Meeting Title: Brainforge x Element Omni Project Sync Date: 2026-03-27 Meeting participants: Uttam Kumaran, Greg Stoutenburg, Awaish Kumar, Jason Wu, Shivani Amar
WEBVTT
1 00:00:13.810 ⇒ 00:00:15.000 Greg Stoutenburg: Hey, Tom.
2 00:00:15.000 ⇒ 00:00:15.880 Uttam Kumaran: Hi, sir.
3 00:00:16.010 ⇒ 00:00:19.609 Uttam Kumaran: What, did the house come painted red, like, originally?
4 00:00:19.780 ⇒ 00:00:21.439 Greg Stoutenburg: It did, this is a rental house.
5 00:00:21.860 ⇒ 00:00:22.780 Uttam Kumaran: Oh, okay, okay.
6 00:00:22.780 ⇒ 00:00:24.249 Greg Stoutenburg: Yup, so I’ll…
7 00:00:24.250 ⇒ 00:00:25.639 Uttam Kumaran: You’re, like, not a fan?
8 00:00:25.780 ⇒ 00:00:26.930 Greg Stoutenburg: I love it, actually.
9 00:00:26.930 ⇒ 00:00:29.699 Uttam Kumaran: No, I like it too, I was just asking.
10 00:00:29.700 ⇒ 00:00:35.259 Greg Stoutenburg: Oh, no, I’m just… yeah, so for me, yeah, it did come that way. I’ll, I’ll pass along compliments to the homeowner.
11 00:00:37.620 ⇒ 00:00:41.959 Uttam Kumaran: Well, yeah, I mean, if it gets you some stuff off rent, sure, but otherwise, don’t pass anything to those…
12 00:00:42.080 ⇒ 00:00:43.580 Uttam Kumaran: Those capitalists.
13 00:00:43.930 ⇒ 00:00:44.520 Greg Stoutenburg: But, yeah.
14 00:00:44.520 ⇒ 00:00:45.910 Uttam Kumaran: Capitalist pigs.
15 00:00:45.910 ⇒ 00:00:51.009 Greg Stoutenburg: Dude, this house… so, this house is actually… I’ll be moving in a couple weeks. This house is…
16 00:00:51.010 ⇒ 00:00:51.940 Uttam Kumaran: Oh, okay.
17 00:00:52.150 ⇒ 00:01:11.890 Greg Stoutenburg: But they… I mean, the family that lived here… Hey, Wish, they’ve owned this house, looks like the last time that it was mortgaged was, like, 2009 or something like that, and now they just sold it for about $300, which is pretty cheap in America. 4 bedrooms, 3 floors, big basement, like.
18 00:01:11.890 ⇒ 00:01:12.580 Uttam Kumaran: Wow.
19 00:01:12.580 ⇒ 00:01:16.040 Greg Stoutenburg: Tenant in the top floor. There’s, like, a full bath up there and stuff.
20 00:01:16.350 ⇒ 00:01:21.300 Greg Stoutenburg: But I know they paid, like, $100,000 for it, and that kills me!
21 00:01:21.300 ⇒ 00:01:24.340 Uttam Kumaran: Hey, dude, you gotta… you gotta be hype for them, Beth, come on, like…
22 00:01:24.340 ⇒ 00:01:35.999 Greg Stoutenburg: Yeah, for them, but I’m also… but also, like, I think they’ve probably had this paid off since forever, and I’m over here paying 0 or $700. True.
23 00:01:37.810 ⇒ 00:01:39.930 Greg Stoutenburg: Anyway, that’s, yeah, that well.
24 00:01:39.930 ⇒ 00:01:42.589 Uttam Kumaran: It pays to think long-term, you know?
25 00:01:44.280 ⇒ 00:01:46.500 Greg Stoutenburg: Yeah, I’ll say. I’ll say.
26 00:01:48.380 ⇒ 00:01:49.979 Greg Stoutenburg: Dot dot dot.
27 00:01:51.200 ⇒ 00:01:55.029 Uttam Kumaran: I’m with you on the, revenue per sale thing.
28 00:01:56.250 ⇒ 00:02:02.889 Uttam Kumaran: Yeah, I mean, I could… we could just talk through stock velocity as an example, too.
29 00:02:04.080 ⇒ 00:02:16.350 Greg Stoutenburg: Sure, yeah, and we… yeah, we can pull up something specific. I think my, my thought is just after yesterday’s conversation, I’m like, I’m not… any time one of those words appears somewhere, like, I want to see it before I show it to her.
30 00:02:16.350 ⇒ 00:02:17.320 Uttam Kumaran: Fair, fair.
31 00:02:17.320 ⇒ 00:02:33.239 Greg Stoutenburg: Yeah, and I think… at least for the Omni stuff, right? I mean, I’ve only got just the one work stream here, but, like, at least for the things that have to do with Omni and what’s going into there, like, I would… I’d rather say, oh, that’s not ready, I need another two days, than to go, alright, hey, YOLO.
32 00:02:33.370 ⇒ 00:02:34.500 Greg Stoutenburg: We’ll do it live.
33 00:02:38.440 ⇒ 00:02:39.830 Greg Stoutenburg: You guys have any weekend plans?
34 00:02:42.290 ⇒ 00:02:47.829 Uttam Kumaran: Welcome, chill weekend. I’m going to California next week. Visit family?
35 00:02:48.280 ⇒ 00:02:49.030 Greg Stoutenburg: Cool.
36 00:02:49.470 ⇒ 00:02:50.010 Uttam Kumaran: Yeah?
37 00:02:50.440 ⇒ 00:02:58.740 Uttam Kumaran: That’s probably it this weekend. We may go to, like, there’s a place in East Austin called Dripping Springs, or West Austin, it’s just, like, a little town.
38 00:02:58.980 ⇒ 00:03:01.140 Greg Stoutenburg: Yeah, I have a friend who lives there.
39 00:03:01.140 ⇒ 00:03:01.880 Uttam Kumaran: Frank?
40 00:03:01.880 ⇒ 00:03:03.920 Greg Stoutenburg: Yeah, I’ve heard I got a hustler a few years ago.
41 00:03:03.920 ⇒ 00:03:04.530 Uttam Kumaran: Oh, yeah.
42 00:03:04.530 ⇒ 00:03:05.500 Greg Stoutenburg: Maybe 10.
43 00:03:05.660 ⇒ 00:03:12.270 Uttam Kumaran: It’s a nice, like, day trip from here, so we’ll just maybe drive through hill country and get lunch and hang out. Yeah. Yeah.
44 00:03:12.970 ⇒ 00:03:17.700 Greg Stoutenburg: Actually, the one time I was in Austin, and actually spent a little time, it was at Dripping Springs, and then…
45 00:03:17.700 ⇒ 00:03:18.170 Uttam Kumaran: Oh, great.
46 00:03:18.170 ⇒ 00:03:21.759 Greg Stoutenburg: It’s for a philosophy conference, so then I would, like, drive downtown, whatever. It’s like an hour, right? It’s like an hour.
47 00:03:21.760 ⇒ 00:03:23.269 Uttam Kumaran: Yeah, it’s about an hour.
48 00:03:23.270 ⇒ 00:03:26.939 Greg Stoutenburg: And then, yeah, did the conference stuff, and then went back.
49 00:03:30.400 ⇒ 00:03:39.209 Greg Stoutenburg: I love it when we’re talking about locations in an enormous state, and I can connect on it because I just, by luck, happen to have been to that one place.
50 00:03:39.210 ⇒ 00:03:44.220 Uttam Kumaran: oh, that’s great, I mean, I was just… sometimes I say things about this area, and I’m like.
51 00:03:45.150 ⇒ 00:03:46.429 Greg Stoutenburg: No one’s gonna know what this is.
52 00:03:46.430 ⇒ 00:03:50.620 Uttam Kumaran: Smile and wave, you know, I just panter.
53 00:03:50.620 ⇒ 00:04:01.109 Greg Stoutenburg: I was talking to an Eden, pharmacy person, and she’s like, oh yeah, I, you know, I live in Michigan. I’m like, cool, where? She’s like, well, just a little tiny town no one’s ever heard of. I was like, where?
