Meeting Title: Uttam Kumaran Date: 2025-02-26 Meeting participants: Uttam Kumaran, Bo Yoon, Robert Tseng, Caio Velasco


WEBVTT

1 00:00:36.510 00:00:37.950 Bo Yoon: Hey! Good morning!

2 00:00:37.950 00:00:38.770 Uttam Kumaran: Hey!

3 00:00:40.990 00:00:41.800 Caio Velasco: Mornings.

4 00:00:47.030 00:00:52.847 Uttam Kumaran: Cool. Okay, so let’s yeah, let’s talk Javi stuff or Eden stuff, or what do we?

5 00:00:53.610 00:00:56.819 Uttam Kumaran: I think this is, yeah, we can talk Javi stuff here.

6 00:00:59.200 00:01:02.431 Uttam Kumaran: Robert, I don’t know. Let me. I guess we have our meeting with

7 00:01:03.790 00:01:06.399 Uttam Kumaran: with them today, but let me know how we like to take this.

8 00:01:08.253 00:01:11.990 Robert Tseng: Yeah, I guess maybe what I wanna hear is just

9 00:01:12.894 00:01:21.386 Robert Tseng: I mean, I guess I can show where I’m where I’m at with the road mapping.

10 00:01:24.970 00:01:26.559 Robert Tseng: okay, I’ll share my screen.

11 00:01:32.340 00:01:33.480 Robert Tseng: So

12 00:01:48.170 00:02:15.280 Robert Tseng: okay, so a few things that I wanted to just kind of review here. So this is the internal joby page. I’ve kind of updated here all the objectives assigned kind of estimated hours, and kind of refresh like what’s in progress. What’s done all these things are kind of the tasks that haven’t been necessarily rolled up, which is fine. I don’t not gonna spend too much time on that I guess what I’d like to get out of this call is just to get a progress check on where we’re at with all these in flight things.

13 00:02:15.748 00:02:36.129 Robert Tseng: I’m kind of filling out some more of these ideas that I wanted to put in front of them on and I think that’s yeah trying to get some. Get a sign off on that. I’m approaching it a bit differently before. These are just kind of like objectives that are kind of, or tasks that are just kind of assigned to different

14 00:02:36.720 00:02:58.330 Robert Tseng: just ad hoc, or one off projects. But I’m trying to think more holistically about the function that we want to go and support. So I’ve kind of actually come up with like a list of like marketing analytics. Okrs, I’m gonna put that in front of him. And I and I know that Aman’s not the main person to make that decision. But that’s how I want to kind of present it. So I think, rather than like

15 00:02:58.560 00:03:00.729 Robert Tseng: asking for his like

16 00:03:01.340 00:03:20.430 Robert Tseng: him to prioritize things. I’m just trying to make a bigger push into. We want to be headed towards working with this function. And this is how the ways we could think we can support them. And yeah, I I’m gonna keep pressing him on the call to make that connection, to let us go go kind of head like

17 00:03:20.610 00:03:33.800 Robert Tseng: head in a specific direction on how to work with the marketing function. I think this is all good stuff to kind of show how we’ve gotten organized over the past couple of weeks. The way I’ll talk about this is like.

18 00:03:34.140 00:03:40.469 Robert Tseng: yeah, I mean, this level of like product categorization. They don’t have right now. Amplitude. So this is kind of like.

19 00:03:40.640 00:03:43.679 Robert Tseng: you know, we have an active effort to

20 00:03:44.900 00:03:50.599 Robert Tseng: just like have more have a more standardized like set of

21 00:03:52.540 00:04:17.420 Robert Tseng: dimensions, I guess. For how we’re going to be able to report on data moving forward right? Because right now, they’re still very much just doing product type like protein, coffee latte, or like, offer. Name this a list of lps. So I think this is us kind of demonstrating, like, okay, we’re ready to push beyond that. And like this is, you know, we want to break things out further. Yep.

22 00:04:17.420 00:04:34.539 Uttam Kumaran: On this, Robert, can you go to product type from shopify the second tab? So this is what I am actually bringing in right now for like short term. But there’s a couple of things one and this is basically anything in shopify that isn’t tagged with a product type product type is a tag that they are

23 00:04:34.710 00:04:51.490 Uttam Kumaran: manually or somehow doing. But it’s sort of like the highest product segmentation category. If you go back to product categorization, there are a couple of product like properties here that that we need to start to break out flavor like

24 00:04:51.610 00:04:59.409 Uttam Kumaran: other subcategories, basically like, for each of these products like these, almost like this isn’t

25 00:04:59.650 00:05:03.949 Uttam Kumaran: these aren’t products like I don’t. I don’t know what to call them. But they’re almost like.

