2025-10-09_default_brainforge_weekly_sync

Meeting Title: Default | Brainforge Weekly Sync Date: 2025-10-09 Meeting participants: Scratchpad Notetaker, Deanna Garcia, Caitlyn Vaughn, Uttam Kumaran, Justin Breshears

WEBVTT

1 00:02:01.510 ⇒ 00:02:03.060 Caitlyn Vaughn: What’s up, Dee?

2 00:02:03.760 ⇒ 00:02:05.190 Deanna Garcia: And we’ve been hanging around.

3 00:02:05.800 ⇒ 00:02:06.850 Caitlyn Vaughn: Just us.

4 00:02:08.100 ⇒ 00:02:09.050 Deanna Garcia: Yes.

5 00:02:10.000 ⇒ 00:02:11.890 Caitlyn Vaughn: Actually gets me.

6 00:02:12.370 ⇒ 00:02:16.979 Caitlyn Vaughn: I’m just gonna… just gonna do this. Okay, how are you?

7 00:02:17.390 ⇒ 00:02:18.950 Deanna Garcia: Yes!

8 00:02:18.950 ⇒ 00:02:21.409 Caitlyn Vaughn: How’s the office this week?

9 00:02:22.050 ⇒ 00:02:24.210 Deanna Garcia: It’s been good. Yeah, I just…

10 00:02:24.640 ⇒ 00:02:30.079 Deanna Garcia: Yeah, the office is… it’s super nice, way bigger than the last one, which is great.

11 00:02:30.220 ⇒ 00:02:32.610 Deanna Garcia: Still, somehow, we run out of, like, rooms.

12 00:02:33.420 ⇒ 00:02:34.150 Caitlyn Vaughn: Oh, really?

13 00:02:34.150 ⇒ 00:02:38.400 Deanna Garcia: always interesting. Yeah, well, I mean, like, not all of the rooms have, like, a desk set up.

14 00:02:38.700 ⇒ 00:02:39.460 Caitlyn Vaughn: Still.

15 00:02:39.460 ⇒ 00:02:43.870 Deanna Garcia: So, like, some rooms are, like, just a vibe room versus, like.

16 00:02:43.960 ⇒ 00:02:45.660 Caitlyn Vaughn: Taking a call in there.

17 00:02:45.660 ⇒ 00:02:49.330 Deanna Garcia: I actually think I’m gonna move rooms, because this one is, like, very echoey.

18 00:02:49.450 ⇒ 00:02:51.209 Deanna Garcia: It’s kind of annoying.

19 00:02:51.380 ⇒ 00:02:53.029 Deanna Garcia: Like, it’s very echoey.

20 00:02:53.030 ⇒ 00:02:56.259 Caitlyn Vaughn: Oh, I can’t hear you. Like, it can’t hear the echo.

21 00:02:56.620 ⇒ 00:02:58.020 Deanna Garcia: Oh, I can.

22 00:02:58.020 ⇒ 00:02:59.500 Caitlyn Vaughn: Okay, that’s annoying.

23 00:03:05.960 ⇒ 00:03:08.469 Deanna Garcia: But no, it’s nice.

24 00:03:08.870 ⇒ 00:03:12.069 Deanna Garcia: And I just realized, like, the lights kind of turn off.

25 00:03:12.350 ⇒ 00:03:13.170 Caitlyn Vaughn: Oh, yeah.

26 00:03:13.480 ⇒ 00:03:17.370 Deanna Garcia: now and again, and the one that I was in did have a window, so it would have been, like, super dark.

27 00:03:17.460 ⇒ 00:03:19.040 Caitlyn Vaughn: With the light turned off.

28 00:03:19.860 ⇒ 00:03:22.860 Caitlyn Vaughn: It’s so nice that there’s, like, more offices in this one, though.

29 00:03:22.860 ⇒ 00:03:24.660 Deanna Garcia: Yes, yeah, yeah, yeah, for sure.

30 00:03:24.660 ⇒ 00:03:26.650 Caitlyn Vaughn: You came to the last office, right?

31 00:03:27.660 ⇒ 00:03:33.209 Caitlyn Vaughn: There was, like… 3 rooms, but, like…

32 00:03:33.520 ⇒ 00:03:36.689 Caitlyn Vaughn: Nico and Victor ended up using two of them, usually, you know?

33 00:03:36.690 ⇒ 00:03:39.030 Deanna Garcia: Yeah. Yeah.

34 00:03:39.500 ⇒ 00:03:41.559 Uttam Kumaran: Hello, good morning, or…

35 00:03:41.560 ⇒ 00:03:42.350 Deanna Garcia: Bye.

36 00:03:42.350 ⇒ 00:03:44.750 Uttam Kumaran: It’s noon in New York, so…

37 00:03:45.130 ⇒ 00:03:47.029 Uttam Kumaran: Hi, Dan, nice to meet you.

38 00:03:47.190 ⇒ 00:03:48.549 Deanna Garcia: Hi, nice to meet you.

39 00:03:49.590 ⇒ 00:03:51.340 Caitlyn Vaughn: Sorry, I’m making myself some coffee here.

40 00:03:51.340 ⇒ 00:03:53.450 Uttam Kumaran: No, you’re fine. No worries.

41 00:03:54.290 ⇒ 00:03:55.480 Caitlyn Vaughn: How are you?

42 00:03:55.670 ⇒ 00:03:56.690 Uttam Kumaran: I’m good.

43 00:03:57.380 ⇒ 00:04:02.169 Uttam Kumaran: I’m good. Yeah, we’re… We’re busy, we were on a…

44 00:04:02.300 ⇒ 00:04:10.349 Uttam Kumaran: we’re doing some data work for a clinic in New York. They’re opening, like, a…

45 00:04:10.740 ⇒ 00:04:17.169 Uttam Kumaran: They provide services for a couple of specific, types of patients, but

46 00:04:17.490 ⇒ 00:04:23.469 Uttam Kumaran: They have, like, a bunch of work they’re trying to do on setting up, like, electronic medical records and a bunch of different things, so…

47 00:04:23.980 ⇒ 00:04:26.140 Uttam Kumaran: We’re a, yeah, interesting client, so…

48 00:04:26.140 ⇒ 00:04:27.000 Caitlyn Vaughn: Yeah.

49 00:04:27.000 ⇒ 00:04:35.909 Uttam Kumaran: I bet you have to be, like, really SOC 2 compliant for that. Yeah, they have, like, a lot of… so that’s the thing, that all their vendors are, like, totally different.

50 00:04:35.910 ⇒ 00:04:48.630 Uttam Kumaran: Yeah. But they do the same thing, like, data storage, forms. It’s like, part of their product is… part of the product we’re looking at, actually, is similar to default. They have forms, routing, inbound, but it’s all, like, for medical.

51 00:04:48.630 ⇒ 00:04:49.220 Caitlyn Vaughn: Oh, wow!

52 00:04:49.220 ⇒ 00:04:50.049 Uttam Kumaran: shake, yeah.

53 00:04:50.940 ⇒ 00:04:53.369 Uttam Kumaran: It’s this company called Healthy.

54 00:04:53.370 ⇒ 00:04:54.410 Caitlyn Vaughn: Huh.

55 00:04:54.470 ⇒ 00:04:58.949 Uttam Kumaran: my… maybe I’ll send it to you. Yeah, I didn’t know if you guys ever thought about getting into, like.

56 00:04:59.740 ⇒ 00:05:06.310 Uttam Kumaran: Medical world, but, yeah, their product rhymes with your product pretty…

57 00:05:06.490 ⇒ 00:05:08.289 Caitlyn Vaughn: healthy default?

58 00:05:08.750 ⇒ 00:05:17.809 Uttam Kumaran: Yeah, or just default, default, default health, you know? Okay. But basically, but these guys also do, like, the, the in-clinic, like.

59 00:05:18.020 ⇒ 00:05:22.660 Uttam Kumaran: The front desk person, like, what do they look at? How do they bring in patients, things like that, so…

60 00:05:22.660 ⇒ 00:05:24.690 Caitlyn Vaughn: Amazing. Are you gonna sell them default?

61 00:05:25.050 ⇒ 00:05:32.889 Uttam Kumaran: Yeah, I guess… I guess I gotta know if you guys… if you guys are HIPAA compliant and all that. I should… I should ask them, yeah. But we’re…

62 00:05:33.170 ⇒ 00:05:35.020 Uttam Kumaran: We’re gonna make decisions on, like.

63 00:05:35.270 ⇒ 00:05:37.349 Uttam Kumaran: There’s just a lot going on with them, so…

64 00:05:37.350 ⇒ 00:05:38.999 Caitlyn Vaughn: I don’t think we’re HIPAA compliant.

