Meeting Title: Omni POS Dashboard Review Date: 2026-04-06 Meeting participants: Greg Stoutenburg, Shivani Amar
WEBVTT
1 00:01:17.960 ⇒ 00:01:19.030 Shivani Amar: Hi, Greg.
2 00:01:19.030 ⇒ 00:01:20.000 Greg Stoutenburg: Bunny.
3 00:01:20.000 ⇒ 00:01:20.980 Shivani Amar: How you doing?
4 00:01:21.380 ⇒ 00:01:23.910 Greg Stoutenburg: Oh, bop, bop, bop. Let’s try again.
5 00:01:24.420 ⇒ 00:01:25.400 Shivani Amar: Hello.
6 00:01:25.400 ⇒ 00:01:28.679 Greg Stoutenburg: There we go, okay.
7 00:01:28.680 ⇒ 00:01:32.930 Shivani Amar: So I know we talked a little bit last week, also.
8 00:01:32.930 ⇒ 00:01:33.550 Greg Stoutenburg: Yep.
9 00:01:34.880 ⇒ 00:01:45.459 Shivani Amar: What would be most helpful today? I have, like, a vague sense, but I have been back-to-back, so, like, not feeling crystal on where to jump in. What’s on your mind?
10 00:01:45.590 ⇒ 00:02:02.409 Greg Stoutenburg: Yeah, sure. So, one thing is I updated the POS dashboard spec, and so we could review that, disambiguated anything that said retail versus POS. So we have that, added a couple fields. The structure is pretty similar.
11 00:02:02.410 ⇒ 00:02:17.859 Greg Stoutenburg: And then I also identified places that were blocked, because there are some data sources that we don’t have coming into Snowflake from Emerson, and the last I knew, you had emailed them, like, 12 days ago or so. I wasn’t sure if there was an update there, and if not…
12 00:02:17.860 ⇒ 00:02:20.729 Shivani Amar: I’m sure that the data should be coming into Emerson now.
13 00:02:21.360 ⇒ 00:02:29.460 Greg Stoutenburg: Okay, so they’re saying they do have it. Okay, do we… does anyone, did anyone receive that update on our side, or was that only…
14 00:02:29.780 ⇒ 00:02:30.700 Greg Stoutenburg: To that.
15 00:02:30.700 ⇒ 00:02:33.989 Shivani Amar: I think Utham’s on that chain.
16 00:02:33.990 ⇒ 00:02:34.680 Greg Stoutenburg: Okay.
17 00:02:35.890 ⇒ 00:02:37.290 Shivani Amar: Let’s see…
18 00:02:37.840 ⇒ 00:02:39.170 Greg Stoutenburg: I can give that a bump.
19 00:02:39.170 ⇒ 00:02:48.009 Shivani Amar: We will check update today and let you know, and… is Oatham not on that chain? Sorry. Maybe Oatham is not. Okay. Okay.
20 00:02:50.500 ⇒ 00:02:59.720 Shivani Amar: And then they said, we can sync any data found in Omega to your cloud instance. Is there anything specific you had in mind? Okay, so… okay, let me just… let’s just ask.
21 00:02:59.720 ⇒ 00:03:02.410 Greg Stoutenburg: Okay, cool. So they’re saying, basically, we’ll start sending it over.
22 00:03:02.410 ⇒ 00:03:03.070 Shivani Amar: Yeah.
23 00:03:03.250 ⇒ 00:03:03.980 Greg Stoutenburg: Okay.
24 00:03:17.930 ⇒ 00:03:19.459 Shivani Amar: Okay, cool.
25 00:03:20.540 ⇒ 00:03:29.239 Shivani Amar: Do you want to show me, like, the tables or anything that you’re like, hey, we’ve… we’ve kind of nailed this now for, like, point-of-sales data and stuff? Like…
26 00:03:29.240 ⇒ 00:03:29.910 Greg Stoutenburg: Yeah.
27 00:03:30.110 ⇒ 00:03:30.570 Shivani Amar: Okay.
28 00:03:31.640 ⇒ 00:03:39.210 Greg Stoutenburg: Okay, cool. So, purchase order data, Walmart and Target. Okay, and that’ll… that’ll include the data then for retail?
29 00:03:39.880 ⇒ 00:03:43.020 Greg Stoutenburg: Right? For any sales from Element to retailers.
30 00:03:43.430 ⇒ 00:03:45.259 Shivani Amar: Yeah, that’s what I… that’s what I’m talking about.
31 00:03:45.260 ⇒ 00:03:45.810 Greg Stoutenburg: Okay, good.
32 00:03:45.810 ⇒ 00:03:48.459 Shivani Amar: Shoulders are… equals that.
33 00:03:48.460 ⇒ 00:03:49.529 Greg Stoutenburg: Got it. Perfect.
34 00:03:49.530 ⇒ 00:04:06.700 Shivani Amar: It’s complicated, Greg, I know that you’ve got this by now, but purchase orders is a PO, like, that’s… but it has nothing to do with point of sales. Yeah. Okay, so POS is point of sales. Purchase orders are, like, what we’re… what we’re getting from the retailer.
35 00:04:06.700 ⇒ 00:04:12.930 Greg Stoutenburg: Yeah, anything that I’ve used to define our fields, I’ve just used retail sales or POS.
36 00:04:13.100 ⇒ 00:04:13.710 Shivani Amar: Perfect.
37 00:04:13.860 ⇒ 00:04:14.670 Greg Stoutenburg: Exclusive.
38 00:04:14.670 ⇒ 00:04:17.920 Shivani Amar: Yeah, and retail sales would be what come in from the purchase orders.
39 00:04:17.920 ⇒ 00:04:23.010 Greg Stoutenburg: Purchase order. Got it. Okay. Yeah, thank you for that.
40 00:04:23.210 ⇒ 00:04:34.389 Greg Stoutenburg: Okay, cool. So, maybe the next best thing to move to is, oh yeah, I had another question, too. For a retailer that is considered at risk, so I was looking at your…
41 00:04:34.390 ⇒ 00:04:35.369 Shivani Amar: A wholesale partner.