26 00:05:04.896 00:05:10.056 Uttam Kumaran: Yeah, they’re just like bundles, or like sort of custom, customized products. Right?

27 00:05:11.150 00:05:16.269 Uttam Kumaran: so like, what is the difference between something that’s caramel versus something that’s vanilla, right? So there is a difference.

28 00:05:16.701 00:05:25.370 Uttam Kumaran: differences, flavor, then we need a flavor dimension, right? So it may even help to add with one or 2 of those here which is like flavor

29 00:05:27.530 00:05:35.799 Uttam Kumaran: like cause cause some of these are bundles. Some of these are offer like, Buy one, get one free is not a product. You get a product. But it’s part of a discount.

30 00:05:36.150 00:05:41.969 Uttam Kumaran: So this is something that like, I feel like they’re they’re definitely losing a lot of context on

31 00:05:43.940 00:05:51.490 Uttam Kumaran: the second tab. I did just to get to to recategorize all this stuff that’s other yesterday with them on. But like I sort of teed this up, which is like

32 00:05:52.270 00:05:57.690 Uttam Kumaran: we’re missing. You’re missing a ton of dimensionality. And you’re comparing stuff that aren’t really products.

33 00:05:59.270 00:06:13.890 Uttam Kumaran: And and also we do know that there is a difference, because the cogs are different. So if and this I’m I may move, I’m I’m eventually I’m gonna move all the cogs to this thing, too. But we’re able to see that like white chocolate is different than French vanilla for the same product.

34 00:06:14.010 00:06:21.030 Uttam Kumaran: So there is a cogs difference. It’s a couple of cents. But, like, you know, for the whole thing, I don’t know. It could be a percent. Some percentage.

35 00:06:21.230 00:06:22.310 Robert Tseng: Yeah.

36 00:06:22.820 00:06:36.749 Uttam Kumaran: Right. And so I think I’ll just send one more link to you in zoom, which is like this could also be valuable just to have up, or you can even just copy this into somewhere into that sheet. Because I would. I think this is also like a huge

37 00:06:38.830 00:06:43.739 Uttam Kumaran: like open area for them is to basically start to do what we did for Eden, which is.

38 00:06:43.740 00:06:44.070 Robert Tseng: Yep.

39 00:06:44.432 00:06:49.180 Uttam Kumaran: Look at product level profitability. Sort of get this sorted out.

40 00:06:50.210 00:06:56.279 Robert Tseng: Yeah, I think that cog sheet that we also have that pie is kind of built out. I don’t know.

41 00:06:56.490 00:06:58.940 Robert Tseng: Well, yeah, I mean, I

42 00:07:00.640 00:07:05.623 Robert Tseng: I feel like there’s more here than there is there? So I mean, I don’t know, I guess.

43 00:07:06.290 00:07:12.649 Robert Tseng: yeah, we’ll have to figure out how to unify those later on. But yeah, ideally, we just have like a master product like

44 00:07:13.110 00:07:20.780 Robert Tseng: cogs sheet, right? That’s kind of like what the other one was supposed to be. But we we’re broke. We have it broken out at this level of of granularity.

45 00:07:24.010 00:07:24.800 Robert Tseng: Yeah.

46 00:07:27.450 00:07:42.889 Robert Tseng: okay, cool. So I mean other than that. And then there’s also kind of this, like customer level dimensionality that I want to resurvice to them. I know you don’t have a tab on this here, but I I have it a notion. So I might just like open a tab and paste something in there. But

47 00:07:43.360 00:07:47.870 Robert Tseng: yeah, I think that’s that’s another thing that I can walk them through today.

48 00:07:48.220 00:07:52.819 Robert Tseng: Okay, yeah. I don’t know. Why don’t. Why don’t we just like kind of talk about like work that’s in flight? And then.

49 00:07:52.820 00:07:54.404 Uttam Kumaran: Yeah. So I think.

