Meeting Title: LMNT QA Sync Date: 2026-05-05 Meeting participants: Greg Stoutenburg, Jasmin Multani, Advait Nandakumar Menon, Shivani Amar
WEBVTT
1 00:00:22.210 ⇒ 00:00:23.260 Greg Stoutenburg: Oh, hey, Jasmine.
2 00:00:24.650 ⇒ 00:00:26.660 Greg Stoutenburg: I had to run behind another window.
3 00:00:27.010 ⇒ 00:00:27.960 Greg Stoutenburg: Go ahead, mate.
4 00:00:28.400 ⇒ 00:00:29.200 Advait Nandakumar Menon: Anyways…
5 00:00:49.500 ⇒ 00:00:50.170 Jasmin Multani: Okay.
6 00:00:55.180 ⇒ 00:00:56.110 Shivani Amar: Hi!
7 00:00:57.330 ⇒ 00:00:58.040 Jasmin Multani: They’re going…
8 00:00:58.040 ⇒ 00:00:58.810 Greg Stoutenburg: Shimani?
9 00:00:58.810 ⇒ 00:00:59.970 Shivani Amar: Hello!
10 00:01:00.230 ⇒ 00:01:01.120 Greg Stoutenburg: Hello!
11 00:01:01.690 ⇒ 00:01:03.060 Shivani Amar: Okay!
12 00:01:04.129 ⇒ 00:01:15.269 Shivani Amar: Sorry for that. I was just, like, my way of QAing is by checking the OKRs, but I didn’t realize that he was just putting, like, placeholder numbers in, because now it’s May 5th, so I thought people were putting
13 00:01:15.830 ⇒ 00:01:17.230 Shivani Amar: accurate numbers.
14 00:01:17.520 ⇒ 00:01:21.129 Jasmin Multani: Are those updated every week?
15 00:01:22.360 ⇒ 00:01:23.050 Shivani Amar: I don’t…
16 00:01:23.290 ⇒ 00:01:30.039 Shivani Amar: I think they’re updated monthly, but because it’s May 5th, I was like, okay, the actuals that he’s putting in for…
17 00:01:30.190 ⇒ 00:01:34.029 Shivani Amar: April must be accurate, but it seems like he’s just pulling…
18 00:01:34.210 ⇒ 00:01:36.579 Shivani Amar: Weekly data and, like, adding it up.
19 00:01:36.810 ⇒ 00:01:40.530 Shivani Amar: And he’s like… but he said something, he’s, like, waiting for the monthly.
20 00:01:42.040 ⇒ 00:01:44.579 Jasmin Multani: Okay, I trust you guys.
21 00:01:44.580 ⇒ 00:01:48.009 Shivani Amar: So, like, that’s why I trust your numbers more.
22 00:01:48.330 ⇒ 00:01:49.340 Shivani Amar: Right?
23 00:01:49.340 ⇒ 00:01:50.320 Jasmin Multani: Realty.
24 00:01:50.320 ⇒ 00:01:57.110 Shivani Amar: So that’s good. I didn’t QA wholesale. I could do that now, actually, just to see.
25 00:01:57.390 ⇒ 00:01:59.510 Shivani Amar: Yeah, why don’t I just do that now? Let’s see.
26 00:01:59.900 ⇒ 00:02:04.649 Jasmin Multani: I’m still doing the internal QA, but you can walk through it as well.
27 00:02:04.930 ⇒ 00:02:11.750 Shivani Amar: Yeah, I just weren’t mean the wholesale numbers for… let me see if they’ve even put them in yet. Let’s see.
28 00:02:11.750 ⇒ 00:02:12.230 Jasmin Multani: Oh, okay.
29 00:02:12.230 ⇒ 00:02:19.300 Shivani Amar: Or, like, the wholesale… for… April…
30 00:02:19.400 ⇒ 00:02:38.650 Shivani Amar: They haven’t even put them in yet, so never mind. Like, basically, like, what we say, the drink mix, sparkling international reseller, like, we have it in our spreadsheet, and I’m wondering when that can be the source of truth. But anyway, okay, cool. So, you can disregard that last thing, but I think the theme of today, at least from my mind, is like.
31 00:02:39.200 ⇒ 00:02:44.000 Shivani Amar: One, as I’m, like, exploring Omni, I don’t always know, like.
32 00:02:44.330 ⇒ 00:03:04.169 Shivani Amar: what we have, what we don’t have, so it was interesting a bit today to hear that, like, we have HazFridge in the data model, and, like, I know you have to construct topics in Omni, but I, like, I still think of it as, like, Omni is just, like, linked to my… all of my data, and I didn’t understand that there are, like, fields that we suppress from Omni.
33 00:03:04.530 ⇒ 00:03:08.699 Shivani Amar: Or don’t include. So, could you guys educate me about that a little bit?
34 00:03:10.090 ⇒ 00:03:18.130 Advait Nandakumar Menon: Yes, so a topic is basically set, based on a set of tables, fact tables, dimension tables, whatever it is.
35 00:03:18.130 ⇒ 00:03:32.229 Advait Nandakumar Menon: And within all the columns in the tables, there is an option within Omni, when you set up a topic, to expose only certain fields to the AI or blobby, or even to be used within the dashboard on top of the topic. So, that’s what happened here.
36 00:03:32.230 ⇒ 00:03:39.380 Advait Nandakumar Menon: I have… we have exposed certain fields that are required currently for the metrics on the dashboards, and…
37 00:03:39.580 ⇒ 00:03:50.639 Advait Nandakumar Menon: possibly on what questions you might ask and things like that, but there’s also the option to expose all the fields to the AI itself, so that’s something that can be…
38 00:03:50.640 ⇒ 00:03:52.279 Shivani Amar: Expose all the fields.
39 00:03:55.170 ⇒ 00:04:08.820 Advait Nandakumar Menon: One reason would be not to particularly overload Blobby with, information like, in a certain topic, it’s useful to have only a curated set of data fields, within the topic, so…
40 00:04:08.870 ⇒ 00:04:17.829 Advait Nandakumar Menon: That’s one use case wherein we can restrict the usage of certain fields which are not relevant to the dashboard or the type of data you’re looking at.
