live edit, like…
6 00:00:37.470 ⇒ 00:00:43.810 Jasmin Multani: we may as well just live edit in the tickets. I’m not even sure if we’re allowed to show the linear tickets, but…
7 00:00:45.070 ⇒ 00:00:45.820 Advait Nandakumar Menon: Okay.
8 00:00:45.950 ⇒ 00:00:47.739 Advait Nandakumar Menon: Even I’m not sure about that.
9 00:00:48.060 ⇒ 00:00:50.260 Jasmin Multani: Hmm, let me…
10 00:01:18.760 ⇒ 00:01:22.679 Advait Nandakumar Menon: I don’t see a response from her to this invite.
11 00:01:23.280 ⇒ 00:01:28.829 Jasmin Multani: She, she just messaged me, she just messaged me that she’s gonna be here in a few minutes.
12 00:01:29.170 ⇒ 00:01:29.780 Advait Nandakumar Menon: Okay.
13 00:01:29.780 ⇒ 00:01:38.930 Jasmin Multani: Yeah. Okay, so I asked Robert and Otam, like, can we show him the tickets?
14 00:01:40.090 ⇒ 00:01:46.610 Jasmin Multani: Hold on. Doesn’t matter to me, just hide other client info, I can add her as a guest to our workplace. Sweet.
15 00:01:46.950 ⇒ 00:01:47.460 Jasmin Multani: Okay.
16 00:01:47.460 ⇒ 00:01:48.150 Advait Nandakumar Menon: Nice.
17 00:01:48.150 ⇒ 00:01:48.780 Jasmin Multani: It helps.
18 00:02:10.710 ⇒ 00:02:18.930 Jasmin Multani: Okay, so that’s good. Let me look at your information. I didn’t really explore, the spreadsheets too much.
19 00:02:19.270 ⇒ 00:02:20.890 Jasmin Multani: the Omni spreadsheets.
20 00:02:22.890 ⇒ 00:02:31.270 Jasmin Multani: You should not. I’m reading your comment. If we are changing our modeling topics to make it like the spreadsheet, we can point selfie to the new topics. Okay.
21 00:02:31.550 ⇒ 00:02:45.939 Jasmin Multani: So, that’s what we’ll tell her, like, hey, there are a lot of benefits from, using the Omni spreadsheets, like, it’s easier to adapt, easier to QA, and, easier to adopt for her as well.
22 00:02:46.010 ⇒ 00:03:04.750 Jasmin Multani: And, if we go this route, it’s just gonna… we’re gonna have to add an additional queuing for Salty, but it shouldn’t be a huge blocker. And I also want her to understand, the pyramid structure that we talked about yesterday. Like, let’s start off in the spreadsheets.
23 00:03:05.130 ⇒ 00:03:06.640 Jasmin Multani: QA those.
24 00:03:06.750 ⇒ 00:03:09.700 Jasmin Multani: Then let’s, QA the graphs that get built.
25 00:03:09.890 ⇒ 00:03:17.070 Jasmin Multani: And then, start queuing Salty with her official questions.
26 00:03:17.750 ⇒ 00:03:18.990 Advait Nandakumar Menon: Okay.
27 00:03:21.240 ⇒ 00:03:25.010 Jasmin Multani: I hope… I hope she’s… I hope it won’t be intense.
28 00:03:29.210 ⇒ 00:03:32.620 Jasmin Multani: It’s just been, like, a mad… mad dash.
29 00:03:33.100 ⇒ 00:03:37.650 Jasmin Multani: to, like, pivot fast. I don’t think she likes pivoting. That’s what my gut says.
30 00:03:38.890 ⇒ 00:03:41.780 Jasmin Multani: But, I’m trying to, like.
31 00:03:43.730 ⇒ 00:03:50.070 Jasmin Multani: Make her understand that, like, we’re trying to be… we’re looking for the most efficient thing to do.
32 00:03:52.030 ⇒ 00:03:55.059 Jasmin Multani: At least she’ll be happy that she can see the linear tickets.
33 00:03:56.610 ⇒ 00:03:57.700 Advait Nandakumar Menon: Yeah, yeah.
34 00:05:13.560 ⇒ 00:05:20.260 Advait Nandakumar Menon: There are these limitations, if you want to keep in mind, if you’re going down this route with respect to spreadsheets, I just sent you a link.
35 00:05:20.780 ⇒ 00:05:24.409 Jasmin Multani: Okay, cool. She’s probably not gonna read this, but.
36 00:05:24.640 ⇒ 00:05:25.850 Advait Nandakumar Menon: No, just photo information.
37 00:05:25.850 ⇒ 00:05:27.780 Jasmin Multani: For us, yeah, thank you, thank you.
38 00:05:28.960 ⇒ 00:05:39.299 Jasmin Multani: I saw, like, the first 5 minutes of the Omni video you had sent, of the alignment, so I’m gonna, after this meeting, getting her alignment, I’m gonna go, like, double-click.
39 00:05:41.380 ⇒ 00:05:43.180 Jasmin Multani: Yeah. Okay.
40 00:05:46.450 ⇒ 00:05:47.130 Shivani Amar: Hello.
41 00:05:47.130 ⇒ 00:05:47.750 Jasmin Multani: funny.
42 00:05:47.750 ⇒ 00:05:48.500 Advait Nandakumar Menon: Hello.
43 00:05:48.730 ⇒ 00:05:49.950 Shivani Amar: How are we doing?
44 00:05:50.670 ⇒ 00:05:54.970 Jasmin Multani: Friday, girl. It’s so close to the deadline.
45 00:05:54.970 ⇒ 00:05:56.019 Shivani Amar: I know you did it.
46 00:05:56.160 ⇒ 00:05:56.750 Jasmin Multani: Yeah.
47 00:05:57.630 ⇒ 00:06:00.160 Jasmin Multani: Are you guys doing anything fun this weekend?
48 00:06:05.560 ⇒ 00:06:11.090 Shivani Amar: Like, what are we doing? I have, like, a birthday party, this dance party tonight, so that should be fun.
49 00:06:13.060 ⇒ 00:06:16.540 Shivani Amar: And then… Wedding planning.
50 00:06:16.540 ⇒ 00:06:18.669 Jasmin Multani: How far away is that?
51 00:06:18.670 ⇒ 00:06:20.370 Shivani Amar: It’s next month.
52 00:06:20.370 ⇒ 00:06:25.280 Jasmin Multani: grass, congrats!
53 00:06:25.280 ⇒ 00:06:26.420 Shivani Amar: Yeah.
54 00:06:26.420 ⇒ 00:06:30.460 Jasmin Multani: Make sure to be up to speed. So, are you gonna be out of office next month?
55 00:06:30.750 ⇒ 00:06:36.159 Shivani Amar: We have a two-week rest and assess in the month of May anyway, so…
56 00:06:37.270 ⇒ 00:06:40.940 Shivani Amar: Have I onboarded you to our Rest and Assess calendars? You guys know what that is?
57 00:06:41.310 ⇒ 00:06:43.150 Jasmin Multani: You, you let us know, yeah.
58 00:06:43.150 ⇒ 00:06:43.540 Shivani Amar: Yeah.
59 00:06:43.540 ⇒ 00:06:43.860 Jasmin Multani: Right?
60 00:06:44.190 ⇒ 00:06:46.450 Jasmin Multani: You know… you know the rest of us? Yes.
61 00:06:46.810 ⇒ 00:06:47.450 Shivani Amar: Yeah.
62 00:06:48.040 ⇒ 00:06:52.089 Shivani Amar: So it’s convenient, because I will be kind of offline at the end of May.
63 00:06:52.690 ⇒ 00:06:56.570 Jasmin Multani: Oh, perfect, okay, so we’ll do a mad dash.
64 00:06:56.570 ⇒ 00:06:59.180 Shivani Amar: We’ll do Mad Dash. Pre-Wedding dash.
65 00:07:01.490 ⇒ 00:07:04.699 Jasmin Multani: So are you gonna be out… you’re gonna be out for 2 weeks.
66 00:07:04.700 ⇒ 00:07:05.110 Shivani Amar: Yeah.
67 00:07:05.110 ⇒ 00:07:05.920 Jasmin Multani: Man.
68 00:07:05.920 ⇒ 00:07:09.579 Shivani Amar: Yeah. Like, the first week of June, the last week of May kind of thing.