42 00:04:36.230 ⇒ 00:04:44.879 Greg Stoutenburg: a wholesale partner that’s at risk. Do we want it, and we’ll leave at risk there, limited there. So we won’t talk about, for example, individual stores.
43 00:04:45.410 ⇒ 00:04:46.989 Greg Stoutenburg: We’ll just say wholesale partner.
44 00:04:48.110 ⇒ 00:04:52.450 Shivani Amar: Like, what would be an individual, like, an individual Target store that’s at risk?
45 00:04:52.450 ⇒ 00:04:55.949 Greg Stoutenburg: Like, an individual target that, yeah, inventory’s not moving or something.
46 00:04:55.950 ⇒ 00:05:09.309 Shivani Amar: Okay, no, that’s fine. I think I… I know the definition for a wholesale partner that they haven’t ordered from us, but if it’s an individual store at risk, I don’t really know what that means. I would need that to be more clear in my mind.
47 00:05:09.310 ⇒ 00:05:12.439 Greg Stoutenburg: Yeah, well, it would just be up for us to define it, and see if this is something
48 00:05:12.850 ⇒ 00:05:27.239 Greg Stoutenburg: want to put in, because it would just be a calculated field, I mean, so we’re getting in this data on POS and, you know, and by individual store and SKU and stuff, so we can then make… create a calculated field that goes, alright, if…
49 00:05:27.290 ⇒ 00:05:33.630 Greg Stoutenburg: only this many purchases in this period of time compared to this period of time, label them this way. Gotcha.
50 00:05:33.630 ⇒ 00:05:34.310 Shivani Amar: Okay.
51 00:05:34.310 ⇒ 00:05:50.479 Greg Stoutenburg: Yeah, if we want to do that. And the reason I’m bringing it up is there was somewhere in… honestly, I’m not even able to find it… there was somewhere where there… it looked like there was a discussion, where someone was asking, like, okay, do we want to apply this to, POS as well as wholesale? And it seemed ambiguous, so I thought I’d bring it up.
52 00:05:50.480 ⇒ 00:05:51.230 Shivani Amar: Okay.
53 00:05:51.470 ⇒ 00:05:54.909 Shivani Amar: I… I don’t think it’s, like, necessary right now.
54 00:05:54.910 ⇒ 00:05:55.990 Greg Stoutenburg: We don’t have to. Nope.
55 00:05:55.990 ⇒ 00:06:09.440 Shivani Amar: I don’t think it’s necessary right now. Like, it doesn’t feel high priority. When we get… we just had a VP of Retail start today, and so I’m very interested in getting to know him so that I can say, like, what would be useful for you?
56 00:06:09.440 ⇒ 00:06:16.910 Shivani Amar: Right? And, like, at one point, the person who was overseeing retail was saying, it’d be helpful for me to see who has
57 00:06:18.220 ⇒ 00:06:36.659 Shivani Amar: low inventory, but high velocity. Yeah. And so, like, that could be defined to some extent around, like, how do you calculate velocity of product moving, and then how do you think about, like, inventory, weeks of stock for the store on hand, and then kind of, like.
58 00:06:36.770 ⇒ 00:06:51.700 Shivani Amar: make a 2x2 or something like that, right? Yeah, yeah. So I think that’s, like, a useful question to explore, but less so, like, that kind of develops some sort of, like, at-risk thing more implicitly versus, like, some explicit category.
59 00:06:51.700 ⇒ 00:06:57.210 Greg Stoutenburg: Yeah, and you really might not even need it, right? Because, again, you can look at velocity and draw the conclusions you want to draw. Yeah.
60 00:06:57.210 ⇒ 00:07:07.629 Shivani Amar: But I do think that there’s something around, like, connecting velocity to inventory. Yeah. It’s just like, oh man, this store sells out of product quickly, and, like, they don’t move enough, right?
61 00:07:07.630 ⇒ 00:07:21.620 Greg Stoutenburg: Yeah. Yep. Yep. Okay, great. Well then, if it’s helpful, I can walk through this and just give… just give context. So this is nearly entirely just focused on point of sale.
62 00:07:21.620 ⇒ 00:07:22.150 Shivani Amar: Okay.
63 00:07:22.150 ⇒ 00:07:38.390 Greg Stoutenburg: And, now, some things are named because it’s the name of a table inside of Snowflake, and so we won’t quibble too much about it. I mean, we can change names of tables, but we also want to make sure we don’t, you know, like, relabel things that already have an existing name and then lose track of them.
64 00:07:38.390 ⇒ 00:07:43.639 Greg Stoutenburg: The primary Omni-topic is called Retail Sales Performance.
65 00:07:45.790 ⇒ 00:07:49.050 Greg Stoutenburg: And, so I came up with…
66 00:07:49.110 ⇒ 00:08:08.789 Greg Stoutenburg: a total of 7 dashboards. Most of them are about point of sale by retailer. There’s velocity in there as well, and then a couple are retail sales specifically, as in element to retailers. But I just noted blocked on those, because… well, until we had the conversation a moment ago.
67 00:08:08.790 ⇒ 00:08:09.230 Shivani Amar: Yeah.
68 00:08:09.230 ⇒ 00:08:23.440 Greg Stoutenburg: We didn’t have that data, so, yeah. So just, I think this is… I’ll re-invite you now. I think this is worth going through, you know, async, but just to, like, I can just give, like, a broad view right now. Okay.
69 00:08:23.720 ⇒ 00:08:29.600 Greg Stoutenburg: One dashboard would be… and I use Heading 2 for these, so if you wanted to scroll and just see them, you know.
70 00:08:29.600 ⇒ 00:08:30.140 Shivani Amar: Cool.
71 00:08:30.140 ⇒ 00:08:33.070 Greg Stoutenburg: At that level. An executive pulse.
72 00:08:33.299 ⇒ 00:08:48.279 Greg Stoutenburg: just tables, looking at retailers, so, POS sales by date in total, comparing across weeks, weekly growth or decline, same date last month, month over month.