41 00:04:17.920 ⇒ 00:04:33.470 Advait Nandakumar Menon: But, having said that, there is the option of exposing all the fields as well, like I said, so I can go ahead and switch it on. So it’s… it shouldn’t cause any issue, but if you have any certain fields, or if…
42 00:04:33.470 ⇒ 00:04:41.919 Advait Nandakumar Menon: like, you prefer, you want to see all the data, all the fields, no matter what, I can, always default to that setting.
43 00:04:41.920 ⇒ 00:04:44.590 Shivani Amar: Okay, cool, like, when I think about it as, like…
44 00:04:44.880 ⇒ 00:04:53.370 Shivani Amar: Okay, when I think about it as… We’ve got… Wholesale customers.
45 00:04:53.540 ⇒ 00:04:55.679 Shivani Amar: Right? And I know this, this kind of…
46 00:04:55.990 ⇒ 00:05:14.399 Shivani Amar: zip code sometimes disappears, but, like, what we… it seems to be there now. So you’ve got wholesale customers, and then you have has fridge, false or true, right? That’s gonna be something that they… especially with sparkling sales. Okay, let me back up and explain why fridge is important to begin with as an example. So.
47 00:05:14.720 ⇒ 00:05:24.760 Shivani Amar: With Sparkling Sales, with some wholesale partners, once they’ve, like, purchased enough, we actually send them a fridge, like, for free. And that way, they have cold sparkling drinks in, like, an Element-branded fridge.
48 00:05:24.890 ⇒ 00:05:26.679 Shivani Amar: And so then we’re trying to see, like.
49 00:05:26.820 ⇒ 00:05:35.760 Shivani Amar: it’s small, because it’s, like, wholesale sales, but, like, Jersey, okay, how effective is that? To send them this free fridge, which is a cost to us.
50 00:05:35.910 ⇒ 00:05:45.679 Shivani Amar: And so, then people want to understand, like, sales of sparkling to these wholesale partners pre and post-fridge, right? So, like, that’s just, like, an example of…
51 00:05:45.970 ⇒ 00:05:52.270 Shivani Amar: something that Madison asked for, because she is going to, like, cut the data that way, right?
52 00:05:52.940 ⇒ 00:05:53.800 Shivani Amar: And then…
53 00:05:55.060 ⇒ 00:06:08.520 Greg Stoutenburg: So, yeah, yeah, so, I mean, I have a suggestion here. So, I think… I think this is one where actually the… the process we’ve been following worked. So, we’re using the potential questions by team that Jasmine sent in the,
54 00:06:08.520 ⇒ 00:06:18.850 Greg Stoutenburg: in the chat here, as success criteria for Blobby. So I think if we… if we go, alright, these are the questions that the team wants to be able to ask, and then you take a look at an answer, and you find
55 00:06:19.050 ⇒ 00:06:37.949 Greg Stoutenburg: for example, here, that you feel like the answer is inadequate because there’s some additional reporting need that you have, I think that’s an opportunity for us to then just go, okay, let’s just make sure the blobby surfaces this as part of the iterative QA process. So I don’t… I don’t think that we want to go all the way to just exposing all fields, because we do want to keep it narrowed to what we know are.
56 00:06:37.950 ⇒ 00:06:52.010 Shivani Amar: I get a little stressed because I’m like, okay, I’m not going into the back end, but I want somebody to walk me through. If that’s the case, I want somebody to walk me through, these are the fields we’re not putting into Blobby, so that I can say, no, no, that’s a key field.
57 00:06:52.010 ⇒ 00:07:02.739 Shivani Amar: And so that’s, like, what we need to be doing in this touchpoint to say, like, hey, just FYI, these are the fields we’ve excluded, do you agree with that? That might be, like, a piece that’s helpful for the sign-off.
58 00:07:02.740 ⇒ 00:07:26.709 Greg Stoutenburg: Yeah, I like that. And, I’d suggest that we handle this just on a topic-by-topic basis. So, we do a review of, separate from the dashboards or any individual reports, we do, hey, here’s the review of what we’ve done for the wholesale topic, and here’s what we’ve exposed, here’s what we’ve not exposed, and make sure that you feel like it’s both answering the questions that we set out as success criteria, and also anywhere you want to go, okay, well, yeah, that was the question I
59 00:07:26.710 ⇒ 00:07:30.699 Greg Stoutenburg: proposed, but I also want to make sure it includes this, then we make those revisions at that time.
60 00:07:30.700 ⇒ 00:07:33.810 Shivani Amar: Okay, that sounds good. So, so then, in that case, like.
61 00:07:34.080 ⇒ 00:07:47.929 Shivani Amar: like, this whole wholesale topic, it seems like we’re not talking about too much today, but, like, do you want to, in the next session, like, go through and show me the backend, or whatever in Omni, which is, like, what you’re excluding, and then I can say yes or no?
62 00:07:48.210 ⇒ 00:07:49.259 Shivani Amar: Does that make sense?
63 00:07:49.260 ⇒ 00:07:49.910 Advait Nandakumar Menon: Oh, that’.
64 00:07:50.930 ⇒ 00:08:05.429 Shivani Amar: this is a great table, right? Like, this is the all-wholesale customers table, and Madison, like, we made this in Google Sheets, but this is what Madison is often going to be, like, referencing. And I would say the join for her that is
65 00:08:05.580 ⇒ 00:08:09.300 Shivani Amar: The join for her that’s relevant is also, like.
66 00:08:09.550 ⇒ 00:08:13.259 Shivani Amar: We tested this in Blobby before, which is, like, surface.
67 00:08:14.620 ⇒ 00:08:20.679 Shivani Amar: This customer’s address, as well as their most recent address that we shipped to.
68 00:08:22.330 ⇒ 00:08:23.250 Shivani Amar: Right?
69 00:08:23.610 ⇒ 00:08:40.110 Shivani Amar: And then, like, you can imagine, like, let’s say we were trying to find bad actors. Like, that’s not necessarily a signal of a bad actor, but, like, let’s say, like, I’m Mike Monarch, and I’m actually sending wholesale-rated stuff to my friends and family.
70 00:08:40.559 ⇒ 00:08:41.119 Greg Stoutenburg: Yeah.
71 00:08:41.120 ⇒ 00:08:45.700 Shivani Amar: Okay? And every week, I’m ordering product for, like, different people in my life.
72 00:08:46.260 ⇒ 00:09:00.220 Shivani Amar: Like, that would be a signal that it’s like, they’ve sent to 10 addresses over time, right? But it might not actually mean they’re a bad actor, because it might be that Mike Monarch is actually just sending to a network of 10 different gyms that he has.