54 00:04:01.530 ⇒ 00:04:12.059 Greg Stoutenburg: Pinckney. I’m like, you’re right, no one’s ever heard of that, but I have. And then, like, we start talking about there’s the paintball place, and then there’s this lake. I can’t believe you’ve heard of Pinckney. I’m like, yeah.
55 00:04:12.750 ⇒ 00:04:14.919 Greg Stoutenburg: Be right.
56 00:04:15.160 ⇒ 00:04:17.750 Uttam Kumaran: Oh wait, do you want to take a look at this modeling slide?
57 00:04:21.620 ⇒ 00:04:22.590 Uttam Kumaran: I’ll send it to you.
58 00:04:29.260 ⇒ 00:04:39.050 Greg Stoutenburg: And just real quick, since I saw Wish’s background, which I always appreciate, I know I mentioned this before, Wish, but I have it in front of me now. Here’s my… here’s my hockey team’s Northern Lights jersey.
59 00:04:40.300 ⇒ 00:04:41.670 Uttam Kumaran: Oh, nice.
60 00:04:41.670 ⇒ 00:04:42.640 Greg Stoutenburg: Yeah, yeah.
61 00:04:42.640 ⇒ 00:04:43.639 Uttam Kumaran: Yeah, hell yeah.
62 00:04:43.640 ⇒ 00:04:46.379 Greg Stoutenburg: That’s when we’re away. That’s when we’re home.
63 00:04:47.020 ⇒ 00:04:48.130 Greg Stoutenburg: Pretty sweet.
64 00:04:48.970 ⇒ 00:04:49.900 Greg Stoutenburg: Yep.
65 00:04:50.070 ⇒ 00:04:51.000 Uttam Kumaran: Nice!
66 00:04:51.850 ⇒ 00:04:52.920 Uttam Kumaran: It looks great!
67 00:04:53.110 ⇒ 00:04:54.250 Greg Stoutenburg: Yeah, they’re good!
68 00:04:55.870 ⇒ 00:04:58.360 Awaish Kumar: Marling…
69 00:05:01.760 ⇒ 00:05:02.470 Greg Stoutenburg: Okay.
70 00:05:04.350 ⇒ 00:05:09.680 Uttam Kumaran: And Greg, we can work on the… maybe we can just even start right now on just working on the 4 box.
71 00:05:10.000 ⇒ 00:05:11.390 Greg Stoutenburg: Yeah, let’s pull it up.
72 00:05:11.650 ⇒ 00:05:13.940 Uttam Kumaran: I just sent it in the Zoom chat.
73 00:05:14.970 ⇒ 00:05:23.030 Uttam Kumaran: So, yeah, there’s… This is 327.
74 00:05:23.740 ⇒ 00:05:26.929 Greg Stoutenburg: It says 320. Does she want us to copy and paste and make a new one?
75 00:05:27.090 ⇒ 00:05:29.560 Uttam Kumaran: I think the top ones are copy and paste.
76 00:05:30.490 ⇒ 00:05:31.490 Greg Stoutenburg: 320.
77 00:05:31.490 ⇒ 00:05:32.649 Uttam Kumaran: I just changed it.
78 00:05:32.650 ⇒ 00:05:32.990 Greg Stoutenburg: Oh.
79 00:05:32.990 ⇒ 00:05:34.609 Awaish Kumar: What is your Bozeman?
80 00:05:35.660 ⇒ 00:05:37.040 Greg Stoutenburg: Sorry?
81 00:05:38.030 ⇒ 00:05:39.629 Awaish Kumar: On the slides, we have this…
82 00:05:40.420 ⇒ 00:05:44.550 Uttam Kumaran: Oh, yeah, I think that’s from last week, like…
83 00:05:44.550 ⇒ 00:05:50.030 Greg Stoutenburg: Okay, alright, here, okay. I was looking at the, second slide in the deck. Alright, let’s go to here.
84 00:05:50.200 ⇒ 00:05:51.070 Uttam Kumaran: Hey, Jason.
85 00:05:52.440 ⇒ 00:05:55.880 Jason Wu: Hey guys, sorry, saw that through the loop.
86 00:05:56.170 ⇒ 00:06:00.590 Jason Wu: All good. Very, very loud coffee shops, I don’t have to stay on mute like him.
87 00:06:01.080 ⇒ 00:06:05.070 Uttam Kumaran: It sounds pretty quiet, so I feel like… Zoom’s doing a good job.
88 00:06:05.440 ⇒ 00:06:07.570 Jason Wu: Yeah. It’s pretty good then, I guess, yeah.
89 00:06:07.570 ⇒ 00:06:09.230 Uttam Kumaran: I don’t hear… we don’t hear a single thing.
90 00:06:09.620 ⇒ 00:06:10.440 Jason Wu: Perfect.
91 00:06:10.830 ⇒ 00:06:13.499 Uttam Kumaran: We hear your voice, but yeah.
92 00:06:13.500 ⇒ 00:06:14.510 Jason Wu: Alright.
93 00:06:15.130 ⇒ 00:06:23.780 Uttam Kumaran: Yeah, we just, maybe we could wait a couple minutes for Shivani. We just started working on the 4 box with the team, but maybe just an update
94 00:06:23.850 ⇒ 00:06:41.659 Uttam Kumaran: From you on… on… on overall. So, we did… we did update our ingestion sort of plan a little bit, based on, some… basically, like, deprioritizing some of the marketing, taking on the things where we know there’s spend. We also are gonna start,
95 00:06:41.700 ⇒ 00:06:44.599 Uttam Kumaran: a recurring call with Polytomic.
96 00:06:44.660 ⇒ 00:06:55.160 Uttam Kumaran: Where… and we’ll all be working from that one spreadsheet. So I’m gonna have them sort of put in their expected timelines for their new sources,
97 00:06:55.500 ⇒ 00:07:00.990 Uttam Kumaran: You know, several of the sources that, we’re gonna ask for next on the marketing side are actually already there, but…
98 00:07:01.110 ⇒ 00:07:02.900 Uttam Kumaran: Just not as high a priority.
99 00:07:03.000 ⇒ 00:07:04.530 Uttam Kumaran: So…
100 00:07:05.830 ⇒ 00:07:09.379 Jason Wu: Yeah, it makes sense. I’ve got the…
101 00:07:09.630 ⇒ 00:07:12.440 Jason Wu: TikTok and the Bing access for Polycomic.
102 00:07:12.850 ⇒ 00:07:17.869 Jason Wu: Although I just got an error trying to connect to TikTok, so I just, messaged Galib right now.
103 00:07:17.970 ⇒ 00:07:24.360 Jason Wu: Okay. I’ll try Bing right after this call. Amazon,
104 00:07:24.920 ⇒ 00:07:43.190 Jason Wu: That’s the one that’s funky. I was reaching out to… I got connected with our ad agency that handles that, and they connected with their tech guy, said there’s a lot… there’s a couple different ways of doing it, it just depends on what data you’re looking for. And they suggested getting a call set up with all the topic of that, to say, kind of, like, what is it you’re looking for, but…
105 00:07:43.240 ⇒ 00:07:47.900 Jason Wu: Hold on, I can’t remember if in your message you said that they already had The connector exists.
106 00:07:48.400 ⇒ 00:07:49.929 Jason Wu: icon? Okay.
107 00:07:50.180 ⇒ 00:07:53.839 Jason Wu: Maybe I just need to log in and kind of play with it. So I’ll look at that today.
108 00:07:54.200 ⇒ 00:07:54.760 Uttam Kumaran: Okay.
109 00:07:57.810 ⇒ 00:07:58.940 Jason Wu: Alright, cool.
110 00:08:00.530 ⇒ 00:08:04.480 Uttam Kumaran: Apart from that, a lot of today’s update’s gonna be on Omni.
111 00:08:04.710 ⇒ 00:08:05.390 Jason Wu: Okay.
112 00:08:05.650 ⇒ 00:08:14.769 Uttam Kumaran: And maybe we can… maybe we can get started, Greg. I just want to make sure that we at least, you know, and I can send this recording to Shivani to catch up.