65 00:05:41.100 ⇒ 00:05:42.439 Deanna Garcia: No, not at all.

66 00:05:42.440 ⇒ 00:05:48.160 Caitlyn Vaughn: I think probably not our forte, but, good to see you! I’m glad that you’ve got some cool clients going.

67 00:05:48.160 ⇒ 00:05:53.579 Uttam Kumaran: Thank you, yeah, and it’s really nice to meet you, Deanna. We’re Brain Forge team, so we’ve been helping with everything

68 00:05:53.810 ⇒ 00:06:12.619 Uttam Kumaran: on the data side of things, we’re helping Caitlin with enrichment. Hopefully, I think you may have seen some of the dashboard work that we’ve done. I think that’s probably what we’ll talk a little bit about today. So, Caitlin, I don’t know, do we want to start on that side? Maybe we can also talk about, like, I know Owler stuff is kind of due today. Do you want to start there?

69 00:06:13.340 ⇒ 00:06:14.680 Uttam Kumaran: Tell you, kind of, the latest.

70 00:06:14.680 ⇒ 00:06:20.749 Caitlyn Vaughn: I have a meeting with Owler tomorrow. So I saw that you guys had sent over the Owler stuff, and I…

71 00:06:21.260 ⇒ 00:06:24.920 Caitlyn Vaughn: I don’t know if you guys sent over anything that was, like, Data?

72 00:06:24.920 ⇒ 00:06:25.310 Uttam Kumaran: Yeah, we didn’t.

73 00:06:25.310 ⇒ 00:06:26.550 Caitlyn Vaughn: the results?

74 00:06:26.550 ⇒ 00:06:32.060 Uttam Kumaran: No, no, no, not yet. Basically, that’s on my to-do list today. I’ve kind of been the blocker.

75 00:06:32.060 ⇒ 00:06:32.510 Caitlyn Vaughn: is…

76 00:06:32.950 ⇒ 00:06:41.729 Uttam Kumaran: We spent, basically, like, we built out, like, 350 companies in each of those different tiers, the enterprise, mid-market, and then,

77 00:06:42.460 ⇒ 00:07:02.189 Uttam Kumaran: I, like, it was funny, because we’re like, okay, what is the source of truth for this? I’m like, I think we need to go through, like, kind of one by one, and to make sure, because otherwise we’re trusting, like, an enrichment source to judge other enrichment sources by. So basically today, we use Owler to scrape data on all 350. I’m gonna go through and just, like.

78 00:07:02.690 ⇒ 00:07:03.520 Uttam Kumaran: Check.

79 00:07:03.850 ⇒ 00:07:11.679 Uttam Kumaran: like, what’s valid or not. And then I’m gonna send you a result set today. I wanted to kind of run through one before we go through

80 00:07:11.840 ⇒ 00:07:26.259 Uttam Kumaran: and do all of them, in case you can tell me, like, hey, I want more information on how accurate this is, or this is… this is good to go, run it for the remaining four. So I’ll give you, like, a… I’ll give you basically the report today on, like, how we feel.

81 00:07:26.750 ⇒ 00:07:29.399 Caitlyn Vaughn: Okay, cool. Because you came up with a list, right?

82 00:07:29.400 ⇒ 00:07:34.880 Uttam Kumaran: Yeah, so we have a list of them, and then we also ran the Owler enrichment on all of them.

83 00:07:34.880 ⇒ 00:07:35.590 Caitlyn Vaughn: Okay.

84 00:07:35.590 ⇒ 00:07:38.929 Uttam Kumaran: We just haven’t done the… Does it all look right?

85 00:07:39.270 ⇒ 00:07:42.990 Uttam Kumaran: Oh, oh, okay, you haven’t, like, sniff tested it yet. Yes, yeah.

86 00:07:43.420 ⇒ 00:07:47.719 Caitlyn Vaughn: Okay, yes, I can see the…

87 00:07:48.700 ⇒ 00:07:52.739 Caitlyn Vaughn: The 350 rows. Is this all of the vendors that we’re testing?

88 00:07:53.310 ⇒ 00:07:57.659 Uttam Kumaran: These are all the companies we’re testing the vendors on.

89 00:07:57.660 ⇒ 00:08:00.460 Caitlyn Vaughn: Yeah, sorry, that’s what I mean. This is all the testing.

90 00:08:00.460 ⇒ 00:08:17.419 Uttam Kumaran: Correct. So you should see that they’re… again, like, we filtered out, like, stuff where they wouldn’t be, like, a potential default customer, but also enterprise stuff, like, these are just the biggest companies. So, mainly, what we did is we looked at, like, people with high web traffic, and, like, and then also that were…

91 00:08:17.570 ⇒ 00:08:26.110 Uttam Kumaran: not, like, Sephora, for example, doesn’t have, like… that’s all e-com, right? So, we’re looking for people that are, like, B2B, they have sales staff.

92 00:08:26.340 ⇒ 00:08:30.289 Uttam Kumaran: And they have, like, heavy inbound, like, routing flows.

93 00:08:30.290 ⇒ 00:08:30.990 Caitlyn Vaughn: Hmm.

94 00:08:31.430 ⇒ 00:08:46.600 Uttam Kumaran: I think I may, like, once I go through today, I may kick a couple more rows out, but I generally think it, like, I look through, these are all kind of potential existing cus… existing customers, immediate, like, next customers, and I think, like, dream customers are all kind of in there.

95 00:08:46.930 ⇒ 00:08:56.110 Caitlyn Vaughn: Yeah, that works. Actually, what would be helpful is if you could just, like, write a quick blurb for us, like, how you came up with this sample.

96 00:08:56.110 ⇒ 00:08:56.760 Uttam Kumaran: Yeah.

97 00:08:56.760 ⇒ 00:09:01.169 Caitlyn Vaughn: Because you’re talking about it, that’s interesting, and I could talk to the team about that.

98 00:09:01.170 ⇒ 00:09:01.820 Uttam Kumaran: Totally.

99 00:09:01.820 ⇒ 00:09:02.349 Caitlyn Vaughn: And then the…

100 00:09:02.350 ⇒ 00:09:06.490 Uttam Kumaran: I feel like it’s a pretty good set, like, to see how, like, how would default fit for, like.

101 00:09:06.710 ⇒ 00:09:08.019 Uttam Kumaran: like a FedEx.

102 00:09:08.270 ⇒ 00:09:08.700 Caitlyn Vaughn: Yeah.

103 00:09:08.700 ⇒ 00:09:14.210 Uttam Kumaran: Versus, like, for, like, you know, another startup or something, yeah.

104 00:09:14.210 ⇒ 00:09:16.339 Caitlyn Vaughn: Yeah, totally. And obviously, these are, like.

105 00:09:16.570 ⇒ 00:09:19.970 Caitlyn Vaughn: Like, the goal is, like, data that our customers want.

106 00:09:19.970 ⇒ 00:09:20.570 Uttam Kumaran: Yes.

107 00:09:20.570 ⇒ 00:09:36.660 Caitlyn Vaughn: For ourselves, but I’m sure all of these are… these look like a good sample size. So yeah, just, like, write up how you came up with this list. Okay. All these people, and then… yes, the actual results of, like, yes, we ran it, but, like, is it good?

108 00:09:37.000 ⇒ 00:09:44.680 Uttam Kumaran: Yes, yeah. So that’s something that where I was like, I think I’m just gonna go through manually and look through each one. I don’t think it’ll… I think it’ll probably just take me, like, an hour, so…

109 00:09:44.680 ⇒ 00:09:46.690 Caitlyn Vaughn: Did you see the test that I had created?

110 00:09:48.030 ⇒ 00:09:50.190 Uttam Kumaran: Maybe?

111 00:09:50.190 ⇒ 00:09:52.079 Caitlyn Vaughn: how I want these to be tested.

112 00:09:52.080 ⇒ 00:09:53.459 Uttam Kumaran: Oh, yeah, yeah, yeah, yeah.

113 00:09:53.460 ⇒ 00:09:54.450 Caitlyn Vaughn: Okay, cool.

114 00:09:56.930 ⇒ 00:09:57.650 Caitlyn Vaughn: Data.

115 00:09:57.650 ⇒ 00:09:59.209 Uttam Kumaran: Yes, yeah, yeah, yeah.

116 00:09:59.210 ⇒ 00:10:01.270 Caitlyn Vaughn: I’ll just send it again right now, just in case.

117 00:10:01.270 ⇒ 00:10:04.660 Uttam Kumaran: Yeah, so we… I copied this over, we… I think we have a running document.

118 00:10:04.840 ⇒ 00:10:09.100 Uttam Kumaran: So, yeah, I’ll make sure that all this is… In there.

