Meeting Title: Brainforge x Default Weekly Sync Date: 2026-03-11 Meeting participants: Uttam Kumaran, Greg Stoutenburg, Mustafa Raja, Nandika Jhunjhunwala, Lev Katreczko, Caitlyn Vaughn
WEBVTT
1 00:00:13.660 ⇒ 00:00:14.700 Uttam Kumaran: Hello!
2 00:00:14.950 ⇒ 00:00:15.580 Greg Stoutenburg: Abe.
3 00:00:16.550 ⇒ 00:00:17.400 Greg Stoutenburg: Ugh.
4 00:00:18.020 ⇒ 00:00:24.340 Greg Stoutenburg: I was like, I was like, oh, if I edit really quickly, they won’t notice it’s not there. Nandica pinged me.
5 00:00:24.340 ⇒ 00:00:32.230 Uttam Kumaran: I thought I… I had a minute, and I was like… I was like, okay, let me just try to join this early, you know, that’s… that’s great. I feel nice joining the meeting early.
6 00:00:32.439 ⇒ 00:00:36.999 Greg Stoutenburg: Nope. I prevented you from doing that.
7 00:00:38.379 ⇒ 00:00:40.329 Greg Stoutenburg: Okay.
8 00:00:44.889 ⇒ 00:00:48.839 Greg Stoutenburg: Hey, Nandika, nice catch on the link, I was hoping to get away with it.
9 00:00:50.380 ⇒ 00:00:52.739 Uttam Kumaran: I think I was a minute ahead of you on the good stuff.
10 00:00:52.740 ⇒ 00:00:56.090 Greg Stoutenburg: I was like, I was like, oh, good. Your time saved the day.
11 00:00:56.270 ⇒ 00:00:58.610 Greg Stoutenburg: And then you ping me at, like, the same time.
12 00:01:03.610 ⇒ 00:01:04.370 Greg Stoutenburg: Alright.
13 00:01:05.860 ⇒ 00:01:06.999 Greg Stoutenburg: How’s it going, Lev?
14 00:01:09.990 ⇒ 00:01:10.600 Uttam Kumaran: A…
15 00:01:10.600 ⇒ 00:01:11.430 Lev Katreczko: up, guys.
16 00:01:11.790 ⇒ 00:01:12.580 Greg Stoutenburg: Hey.
17 00:01:20.160 ⇒ 00:01:21.479 Greg Stoutenburg: All right.
18 00:01:23.630 ⇒ 00:01:25.899 Greg Stoutenburg: Ugh, how’s everybody’s week so far?
19 00:01:27.580 ⇒ 00:01:28.060 Nandika Jhunjhunwala: Hello.
20 00:01:28.060 ⇒ 00:01:32.370 Uttam Kumaran: I’m jealous you’re go… I’m jealous you’re going on vacation, Greg. I need a vacation.
21 00:01:32.640 ⇒ 00:01:33.689 Greg Stoutenburg: You should be.
22 00:01:33.690 ⇒ 00:01:34.190 Uttam Kumaran: I’m going…
23 00:01:34.190 ⇒ 00:01:35.070 Greg Stoutenburg: Bring it up.
24 00:01:35.410 ⇒ 00:01:39.719 Greg Stoutenburg: I will try to avoid Slack, except to post pictures of how much fun I’m having.
25 00:01:40.490 ⇒ 00:01:42.119 Uttam Kumaran: Where are you going to… where are you going?
26 00:01:42.350 ⇒ 00:01:58.140 Greg Stoutenburg: It’ll be near Tampa. This is gonna be at my, my girlfriend’s father’s house. He lives down in Florida. So, bring the kids. We’re gonna do things like play pickleball for 3 hours, because that’s what a retiree does in Florida.
27 00:01:58.140 ⇒ 00:02:00.240 Uttam Kumaran: Things like play pickleball.
28 00:02:00.240 ⇒ 00:02:03.159 Greg Stoutenburg: Yeah, she’s already warned me, she’s like, hey Caitlin.
29 00:02:03.160 ⇒ 00:02:03.890 Caitlyn Vaughn: Hello!
30 00:02:04.810 ⇒ 00:02:17.980 Greg Stoutenburg: She’s already warned me, like, they don’t eat until they eat dinner. Dinner is at 4.30, and they will make you play pickleball for, like, 3 hours, unless you explicitly state that you need to stop. So, like…
31 00:02:18.420 ⇒ 00:02:28.530 Greg Stoutenburg: That’s not so bad. That’s not so bad. So, Caleb, we’re discussing my plans for the rest of the week. I’ll be leaving for Florida this afternoon. Oh, nice! You’re on a bender here.
32 00:02:28.820 ⇒ 00:02:36.940 Greg Stoutenburg: I know, you’d think, you know, it really… I hope so. You know what I should have done? I should have done the responsible thing and, like, actually just taken off one week, like, straight through.
33 00:02:37.210 ⇒ 00:02:46.600 Greg Stoutenburg: Right. Instead, I did what feels like… it already feels like I didn’t take a break at all. Like, I took 2 days, and then I’m taking, like, 2 more days, and then I’m taking 3 more days. That’s only 5 days.
34 00:02:46.600 ⇒ 00:02:47.080 Caitlyn Vaughn: Yeah.
35 00:02:47.080 ⇒ 00:02:53.290 Greg Stoutenburg: But it feels like I’ve been away for a month, and then I have to, like, prepare to leave, and then catch up after having been gone, while preparing to leave again.
36 00:02:53.290 ⇒ 00:02:53.700 Caitlyn Vaughn: Yeah.
37 00:02:53.700 ⇒ 00:02:55.449 Greg Stoutenburg: So, I learned something this week.
38 00:02:55.790 ⇒ 00:02:56.810 Greg Stoutenburg: Don’t do that.
39 00:02:57.190 ⇒ 00:02:59.930 Caitlyn Vaughn: Yeah, boundaries, such a hard thing to learn.
40 00:02:59.930 ⇒ 00:03:01.430 Greg Stoutenburg: Yeah, yeah, yeah.
41 00:03:03.100 ⇒ 00:03:10.430 Greg Stoutenburg: Okay, alright. Well, I think we’re all here. Demi said he was going to be away…
42 00:03:10.660 ⇒ 00:03:20.289 Greg Stoutenburg: Looking at the calendar… Okay, this is our crew, so let’s just dive in.
43 00:03:29.190 ⇒ 00:03:45.069 Greg Stoutenburg: Alright, here is what we have been up to. So we’ll go through the normal stuff, we’ll summarize, talk about wins, progress on dashboard requests, some timelines, some more wins, how things are going for the metrics, definitions, and schema.
44 00:03:45.190 ⇒ 00:04:05.829 Greg Stoutenburg: and sensitive data, and, yeah, the rest of it. So, the GTM metrics dashboard, I know that we were… one of the things we’re working on for a while is getting the, dbt work done so that we could stand up a couple of dashboards. The GTM metrics dashboard is done, and awaiting QA, and, Uten will talk about that, a little bit later.
45 00:04:05.830 ⇒ 00:04:11.510 Greg Stoutenburg: The financial summary will also be ready for review. Goal is by end of the week.
46 00:04:11.510 ⇒ 00:04:16.779 Greg Stoutenburg: And so, we’re ready to schedule those stakeholder reviews with Laura and sign off.
47 00:04:17.550 ⇒ 00:04:28.309 Greg Stoutenburg: On the product analytics side, we’re continuing to instrument as pieces of Phoenix go into staging. I shared that dashboard, and Nandaka, I saw that you were able to take a look at that, so thanks.