113 00:08:14.770 ⇒ 00:08:21.800 Greg Stoutenburg: Yeah, yeah, yeah. And I see that she’s looking at the 4 box, so I think if we chat through stuff, we’ll be fine.
114 00:08:22.000 ⇒ 00:08:28.659 Greg Stoutenburg: So… Yeah, okay, so on our side, I mean, do we wanna…
115 00:08:28.660 ⇒ 00:08:29.100 Uttam Kumaran: Yeah.
116 00:08:29.100 ⇒ 00:08:30.020 Greg Stoutenburg: I can…
117 00:08:30.020 ⇒ 00:08:31.300 Uttam Kumaran: I’ll pull it up, yeah.
118 00:08:31.300 ⇒ 00:08:32.049 Greg Stoutenburg: Okay.
119 00:08:36.549 ⇒ 00:08:37.949 Uttam Kumaran: Sorry, one second.
120 00:08:44.409 ⇒ 00:08:48.969 Uttam Kumaran: Oh, it’s just maybe the wrong.
121 00:08:49.140 ⇒ 00:08:50.140 Greg Stoutenburg: That’s the gearbox.
122 00:08:51.870 ⇒ 00:08:54.949 Uttam Kumaran: Okay, cool. So, maybe I can just start,
123 00:08:54.950 ⇒ 00:09:17.119 Uttam Kumaran: So, looks like we’re, you know, towards the finish line on getting the next, you know, renewal signed, and, I think, Jason, the way we’re gonna kind of plan phase out is I’m actually gonna go to New York, and Robert and I are gonna spend some time with Shivani, plan out the next, you know, 6 months or so, and then there’s a couple other people that we’re ramping up on our side that we’ll probably do a couple of introductory sessions with
124 00:09:17.230 ⇒ 00:09:26.159 Uttam Kumaran: Over the next, you know, week or two, to sort of ramp up a few more people, especially on the strategy and analytics side of the house.
125 00:09:26.240 ⇒ 00:09:44.400 Uttam Kumaran: So that’s on, like, next contract, sort of next immediate, phase. One other thing I kind of wanted to share, was just, like, how we’re starting to do, some project planning on our side. So we’re… we’re starting to use Linear internally for all of our project
126 00:09:44.550 ⇒ 00:09:49.590 Uttam Kumaran: management, and I’ll go ahead and just see if I can pull this up.
127 00:09:49.980 ⇒ 00:09:54.220 Uttam Kumaran: Let’s see if it opens…
128 00:09:57.930 ⇒ 00:10:01.670 Uttam Kumaran: Which should be…
129 00:10:06.490 ⇒ 00:10:07.360 Uttam Kumaran: Okay…
130 00:10:16.010 ⇒ 00:10:24.050 Uttam Kumaran: So, in terms of our… we’re kind of gonna move from, you know, our Gantt chart view into this view, the reason being is
131 00:10:24.050 ⇒ 00:10:35.059 Uttam Kumaran: You know, we ticket everything out for all of our processes, and it’s actually a little bit easier for us to go into an individual project and tie that back to, you know, a higher level initiative.
132 00:10:35.060 ⇒ 00:10:44.710 Uttam Kumaran: And so the way, linear is structured is, like, we have several initiatives as part of the, you know, Brainforge element plan. As you see here, we have…
133 00:10:44.710 ⇒ 00:10:51.610 Uttam Kumaran: Data modeling, we have some couple things that we’re familiar with, so data modeling, reporting analytics, and ingestion.
134 00:10:51.610 ⇒ 00:11:05.189 Uttam Kumaran: We also have a plan for Omni, which includes this pilot, and then we’re also going to be adding pieces around strategy, documentation, and, like, broader adoption. So it’s just ways for us to store these projects.
135 00:11:05.190 ⇒ 00:11:15.249 Uttam Kumaran: If I was to drill into just one area, which is the Omni pilot, you can see, like, Greg is driving that forward. We can actually look into an individual project.
136 00:11:15.250 ⇒ 00:11:34.210 Uttam Kumaran: and basically be able to see, the milestones associated with it, and the issues associated with it. So this is just setting the foundation for the pilot, and then we’re going into the topics and draft dashboards now, and this is sort of, like, what we’re in the middle of. So you can see there’s several tickets.
137 00:11:34.210 ⇒ 00:11:47.979 Uttam Kumaran: around, creating topics, dashboard launches, ensuring that the AI system is configured. And so, I’ll go ahead and I’m gonna give… make sure that you and Shivani have access, to this, but…
138 00:11:48.040 ⇒ 00:11:53.230 Uttam Kumaran: This way, basically, you’ll be able to drill all the way down into the tickets that are, you know, getting executed.
139 00:11:53.410 ⇒ 00:11:54.639 Uttam Kumaran: And so…
140 00:11:55.200 ⇒ 00:12:01.939 Uttam Kumaran: Broadly looking at the plan and just, again, focusing on sort of the things that are in flight now,
141 00:12:02.590 ⇒ 00:12:09.190 Uttam Kumaran: I break up the project into ingestion, modeling, and then right now is our Omni pilot, as well as, like.
142 00:12:09.320 ⇒ 00:12:12.359 Uttam Kumaran: larger definition. So, in terms of ingestion.
143 00:12:12.880 ⇒ 00:12:23.889 Uttam Kumaran: We are closing out Amazon and Walmart, and so that is the primary focus. We actually got… we have quite a bit of Amazon data that’s landed this week.
144 00:12:24.450 ⇒ 00:12:35.440 Uttam Kumaran: There’s one ask that I’m gonna have that I’ll share around one specific endpoint for Amazon, that the volume is actually quite enormous, and the ingestion may take a little bit of time.
145 00:12:35.440 ⇒ 00:12:44.679 Uttam Kumaran: I may collaborate with you, Jason, on just putting together an ask, you know, potentially to Amazon support to see if they can maybe deliver us a one-time export of some data.
146 00:12:44.860 ⇒ 00:12:53.619 Uttam Kumaran: it’s… it’s our order item level data on Amazon, so of course, every… every, order may have several items, and
147 00:12:53.850 ⇒ 00:13:05.070 Uttam Kumaran: you guys sell a lot of… on Amazon, and so there’s just a lot of data there that may just take a while to sync. I don’t know if you’ve worked with the Amazon team at all on anything on the technical side, or if this would be, like, a net new ask.
148 00:13:05.530 ⇒ 00:13:07.750 Jason Wu: I… I haven’t,
149 00:13:08.750 ⇒ 00:13:15.940 Jason Wu: I’m trying to think, yeah, like, how to accelerate that. When we have that conversation, let’s also pull in Andy, and…
150 00:13:16.200 ⇒ 00:13:18.299 Jason Wu: Carlos? Into that?
151 00:13:18.300 ⇒ 00:13:18.690 Uttam Kumaran: Okay.
152 00:13:18.690 ⇒ 00:13:26.290 Jason Wu: Carlos, he’s mainly kind of owning that business relationship, so if there’s anyone that he might know that can help escalate, it might be him as well.
153 00:13:28.160 ⇒ 00:13:28.890 Jason Wu: Okay.
154 00:13:28.890 ⇒ 00:13:29.450 Uttam Kumaran: Great.
155 00:13:29.660 ⇒ 00:13:32.810 Uttam Kumaran: So I will… I’m just kind of, like, putting together the ask there.
156 00:13:33.000 ⇒ 00:13:35.949 Uttam Kumaran: And then I can, yeah, run that by you.
157 00:13:36.450 ⇒ 00:13:39.090 Uttam Kumaran: And then in terms of the data modeling side, so…
158 00:13:39.530 ⇒ 00:13:40.120 Jason Wu: You know.