119 00:10:10.560 ⇒ 00:10:16.880 Uttam Kumaran: Because the other thing you mentioned is, like, latency, and we want to make sure that there’s some response.

120 00:10:17.540 ⇒ 00:10:21.040 Uttam Kumaran: Versus a non-response. So, like, that’s what I’ll be looking at.

121 00:10:21.040 ⇒ 00:10:26.650 Caitlyn Vaughn: Latency’s a big one. There’s honestly, like, pretty easy ways to test that.

122 00:10:26.650 ⇒ 00:10:27.260 Uttam Kumaran: Yeah.

123 00:10:27.700 ⇒ 00:10:31.300 Uttam Kumaran: Also, like, that’s something that I’ll, like, I can ask them.

124 00:10:31.710 ⇒ 00:10:43.619 Uttam Kumaran: to be like, give us… give us, like, a load sheet, like, how… how your… the API scales at, like, different number of requests. Okay. Or, like, they can run that for us if we give them a test, like.

125 00:10:43.620 ⇒ 00:10:50.219 Caitlyn Vaughn: Oh, actually, that reminds me, I have a call tomorrow with, with Owler. Can I add you to it, Utam?

126 00:10:50.220 ⇒ 00:10:53.029 Uttam Kumaran: Sure. Oh, yeah, you mentioned you wanna… yeah, please.

127 00:10:53.260 ⇒ 00:11:00.329 Caitlyn Vaughn: Because we actually have a new sales guy, which I’m super glad, because the last one was, like, a huge bully. So this guy seems nice.

128 00:11:00.870 ⇒ 00:11:03.550 Caitlyn Vaughn: really mean. This guy seems.

129 00:11:03.550 ⇒ 00:11:04.250 Uttam Kumaran: Next, please.

130 00:11:04.250 ⇒ 00:11:09.189 Caitlyn Vaughn: And if… so we’re, like, running up on our POC right now.

131 00:11:09.190 ⇒ 00:11:09.690 Uttam Kumaran: Yes.

132 00:11:11.390 ⇒ 00:11:17.229 Caitlyn Vaughn: we’re coming up on, like, a one-year contract, and, like, obviously we didn’t use Owler data yet, but we.

133 00:11:17.230 ⇒ 00:11:17.820 Uttam Kumaran: yet.

134 00:11:17.820 ⇒ 00:11:24.549 Caitlyn Vaughn: on it already, and they want to, like, charge us another $50 for the next year. Yeah. So that’s, like, crazy. I think…

135 00:11:25.020 ⇒ 00:11:26.199 Caitlyn Vaughn: I don’t know.

136 00:11:26.480 ⇒ 00:11:33.200 Uttam Kumaran: Well, let’s, I can help you, so one is, I’ll just say that, like, hey, we didn’t use it much, but we need a break.

137 00:11:33.340 ⇒ 00:11:33.860 Caitlyn Vaughn: And we’ll see what.

138 00:11:33.860 ⇒ 00:11:42.519 Uttam Kumaran: what they say, and then I’ll… well, I think we’ll try to go through the result set, so at least coming to that meeting, we’ll know, like, whether we like them or not. That’ll dictate, okay, like.

139 00:11:42.920 ⇒ 00:11:45.029 Uttam Kumaran: Let’s see what we can get out of them.

140 00:11:47.680 ⇒ 00:11:51.790 Uttam Kumaran: I mean, this is the thing for… I would rather get you guys some contract that, like.

141 00:11:51.920 ⇒ 00:11:53.330 Uttam Kumaran: Scales with usage.

142 00:11:53.440 ⇒ 00:11:59.180 Uttam Kumaran: Or, like, there’s some minimum, and then it scales up. They’re gonna… these guys are selling, like… they’re selling, like.

143 00:11:59.650 ⇒ 00:12:12.540 Uttam Kumaran: like, credits, right? There’s… so… you don’t want to buy… like, it’s, like, sort of like everything. They want to sell you, like, the max amount of credits, like, oh, you can use it whenever you want. Instead, I’m like, let me buy it when I need it, and then, like, let’s meet in the middle somewhere.

144 00:12:12.540 ⇒ 00:12:13.550 Caitlyn Vaughn: Yeah.

145 00:12:13.630 ⇒ 00:12:14.400 Uttam Kumaran: Yeah.

146 00:12:14.620 ⇒ 00:12:17.799 Caitlyn Vaughn: Okay, cool. I think, as I was thinking about it more.

147 00:12:18.000 ⇒ 00:12:24.620 Caitlyn Vaughn: Did we… we didn’t test them for any of the, like, job change data, right? Like, we’re just testing them on, like, company in person.

148 00:12:25.220 ⇒ 00:12:26.419 Uttam Kumaran: Yeah, I believe so.

149 00:12:26.420 ⇒ 00:12:38.150 Caitlyn Vaughn: Yeah, because I’ve heard, like, I was in the 5x5 office yesterday, I was talking to them about Owler, and they were like, they have better data on, like, job change and, like, signal-based data versus, like.

150 00:12:38.200 ⇒ 00:13:00.340 Caitlyn Vaughn: the actual person versus company. I mean, we can look at it, too. Like, you already ran the numbers, and I’m curious to see what the results are, but potentially, I think an angle for tomorrow is, like, one, we probably want to delay the contract, and two, we are more interested in just having their signal data versus, like, their actual enrichment, because I don’t care to pay $50K for enrichment, where we can, like.

151 00:13:00.430 ⇒ 00:13:04.999 Caitlyn Vaughn: pay Clearbit, like.00015 per credit, you know?

152 00:13:05.000 ⇒ 00:13:05.370 Uttam Kumaran: Yeah.

153 00:13:05.860 ⇒ 00:13:09.650 Uttam Kumaran: PeopleData has some of that, so I want to look at the overlap among the five.

154 00:13:09.650 ⇒ 00:13:11.269 Caitlyn Vaughn: That we’re looking.

155 00:13:11.270 ⇒ 00:13:16.700 Uttam Kumaran: And then that way you can… you can make a decision. Do you have a copy of the last contract?

156 00:13:17.650 ⇒ 00:13:19.700 Caitlyn Vaughn: Probably. I’ll send it to you.

157 00:13:19.700 ⇒ 00:13:23.260 Uttam Kumaran: PDF, you can just send it to me, and then… yeah, cool.

158 00:13:24.610 ⇒ 00:13:28.230 Uttam Kumaran: And then did… I just want to confirm if you added me to that meeting.

159 00:13:28.570 ⇒ 00:13:31.290 Caitlyn Vaughn: Hi, Dad It’s tomorrow at 1.30.

160 00:13:31.510 ⇒ 00:13:32.610 Uttam Kumaran: Nice. Okay, perfect.

161 00:13:32.610 ⇒ 00:13:33.580 Caitlyn Vaughn: Okay.

162 00:13:33.580 ⇒ 00:13:34.180 Uttam Kumaran: Yeah.

163 00:13:35.040 ⇒ 00:13:35.960 Caitlyn Vaughn: Sick.

164 00:13:36.500 ⇒ 00:13:42.310 Caitlyn Vaughn: And then, as for the other vendors, have we tested, like, PDL or anything else yet?

165 00:13:42.510 ⇒ 00:13:51.440 Uttam Kumaran: No, so I wanted to basically give you this first report, and then we’ll run the same… we’ll basically run the same for the other four. So it took me not that much time to run the test.

166 00:13:51.440 ⇒ 00:13:53.200 Caitlyn Vaughn: Yeah. But I just didn’t want to, like…

167 00:13:53.690 ⇒ 00:13:58.320 Uttam Kumaran: burned through all of them if we, like, wanted to test anything else, or… I want to give you, like, what a sense of, like.

168 00:13:58.460 ⇒ 00:14:01.689 Uttam Kumaran: Here’s what we think, and then you can tell me, like.

169 00:14:02.090 ⇒ 00:14:03.620 Uttam Kumaran: that’s all I need, or like…

170 00:14:03.780 ⇒ 00:14:05.870 Uttam Kumaran: Can you test each one a little bit more? Yeah.

171 00:14:06.060 ⇒ 00:14:08.100 Caitlyn Vaughn: Okay, cool, that sounds… that sounds perfect.

172 00:14:08.100 ⇒ 00:14:13.599 Uttam Kumaran: And then that way, you also have, like, this, so anytime you guys want to test a new vendor, you have, like, a framework to do that.

173 00:14:13.600 ⇒ 00:14:15.580 Caitlyn Vaughn: You have a sample data set.

174 00:14:15.650 ⇒ 00:14:16.590 Uttam Kumaran: Yeah.