48 00:04:28.310 ⇒ 00:04:53.309 Greg Stoutenburg: for looking at engagement for the tables feature. We’ll continue to build that out, because there are more things that you can do with the tables feature, but this was sort of intended, like, as an example. Well, one, proof we’ve got data coming in from those clicks, so that’s good. And then two, one of the kinds of charts we’ll be building in post-hog is just going to show how users are using a feature. So, like, one of the things you can see right away, actually, just looking at that chart, is there are a lot more clicks just to open the tables feature.
49 00:04:53.310 ⇒ 00:05:04.150 Greg Stoutenburg: than there are anything else. Now, if this weren’t testing and this were actually live, one of the things we would learn from that is we’d go, okay, why is it that users are clicking this, but then not actually doing anything with it?
50 00:05:04.150 ⇒ 00:05:11.999 Greg Stoutenburg: maybe they’re confused, maybe this looks like a drop-off. So, so that’s the goal there. We’ll continue to instrument as we, as we go.
51 00:05:12.000 ⇒ 00:05:26.619 Greg Stoutenburg: on the data platform and analytics side, we’re, yeah, documenting those QA steps needed for… needed from you for those dashboards that are getting stood up now, and, we’ll meet with Laura and get… get sign-off on those.
52 00:05:27.580 ⇒ 00:05:45.649 Greg Stoutenburg: On the data platform and analytics side, wins are, you know, as I already mentioned, those dashboards getting bleeded. We’ve really sped up things a lot there. The warehouse scheme has been shared with stakeholders for alignment, and so you can actually see what’s there and align on metrics, make sure that we’re measuring the right things, and allow you to make sure that you’re hitting your goals.
53 00:05:45.650 ⇒ 00:06:05.790 Greg Stoutenburg: V1 of the Omni dashboards is up. Excited about that, big Omni fans over here. The dashboards then are ready for QA, and and we’ll start that feedback loop. So we do want you to, you know, we want you to take a look, we want you to love it. Also, we want you to weigh in and say where, you know, it looks like something isn’t quite as expected, provide that feedback, and we’ll continue to improve it.
54 00:06:05.890 ⇒ 00:06:09.720 Greg Stoutenburg: This gives you your first integrated view of the business data.
55 00:06:09.920 ⇒ 00:06:26.840 Greg Stoutenburg: Also, it’s just awesome using the AI tool to ask questions, you can build new charts, it’s fantastic. dbt modeling’s been completed for those dashboards mentioned already, so now the foundation’s in place for, for those dashboard work… that dashboard work to keep moving forward.
56 00:06:27.290 ⇒ 00:06:29.590 Greg Stoutenburg: Utam, did you want to talk through this one?
57 00:06:30.030 ⇒ 00:06:31.989 Uttam Kumaran: Yes, I can share.
58 00:06:36.020 ⇒ 00:06:36.900 Greg Stoutenburg: Let me stop mine.
59 00:06:37.260 ⇒ 00:06:38.470 Uttam Kumaran: Yeah.
60 00:06:39.500 ⇒ 00:06:48.929 Uttam Kumaran: Okay, so I think our initial goal was basically to recreate what’s in equals, and I feel like we’ve accomplished that, but there are some things that are…
61 00:06:49.010 ⇒ 00:07:02.629 Uttam Kumaran: not great in equals, that I think we’re at the point where we can start talking about that. But I think what you see compared to, Caitlin, what we showed on Friday, is just, like, a lot of those tweaks that, Laura flagged.
62 00:07:02.630 ⇒ 00:07:03.080 Caitlyn Vaughn: In particular.
63 00:07:03.080 ⇒ 00:07:17.730 Uttam Kumaran: like, we just updated a lot of coloring, we’ve… a lot of the data is basically accurate and filled out. Some of the things you’re seeing zero, and I went in and QA’d, and it’s accurate, so these are things that I want to just talk to Laura and just
64 00:07:18.170 ⇒ 00:07:21.010 Uttam Kumaran: Just be like, yeah, are we… are we…
65 00:07:21.250 ⇒ 00:07:41.060 Uttam Kumaran: are we confident that, like, yeah, there are no restarts or contractions, and just make sure? We have both… so as I mentioned in the call, and for everybody here, like, we are both looking at revenue and customer counts, typically, all at the same time. And so one thing that… I don’t know, Caitlin, is there… are there definitions yet on…
66 00:07:41.130 ⇒ 00:07:45.429 Uttam Kumaran: customer segmentations, because that’s something that wasn’t in equals.
67 00:07:45.430 ⇒ 00:07:46.390 Caitlyn Vaughn: And…
68 00:07:46.390 ⇒ 00:07:50.570 Uttam Kumaran: I don’t know if we… if… If default internally has, like.
69 00:07:50.760 ⇒ 00:07:56.439 Uttam Kumaran: small, medium, enterprise, or if, like, that’s something that we want to add. That was the only thing I feel like is…
70 00:07:56.610 ⇒ 00:08:00.330 Uttam Kumaran: Missing from my perspective in terms of segmentation on the customer side.
71 00:08:01.040 ⇒ 00:08:19.069 Caitlyn Vaughn: Yeah, I mean, we have, like, a basic version that we created maybe 6 months ago with, like, Tier 3, Tier 2, Tier 1 customers, but yeah, Sid was, working on something for Laura, so I think this is probably new news for us that will come out in the next week or so.
72 00:08:19.120 ⇒ 00:08:34.190 Uttam Kumaran: Cool, so basically, this will look like this is split up into colors, and so ideally, similar to how we see revenue flow throughout, we want to see what segments are growing, and then ultimately, you get to answer the question, like, what segment is growing the fastest, as
73 00:08:34.190 ⇒ 00:08:44.759 Uttam Kumaran: like, in terms of number of customers, or in terms of percent of total revenue. And that sort of wraps up a lot of, like, the basics of customer change and, like, ARR change.
74 00:08:44.820 ⇒ 00:09:01.369 Uttam Kumaran: So one thing that I… kind of next step for us here is, like, I want to talk to Laura about a couple of pieces. I think… I think this is in a great spot. I think I want to ask her what parts of these are, like, operational for her, where she’s, like, checking numbers versus
75 00:09:01.810 ⇒ 00:09:14.990 Uttam Kumaran: is this something she could screenshot for her, like, for the investor decks and things like that? Like, how far are we from, like, now I don’t have to prepare something, I can just screenshot this? So that’s, like, a clear attack of, like, hey, it takes me 3-4 hours to, like.
76 00:09:15.120 ⇒ 00:09:27.000 Uttam Kumaran: put this together in Excel, I can now screenshot Omni. The second piece is there’s some things about, like, current month versus last month. For example, you’ll see, like, these are, like, 0% or 100%. We actually just mimicked
77 00:09:27.160 ⇒ 00:09:34.920 Uttam Kumaran: I asked the team, and they’re like, this is what’s an equal, so I want to call her and be like, do you want these numbers to show last month versus the month before?
78 00:09:35.250 ⇒ 00:09:43.300 Uttam Kumaran: another way to think about this is how do you think about pacing in a given month? For example, it’s March 11th. Do you want to see, like.
79 00:09:43.480 ⇒ 00:09:47.020 Uttam Kumaran: The number we’re trending to, given, like, a linear forecast.
80 00:09:47.330 ⇒ 00:09:52.179 Uttam Kumaran: to see… Last… the closed month versus the previous.
81 00:09:52.380 ⇒ 00:09:52.780 Caitlyn Vaughn: Yeah.