50 00:07:56.501 00:08:13.719 Uttam Kumaran: let’s kick this migrate Meta base cause dude like I don’t want to talk about that with him. The migrate Meta base from analytics. dB, that one and the bottom one. I think these are distraction tickets. Like, let’s just let’s just crush these, because otherwise he’s gonna ask about these like I have to get this done. But

51 00:08:13.850 00:08:17.389 Uttam Kumaran: it’s gonna require, like us, to migrate some dashboards.

52 00:08:17.610 00:08:21.659 Uttam Kumaran: We’ve been talking about this for 3 weeks. I just don’t wanna like I don’t know. I would suggest we just

53 00:08:22.540 00:08:25.170 Uttam Kumaran: either delete these or kick these to Don.

54 00:08:26.060 00:08:29.660 Robert Tseng: Okay, I’m just gonna I mean, he won’t know. So whatever just.

55 00:08:29.660 00:08:30.960 Uttam Kumaran: That’s I just like

56 00:08:31.370 00:08:41.450 Uttam Kumaran: I want us to stop talking about anything related to portable anything related to metabase fucking up like any I don’t like we’ve been talk, that’s all we talked to him about for like 3 weeks. It’s like

57 00:08:42.110 00:08:43.230 Uttam Kumaran: complete waste.

58 00:08:43.230 00:08:49.300 Robert Tseng: Agreed. Okay, portable stuff, too, should we? Just, I mean Northeas already done right, or like you’ve.

59 00:08:49.300 00:08:57.349 Uttam Kumaran: Yeah, so yeah, yeah, I would. I would just mark this as done, this is coming. This is actually coming in. They’re just doing like a 5 year backfill, basically.

60 00:08:58.200 00:09:01.440 Uttam Kumaran: So that’s great. The address matching is done

61 00:09:02.150 00:09:05.450 Uttam Kumaran: right? Like, yeah, let’s just let’s mark that as done.

62 00:09:06.194 00:09:12.159 Uttam Kumaran: Install the nightly rail thing. I don’t dude. I don’t know. We shouldn’t talk to him about that like.

63 00:09:12.160 00:09:13.386 Robert Tseng: Yeah, let’s

64 00:09:14.000 00:09:17.280 Uttam Kumaran: Just done this or backlog it. Yeah.

65 00:09:17.280 00:09:17.880 Robert Tseng: Yeah.

66 00:09:18.300 00:09:23.840 Uttam Kumaran: Anything that’s like data engineering. I would be very cautious to tell. Talk to like

67 00:09:24.320 00:09:28.490 Uttam Kumaran: any client about, unless we have a very clear through line to like how it helps them.

68 00:09:28.650 00:09:33.428 Uttam Kumaran: It’s like we’ve dealt this with with 5 Tran. We’ve dealt this with calls about 5 tran.

69 00:09:33.860 00:09:40.769 Uttam Kumaran: Dbt. It just lead, especially with them on. I feel like he just gets hooked on like these platform issues that like have. No.

70 00:09:40.960 00:09:43.500 Uttam Kumaran: that aren’t important. So I just want to avoid that.

71 00:09:43.890 00:09:47.580 Robert Tseng: Yeah, agreed I. Wanna spend most of my time talking to him about stuff that’s here.

72 00:09:48.520 00:09:49.120 Uttam Kumaran: Yes.

73 00:09:50.490 00:09:51.230 Uttam Kumaran: Yeah.

74 00:09:51.230 00:09:51.710 Robert Tseng: Okay.

75 00:09:51.710 00:09:56.830 Uttam Kumaran: I think, okay, recurring weekly, I just, I guess, is, are we just gonna leave that in here forever?

76 00:09:56.830 00:09:59.589 Robert Tseng: He, Aman, asked us to build like a tool.

77 00:09:59.590 00:10:00.150 Uttam Kumaran: Okay. Okay.

78 00:10:00.527 00:10:03.850 Robert Tseng: I kind of want to just ignore him. So I kind of want to just like.

79 00:10:04.350 00:10:10.909 Uttam Kumaran: Just leave it. Just leave it, and like, if it brings it up I can. I’ll just like I’ll take on the question, and we could just move past that.

80 00:10:11.140 00:10:15.050 Robert Tseng: Okay, yeah. I mean, Pia said, he, speed sped it up to a point where like, he doesn’t really.

81 00:10:15.230 00:10:18.050 Robert Tseng: Yeah, yeah, much to do it. So yeah.