175 00:14:16.590 ⇒ 00:14:22.769 Caitlyn Vaughn: Yeah. Okay, perfect. Yeah, that sounds great. Let’s do the first one first, and, like, detail it, and then…

176 00:14:23.100 ⇒ 00:14:25.969 Caitlyn Vaughn: Do them all! We have, like, a dozen.

177 00:14:25.970 ⇒ 00:14:26.600 Uttam Kumaran: Yeah.

178 00:14:26.680 ⇒ 00:14:30.360 Caitlyn Vaughn: No, this is great. I’m learning a lot, too, about all of these. Like, I wanna…

179 00:14:30.670 ⇒ 00:14:37.049 Uttam Kumaran: I want to use some of these for our business, so I want to use it through default for Brave Forge, so…

180 00:14:37.050 ⇒ 00:14:38.290 Caitlyn Vaughn: Let’s go!

181 00:14:38.290 ⇒ 00:14:39.110 Uttam Kumaran: Yeah.

182 00:14:39.110 ⇒ 00:14:39.850 Caitlyn Vaughn: Nice.

183 00:14:40.290 ⇒ 00:14:45.900 Uttam Kumaran: Cool. So then, I also saw that a bunch of people are getting access to the dashboard.

184 00:14:46.320 ⇒ 00:14:56.239 Uttam Kumaran: So I do want to talk today a little bit about getting you guys, like, on… probably on your own Omni, and I did get some pricing for them, and I…

185 00:14:56.810 ⇒ 00:15:01.469 Uttam Kumaran: I think we can get you guys a pretty solid deal. Do you want to talk about that, or… I don’t want to waste…

186 00:15:01.750 ⇒ 00:15:05.700 Uttam Kumaran: Deanna, your time, maybe we could talk about that and… And then you can…

187 00:15:05.700 ⇒ 00:15:25.590 Caitlyn Vaughn: Yeah, we can talk about that at the end, the, like, pricing and stuff. Sure. What I would probably want to talk about right now with Deanna on the call is, like, one, I was going through with Dee the actual, like, Omni charts and everything, and I think we need to, like, sniff test it a little more. There’s some more data where I’m like.

188 00:15:25.590 ⇒ 00:15:26.200 Uttam Kumaran: Yes.

189 00:15:26.200 ⇒ 00:15:28.129 Caitlyn Vaughn: Not correct.

190 00:15:28.130 ⇒ 00:15:28.650 Uttam Kumaran: Yes.

191 00:15:28.650 ⇒ 00:15:47.039 Caitlyn Vaughn: Just, like, gut feeling. But also, if it is correct, that’s fucking crazy, and we should also know that. And then the second thing is, so Deanna’s our head of support, and I know she’s, like, they’re kind of moving to, like, a account management role, so I know they’re wanting to, like, she’s wanting to build out a, like, dashboard.

192 00:15:47.040 ⇒ 00:16:01.130 Caitlyn Vaughn: for each customer, and then as they’re doing renewals, like, bring up all the data and be like, hey, you guys, you know, submitted 50,000 forms and, like, ran this much enrichment, whatever. So, that’s why she’s on. I don’t know if she’s talked to you yet, but I’m gonna pass it over to her.

193 00:16:01.130 ⇒ 00:16:09.789 Uttam Kumaran: No, perfect, and then to give you a little bit of background, this is, like, I worked… so when I was at WeWork, I basically supported the entire, like, account management team, so we built out all of their, like.

194 00:16:10.090 ⇒ 00:16:25.100 Uttam Kumaran: customer health dash, you filter to one customer, you see everything about, like, the products they’re using, their usage, the team, the amount of users on there, and then… so that’s certainly something. And then, yeah, I’d say best way to work with us is to hit us with, like, a wish list.

195 00:16:25.110 ⇒ 00:16:36.739 Uttam Kumaran: Or, like, even hit us with, like, what you wish you would… I was… like, even scenarios, like, I was talking to this customer, I wish I had this data available. We can build a first version of that, and then…

196 00:16:36.760 ⇒ 00:16:41.989 Uttam Kumaran: Sort of start to… Kind of gather more requirements and support, but also definitely interested in

197 00:16:42.090 ⇒ 00:16:46.639 Uttam Kumaran: Like, your team and sort of how the state of the world is on your side.

198 00:16:47.930 ⇒ 00:16:48.460 Caitlyn Vaughn: Totally.

199 00:16:48.460 ⇒ 00:16:54.859 Deanna Garcia: Yeah, yeah, I can, I can jump in and maybe give you some context, but, john, can you guys hear me okay?

200 00:16:54.860 ⇒ 00:16:55.530 Uttam Kumaran: Yes.

201 00:16:55.860 ⇒ 00:17:12.589 Deanna Garcia: Okay, sometimes the AirPods are cut in and out. But yeah, I mean, on the CS side, so essentially what we’re doing, or trying to do more of, right, we’re really heavily focused on onboarding and implementation, and kind of not worrying about, like, what’s happening after the fact, right? So what we’re trying to do…

202 00:17:12.589 ⇒ 00:17:25.660 Deanna Garcia: you, Tom, to kind of, like, what you mentioned, what you guys were doing at WeWork, is just kind of better understand just metrics around usage from a customer perspective, right? So as we’re having, like, some of our midterm conversations, trying to understand, like.

203 00:17:25.660 ⇒ 00:17:36.910 Deanna Garcia: Are they using the platform? How many meetings have they booked? How many form submissions? Like, all that, all the nitty-gritty. So a lot of the stuff that I found on the dashboard that Caitlin shared with us yesterday, or earlier this week.

204 00:17:36.950 ⇒ 00:17:42.139 Deanna Garcia: I felt like could probably double as, like, a customer dashboard, just, like.

205 00:17:42.140 ⇒ 00:17:42.590 Uttam Kumaran: Yes.

206 00:17:42.590 ⇒ 00:17:47.220 Deanna Garcia: some of the stuff. And at, like, the most basic level, I can, I can kind of…

207 00:17:47.390 ⇒ 00:17:51.299 Deanna Garcia: give you a wish list, and I can kind of put something together of, like.

208 00:17:51.510 ⇒ 00:17:59.329 Deanna Garcia: ideal, what we would like to see, right? And I’m sure Caitlin’s giving you some context in terms of, like, how the product is shifting a little bit as well, so, like.

209 00:17:59.330 ⇒ 00:17:59.730 Uttam Kumaran: Yes.

210 00:17:59.730 ⇒ 00:18:05.530 Deanna Garcia: Probably an ever-changing wishlist, especially as we, like, see what the new world looks like.

211 00:18:05.550 ⇒ 00:18:19.399 Deanna Garcia: But I would say at the most basic level, like, meetings booked, form submissions, like, how many users you’re adding, like, things like that are probably helpful, just to help CS just have, like, a more well-rounded conversation, provide, like, showcase value.

212 00:18:19.400 ⇒ 00:18:26.530 Deanna Garcia: And then as we jump into these renewal conversations as well, like, showcase value they’ve had, you know, year over year, month over month, things like that.

213 00:18:26.530 ⇒ 00:18:33.649 Deanna Garcia: So… really my main question, and I think this probably comes after we figure out, like, whether we

214 00:18:33.760 ⇒ 00:18:38.350 Deanna Garcia: have our own Omni, or if we, like, continue using this one, right? It’s like.

215 00:18:38.380 ⇒ 00:18:52.049 Deanna Garcia: just how we build out that dashboard, how we, like, where the data’s actually coming from. I think to Caitlin’s point as well, like, once we validate that, like, the data we feel confident in, right, then we can start to use that and, like, actually showcase that to customers.

216 00:18:52.050 ⇒ 00:19:00.050 Deanna Garcia: I know we’re not there yet, and part of me jumping on today was just to kind of understand what some of the data meant, and maybe that’s a little preliminary now, but

217 00:19:00.460 ⇒ 00:19:05.459 Deanna Garcia: Yeah, I mean, I think… I think, really, that’s the biggest thing for CS, is, like, just to have a place where we can

218 00:19:05.590 ⇒ 00:19:19.349 Deanna Garcia: pull, like, easily digest the customer data, and then showcase it to customers, whether it’s, like, showing this dashboard, whether it’s pulling this into decks, or just into some of the messaging that we send out, but, that’s really my biggest…

219 00:19:19.530 ⇒ 00:19:22.280 Deanna Garcia: hope with what we can capture with Omni.

220 00:19:22.860 ⇒ 00:19:27.860 Uttam Kumaran: Cool, yeah, I think that’s all, like, perfect, and I actually think we’re not, like, too far away from that. I think…

221 00:19:27.860 ⇒ 00:19:28.260 Deanna Garcia: Yeah.