159 00:13:40.120 ⇒ 00:13:49.309 Uttam Kumaran: we’re basically through on wholesale. We’re working on retail pretty heavily now, and in addition to e-commerce. And so…
160 00:13:49.590 ⇒ 00:14:04.869 Uttam Kumaran: like, I think we’re making really, really strong progress here. I think the fact that the Omnis pilot is moving this fast is really, I think, a lot of kudos to Awash for moving quickly on a lot of these models. We’re gonna be putting together
161 00:14:05.530 ⇒ 00:14:25.159 Uttam Kumaran: really, this is what funds… what sort of funds the success of Omni, is, like, having these clear marts. So where we’re gonna sort of bobble back and forth is when we, have a… have, like, a retail mart, and the strategy team says, hey, there’s a new definition for a piece, we can go make a modification in… in dbt for logic. And so…
162 00:14:25.810 ⇒ 00:14:32.420 Uttam Kumaran: I feel that we’re gonna have a pretty good version of the V1 with our existing retail data ready
163 00:14:32.580 ⇒ 00:14:47.139 Uttam Kumaran: you know, fairly soon. So thinking about this sprint, basically by the end of this sprint, we have our Target and, Walmart data, you know, for our V1 models, and then we’re starting to work on Shopify. We’re trying to work on Amazon.
164 00:14:47.390 ⇒ 00:15:01.659 Uttam Kumaran: And then across retail, across re-ecom, there will be, like, a combined model, and then there’ll be, of course, like, the omni-channel set of marts. So, hey, Shivani, pretty good progress on, the modeling side as well.
165 00:15:02.770 ⇒ 00:15:04.310 Uttam Kumaran: I’m just sort of,
166 00:15:04.560 ⇒ 00:15:16.979 Uttam Kumaran: showing Shivani, like, we’re gonna start to transition to linear for our, like, project planning. That way, you’ll be able to actually drill down into all the tickets, and it’s just a lot easier for us to maintain.
167 00:15:17.550 ⇒ 00:15:23.540 Uttam Kumaran: And so I just went through, sort of, where we are on the ingestion and the modeling side.
168 00:15:23.650 ⇒ 00:15:26.850 Uttam Kumaran: And our spreadsheet is also,
169 00:15:27.030 ⇒ 00:15:44.500 Uttam Kumaran: fairly up-to-date. The only changes I’ve made on the ingestion is we’ve deprioritized several things, just based on items that we’re no… Elements no longer spending on, or have just, you know, we wanted to prioritize a couple of the core areas, like TikTok, like Amazon ads, where there is active spend and CAC measurement.
170 00:15:45.810 ⇒ 00:15:51.370 Uttam Kumaran: And then we are… we have a couple follow-ups on Amazon that I just spoke to Jason about, so…
171 00:15:54.230 ⇒ 00:16:04.930 Uttam Kumaran: Great. Yeah, I think I can hand it off to… to you, Greg, just to focus on Omni. I know we… we don’t have a ton of time, so I want to do that, and then make sure we have time for the 4 box today.
172 00:16:06.950 ⇒ 00:16:17.329 Greg Stoutenburg: Should we just jump into the 4 box now? Actually, I put the… I put the Omni updates into the 4 box, so, if we want to, we can switch over, do you want to just pull that up?
173 00:16:17.330 ⇒ 00:16:17.990 Uttam Kumaran: Yeah.
174 00:16:17.990 ⇒ 00:16:18.750 Greg Stoutenburg: Okay.
175 00:16:24.890 ⇒ 00:16:26.629 Greg Stoutenburg: Yeah, let’s go to…
176 00:16:26.820 ⇒ 00:16:38.879 Greg Stoutenburg: Yeah, let’s go there. Cool. Yeah, so I put, I put updates in the, top left there. So, yeah, what we’ve been up to is, yeah, we created those initial retail topics. As you’ve seen, we’ve been working on those.
177 00:16:38.880 ⇒ 00:16:55.349 Greg Stoutenburg: So, they’re in now, they’re being refined, but we’ve got them started and begun dashboarding. We have clarified core metric definitions and drafted a dashboarding spec for internal revision. It’s very much work in progress. Shivani had a peek at it the other day, we’re still working on it, but that is there.
178 00:16:55.360 ⇒ 00:17:20.330 Greg Stoutenburg: We were able to introduce the Element team to Omni on Wednesday, show them sort of what the goal is, why someone would want to use a tool like this, what the promise of it is, and how topics work, and so they’ve been invited to get in and sort of, you know, kick the tires, take a look, and submit feedback in the channel that we have created for soliciting that feedback. And I clarified that I’ll look at things that are coming in and create
179 00:17:20.329 ⇒ 00:17:24.730 Greg Stoutenburg: So that we can action those items that, that they raise.
180 00:17:25.500 ⇒ 00:17:28.009 Greg Stoutenburg: There was a reporting error in Google.
181 00:17:28.010 ⇒ 00:17:37.279 Shivani Amar: I like this update. I deleted it because it’s not necessary for other people. It’s kind of in the weeds for, like, leadership who looks at the scorebox, but you can continue.
182 00:17:37.280 ⇒ 00:17:37.850 Greg Stoutenburg: Okay.
183 00:17:37.850 ⇒ 00:17:39.610 Shivani Amar: It’s helpful for you to voice it here.
184 00:17:39.780 ⇒ 00:18:04.770 Greg Stoutenburg: Yeah, yeah, yeah, good, okay. Yeah, that’s… and that’s helpful on level of, detail for this. So, there was a reporting error where there were non-zipcode values in the zip code sheet, and so, we were able to figure out that what happened is that someone had deleted the column by accident, and so, the script was running correctly, but when that column was deleted, then the rest of the fields were… were populating with data that belonged
185 00:18:04.770 ⇒ 00:18:14.460 Greg Stoutenburg: elsewhere. So, that was restored, and the sheet locked, so that that won’t happen again, as it’s an important piece of reporting until we’ve got Omni stood up.
186 00:18:14.830 ⇒ 00:18:20.169 Greg Stoutenburg: And then, we drafted definitions of sales velocity, weeks of stock, and sell-through.
187 00:18:20.170 ⇒ 00:18:24.319 Shivani Amar: Are those now in the metric definitions section with them?
188 00:18:24.320 ⇒ 00:18:25.470 Uttam Kumaran: Yes.
189 00:18:25.470 ⇒ 00:18:27.379 Shivani Amar: Like, in that spreadsheet.
190 00:18:27.530 ⇒ 00:18:40.939 Uttam Kumaran: Yeah, so one thing I wanted to ask you about is, like, if you thought that format was good, like, basically, for some of these, we’re gonna have, like, our recommendation. We’re gonna run it all through that part of the spreadsheet, so I can link to just the block, basically.
191 00:18:40.940 ⇒ 00:18:50.580 Shivani Amar: So, sorry, when I go… when I’m asking about the spreadsheet, so should I go to Brain Forge Data Document… Data Platform Documentation, and then I go to Core Metrics, is that where it would be?
192 00:18:50.760 ⇒ 00:18:56.650 Uttam Kumaran: Yeah, so we… if you can go just the sheet to the right, I actually want to swap it for our new format.
193 00:18:56.870 ⇒ 00:19:00.779 Uttam Kumaran: If you want to just double check, and I can just make that change.
194 00:19:01.320 ⇒ 00:19:05.369 Uttam Kumaran: I didn’t… I haven’t… like, it’s literally work-in-progress core metrics.
195 00:19:05.860 ⇒ 00:19:09.400 Uttam Kumaran: We just added some, like, conditional formatting.
196 00:19:12.040 ⇒ 00:19:17.280 Shivani Amar: Mmm… I… .
197 00:19:17.280 ⇒ 00:19:19.799 Uttam Kumaran: And a few more, like, columns to the right.
198 00:19:20.990 ⇒ 00:19:24.189 Shivani Amar: I think of channel as retail…
199 00:19:24.330 ⇒ 00:19:26.469 Shivani Amar: Can you… should I open it? Can I…
200 00:19:26.470 ⇒ 00:19:29.740 Uttam Kumaran: Yeah, I… I have it, here, I can share it.
201 00:19:35.660 ⇒ 00:19:44.289 Shivani Amar: Jason, I’d be curious to get your take on this, if you have a perspective. Like, I think of channel as either e-commerce, retail, wholesale.
202 00:19:44.710 ⇒ 00:19:46.520 Uttam Kumaran: Sales channel, like, revenue channel.