73 00:08:48.450 ⇒ 00:08:53.979 Greg Stoutenburg: Same day last year, year over year. To just get that really high-level view.
74 00:08:55.440 ⇒ 00:09:03.899 Greg Stoutenburg: As well, another table would just break down the same, but by units sold. Here, I think we’d probably want to do per SKU.
75 00:09:04.060 ⇒ 00:09:05.200 Greg Stoutenburg: Per retailer?
76 00:09:05.670 ⇒ 00:09:08.970 Greg Stoutenburg: By category.
77 00:09:09.540 ⇒ 00:09:18.179 Greg Stoutenburg: I mean, we know what these are already, and also they can be inferred by SKU, but this is nice because you can just see, again, at a glance, movement by category.
78 00:09:18.730 ⇒ 00:09:22.549 Greg Stoutenburg: Yeah, buy sales as in POS sales.
79 00:09:22.910 ⇒ 00:09:24.130 Greg Stoutenburg: By units.
80 00:09:24.320 ⇒ 00:09:30.840 Shivani Amar: And it’s, like, it’s funny, because it’s like, if you just have a bunch of charts or a bunch of tables, that’s one thing, or, like.
81 00:09:31.060 ⇒ 00:09:46.080 Shivani Amar: it’s, like, I don’t remember what we were talking about with, like, the filters that you can pick? I don’t know if it should be, like… I actually, right now, I’m just curious about it by category, so I want it to be, like… and we can figure that out later, but, like, I think this is a fine list right now.
82 00:09:46.090 ⇒ 00:09:53.720 Greg Stoutenburg: Yeah, yeah, and this is more about, like, this is more about, is this going to provide all of the detail that you need to see what you want to be able to see?
83 00:09:53.720 ⇒ 00:09:54.180 Shivani Amar: Yeah.
84 00:09:54.180 ⇒ 00:10:01.109 Greg Stoutenburg: And then, when we get to the engineering step, you know, I’ll work with others on my team to ask exactly that kind of question, like.
85 00:10:01.300 ⇒ 00:10:07.310 Greg Stoutenburg: Of course, you know, as much as possible, if there’s… if there’s a way that this just becomes one drop-down filter or a button, then we do that. We don’t have.
86 00:10:07.310 ⇒ 00:10:15.280 Shivani Amar: Yeah, because, like, even, like, daily versus weekly versus monthly or whatever, I’d rather just be able to pick the time frame than, like, have to have…
87 00:10:15.280 ⇒ 00:10:32.570 Shivani Amar: the same graph over and over, or the same table over and over. If it’s structured well, then I might say I actually want it monthly, and then that way it’s consolidating everything in a way that I can report out on my monthly performance, versus saying, hey, by the way, can you add a section for each of these to also be monthly?
88 00:10:32.570 ⇒ 00:10:47.039 Greg Stoutenburg: Yeah, no, fully agree. And one of the things that, one of the things that I noted from our conversation last week is, wherever possible, you’d like to have selectors for product time period, that can just be applied globally across a dashboard.
89 00:10:47.040 ⇒ 00:10:47.570 Shivani Amar: Yeah.
90 00:10:47.730 ⇒ 00:10:48.450 Greg Stoutenburg: Yeah.
91 00:10:48.780 ⇒ 00:10:53.300 Greg Stoutenburg: Yeah, and then, okay, and then basically it’s just gonna repeat. Daily, weekly.
92 00:10:55.210 ⇒ 00:10:57.520 Greg Stoutenburg: Okay. Dashboard 2.
93 00:10:57.970 ⇒ 00:11:01.889 Greg Stoutenburg: Walmart versus Target, and performance summary.
94 00:11:02.140 ⇒ 00:11:14.959 Greg Stoutenburg: these are some things that you’ve got reporting on already, so a lot of this is taking what’s been in spreadsheets and just putting it into Omni, where the data is constantly fresh. So, help us answer those questions. How is Target performing versus Walmart?
95 00:11:15.070 ⇒ 00:11:18.379 Greg Stoutenburg: However, sales, for a given day or week.
96 00:11:19.040 ⇒ 00:11:28.540 Greg Stoutenburg: And we would look at… Sales by retailer, latest day, week over week, units, POS sales.
97 00:11:28.880 ⇒ 00:11:30.690 Greg Stoutenburg: In the most recent week.
98 00:11:31.060 ⇒ 00:11:38.369 Greg Stoutenburg: Active stores, where we’re understanding this as, stores that have a sale during this time period.
99 00:11:38.370 ⇒ 00:11:38.950 Shivani Amar: Yep.
100 00:11:40.110 ⇒ 00:11:45.460 Greg Stoutenburg: store rank. This is one we thought you’d think would be helpful,
101 00:11:45.820 ⇒ 00:11:51.820 Greg Stoutenburg: be… this would be a calculated field, as well, that would look at That would be…
102 00:11:51.820 ⇒ 00:11:52.310 Shivani Amar: Right now.
103 00:11:52.310 ⇒ 00:11:54.309 Greg Stoutenburg: stores, basically. You like that?
104 00:11:55.570 ⇒ 00:12:04.530 Shivani Amar: That… that’s… POS volume is kind of, like, a weird, phrase. Movement.
105 00:12:05.200 ⇒ 00:12:11.539 Shivani Amar: Yeah, but, like, is it… is it movement? Is that velocity? Or is it store percentile and point of sales, like, total?
106 00:12:11.540 ⇒ 00:12:12.280 Greg Stoutenburg: Total.
107 00:12:12.280 ⇒ 00:12:14.359 Shivani Amar: Yeah, so then it’s just the sum of…
108 00:12:14.600 ⇒ 00:12:15.590 Greg Stoutenburg: Just say some.
109 00:12:15.590 ⇒ 00:12:21.819 Shivani Amar: Or just delete the word volume, and just say store percentile at point of sales. Okay.