82 00:10:21.130 00:10:28.819 Uttam Kumaran: Okay, yeah, I feel like, yeah, this is all fine. We the stuff that’s

83 00:10:28.980 00:10:33.259 Uttam Kumaran: I sent a little bit of note of, like, yeah, we want to do the gorgeous dashboard. The okendo.

84 00:10:33.730 00:10:34.200 Uttam Kumaran: yeah.

85 00:10:34.200 00:10:34.780 Uttam Kumaran: Service.

86 00:10:35.730 00:10:43.870 Robert Tseng: That’s where I’m kind of just like we have stuff here. And it’s not really. There’s no tickets for it. So I mean, that’s fine. I can just like throw something out there.

87 00:10:43.870 00:10:48.279 Uttam Kumaran: I think we’ll have something. I mean, Kai. I think it’s between you and pious, like

88 00:10:48.780 00:10:51.969 Uttam Kumaran: something related to gorgeous in metabase.

89 00:10:52.240 00:10:54.509 Uttam Kumaran: I think by this week.

90 00:10:55.441 00:11:01.000 Uttam Kumaran: If not today. But if you guys want to comment on like where tougher, that is.

91 00:11:01.540 00:11:10.130 Payas Parab (TikTok): I think today is today is reasonable, especially like the the data isn’t like anything complicated to dashboard. Right? It’s not like any product, weird shit. It’s just like.

92 00:11:10.270 00:11:15.099 Payas Parab (TikTok): yeah tickets. Did each agent look at? That’s like unbelievably straightforward. I just need a

93 00:11:15.210 00:11:25.280 Payas Parab (TikTok): Kyle’s working on the database. Maybe we me and him can just huddle like after this or like whatever whenever. And we can just like, try and get that out today, it seems pretty reasonable data, simple

94 00:11:25.410 00:11:28.919 Payas Parab (TikTok): like we just need to. I think there’s some backlog data and stuff.

95 00:11:29.030 00:11:38.859 Payas Parab (TikTok): And kai utam, you’re in that thread with portable right? That’s just like a lot of the historical data and stuff. But I think we’ll just focus on like, what can we get up and running today?

96 00:11:39.110 00:11:43.430 Payas Parab (TikTok): And just we’ve already flagged it to portable on the historical data.

97 00:11:45.540 00:11:47.660 Robert Tseng: Yeah, I would say that in general.

98 00:11:47.770 00:11:48.860 Robert Tseng: Sorry. Go ahead.

99 00:11:48.980 00:11:49.420 Uttam Kumaran: Go ahead! Go ahead!

100 00:11:49.420 00:11:50.399 Caio Velasco: No, no, I was. Gonna say that.

101 00:11:50.400 00:11:52.450 Caio Velasco: Okay, I’ll do my side. I can talk to Paris here.

102 00:11:53.010 00:12:16.500 Robert Tseng: Okay, I was just gonna say, in general, I know we every time we connect a new source we’re always asking like, Oh, do is, do we have, like the whole picture, like including historicals? Let’s not let historicals like slow us down like we can always add as a caveat like, hey, we have this set up, and it’s working, moving forward. We can work it to fill to backfill this historical stuff later. Like, I think that’s that’s generally how we should handle stuff like that.

103 00:12:16.500 00:12:23.509 Payas Parab (TikTok): Yep. Also shout out to Kyle, Dude like you’re asking all the right questions as you’re doing the work which helps a ton. Just wanted to throw that out there.

104 00:12:25.000 00:12:26.420 Caio Velasco: Appreciate it. Thank you.

105 00:12:28.860 00:12:39.453 Robert Tseng: Cool alright. Well, I mean, I feel good about this. I I feel like we have plenty to talk about. I don’t know if you wanted to call, mention anything else, but otherwise we could move on to the next client too.

106 00:12:39.700 00:12:44.729 Uttam Kumaran: I feel pretty good. Yeah, just tell me if there’s anything like we wanna

107 00:12:45.280 00:12:48.509 Uttam Kumaran: hustle on between now and then. But otherwise, yeah, I feel

108 00:12:48.670 00:12:55.609 Uttam Kumaran: feel as good as we could have been by today. And then if we wanna, I think for me, I want to get a sense of if we need to do anything else by the end of the week.

109 00:12:55.770 00:12:56.770 Uttam Kumaran: Oh, yeah.

110 00:12:57.027 00:13:00.120 Payas Parab (TikTok): Quick question. Did you get a chance to review the cross platform?