222 00:19:28.260 ⇒ 00:19:37.070 Uttam Kumaran: The data accuracy piece is just something that we’re going to continue to chip at, so it’s actually great that more people are looking at it, because we’ll… you’ll know your accounts really well, and you’ll say, like, that’s wrong.

223 00:19:37.070 ⇒ 00:19:37.460 Deanna Garcia: deck.

224 00:19:37.460 ⇒ 00:19:54.490 Uttam Kumaran: probably just a couple line change for us to make to make sure that’s fixed. So, one, we’re getting all of our product data just from an export that Thomas has sent us before, so one thing on our list is to figure out, like, getting a continuous export, so I can

225 00:19:54.490 ⇒ 00:20:02.959 Uttam Kumaran: re-up that conversation with them, so that’s something that I can do. The other thing is, I… I… I think…

226 00:20:03.540 ⇒ 00:20:15.910 Uttam Kumaran: I can build you guys a really great dashboard for active, like, user usage, but I’m curious about, like, what is going on on the support side. Like, are you guys… do you guys have a platform for, like, support tickets? Is that, like, a big…

227 00:20:15.970 ⇒ 00:20:26.549 Uttam Kumaran: thing right now, and, like, wondering how you’re… how you’re managing that. Again, for our background, we’ve done a ton of work on, like, Zendesk and general, like, tickets and stuff like that, so…

228 00:20:26.550 ⇒ 00:20:46.300 Deanna Garcia: Yeah, so support, support is actually… we’re using Plain right now for support, because we have Slack channels, so everything kind of goes into that, into Plain. Jonathan, which I don’t know if you’ve met yet, he heads up on the support side, I head up on the CS side, which we’re kind of, like, one and the same, trying to, like, work in tandem, but, right now we use Plain.

229 00:20:46.300 ⇒ 00:20:54.040 Deanna Garcia: And I would say, really, the biggest need is going to be more on the CS account management side than on the support side, just, like, to start.

230 00:20:54.040 ⇒ 00:20:59.780 Deanna Garcia: Because support right now is just, like, it’s just reaction. Like, it’s just… that’s what support is, right? So…

231 00:20:59.780 ⇒ 00:21:07.270 Deanna Garcia: On the CS side, it’s like, we want to be able to showcase value, have, like, informed conversations with people,

232 00:21:07.270 ⇒ 00:21:24.189 Deanna Garcia: be able to push back when people say that they don’t get value, like, things like that, right? Which right now, we just don’t have visibility into. But yeah, we’re using Plain today. If there is a way to maybe see, you know, how… how many tickets have been created, like, threads have been created, things like that. Just to give you a little bit more context in terms of, like.

233 00:21:24.190 ⇒ 00:21:31.029 Deanna Garcia: tooling, on the CS side, we’re actually looking into a customer success platform, called Catalyst. I’m not sure if you’ve heard of it.

234 00:21:31.030 ⇒ 00:21:31.490 Uttam Kumaran: to work.

235 00:21:31.490 ⇒ 00:21:40.240 Deanna Garcia: with Catalysts in the past, but, essentially, we… that’s kind of, like, we’re managing our customer insights, like, managing note-taking, Gong calls.

236 00:21:40.240 ⇒ 00:21:42.359 Uttam Kumaran: I know, like, Gainsight, I’ve used, like, Gainsight.

237 00:21:42.360 ⇒ 00:21:44.490 Deanna Garcia: Yeah, it’s a competitor, yeah, yeah.

238 00:21:44.490 ⇒ 00:21:45.360 Uttam Kumaran: There’s, like, a bunch of them.

239 00:21:45.360 ⇒ 00:21:57.600 Deanna Garcia: Cool. Same concept, just different and prettier. Just way better. My biased opinion, yeah. Much prettier, much more pretty. So that being said, I would say that the biggest

240 00:21:57.600 ⇒ 00:22:09.400 Deanna Garcia: piece that we want to… I know that Omni and Catalyst don’t… they don’t integrate today, but we can pull usage into the platform via, like, S3 Bucket, Snowflake, BigQuery, however it is that we’re…

241 00:22:09.400 ⇒ 00:22:18.160 Deanna Garcia: That… that we’re… we’re pulling it from. So, that being said, like, things like expansions, like seat used, that kind of information we would definitely want in catalog.

242 00:22:18.160 ⇒ 00:22:29.039 Uttam Kumaran: Well, talk to me about what’s in Catalyst now, because… so, Omni… Omni is just, like, a view on top of, like, raw data we have in a… in a bucket. So, Omni is, like, we… basically…

243 00:22:29.040 ⇒ 00:22:29.660 Deanna Garcia: Sure.

244 00:22:29.780 ⇒ 00:22:46.240 Uttam Kumaran: Yeah, it’s a pure BI tool. So, again, like, roughly what we’re gonna do is you’re gonna have some financial data in Stripe, but you’re not gonna have all the product data. You have, sort of, everything in Postgres, but there’s no view on top of it, right? You’re gonna have some stuff in Catalyst, you’re also gonna have, like, Amplitude and, like, web events.

245 00:22:46.270 ⇒ 00:22:55.250 Uttam Kumaran: all of those products are all gonna have, like, dashboards, but again, they’re not gonna have information from us. So, what we do on the data team is, like, consolidate report, but

246 00:22:55.250 ⇒ 00:23:09.069 Uttam Kumaran: it’s not… it’s not a replacement for Catalyst at all. In fact, what I’m interested in is, like, do you need additional information in there that you don’t have today? And second, like, what stuff do you want to remain in Catalyst so we don’t duplicate

247 00:23:09.260 ⇒ 00:23:12.870 Uttam Kumaran: work, right? Like, that’s what I’m curious about.

248 00:23:13.110 ⇒ 00:23:34.830 Deanna Garcia: Yeah, so Catalyst, would sit… we’re pulling data from Salesforce as, like, the source of truth in terms of, like, account opportunity and, like, record, information. Outside of that, what we’re pulling in is, like, Gong information as well, so, like, Gong Insights and, like, Next Steps can pull into, like, Catalyst to automatically create tasks and notes and stuff, so the rest of the org just kind of sees high level what’s happening with an account.

249 00:23:34.830 ⇒ 00:23:59.169 Deanna Garcia: Outside of that, Catalyst can pull, like, embedded iframes into their dashboards for things like Tableau, Mode, Looker, Sigma.domo. So, like, if we have that data in one of those, for example, we can pull in that dashboard and just see that in the Catalyst platform, so CS doesn’t have to leave the tab to, like, see the reports. Oh, awesome. Yeah, outside of that, what we can also pull into Catalyst, we can pull

250 00:23:59.170 ⇒ 00:24:13.499 Deanna Garcia: like, we can sync up with Data Warehouse, right? So we can do, BigQuery, Google Data Cloud Storage, we can pull Postgres data into it as well, Redshift, S3, Snowflake, Mixpanel, Pendo, Segment, so it just kind of depends, like, where it’s coming from, but, like.

251 00:24:13.500 ⇒ 00:24:23.990 Deanna Garcia: we can pull that into Catalyst, see that against Salesforce data, and then build dashboards, in Catalyst with that information. I would say from, like, a

252 00:24:24.520 ⇒ 00:24:28.450 Deanna Garcia: BI perspective, Catalyst is more of, like, a…

253 00:24:28.600 ⇒ 00:24:37.560 Deanna Garcia: source of truth dashboarding tool, rather than, like, a replacement for Tableau, Looker, or Omni, right? Like, there might be additional pie charts and bars.

254 00:24:37.560 ⇒ 00:24:50.660 Uttam Kumaran: Well, I guess my question is more of, like, is there anything that isn’t there about the product, whether it’s, like, for example, a very common use case is, like, I need to see form submissions meetings booked in Catalyst attached to.

255 00:24:50.660 ⇒ 00:24:53.740 Deanna Garcia: Yeah, none of that’s in the… yeah, we don’t have any of that information.

256 00:24:53.740 ⇒ 00:25:09.670 Uttam Kumaran: Well, I guess, like, tell me… I guess what I’m asking is, like, is that… would that be important? Because that… that is what I would… what we typically call, like, reverse ETL, or it’s basically, like, taking data and pushing it into there. So if that’s important to have in Catalyst, because I assume Catalyst, you can have triggers and things like that, like.

257 00:25:09.670 ⇒ 00:25:10.190 Deanna Garcia: Exactly.

258 00:25:10.190 ⇒ 00:25:13.020 Uttam Kumaran: Form submissions goes off a cliff, it needs to get flagged.

259 00:25:13.020 ⇒ 00:25:13.570 Deanna Garcia: like…

260 00:25:13.570 ⇒ 00:25:14.320 Uttam Kumaran: That’s the sort of.