110 00:12:21.820 ⇒ 00:12:25.429 Greg Stoutenburg: Love it. Yeah, less is more this time.
111 00:12:25.600 ⇒ 00:12:27.610 Greg Stoutenburg: Average sales per store.
112 00:12:28.620 ⇒ 00:12:29.350 Shivani Amar: Cool.
113 00:12:29.350 ⇒ 00:12:30.989 Greg Stoutenburg: Then we can compare as well.
114 00:12:31.980 ⇒ 00:12:35.610 Greg Stoutenburg: Similar fields from before, but now, you know, broken down by retailer.
115 00:12:36.660 ⇒ 00:12:38.200 Greg Stoutenburg: Including share.
116 00:12:38.380 ⇒ 00:12:40.430 Greg Stoutenburg: percent of POS sales.
117 00:12:42.980 ⇒ 00:12:52.460 Shivani Amar: Like, like, what level of feedback do you want for me on this? I’m kind of like, right now, I’m kind of like, okay, cool, but, like, I think that, can you scroll up to that again?
118 00:12:53.410 ⇒ 00:12:56.939 Shivani Amar: Latest day, latest week.
119 00:12:57.130 ⇒ 00:13:02.379 Shivani Amar: It’s just, like, a comparison table to tell you what’s happening with Walmart versus Target, I guess. Yes.
120 00:13:02.380 ⇒ 00:13:04.090 Greg Stoutenburg: Yep, that’s the purpose of this dashboard.
121 00:13:04.090 ⇒ 00:13:16.849 Shivani Amar: Yeah, and I’m like, okay, like, sure, right? Good. Or it’s kind of like, you know how I say, like, I like seeing the times, I like seeing the rows being the…
122 00:13:17.540 ⇒ 00:13:26.499 Shivani Amar: store, and the times being the columns, so you could also have, like, fields for… like… like, I think in general, if we have, like.
123 00:13:28.550 ⇒ 00:13:33.829 Shivani Amar: If we move towards A time range being in the columns.
124 00:13:33.990 ⇒ 00:13:38.219 Shivani Amar: And then the other things being the rows, that’s, like, helpful orientation, but it’s.
125 00:13:38.770 ⇒ 00:13:46.909 Shivani Amar: I’m kind of like, this is fine. If you’re just saying, like, what happened yesterday at Walmart versus Target, and you think people are going to want to know about that, that’s fine. Like… Yeah.
126 00:13:47.060 ⇒ 00:13:53.660 Shivani Amar: It’s… it’s okay. I’m just like, what is somebody actually going to want to know? And it’s…
127 00:13:53.820 ⇒ 00:14:05.659 Shivani Amar: TBD. I think this is… this is, like, where, like, I kind of can weigh in and say, like, I think that what I’d want to know is ABC, but, like, we had the VP of Retail start today. So, my thought is, like.
128 00:14:06.730 ⇒ 00:14:08.849 Shivani Amar: We can present some things, and then, like.
129 00:14:08.850 ⇒ 00:14:09.310 Greg Stoutenburg: Yup.
130 00:14:09.310 ⇒ 00:14:17.200 Shivani Amar: The hope is, by the end of May, he has looked at this stuff, given us feedback, and then we’ve reoriented things to work for.
131 00:14:17.200 ⇒ 00:14:17.770 Greg Stoutenburg: Totally.
132 00:14:17.950 ⇒ 00:14:31.219 Greg Stoutenburg: Yep, yeah, absolutely. So this is, let’s think of this as a… this is a true first pass of, here’s a proposal for data for us to include and a way to organize it.
133 00:14:31.220 ⇒ 00:14:39.569 Greg Stoutenburg: It doesn’t need to be perfect, so, like, feedback at that level is perfect. So if you see something like this, and you think, yeah, this would do the job.
134 00:14:39.670 ⇒ 00:14:56.770 Greg Stoutenburg: probably we just go, okay, we can build this. If you see something like this, and you think, actually, I think this sort of gets it backwards, what I’d really like to see is just, you know, date… is just date, retailer, and so on and so on, then we go, okay, good, I’ll make a note, and we’ll basically, you know, pivot this.
135 00:14:56.960 ⇒ 00:14:58.050 Greg Stoutenburg: Instead.
136 00:14:58.050 ⇒ 00:14:59.249 Shivani Amar: That sounds good.
137 00:14:59.250 ⇒ 00:14:59.860 Greg Stoutenburg: Yep.
138 00:15:00.200 ⇒ 00:15:05.000 Greg Stoutenburg: Cool. Okay. Maybe for the sake of time, I won’t jump in on all the charts, and we’ll just…
139 00:15:05.000 ⇒ 00:15:05.680 Shivani Amar: Fine.
140 00:15:05.860 ⇒ 00:15:11.850 Greg Stoutenburg: just go to the next dashboard. I… there were some places where there were some comments internally, and I just left them for follow-ups later.
141 00:15:11.850 ⇒ 00:15:12.600 Shivani Amar: Yeah.
142 00:15:12.600 ⇒ 00:15:17.380 Greg Stoutenburg: The third dashboard can look at geography and regional sales. Omni has some pretty…
143 00:15:17.830 ⇒ 00:15:34.149 Greg Stoutenburg: for maps, and so it’s gonna be a place where you can, like, really see the data and interact with it in ways that are neat. So we can answer questions about retail sales, retail… oh, careful, careful, let’s get in here, by region.
144 00:15:34.150 ⇒ 00:15:35.290 Shivani Amar: sales. Yeah.
145 00:15:35.990 ⇒ 00:15:39.240 Shivani Amar: Just say point of… just type out point of sales.
146 00:15:39.650 ⇒ 00:15:44.160 Shivani Amar: Or, yeah, let’s just type out point of sales where, like, where possible.
147 00:15:44.160 ⇒ 00:15:44.750 Greg Stoutenburg: Sure.
148 00:15:44.750 ⇒ 00:15:46.140 Shivani Amar: Yeah, that’s fine.
149 00:15:47.370 ⇒ 00:15:47.930 Greg Stoutenburg: Yep.