203 00:19:46.520 ⇒ 00:19:54.650 Shivani Amar: Yeah. So then when I see business domain is retail, and then channel is target, that’s like… .
204 00:19:55.440 ⇒ 00:20:02.860 Uttam Kumaran: I guess maybe let’s start with just the business domain side. How would you think about these
205 00:20:03.280 ⇒ 00:20:08.069 Uttam Kumaran: I guess we are… we are… we were initially just allocating them by team. We can actually…
206 00:20:09.480 ⇒ 00:20:13.920 Uttam Kumaran: Basically, just consider it, like, let’s take finance metrics, for example.
207 00:20:14.550 ⇒ 00:20:15.990 Uttam Kumaran: Would we consider them…
208 00:20:16.710 ⇒ 00:20:21.839 Uttam Kumaran: Those may be metrics that are… those aren’t… that’s not a sales channel, necessarily a revenue channel.
209 00:20:22.130 ⇒ 00:20:23.699 Shivani Amar: Oh, I see, okay.
210 00:20:23.700 ⇒ 00:20:27.930 Uttam Kumaran: Yeah, so… I think… I think having.
211 00:20:27.930 ⇒ 00:20:32.119 Shivani Amar: Is the channel column necessary? Like, what is in the channel… like…
212 00:20:33.140 ⇒ 00:20:39.830 Uttam Kumaran: Yeah, I think the channel column is probably just for commercial sources, so I can put sales channel
213 00:20:39.930 ⇒ 00:20:43.550 Uttam Kumaran: As if relevant, and otherwise… It’s NA.
214 00:20:43.900 ⇒ 00:20:55.059 Shivani Amar: But, like, it seems like it’s just to call out which retailers. Like, is there anything else in that column? Like, oh, okay, so you’re saying GA4, like, where does GA4…
215 00:20:55.500 ⇒ 00:20:56.250 Shivani Amar: What is that?
216 00:20:56.250 ⇒ 00:21:06.340 Uttam Kumaran: Yeah, so… so a good way to think about it is, like, if we have a… if we have a metric for a certain sales… for a certain channel that we don’t have, like, for another.
217 00:21:07.170 ⇒ 00:21:12.230 Uttam Kumaran: we would try to call that out here. For example, there may be some Amazon-specific things.
218 00:21:12.350 ⇒ 00:21:12.870 Shivani Amar: Hmm…
219 00:21:12.870 ⇒ 00:21:15.559 Uttam Kumaran: versus Shopify, and then there’s, like.
220 00:21:16.940 ⇒ 00:21:21.289 Uttam Kumaran: Basically, we’ll… for the e-commerce smart, we’ll take whatever is… exists in both.
221 00:21:21.400 ⇒ 00:21:22.740 Uttam Kumaran: to create a unified Mars.
222 00:21:22.740 ⇒ 00:21:25.090 Shivani Amar: Can you just call that, like, source, then?
223 00:21:25.090 ⇒ 00:21:25.410 Jason Wu: Of course.
224 00:21:26.110 ⇒ 00:21:28.700 Jason Wu: Just subdomain? Oh, subdomain, maybe?
225 00:21:28.700 ⇒ 00:21:33.459 Shivani Amar: Yeah, like, something like, you’re like, this source has it, this source doesn’t.
226 00:21:33.460 ⇒ 00:21:34.220 Uttam Kumaran: Okay.
227 00:21:34.220 ⇒ 00:21:36.549 Shivani Amar: Yeah, something… subdomain is fine.
228 00:21:36.550 ⇒ 00:21:37.080 Uttam Kumaran: Okay.
229 00:21:37.080 ⇒ 00:21:39.250 Awaish Kumar: Maybe we can call it platform, so it’s…
230 00:21:39.640 ⇒ 00:21:48.360 Shivani Amar: Or platform, yeah, platform makes sense. Like, channel to me is, like, the revenue channel, so I’m just trying to be… okay. Then…
231 00:21:48.580 ⇒ 00:21:59.809 Shivani Amar: Okay… Sorry, so many colors. Okay, so, alignment status, definitions align, discussing definitions.
232 00:22:01.850 ⇒ 00:22:05.300 Uttam Kumaran: So, these are the ones, that, like, we have.
233 00:22:06.140 ⇒ 00:22:10.569 Uttam Kumaran: we already are sort of confirmed. These are the ones that we…
234 00:22:10.670 ⇒ 00:22:12.529 Uttam Kumaran: Wanted to talk about this week.
235 00:22:12.800 ⇒ 00:22:13.879 Uttam Kumaran: I guess I want.
236 00:22:13.880 ⇒ 00:22:15.339 Shivani Amar: They’re the ones who sent in Slack, and I was like.
237 00:22:15.760 ⇒ 00:22:19.459 Shivani Amar: Then I think definitions are aligned. I don’t think we need…
238 00:22:19.470 ⇒ 00:22:23.600 Uttam Kumaran: Okay. Well, I guess I want to take that example as, like, we’re gonna come up with…
239 00:22:23.940 ⇒ 00:22:25.560 Uttam Kumaran: Like, our definition.
240 00:22:25.560 ⇒ 00:22:26.410 Shivani Amar: Yeah.
241 00:22:26.980 ⇒ 00:22:31.179 Uttam Kumaran: If you’re, like, that’s good. Again, these are all, like, V1, so…
242 00:22:31.910 ⇒ 00:22:39.789 Uttam Kumaran: if that’s a fine process, then we’ll sort of just use the Slack approval process as that, and then I can link… I can literally link this
243 00:22:39.900 ⇒ 00:22:41.600 Uttam Kumaran: Section in the doc.
244 00:22:41.600 ⇒ 00:22:45.249 Shivani Amar: And eventually, it’s like these definitions, I’m saying yes right now.
245 00:22:45.810 ⇒ 00:22:51.100 Shivani Amar: And eventually, Phil might get in here and be like, I actually wanted it called subdomain, and…
246 00:22:51.360 ⇒ 00:22:52.300 Uttam Kumaran: There will be revisions, totally.
247 00:22:52.300 ⇒ 00:22:53.919 Shivani Amar: Whatever, and so, like.
248 00:22:54.570 ⇒ 00:23:06.949 Uttam Kumaran: Yeah, so on the right here, we’re gonna have, like, any commentary, so as… even as we come back and look at stuff, I may leave some commentary or other people on the team to then, like, update definitions.
249 00:23:06.950 ⇒ 00:23:07.830 Shivani Amar: Okay.
250 00:23:08.460 ⇒ 00:23:10.310 Shivani Amar: It’s a lot of columns, so I’m just.
251 00:23:10.310 ⇒ 00:23:12.270 Uttam Kumaran: It is a lot of columns. Some of this…
252 00:23:12.270 ⇒ 00:23:16.680 Shivani Amar: I have this many columns, but for now… It’s fine.
253 00:23:16.680 ⇒ 00:23:21.380 Uttam Kumaran: Yeah, some of this is just to give an understanding of, like, all the taxonomy. Yeah.
254 00:23:21.500 ⇒ 00:23:25.959 Uttam Kumaran: But, as you can see, some of these… this is more about, like, the source systems themselves.
255 00:23:25.960 ⇒ 00:23:27.009 Shivani Amar: Oh, okay, yeah.
256 00:23:27.010 ⇒ 00:23:28.630 Uttam Kumaran: Pulling from the other sheet.
257 00:23:28.970 ⇒ 00:23:33.029 Uttam Kumaran: And then we’re gonna also do, like, where is it referenced?
258 00:23:34.450 ⇒ 00:23:42.610 Uttam Kumaran: So this should… this should be pretty expansive. So let’s… I think we rerun with this, and then… ideally, all of the things are in the first…
259 00:23:43.100 ⇒ 00:23:47.680 Uttam Kumaran: Like, section, so we can move anything back that’s, like, not super operational.
260 00:23:47.680 ⇒ 00:23:56.069 Shivani Amar: Yeah, like, I’m like, the development status, alignment status can be, like, I think the main thing people want to see is, like, can I just read the definition clearly?
261 00:23:56.070 ⇒ 00:23:56.900 Uttam Kumaran: Yeah.