82 00:09:52.780 ⇒ 00:10:07.620 Uttam Kumaran: like, the… another problem is you guys invoice, like, typically on the first day of the month, right? So, like, the revenue, we would have to spread out. So these are some of the things I just need to… I think we’re, like, hopefully past the QA stage, and now more on, like, the…
83 00:10:07.820 ⇒ 00:10:09.349 Uttam Kumaran: How does the dashboard?
84 00:10:09.890 ⇒ 00:10:10.930 Uttam Kumaran: Look, and it…
85 00:10:10.930 ⇒ 00:10:11.400 Caitlyn Vaughn: Yeah.
86 00:10:11.400 ⇒ 00:10:14.940 Uttam Kumaran: And… and who… who needs it in what shape, basically?
87 00:10:15.100 ⇒ 00:10:26.310 Caitlyn Vaughn: Yeah. Okay, cool, this is looking good. I’m fairly sure we have had a few, like, restarted accounts, like… Okay. This is, like, accounts that have churned and then come back, right?
88 00:10:26.310 ⇒ 00:10:29.600 Uttam Kumaran: Yeah, not an adjacent period, basically.
89 00:10:29.730 ⇒ 00:10:34.349 Uttam Kumaran: So, like, it’s not… it’s… it’s not like… you could… if you canceled last month.
90 00:10:34.350 ⇒ 00:10:35.900 Caitlyn Vaughn: Started this month.
91 00:10:35.960 ⇒ 00:10:41.570 Uttam Kumaran: There’s, like, no… It’s a definition thing, but it’s not like there’s not a period of absence in between.
92 00:10:42.040 ⇒ 00:10:43.040 Uttam Kumaran: So…
93 00:10:45.150 ⇒ 00:10:48.459 Caitlyn Vaughn: There’s not a period of absence, and that is restarted ARR.
94 00:10:48.460 ⇒ 00:10:49.680 Uttam Kumaran: That’s not restarted.
95 00:10:49.680 ⇒ 00:10:56.699 Caitlyn Vaughn: Oh, okay, yeah, yeah, yeah. Okay, so most of… I guess all the customers I have in mind have, like, churned for 6 months, and then come back.
96 00:10:56.700 ⇒ 00:10:59.800 Uttam Kumaran: Yeah, okay. Then that would count, so I would like to go…
97 00:10:59.810 ⇒ 00:11:02.969 Caitlyn Vaughn: Basically, you can drill into one of these values and see, like.
98 00:11:03.030 ⇒ 00:11:07.970 Uttam Kumaran: What is the contributing… You know, who’s contributing to this amount?
99 00:11:07.970 ⇒ 00:11:08.839 Caitlyn Vaughn: So… Yeah.
100 00:11:08.840 ⇒ 00:11:10.730 Uttam Kumaran: If you have some of those in mind.
101 00:11:11.000 ⇒ 00:11:15.250 Uttam Kumaran: and they’re notable, like, I would love to go make sure what category they fall in.
102 00:11:15.250 ⇒ 00:11:15.660 Caitlyn Vaughn: Yeah.
103 00:11:15.770 ⇒ 00:11:20.770 Uttam Kumaran: Or if you have… you can have… if you have those now, I can just note them down, and I can… we can go look at that.
104 00:11:21.010 ⇒ 00:11:31.310 Caitlyn Vaughn: Let me make sure I’m not talking out of my ass, okay. Companies that restarted. Yeah, I’ll get you a list right after this.
105 00:11:31.940 ⇒ 00:11:35.539 Uttam Kumaran: Okay, that’s helpful. But yes, we should basically expect
106 00:11:35.810 ⇒ 00:11:38.330 Uttam Kumaran: Like, expect to see their churn.
107 00:11:39.100 ⇒ 00:11:44.940 Uttam Kumaran: And we should… so they shouldn’t show up as a contraction, they should show up as a churn, and then we should see their…
108 00:11:45.290 ⇒ 00:11:59.780 Uttam Kumaran: their restart show up in the… in the month that they… that they restarted. But also, again, like, it’ll be the amount restarted in the month they restarted, but the following month, there will be…
109 00:12:00.030 ⇒ 00:12:04.940 Uttam Kumaran: They moved to just the… the existing ARR. Does that make sense?
110 00:12:04.940 ⇒ 00:12:08.000 Caitlyn Vaughn: Yeah. Okay, so there is, there is one in here.
111 00:12:08.180 ⇒ 00:12:12.540 Uttam Kumaran: There is one, but that’s why I just wanted to… confirm…
112 00:12:12.810 ⇒ 00:12:17.070 Uttam Kumaran: If that’s correct. The churn… the churn looks okay.
113 00:12:17.230 ⇒ 00:12:17.590 Caitlyn Vaughn: Yeah.
114 00:12:17.940 ⇒ 00:12:19.939 Uttam Kumaran: If you have a couple, we can spot check.
115 00:12:21.070 ⇒ 00:12:21.939 Uttam Kumaran: That would be great.
116 00:12:22.130 ⇒ 00:12:23.210 Caitlyn Vaughn: Yeah, yeah, yeah.
117 00:12:24.010 ⇒ 00:12:28.420 Caitlyn Vaughn: Let me just quickly send a message…
118 00:12:40.290 ⇒ 00:12:49.280 Uttam Kumaran: So, I think the other way to kind of think about it, and Greg and I were talking about this today, too, is, like, thinking about personas in terms of dashboard viewers. For example, like.
119 00:12:49.590 ⇒ 00:12:58.399 Uttam Kumaran: Like, the exec team at default may want a certain view versus Laura may want a certain view versus someone on the sales team, so…
120 00:12:58.520 ⇒ 00:13:13.670 Uttam Kumaran: it’s… I actually think we have a lot of flexibility now to collect some of those requirements and work on these. Ideally, everybody’s can get… look at one, and it sort of satisfies, so if… if your recommendation is like, yeah, I don’t think we need, like.
121 00:13:13.830 ⇒ 00:13:17.160 Uttam Kumaran: People need that, let’s just try to all look at the same thing.
122 00:13:17.340 ⇒ 00:13:21.500 Uttam Kumaran: then I think we can just consider this, like, the core ARR.
123 00:13:22.300 ⇒ 00:13:28.800 Uttam Kumaran: Dashboard, and then we can move to the next, which is going to be looking at, like, customer and product usage.
124 00:13:29.040 ⇒ 00:13:36.259 Caitlyn Vaughn: I’m imagining for our sales team, there might be some, like, different metrics that they’re gonna care about.
125 00:13:36.370 ⇒ 00:13:37.210 Caitlyn Vaughn: Yeah.
126 00:13:37.210 ⇒ 00:13:45.490 Uttam Kumaran: I also… for the sales, typically, they also are doing, like, single account lookups, commonly. They’re like, let me just, like, look up this account and see everything.
127 00:13:45.490 ⇒ 00:13:45.880 Caitlyn Vaughn: Yeah.
128 00:13:45.880 ⇒ 00:13:53.919 Uttam Kumaran: So, like, this doesn’t have information about, like, people or, like, more specific customer information.
129 00:13:53.920 ⇒ 00:13:54.280 Caitlyn Vaughn: Yeah.
130 00:13:54.280 ⇒ 00:13:58.030 Uttam Kumaran: customer dashboard, We can add features for, like.
131 00:13:58.590 ⇒ 00:14:01.220 Uttam Kumaran: Like, you can imagine you scroll down, and there’s, like.