150 00:15:48.070 ⇒ 00:15:48.720 Shivani Amar: Yeah.
151 00:15:50.370 ⇒ 00:15:56.939 Shivani Amar: But then it says… And what is revenue by state for retail is, like, totally different, which is…
152 00:15:57.130 ⇒ 00:15:59.130 Shivani Amar: Like, I…
153 00:15:59.740 ⇒ 00:16:04.030 Shivani Amar: Revenue just gets it to, like, a whole host of things that we just can’t answer right now, so…
154 00:16:04.030 ⇒ 00:16:06.319 Greg Stoutenburg: Yeah, yeah. So we can just strike this, then?
155 00:16:06.320 ⇒ 00:16:07.659 Shivani Amar: Yeah, delete that.
156 00:16:07.660 ⇒ 00:16:10.439 Greg Stoutenburg: Yeah, because the whole point is we’re just trying to introduce.
157 00:16:10.440 ⇒ 00:16:11.729 Shivani Amar: You’re just trying to show you can do GS.
158 00:16:11.730 ⇒ 00:16:13.740 Greg Stoutenburg: Regions and geo. That’s it. Yep.
159 00:16:13.740 ⇒ 00:16:14.490 Shivani Amar: Yeah.
160 00:16:14.490 ⇒ 00:16:18.900 Greg Stoutenburg: Okay. So, we can do…
161 00:16:19.390 ⇒ 00:16:22.200 Greg Stoutenburg: By region, we can group regions this way.
162 00:16:22.630 ⇒ 00:16:24.129 Greg Stoutenburg: We can do by state.
163 00:16:25.120 ⇒ 00:16:27.060 Greg Stoutenburg: And include retailer.
164 00:16:28.030 ⇒ 00:16:34.399 Shivani Amar: And, like, people seem to have a desire for, like, zip code analysis. Is that something that can be done as well?
165 00:16:35.330 ⇒ 00:16:35.770 Greg Stoutenburg: Yep.
166 00:16:35.770 ⇒ 00:16:46.470 Shivani Amar: understanding, I think, where we have zip code in our data versus where we might be lacking zip code data is helpful. Like, do all of these stores for Target and Walmart have zip codes?
167 00:16:47.240 ⇒ 00:16:50.140 Shivani Amar: Yeah. And eventually, if I wanted to say, I want to see…
168 00:16:50.730 ⇒ 00:17:01.700 Shivani Amar: like, the omni-channel view is saying, I want to see sales… by zip code… combined for…
169 00:17:01.910 ⇒ 00:17:06.550 Shivani Amar: retail, Target and Walmart, and wholesale.
170 00:17:08.140 ⇒ 00:17:08.530 Shivani Amar: Right?
171 00:17:08.530 ⇒ 00:17:09.140 Greg Stoutenburg: Yeah.
172 00:17:09.339 ⇒ 00:17:18.209 Shivani Amar: And it’s like, what are our biggest markets across those two channels? Right. Then, when you have e-commerce data, you want to say, what’s the mix in…
173 00:17:18.449 ⇒ 00:17:26.339 Shivani Amar: this zip code. And it’s like, in this zip code, 70% of your customers are e-commerce, because there’s no target close by.
174 00:17:26.339 ⇒ 00:17:27.049 Greg Stoutenburg: Right.
175 00:17:27.050 ⇒ 00:17:29.790 Shivani Amar: Right? And so, like, that’s, like, the…
176 00:17:30.240 ⇒ 00:17:33.410 Shivani Amar: Like, the place to get… want to get to.
177 00:17:33.410 ⇒ 00:17:43.679 Greg Stoutenburg: Yeah, yeah, yeah. Yeah, no, heard. Right. Good, very good. Yeah, so we can… let me just find out what zip code data is available.
178 00:17:43.920 ⇒ 00:17:50.709 Greg Stoutenburg: I am very confident that if it isn’t a field already, then it’s something that’s inferred by,
179 00:17:51.390 ⇒ 00:17:54.419 Greg Stoutenburg: other information about the store. So, I’ll find out.
180 00:17:56.620 ⇒ 00:17:58.290 Greg Stoutenburg: Okay, and the sales by state.
181 00:17:59.730 ⇒ 00:18:00.420 Greg Stoutenburg: Alright.
182 00:18:01.680 ⇒ 00:18:04.070 Greg Stoutenburg: POS velocity by SKU and retailer.
183 00:18:04.950 ⇒ 00:18:07.530 Greg Stoutenburg: Primarily this sort of table.
184 00:18:08.590 ⇒ 00:18:13.120 Greg Stoutenburg: That would give us daily units for POS velocity per store.
185 00:18:13.620 ⇒ 00:18:15.359 Greg Stoutenburg: Total units, period.
186 00:18:15.850 ⇒ 00:18:17.530 Greg Stoutenburg: Total sales in dollars.
187 00:18:18.340 ⇒ 00:18:20.879 Greg Stoutenburg: And then information about the stores themselves.
188 00:18:21.450 ⇒ 00:18:25.769 Greg Stoutenburg: Percent selling, carrying, And number. Selling.
189 00:18:25.960 ⇒ 00:18:27.880 Shivani Amar: Oh, so this is,
190 00:18:31.730 ⇒ 00:18:32.610 Shivani Amar: Hmm.
191 00:18:36.070 ⇒ 00:18:40.150 Greg Stoutenburg: And maybe this is the sort of question that’s going to go to the VP of Retail.
192 00:18:40.350 ⇒ 00:18:49.410 Greg Stoutenburg: Because this is related… so this is the second one that we’ve come to where we’re looking at information about stores specifically, and we’ll find out.
193 00:18:49.890 ⇒ 00:18:53.289 Greg Stoutenburg: What they think of that kind of, level of detail.
194 00:18:56.200 ⇒ 00:18:56.780 Shivani Amar: Hmm.