69 00:07:10.120 ⇒ 00:07:16.100 Jasmin Multani: Perfect, that’s really good information for us. Congrats! Are you gonna do the full… the full wedding?
70 00:07:16.100 ⇒ 00:07:20.400 Shivani Amar: We’re doing a full wedding, but a mini moon, because,
71 00:07:20.510 ⇒ 00:07:36.019 Shivani Amar: he has a show, he’s drum… he’s a drummer, so he’s, like, playing a show in Denver the weekend after we get married, so we’re just gonna go to, like, a beach for 3 days, and then I’ll go with him to Denver, and then we’ll, like, maybe do some mountain adventures, but then we’ll try to do, like, a proper honeymoon later, so…
72 00:07:37.010 ⇒ 00:07:38.399 Shivani Amar: That’s what we’re at.
73 00:07:39.670 ⇒ 00:07:43.110 Jasmin Multani: I hope you get a good break from the wedding, I feel like.
74 00:07:44.830 ⇒ 00:07:46.080 Jasmin Multani: Paris is hard.
75 00:07:46.080 ⇒ 00:07:53.699 Shivani Amar: I know… by the way, like, I know I was like, it’s hard to disaggregate visuals and.
76 00:07:54.570 ⇒ 00:07:57.210 Shivani Amar: UAs at the same time, and so…
77 00:07:58.710 ⇒ 00:08:03.839 Shivani Amar: like, I don’t know if your goal was to go through just retail today, but I wanted to shh…
78 00:08:04.120 ⇒ 00:08:09.360 Shivani Amar: talk a little bit about… before we even get started, I think I just, like, want to talk about, like…
79 00:08:09.490 ⇒ 00:08:19.420 Shivani Amar: It’s, like, the topic I keep talking about. Revenue versus sales versus point of sales. Just, like, really, like, be like, let’s make sure we’re all super clear on the definitions. So…
80 00:08:19.780 ⇒ 00:08:23.799 Shivani Amar: if we want to, like, not even share screens, I’m curious, like.
81 00:08:24.460 ⇒ 00:08:30.659 Shivani Amar: I’m curious, like, Aveyeth, could you describe what the difference is between wholesale sales and revenue?
82 00:08:33.570 ⇒ 00:08:39.760 Advait Nandakumar Menon: Yeah, wholesale sales, is, as per my understanding, is… Something like…
83 00:08:39.870 ⇒ 00:08:43.769 Advait Nandakumar Menon: what you guys sell to Shopify, or Shopify makes sales.
84 00:08:44.990 ⇒ 00:08:54.350 Advait Nandakumar Menon: as a wholesale retailer, that’s how I understand sales are, and revenues, anything, like, total sales, and any, discounts, or refunds, or whatever you got.
85 00:08:54.350 ⇒ 00:08:56.829 Shivani Amar: Absolutely. Nailed it. Okay, great.
86 00:08:57.120 ⇒ 00:09:00.060 Shivani Amar: And…
87 00:09:01.720 ⇒ 00:09:19.379 Shivani Amar: currently… oh, that’s, like, an interesting thing that, like, our numbers could be off a little bit if there are manual orders, because we’re not currently ingesting manual orders, okay? I forgot about that, but that’s… you know, I just went through, like, as an aside, I just went through these numbers with,
88 00:09:19.380 ⇒ 00:09:22.129 Shivani Amar: Bess, who’s on our, like, finance team.
89 00:09:22.190 ⇒ 00:09:28.090 Shivani Amar: No, sorry, that’s not the right tab. Where is it? The wholesale tab?
90 00:09:28.910 ⇒ 00:09:34.670 Shivani Amar: And… I was, like, sharing these numbers with her in this table, and she was, like.
91 00:09:35.470 ⇒ 00:09:37.380 Shivani Amar: She was like, that looks really…
92 00:09:37.950 ⇒ 00:09:44.450 Shivani Amar: She was like, this is missing a couple of SKUs, I think. The D2C Sparkling is missing a couple of SKUs, I think I tried to
93 00:09:45.260 ⇒ 00:09:55.230 Shivani Amar: Lara comment in somewhere, I don’t know. Did I let Lara comment in? Yes, it’s missing bundle SKUs, so this is not comprehensive of all SKUs right now.
94 00:09:55.230 ⇒ 00:09:55.760 Jasmin Multani: Okay.
95 00:09:55.760 ⇒ 00:09:58.989 Shivani Amar: And then this, she was like.
96 00:10:00.020 ⇒ 00:10:06.609 Shivani Amar: this looks… I think this one… there was one that was, like, 10 cents off, like, it was, like, beautiful.
97 00:10:07.630 ⇒ 00:10:12.200 Shivani Amar: I think this one was good, which is, like, the D2C… drink mix?
98 00:10:12.310 ⇒ 00:10:28.470 Shivani Amar: And then wholesale drink mix, she said she had a bit of a different number. So, it could be the SKUs thing, it could be the manual orders, but, like, it’s, like, we’re getting… D2C drink mix sales makes sense to me, as being spot on, because…
99 00:10:29.790 ⇒ 00:10:34.820 Shivani Amar: we probably haven’t added many new… now we have pink lemonade, but in the month of March, we didn’t.
100 00:10:35.050 ⇒ 00:10:35.930 Shivani Amar: Right?
101 00:10:36.410 ⇒ 00:10:46.169 Shivani Amar: So, in the month of March, it was probably the same SKUs we’ve been having, and then it’s possible for April, this will be off if we don’t also include the new SKUs.
102 00:10:46.830 ⇒ 00:10:56.049 Shivani Amar: I don’t know how the sheep works exactly, and eventually we’re shifting to Omni, but that’s… I just wanted to share that I, like, was trying to do a QA check, and my goal…
103 00:10:56.730 ⇒ 00:11:10.120 Shivani Amar: Especially for wholesale, when we get to the point where we’re, like, stamp of approval on the dashboard, is that Bess feels like she no longer has to do wholesale revenue reconciliation, and, like, she can trust Omni to do it.
104 00:11:11.860 ⇒ 00:11:12.450 Jasmin Multani: Okay.
105 00:11:12.450 ⇒ 00:11:18.249 Shivani Amar: Okay. I’ve talked, like, with them about this, but I’m just trying to bring you guys along for the ride.
106 00:11:18.440 ⇒ 00:11:24.619 Shivani Amar: So yeah, so it’s like, I think it’s fun when a number is, like, 10 cents off, and you’re like, okay, this, like, this thing is working.
107 00:11:28.460 ⇒ 00:11:29.680 Jasmin Multani: We want you to get fun.
108 00:11:29.960 ⇒ 00:11:36.799 Jasmin Multani: Huh? Why do you think it’s fine? You’re saying that, like, it is where you’re able to see the delta instead of it being…
109 00:11:37.770 ⇒ 00:11:53.399 Shivani Amar: No, I’m saying when it’s only 10 cents off, that feels, like, negligible to me. If it’s, like, 2,200,000 off doesn’t work.
110 00:11:53.400 ⇒ 00:12:00.719 Shivani Amar: Right? But, like, when I see that something’s only 10 cents off from what she had, I’m like, we’re getting closer to the source of truth, this being the source of truth.
111 00:12:01.010 ⇒ 00:12:13.189 Jasmin Multani: Okay, that’s really helpful, because we’re tracking Omni’s, accuracy, back to those Google Sheets, so I’ll write those variances down. Yeah. Number of SKUs…
112 00:12:13.190 ⇒ 00:12:18.150 Shivani Amar: Like, it’s always making sure we have the latest and greatest SKUs, and like, I don’t know if that requires
113 00:12:18.230 ⇒ 00:12:36.009 Shivani Amar: an input from finance to say, like… like, I don’t… like, I don’t know if we need a master SKU list, or if the data model can pick up on, like, this is a new SKU, and it’s D2C. But, like, if a new SKU is presented, and it’s for wholesale, how do we make sure that the wholesale sales is, like, constantly taking whatever new SKUs they have?
114 00:12:38.320 ⇒ 00:12:40.820 Shivani Amar: How do we make sure that our database knows what this
115 00:12:40.970 ⇒ 00:12:44.450 Shivani Amar: SKUs are to pull at any given point. And is that…
116 00:12:45.110 ⇒ 00:12:57.470 Shivani Amar: is that, like, a separate, like, piece of work to make all the stuff… is that, like, a file that we need to be ingesting regularly, or is it, like, it can kind of intuit from your master SKUs on Shopify and, like, kind of know how to bucket them?