132 00:14:01.670 ⇒ 00:14:19.969 Uttam Kumaran: pinpoint one customer and give me just the, like, overview of that customer. When did they start? When do they upgrade, downgrade, churn? Who’s onboarded? Sort of, like, it’s actually really related to the first dashboard we created for Deanna. So, like, that’s what I’m expecting, like, they’re often using, in addition to, like.
133 00:14:20.260 ⇒ 00:14:23.519 Uttam Kumaran: Salesforce opportunities, like, all Salesforce-focused stuff.
134 00:14:23.870 ⇒ 00:14:24.600 Caitlyn Vaughn: Okay.
135 00:14:24.900 ⇒ 00:14:33.549 Caitlyn Vaughn: Yeah, I think, yeah, we’ll probably have, like, a less robust board for sales, but probably a separate one, I imagine.
136 00:14:34.020 ⇒ 00:14:34.600 Uttam Kumaran: Okay.
137 00:14:34.860 ⇒ 00:14:37.340 Uttam Kumaran: And then my next question is, like.
138 00:14:37.470 ⇒ 00:14:47.889 Uttam Kumaran: like, we’re gonna keep ripping through the first versions of these, but, like, I don’t know, Nandika, do you feel comfortable enough to start to be able to, like, tweak some of these? And, like, I think that’s gonna be…
139 00:14:48.000 ⇒ 00:14:54.160 Uttam Kumaran: easy… that’s gonna be really helpful, I think, for folks inside default, to be like, hey, this chart is good, like, can you add a label, or…
140 00:14:54.750 ⇒ 00:15:07.759 Uttam Kumaran: you, like, add this, I want to make sure that we’re not the blocker there, so that we can just keep going, like, making sure all the models are ready, and then we’re gonna do the first version of the dash based on, like, best practice, but…
141 00:15:07.760 ⇒ 00:15:10.239 Nandika Jhunjhunwala: This is, like, this is subjective, so, like.
142 00:15:10.330 ⇒ 00:15:16.670 Uttam Kumaran: what’s gonna happen is people are gonna be like, oh, like, can I add this one chart, or can you add this thing, or can you add a drill down, and…
143 00:15:17.130 ⇒ 00:15:21.040 Uttam Kumaran: I want to make it so, like, the default team, you guys are able to do that.
144 00:15:22.100 ⇒ 00:15:24.430 Nandika Jhunjhunwala: I think I’m able to do that.
145 00:15:24.430 ⇒ 00:15:27.080 Uttam Kumaran: Okay. Maybe give it a shot, too, because there’s a lot.
146 00:15:27.080 ⇒ 00:15:27.440 Nandika Jhunjhunwala: Yes.
147 00:15:27.440 ⇒ 00:15:36.800 Uttam Kumaran: in Ami, so, like, try and break it, and then be like, I broke something, because… and I think just get the hang of it.
148 00:15:37.590 ⇒ 00:15:38.340 Uttam Kumaran: Yeah.
149 00:15:38.940 ⇒ 00:15:40.130 Nandika Jhunjhunwala: No, for sure, yeah.
150 00:15:40.130 ⇒ 00:15:40.590 Uttam Kumaran: Okay.
151 00:15:40.590 ⇒ 00:15:45.709 Nandika Jhunjhunwala: Yeah, I’m gonna… I’m gonna play around with Omni more, and…
152 00:15:46.180 ⇒ 00:15:49.170 Nandika Jhunjhunwala: If I have questions, I’ll let you guys know if that sounds good.
153 00:15:49.510 ⇒ 00:15:51.040 Uttam Kumaran: Okay. Okay, great.
154 00:15:51.040 ⇒ 00:15:51.800 Nandika Jhunjhunwala: Thank you.
155 00:15:52.550 ⇒ 00:15:53.130 Uttam Kumaran: Cool.
156 00:15:53.530 ⇒ 00:16:03.249 Uttam Kumaran: So I think maybe, Caitlin, I’ll try to grab time with Laura just to go through this in detail. We compared, like, apples to apples with
157 00:16:03.250 ⇒ 00:16:14.969 Uttam Kumaran: the equals board. I still would like to… I think I… I think I bumped it to her, but we still don’t have, like, the underlying data in equals. We have just, like, the dashboard, and we don’t have the views.
158 00:16:15.000 ⇒ 00:16:24.399 Uttam Kumaran: Is there any way, like, I… I can… you can bump her? I just need… we just need to con… contact the FTE from Equals to give our account access.
159 00:16:24.770 ⇒ 00:16:25.600 Caitlyn Vaughn: Yes.
160 00:16:26.020 ⇒ 00:16:32.069 Caitlyn Vaughn: I have equals defaults… Join channel…
161 00:16:32.660 ⇒ 00:16:37.779 Caitlyn Vaughn: Is it a view, like, a specific view that you needed access to?
162 00:16:38.170 ⇒ 00:16:44.770 Uttam Kumaran: It’s the underlying views for Laura’s, like, ARR dashboard in there.
163 00:16:45.070 ⇒ 00:16:49.799 Uttam Kumaran: Let me just show you what, what that… Kinda, like, what I mean.
164 00:16:50.190 ⇒ 00:16:52.020 Uttam Kumaran: Give me a sec…
165 00:17:02.000 ⇒ 00:17:03.090 Uttam Kumaran: I’ll just show you, like, kind of…
166 00:17:03.090 ⇒ 00:17:06.389 Caitlyn Vaughn: I know what you mean. Like, I’ve seen it from Laura.
167 00:17:06.390 ⇒ 00:17:10.279 Uttam Kumaran: It’s like, it’s like, we have this, but then I can’t, like, go and, like,
168 00:17:11.250 ⇒ 00:17:15.060 Uttam Kumaran: I can’t do this, which is, like, go look at, like, all the rows that contributed.
169 00:17:15.240 ⇒ 00:17:15.930 Caitlyn Vaughn: Mmm…
170 00:17:15.930 ⇒ 00:17:16.690 Uttam Kumaran: You know?
171 00:17:16.910 ⇒ 00:17:25.500 Uttam Kumaran: for example, like, now I can go see these, and then I can add more. Yeah. Because we’re matching to the total, but for the stuff that’s off.
172 00:17:26.130 ⇒ 00:17:26.890 Caitlyn Vaughn: You don’t know why.
173 00:17:26.890 ⇒ 00:17:33.490 Uttam Kumaran: Maybe a small, like, a small definition that we just… it’s just gonna be way faster for us to QA and check that, yeah.
174 00:17:33.490 ⇒ 00:17:37.859 Caitlyn Vaughn: Okay, yeah, yeah, yeah, absolutely. I’m gonna ping them right now as well.
175 00:17:38.750 ⇒ 00:17:43.469 Uttam Kumaran: Okay, and I’m just… Trying to see if I can…
176 00:17:48.180 ⇒ 00:17:50.110 Uttam Kumaran: Yeah, let me just see…
177 00:17:54.560 ⇒ 00:17:56.750 Uttam Kumaran: It is this view.
178 00:18:05.200 ⇒ 00:18:08.129 Caitlyn Vaughn: Arr Monthly Overview, that one.
179 00:18:08.320 ⇒ 00:18:12.789 Uttam Kumaran: Yeah… yeah, so it’s, here, let’s see, I have it up.
180 00:18:18.400 ⇒ 00:18:19.970 Uttam Kumaran: God, what is it called?
181 00:18:23.320 ⇒ 00:18:24.640 Uttam Kumaran: Yeah, this one.