195 00:18:59.840 ⇒ 00:19:04.550 Shivani Amar: Velocity Leaderboard, just as, like, a con… I’m like, what… what is this…
196 00:19:05.200 ⇒ 00:19:10.840 Shivani Amar: trying to answer. It’s saying… so it’s saying which… SKUs are moving quickly.
197 00:19:11.470 ⇒ 00:19:15.909 Greg Stoutenburg: Yep, which SKUs are moving quickly? By how much.
198 00:19:16.130 ⇒ 00:19:17.750 Greg Stoutenburg: What’s their dollar value?
199 00:19:18.980 ⇒ 00:19:20.450 Greg Stoutenburg: At what retailer?
200 00:19:21.220 ⇒ 00:19:21.950 Shivani Amar: Okay.
201 00:19:23.350 ⇒ 00:19:24.060 Shivani Amar: Cool.
202 00:19:24.700 ⇒ 00:19:27.999 Greg Stoutenburg: I could see… I see this,
203 00:19:28.370 ⇒ 00:19:31.359 Greg Stoutenburg: Valuable in conjunction with the regional data.
204 00:19:31.770 ⇒ 00:19:33.629 Greg Stoutenburg: Because it tells you what’s hot where.
205 00:19:36.590 ⇒ 00:19:38.169 Greg Stoutenburg: Or what’s cold, where.
206 00:19:40.760 ⇒ 00:19:43.869 Greg Stoutenburg: Similar but different graphic. We could put it into a heat map.
207 00:19:44.450 ⇒ 00:19:47.580 Greg Stoutenburg: So you can just sort of see at a glance and look for patterns that way.
208 00:19:49.140 ⇒ 00:19:51.139 Greg Stoutenburg: And then trend as a line.
209 00:19:54.540 ⇒ 00:19:56.120 Greg Stoutenburg: I put last 12 weeks here.
210 00:19:56.120 ⇒ 00:19:57.370 Shivani Amar: per SKU.
211 00:19:58.240 ⇒ 00:20:01.609 Greg Stoutenburg: For velocity trend? Yeah, we can do…
212 00:20:01.610 ⇒ 00:20:02.660 Shivani Amar: For, like, the…
213 00:20:02.950 ⇒ 00:20:04.760 Greg Stoutenburg: Yeah, I suggested top 5 SKUs.
214 00:20:06.040 ⇒ 00:20:07.199 Greg Stoutenburg: Or filter.
215 00:20:08.370 ⇒ 00:20:09.060 Shivani Amar: Hmm.
216 00:20:10.360 ⇒ 00:20:11.230 Shivani Amar: Cool.
217 00:20:13.050 ⇒ 00:20:19.109 Shivani Amar: So I could pick any SKU and see what its velocity has been over time, and then velocity will be very clearly defined.
218 00:20:19.390 ⇒ 00:20:19.980 Greg Stoutenburg: Yep.
219 00:20:19.980 ⇒ 00:20:20.610 Shivani Amar: Okay.
220 00:20:20.780 ⇒ 00:20:22.910 Greg Stoutenburg: Yeah, according to…
221 00:20:27.150 ⇒ 00:20:28.880 Greg Stoutenburg: Got all your tabs here.
222 00:20:29.410 ⇒ 00:20:31.929 Greg Stoutenburg: I think you defined velocity…
223 00:20:31.930 ⇒ 00:20:33.249 Shivani Amar: I think Utem and…
224 00:20:33.250 ⇒ 00:20:33.760 Greg Stoutenburg: this table.
225 00:20:34.030 ⇒ 00:20:36.190 Shivani Amar: Defined it in this table, yeah.
226 00:20:36.820 ⇒ 00:20:39.430 Greg Stoutenburg: I get all my… well, no, I don’t want that there.
227 00:20:40.550 ⇒ 00:20:43.059 Shivani Amar: It’s in the WIP core metrics or something? Okay.
228 00:20:43.060 ⇒ 00:20:45.850 Greg Stoutenburg: Yep, units sold by store in time frame.
229 00:20:47.400 ⇒ 00:20:49.880 Greg Stoutenburg: Looks like they haven’t finished the…
230 00:20:50.220 ⇒ 00:20:50.810 Shivani Amar: go to court.
231 00:20:50.810 ⇒ 00:20:53.380 Greg Stoutenburg: Oh, there’s the formula. There’s the formula right there.
232 00:20:53.380 ⇒ 00:20:54.449 Shivani Amar: What is it?
233 00:20:54.820 ⇒ 00:20:57.169 Greg Stoutenburg: Sum of units sold by store.
234 00:21:00.020 ⇒ 00:21:03.939 Shivani Amar: In what time… like, in the… that’s not velocity.
235 00:21:04.060 ⇒ 00:21:04.980 Greg Stoutenburg: Yeah, and that should.
236 00:21:04.980 ⇒ 00:21:07.030 Shivani Amar: Could you go to… could you go to the next tab?
237 00:21:07.200 ⇒ 00:21:08.650 Shivani Amar: with core metrics?
238 00:21:10.370 ⇒ 00:21:12.360 Greg Stoutenburg: Yeah, this is the one you’ve been working on.
239 00:21:12.610 ⇒ 00:21:19.580 Shivani Amar: Sales velocity, rolling 4-week daily average of units sold. Units sold in the last 28 days divided by 28.
240 00:21:19.870 ⇒ 00:21:20.740 Greg Stoutenburg: Yeah.
241 00:21:22.410 ⇒ 00:21:25.260 Greg Stoutenburg: So, this one needs to be updated.
242 00:21:25.500 ⇒ 00:21:29.079 Shivani Amar: I think that this tab is old, like, so I think…
243 00:21:29.370 ⇒ 00:21:36.579 Shivani Amar: you should just talk with them about that, because I feel like he was like, no, we’re gonna start working on this other tab. Rather than updating this one, I think you should just delete.