117 00:12:58.070 ⇒ 00:13:05.270 Jasmin Multani: I would have to… we would have to see what triggers the data in the tables, because let’s say…
118 00:13:05.620 ⇒ 00:13:13.090 Jasmin Multani: let’s say, like, you guys have a catalog of all of the full SKUs, but if the Shopify…
119 00:13:13.970 ⇒ 00:13:16.499 Jasmin Multani: Is triggered by sales.
120 00:13:16.810 ⇒ 00:13:22.280 Jasmin Multani: Then, and the way we do… we pull the numbers, it’s only gonna show…
121 00:13:23.450 ⇒ 00:13:33.069 Jasmin Multani: the SKUs that have already been sold, because what if there are SKUs that have been deployed to the shop, but they just haven’t been sold yet?
122 00:13:33.730 ⇒ 00:13:38.370 Jasmin Multani: Is that gonna show up at all? Or is that just gonna be null? Or is that just not gonna be good?
123 00:13:38.370 ⇒ 00:13:39.370 Shivani Amar: be null, I guess.
124 00:13:39.370 ⇒ 00:13:49.159 Jasmin Multani: Yeah, yeah. So let’s validate that, too. How is Shopify… Triggering with the data.
125 00:13:49.570 ⇒ 00:14:00.600 Jasmin Multani: When, triggering, or… I’m wondering, Dana, when… There is a new SKU.
126 00:14:03.880 ⇒ 00:14:11.680 Jasmin Multani: Holy… I don’t… Shift.
127 00:14:14.570 ⇒ 00:14:22.669 Jasmin Multani: If there’s an issue, then we would need, like, a… robust, full,
128 00:14:23.270 ⇒ 00:14:26.240 Jasmin Multani: Data table that just documents things.
129 00:14:27.130 ⇒ 00:14:36.780 Jasmin Multani: And because it sounds like a SKU launch is pretty rare, like, item net new launches is pretty rare, I feel like that’ll end up being easier to maintain.
130 00:14:37.370 ⇒ 00:14:38.220 Shivani Amar: Maybe.
131 00:14:38.470 ⇒ 00:14:56.650 Shivani Amar: Yeah. I don’t know, as we go into, like, let’s say we’re going to, like, 3 retailers, there might be, like, a proliferation of SKUs. I’m not sure. I don’t want to make the assumption that it’s right, I just want to make sure that we have a process for capturing SKUs, and, like, making sure every SKU that needs to be a part of a sales number is, and I just want that to be, like, named.
132 00:14:57.400 ⇒ 00:15:02.560 Jasmin Multani: Yeah, I think even when, we looked at, like, Avid made the product velocity.
133 00:15:02.660 ⇒ 00:15:10.300 Jasmin Multani: of SKUs, I… there were some nulls, and we were like, oh, we should double check, we should double check, like, are they nulls? Because…
134 00:15:11.000 ⇒ 00:15:16.660 Jasmin Multani: Element shipped it out, in the stores that have not been selling, or…
135 00:15:17.650 ⇒ 00:15:19.810 Shivani Amar: We didn’t have sparkling in Walmart.
136 00:15:19.960 ⇒ 00:15:27.260 Shivani Amar: Like, until recently. So it makes sense that it would show us, like, nulls at Walmart, but show us something at Target, for example.
137 00:15:28.210 ⇒ 00:15:30.850 Shivani Amar: Okay.
138 00:15:31.170 ⇒ 00:15:38.509 Jasmin Multani: But, like, what would happen if, like, you guys have that launch, and you just haven’t shipped it to Walmart or Target yet? I’m curious, like…
139 00:15:38.510 ⇒ 00:15:42.639 Shivani Amar: then that can just show up as null for the time. People can contextualize that, I think that’s fine.
140 00:15:42.970 ⇒ 00:15:43.610 Jasmin Multani: Okay.
141 00:15:43.610 ⇒ 00:15:50.530 Shivani Amar: Yeah. Okay. So, I’m gonna go back to the wholesale thing and just share some high-level notes about, about,
142 00:15:50.640 ⇒ 00:15:56.460 Shivani Amar: my style of graphs, we don’t have to, like, go… like, we’re gonna have more time together. Okay, this is…
143 00:15:56.490 ⇒ 00:16:13.170 Shivani Amar: a big… it’s a thick graph, if you will. I find it disorienting, and like, again, this is me, Shivani, and our VPs could feel differently in time. I find it disorienting to go from vertical to horizontal.
144 00:16:14.540 ⇒ 00:16:15.210 Jasmin Multani: Okay.
145 00:16:15.210 ⇒ 00:16:21.860 Shivani Amar: Okay, so I’m like, just to have variety, like, to me feels like… kind of unnecessary. It’s like…
146 00:16:22.580 ⇒ 00:16:33.760 Shivani Amar: I’m like, let’s just have it be orientation, where sales is the y-axis, and then, yeah, you can show it by segment, and you can show it by category, just, like, as a mix.
147 00:16:34.210 ⇒ 00:16:36.059 Shivani Amar: I think for this top graph.
148 00:16:36.230 ⇒ 00:16:39.840 Shivani Amar: Just having it be a stacked bar, Right off the bat.
149 00:16:39.840 ⇒ 00:16:40.820 Jasmin Multani: That’s the neat.
150 00:16:40.820 ⇒ 00:16:56.290 Shivani Amar: shows you drink mix versus sparkling, I might have already typed that in somewhere, so I realized that. I’m, like, just showing it, like, as a stack bar, and, like, maybe… maybe it’s showing it as a stack bar by drink mix, and then you have a similar stacked bar that’s showing it to you by segment.
151 00:16:57.890 ⇒ 00:17:22.750 Shivani Amar: Right? Instead of having it be, like, you just have, like, two versions of this. So it’s, like, stacked bar, wholesale revenue trend by product type, wholesale revenue trend by… or, like, wholesale revenue by partner segment, and both of them are the same. They’re just the same stacked bars, but you can see, like, oh, Trusted Health is clearly the biggest one, or maybe trusted health dwindled last month, or whatever. Oh, it’s interesting, specialty retail had a, like, big bump in February.
152 00:17:22.750 ⇒ 00:17:27.670 Shivani Amar: Like, that would just be nice to see it over time also, instead of, like, Cumulating…
153 00:17:27.670 ⇒ 00:17:32.499 Shivani Amar: Yeah. Which, like, don’t really tell me that. Cumulatives don’t tell me that much, in my opinion.
154 00:17:32.700 ⇒ 00:17:33.870 Shivani Amar: Right now.
155 00:17:33.870 ⇒ 00:17:40.680 Jasmin Multani: Okay, okay, okay. So, up and down, it’s gonna be like… Going month over month, and…
156 00:17:40.680 ⇒ 00:17:43.620 Shivani Amar: No, so I like this orientation, right? Months?
157 00:17:44.310 ⇒ 00:17:53.989 Shivani Amar: Revenue. Revenue on the Y… or, like, revenue on the y-axis, time series going across. Yes. Sacked bar, sparkling.
158 00:17:54.170 ⇒ 00:18:01.649 Shivani Amar: Drink mix, sparkling drink mix, sparkling drink mix, and you can see. Then the same exact graph, but actually split by partner segment.
159 00:18:02.020 ⇒ 00:18:03.230 Jasmin Multani: Yeah, that makes sense.
160 00:18:03.230 ⇒ 00:18:03.710 Shivani Amar: Okay?
161 00:18:03.840 ⇒ 00:18:10.579 Jasmin Multani: So you can see how the width changes over time, and you can easily track growth. That makes sense.
162 00:18:10.830 ⇒ 00:18:15.360 Jasmin Multani: We have that cut for Advid,
163 00:18:15.680 ⇒ 00:18:21.229 Jasmin Multani: that I’ll update your linear ticket so that it also includes the partner segmentation.
164 00:18:21.580 ⇒ 00:18:22.270 Shivani Amar: Cool.
165 00:18:22.640 ⇒ 00:18:26.619 Jasmin Multani: I was gonna say something here.
166 00:18:26.620 ⇒ 00:18:38.160 Shivani Amar: And you could just scrap these horizontal guys, because I don’t think they’re giving me much, and I can see it from the… I can see it from the stack bar myself, that obviously it’s a lot more drink mix, we’re starting to sell sparkling, you can see, like, drink mix.