73 00:09:01.190 ⇒ 00:09:19.439 Shivani Amar: Okay? So it’s not that the number itself tells you it’s a bad actor, it’s, like, more like, let’s investigate this, and, like, does it check out? Because we know this is, like, a franchise of gyms anyway, right? Or, like, a franchise of, like, chiropractor clinics that, like, all sell Element. I’m just making things up. So… so I think, like.
74 00:09:19.580 ⇒ 00:09:22.519 Shivani Amar: If I go back to…
75 00:09:22.970 ⇒ 00:09:36.189 Shivani Amar: the basics, also, of just, like, what… if I’m putting myself into Madison’s shoes, she wants to just know some stuff about the customers, right? And you have some of that in, like… you have a lot of that in this table. You don’t necessarily have…
76 00:09:36.720 ⇒ 00:09:42.300 Shivani Amar: I don’t know if you necessarily have in this table how many addresses they’ve… they’ve ordered to.
77 00:09:43.750 ⇒ 00:09:47.890 Shivani Amar: Right, okay, I think you have one address listed here, which is probably the address that’s on there.
78 00:09:47.890 ⇒ 00:09:48.490 Advait Nandakumar Menon: Yeah.
79 00:09:48.920 ⇒ 00:09:54.880 Shivani Amar: But, like, I think she wants to know, like, just, like, number of addresses that we’ve sent them to.
80 00:09:55.120 ⇒ 00:10:04.169 Shivani Amar: we’ve said product to under this account name, or whatever. And so, I think that this is, like, a great table, and so today, what I’m realizing is that, like.
81 00:10:05.000 ⇒ 00:10:16.720 Shivani Amar: this is all really good stuff, and so if we can add this stuff to Omni, that’s super helpful, because eventually, Madison’s going to want to be able to, like, export this…
82 00:10:17.660 ⇒ 00:10:23.359 Shivani Amar: Like, I’ll… let me type in the chat of what she asked for, just so you have a feel for it. She was just like…
83 00:10:23.360 ⇒ 00:10:27.030 Greg Stoutenburg: Perfect. And should she go in the Element Omni Feedback channel?
84 00:10:27.670 ⇒ 00:10:30.270 Shivani Amar: No, I don’t think so.
85 00:10:30.270 ⇒ 00:10:30.850 Greg Stoutenburg: Okay.
86 00:10:31.460 ⇒ 00:10:41.469 Shivani Amar: she’s kind of like this, like, interesting… she was like, I did wholesale discovery as the first discovery phase, but, like, technically, I’m like, Omni will be rolled out to stakeholders, like.
87 00:10:42.240 ⇒ 00:10:58.840 Shivani Amar: Omni will be rolled out to stakeholders in, like, September. But, like, I’ve given her a preview of it, so she was like, oh, like, can Omni do XYZ? And it’s helpful for me when she does that, because then I can see what the limitations are. Yeah. So it’s less about, like, giving her access today, it’s more about,
88 00:10:59.620 ⇒ 00:11:04.019 Shivani Amar: Because that’s, like, too many cooks in the kitchen. I think it’s more, like, let me give you…
89 00:11:04.340 ⇒ 00:11:06.899 Shivani Amar: This is… this is a foreword of…
90 00:11:06.900 ⇒ 00:11:07.500 Greg Stoutenburg: Yeah, fair.
91 00:11:08.060 ⇒ 00:11:16.640 Shivani Amar: message. So, like, you can see in Slack, it’s like, okay.
92 00:11:19.440 ⇒ 00:11:21.609 Shivani Amar: Did I send that to the right chat? Yeah.
93 00:11:21.610 ⇒ 00:11:23.780 Greg Stoutenburg: You put it in Element Omni Feedback, yeah.
94 00:11:23.780 ⇒ 00:11:24.410 Shivani Amar: Okay.
95 00:11:24.600 ⇒ 00:11:27.130 Shivani Amar: I’m like, sometimes it has a bunch of people from…
96 00:11:27.710 ⇒ 00:11:36.849 Shivani Amar: from Omni, which I forget that. Okay, that’s just Omni Element. And so she was like, hey, for these wholesale emails, I want to know
97 00:11:37.040 ⇒ 00:11:42.980 Shivani Amar: Count of addresses they ship to, do they order drink mix? Do they order sparkling? Do they have a fridge?
98 00:11:43.230 ⇒ 00:11:52.139 Shivani Amar: And eventually, this is probably what Madison’s gonna need a lot of. She’s gonna have, like, a specific wholesale partner in mind they’re thinking about doing. Let’s say, like.
99 00:11:52.260 ⇒ 00:11:59.820 Shivani Amar: Okay, more context for you guys. We’re gonna start launching distribution of Element in New York City.
100 00:12:00.400 ⇒ 00:12:02.039 Shivani Amar: What that means is.
101 00:12:02.440 ⇒ 00:12:14.010 Shivani Amar: we’re gonna partner with a distributor and… and actually have product show up in bodegas and, like, all these different things, right? So that’ll be, like, the… a channel kind of thing as part of our distribution.
102 00:12:14.340 ⇒ 00:12:18.950 Shivani Amar: And so… as we’re doing this, like, launch in New York City.
103 00:12:19.420 ⇒ 00:12:28.059 Shivani Amar: they’re also thinking through where should we do events to, like, get people hyped about, like, Element growing? So they might be like, I want to pull a list of all New York City
104 00:12:28.490 ⇒ 00:12:43.840 Shivani Amar: wholesalers that we have, right? All New York City and Brooklyn wholesalers that we have, and see which ones our largest ones are, to see if we want to do an event to build hype at those wholesale partners. Like, those are the kinds of things that I’m like.
105 00:12:44.070 ⇒ 00:12:45.940 Shivani Amar: I can imagine…
106 00:12:46.060 ⇒ 00:12:51.620 Shivani Amar: myself, like, I feel like Omni probably could do that. Show me… give me a list.
107 00:12:51.980 ⇒ 00:12:58.310 Shivani Amar: of the top wholesale partners in New York City, and Brooklyn.
108 00:13:01.520 ⇒ 00:13:15.439 Shivani Amar: like, let’s see, right? And then it’ll… like, I feel like it could probably do this, but anyways, that’s just to give you an example of the kinds of things that Madison will probably want to be doing. It’s like, there are the dashboards, and there’s, like, specific cuts of trying to find things.