111 00:13:00.600 00:13:01.750 Payas Parab (TikTok): Prd.

112 00:13:03.860 00:13:08.430 Uttam Kumaran: No, okay, wait. What is that? Wait. Where is that?

113 00:13:08.960 00:13:28.449 Payas Parab (TikTok): So Google? Let me, Robert, I don’t even know if you’ve seen it. Basically like, can you, you know, like basically all the address matching work we did. We now have, like Amazon, and shopify duplicate customers. We want to like build out some sort of like a dimension of customers that are like on both platforms. So we can like see who does what.

114 00:13:28.450 00:13:29.169 Uttam Kumaran: Yeah, yeah.

115 00:13:29.170 00:13:33.769 Payas Parab (TikTok): First, st that type of thing. I basically mocked that up. It’s kind of its own project. But.

116 00:13:33.770 00:13:36.290 Uttam Kumaran: Is that this loom is that the loom thing you send.

117 00:13:36.290 00:13:40.459 Payas Parab (TikTok): No, it’s not the loom, it’s the let me just quickly find it. It’s the

118 00:13:41.740 00:13:45.839 Payas Parab (TikTok): pr, it’s a Google Doc that’s just like Prd

119 00:13:46.050 00:13:48.960 Payas Parab (TikTok): Javi cross-platform analytics. Hold on! I’m sharing it here.

120 00:13:49.970 00:13:51.350 Uttam Kumaran: I would love to read it.

121 00:13:51.950 00:13:58.080 Payas Parab (TikTok): And Robert, it’s also like, it’s another thing you can tee up. Basically right is like, I was trying to think about like

122 00:13:58.290 00:14:14.419 Payas Parab (TikTok): this side of side quest. We went on with the address thing is there like an analytics and dashboarding win there to be able to deliver that on Friday it’d be pretty challenging, just based on like what we would need to do. But I mocked it up in this document I just shared, but we could sort of be like. Sorry. I just sent to Utam.

123 00:14:14.830 00:14:16.560 Uttam Kumaran: I just bumped it up in the.

124 00:14:16.920 00:14:17.600 Payas Parab (TikTok): Okay.

125 00:14:20.080 00:14:21.409 Robert Tseng: Yeah, let me take a look, too.

126 00:14:22.610 00:14:23.730 Uttam Kumaran: Okay. Nice.

127 00:14:24.420 00:14:46.870 Payas Parab (TikTok): It’s an additional item we can pitch right is like, Hey, we got everything working and running. Now we know cross platform, and like there’s sort of an analysis there of like are you losing customers for via Amazon? Are you gaining them? Are they spending more on Amazon? Are they spending more on shopify doing some of those joints. It’s just like building all that. I’ve laid out the tables, but it’s not trivial.

128 00:14:48.480 00:14:49.639 Robert Tseng: Got it.

129 00:14:50.710 00:14:54.260 Payas Parab (TikTok): And I’m giving you as editor access to everyone on the Javi team.

130 00:15:01.770 00:15:05.019 Robert Tseng: Okay, cool. Yeah, no, I’ll I’ll definitely bring this up, too.

131 00:15:06.470 00:15:11.929 Uttam Kumaran: Yeah. And I think the only other things is as we have. The next set of dashboard requests. I want to hand them to

132 00:15:12.280 00:15:16.500 Uttam Kumaran: to bow to do, I think? Pies, yeah, if you want to get something out for gorgeous.

133 00:15:16.610 00:15:17.449 Uttam Kumaran: let’s do that.

134 00:15:17.450 00:15:20.769 Payas Parab (TikTok): Gorgeous. I don’t want to add this for now, but I thought.

135 00:15:20.770 00:15:21.210 Uttam Kumaran: Yeah.

136 00:15:21.270 00:15:35.210 Payas Parab (TikTok): I can just map it out and be like, here’s another idea you want to throw. If you want to throw it, I’ll leave it up to you guys. Right? I’m just like giving you the information of like, we have this data. And like, there’s a logical next step we could take with like analytics for this aspect, now that we have it.

137 00:15:35.210 00:15:43.430 Uttam Kumaran: So there’s that, and there’s the okendo and so any next, like dashboarding stuff, I wanna hand

138 00:15:44.174 00:15:47.260 Uttam Kumaran: to bow. So if there’s anything

139 00:15:47.500 00:15:54.540 Uttam Kumaran: like if if I can get the Okendo data in some place today, then maybe we can also spit something out for that.