167 00:18:38.160 ⇒ 00:18:46.949 Shivani Amar: Sparkling didn’t exist, then Sparkling pops up, like, how’s it going, right? Like, that will be cool. And then this is, like, fine. Like, I think this is…
168 00:18:47.380 ⇒ 00:18:51.159 Shivani Amar: you know, we talked about the totals being a little, like, funky, but, like, I think…
169 00:18:51.180 ⇒ 00:19:10.399 Shivani Amar: I think just as a… somebody were curious, like, okay, like, how is wholesale revenue doing over time? And, like, by the way, who’s contributing to revenue? I think that… that’s a fine thing to have here. And then the, like, maybe in between, it’s like, revenue, revenue, funnel, like, maybe the funnel could sit here, which is, like.
170 00:19:10.410 ⇒ 00:19:27.200 Shivani Amar: who… how many wholesale partners do I have? Like, if I were an executive, I’d be like, how much money am I getting from wholesale? How many wholesale partners do I have? Why are a bunch of wholesale partners churning, right? Like, it’s like the basic high-level funnel. And then it’s like, oh, who are my, like, some of my biggest partners? And then I think that this dashboard will be complete.
171 00:19:27.600 ⇒ 00:19:30.150 Jasmin Multani: Okay. So…
172 00:19:30.290 ⇒ 00:19:38.909 Jasmin Multani: Because we were talking about this yesterday, too, so you don’t want to… this top 20 by partners, you don’t want to delete that total at the end.
173 00:19:39.220 ⇒ 00:19:43.519 Shivani Amar: No, I don’t think the total… I think the total can be… I don’t think the total is…
174 00:19:44.350 ⇒ 00:19:56.550 Shivani Amar: necessary when it’s totaling all partners, so, like, I would rather it be, like, you have the funnel in between this, you have new applicants, approved,
175 00:19:56.550 ⇒ 00:20:10.770 Shivani Amar: whatever the funnel is, like, first order, second order, you just get a feel for how many customers you have, who are active, how many churned, like, over time. You have, like, a grid for that, like a table, and then you say, by the way, these are your top 20 partners, and you just don’t need the total line.
176 00:20:10.770 ⇒ 00:20:17.449 Jasmin Multani: Okay, that might be a little bit tricky, just because, you know that percent of total revenue to the right?
177 00:20:17.450 ⇒ 00:20:19.410 Shivani Amar: Is what’s defining the thing.
178 00:20:19.690 ⇒ 00:20:23.309 Jasmin Multani: It’s… it relies on this bottom row.
179 00:20:23.690 ⇒ 00:20:25.290 Jasmin Multani: To do the calculation.
180 00:20:25.290 ⇒ 00:20:27.000 Shivani Amar: Yeah.
181 00:20:27.290 ⇒ 00:20:35.749 Jasmin Multani: But we can… we don’t have… if you don’t feel like you need that calm to the further right, that’s… we can… we can just edit it out then.
182 00:20:38.590 ⇒ 00:20:40.689 Shivani Amar: Yeah, I don’t think I need it.
183 00:20:41.420 ⇒ 00:20:42.430 Shivani Amar: That’s fine.
184 00:20:42.940 ⇒ 00:20:47.289 Jasmin Multani: Yeah. I think eventually, like, I think it’ll be, like, down the road, but for
185 00:20:47.710 ⇒ 00:20:55.560 Jasmin Multani: We can just knock it out. So, at the… I’m just gonna write that. Just delete the… that column.
186 00:20:56.270 ⇒ 00:20:57.800 Jasmin Multani: itself.
187 00:20:58.050 ⇒ 00:21:04.680 Jasmin Multani: And maybe this information of top 20 can sit in the funnel with a labeling.
188 00:21:06.010 ⇒ 00:21:12.540 Jasmin Multani: Let me write that down… Delete…
189 00:21:16.900 ⇒ 00:21:27.210 Jasmin Multani: So… Plus, the, ranking of… partners… Yeah.
190 00:21:27.730 ⇒ 00:21:31.809 Jasmin Multani: So people, people can toggle and filter as they need.
191 00:21:36.190 ⇒ 00:21:44.700 Jasmin Multani: And one more question about the funnel. Do you just want that information to sit as a table, or do you want it to be presented as a graph as well?
192 00:21:46.210 ⇒ 00:21:51.500 Shivani Amar: I like it as a table right now. Some people might, like… you know, it’s interesting, at,
193 00:21:57.170 ⇒ 00:22:02.419 Shivani Amar: How did I visualize it at Brave? Like, I would… Brave, it was more like an actual…
194 00:22:03.300 ⇒ 00:22:10.360 Shivani Amar: like, first appointment to second appointment to third appointment, and you could, like, see the drop-off between stages.
195 00:22:10.770 ⇒ 00:22:12.089 Shivani Amar: Which, in this case, would be, like.
196 00:22:12.250 ⇒ 00:22:31.649 Shivani Amar: in a period of time, like, how many first orders did you get, how many second orders did you get, how many third orders did you get, how many ongoing orders did you get? That might be an interesting, like, literal, like, pyramid that exists, so you can, like, you can see the funnel of, like, the customers, but then, like, if you’re curious about, like.
197 00:22:32.120 ⇒ 00:22:37.640 Shivani Amar: like, what’s my drop-off between first and second order? That might be, like, a nice little…
198 00:22:37.640 ⇒ 00:22:38.260 Jasmin Multani: Visual.
199 00:22:38.260 ⇒ 00:22:39.330 Shivani Amar: Might, yeah.
200 00:22:39.330 ⇒ 00:22:41.620 Jasmin Multani: Okay, drop off of 1st, 2nd, and third.
201 00:22:42.400 ⇒ 00:22:46.940 Shivani Amar: I think that’s just interesting to see, like, okay, we usually get, like.
202 00:22:47.130 ⇒ 00:22:59.019 Shivani Amar: we get, like, 100 first orders a week, but, like, only 20 second orders, or… I’m kind of making stuff up right now. I would need to see it, and then I’d get a feel for, like, okay, what looks good.
203 00:22:59.020 ⇒ 00:23:08.450 Jasmin Multani: And Edveith and I were talking yesterday, just as a meta point of how the queuing has been going.
204 00:23:08.500 ⇒ 00:23:22.340 Jasmin Multani: I think, for our future net new dashboards that we’re gonna push out for, like, e-commerce, supply chain, and so forth, I think the process should be, A, I literally just draw tables.
205 00:23:22.380 ⇒ 00:23:31.479 Jasmin Multani: or whiteboard it with you, before sending it over to Adviv to be like, okay, how do we visually want to look at this?
206 00:23:31.480 ⇒ 00:23:31.880 Shivani Amar: Yeah, that’.
207 00:23:31.880 ⇒ 00:23:32.880 Jasmin Multani: It’s fine.
208 00:23:32.880 ⇒ 00:23:44.860 Shivani Amar: This is the spec document. The spec document is, like, not how my brain works. It’s like reading about a dashboard is very different than just us being like, I think I’m gonna want it to start like this. Like, we could do it in Google Sheets, right? Just be like.
209 00:23:44.860 ⇒ 00:23:45.380 Jasmin Multani: Yes.
210 00:23:45.380 ⇒ 00:23:46.810 Shivani Amar: And, like, I love…
211 00:23:46.990 ⇒ 00:23:54.119 Shivani Amar: I don’t even know if Omni is capable of, like, doing clustered bar charts really well, but I like a clustered bar chart.
212 00:23:54.230 ⇒ 00:23:54.889 Shivani Amar: In life.
213 00:23:54.890 ⇒ 00:23:55.450 Jasmin Multani: I’m totally…
214 00:23:55.450 ⇒ 00:23:59.390 Shivani Amar: So, like, let me, let me show you what I’m talking about.
215 00:23:59.560 ⇒ 00:24:03.330 Shivani Amar: Let’s look at clustered margins. This is very painy.
216 00:24:03.330 ⇒ 00:24:04.590 Jasmin Multani: No, it’s all good.
217 00:24:04.590 ⇒ 00:24:10.740 Shivani Amar: This is a clustered bar chart, so… It’s like…
218 00:24:11.380 ⇒ 00:24:12.030 Jasmin Multani: Yeah.