109 00:13:17.560 ⇒ 00:13:25.220 Shivani Amar: And, like, the example that I come back to is, like, when we can get every data point, or, like, every,
110 00:13:25.580 ⇒ 00:13:31.969 Shivani Amar: Every channel’s data to be in zip code format, we’ll definitely want to do cuts by zip code in an omni-channel view.
111 00:13:33.590 ⇒ 00:13:37.450 Shivani Amar: Which is, like, where do we have the top sales of Element product?
112 00:13:37.560 ⇒ 00:13:40.719 Shivani Amar: Or wholesale, and where do we have the top point of sales?
113 00:13:40.820 ⇒ 00:13:41.930 Shivani Amar: 4 element.
114 00:13:42.070 ⇒ 00:13:43.940 Shivani Amar: In retail. So…
115 00:13:44.160 ⇒ 00:13:51.970 Shivani Amar: Anyway, that’s to give you an example. So this is, like, pretty good, right? Like, it’s like, hey, like, these are… then maybe she can go into the customer database and be like, okay.
116 00:13:52.200 ⇒ 00:13:56.790 Shivani Amar: Like, or I just keep asking Omni, like, okay, for these 10, also show me their last order date.
117 00:13:57.150 ⇒ 00:13:57.670 Greg Stoutenburg: Yep.
118 00:13:57.670 ⇒ 00:14:03.120 Shivani Amar: Are they active? I guess it’s showing if they’re active at risk return, right? And then they can, like, do some digging.
119 00:14:03.290 ⇒ 00:14:09.380 Shivani Amar: My hope is that they don’t have to do digging in any system other than, like, Omni, eventually.
120 00:14:09.530 ⇒ 00:14:14.559 Greg Stoutenburg: Yeah, or maybe OmniPlus, figuring out, you know, where… what Robbie Bent’s phone number is.
121 00:14:14.720 ⇒ 00:14:24.020 Shivani Amar: Yeah, exactly. If I were to say, which of these have fridges, and can you give me their phone number, their full addresses, or something, right?
122 00:14:24.420 ⇒ 00:14:26.719 Shivani Amar: Like, that’s, like, the level of…
123 00:14:26.960 ⇒ 00:14:30.590 Shivani Amar: whatever, that I think… I think it can get to. Okay.
124 00:14:30.940 ⇒ 00:14:42.939 Advait Nandakumar Menon: So, I’m just jumping in here quickly. So, here, if you notice that, wholesale customer sheet, which you showed, Shivani, it’s basically the wholesale customers table. It is…
125 00:14:43.110 ⇒ 00:14:55.649 Advait Nandakumar Menon: present in Omni right now, and we do have a topic for it, so it’s all about exposing the right fields to Blobby, and I think it should take care of whatever questions you want to throw at it. So, yeah.
126 00:14:57.160 ⇒ 00:15:07.000 Shivani Amar: Awesome. Okay. Yeah, if you want to pivot and just, like, talk to me about the fields now, I’m game to do that, but if you’re like, you want more time, then we can do it on Thursday, so you let me know.
127 00:15:07.730 ⇒ 00:15:12.989 Advait Nandakumar Menon: Yeah, we can do it in the next session, maybe we can pull up all the list of topics we have, and, like.
128 00:15:13.090 ⇒ 00:15:17.719 Advait Nandakumar Menon: what kind of fields do you want to look in each topic? We can maybe have a session like that.
129 00:15:17.720 ⇒ 00:15:18.730 Shivani Amar: Okay, perfect.
130 00:15:18.730 ⇒ 00:15:19.280 Greg Stoutenburg: No.
131 00:15:19.450 ⇒ 00:15:44.059 Greg Stoutenburg: And, Jasmine, can you… so I know we, traded messages about this the other day. Could you put together, like, a spec sheet for, what counts as validating a topic? And that can include things like, once we’ve got the questions that are intended to be used as success criteria for Blobby on a topic, then we can go, alright, you know, we’ve run this… we’ve run this question, we got an answer, the answer looked good, you know, check, or it needed to be revised, you know.
132 00:15:44.060 ⇒ 00:15:48.340 Greg Stoutenburg: then check. And then anything that gets added to it. So I think, for example.
133 00:15:48.340 ⇒ 00:16:05.620 Greg Stoutenburg: If we’ve already cleared the other questions for wholesale, then we can go… well, the follow-up question about, being able to analyze the relationship between fridges and repeat purchases and so on, that’s just, like, an additional question that we go, alright, now we’re actually going to add this to this success criteria.
134 00:16:05.620 ⇒ 00:16:19.569 Shivani Amar: And, like, I can, like, if this is the lens, this is very helpful, because it’s, like, the success criteria were, like, a moment in time. I can, like… I think I even, like, deleted the fridge questions, like, whatever, but they… I know they care about the fridges, right? Yeah.
135 00:16:19.570 ⇒ 00:16:28.170 Shivani Amar: So I think if… if my homework is, like, hey, by Thursday, Shivani, which I think Garrett, like, had tagged me at one point, he was like.
136 00:16:28.400 ⇒ 00:16:41.619 Shivani Amar: are these questions still the right questions? And I was like, I don’t know why they would be different, but, like, I think if the question is, is this the comprehensive list of questions, that prompts me to then be like, let me actually read them again. But the question was just, like.
137 00:16:41.750 ⇒ 00:16:45.270 Shivani Amar: Is this still the list of questions? I was like, I don’t know, I haven’t changed anything.
138 00:16:45.270 ⇒ 00:16:47.000 Greg Stoutenburg: Yeah, right, you’re like, I still want to know that, why are you
139 00:16:47.340 ⇒ 00:16:49.119 Greg Stoutenburg: Lobby to unlearn the answer to that question.
140 00:16:49.120 ⇒ 00:16:53.129 Shivani Amar: Even if you’re like, hey, green light something, I want to make sure we can answer all these questions.
141 00:16:53.130 ⇒ 00:16:53.800 Greg Stoutenburg: Yeah.
142 00:16:53.800 ⇒ 00:16:57.919 Shivani Amar: And I can go through and be like, no, there’s actually more questions. Right.