182 00:18:26.290 ⇒ 00:18:28.730 Uttam Kumaran: So this is kind of, like, what we…
183 00:18:28.860 ⇒ 00:18:33.650 Uttam Kumaran: What we’re looking to replicate, but actually being able to, like,
184 00:18:34.220 ⇒ 00:18:37.229 Uttam Kumaran: We just don’t have the views that contribute to this.
185 00:18:37.680 ⇒ 00:18:39.489 Caitlyn Vaughn: Okay, wait, can you scroll up to the top?
186 00:18:39.490 ⇒ 00:18:40.170 Uttam Kumaran: Yes.
187 00:18:41.110 ⇒ 00:18:43.400 Caitlyn Vaughn: Okay, I’m just gonna screen grab it and send it.
188 00:18:43.730 ⇒ 00:18:44.290 Uttam Kumaran: Okay.
189 00:18:45.340 ⇒ 00:18:48.990 Caitlyn Vaughn: Okay, cool. And then the email that you want to access is…
190 00:18:49.360 ⇒ 00:18:50.630 Uttam Kumaran: Brainforge at default.com.
191 00:18:50.630 ⇒ 00:18:52.420 Caitlyn Vaughn: forage, right? Thank you.
192 00:18:53.680 ⇒ 00:18:58.740 Caitlyn Vaughn: storage at default.com… Okay, amazing.
193 00:18:59.720 ⇒ 00:19:02.140 Caitlyn Vaughn: We will get that done for you.
194 00:19:02.840 ⇒ 00:19:03.410 Uttam Kumaran: Great.
195 00:19:08.630 ⇒ 00:19:09.380 Uttam Kumaran: Okay.
196 00:19:10.110 ⇒ 00:19:10.780 Greg Stoutenburg: What?
197 00:19:11.270 ⇒ 00:19:14.299 Uttam Kumaran: Yeah, I think, Craig, is the next slide about, like, governance?
198 00:19:15.030 ⇒ 00:19:16.100 Greg Stoutenburg: Product analytics.
199 00:19:16.410 ⇒ 00:19:17.550 Uttam Kumaran: Oh, okay, sorry, go ahead.
200 00:19:18.030 ⇒ 00:19:19.480 Greg Stoutenburg: Yep.
201 00:19:19.500 ⇒ 00:19:34.579 Greg Stoutenburg: this is, you know, we’ve been chatting about this on the channel, but, well, you know, call it a win. Got signed off on the initial version of the tracking plan, so we’re closer to making sure that we have everything tracked that we want to have tracked to show what users are up to, as we get closer to Phoenix rolling out.
202 00:19:34.580 ⇒ 00:19:59.419 Greg Stoutenburg: and have started, building feature engagement charts. So, again, as mentioned before, starting with the tables feature. And, especially while I’m away, I won’t be able to build anything new in post-tog for the rest of this week, but, Nadica, if you want to go in and just, like, simulate really being a user who’s trying stuff out with tables, you know, to whatever degree is possible with what’s in staging so far, then we can make sure that, the chart
203 00:19:59.420 ⇒ 00:20:11.230 Greg Stoutenburg: reflects what that usage pattern looks like with the events that you’ve set up already. And then, if anything’s missing, we’ll add it, but, you know, we can get a little bit closer to, like, a realistic picture now that that first part’s in place.
204 00:20:13.370 ⇒ 00:20:15.069 Greg Stoutenburg: Cool. Alright.
205 00:20:18.030 ⇒ 00:20:23.600 Greg Stoutenburg: metric definitions and schema. Utam, were you going to speak to this? Is that what you’re asking about this one? Okay.
206 00:20:23.600 ⇒ 00:20:40.940 Uttam Kumaran: Yeah, so we also work… like, we have all the core dbt models for the next set of dashboards, so now we’re just working on, customizing the topic and then creating that, and I think you guys saw we’re basically at the finish line for this ARR dashboard.
207 00:20:41.740 ⇒ 00:20:50.569 Uttam Kumaran: So, I’m feeling pretty good. I think, like, the AR dashboard is something that’s gonna flow into everything, so I want to make sure that that’s really, like, nailed down.
208 00:20:50.910 ⇒ 00:20:59.590 Uttam Kumaran: And it seemed like from our last call, Laura was, like, aware of, like, yeah, this is… this is, like, people are gonna use this. People are gonna use Salesforce, and, like, we’re good with that, so…
209 00:20:59.850 ⇒ 00:21:05.739 Uttam Kumaran: That was great. So yeah, I feel pretty good. We haven’t had to talk about
210 00:21:06.640 ⇒ 00:21:13.810 Uttam Kumaran: data warehouse in a while, which is great, and I think, like, still, I think what we’re gonna do is just drive with whatever flat files we have.
211 00:21:13.810 ⇒ 00:21:14.190 Caitlyn Vaughn: moose.
212 00:21:14.190 ⇒ 00:21:18.690 Uttam Kumaran: The dashboards will continue to push the need for those pipelines, so…
213 00:21:18.690 ⇒ 00:21:19.010 Caitlyn Vaughn: Yeah.
214 00:21:19.010 ⇒ 00:21:23.990 Uttam Kumaran: I feel good about that. I guess, Caitlin, if there’s… you just let me know if there’s anything that changes on, like.
215 00:21:24.330 ⇒ 00:21:28.429 Uttam Kumaran: reverse ETL need, or if there’s any other data info stuff that,
216 00:21:29.160 ⇒ 00:21:31.259 Uttam Kumaran: Comes up, just let me know.
217 00:21:31.300 ⇒ 00:21:32.380 Caitlyn Vaughn: Okay.
218 00:21:32.490 ⇒ 00:21:50.960 Uttam Kumaran: The other piece, is, like, and I think Greg, like, we can start this when you’re back next week, is, being able to use the AI features in Omni. In particular, you can actually query Omni from Slack, and I feel like a lot of people in default are gonna probably use that, and so I think maybe, Greg, next week.
219 00:21:51.170 ⇒ 00:21:54.120 Uttam Kumaran: I just want to make sure that everybody here is, like.
220 00:21:54.320 ⇒ 00:21:57.039 Uttam Kumaran: a super user of, like, the blobby features.
221 00:21:57.260 ⇒ 00:22:03.069 Uttam Kumaran: And then… that way, like, I have a feeling, like, a lot of people there are gonna start to use that to ask questions.
222 00:22:03.890 ⇒ 00:22:04.440 Greg Stoutenburg: Yeah.
223 00:22:04.440 ⇒ 00:22:07.689 Nandika Jhunjhunwala: I can try and set up Omni in Slack, if it’s…
224 00:22:07.690 ⇒ 00:22:13.320 Uttam Kumaran: Oh, yeah. Yeah, I would say set it up, set it up in, like a R group or something.
225 00:22:13.860 ⇒ 00:22:15.620 Nandika Jhunjhunwala: Let’s test it out.
226 00:22:15.620 ⇒ 00:22:18.069 Uttam Kumaran: And make sure the answers are right.
227 00:22:19.000 ⇒ 00:22:22.980 Uttam Kumaran: They should be right, but you never know what the AI’s gonna do unless we have, like.
228 00:22:22.980 ⇒ 00:22:23.660 Nandika Jhunjhunwala: Definitely.
229 00:22:23.660 ⇒ 00:22:24.280 Uttam Kumaran: Yeah.
230 00:22:24.490 ⇒ 00:22:29.909 Uttam Kumaran: Until we teach it, yeah. Teach it and build that sandbox. Nandika, have you used Blobby at all?
231 00:22:29.960 ⇒ 00:22:30.749 Greg Stoutenburg: You know what I mean?