261 00:25:14.320 ⇒ 00:25:14.850 Deanna Garcia: Exactly.

262 00:25:14.850 ⇒ 00:25:16.059 Uttam Kumaran: I think we can support.

263 00:25:16.240 ⇒ 00:25:34.309 Deanna Garcia: Yeah, yeah, so the wishlist that I’ll put together for you, I would say I would love that information to be in Catalyst in some way, shape, or form, right? Like, whether it’s through… like, we’re just pulling it directly from Postgres if it’s there, instead of, like, it being… and then it’s, like, separately pulled into, like, an Omni or something else that, like, puts it into a pretty dashboard, right? But yeah, in Catalyst.

264 00:25:34.310 ⇒ 00:25:56.759 Deanna Garcia: Really the ideal state, of what we built in Catalyst, like, we can build health scoring, weighted health scoring, we can include usage data against, like, billing data against, Salesforce data, right? So, like, things like form submissions takes a plummet in 30 days, right? CS can get an automatic notification that, hey, form submissions have gone down, or, like, workflow… number of workflows created has gone down, or an integration.

265 00:25:56.760 ⇒ 00:26:07.949 Deanna Garcia: has, you know, broken, or whatever it might be. So yeah, I mean, as much product data as we can get into Catalyst, the better, because then we can start to build and automate some stuff, and some triggers and health scoring on the Catalyst end with it.

266 00:26:08.290 ⇒ 00:26:19.089 Uttam Kumaran: Okay, okay, great. So that gives me a good sense of that. I mean, I think it’s… it actually would be pretty easy for us to create for you a dashboard in Omni that’s just, like, filtered to a customer and see that.

267 00:26:19.430 ⇒ 00:26:22.790 Uttam Kumaran: Additionally, I think I don’t want to, like, for example.

268 00:26:23.170 ⇒ 00:26:31.939 Uttam Kumaran: if we… there’s an option to, like, build lead scoring in the data warehouse. If you guys are… if we’re just comfortable doing all that in Catalyst and Leaning, I would rather do that, because that…

269 00:26:31.940 ⇒ 00:26:32.570 Deanna Garcia: Yeah.

270 00:26:32.570 ⇒ 00:26:50.220 Uttam Kumaran: they probably have a bunch of ways to do that, that’s great, and they have the operational, like, hook into Slack or whatever to… to do those triggers and things, so that’s a great use case for Catalyst, and then we’ll… we’ll see, like, I don’t… I don’t… I don’t know if there’s any data from Catalyst, because we’re gonna… we can get the Salesforce data

271 00:26:50.500 ⇒ 00:27:08.570 Uttam Kumaran: you know, directly from Salesforce, so if there’s no direct catalyst-related information, then we’ll leave it at that for now, and then I’ll just try to basically plan on waiting for what you need to see in, like, a customer health dashboard, right? I guess my other question is, if there’s nothing else, so even just walk me through, if you have a

272 00:27:08.780 ⇒ 00:27:17.639 Uttam Kumaran: if you can walk me through a recent, like, renewal conversation where, like, you didn’t have the data, or a good one or a bad one, or, like, I’m just interested to hear

273 00:27:17.670 ⇒ 00:27:29.190 Uttam Kumaran: like, how these are going, and, like, what are the objections that you’re hearing? To give you, like, a bit more context, like, again, at WeWork, and even, like, when we were building this at Flowcode and stuff, basically you’re talking about

274 00:27:29.400 ⇒ 00:27:43.669 Uttam Kumaran: you’re talking about, like, okay, you saw this much growth, we’re seeing these many meetings. Ideally, like, this is the proposal we put together for the next year, or we want to graduate you into the next service. Additionally, I think

275 00:27:43.930 ⇒ 00:27:58.399 Uttam Kumaran: ideally, you want to be more proactive in finding customers that are, like, declining in usage, and identify, like, is this us problem, right? Like, why should we be proactively reaching out? Like, hey, you guys haven’t invited anybody, things like that, right? Is that generally.

276 00:27:58.400 ⇒ 00:27:58.920 Deanna Garcia: Yeah.

277 00:27:58.920 ⇒ 00:27:59.530 Uttam Kumaran: theme.

278 00:28:00.250 ⇒ 00:28:04.409 Deanna Garcia: Yeah, yeah, I mean, right now, everything is, like.

279 00:28:05.020 ⇒ 00:28:22.330 Deanna Garcia: as it comes, right? Like, within the current month of that renewal. Like, I think this month is the first time that we’ve actually… we’re, like, 90 days out of renewal. Right. Like, we finally fully caught up. But that being said, everything right now is just good, right? Like, how’s… like, what’s the health of the customer? I think it’s good, because they haven’t complained, right? Or, like, they’re quiet.

280 00:28:22.330 ⇒ 00:28:22.840 Uttam Kumaran: Yeah. So, like.

281 00:28:22.840 ⇒ 00:28:33.420 Deanna Garcia: it’s neutral, right? So, I would say ideal state, because we also haven’t had this data in terms of meeting books, form submissions, all that stuff, at least in a way that we can trust.

282 00:28:33.450 ⇒ 00:28:48.619 Deanna Garcia: And, like, actually rely on it in conversation. Most of it, I would say most of the pushback that we get is really just also, in addition to, the renewals that we’re doing this year, we had, like, changed our pricing as well, so everybody coming up against renewal is also coming against new pricing.

283 00:28:48.620 ⇒ 00:29:06.249 Deanna Garcia: So, like, any rebuttals we’re getting is just, like, on the pricing perspective, but then we also don’t have the data to back it up, to be like, well, this shouldn’t cost… this shouldn’t be a big cost to you, since you’ve booked X amount of meetings, and then depending on how many you’ve closed, and how many, you know, what your contract value is, like, you should have already made a return on investment.

284 00:29:06.250 ⇒ 00:29:11.859 Deanna Garcia: But again, that information we just don’t have. So, in the ideal world, we’re able to,

285 00:29:12.000 ⇒ 00:29:27.020 Deanna Garcia: full information, like meetings booked, form submissions, you know, how many meetings were actually held versus booked, things like that. And then through, like, CS conversation, we can get a little bit more context into, like, contract values and things like that for customers, so we can also kind of, like.

286 00:29:27.020 ⇒ 00:29:32.179 Deanna Garcia: start to form and calculate what, you know, how much revenue default has

287 00:29:32.180 ⇒ 00:29:39.080 Deanna Garcia: like, actually manage for you, right? Yes. That then offsets that conversation around how much your contract is now gonna be.

288 00:29:39.340 ⇒ 00:29:55.110 Uttam Kumaran: Okay, okay. Makes a lot of sense. Okay, perfect. So yeah, I mean, I think a great outcome here is, like, you have at least a data story to then fill out, and then ideally you move to a mode where, like, you can also proactively identify customers that are falling off.

289 00:29:55.140 ⇒ 00:30:02.599 Uttam Kumaran: I mean, it’s, again, very similar in our business, like, no news is, like, not good news. You want to be engaging with people.

290 00:30:02.600 ⇒ 00:30:03.480 Deanna Garcia: Exactly.

291 00:30:03.480 ⇒ 00:30:15.600 Uttam Kumaran: But I think you guys have all the infrastructure to do it, and we have form submissions, meetings booked, new users, existing users, and as well as, like, who those users are, like, their roles and stuff.

292 00:30:16.050 ⇒ 00:30:24.060 Uttam Kumaran: So ideally, if I can give you that whole story in one dashboard, then it allows you to go into that renewal conversation pretty…

293 00:30:24.470 ⇒ 00:30:30.680 Uttam Kumaran: well, and then ideally, if I can get you that data into Catalyst, I assume you can build workflows there to track.

294 00:30:31.150 ⇒ 00:30:35.270 Deanna Garcia: Exactly, exactly. And we can, we can pull things, I would say, like.

295 00:30:35.430 ⇒ 00:30:47.589 Deanna Garcia: the other big initiative for CS is gonna be, like, upsells and expansions, right? Like, direct new users, right? So we can pull that information against what we already have in Salesforce, which is, like, seats purchased and things like that against seats used.

296 00:30:47.590 ⇒ 00:30:57.669 Deanna Garcia: or seats… yeah, seats used that you’re pulling from product, right, into Catalyst, and then we can build, like, expansion signals and things like that, in the platform. So, so yeah, I would think of,

297 00:30:57.710 ⇒ 00:31:07.490 Deanna Garcia: I would think of Catalyst as a way that we can marry both the Salesforce data and the product data in a way that we can, like, build triggers. And then I would say, for the product data stuff.