140 00:15:55.810 00:15:58.490 Uttam Kumaran: But otherwise, yeah, I feel pretty good about this week.

141 00:15:59.024 00:16:03.140 Uttam Kumaran: We’re still working on some getting this product categorization fixed. And

142 00:16:03.716 00:16:07.720 Uttam Kumaran: it’s been a little bit complicated. But yeah, I feel pretty good compared to Monday.

143 00:16:11.450 00:16:16.609 Uttam Kumaran: Okay, let’s talk about today for pool parts.

144 00:16:17.910 00:16:20.860 Uttam Kumaran: Same deal. Any sort of changes.

145 00:16:22.250 00:16:25.840 Uttam Kumaran: And did we want to try and talk to him about like

146 00:16:26.260 00:16:29.033 Uttam Kumaran: the opportunity work we were thinking about.

147 00:16:32.548 00:17:00.909 Payas Parab (TikTok): So on that on that note we actually have quite a lot to cover with the skew stuff. We made some really good like understanding the Asia connection stuff. And there’s like a lot of questions that will probably take up the full hour with him today. But Bo and I have a meeting. I did my, we we both did like independent analysis on 2 categories and like are sharing that out today discussing like our spark notes of like. Here’s the opportunity. Here’s that we’ll determine if, like, it seems viable to share at the end of that meeting today.

148 00:17:00.950 00:17:05.410 Payas Parab (TikTok): and then we’ll maybe like throw it in the mix to Dan, or maybe we’ll

149 00:17:05.480 00:17:10.759 Payas Parab (TikTok): what’s the best way to do that. But we’re trying to make that like 4 sentence elevator pitch.

150 00:17:10.930 00:17:11.560 Payas Parab (TikTok): Let’s.

151 00:17:11.990 00:17:15.000 Uttam Kumaran: Let’s just hammer this the Asia work

152 00:17:15.619 00:17:19.800 Uttam Kumaran: we’ll be. We’ll win us. It’ll win us some brownie points, and then

153 00:17:20.079 00:17:26.650 Uttam Kumaran: one. The big objectives is one. Get them on a hook for another meeting, and then, second is.

154 00:17:27.210 00:17:32.889 Uttam Kumaran: I’ll kind of see how the meeting goes. If you guys have that beforehand. I can try and tee it up. Otherwise

155 00:17:33.220 00:17:35.140 Uttam Kumaran: I sort of don’t want to lose, like

156 00:17:35.800 00:17:39.419 Uttam Kumaran: I’m pretty sure if we bring it up you’ll be like, let’s just focus on this. So I’ll find.

157 00:17:39.420 00:17:39.960 Payas Parab (TikTok): Yeah, yeah.

158 00:17:39.960 00:17:42.470 Uttam Kumaran: Get that going as well on the side.

159 00:17:43.024 00:17:50.860 Uttam Kumaran: But yeah, if we can fill the whole hour I’ll be there, and I’ll make sure that that meeting sort of moves forward, and like I can take notes and things so.

160 00:17:50.860 00:18:02.080 Payas Parab (TikTok): Well, by the end of me and Bose and Nico’s meeting today we’ll have that couple sentence elevator pitch ready, at least. So then we’ll send that to you before the hour meeting with Dan, and then we’ll leave it like we’ll we’ll focus.

161 00:18:02.080 00:18:02.450 Uttam Kumaran: Yeah.

162 00:18:02.450 00:18:13.589 Payas Parab (TikTok): Few questions, but we’ll kind of leave it up to you, if, like. The vibe is right to tee that up or throw that in the mix. We’ll leave that up to you. We can always follow up via like text, or something, too. Right is like, Hey, like, whatever’s the best

163 00:18:13.750 00:18:18.849 Payas Parab (TikTok): method. We’ll leave that up to you. We’ll have it ready for you, though. Those like elevator pitches.

164 00:18:18.850 00:18:30.309 Uttam Kumaran: Okay, yeah, ideally, I want to buy some like, at least another week of work. And this is great work. Because we don’t. We’re not really talking to many people so ideally, we can buy ourselves a week of work.

165 00:18:30.620 00:18:33.740 Uttam Kumaran: And I just want to get through as many questions as we have

166 00:18:34.020 00:18:37.089 Uttam Kumaran: so great. Okay? Should be a good meeting.