143 00:16:57.920 ⇒ 00:17:21.149 Greg Stoutenburg: Yeah, and you know, and I hope that that way of going about it makes sense, right? Because it’s like, when you’ve got something that’s an LLM that we’ve got pointed at your data, like, there’s an indefinite number of questions you could ask and get the right answers to that wouldn’t help you in making up your mind about if the topic is doing what you want it to do. Right. Like, we could, you know, you could spend all day with dumb examples, like, give me the…
144 00:17:21.150 ⇒ 00:17:24.310 Greg Stoutenburg: 3rd column, 17th cell value.
145 00:17:24.310 ⇒ 00:17:26.749 Greg Stoutenburg: Right, you can, like, go on with questions like that, so we want to know…
146 00:17:26.750 ⇒ 00:17:32.840 Shivani Amar: So the tab that I should be looking at is the potential questions by team, right?
147 00:17:33.930 ⇒ 00:17:44.129 Jasmin Multani: That’s what we are going off of, that’s how we’re building our dashboard specs, but then I flipped through the other tabs, and I was like, oh, I’m getting kind of, like, conflicting information.
148 00:17:44.130 ⇒ 00:17:46.460 Shivani Amar: Because dashboard specs has one thing.
149 00:17:48.110 ⇒ 00:17:50.140 Shivani Amar: Retail spend has one thing. Okay.
150 00:17:50.440 ⇒ 00:17:51.100 Jasmin Multani: Yeah.
151 00:17:51.100 ⇒ 00:18:02.759 Shivani Amar: I think I did them at different times. I don’t remember anymore, like, which came first, what it is. So, yes. Because as you can see with the dashboard specs, right, we have…
152 00:18:03.380 ⇒ 00:18:09.459 Shivani Amar: For the dashboard specs, it says retail, Target, Walmart, point of sale, and then you also have retail spend.
153 00:18:09.460 ⇒ 00:18:11.080 Jasmin Multani: Yeah, that’s what we wanna…
154 00:18:11.080 ⇒ 00:18:25.989 Shivani Amar: And retail spend is actually very different. It’s actually trade, spend, whatever, and then I’m like, I don’t even know if we have, retail sales, right? This is just how we’re selling in retail, that’s point of sale. I think it’s just because we didn’t have it at the time, but now we do.
155 00:18:26.180 ⇒ 00:18:26.860 Jasmin Multani: Yeah.
156 00:18:26.860 ⇒ 00:18:37.690 Shivani Amar: So, I think they’ve modeled it. I think it’s done, so it’s like, to me, retail sales, they’re like, how are we selling retail? Are we growing velocity? Where are we winning, losing, fine. But then there’s also,
157 00:18:40.510 ⇒ 00:18:46.620 Shivani Amar: how often are… like, I can edit this later, we don’t have to do this now. How often are retailers ordering?
158 00:18:47.030 ⇒ 00:18:48.720 Shivani Amar: like.
159 00:18:50.130 ⇒ 00:19:05.760 Shivani Amar: and this will be, like, order size, order this, you know, like, the… I don’t know, like, we’ll just get a feel for, like, the… Target’s ordering every 45 days on average, but it’s really spiky or something, like, I think that’s… will help be helpful for supply chain. Yeah. And then it’s, like, elements…
160 00:19:06.180 ⇒ 00:19:08.520 Shivani Amar: sales from retail.
161 00:19:08.880 ⇒ 00:19:09.420 Greg Stoutenburg: Yep.
162 00:19:09.420 ⇒ 00:19:10.270 Shivani Amar: Right?
163 00:19:10.270 ⇒ 00:19:13.769 Greg Stoutenburg: I’m gonna grab that and move that into a new column, since we’ve already made the retail topic.
164 00:19:13.770 ⇒ 00:19:14.490 Shivani Amar: I just want to make…
165 00:19:14.490 ⇒ 00:19:19.500 Greg Stoutenburg: that any new questions that we’re adding are, you know, we can see that it’s new, so we don’t.
166 00:19:19.500 ⇒ 00:19:22.920 Shivani Amar: Then maybe it’s just… we keep this one as just retail point of sale.
167 00:19:22.920 ⇒ 00:19:35.100 Greg Stoutenburg: Yeah, I think let’s… yeah, let’s maybe adopt a policy that anything that we’re… once something is in progress or finished, if we’re going to say, okay, we need to make sure that it also answers these questions, let’s just mark that as separate.
168 00:19:35.300 ⇒ 00:19:49.479 Shivani Amar: Yeah. Okay, so this is helpful. So if this is, like, the, hey, Shivani, make sure that you feel really bought into, like, what’s on this, because this is what we’re using as our scorecard, then that will orient my brain. I think I got thrown last week when it was just, like.
169 00:19:49.680 ⇒ 00:19:51.919 Shivani Amar: Is this still applicable? I was like, yeah!
170 00:19:51.920 ⇒ 00:19:56.420 Greg Stoutenburg: Yeah, yeah, I understand why that would be. So you’re in 5 there?
171 00:19:57.140 ⇒ 00:20:00.719 Shivani Amar: Yeah, so this was what it was originally.
172 00:20:01.260 ⇒ 00:20:04.919 Shivani Amar: I haven’t changed anything now. And then this is the new row that I’m adding.
173 00:20:05.300 ⇒ 00:20:06.130 Greg Stoutenburg: Okay.
174 00:20:06.740 ⇒ 00:20:10.009 Jasmin Multani: And you expect… like, we also wanted
175 00:20:10.520 ⇒ 00:20:13.870 Jasmin Multani: to have the right expectations of what’s being landed on May 22nd.
176 00:20:14.150 ⇒ 00:20:14.840 Shivani Amar: Yeah.
177 00:20:15.130 ⇒ 00:20:20.059 Jasmin Multani: Is retail sales… was that always part of your initial scoping for May 22nd?
178 00:20:20.060 ⇒ 00:20:25.180 Shivani Amar: It was, but, like, you would tell me, right? If you’re like, hey, when I think about, like.
179 00:20:25.360 ⇒ 00:20:28.800 Shivani Amar: The… the…
180 00:20:29.930 ⇒ 00:20:41.769 Shivani Amar: milestone tracker. Yes, this says point of sale, right? Like, this says point of sale, so I don’t… like, I think it was just a matter of, like, I want everything that we have on retail to be, like, organized well.
181 00:20:42.020 ⇒ 00:20:42.420 Jasmin Multani: Okay.
182 00:20:42.420 ⇒ 00:20:45.920 Shivani Amar: And then we’re going retail, wholesale, e-commerce. Now, I don’t have, like.