219 00:24:12.250 ⇒ 00:24:33.849 Shivani Amar: Right? And then this could be Target, drink mix and sparkling. Walmart, drink mix and sparkling. Vitamin Shop, drink mix and sparkling. Eventually, you could have all your retailers, and then you have Jan, Feb, March. And then you could be like, okay, what’s my mix across all of these? It’s… I love a cl… it’s so funny to me, like, I’m listening to myself talk, I’ve, like, had a little bit of coffee today, like, I love a clustered bar!
220 00:24:35.120 ⇒ 00:24:35.580 Shivani Amar: That’s my best.
221 00:24:35.580 ⇒ 00:24:39.520 Jasmin Multani: She’s in data. You’re in the right spot.
222 00:24:39.520 ⇒ 00:24:41.530 Shivani Amar: I didn’t realize I was such a data nerd.
223 00:24:41.530 ⇒ 00:24:46.350 Jasmin Multani: You know what? It’s… it’s just super niche when you’re like.
224 00:24:46.350 ⇒ 00:24:46.670 Shivani Amar: Oh, yeah.
225 00:24:46.670 ⇒ 00:24:49.310 Jasmin Multani: You just love a story, I think that’s what I tell people.
226 00:24:49.310 ⇒ 00:24:58.100 Shivani Amar: I’m gonna tell a story, but yeah, I haven’t seen Omni’s functionality, like, nail a clustered bar chart, so that’s, like, something I’m curious about, is, like.
227 00:24:58.100 ⇒ 00:24:58.640 Jasmin Multani: God.
228 00:24:58.640 ⇒ 00:24:59.270 Shivani Amar: this.
229 00:24:59.270 ⇒ 00:25:07.810 Jasmin Multani: Yeah, yeah, I, let’s explore it, but I just want to, like, go back to that graph. So, you know how it says that yellow and that blue?
230 00:25:07.810 ⇒ 00:25:08.220 Shivani Amar: Yeah.
231 00:25:08.220 ⇒ 00:25:15.099 Jasmin Multani: It should be… the yellow could be mixed, the blue should be sparkling, and then… So it’s like…
232 00:25:15.100 ⇒ 00:25:18.169 Shivani Amar: Then you have your retailers, and then you have your time.
233 00:25:18.940 ⇒ 00:25:21.419 Jasmin Multani: Yeah. Yeah, we could do that, yeah. I think…
234 00:25:21.420 ⇒ 00:25:23.009 Shivani Amar: That’s what I… that’s my dream.
235 00:25:23.510 ⇒ 00:25:27.160 Jasmin Multani: I think this is how we should have started off our spec.
236 00:25:27.160 ⇒ 00:25:29.760 Shivani Amar: I can tell you what kind of charts I want.
237 00:25:29.760 ⇒ 00:25:30.520 Jasmin Multani: Literally.
238 00:25:30.520 ⇒ 00:25:34.950 Shivani Amar: Why is the bar going horizontally? Like, what is that supposed to do for my brain?
239 00:25:34.950 ⇒ 00:25:35.410 Jasmin Multani: No, you’re.
240 00:25:35.410 ⇒ 00:25:37.970 Shivani Amar: Why should I suddenly flip my lens?
241 00:25:38.030 ⇒ 00:25:41.759 Jasmin Multani: Yeah, I mean, that’s on me, sorry, that’s on me.
242 00:25:42.560 ⇒ 00:25:44.940 Jasmin Multani: Trying to figure out, like, what’s the best way?
243 00:25:45.350 ⇒ 00:25:48.940 Jasmin Multani: where I can write this thing once, and the engineers can understand it, but I
244 00:25:49.280 ⇒ 00:25:56.350 Jasmin Multani: engineers also would like this, like, I was talking to OH, and I realized, okay, for…
245 00:25:57.020 ⇒ 00:26:10.279 Jasmin Multani: whatever requirements I’m gonna give you, like, we’re gonna build out together, hand it off to Edvit and OH, it would be, like, a mock-up table with the rows and the columns, and then, which
246 00:26:10.850 ⇒ 00:26:25.239 Jasmin Multani: types of bar charts, or charts, or whatever, are gonna be sourced from that table. So that’s gonna help Awash. I’m… I’m just being very meta at this point about, like, how we can reduce touchpoints.
247 00:26:26.890 ⇒ 00:26:36.969 Jasmin Multani: Okay, I’m glad we talked about that. Other thing, wanted to shout out, Adviv put on his, like, research hat, and he actually found,
248 00:26:37.390 ⇒ 00:26:45.899 Jasmin Multani: this feature on Omni that you can create an Omni documentation… Omni spreadsheet that mirrors exactly like Google Sheets.
249 00:26:47.520 ⇒ 00:26:51.219 Jasmin Multani: So, is that something that you’d be interested in?
250 00:26:51.830 ⇒ 00:26:56.130 Shivani Amar: Explain it again. So… Tell me.
251 00:26:56.490 ⇒ 00:27:04.450 Jasmin Multani: So, instead of… On top of, like, making dashboards, we can migrate the data
252 00:27:04.910 ⇒ 00:27:11.630 Jasmin Multani: And format it so that it’s looking exactly like the Google Sheets. There’s, like, an Omni Sheets.
253 00:27:11.860 ⇒ 00:27:23.490 Shivani Amar: Yeah, when I was at Omnivision, they kind of talked about that. They’re like, some people are just gonna like spreadsheets, so it’s… it’s interesting. I’m the kind of person that’s like, I want to see the funnel, and then I want to be able to, like, calculate things, and I want to be able to play.
254 00:27:23.490 ⇒ 00:27:23.970 Jasmin Multani: Yeah.
255 00:27:23.970 ⇒ 00:27:36.379 Shivani Amar: So, I love when it can go to spreadsheet form, but for other people, they’re gonna be like, I literally need a visual. And so that’s where it’s gonna be, like, we’re gonna take this to 80% for what works for my brain, and then the VPs are gonna come in and be like.
256 00:27:36.930 ⇒ 00:27:50.910 Shivani Amar: I wanted this to be a visual, I wanted this to be whatever, but, like, I’m trying to make it as consistent as possible now, so people can get on my wavelength of, like, bar charts are up and y-axis is always revenue, like, x-axis is series of time, like…
257 00:27:51.370 ⇒ 00:27:58.979 Shivani Amar: I’m trying to get everybody to, like, see something that, like, isn’t varied, so then they’re kind of, like, bought in to my way of seeing the world, because…
258 00:27:59.560 ⇒ 00:28:05.909 Shivani Amar: That’s very selfish sounding, but if they have a better way, that’s fine, but I think I have, like, a pretty good sense about me, like, when.
259 00:28:05.910 ⇒ 00:28:06.330 Jasmin Multani: Yeah.
260 00:28:06.330 ⇒ 00:28:07.520 Shivani Amar: look like.
261 00:28:07.770 ⇒ 00:28:20.610 Jasmin Multani: I’m also, like, you’re gonna be repping. You’re gonna be repping and discussing it, so it’s like, let’s get you up to success. And, just as that meta point, it’s like, let’s first, when we migrate things over from Google to…
262 00:28:20.730 ⇒ 00:28:25.559 Jasmin Multani: Omni, on top of doing the visuals and validating the data integrity.
263 00:28:25.880 ⇒ 00:28:40.800 Jasmin Multani: when we now migrate from Google Sheets to OmniSheets, that should be the first layer, and then once that’s validated, then we also start jamming on the dashboard visuals. So, we want to make the OmniSheets additive, not a replacement.
264 00:28:40.970 ⇒ 00:28:41.700 Shivani Amar: Yeah.
265 00:28:41.880 ⇒ 00:28:43.039 Shivani Amar: That sounds good.
266 00:28:43.040 ⇒ 00:28:58.900 Jasmin Multani: And then, that also means that, like, we have to do an additional layer of letting Salty know where to look for information, and it’s gonna be like, oh, look at this OmniSheets for more info, but I think that’ll be, that’s not a huge lift.
267 00:28:58.900 ⇒ 00:28:59.640 Shivani Amar: Yes.
268 00:28:59.640 ⇒ 00:29:02.309 Jasmin Multani: Cool, cool, cool.
269 00:29:02.450 ⇒ 00:29:04.360 Jasmin Multani: Alright, do we feel good?
270 00:29:04.870 ⇒ 00:29:06.120 Shivani Amar: I’m feeling good.
271 00:29:06.720 ⇒ 00:29:12.429 Jasmin Multani: Okay, Avid, I’m gonna also re-scope. The one thing I want to double-check is, like.