298 00:31:07.560 ⇒ 00:31:09.139 Deanna Garcia: Ideally, we have…

299 00:31:09.420 ⇒ 00:31:22.250 Deanna Garcia: just a simple dashboard that CS can easily pull up before they’re jumping into a meeting, just to kind of pull agenda items. But we would already have, like, our triggers and stuff built in Catalyst with that data that we’re pulling directly from the source.

300 00:31:22.580 ⇒ 00:31:29.669 Uttam Kumaran: Yeah, and I actually think it’s a great idea if you can iframe the Omni thing in, which I’ll see, like, what their options are.

301 00:31:29.820 ⇒ 00:31:36.439 Uttam Kumaran: That’s… that’s honestly great, because then you can keep all your team in just one tool. Yeah, yeah, I don’t think…

302 00:31:36.440 ⇒ 00:31:44.700 Deanna Garcia: that Catalyst integrates with Omni as an embedded option. I think right now it’s just Tableau Mode, Looker, Sigma, Domo, Google Data Studio.

303 00:31:44.700 ⇒ 00:31:48.330 Uttam Kumaran: Well, if they support iframe, then I… then I think they’ll be…

304 00:31:48.700 ⇒ 00:31:51.869 Uttam Kumaran: Because the iframe is just, like, basically like a CSV, it’s just a…

305 00:31:51.870 ⇒ 00:31:56.630 Deanna Garcia: Well, it’s a… yeah, so they have, like, a… the way that they set it up.

306 00:31:56.630 ⇒ 00:31:57.370 Uttam Kumaran: Oh, they couldn’t.

307 00:31:57.370 ⇒ 00:31:58.730 Deanna Garcia: embedded module, yeah.

308 00:31:58.730 ⇒ 00:31:59.230 Uttam Kumaran: It’s an investment.

309 00:31:59.230 ⇒ 00:32:12.329 Deanna Garcia: module, and you connect directly to, like, you would choose which one you’re using, and then you would connect and add, like, the URL source code there, so I don’t know that we can, like, finagle Omni into it, but I think either way, even if it’s…

310 00:32:12.700 ⇒ 00:32:21.910 Deanna Garcia: if, like, we can link… we can link out to different things in the dashboards too, right? So we can have a section where it’s, like, for product data, click here, right? And then it’s not a huge deal.

311 00:32:21.910 ⇒ 00:32:31.820 Uttam Kumaran: The other thing, you know, it’s also helpful is Omni looks really amazing, but one thing at WeWork is we worked on basically a pretty large

312 00:32:31.820 ⇒ 00:32:42.400 Uttam Kumaran: like, Tableau dashboard that they would present during QBRs. So it’s actually, I think, could be great for even to present to a customer where the data looks good, like, here’s what your usage and default is.

313 00:32:42.800 ⇒ 00:32:43.970 Uttam Kumaran: I would say…

314 00:32:44.210 ⇒ 00:33:00.019 Uttam Kumaran: there… one, I know that, like, Caitlin and team are building out the product analytics in the product, but I’m sure that the data you guys have is probably stuff that they’re not even… their customers aren’t even really looking at, so that’s a great opportunity to say, like, well, here’s, like, your snapshot of…

315 00:33:00.020 ⇒ 00:33:00.470 Deanna Garcia: Exactly.

316 00:33:00.470 ⇒ 00:33:19.059 Uttam Kumaran: your default usage, which is your meetings and stuff like that. Yeah. And I think that’s, like, another great win, like, from our past, we saw is, like, really, really helpful. I worked at Flowcode before, it’s this QR code company. We’d always present, like, here’s the number of codes created, the number of scans you had, where the distribution was, and I think

317 00:33:19.690 ⇒ 00:33:23.549 Uttam Kumaran: It’s just a nice win for those conversations to, like, lead with that, because you talk about, like.

318 00:33:23.550 ⇒ 00:33:24.040 Deanna Garcia: Yeah.

319 00:33:24.590 ⇒ 00:33:25.120 Uttam Kumaran: You know.

320 00:33:25.120 ⇒ 00:33:26.350 Deanna Garcia: Yeah, exactly.

321 00:33:26.350 ⇒ 00:33:27.380 Uttam Kumaran: Yeah. Yeah.

322 00:33:28.160 ⇒ 00:33:35.530 Uttam Kumaran: Okay, cool. So I think if we can… if we can get an invite, too, to… to Catalyst, we have a brainforge at default.com.

323 00:33:35.530 ⇒ 00:33:36.240 Deanna Garcia: Okay.

324 00:33:36.240 ⇒ 00:33:52.200 Uttam Kumaran: So that would be great, and that way I can just see, like, what we need from, like, being able to add stuff into there. And then a wish list in terms of, like, what you need to see from a customer health dashboard. And once we get the basics in there,

325 00:33:52.340 ⇒ 00:34:00.529 Uttam Kumaran: We can poke at a couple of, like, the accounts coming up to, like, verify all the data, and ideally that’ll help, like, clean up a bunch of stuff.

326 00:34:00.680 ⇒ 00:34:06.060 Uttam Kumaran: And then, once we’re comfortable with the data, we can make it really pretty, so that’s something that’s client.

327 00:34:06.060 ⇒ 00:34:06.460 Deanna Garcia: Yeah.

328 00:34:06.660 ⇒ 00:34:07.600 Uttam Kumaran: Yeah. Presentable.

329 00:34:08.400 ⇒ 00:34:14.009 Deanna Garcia: Cool, yeah, as… right now, this week, we’re actually, like, in talks with signing with Catalyst. Like, I have a… I have a…

330 00:34:14.010 ⇒ 00:34:14.669 Uttam Kumaran: Okay, okay, okay.

331 00:34:14.679 ⇒ 00:34:16.569 Deanna Garcia: I can always add you just so you could see it, but…

332 00:34:16.770 ⇒ 00:34:20.129 Uttam Kumaran: That’s fine, then it’s… then it’s whenever you guys are ready for that, so…

333 00:34:20.139 ⇒ 00:34:20.719 Deanna Garcia: Yeah.

334 00:34:20.829 ⇒ 00:34:21.739 Deanna Garcia: Awesome.

335 00:34:23.429 ⇒ 00:34:30.519 Uttam Kumaran: Okay, cool. So I… if you… if you don’t have, like, a… if there’s not, like, a super strict deadline, I think we… Caitlin and I meet.

336 00:34:30.809 ⇒ 00:34:32.369 Uttam Kumaran: here on Thursdays.

337 00:34:32.589 ⇒ 00:34:39.699 Uttam Kumaran: I don’t know, Caleb, maybe just… we can plan to talk async and keep Thursdays, and we’ll try to send something over.

338 00:34:40.219 ⇒ 00:34:40.579 Deanna Garcia: Yeah.

339 00:34:40.580 ⇒ 00:34:46.590 Uttam Kumaran: probably next week. I know we have this data vendor workstream that we wanna… Fresh through first, so…

340 00:34:47.400 ⇒ 00:34:51.919 Deanna Garcia: Yeah, I can send you the wishlist stuff, I can talk to the team.

341 00:34:52.070 ⇒ 00:34:55.869 Uttam Kumaran: When’s the next, like, major renewal coming up?

342 00:34:56.520 ⇒ 00:34:59.600 Uttam Kumaran: That maybe you’d want to use something like this for.

343 00:35:01.430 ⇒ 00:35:02.090 Deanna Garcia: Mmm…

344 00:35:04.690 ⇒ 00:35:21.599 Deanna Garcia: I mean, since we’re… since we can now say that we’re, like, 90 days ahead, I think we have folks that are renewing in December that we could start to use some of this for. I would say we don’t have, like, a major deadline that I’m thinking through, other than I would love to get this stood up. I… just some context, I’m going on maternity leave in January.

345 00:35:21.600 ⇒ 00:35:21.960 Uttam Kumaran: Okay.

346 00:35:21.960 ⇒ 00:35:40.000 Deanna Garcia: I would love that, like, this stuff is, like, in place. Okay, that’s a good deadline. Which is, yeah, I mean, we’ve got a few months, but I would love to kind of get all of this settled so that while I’m out, CS has, like, everything they need to actually, like, even better than it, than, you know, before I left.

347 00:35:40.190 ⇒ 00:35:55.949 Deanna Garcia: So that’s kind of, like, my internal deadline for everything that I’m doing here, is, like, I would love, just before the holidays, like, we have all of this stuff nailed down, so that come January, towards the end of January when I’m out, everybody can already, like, kind of get accustomed to all of the new stuff that we’re gonna be doing in CS.

348 00:35:56.420 ⇒ 00:35:56.990 Uttam Kumaran: Okay.

349 00:35:57.320 ⇒ 00:36:08.819 Uttam Kumaran: Okay, yeah, I mean, we could easily hit that. I think in the next, probably, week or two, we’ll… we’ll have something that we can poke at, and then as soon as Catalyst is set up, we’ll focus on getting data into there, so you can set up triggers and stuff.