244 00:21:36.580 ⇒ 00:21:40.479 Greg Stoutenburg: Yeah. Yeah, but that’s exactly what I was thinking, like,
245 00:21:42.350 ⇒ 00:21:47.149 Greg Stoutenburg: I would rather, instead of saying, this one’s work in progress, I’d rather say, this is the…
246 00:21:47.460 ⇒ 00:21:54.159 Shivani Amar: Yeah, I think… I think that’s what he said to me, he’s like, we’re gonna start working off of this one, and I was like, okay, then I won’t even look at this one anymore.
247 00:22:12.280 ⇒ 00:22:12.850 Greg Stoutenburg: Yup.
248 00:22:13.700 ⇒ 00:22:14.650 Greg Stoutenburg: Okay.
249 00:22:16.180 ⇒ 00:22:20.449 Greg Stoutenburg: Cool. So… This one is the definition.
250 00:22:23.640 ⇒ 00:22:24.990 Greg Stoutenburg: Okay.
251 00:22:25.310 ⇒ 00:22:33.180 Greg Stoutenburg: Category SKU drill down. Some of this replicates data that’s been suggested in other tables, but it matches
252 00:22:33.430 ⇒ 00:22:49.000 Greg Stoutenburg: fill skew daily wireframe. So the thought here is, this is probably one of those places where you’re able to say, hey, you know, stakeholder, I know the way that you’re used to digesting this data, and we can just move it here while you adjust to the new format of the dashboards that we have.
253 00:22:52.400 ⇒ 00:22:54.979 Greg Stoutenburg: Maybe we build it, and he goes, Sorry, hang on.
254 00:22:55.450 ⇒ 00:22:58.770 Greg Stoutenburg: Hey, buddy, you okay? Hey, you check on him?
255 00:22:59.360 ⇒ 00:23:01.039 Greg Stoutenburg: Hey, bud! Hey!
256 00:23:01.330 ⇒ 00:23:03.129 Greg Stoutenburg: Hey, Bass, can you come check on him?
257 00:23:04.480 ⇒ 00:23:07.779 Greg Stoutenburg: Sebastian, can you check on the dog, please? Thank you.
258 00:23:09.700 ⇒ 00:23:13.009 Greg Stoutenburg: My dog’s, like, screeching and eating his paw or something, so…
259 00:23:13.010 ⇒ 00:23:13.660 Shivani Amar: Okay.
260 00:23:13.660 ⇒ 00:23:15.309 Greg Stoutenburg: Sorry about that.
261 00:23:15.310 ⇒ 00:23:16.389 Shivani Amar: No, no, you’re good.
262 00:23:20.800 ⇒ 00:23:33.469 Greg Stoutenburg: And then this is one that I marked as on hold. So, I have not gone through and updated everything to be precise about retail here. Yeah, don’t look at that one.
263 00:23:33.820 ⇒ 00:23:38.479 Greg Stoutenburg: Yeah, and that applies for… yeah, that applies for all of the Section 6 dashboards. Okay. 7.
264 00:23:38.610 ⇒ 00:23:41.119 Greg Stoutenburg: Also on hold, yeah.
265 00:23:41.120 ⇒ 00:23:42.290 Shivani Amar: Okay, perfect.
266 00:23:43.110 ⇒ 00:23:44.229 Shivani Amar: Cool. That sounds great.
267 00:23:44.230 ⇒ 00:23:45.259 Greg Stoutenburg: So, I’m gonna invite you in.
268 00:23:45.260 ⇒ 00:23:47.399 Shivani Amar: through 1 through 5. That sounds good.
269 00:23:47.400 ⇒ 00:23:48.140 Greg Stoutenburg: Yeah.
270 00:23:49.860 ⇒ 00:23:50.790 Shivani Amar: Okay, great.
271 00:23:51.220 ⇒ 00:23:55.490 Shivani Amar: Hey, so if we pivot away from this for a second.
272 00:23:55.490 ⇒ 00:23:56.070 Greg Stoutenburg: Yep.
273 00:23:59.570 ⇒ 00:24:12.719 Shivani Amar: can we go into Omni itself and just look at what exists, and any of the dashboards that you’re like, I actually think this one is good, and like… like, this would be one you could show to somebody? I have a conversation with the COO of Element this week, and…
274 00:24:12.720 ⇒ 00:24:26.169 Shivani Amar: I want to ground her in, like, where we’re trying to get to, and, like, show her one example of, like, something that is now accessible to her, and I’m curious if you have, like, one dashboard that you’re like, this one feels like it’s in a good place.
275 00:24:27.350 ⇒ 00:24:36.900 Greg Stoutenburg: I would say I… I would like time to answer that question. And that time can be even a half an hour. We can click through some of these right now if you want to.
276 00:24:37.610 ⇒ 00:24:39.059 Greg Stoutenburg: But I… I don’t…
277 00:24:39.280 ⇒ 00:24:43.759 Greg Stoutenburg: immediately top of mind, think, like, oh, I know, let’s, you know, let’s check this one.
278 00:24:47.310 ⇒ 00:24:49.089 Greg Stoutenburg: Looks like Amber did this one.
279 00:24:55.800 ⇒ 00:24:59.110 Greg Stoutenburg: Yeah, see this? I… yeah, that would need to be cleaned up.
280 00:24:59.110 ⇒ 00:25:00.060 Shivani Amar: Yeah.
281 00:25:02.120 ⇒ 00:25:19.709 Greg Stoutenburg: This is on a branch, this is the most recent work that’s been done, so your… your instance of Omni wouldn’t show this unless you went into the develop area and opened this branch, but I can make sure that we’ve got something that’s polished up enough to show off, at least… at least it’s directionally adequate, so you can go, like, hey.
282 00:25:19.710 ⇒ 00:25:24.310 Shivani Amar: Yeah, look, I can say that this is directional, but, like, I think,
283 00:25:24.580 ⇒ 00:25:27.229 Shivani Amar: like, even when I go to the wholesale…
284 00:25:27.350 ⇒ 00:25:34.320 Shivani Amar: summary report. Average order value doesn’t have dollar signs yet, in my view. And so, like.