272 00:29:13.170 ⇒ 00:29:18.499 Jasmin Multani: If we can extract, if we can build omniSheets.
273 00:29:19.050 ⇒ 00:29:22.729 Jasmin Multani: Once, extract it, and then put that
274 00:29:23.090 ⇒ 00:29:29.050 Jasmin Multani: Dynamically into the dashboard, so that we don’t have to create a new table in the dashboard for the funnel.
275 00:29:29.050 ⇒ 00:29:29.640 Shivani Amar: Okay.
276 00:29:29.640 ⇒ 00:29:31.679 Jasmin Multani: That would be… I know some…
277 00:29:32.010 ⇒ 00:29:46.500 Jasmin Multani: some software, dashboard softwares can do that. I would love to have that. If there’s, an issue, or if, like, we have to do double work, then I’ll flag it over, and we can discuss deadlines then. But fingers crossed that works.
278 00:29:47.980 ⇒ 00:29:48.730 Shivani Amar: Perfect.
279 00:29:48.960 ⇒ 00:29:55.259 Jasmin Multani: Cool. Are we good to… is there anything else to discuss, or can we start QAing that retail?
280 00:29:56.440 ⇒ 00:30:00.580 Shivani Amar: I think let’s go to retail.
281 00:30:00.730 ⇒ 00:30:07.439 Shivani Amar: I’m messaging Max from Omni, and I’m saying, Salty is not great at understanding my desire for clustered bar charts.
282 00:30:08.940 ⇒ 00:30:12.320 Shivani Amar: Is that gonna be?
283 00:30:12.810 ⇒ 00:30:16.190 Shivani Amar: Prevention. I’m happy to explain more. Okay.
284 00:30:16.340 ⇒ 00:30:18.240 Shivani Amar: Perfect, let’s go to retail.
285 00:30:18.460 ⇒ 00:30:29.149 Jasmin Multani: Okay, and I’m also gonna give you insights of… I’m just gonna show you from our linear tickets directly of how,
286 00:30:29.300 ⇒ 00:30:33.240 Jasmin Multani: Where, we’re setting up
287 00:30:34.020 ⇒ 00:30:37.779 Jasmin Multani: information for Advent to just start immediately working on it.
288 00:30:37.990 ⇒ 00:30:43.280 Jasmin Multani: Okay, so let’s start off in Retail Executive Hills.
289 00:30:43.430 ⇒ 00:30:44.709 Jasmin Multani: This is the link.
290 00:30:45.850 ⇒ 00:30:47.199 Jasmin Multani: Let’s go to the link…
291 00:30:49.950 ⇒ 00:30:56.700 Jasmin Multani: So, here are you on POS… point of sales… oh, I think, did you already update this, Avik?
292 00:30:58.160 ⇒ 00:31:02.560 Advait Nandakumar Menon: Nope, I updated the geographic one this summer, too.
293 00:31:05.490 ⇒ 00:31:06.820 Jasmin Multani: Okay, perfect.
294 00:31:06.950 ⇒ 00:31:16.159 Jasmin Multani: I remember one of the issues you had faced was seeing redundancy POS sales. At least it’s not in here.
295 00:31:16.430 ⇒ 00:31:20.519 Jasmin Multani: Let me also look over what we had.
296 00:31:23.600 ⇒ 00:31:31.850 Jasmin Multani: Okay, I think your feedback was… Basically, redesigning this.
297 00:31:32.120 ⇒ 00:31:34.000 Jasmin Multani: Into this, right?
298 00:31:35.120 ⇒ 00:31:38.700 Shivani Amar: Hold on, so… Okay, no, no, so, okay.
299 00:31:39.720 ⇒ 00:31:41.860 Shivani Amar: Click on the next tab over for a second.
300 00:31:41.860 ⇒ 00:31:42.500 Jasmin Multani: Okay.
301 00:31:42.670 ⇒ 00:31:45.930 Shivani Amar: Okay, this was nice. This was what Phil wanted.
302 00:31:46.320 ⇒ 00:31:47.230 Shivani Amar: Cool?
303 00:31:47.370 ⇒ 00:31:54.919 Shivani Amar: Let’s give Phil what he wanted. It’s like, I want him to have what he… the version that he wanted, which is good, and it’s what you built.
304 00:31:55.250 ⇒ 00:31:55.770 Shivani Amar: Right?
305 00:31:55.770 ⇒ 00:31:56.170 Jasmin Multani: rec…
306 00:31:56.170 ⇒ 00:31:59.110 Advait Nandakumar Menon: Yeah, this should match whatever’s here, yeah.
307 00:31:59.110 ⇒ 00:32:00.700 Shivani Amar: Great. So I’m like.
308 00:32:00.850 ⇒ 00:32:12.869 Shivani Amar: like, in terms of coloring, and like, I don’t know, like, how to make it pop more, like, it’s fine. Like, I think this does the job, it’s okay. If you go back to the retail file.
309 00:32:13.080 ⇒ 00:32:13.910 Jasmin Multani: Nope.
310 00:32:14.450 ⇒ 00:32:19.039 Shivani Amar: And go to the retail summary report now? This is similar to…
311 00:32:19.350 ⇒ 00:32:23.109 Shivani Amar: the funnel in wholesale. It’s like, how many stores are we in?
312 00:32:23.500 ⇒ 00:32:35.279 Shivani Amar: how many are active? Is… is churn store even a concept in this, like… maybe churned is not a thing, right? Like, I’m not seeing that as populating any numbers. Then it’s like…
313 00:32:36.040 ⇒ 00:32:38.429 Shivani Amar: How much,
314 00:32:39.080 ⇒ 00:32:48.999 Shivani Amar: Retail Omni Gross Sales, drink mix, POS, sprinkling POS. It’s just showing me how, like, I think there’s something to it, because it’s like.
315 00:32:49.790 ⇒ 00:33:01.489 Shivani Amar: we might add more Target stores later, or, like, in Costco, we might start out in, like, 200 Costco stores, or, like, 50 Costco stores. I think we’re starting in Costco only in, like, Texas, for example. And then…
316 00:33:02.510 ⇒ 00:33:12.800 Shivani Amar: And then it’s, like, Costco rollout nationally, so I kind of want to see, like… it’s a separate thing, kind of, but it’s like, how many stores are we in? And maybe one day there’s also, like.
317 00:33:13.130 ⇒ 00:33:20.910 Shivani Amar: Does it mix in, like… How many stores are we in? And then how many…
318 00:33:30.400 ⇒ 00:33:32.230 Shivani Amar: How many stores are we in?
319 00:33:32.490 ⇒ 00:33:36.290 Shivani Amar: I’m trying to see if that… does that, like, blend with velocity in a way, that you’re like…
320 00:33:39.120 ⇒ 00:33:44.769 Shivani Amar: How many stores are we in? By the way, we sell about 2 cans a day of this.
321 00:33:44.970 ⇒ 00:33:48.100 Shivani Amar: across each store of Target. Like, would it be the…
322 00:33:48.980 ⇒ 00:33:56.410 Shivani Amar: would it make sense in the velocity dashboard to be like, I’m telling you a bunch of things per store, but don’t you want to know how many stores we’re in?
323 00:33:56.580 ⇒ 00:34:06.800 Jasmin Multani: Yes, that is a feedback that we have. So in POS velocity, add number of stores so people can understand the average daily units per store.
324 00:34:06.800 ⇒ 00:34:19.589 Shivani Amar: So maybe just having, like, another section that’s just, like, how many stores are we in as a standalone thing, like, is, like, just helpful. Like, trailing, like, the number of stores we’re in for, like, each retailer.
325 00:34:22.219 ⇒ 00:34:30.319 Jasmin Multani: And do you want that? Because I remember you mentioned that you don’t like the high-level standalone numbers, so,
326 00:34:30.320 ⇒ 00:34:33.960 Shivani Amar: Oh yeah, that’s, like, that would also be, like, an overtime series, I think.
327 00:34:33.969 ⇒ 00:34:37.019 Jasmin Multani: It’s over… over time is very insane.
328 00:34:37.020 ⇒ 00:34:37.790 Shivani Amar: Yeah.
329 00:34:37.790 ⇒ 00:34:39.359 Jasmin Multani: And this should be tabular.
330 00:34:40.350 ⇒ 00:34:41.280 Jasmin Multani: table.