183 00:20:46.430 ⇒ 00:20:52.840 Shivani Amar: you see here, I have retail… I have spend buckets coming later, but I don’t have, like… before we get to omnichannel.
184 00:20:53.160 ⇒ 00:20:54.550 Shivani Amar: We need sales.
185 00:20:55.580 ⇒ 00:20:56.690 Shivani Amar: Right? So…
186 00:20:57.010 ⇒ 00:20:57.700 Greg Stoutenburg: Yep.
187 00:21:00.310 ⇒ 00:21:04.209 Shivani Amar: I also don’t think supply chain dashboarding is realistic for June.
188 00:21:04.700 ⇒ 00:21:13.930 Jasmin Multani: I think we internally said… like, Robert and I were talking about it, I want to push it to July, but, we’re also trying to…
189 00:21:14.710 ⇒ 00:21:19.340 Jasmin Multani: make sure we have an accurate walk, and, like, prove, like, July is the accurate date.
190 00:21:21.560 ⇒ 00:21:24.670 Jasmin Multani: So… yeah, that could work.
191 00:21:24.670 ⇒ 00:21:36.539 Shivani Amar: So there’s retail sales, there’s purchase order timing, and there’s retail inventory, right? So it’s like, I think just like you have, here, you have, like, this at the bottom, you have…
192 00:21:36.750 ⇒ 00:21:45.690 Shivani Amar: retail inventory, like, how much do they have on hand on order and transit, you probably could make a better, sorry, I’m losing my…
193 00:21:46.040 ⇒ 00:22:02.499 Shivani Amar: you probably could make a better inventory report than what was in that Google spreadsheet, but, like, that’s fine if this is just point of sale. I can, like, I can articulate that, that’s fine, but then let’s make June’s timing the, like, the rest of what you have from retail.
194 00:22:03.150 ⇒ 00:22:08.849 Jasmin Multani: That makes sense. Just because I want to bake in a rigorous QAing session for Blobby.
195 00:22:08.850 ⇒ 00:22:09.180 Shivani Amar: Yeah.
196 00:22:09.180 ⇒ 00:22:10.770 Jasmin Multani: for May 22nd.
197 00:22:10.770 ⇒ 00:22:11.390 Shivani Amar: Okay.
198 00:22:11.390 ⇒ 00:22:18.430 Jasmin Multani: And that way, we have, like, a template ready to go, using these dashboards that, hey, this is the process of QAing, end-to-end.
199 00:22:18.870 ⇒ 00:22:24.029 Jasmin Multani: And that way we can go faster. So that’s… that’s… I’m just trying to figure out what to do in the next two weeks.
200 00:22:24.030 ⇒ 00:22:29.030 Shivani Amar: Yeah, that sounds good, that sounds good. So, I’m like, I think that’s totally fair, let’s…
201 00:22:29.030 ⇒ 00:22:29.610 Jasmin Multani: What’s…
202 00:22:29.610 ⇒ 00:22:39.210 Shivani Amar: say, this will be approved when I feel like… So… I’m gonna just…
203 00:22:39.540 ⇒ 00:22:46.570 Shivani Amar: I’m gonna put it as this for now, but, like, I think that, like, this will be approved once I feel like it’s, like.
204 00:22:48.960 ⇒ 00:22:50.959 Shivani Amar: I almost feel like it’s, like.
205 00:22:51.100 ⇒ 00:22:55.589 Shivani Amar: Russell would not need to look at SOAR systems for point of sale.
206 00:22:55.750 ⇒ 00:22:56.720 Shivani Amar: for…
207 00:22:57.360 ⇒ 00:23:06.189 Shivani Amar: Target and Walmart, and, like, fully trust our numbers, and I realize that’s socializing… I’m not saying he’s actually going to be involved in this QA, but, like, my brain would be like.
208 00:23:06.190 ⇒ 00:23:06.520 Jasmin Multani: Yeah.
209 00:23:06.520 ⇒ 00:23:22.689 Shivani Amar: I feel so confident in these numbers for point of sales for Target and Walmart, that they’re, like, very close to what he’s reporting out on, that I feel like this could be the source of truth going forward, even if I’m not, like, giving that to him. So, I think that that… I just need… like, we’ll see how the April numbers
210 00:23:22.880 ⇒ 00:23:25.830 Shivani Amar: Go, and then we can do a retro of saying.
211 00:23:26.260 ⇒ 00:23:36.280 Shivani Amar: for every month of 2026, do we feel like point of sale in what you’re reporting out on is the same as what we were… is very close to what we reported out on our OKRs?
212 00:23:36.550 ⇒ 00:23:37.749 Shivani Amar: And if not, why?
213 00:23:38.260 ⇒ 00:23:40.929 Jasmin Multani: Yeah, I love a hard number to track against.
214 00:23:40.930 ⇒ 00:23:41.560 Shivani Amar: Exactly.
215 00:23:41.560 ⇒ 00:23:47.590 Jasmin Multani: As a success metric, because right now, I feel like, visually, we’ve landed on what you want to see.
216 00:23:47.590 ⇒ 00:23:47.970 Shivani Amar: Yeah.
217 00:23:47.970 ⇒ 00:23:51.189 Jasmin Multani: It’s just a matter of, like, data integrity. Yup.
218 00:23:51.420 ⇒ 00:24:06.390 Jasmin Multani: Cool. So that should be part of the AB&QA blobby, and, in the meantime, I’ll at least do, like, the spec builds for the retail sales and KPI stuff, and see where we can recycle stuff from here. Yeah.
219 00:24:06.390 ⇒ 00:24:12.649 Shivani Amar: And, like, then your question also, like, the question also becomes, like, like, if I’m looking at…
220 00:24:13.350 ⇒ 00:24:19.409 Shivani Amar: Okay. If I’m looking at how Russell is reporting out on something, let’s just look at an example.
221 00:24:20.000 ⇒ 00:24:23.370 Shivani Amar: He’s reporting out on… drink mix sales.
222 00:24:23.660 ⇒ 00:24:27.010 Shivani Amar: Okay? Drink mix sales as a whole.
223 00:24:27.860 ⇒ 00:24:32.670 Shivani Amar: He’s reporting on… sorry, string makes point of sale, so, like, what’s happening at the register.
224 00:24:34.040 ⇒ 00:24:36.760 Shivani Amar: reporting on during mixed sale, which is, what are they buying from us?