262 00:23:57.150 ⇒ 00:23:59.129 Shivani Amar: But we can restructure this later.
263 00:23:59.810 ⇒ 00:24:08.430 Uttam Kumaran: I feel it’s fair, let’s just… I’ll just move this… move this here, and that’s a good way to go. Okay, great, so I’m gonna go… I’ll go ahead and, link…
264 00:24:09.430 ⇒ 00:24:11.300 Uttam Kumaran: this section…
265 00:24:11.730 ⇒ 00:24:14.429 Shivani Amar: You don’t have to link the section, I’ll just link the sheet.
266 00:24:14.430 ⇒ 00:24:15.480 Uttam Kumaran: Oh, okay, okay, okay.
267 00:24:16.520 ⇒ 00:24:21.400 Shivani Amar: Okay…
268 00:24:22.700 ⇒ 00:24:38.960 Uttam Kumaran: And then I think similarly, on the marts side, like, what is a good, way for Echevani to show, like, we have some traditional marts-type documentation. The way that we typically would do it is every column in the marts
269 00:24:39.770 ⇒ 00:24:42.720 Uttam Kumaran: Like, as a definition, but ultimately, like.
270 00:24:43.400 ⇒ 00:24:47.940 Uttam Kumaran: this is really, like, the core piece, so I wonder if it’s helpful to show
271 00:24:48.360 ⇒ 00:24:51.509 Uttam Kumaran: progress on the MARC development in a different way.
272 00:24:52.360 ⇒ 00:24:56.350 Uttam Kumaran: Because it’s a little bit technical.
273 00:24:59.940 ⇒ 00:25:05.389 Uttam Kumaran: Like, is it helpful at all to see, like, hey, this is the… these are the five tables in the retail data mart?
274 00:25:06.930 ⇒ 00:25:07.650 Uttam Kumaran: Okay.
275 00:25:08.050 ⇒ 00:25:11.329 Uttam Kumaran: then I think I would prefer us to just focus on metrics.
276 00:25:11.580 ⇒ 00:25:13.310 Uttam Kumaran: It comes from the marts.
277 00:25:13.720 ⇒ 00:25:14.420 Shivani Amar: Okay.
278 00:25:15.810 ⇒ 00:25:21.899 Uttam Kumaran: Yeah, go ahead, Greg, I don’t know if you had any other pieces on your workstream.
279 00:25:22.150 ⇒ 00:25:30.680 Greg Stoutenburg: Yeah, I mean, that’s… so that’s what we got done, this week, and moving forward, I put next actions, just completing creation of the retail definitions.
280 00:25:30.680 ⇒ 00:25:43.040 Greg Stoutenburg: And those dashboards, as the team has been invited in, we know that the dashboards are incomplete and some pieces of them are broken, but the next steps is just keep moving forward on that piece of the project.
281 00:25:43.040 ⇒ 00:25:46.489 Greg Stoutenburg: As we begin ramping up on wholesale and e-commerce as well.
282 00:25:47.570 ⇒ 00:25:57.050 Uttam Kumaran: Shivani, for Dan, and for, like, other folks, should we do a working session? Do we feel like they’ll take time to play around with things?
283 00:25:57.270 ⇒ 00:25:59.940 Uttam Kumaran: Like, what, what is the move there?
284 00:26:00.640 ⇒ 00:26:11.929 Shivani Amar: I think just re-ping them the link to Omni is sufficient for now, yeah. Okay. We have Reston Assessed anyway next week, so I don’t know if anybody’s gonna be doing this, but, like, maybe the following Monday, just, like, be okay.
285 00:26:12.090 ⇒ 00:26:22.729 Uttam Kumaran: So I think, Greg, like, one thing, yeah, as you unlock more blobby functionality, maybe we decide, and we can just send… sort of use that channel as, like, status update, here’s what functionality is available now.
286 00:26:23.190 ⇒ 00:26:24.149 Greg Stoutenburg: Yep, sounds good.
287 00:26:24.760 ⇒ 00:26:32.520 Uttam Kumaran: And then, yeah, I know we’re, yeah, I’m… I’m excited, Shivani, for the Emerson conversation. I feel like they should give us whatever they have.
288 00:26:32.710 ⇒ 00:26:35.890 Uttam Kumaran: We’re paying for everything, so… .
289 00:26:36.090 ⇒ 00:26:39.370 Shivani Amar: Yeah, Jason, did you realize that? I don’t know if you followed that thread.
290 00:26:39.730 ⇒ 00:26:42.720 Shivani Amar: That, like, we don’t have purchase orders in Snowflake.
291 00:26:44.740 ⇒ 00:26:45.790 Jason Wu: for Walmart.
292 00:26:46.230 ⇒ 00:26:47.200 Shivani Amar: for both.
293 00:26:47.480 ⇒ 00:26:52.660 Jason Wu: Yeah, no, I… I think it just came down to…
294 00:26:53.680 ⇒ 00:26:58.660 Jason Wu: Frankly, no one was really digging into the snowflake data to identify that they wanted.
295 00:26:59.110 ⇒ 00:26:59.430 Shivani Amar: Yeah.
296 00:26:59.430 ⇒ 00:27:03.130 Jason Wu: So, at that point, they were so focused on, I think, just sales data.
297 00:27:03.130 ⇒ 00:27:03.960 Shivani Amar: Yeah.
298 00:27:03.960 ⇒ 00:27:04.490 Jason Wu: Yeah.
299 00:27:04.630 ⇒ 00:27:07.629 Jason Wu: So, no, I didn’t realize it either, because,
300 00:27:08.010 ⇒ 00:27:09.729 Jason Wu: Yeah, we weren’t put part on the table.
301 00:27:09.950 ⇒ 00:27:10.580 Shivani Amar: Yeah.
302 00:27:14.170 ⇒ 00:27:16.469 Uttam Kumaran: For non-Emerson, can I change this to, like.
303 00:27:17.760 ⇒ 00:27:21.349 Uttam Kumaran: are, like, I guess this is, like, outside of Target, Walmart.
304 00:27:21.830 ⇒ 00:27:22.640 Uttam Kumaran: Alright.
305 00:27:23.230 ⇒ 00:27:24.110 Uttam Kumaran: Yeah.
306 00:27:25.490 ⇒ 00:27:27.769 Uttam Kumaran: Atomic connection, encompass of…
307 00:27:36.540 ⇒ 00:27:43.010 Uttam Kumaran: And then did… did we find out anything about, like, the necessity of spins shorter term?
308 00:27:44.920 ⇒ 00:27:45.739 Uttam Kumaran: You, Ivor.
309 00:27:45.740 ⇒ 00:27:58.439 Shivani Amar: I was looking… I’m looking for you guys to finalize, like, a couple paragraphs about, like, this is what we get from this, so that I can, like, I can, like, speak to it, because I was just googling around, and, like, it was, like.
310 00:27:59.000 ⇒ 00:28:18.760 Shivani Amar: oh, you get more from the UI, you get, like, to do more cuts, so it’s like, if ultimately we get, like, a… we pay 15K, and it’s, like, a basic amount of data, and it’s, like, not… people are still going into the system, it’s a waste of money. Yeah. So, I’m just trying to understand, like, what we get from both, and I don’t know if I’ve been able… I’ve, like… I have that crystal for myself yet.
311 00:28:18.760 ⇒ 00:28:20.240 Uttam Kumaran: Okay, I can deliver that.
312 00:28:22.440 ⇒ 00:28:25.459 Uttam Kumaran: Yeah, maybe a way, Shmi and you can pair on that one.
313 00:28:25.820 ⇒ 00:28:27.180 Uttam Kumaran: And then…
314 00:28:30.190 ⇒ 00:28:38.739 Uttam Kumaran: Yeah, completion, creation of retail definitions, and V1 dashboard. I think it’s also helpful, Greg, for us to put something around, like.
315 00:28:39.150 ⇒ 00:28:44.109 Uttam Kumaran: In terms of, like, blobby functionality, Or salt, whatever, like…
316 00:28:44.110 ⇒ 00:28:44.770 Greg Stoutenburg: Yeah.