331 00:34:41.530 ⇒ 00:34:43.330 Shivani Amar: Yeah, and like, that, like…
332 00:34:43.449 ⇒ 00:34:52.679 Shivani Amar: I think tabular is fine, because otherwise what it’s gonna do is, like, be a graph that, like, week over week just kind of, like, looks the same. So it’s like… it’s like, when I see a graph like that, and it’s like…
333 00:34:53.139 ⇒ 00:35:02.699 Shivani Amar: this isn’t telling me much, then I’m like, okay, at least I can eyeball and say, we’re currently in 500 stores, but hey, 2 years ago, we were only in 3. Yeah. Like, that’s, like, nice to see.
334 00:35:03.480 ⇒ 00:35:11.660 Jasmin Multani: I think we should do a number of stores launched in, and then number of stores actively
335 00:35:12.590 ⇒ 00:35:14.569 Jasmin Multani: Making sales, right?
336 00:35:14.570 ⇒ 00:35:18.639 Shivani Amar: What is… what do I have in the other one? The retail summary? How did I do it there?
337 00:35:20.790 ⇒ 00:35:21.500 Jasmin Multani: Mute.
338 00:35:21.500 ⇒ 00:35:22.460 Shivani Amar: Oh, no.
339 00:35:23.130 ⇒ 00:35:24.490 Shivani Amar: I said…
340 00:35:24.820 ⇒ 00:35:32.200 Shivani Amar: Total stores, active stores, new stores. Scroll to the right on new stores, scroll all the way to the right, like, do we actually have anything when it says new stores, so…
341 00:35:32.860 ⇒ 00:35:35.560 Shivani Amar: Ye, no data, no data, no data.
342 00:35:36.260 ⇒ 00:35:39.789 Shivani Amar: So it’s like, we kind of went, like, full force, it seems like, with…
343 00:35:40.140 ⇒ 00:35:51.710 Shivani Amar: Target, so it’s, like, not a ton, but, like, hey, we added 7 stores in March, apparently. Like, okay, good to know. Like, I’m like, I kind of like the, the monthly view of this, maybe.
344 00:35:51.710 ⇒ 00:36:00.309 Jasmin Multani: Okay, okay. And I’m curious about this, like, this last 365 days. Do you want to keep it to that range, or should we cut shorter?
345 00:36:03.320 ⇒ 00:36:17.480 Shivani Amar: That’s what we said for active wholesale Partner. Did you, like, place an order? I… I don’t… I don’t even know if active, like, makes sense here, because literally you’re seeing that the numbers are the same. Like, I would just say… I would just say total stores. Like, new stores, total stores.
346 00:36:17.690 ⇒ 00:36:27.610 Shivani Amar: I don’t think in a big retail… I don’t think we’re charting stores like we are in Wholesale Partners, which is, like, an individual gym or something like that that hasn’t placed an order in a year.
347 00:36:28.010 ⇒ 00:36:28.810 Jasmin Multani: Like…
348 00:36:28.810 ⇒ 00:36:33.979 Shivani Amar: Target is placing orders at a purchase order level at Target, and then disseminating to stores.
349 00:36:33.980 ⇒ 00:36:34.910 Jasmin Multani: I just think of them.
350 00:36:34.910 ⇒ 00:36:35.450 Shivani Amar: print.
351 00:36:36.830 ⇒ 00:36:41.649 Shivani Amar: me taking a stab at what the funnel could be, but I don’t think it’s, like, the same funnel as it is in wholesale, basically.
352 00:36:41.880 ⇒ 00:36:50.119 Jasmin Multani: Okay, that’s good. And I guess, like, we can kind of address this in the revenue at store level.
353 00:36:50.310 ⇒ 00:36:57.510 Jasmin Multani: The month over month. So… Yeah, we’ll delete this, right?
354 00:36:58.010 ⇒ 00:37:01.610 Shivani Amar: Yeah, I don’t think active stores is a thing as much, yeah.
355 00:37:03.150 ⇒ 00:37:05.699 Jasmin Multani: This is re… this is…
356 00:37:10.480 ⇒ 00:37:16.229 Jasmin Multani: So, you want the funnel to be in product, POS velocity or executive.
357 00:37:20.720 ⇒ 00:37:21.640 Jasmin Multani: Executive.
358 00:37:25.990 ⇒ 00:37:41.629 Shivani Amar: you know this executive one is really just, like, a fill version of the dashboard, and if I had it my way, I probably would, like, have a graph. So, like, so, like, if you think about it, like, I… this is just, like, a replication of Phil’s dashboard. We can call it an executive pulse, but, like, I think I would need to go from first principles to be, like.
359 00:37:41.870 ⇒ 00:37:49.189 Shivani Amar: this is good, but, like, why is it, like, is daily the thing that, like, maybe Phil will want to see this daily, maybe, like, a person will want to see this daily.
360 00:37:49.260 ⇒ 00:38:02.320 Shivani Amar: But I imagine I would want, like, similar to what we have in wholesale, like, what are my monthly point of sales, buy drink mix, and sparkling across each retailer? Like, I want that cluster bar chart for Walmart and Target.
361 00:38:03.910 ⇒ 00:38:05.790 Jasmin Multani: Yeah, should we just…
362 00:38:05.790 ⇒ 00:38:21.119 Shivani Amar: Like, that is the executive view. Like, I would just call this, right? You can just name it as, like, Phil’s request, and that’s okay, and that’s fine. Like, that one can… it’s just, like, literally, like, Phil asked for that exact format, but when I think about, like, the exec level, I’m like.
363 00:38:21.160 ⇒ 00:38:36.960 Shivani Amar: I’m not thinking daily, I’m thinking, like, a monthly series or a weekly series that you can fit the time grain, and you can see what the variation is by product type and across stores. So I’m, like, clustered bar chart for the exec pulse, which is new.
364 00:38:38.050 ⇒ 00:38:38.890 Jasmin Multani: Okay.
365 00:38:39.310 ⇒ 00:38:47.130 Jasmin Multani: I just want to reduce the number of, like, floating dashboards. So, Abedith, can we,
366 00:38:47.390 ⇒ 00:38:51.050 Jasmin Multani: Keep this as a tab, and then add an additional tab.
367 00:38:51.050 ⇒ 00:38:51.820 Shivani Amar: That sounds great.
368 00:38:52.990 ⇒ 00:38:53.650 Advait Nandakumar Menon: Yeah.
369 00:38:53.650 ⇒ 00:38:58.929 Jasmin Multani: Yes, okay, thank you for Shivani’s, like, clustered version.
370 00:38:59.300 ⇒ 00:39:00.359 Jasmin Multani: funnel, okay.
371 00:39:00.360 ⇒ 00:39:03.159 Shivani Amar: Does that land for you? My clustered bar chart love?
372 00:39:05.950 ⇒ 00:39:07.249 Jasmin Multani: Oh, this is good.
373 00:39:07.420 ⇒ 00:39:11.580 Jasmin Multani: Or field view… You know…
374 00:39:11.580 ⇒ 00:39:19.270 Shivani Amar: I texted some Bain friends, and I was like, I just… I just explained, I just exclaimed, I love a clustered bar chart, and then my Bain friends said, oh, don’t we all?
375 00:39:22.010 ⇒ 00:39:29.149 Jasmin Multani: It’s… it’s a disease, honestly. I’m also like, oh, I miss detection work, I miss… I miss live detections, but…
376 00:39:29.980 ⇒ 00:39:38.279 Jasmin Multani: when I talk to my, like, non-tech friends, I’m like, yeah, I’m just storytelling. I just love to don’t worry about what I’m doing, actually.
377 00:39:39.650 ⇒ 00:39:45.600 Jasmin Multani: Okay, so Phil’s chart is good.
378 00:39:46.980 ⇒ 00:39:51.590 Jasmin Multani: May revisit for, color coding.
379 00:39:52.220 ⇒ 00:39:52.730 Shivani Amar: Yeah.
380 00:39:52.730 ⇒ 00:39:53.360 Jasmin Multani: Jane.
381 00:39:54.070 ⇒ 00:40:00.940 Shivani Amar: I kind of like the, like, when you look at the retail version, whoever formatted that one, like, if you look at the spreadsheet version.
382 00:40:01.320 ⇒ 00:40:08.810 Shivani Amar: It was… it was nice to have, like, The header of retailer, like.