225 00:24:38.380 ⇒ 00:24:42.450 Shivani Amar: Right? Then he’s doing trade spend, which…
226 00:24:42.960 ⇒ 00:24:57.379 Shivani Amar: will come from Convido and, like, other… like, that’ll… we don’t have that data source right now. Ben is reporting out on Sparkling Point of Sale, Sparkling Sales, and Sparkling TradeSpin. So, to me, the minute that we have these fields.
227 00:24:57.570 ⇒ 00:24:58.660 Shivani Amar: as, like.
228 00:24:58.880 ⇒ 00:25:18.019 Shivani Amar: That’s, like, the retail dashboard. It’s, like, the retail OKRs, top-level OKRs, are, like, we have all of them in one place. So you can imagine an Omni dashboard right now, we have point of sale, and then you have sales, and whatever, but, like, a macro retail dashboard that’s just telling you, this was our sales, this was our point of sales, this was our…
229 00:25:18.020 ⇒ 00:25:23.050 Shivani Amar: Trade spend, and this is our, like, split by product type, or whatever.
230 00:25:23.050 ⇒ 00:25:24.190 Jasmin Multani: Right?
231 00:25:24.190 ⇒ 00:25:42.839 Shivani Amar: And so, like, that’s where we want to get to, and then people can drill into it to say, like, well, what happened at Target? Or, like, well, I really just want to think about point of sales historically, and, like, I want to go into that. So, like, what we’re… what you’ve been building is, like, a drill down on point of sale, but there’s, like, more stuff macro in what’s going on.
232 00:25:42.840 ⇒ 00:25:44.330 Jasmin Multani: Okay. Okay, okay.
233 00:25:44.680 ⇒ 00:25:49.780 Jasmin Multani: This helps. Could we… get access to the spreadsheet?
234 00:25:49.780 ⇒ 00:25:51.130 Shivani Amar: It’s in the.
235 00:25:51.130 ⇒ 00:25:53.370 Jasmin Multani: Okay, okay. I think I just haven’t exported.
236 00:25:53.370 ⇒ 00:26:08.199 Shivani Amar: No, so if you go to the, like, there’s a copy of it, you don’t necessarily need the full numbers, but, like, in terms of… if you’re ever, like, okay, am I hitting… like, what questions do I need to be answering? Like, yes, I can do that, but if you’re just, like, am I hitting the top-level things that, like.
237 00:26:08.200 ⇒ 00:26:17.299 Shivani Amar: element cares about, you can go tab by tab here. Say, okay, they’re doing drink mix point of sales, they’re doing drink mix sales. Do I have questions about what any of these things mean?
238 00:26:17.300 ⇒ 00:26:18.250 Jasmin Multani: Okay, perfect.
239 00:26:18.250 ⇒ 00:26:19.830 Shivani Amar: Sparkling Tradespin.
240 00:26:19.970 ⇒ 00:26:30.240 Shivani Amar: And so, this is, like, the top level, and then you can do the same thing for wholesale. You can be like, they’re reporting on drink mix versus drink mix, revenue for drink mix, sparkling.
241 00:26:30.530 ⇒ 00:26:41.229 Shivani Amar: how many partners they have, how many new accounts are created, revenue by U.S. resellers, international distributors, I think the ordering of these rows is kind of weird. They should just have the revenue all together. But, like.
242 00:26:41.580 ⇒ 00:26:59.140 Shivani Amar: This is an old file, so that’s why you’re gonna see references and stuff like that, but at least in terms of the buckets of things, this is, like, what you need to know. You’ll see, oh, they’re saying the word revenue here. That means that they might be, like, dis- removing refunds, or discounts, or whatever.
243 00:26:59.250 ⇒ 00:27:05.240 Shivani Amar: Right? Whereas in… In retail, they’re not actually saying revenue, they’re saying sales.
244 00:27:05.710 ⇒ 00:27:14.539 Jasmin Multani: okay, so for, like, any… I like this, run-through. I think for future, spec builds.
245 00:27:14.800 ⇒ 00:27:19.210 Jasmin Multani: What we should do is, like, backtrack from your questions teams are asking.
246 00:27:19.210 ⇒ 00:27:19.850 Shivani Amar: Yeah.
247 00:27:19.850 ⇒ 00:27:31.009 Jasmin Multani: And then, laterally be like, okay, these questions today are being answered in this spreadsheet through these columns, this is the context, because that also helps us train Bobby with the context.
248 00:27:31.020 ⇒ 00:27:42.290 Jasmin Multani: And from there, we look at the raw data modeling that OASH has created and be like, okay, these are literally the columns that will be needed to answer, and this is how we build the topic.
249 00:27:42.290 ⇒ 00:27:42.820 Shivani Amar: Yeah.
250 00:27:42.820 ⇒ 00:27:44.140 Jasmin Multani: So that’s just the workflow.
251 00:27:44.140 ⇒ 00:27:53.399 Shivani Amar: That sounds good, and you’ll see, like, the wholesale… nothing about fridges is gonna be in wholesale. Like, this is, like, the top-level metrics for the thing, so it’s like…
252 00:27:53.400 ⇒ 00:27:53.800 Jasmin Multani: Hmm.
253 00:27:53.800 ⇒ 00:27:56.290 Shivani Amar: bring… like, this is why…
254 00:27:58.090 ⇒ 00:28:06.270 Shivani Amar: go in a second, but this is why, like, this summary report I love, right? Because it’s like, okay, it tells me,
255 00:28:09.460 ⇒ 00:28:17.300 Shivani Amar: you know, you built this, but it just, like, tells me, like, sales, and then I’m kind of like, is this revenue yet? Like, is this gonna match up with what they’re doing?
256 00:28:17.660 ⇒ 00:28:30.960 Shivani Amar: when does it get to match revenue? Because eventually, you’re like, okay, if that’s how they’re reporting it here, I want to… I wanna be able to serve up… I want to be able to replace… the long-term thing is, like, replacing anybody typing any OKRs into anything.
257 00:28:32.260 ⇒ 00:28:39.539 Shivani Amar: People are hard-coding these numbers, right? They’re, like, finding it from the source, then hardcoding it here so we can all review it together.
258 00:28:42.860 ⇒ 00:28:52.030 Jasmin Multani: Okay, this is helpful, I wanna go back to the deliverable you asked for Matt Faith for Thursday.
259 00:28:53.860 ⇒ 00:28:59.910 Jasmin Multani: What… so we want to look into the retail reporting summary?