285 00:25:34.640 ⇒ 00:25:43.529 Shivani Amar: I think, like, adding dollar signs would be useful to where, like, if you were to pick one and just be like, okay, I’m gonna try to just make one look really nice.
286 00:25:43.720 ⇒ 00:25:44.300 Greg Stoutenburg: Yeah.
287 00:25:44.300 ⇒ 00:25:48.010 Shivani Amar: By the end of today, or by… in the next hour, or whatever, like…
288 00:25:48.010 ⇒ 00:25:48.740 Greg Stoutenburg: Yep.
289 00:25:48.740 ⇒ 00:26:06.940 Shivani Amar: she hasn’t gotten back to me about when she wants to meet, but she was like, I want to start… she’s going on maternity leave, and she’s like, I want to, like, feel like I fully understand the data project, and, like, while it’s, like, happening in the background, like, my boss is the chief business officer, whatever, and she was the COO, and I think that she feels a little bit, like.
290 00:26:07.050 ⇒ 00:26:23.560 Shivani Amar: I’m going around to different teams and trying to, like, understand what this, like, future of our OKRs and stuff will look like, and that was previously her realm. So I think she just wants to get comfortable with it, understand what it is, understand how we’re talking about it in the company, how we’re, like, approaching different teams, which I would love her help on, but I’m like.
291 00:26:23.560 ⇒ 00:26:30.229 Shivani Amar: I feel like the easiest way to orient her to it is, like, saying, like, this is the end in mind. Like… Yeah.
292 00:26:30.230 ⇒ 00:26:41.949 Shivani Amar: where we’re trying to get to, and, like, everybody just did this, like, project last week where everybody’s saying, how can I use AI to, like, better, you know, my processes, my systems? And, like, where we’re trying to get to is having
293 00:26:42.560 ⇒ 00:27:00.299 Shivani Amar: an AI-enabled business intelligence tool, where we can, like, one, look at dashboards regularly to track what’s going on in the business, and then two, be able to actually query things relevant, like, that are, like, we’re all speaking the same language about the metrics in this organization. And so I want to show her…
294 00:27:00.480 ⇒ 00:27:05.020 Shivani Amar: A slice of this, and not get her distracted, and just be like.
295 00:27:05.020 ⇒ 00:27:05.510 Greg Stoutenburg: Yeah.
296 00:27:05.510 ⇒ 00:27:21.630 Shivani Amar: I want to show you, like, in theory, you could have dashboards that you could look at, like, being an executive, you could say, I’m curious about wholesale today, let me go in and look at wholesale. I’m curious about retail today. One day it could be, I’m curious about how we are performing across channel in a particular geo, right?
297 00:27:21.630 ⇒ 00:27:22.070 Greg Stoutenburg: Yup.
298 00:27:22.070 ⇒ 00:27:32.170 Shivani Amar: And so, like, I know we’re saying that the OmniView, the Omni dashboards are not gonna come for a couple months, but, like, I’m just trying to, like, orient her, and I want to open one that just, like, looks
299 00:27:32.170 ⇒ 00:27:42.299 Shivani Amar: decently nice, even if the numbers… it’s not to say, let’s check that 248,82693 and see if that’s exactly in line with whatever. No, it’s like…
300 00:27:42.450 ⇒ 00:27:49.520 Shivani Amar: It’s, it’s more just, oh, the daily revenue is… Pretty high.
301 00:27:50.870 ⇒ 00:27:52.430 Shivani Amar: So, right?
302 00:27:53.240 ⇒ 00:27:55.990 Shivani Amar: $1,000 in 300 days.
303 00:27:56.140 ⇒ 00:27:59.020 Shivani Amar: Great, yeah, makes sense. Okay.
304 00:27:59.910 ⇒ 00:28:04.999 Greg Stoutenburg: Yeah, so something… basically, you want a demo. So, a demo for feedback, a demo to paint some.
305 00:28:05.000 ⇒ 00:28:13.120 Shivani Amar: But it’s not a demo for her to, like, sit in Omni, it’s a demo… it’s purely just for her to understand, like, this is… this is what we’re trying to get to.
306 00:28:13.120 ⇒ 00:28:13.720 Greg Stoutenburg: Yeah.
307 00:28:13.870 ⇒ 00:28:25.080 Shivani Amar: You’ll have all the dashboards in one place, you can see every part of the business, you can, like, understand and query things, and then pull out of the demo and say, what does the work involve? How are we tracking the work?
308 00:28:25.080 ⇒ 00:28:35.219 Shivani Amar: how am I going from team to team? How am I thinking about, like, spreading out the work? It’s more like getting her on board with the project plan than it is, like, the Omni tool itself. She’s not gonna be, like.
309 00:28:35.300 ⇒ 00:28:38.659 Shivani Amar: in Omni, I just want to say, like, this is, like, an…
310 00:28:38.860 ⇒ 00:28:50.870 Shivani Amar: to anchor us in, like, what this work is trying to do, it’s getting us to a place where, like, Omni becomes this source of truth that anybody can go to. Anyways, I have to interview somebody in, like, a minute, and I have to run to the bathroom for them.
311 00:28:50.870 ⇒ 00:28:53.920 Greg Stoutenburg: reach out to the team, and I think I’ll also… this is the nicest-looking one.
312 00:28:53.920 ⇒ 00:28:54.600 Shivani Amar: Yeah.
313 00:28:54.600 ⇒ 00:28:57.519 Greg Stoutenburg: And I’ll say, hey, let’s clean this up in the next…
314 00:28:57.800 ⇒ 00:29:05.569 Greg Stoutenburg: hour or a couple of hours so that you can show it off, and I’ll review a couple of questions that you could wower by, using AI.
315 00:29:05.570 ⇒ 00:29:06.929 Shivani Amar: Perfect. Thank you so much.
316 00:29:06.930 ⇒ 00:29:08.940 Greg Stoutenburg: Cool. Alright, see ya. Thanks.
317 00:29:28.420 ⇒ 00:29:34.250 Greg Stoutenburg: I’m gonna have funny, and I’m… Still in the meeting.