383 00:40:09.310 ⇒ 00:40:15.619 Shivani Amar: I liked… this was actually quite nice to me, like, for what he wanted. I was like, the formatting was… this was pleasant.
384 00:40:15.950 ⇒ 00:40:17.250 Jasmin Multani: Yeah.
385 00:40:19.310 ⇒ 00:40:32.210 Shivani Amar: And, like, eventually we might be like, you know what, we like black bars, because it’s Element, and, like, here’s our color palette of Element, which right now I’m not thinking about, but I can imagine James, the CEO, being like, we need to elementify this, so…
386 00:40:32.660 ⇒ 00:40:33.729 Shivani Amar: That’s justice.
387 00:40:34.010 ⇒ 00:40:40.389 Shivani Amar: I can send you guys stuff on element design, but that doesn’t feel, like, pressing to me. Color changes right now.
388 00:40:40.390 ⇒ 00:40:44.239 Jasmin Multani: I do want to get to that. Yeah. If it’s helpful.
389 00:40:44.240 ⇒ 00:40:48.909 Shivani Amar: I can just send you our color… like, I can try to find your color palette.
390 00:40:48.910 ⇒ 00:40:58.719 Jasmin Multani: I think it’s classy, and I do prefer it, because it reduces the mental load of being like, what looks good? Let’s just stick to, like, the default formats that you guys.
391 00:40:58.720 ⇒ 00:40:59.590 Shivani Amar: Yeah.
392 00:40:59.760 ⇒ 00:41:02.630 Jasmin Multani: And it’s just less work.
393 00:41:02.750 ⇒ 00:41:14.660 Jasmin Multani: Okay. So, because this format is already… I know we only have, like, 3 minutes. Because this format is already, built out in table.
394 00:41:15.570 ⇒ 00:41:20.669 Jasmin Multani: Can I just make the decision to say, like, this should not be converted to OmniSpreadsheet?
395 00:41:23.560 ⇒ 00:41:24.980 Shivani Amar: Yeah, that’s fine.
396 00:41:24.980 ⇒ 00:41:26.829 Jasmin Multani: Because it’s already done.
397 00:41:26.830 ⇒ 00:41:32.150 Shivani Amar: And again, like, it feels like I want to be able to manipulate this later, we can decide that, but I think it’s fine.
398 00:41:32.660 ⇒ 00:41:33.649 Jasmin Multani: Okay, cool.
399 00:41:33.650 ⇒ 00:41:34.170 Shivani Amar: Yeah.
400 00:41:34.220 ⇒ 00:41:42.149 Jasmin Multani: So I’m gonna write in, mirror Retail Summary Report.
401 00:41:43.850 ⇒ 00:41:44.830 Jasmin Multani: All right.
402 00:41:46.420 ⇒ 00:41:48.740 Jasmin Multani: Sheets?
403 00:41:48.960 ⇒ 00:41:54.380 Jasmin Multani: Add as an extra tab.
404 00:41:54.800 ⇒ 00:42:06.920 Jasmin Multani: And for moving forward, I think you and I have to figure out the lift between, making the omni sheet and, making…
405 00:42:07.320 ⇒ 00:42:09.450 Jasmin Multani: The dashboard visuals.
406 00:42:10.680 ⇒ 00:42:18.589 Jasmin Multani: And seeing… we have to track… track time to leave the beach, and then we have to track, overlap.
407 00:42:19.210 ⇒ 00:42:21.249 Jasmin Multani: Overlap versus overkill.
408 00:42:22.670 ⇒ 00:42:23.750 Jasmin Multani: of information.
409 00:42:23.750 ⇒ 00:42:24.370 Advait Nandakumar Menon: Yep.
410 00:42:25.420 ⇒ 00:42:28.880 Jasmin Multani: But cool. We have one minute left. Do you guys want to do an…
411 00:42:29.390 ⇒ 00:42:31.190 Jasmin Multani: Should we rebook a time?
412 00:42:31.380 ⇒ 00:42:32.020 Shivani Amar: Yes.
413 00:42:32.810 ⇒ 00:42:35.929 Shivani Amar: I like this. Just so you know, this is like…
414 00:42:35.930 ⇒ 00:42:36.490 Jasmin Multani: Yeah.
415 00:42:36.490 ⇒ 00:42:47.159 Shivani Amar: Like, I like just, like, going through it. I don’t like looking at the Word document. It’s, like, too much intel, so we’re just gonna figure this out together. When can I expect some of these changes? So it’s like…
416 00:42:47.540 ⇒ 00:42:54.199 Shivani Amar: when can I react to, like, the clustered bar chart, or, like, whatever, like, we’re working through it? Would it be, like.
417 00:42:54.440 ⇒ 00:42:59.519 Shivani Amar: Would that be the time to connect next, or do you want to keep going? Like, what… what would be helpful?
418 00:43:00.660 ⇒ 00:43:02.550 Jasmin Multani: So, we have about, like.
419 00:43:03.460 ⇒ 00:43:19.759 Jasmin Multani: I think, 6 tickets, one ticket per dashboard, and one of those… like, five of those are live, that we already reviewed. That sixth one is the one where you’re like, let’s do, like, an extra review, like, just create another dashboard.
420 00:43:20.020 ⇒ 00:43:30.439 Jasmin Multani: tickets. I would say… We review one dashboard at a time, one dashboard per day.
421 00:43:30.560 ⇒ 00:43:32.699 Jasmin Multani: Evan, do you think that’s possible?
422 00:43:33.470 ⇒ 00:43:34.170 Advait Nandakumar Menon: Yep, yep.
423 00:43:34.170 ⇒ 00:43:39.500 Jasmin Multani: Certain ones are easy lifts. Like, I feel like the retail ones are more straightforward.
424 00:43:39.500 ⇒ 00:43:40.150 Advait Nandakumar Menon: Yeah.
425 00:43:41.230 ⇒ 00:43:42.410 Shivani Amar: Why don’t we do our, like…
426 00:43:42.560 ⇒ 00:43:45.339 Shivani Amar: Well, what do you want to do? Do you want to do.
427 00:43:47.820 ⇒ 00:43:49.520 Jasmin Multani: Every other day, saying…
428 00:43:50.400 ⇒ 00:43:52.160 Shivani Amar: Yeah, that seems good.
429 00:43:52.710 ⇒ 00:43:54.650 Jasmin Multani: Yeah, and then,
430 00:43:55.680 ⇒ 00:44:15.430 Jasmin Multani: I’m also of it, like, don’t feel like this is all just lumped on you. For the Google stuff, like, the point-of-sale stuff, I’m gonna coordinate with Robert, with them and folks, to be like, what is the true definition? So I know, like, the wholesale stuff is very chunky.
431 00:44:15.920 ⇒ 00:44:20.229 Jasmin Multani: So hit publish after each segment.
432 00:44:20.350 ⇒ 00:44:37.770 Jasmin Multani: Lamif, and, like, be… be… I’d rather… because we have so much time now, now that we’re not, releasing to VP, so let’s keep, like, a honest, feedback about, like, how long the turnaround is, and just keep it honest with me, like…
433 00:44:37.840 ⇒ 00:44:48.770 Jasmin Multani: Jasmine, you didn’t cut a ticket, like, this morning, too, right? So, I think even from now, like, after… when we meet, Shivani, let’s get… let’s cut tickets together.
434 00:44:48.770 ⇒ 00:44:49.240 Advait Nandakumar Menon: Okay.
435 00:44:49.240 ⇒ 00:45:02.439 Jasmin Multani: And, like, let’s throw the screenshots in together and, like, make the tickets the source of truth. But if you, Shivani, if you still want to, like, record feedback notes, I like the Google Sheets.
436 00:45:03.060 ⇒ 00:45:11.400 Jasmin Multani: Because, like, we’re gonna be offline different time zones, so you can house it there. I can’t… we can each check, but then when we meet, let’s cut tickets together.
437 00:45:11.400 ⇒ 00:45:17.079 Shivani Amar: That sounds great. Okay, thanks, have a wonderful weekend. This is… we’re making progress, so I appreciate it.
438 00:45:17.620 ⇒ 00:45:18.730 Jasmin Multani: Okay, thank you for…
439 00:45:18.730 ⇒ 00:45:20.590 Shivani Amar: Okay, good care! Bye!
440 00:45:20.590 ⇒ 00:45:22.410 Advait Nandakumar Menon: Hi, everyone. Hey.