260 00:29:00.860 ⇒ 00:29:07.470 Jasmin Multani: That spreadsheet, and we wanna… you want us to sit down and backtrack every metric, every raw data metric.
261 00:29:07.610 ⇒ 00:29:14.669 Jasmin Multani: And give a assessment of, like, yes, this is in Blobby today, versus no, it’s not in Blobby today. Is that what you’re asking?
262 00:29:14.670 ⇒ 00:29:15.910 Shivani Amar: Oh, for Thursday?
263 00:29:15.910 ⇒ 00:29:16.430 Jasmin Multani: Yum.
264 00:29:16.430 ⇒ 00:29:33.980 Shivani Amar: I think I want to just look at it, like, what fields are you excluding? So if you just pull it up, and you just show me if it’s palatable for me to look at, hey, these are all the fields we’re excluding, I could be like, I think that we would need this field. If it’s all really a different language, and it’s gonna be really hard for my brain to compute it, you could…
265 00:29:34.010 ⇒ 00:29:40.979 Shivani Amar: you know, or you can copy and paste the list that you’re excluding and send it to me to look at beforehand, like, that’s kind of… I think that you know what I’m looking for.
266 00:29:40.980 ⇒ 00:29:42.080 Greg Stoutenburg: We’ll organize it, yeah.
267 00:29:42.080 ⇒ 00:29:42.860 Shivani Amar: Yeah, that’s fine.
268 00:29:42.860 ⇒ 00:29:43.400 Advait Nandakumar Menon: Yep.
269 00:29:43.400 ⇒ 00:30:02.770 Shivani Amar: Okay, I know we didn’t look at the point-of-sale velocity thing, but I think this was still a very good conversation. And so, Jasmine, if you’re… if you’re like, hey, I actually just want the latest copy of the OKR document so I can actually start, like, looking and comparing some numbers, then I can just replace this one with the latest copy.
270 00:30:02.980 ⇒ 00:30:03.690 Jasmin Multani: Okay.
271 00:30:03.690 ⇒ 00:30:06.330 Shivani Amar: Okay, so just let me know.
272 00:30:06.740 ⇒ 00:30:15.079 Shivani Amar: And then, we didn’t talk about UPC… the UPC thing, but I synced with Hannah, and she was like, look.
273 00:30:15.420 ⇒ 00:30:28.550 Shivani Amar: I don’t think UPC is, like, the cleanest thing, I think SKUID is better. And then she’s like, I’m just gonna pull a product catalog from Stored that, like, I think is clean, and then there’s an equivalent one on the retail side. Stored is, like, D2C…
274 00:30:28.930 ⇒ 00:30:31.239 Shivani Amar: And wholesale, and
275 00:30:31.890 ⇒ 00:30:40.920 Shivani Amar: sampling, I guess, right? And so then, like, the equivalent… there’s, like, an equivalent product catalog for… Retail?
276 00:30:41.090 ⇒ 00:30:45.560 Shivani Amar: And so she was saying, like, why don’t we give BrainForge these product catalogs?
277 00:30:45.660 ⇒ 00:30:53.080 Shivani Amar: And I was like, okay, that sounds good, so we’ll give you guys the product catalogs, and then I want you to assess, like, okay, do you have everything you need? Or, like, what do you feel is still missing?
278 00:30:53.850 ⇒ 00:31:09.060 Shivani Amar: concurrently, just for context for you guys, and then I will let you go, is we’re re-engaging with this company called Atomic that’s gonna, like, eventually do our supply forecasting, and they also said they wanted a master SKU list at some point. So, like, I kind of, like, I’m almost, like.
279 00:31:09.410 ⇒ 00:31:20.400 Shivani Amar: they… I want their specs on what they’re looking for. I want you to do a review of what we already have, and then, like, if we’re gonna ask the supply chain team to do anything, we should make sure that it’s aligned with both parties.
280 00:31:20.400 ⇒ 00:31:32.230 Shivani Amar: Versus, like, Brainforge want this, actually, Atomic wants it structured this way. So if, like, we need to have a meeting with Atomic to be like, what is the vision you have on Master SKU list, here’s what we think is missing, then we can arrange that.
281 00:31:32.510 ⇒ 00:31:33.230 Jasmin Multani: Perfect, yeah.
282 00:31:33.230 ⇒ 00:31:34.010 Shivani Amar: Okay?
283 00:31:34.010 ⇒ 00:31:36.340 Jasmin Multani: When is that conversation happening with Atomic?
284 00:31:36.340 ⇒ 00:31:52.939 Shivani Amar: I’m talking to them on Friday, but it’s not purely about the SKU list. It’s about generally what their data needs are, and, like, how they want to get the data in. And I think, like, the macro thing that I’m thinking about is, like, who’s going to ingest supply chain data? What goes into the ERP first? What goes into,
285 00:31:53.620 ⇒ 00:32:03.579 Shivani Amar: what goes into Snowflake first? What can Atomic ingest? Because they seem to have relationships, like, all these warehouses. So, like, instead of using Polyatomic, could they ingest some of the data and model it?
286 00:32:03.910 ⇒ 00:32:12.029 Shivani Amar: we’re actually taking their model data and putting it into Snowflake, right? So, like, I’m a little confused on… my question of the week is, who ingests supply chain data?
287 00:32:13.140 ⇒ 00:32:16.909 Jasmin Multani: Thank you. Just having one person that’s, like, consistent, reliant.
288 00:32:16.910 ⇒ 00:32:17.300 Shivani Amar: Yeah.
289 00:32:17.300 ⇒ 00:32:19.160 Jasmin Multani: One company that’s considered.
290 00:32:19.160 ⇒ 00:32:19.700 Shivani Amar: Yeah.
291 00:32:19.700 ⇒ 00:32:23.460 Jasmin Multani: Makes less queuing work for us.
292 00:32:23.460 ⇒ 00:32:28.599 Shivani Amar: Perfect. Okay, well, thank you guys for the conversation. I’m gonna go chat with Utham, and…
293 00:32:29.000 ⇒ 00:32:29.340 Greg Stoutenburg: Alright.
294 00:32:29.340 ⇒ 00:32:30.680 Shivani Amar: Talk to you later. Bye.
295 00:32:30.680 ⇒ 00:32:31.799 Jasmin Multani: Next up, bye.
296 00:32:31.800 ⇒ 00:32:32.630 Advait Nandakumar Menon: I’m good.