232 00:22:30.750 ⇒ 00:22:46.079 Nandika Jhunjhunwala: I have, yeah. I, like, my experience wasn’t the greatest, but I think I’m getting used to how to interact with Bobby. I think you have to be really specific with, like, what data you’re referencing and, like, what you exactly wanted to do, so, like, I found that you need clarity
233 00:22:46.250 ⇒ 00:22:50.239 Nandika Jhunjhunwala: When querying it, for it to, like, give you what you need.
234 00:22:50.240 ⇒ 00:23:13.729 Greg Stoutenburg: Okay, alright, alright, that’s good, yeah. We’ll, yeah, good, good insight, good feedback. Yeah, we want to make sure that everyone who would want to use Omni can just get in there and type a question the way that they would want to type the question, and have it make sense, provided that they’re, you know, reasonably clear. It shouldn’t feel like, you know, great, hey, great news, you don’t have to know how to use SQL anymore, but you have to know how to write like you’re using SQL. Like, that would not be that much of an improvement.
235 00:23:14.100 ⇒ 00:23:19.269 Nandika Jhunjhunwala: Yeah, that’s what I found so far, but maybe I didn’t test it enough. Yeah, no.
236 00:23:19.270 ⇒ 00:23:26.839 Greg Stoutenburg: Yeah, okay. Okay, yeah, yeah, no, that’s good, and we’ll, we’ll see what we can do to, make that experience a little better.
237 00:23:27.850 ⇒ 00:23:28.720 Greg Stoutenburg: Alright.
238 00:23:30.780 ⇒ 00:23:50.499 Greg Stoutenburg: Okay, data governance, so we’ve got access control in Omni in progress. What we want to do is to implement row or dashboard-level access so that only the right people can see the financial summary dashboard. There’s, Omni’s got pretty sophisticated, actually, RBAC features.
239 00:23:50.500 ⇒ 00:23:53.829 Greg Stoutenburg: That, that we’re able to make use of that will…
240 00:23:53.830 ⇒ 00:24:11.250 Greg Stoutenburg: enable this. So, something that we’ll be doing that’s coming down the pike is to confirm exactly who should have access to what, so you can supply us with that, and then we’ll set it up for you, and then confirm that it looks right before, you know, exposing anything that’s sensitive to someone who shouldn’t see it.
241 00:24:13.680 ⇒ 00:24:23.450 Greg Stoutenburg: Things are in progress, this has been mentioned already, the customer reporting and enablement, gonna align on QA, and want to have that dashboard finished by Friday.
242 00:24:24.230 ⇒ 00:24:32.369 Greg Stoutenburg: We’ve got a path forward on the reverse ETL. It’s paused until after the core project, and we’ll be coming back to that.
243 00:24:33.720 ⇒ 00:24:45.040 Greg Stoutenburg: And, and finally, LinkedIn Attribution Dashboard. It’s coming, need that polyatomic work on factors done, and, the AI connector should be done soon.
244 00:24:45.630 ⇒ 00:24:48.689 Caitlyn Vaughn: Okay, great. And that’s on the polyatomic side, right?
245 00:24:49.220 ⇒ 00:24:49.920 Uttam Kumaran: Yes.
246 00:24:49.920 ⇒ 00:24:54.360 Caitlyn Vaughn: Okay. Cool. Also, Deanna is back starting next week.
247 00:24:54.930 ⇒ 00:24:55.470 Greg Stoutenburg: Okay.
248 00:24:55.470 ⇒ 00:24:56.040 Uttam Kumaran: Okay.
249 00:24:56.330 ⇒ 00:24:57.000 Caitlyn Vaughn: Great to know.
250 00:24:57.000 ⇒ 00:25:01.299 Uttam Kumaran: So maybe I can schedule… I can schedule a chat with her, and just catch her up on where we’re at.
251 00:25:01.300 ⇒ 00:25:01.700 Caitlyn Vaughn: Yeah.
252 00:25:01.700 ⇒ 00:25:03.140 Greg Stoutenburg: Yeah, great. Perfect.
253 00:25:04.400 ⇒ 00:25:07.810 Greg Stoutenburg: Perfect. Okay. Need e-learning?
254 00:25:07.810 ⇒ 00:25:08.450 Uttam Kumaran: Got it.
255 00:25:08.450 ⇒ 00:25:18.519 Greg Stoutenburg: But yeah, that’s… that’s one we wanted to raise again. And then, ta-da! Thank you, okay. That’s it from our side.
256 00:25:18.520 ⇒ 00:25:35.720 Uttam Kumaran: I had a question, too, about… Greg, I was reading a lot about Posthog, like, and their AI features. I’m wondering, like, what we’re using right now, to both, like, edit posthog things and, like, create dashboards? Or, like, are we leveraging any of the AI features in Post Hog? I was just reading about something this morning.
257 00:25:35.720 ⇒ 00:25:48.830 Greg Stoutenburg: Yeah. We haven’t been yet, and most of the reason why is just because we’re still setting up some initial things, so, using AI to try to, like, write queries wouldn’t be very valuable at this stage. But, yeah, I mean.
258 00:25:48.830 ⇒ 00:26:11.629 Greg Stoutenburg: all the big analytics players right now, the big product analytics players, are coming out with pretty sweet AI tools, and so we will be making use of those connectors. I have been reading about some of them, and I’m like, this is pretty exciting. I would… I would love to see something like, hey, take my tracking plan, and I’ve just labeled which things are, like, features, or which things are funnels, right? And just go, you know, here, go build it.
259 00:26:11.630 ⇒ 00:26:17.060 Greg Stoutenburg: And I’ve seen versions of things like that in pretty organized environments.
260 00:26:17.080 ⇒ 00:26:36.139 Greg Stoutenburg: There’s… I’ve seen a connector do that kind of thing. I don’t know if this should have been shared in the Amplitude cohort Slack, but a former colleague at Stack Overflow, where they’ve got, like, they’ve got a very organized, event set up there, was able to use their MCP to just, like, create an activation dashboard using natural.
261 00:26:36.140 ⇒ 00:26:36.860 Uttam Kumaran: Nice.
262 00:26:37.530 ⇒ 00:26:48.440 Greg Stoutenburg: yeah, straight and clawed, and then it just… appeared. It wasn’t perfect, but it was, like, pretty darn good, you know? It would have taken half a day, or three-quarters of a day, or something like that, so…
263 00:26:49.150 ⇒ 00:26:54.219 Greg Stoutenburg: Good idea. Yeah. Anyway, we’ll do things like that with Post Hog.
264 00:26:54.220 ⇒ 00:26:58.970 Uttam Kumaran: Yeah, I’m wondering if we can also, like, next week, see some of, like, the…
265 00:26:59.270 ⇒ 00:27:04.450 Uttam Kumaran: Demo, like, walkthroughs of, like, what funnels look like currently with our event tracking setup.
266 00:27:04.500 ⇒ 00:27:20.639 Uttam Kumaran: Like, I think that was sort of probably what you mentioned before, is, like, walkthrough as a user. I think it’d be helpful for this whole group to see, finally, like, what are some of the dashboards that we’ll get out. Because for me, I think it’s similar to, like, displaying these dashboards. I think looking at that.
267 00:27:20.710 ⇒ 00:27:25.459 Uttam Kumaran: Every week, will start to be really, really helpful.
268 00:27:25.660 ⇒ 00:27:26.260 Uttam Kumaran: Yeah.
269 00:27:26.260 ⇒ 00:27:26.710 Greg Stoutenburg: Yeah.