167 00:18:39.480 00:18:51.469 Uttam Kumaran: Okay? And then the only other client I think to talk about is, it’s still me and Luke sort of helping on stuff for Stack Blitz. The only thing we’re working on is we’re working on subscription revenue subscription.

168 00:18:51.670 00:18:53.620 Uttam Kumaran: churn revenue, etc.

169 00:18:54.626 00:18:56.773 Uttam Kumaran: I think. Probably.

170 00:18:57.610 00:19:01.894 Uttam Kumaran: Robert, I wouldn’t. I need your help. Kind of thinking through renewal for them.

171 00:19:02.200 00:19:02.550 Robert Tseng: Okay.

172 00:19:02.550 00:19:03.950 Uttam Kumaran: I think we’re we’re coming up.

173 00:19:04.680 00:19:08.097 Uttam Kumaran: God, I don’t even know when the day is we’re coming up on

174 00:19:09.690 00:19:15.130 Uttam Kumaran: yeah, I think we basically started on

175 00:19:16.620 00:19:20.470 Uttam Kumaran: like probably a month ago. So I think our contract may be up.

176 00:19:20.710 00:19:26.710 Uttam Kumaran: But like, we’re just, we’re just basically billing hourly. So it’s okay. But I would love your help

177 00:19:26.880 00:19:45.979 Uttam Kumaran: to sort of do the handoff of like how we want to think of the renewal there, and I can give you an overall scope of what we’ve done. We’ve been mainly doing so to give you the gist, and everyone on the call who’s probably not familiar. Stack Blitz runs bolt on new. They’re just like this really popular. AI full stack app generator.

178 00:19:46.613 00:19:54.660 Uttam Kumaran: It’s all subscription product usage and segment. Actually, very easy data. Just a lot of it.

179 00:19:55.223 00:20:13.890 Uttam Kumaran: And they have no data team. They have one sort of interim head of marketing. Mitchell, who’s a connection of mine. We’re working directly and just basically setting the analytics infrastructure up. He’s planning on hiring 2 data people. I don’t know whether they’re gonna be aes or analysts.

180 00:20:14.332 00:20:23.389 Uttam Kumaran: But I do think there’s an opportunity for us to just continue to support them on the, on both the Ae side and the potentially like the analysis side.

181 00:20:25.030 00:20:28.290 Uttam Kumaran: So I think we should probably see if we can sign for a

182 00:20:28.550 00:20:31.389 Uttam Kumaran: just a longer deal. Initially, they were like, let’s just do one month.

183 00:20:31.700 00:20:34.400 Uttam Kumaran: and they’re definitely not close to hiring.

184 00:20:34.700 00:20:42.240 Uttam Kumaran: They’re just really busy. So I think there’s something we can do there to try to get a get them on the hook for longer, and and maybe even move them to

185 00:20:42.410 00:20:44.689 Uttam Kumaran: ideally. Move them to a package pricing

186 00:20:50.810 00:20:56.919 Uttam Kumaran: so I can. I’ll try and maybe grab some time before the end of the week or on Friday, and we can talk through that.

187 00:20:57.250 00:20:57.920 Robert Tseng: Yeah.

188 00:20:58.160 00:20:58.750 Uttam Kumaran: Okay.

189 00:21:02.950 00:21:05.000 Uttam Kumaran: okay, cool.

190 00:21:07.470 00:21:09.040 Uttam Kumaran: Anything else.

191 00:21:10.280 00:21:15.099 Uttam Kumaran: We’ll talk even in the next meeting. But anything else on any of these clients.

192 00:21:18.650 00:21:32.870 Uttam Kumaran: Okay, cool. Yeah, I’m I’m gonna be basically be like, I’m trying to block out like half my day for just data work and documentation work. So I’ll be on slack, just ping me. If you need anything, I’m gonna be working on improving this data platform documentation across all clients

193 00:21:33.020 00:21:37.779 Uttam Kumaran: and the architecture diagrams across all clients. So some stuff’s getting a little bit better there.

194 00:21:37.890 00:21:41.999 Uttam Kumaran: If there’s if there needs to be reviews or anything, just ping me, and we’ll get stuff out.

195 00:21:42.717 00:21:44.309 Uttam Kumaran: Cool. Okay. Thanks. Everyone.

196 00:21:46.520 00:21:47.419 Robert Tseng: Thanks, talk soon.

197 00:21:48.075 00:21:48.380 Bo Yoon: You.