317 00:28:44.770 ⇒ 00:28:49.500 Uttam Kumaran: what you expect, Like, functionality unlocks, either in terms of, like.
318 00:28:49.620 ⇒ 00:28:59.620 Uttam Kumaran: accuracy or, like, steering, I think it’s helpful, because otherwise it could seem like it’s sort of, like, it’s an endless mission, but for us to confirm, like, certain pieces are functional…
319 00:28:59.620 ⇒ 00:29:00.280 Greg Stoutenburg: Yep.
320 00:29:00.280 ⇒ 00:29:06.390 Uttam Kumaran: You know, and then I think, ultimately, we’re looking for…
321 00:29:06.750 ⇒ 00:29:13.639 Uttam Kumaran: We’re looking for pretty… we’re looking for sign-off from everyone in this sort of omni-pilot committee, you know, ultimately, so…
322 00:29:20.720 ⇒ 00:29:21.570 Greg Stoutenburg: How about that?
323 00:29:24.510 ⇒ 00:29:25.120 Uttam Kumaran: Yeah.
324 00:29:32.200 ⇒ 00:29:39.900 Uttam Kumaran: Okay, and then the modeling side, we are doing, Amazon right now…
325 00:29:43.090 ⇒ 00:29:52.580 Uttam Kumaran: Amazon, and then, I guess I’ll say, like, combined… Or… Combine e-commerce.
326 00:29:53.760 ⇒ 00:30:00.409 Uttam Kumaran: I don’t know if that’s, like, makes sense to anybody. Like, basically Amazon plus Shopify into, like, a combined e-commerce.
327 00:30:00.780 ⇒ 00:30:01.640 Uttam Kumaran: table.
328 00:30:13.150 ⇒ 00:30:15.569 Uttam Kumaran: Anything else, Awash, on the modeling side?
329 00:30:19.300 ⇒ 00:30:25.730 Awaish Kumar: Yeah, we also did marketing modeling, like Facebook and Google Ads.
330 00:30:26.600 ⇒ 00:30:27.270 Uttam Kumaran: Okay.
331 00:30:28.480 ⇒ 00:30:31.570 Uttam Kumaran: Yeah, it’s just gonna… it’s probably just gonna…
332 00:30:32.780 ⇒ 00:30:35.030 Uttam Kumaran: Sit for a sec there, but yeah.
333 00:30:37.060 ⇒ 00:30:46.069 Uttam Kumaran: I think what’s gonna change, Awash, is, like, as, Greg… Greg’s workstream starts to have changes for reporting, we’ll probably do a lot more about, like, going back.
334 00:30:49.170 ⇒ 00:30:50.810 Uttam Kumaran: I’m editing logic.
335 00:30:51.550 ⇒ 00:30:52.330 Uttam Kumaran: Okay.
336 00:31:00.930 ⇒ 00:31:06.919 Uttam Kumaran: I feel good here, we can… yeah, we can work on… This piece, so…
337 00:31:09.450 ⇒ 00:31:11.800 Uttam Kumaran: Yeah, I feel like since last time, it’s…
338 00:31:12.510 ⇒ 00:31:16.119 Uttam Kumaran: We probably are gonna move some of the milestones, right, Shivani?
339 00:31:16.550 ⇒ 00:31:17.820 Uttam Kumaran: later. Yeah.
340 00:31:20.680 ⇒ 00:31:24.149 Uttam Kumaran: So, I think finalized data sources can change…
341 00:31:43.850 ⇒ 00:31:47.359 Shivani Amar: Yeah, these dates are gonna just change, it doesn’t matter. Yeah, I think it’s fine.
342 00:31:47.360 ⇒ 00:31:48.380 Uttam Kumaran: Yeah. Okay, okay.
343 00:32:03.050 ⇒ 00:32:03.890 Shivani Amar: Cool.
344 00:32:04.530 ⇒ 00:32:05.760 Shivani Amar: Okay!
345 00:32:06.060 ⇒ 00:32:07.369 Shivani Amar: I feel good on this.
346 00:32:07.580 ⇒ 00:32:08.140 Uttam Kumaran: Okay.
347 00:32:09.040 ⇒ 00:32:09.810 Greg Stoutenburg: Alright.
348 00:32:14.100 ⇒ 00:32:16.719 Uttam Kumaran: Okay, we can… I mean, for me, we can spend more time
349 00:32:16.880 ⇒ 00:32:20.579 Uttam Kumaran: playing with Blobby together, if we want to use the rest of time.
350 00:32:20.580 ⇒ 00:32:21.310 Shivani Amar: I think…
351 00:32:21.450 ⇒ 00:32:29.269 Shivani Amar: I think it’s okay. I think Utham, Jason, for context for you, Utham’s gonna come to New York next week, and we’re gonna do a sesh with…
352 00:32:29.440 ⇒ 00:32:33.559 Shivani Amar: Robert and me and Utam, and, like, we’ve approved the contract.
353 00:32:33.700 ⇒ 00:32:38.740 Shivani Amar: I think the new contract will start, like…
354 00:32:39.520 ⇒ 00:32:53.649 Shivani Amar: April 6th or April 13th, or something like that. And so, maybe I can share my screen quickly with them, but, like, so we’re gonna… we’re gonna meet in person on Tuesday, Jason, and just kind of, like, basically redo the Gantt.
355 00:32:53.920 ⇒ 00:32:54.909 Shivani Amar: To be, like.
356 00:32:55.410 ⇒ 00:33:12.739 Shivani Amar: like, the way I’m framing it is, like, I need to, like, fully understand every, like, every row, and, like, it needs to be kind of in my words if I’m project planning, managing this, so we’re gonna kind of just redo it slowly and, like, build it out so that it’s, like, makes sense for me. And…
357 00:33:12.740 ⇒ 00:33:17.189 Shivani Amar: And then that’ll be, like, our new flow, and I think we’re, like, realizing
358 00:33:17.190 ⇒ 00:33:28.939 Shivani Amar: Maybe you want to do supply chain discovery sooner, and then pause on, like, pushing on marketing. Like, it seems like getting the supply-demand inputs is more important than getting, like, the marketing math done.
359 00:33:28.970 ⇒ 00:33:36.490 Shivani Amar: So… I think we can, like, refresh the Gantt together, and then what I would love to do is…
360 00:33:36.820 ⇒ 00:33:41.869 Shivani Amar: This will be very quick. But basically, with them, like, just to give you a sense
361 00:33:42.200 ⇒ 00:33:52.839 Shivani Amar: what I’m trying to do. I would love to have a kickoff with your… with the team that’s, like, fully working on this project, and kind of just go through, like, some rah-rah stuff about Element.
362 00:33:52.840 ⇒ 00:33:53.200 Uttam Kumaran: Cool.
363 00:33:53.200 ⇒ 00:34:07.190 Shivani Amar: So that it’s, like, we’re like, okay, this is, like, the Brainforge team, like, let’s go, introduction to our product lineup, like, just some, like, excitement about, like, connecting with the brand. I’m gonna edit these a little bit more, and then it gets into a little bit about, like.
364 00:34:07.190 ⇒ 00:34:17.979 Shivani Amar: okay, like, what channels do we have? And, like, what are some examples? Like, then it can transition to, like, what are things that we should be thinking about? So it’s gonna start sexy, go a little bit less sexy with my taste.
365 00:34:17.989 ⇒ 00:34:24.179 Shivani Amar: Google Slides right now, but I’m hoping we can figure out a date for a kickoff for, like, the whole team and intros.
366 00:34:24.179 ⇒ 00:34:24.709 Greg Stoutenburg: Great.
367 00:34:26.040 ⇒ 00:34:26.679 Greg Stoutenburg: Cool.
368 00:34:26.880 ⇒ 00:34:28.019 Greg Stoutenburg: I like that. Okay.
369 00:34:28.139 ⇒ 00:34:30.100 Greg Stoutenburg: Shivani, how was the Omni event?
370 00:34:30.389 ⇒ 00:34:38.649 Shivani Amar: It was good. I… like, it was funny, I was listening to somebody from Condi Nast talk about using Omni, and they were like.
371 00:34:38.949 ⇒ 00:34:44.999 Shivani Amar: When somebody on my team asks, Blobby for, like, for revenue.