270 00:27:26.710 ⇒ 00:27:37.739 Uttam Kumaran: You know, and I think, like, I want to spend more time, like, discussing what else we want to see there. Like, that’s why, hopefully, I think, once these first dashboards are through, Caitlin, part of this meeting, I want it to be more, like.
271 00:27:38.260 ⇒ 00:27:43.060 Uttam Kumaran: kind of like when we… me, you, and Amber were meeting about, like, we saw this in the data, like.
272 00:27:43.310 ⇒ 00:27:49.679 Uttam Kumaran: what do we think, or who can we flag this to for a question? Like, more discussion about metrics and definitions.
273 00:27:50.240 ⇒ 00:27:54.409 Uttam Kumaran: because I think we’re close to having, like, the baseline ready for everything.
274 00:27:54.940 ⇒ 00:27:55.950 Caitlyn Vaughn: Oh, really?
275 00:27:55.950 ⇒ 00:28:04.360 Uttam Kumaran: Yeah, meaning, like, I want… I want part of this meeting to be like, hey, we saw… we… we visualized the data, and we saw this piece, like.
276 00:28:04.930 ⇒ 00:28:16.690 Uttam Kumaran: who can we flag this to, or there’s someone in the company that would be interested in this, right? And, like, sometimes if you have, like, a narrative around, like, a metric has changed drastically, or something is on the decline, like, it’s a good way to loop that person into, like.
277 00:28:16.840 ⇒ 00:28:20.860 Uttam Kumaran: owning a dashboard or starting to use it to be like, oh yeah, I never saw it this way.
278 00:28:21.170 ⇒ 00:28:25.390 Uttam Kumaran: Like, if part of this meeting can be like, oh yeah, that’s this person, like, they would love to see this.
279 00:28:25.390 ⇒ 00:28:25.770 Caitlyn Vaughn: Yeah.
280 00:28:25.770 ⇒ 00:28:28.209 Uttam Kumaran: then I think it’ll help, like, adoption.
281 00:28:28.810 ⇒ 00:28:30.010 Uttam Kumaran: You know, a lot more.
282 00:28:30.010 ⇒ 00:28:35.170 Caitlyn Vaughn: Yeah, that would be great. You guys created all of the dashboards. Nantika, did we end up sharing.
283 00:28:35.170 ⇒ 00:28:41.039 Uttam Kumaran: Not all of them. Just… we’re gonna have the other customer one on this Friday.
284 00:28:41.300 ⇒ 00:28:42.980 Caitlyn Vaughn: There’s gonna be password ones?
285 00:28:43.530 ⇒ 00:28:50.619 Uttam Kumaran: We’re gonna have one as missing, like… I feel like the requirements for this dashboard is slightly different than the other one.
286 00:28:50.640 ⇒ 00:28:51.720 Caitlyn Vaughn: That exists.
287 00:28:52.000 ⇒ 00:28:53.449 Caitlyn Vaughn: Okay, so…
288 00:28:53.710 ⇒ 00:28:54.290 Uttam Kumaran: Yeah.
289 00:28:54.650 ⇒ 00:28:55.500 Caitlyn Vaughn: Okay.
290 00:28:56.700 ⇒ 00:29:01.579 Caitlyn Vaughn: Let me look at our list… our Instagant.
291 00:29:04.780 ⇒ 00:29:10.660 Caitlyn Vaughn: Okay, cool. Yeah, that sounds good. I think… Obviously, like, looping people in.
292 00:29:10.940 ⇒ 00:29:16.900 Caitlyn Vaughn: Especially on their own dashboards, will probably be a great place for people to start caring about this.
293 00:29:16.920 ⇒ 00:29:17.780 Uttam Kumaran: Yeah.
294 00:29:17.780 ⇒ 00:29:24.579 Caitlyn Vaughn: And we’re obviously gonna have to do, like, a big culture shift, company-wise, to be more, like, data-centric, so…
295 00:29:25.050 ⇒ 00:29:27.950 Uttam Kumaran: Yeah, so the first 3 dashboards that you see on the list.
296 00:29:28.130 ⇒ 00:29:35.330 Uttam Kumaran: The revenues, the customer reporting, and the productivity is, like, really the core.
297 00:29:37.060 ⇒ 00:29:42.080 Caitlyn Vaughn: It’s like, what are people… what are people doing? How are, like, what are we earning?
298 00:29:42.500 ⇒ 00:29:47.600 Uttam Kumaran: You know, and then, like, how are we selling, right? So it’s, like, the demand, this actual service side, and, like, the…
299 00:29:47.700 ⇒ 00:29:49.699 Uttam Kumaran: The sort of collection side, so…
300 00:29:50.060 ⇒ 00:29:59.230 Uttam Kumaran: That’s the three. We actually have a lot of the data modeling work ready to go, that’s why we really only have, like, a week just to make sure we have everything next week.
301 00:29:59.580 ⇒ 00:30:04.540 Uttam Kumaran: And so I actually think there’s a good chance we, like, knock out the dashboard next week as well.
302 00:30:04.620 ⇒ 00:30:05.860 Caitlyn Vaughn: They were cool.
303 00:30:06.060 ⇒ 00:30:06.630 Uttam Kumaran: Yeah.
304 00:30:07.570 ⇒ 00:30:13.990 Caitlyn Vaughn: Okay, yeah, this looks good. I see, so the customer product activity, and then the customer reporting and enablement.
305 00:30:13.990 ⇒ 00:30:23.090 Uttam Kumaran: Yes. So that’s why it’s a little bit different. It’s both, like, what are people doing? So, like, how many people are getting added to teams, like, what are some of the events we have as part of the…
306 00:30:23.370 ⇒ 00:30:25.350 Uttam Kumaran: Product… product data.
307 00:30:25.890 ⇒ 00:30:29.560 Uttam Kumaran: And then longer term, I think if there are things we need from
308 00:30:30.010 ⇒ 00:30:45.639 Uttam Kumaran: postog in there, we’ll bring those in. Yeah. So I think, yeah, that’ll be… so that’s… I want to start looking at both the dashboards, or as many each week, so that we can start to think about, like, okay, are people… one, are, like, people starting to use this stuff? Like, what’s the gap if they aren’t?
309 00:30:45.730 ⇒ 00:30:50.760 Uttam Kumaran: And making sure, like, okay, if the gap is just, like, people need trainings or whatever, we’re like.
310 00:30:50.980 ⇒ 00:30:52.710 Uttam Kumaran: Really much more focused on that.
311 00:30:52.940 ⇒ 00:30:57.180 Caitlyn Vaughn: Okay, cool. I feel like we’re, like, getting back on track, so this is really exciting.
312 00:30:57.180 ⇒ 00:30:57.710 Uttam Kumaran: Yeah.
313 00:30:57.710 ⇒ 00:30:58.910 Caitlyn Vaughn: Appreciate your push.
314 00:30:59.220 ⇒ 00:30:59.760 Uttam Kumaran: Yeah, yeah, of course.
315 00:30:59.760 ⇒ 00:31:01.550 Nandika Jhunjhunwala: Thank you so much. Cool. This was great.
316 00:31:01.550 ⇒ 00:31:07.130 Uttam Kumaran: Yeah, so I think, give it a shot. I think love to… give Omni a shot if you want to try playing around.
317 00:31:07.290 ⇒ 00:31:11.489 Uttam Kumaran: And then, yeah, if you’re, like, I’m stuck or whatever, just, like, it’s really powerful.
318 00:31:11.740 ⇒ 00:31:18.239 Uttam Kumaran: Especially the AI pieces, so if you have feedback, just, like, try to play around in there and answer some questions, and then let us know.
319 00:31:20.620 ⇒ 00:31:28.039 Greg Stoutenburg: Yeah, yeah, and honestly, like, just for the purposes of us improving it, if you want to do things like… like Nautica, you know, you ran into some kind of…
320 00:31:28.040 ⇒ 00:31:40.739 Greg Stoutenburg: issue where Blobby wanted you to speak up like you’re writing SQL. If you want to record that, and just show, like, here’s the question I ask, and here’s this, you know, dumb response I got from Blobby, like, that… that is something that can be fixed. You know, it should be…
321 00:31:40.760 ⇒ 00:31:45.730 Greg Stoutenburg: It should be impressive, so, that’s what we wanna… that’s what we’re aspiring to here.
322 00:31:46.480 ⇒ 00:31:50.480 Caitlyn Vaughn: Cool. And then, Utam, will you just add me? Are you doing another meeting with Laura?
323 00:31:50.730 ⇒ 00:31:53.489 Uttam Kumaran: I am going to…
324 00:31:53.660 ⇒ 00:31:58.740 Uttam Kumaran: Yeah, I would prefer to get equals so that I don’t, like, use our time to, like, do as much QA.
325 00:31:58.740 ⇒ 00:32:00.679 Caitlyn Vaughn: Okay, we’ll do that first.
326 00:32:00.770 ⇒ 00:32:06.130 Uttam Kumaran: Regardless, I will try to meet with her, but if I can get that, then we can go further on our side.
327 00:32:06.130 ⇒ 00:32:06.970 Caitlyn Vaughn: Okay.
328 00:32:07.220 ⇒ 00:32:07.960 Uttam Kumaran: Yeah.
329 00:32:08.160 ⇒ 00:32:09.550 Caitlyn Vaughn: Yeah, yeah, that sounds great.
330 00:32:09.550 ⇒ 00:32:18.260 Uttam Kumaran: If she’s okay with Slack, too, I just have some questions about, like, the visualizations, but, like, it’ll be 30 minutes, so if I can get the equals thing…
331 00:32:18.890 ⇒ 00:32:22.050 Uttam Kumaran: Then we can wrap everything up, and that meeting will be productive.
332 00:32:22.420 ⇒ 00:32:33.350 Caitlyn Vaughn: Okay, cool. I pinged the Equals team to get you guys access. I pinged Victor about the product data. I asked Sid… oh yeah, I asked Sid about.
333 00:32:34.490 ⇒ 00:32:35.420 Uttam Kumaran: Segmentation.
334 00:32:36.220 ⇒ 00:32:44.170 Caitlyn Vaughn: the, yeah, two things, the, customer definition, and then, like, the tiering, and then if we have restarted ARR,
335 00:32:44.650 ⇒ 00:32:54.509 Caitlyn Vaughn: We technically… he says we technically have one, but it’s kind of blurry, so… it should be, like, 4K total ever. I mean, zero is also fine, honestly.
336 00:32:54.880 ⇒ 00:32:55.600 Uttam Kumaran: Okay.
337 00:32:55.600 ⇒ 00:32:57.440 Caitlyn Vaughn: But I don’t know where we’re getting 12 from.
338 00:32:58.060 ⇒ 00:33:02.319 Uttam Kumaran: If you prefer, we do it. The thing is, if we do it, then people will start referring to it, so…
339 00:33:02.860 ⇒ 00:33:03.860 Uttam Kumaran: It’s like…
340 00:33:05.110 ⇒ 00:33:08.239 Caitlyn Vaughn: Like, you mean to, like, remove it completely, the restarted ARR?
341 00:33:08.440 ⇒ 00:33:19.409 Uttam Kumaran: Well, I guess on both… yeah, like, on two things. One is, like, on the customer segmentation. Yeah. Like, small, medium, large, or small, medium, enterprise.
342 00:33:19.410 ⇒ 00:33:19.870 Caitlyn Vaughn: Yeah.
343 00:33:19.870 ⇒ 00:33:21.900 Uttam Kumaran: Like, if there isn’t a definition.
344 00:33:22.160 ⇒ 00:33:24.910 Uttam Kumaran: Then, and we do one, people will start using, because it’ll just…
345 00:33:24.910 ⇒ 00:33:36.989 Caitlyn Vaughn: Oh, yeah, yeah, yeah. I mean, wait on that. Okay, okay, okay. Sid’s putting something together right now. Okay, okay. Just, like, working on that actively, so in the next couple of days to, like, a week, I would say we’ll have something, but that would be really interesting for the company.
346 00:33:36.990 ⇒ 00:33:44.459 Uttam Kumaran: He should try… he should work with us to, like, do it, and then we can… he can easily visualize, like, changing the parameters, you know?
347 00:33:44.460 ⇒ 00:33:45.539 Caitlyn Vaughn: Yeah, that’s true.
348 00:33:45.540 ⇒ 00:33:46.100 Uttam Kumaran: Yeah.
349 00:33:47.460 ⇒ 00:33:50.890 Caitlyn Vaughn: It’s a demand of many opinions and feelings.
350 00:33:50.890 ⇒ 00:33:55.720 Uttam Kumaran: Cool. And then, yeah, send me any of the restarted ones, or, like, if he has that list.
351 00:33:56.200 ⇒ 00:33:57.460 Caitlyn Vaughn: Yeah, it’s just one.
352 00:33:57.620 ⇒ 00:33:58.270 Uttam Kumaran: Okay.
353 00:33:58.270 ⇒ 00:34:00.079 Caitlyn Vaughn: I’m gonna just… I’ll ping it to you.
354 00:34:00.080 ⇒ 00:34:00.840 Uttam Kumaran: Okay.
355 00:34:00.840 ⇒ 00:34:04.260 Caitlyn Vaughn: anti-metal, I think is what it’s called? Anti-metal?
356 00:34:04.870 ⇒ 00:34:06.130 Uttam Kumaran: Oh, right, okay, cool.
357 00:34:06.130 ⇒ 00:34:08.140 Caitlyn Vaughn: like that. Yeah, that’s it.
358 00:34:08.920 ⇒ 00:34:10.189 Greg Stoutenburg: It’d be a sick band name.
359 00:34:10.820 ⇒ 00:34:11.630 Caitlyn Vaughn: Yeah, it would.
360 00:34:12.770 ⇒ 00:34:16.729 Uttam Kumaran: They have cool branding, this company. Yeah, do they? I’ve heard about them, yeah.
361 00:34:16.730 ⇒ 00:34:17.670 Caitlyn Vaughn: Oh, man.
362 00:34:17.989 ⇒ 00:34:25.710 Caitlyn Vaughn: That’s so funny. Okay, cool. Well, all of this sounds good. Any other questions from the team, Lev, Nautica?
363 00:34:27.020 ⇒ 00:34:28.860 Nandika Jhunjhunwala: That sounds great. Thank you so much.
364 00:34:29.330 ⇒ 00:34:30.120 Caitlyn Vaughn: Coolie.
365 00:34:30.900 ⇒ 00:34:32.569 Greg Stoutenburg: Alright, let’s keep it rolling.
366 00:34:32.920 ⇒ 00:34:34.400 Caitlyn Vaughn: Thanks, guys, we’ll see you soon.
367 00:34:35.139 ⇒ 00:34:35.829 Uttam Kumaran: Like I said.
368 00:34:35.830 ⇒ 00:34:36.600 Caitlyn Vaughn: Bye.