372 00:34:45.139 ⇒ 00:34:47.749 Shivani Amar: I have the AI bot give it
373 00:34:47.859 ⇒ 00:35:10.769 Shivani Amar: the seven versions of revenue you have gross, net, da-da-da-da-da, revenue before this, revenue before that, and it’s, like, seven different versions. Yeah. And I was like, oh, that’s comfort… like, it’s like, everybody struggles with what is the definition of revenue, like, across every business. In our case, it’s like, oh, but it’s minus trade spend, and it’s minus chargebacks for retail, but it’s minus this for e-commerce. And so, it was just, like, that was nice
374 00:35:10.769 ⇒ 00:35:16.689 Shivani Amar: to see, like, okay, you can, like, program it to be like, I’d rather cover my ass and have it give the…
375 00:35:16.690 ⇒ 00:35:17.190 Uttam Kumaran: Yeah, everything.
376 00:35:17.190 ⇒ 00:35:28.910 Shivani Amar: asking, like, the four different versions it could want, and then it could say, oh, I’m, like, looking for this definition in particular, instead of it just being, like, here’s wholesale revenue, and then I sit here and say, like, no, that’s actually wholesale sales.
377 00:35:28.910 ⇒ 00:35:30.540 Greg Stoutenburg: Right? That’s not what we mean here, yeah.
378 00:35:30.540 ⇒ 00:35:33.209 Shivani Amar: Yeah, exactly. So it’s like, it’s like…
379 00:35:33.340 ⇒ 00:35:55.559 Shivani Amar: having… building it out to say, like, when somebody asks about sales, even if it’s clarifying, I want to clarify that you’re looking for, like, gross sales in a time period, but not looking for, like, revenue… net revenue, which would be this. Like, that would be a good back and forth for somebody engaging with the AI to, like, have to clarify what they’re looking for. So I thought it was very helpful.
380 00:35:55.580 ⇒ 00:36:00.200 Shivani Amar: Otherwise, it was, like, I think it was… it was nice to just hear, like, people…
381 00:36:00.500 ⇒ 00:36:18.850 Shivani Amar: saying how much, like, time it’s saving them, and, like, how they’ve been able to have scrappy data teams because of Omni, and how much adoption they’ve gotten. Like, one person was saying, I think she was coming from Caraway, like, the, like, pots and pans company. She was saying there used to be, like, 15 people who looked at the BI tool, and now it’s 75 will engage.
382 00:36:18.850 ⇒ 00:36:19.260 Greg Stoutenburg: No, nice.
383 00:36:19.260 ⇒ 00:36:20.050 Shivani Amar: a guy.
384 00:36:20.410 ⇒ 00:36:20.740 Uttam Kumaran: Wow.
385 00:36:20.740 ⇒ 00:36:28.970 Shivani Amar: like, a 100-person team. So, like, the adoption stats were cool, and it was helpful to see, like, how people get the, like.
386 00:36:29.640 ⇒ 00:36:33.839 Shivani Amar: energy going about the BI tool, like, I can imagine, Jason, like, me and you, like.
387 00:36:33.930 ⇒ 00:36:52.459 Shivani Amar: presenting, like, Omni to people at the annual planning, and, like, showing them some cool things, right? Like, I can imagine this being, like, a debut of sorts for the company, to be, like, we’re, like, unveiling, like, a really awesome tool. And so that, like, got the juices flowing on, like, how to, like…
388 00:36:52.710 ⇒ 00:37:04.070 Shivani Amar: how to get that, but I would say otherwise, the funny thing is, like, everybody’s examples are about time-saving and finding data. Nobody necessarily gave an example that was like, which is fine, any BI tool is, like.
389 00:37:04.070 ⇒ 00:37:13.640 Shivani Amar: gathering data and coming with insights, like, nobody was like, because I saved the time and I had more time to analyze, I actually was able to run this experiment and, like, get more revenue. Right.
390 00:37:13.730 ⇒ 00:37:18.859 Shivani Amar: get more, like, for the P&L, which, what would excite CEO type is, like, more that it’s, like.
391 00:37:18.860 ⇒ 00:37:19.380 Greg Stoutenburg: Yeah.
392 00:37:19.380 ⇒ 00:37:28.880 Shivani Amar: the back and forth of investigating a dip actually helped us uncover an experiment that we should run, and that’s a sexier example to me than.
393 00:37:28.880 ⇒ 00:37:29.240 Greg Stoutenburg: Yeah.
394 00:37:29.240 ⇒ 00:37:32.069 Shivani Amar: like, look how fast I can do this now. Right.
395 00:37:32.070 ⇒ 00:37:35.439 Greg Stoutenburg: Right. Now I spend even more time with my BI tool, because.
396 00:37:35.440 ⇒ 00:37:53.119 Shivani Amar: Yeah, like, the one other thing that I thought was cool that they were saying is, like, the beauty will be when the, like, AI can code to, like, actually conduct actions for the business also. So in this one example that I was thinking about, Madison is trying to come up with a list of, like, wholesale partners to give
397 00:37:53.560 ⇒ 00:37:55.519 Shivani Amar: Pink lemonade samples, too.
398 00:37:55.920 ⇒ 00:38:03.500 Shivani Amar: And she’s like, okay, I want to look at who’s active, I want to figure out which address I should send it to, and I want to figure out,
399 00:38:03.950 ⇒ 00:38:23.609 Shivani Amar: I only want it to be from the, like, trusted health segment, or something like that, right? So she has some criteria, but then, like, the cool thing eventually would be, like, can we have a button for Madison to be like, now you can click this, and it’ll actually, like, once you approve of this list and the addresses, you can click this and it’ll just go into your sample system.
400 00:38:24.570 ⇒ 00:38:31.440 Greg Stoutenburg: I mean, Utam, once we’ve got topics locked down, I mean, I think we’re… I think that technology exists already. Yeah.
401 00:38:31.440 ⇒ 00:38:52.449 Shivani Amar: So that’s, like, where I was kind of, like, excited about, like, going from the AI to actually just completing the action fully, and I was like, I don’t know how often she needs lists of… how often people need lists of… bespoke lists to give samples to, but if, like, if it could go from the BI tool to be like, I need to give samples to this subset of
402 00:38:52.520 ⇒ 00:39:04.849 Shivani Amar: customers or people, and, like, this is what I need to pull from, that’s, like, a nice feature, right? Yeah. So, that, like, got the excitement flowing on, like, what could be possible to actually complete the thing entirely, like, end-to-end. Yeah.
403 00:39:04.850 ⇒ 00:39:16.319 Greg Stoutenburg: Yeah, that is exciting, yeah. It’s… I mean, it’s a victory if you can go to something like a BI tool conference and actually get excited, because it points the way to this bright future, so…
404 00:39:16.320 ⇒ 00:39:16.750 Shivani Amar: Yeah.
405 00:39:16.750 ⇒ 00:39:24.300 Greg Stoutenburg: Yeah, that’s, yeah, that’s good, that’s good feedback, and yeah, we’ll keep that in mind as we, you know, as we do polish up the topics, like, to start.
406 00:39:24.300 ⇒ 00:39:24.630 Shivani Amar: Yeah.
407 00:39:24.630 ⇒ 00:39:35.849 Greg Stoutenburg: looking around for how we can connect, you know, connect some AI tool to, Omni’s MCP for Element, and, you know, what else we can automate from there.
408 00:39:35.850 ⇒ 00:39:36.720 Shivani Amar: Okay, perfect.
409 00:39:36.720 ⇒ 00:39:37.310 Greg Stoutenburg: Yeah.
410 00:39:37.310 ⇒ 00:39:40.389 Shivani Amar: Well, well, thank you guys, Utham. I look forward to seeing you on Tuesday.
411 00:39:40.540 ⇒ 00:39:43.140 Uttam Kumaran: Perfect, yes. Thank you, thank you.
412 00:39:43.140 ⇒ 00:39:43.610 Greg Stoutenburg: See y’all.
413 00:39:43.610 ⇒ 00:39:44.130 Uttam Kumaran: Bye.