350 00:36:09.430 ⇒ 00:36:10.100 Deanna Garcia: Cold?

351 00:36:11.150 ⇒ 00:36:11.770 Uttam Kumaran: Okay.

352 00:36:12.670 ⇒ 00:36:15.820 Uttam Kumaran: That’s all I had on that side.

353 00:36:16.070 ⇒ 00:36:19.919 Uttam Kumaran: I guess, Caitlin, we can talk about BI tools and…

354 00:36:20.330 ⇒ 00:36:20.690 Caitlyn Vaughn: Mmm.

355 00:36:20.690 ⇒ 00:36:22.739 Uttam Kumaran: if that’s interesting or not.

356 00:36:22.740 ⇒ 00:36:24.120 Caitlyn Vaughn: I guess.

357 00:36:24.120 ⇒ 00:36:26.549 Deanna Garcia: Yeah. Yeah, I can hop.

358 00:36:26.940 ⇒ 00:36:30.000 Deanna Garcia: Unless you guys think that it’s helpful here, but yeah, I can help.

359 00:36:30.000 ⇒ 00:36:32.399 Caitlyn Vaughn: We’re just gonna talk through, like, what BI tool we’re gonna use.

360 00:36:32.400 ⇒ 00:36:37.229 Deanna Garcia: Okay, cool. Cool. Thank you, Tom.

361 00:36:37.480 ⇒ 00:36:39.120 Deanna Garcia: Thank you, bye. Thank you, sir.

362 00:36:40.910 ⇒ 00:36:42.580 Uttam Kumaran: That’s great. That’s a great use case.

363 00:36:42.820 ⇒ 00:36:49.379 Caitlyn Vaughn: Yeah, that is a really good use case. She’s, like, so… she’s so clever, she’s so smart, she’ll just, like.

364 00:36:49.570 ⇒ 00:36:55.650 Caitlyn Vaughn: come to me and just be like, hey, what about this really obvious, super valuable thing? It’s like.

365 00:36:55.940 ⇒ 00:37:00.550 Caitlyn Vaughn: Mind… it’s probably, like, once a quarter she does this, where she just blows my mind.

366 00:37:00.830 ⇒ 00:37:07.739 Uttam Kumaran: No, I’m glad that she, like, noticed it, and it gives us another person to sort of have pick at the dashboard and make sure everything’s accurate, so…

367 00:37:07.740 ⇒ 00:37:12.430 Caitlyn Vaughn: Yeah, totally. Yeah, so, Omni.

368 00:37:12.710 ⇒ 00:37:22.099 Caitlyn Vaughn: We are using equals for, like, revenue data specifically, but it’s just, like, another layer on top of Hyperline, essentially. Yeah.

369 00:37:22.220 ⇒ 00:37:29.719 Caitlyn Vaughn: I feel like Omni’s cool, I have no, like, strong opinions on it, honestly, because I haven’t, like, used all of them.

370 00:37:30.200 ⇒ 00:37:32.840 Uttam Kumaran: Yeah, I would say I have…

371 00:37:33.420 ⇒ 00:37:40.190 Uttam Kumaran: Really strong opinions when it comes to data tools, because you can get jammed, and you can get

372 00:37:40.460 ⇒ 00:37:53.160 Uttam Kumaran: basically shafted on price. We actually put this together this week, kind of for this conversation, but it’s something that we wanted to have for a bunch of customers. I don’t know if this is, like, way too big or small.

373 00:37:53.380 ⇒ 00:37:55.439 Uttam Kumaran: Is that any better?

374 00:37:56.020 ⇒ 00:37:57.839 Caitlyn Vaughn: That’s good, yeah.

375 00:37:57.960 ⇒ 00:38:14.809 Uttam Kumaran: Okay, so basically, like, when you’re picking BI tools, typically the way they price is they price on, like, a platform, which is, like, how much it costs for the platform, and then they also typically price on seats. What you’re seeing here is these are, like, the most common

376 00:38:14.910 ⇒ 00:38:30.900 Uttam Kumaran: tools in the market for, like, fast-growing companies, where you kind of want to assume two things. One, there’s people in your company that have used probably one or many of these tools in their past, so that’s good, so you don’t want to go with, like, some random, obscure

377 00:38:30.920 ⇒ 00:38:39.590 Uttam Kumaran: like, I would kind of put equals a little bit in that category, because they were just created, like, 3 years ago, and really focus on financial data.

378 00:38:39.640 ⇒ 00:38:48.299 Uttam Kumaran: most people in your business probably use Looker, Tableau, or Power BI, and in the startup world, people would have used probably Sigma,

379 00:38:48.380 ⇒ 00:38:53.700 Uttam Kumaran: Omni is basically, like, a combination of Looker and Tableau.

380 00:38:53.850 ⇒ 00:38:55.510 Uttam Kumaran: like, 3.0?

381 00:38:56.160 ⇒ 00:39:11.709 Uttam Kumaran: I think the product is actually, like, really, really incredible. The team is also really incredible, and, like, we’ve been using it… we use it for our stuff internally, and it’s kind of just, like, the best… basically the best tool on the market.

382 00:39:11.900 ⇒ 00:39:17.969 Uttam Kumaran: I would say the two tools that are, like, kind of, like, best in class right now are Omni and Sigma.

383 00:39:18.250 ⇒ 00:39:20.019 Uttam Kumaran: sig… I would say the…

384 00:39:20.270 ⇒ 00:39:27.350 Uttam Kumaran: the downside to a tool like this is there’s just, like, a lot. Like, there’s, like, models and views and stuff like that, so…

385 00:39:27.350 ⇒ 00:39:27.920 Caitlyn Vaughn: Hmm.

386 00:39:27.920 ⇒ 00:39:33.270 Uttam Kumaran: It’s not as simple as, like, an equals, or, like, but it’s also…

387 00:39:33.450 ⇒ 00:39:49.969 Uttam Kumaran: it’s a tool that you can kind of trust that whoever comes on and does data work is going to be able to use and is going to be very happy with the decision there. Basically, the cheapest option on this list is Power BI. It’s the worst product. Yeah.

388 00:39:50.650 ⇒ 00:40:08.250 Uttam Kumaran: Yeah, so I would urge against not using that. The other product we tried for y’all is Rill. I would say RIL is really good if you just have a couple of data sets, but the dashboarding functionality is not nearly as robust as Omni. Like, I wouldn’t use it for, like, a customer health

389 00:40:08.300 ⇒ 00:40:11.660 Uttam Kumaran: dashboard. They’re mainly pitching to, like, ad tech.

390 00:40:11.830 ⇒ 00:40:21.079 Uttam Kumaran: media, like, tons of time series data. You guys have multiple different types, so for y’all, it would either be, like, Looker, Sigma, Tableau, or Omni.

391 00:40:21.080 ⇒ 00:40:22.100 Caitlyn Vaughn: Right.

392 00:40:22.100 ⇒ 00:40:30.370 Uttam Kumaran: in terms of Omni pricing, their list price is, like, $36K for 10 seats. I can probably get…

393 00:40:30.870 ⇒ 00:40:35.679 Uttam Kumaran: like, well, one, you guys qualify for, I think, the 24K.

394 00:40:35.680 ⇒ 00:40:36.720 Caitlyn Vaughn: Huh.

395 00:40:36.800 ⇒ 00:40:50.529 Uttam Kumaran: So that’s another 10K off, and I think I can probably even do more damage, on that. So that puts you at roughly, like, $1 t o$ 2K a month. You get 10 seats out of the box. I need to confirm…

396 00:40:50.660 ⇒ 00:40:53.959 Uttam Kumaran: Like, what the makeup of that is, but…

397 00:40:54.300 ⇒ 00:40:59.079 Uttam Kumaran: I also think that most of your folks will just be, like, within these two buckets.

398 00:40:59.080 ⇒ 00:41:03.320 Caitlyn Vaughn: Yeah. So you’re probably looking at, like, 1K to 2K a month. Okay.

399 00:41:03.620 ⇒ 00:41:15.629 Uttam Kumaran: for Omni. The other benefits is they have a lot of great, like, AI features. So, being like, hey, what’s our top user, stuff like that. They’re, like, the best in class for

400 00:41:15.910 ⇒ 00:41:18.550 Uttam Kumaran: For that. Which is actually why

401 00:41:18.730 ⇒ 00:41:33.830 Uttam Kumaran: I’m, like, a big fanboy, because they’re really, really good at that. I don’t want to build that for our clients. It’s very hard to build, like, chat with your data type stuff, but it’s something that, like, there will be a lot of people in your company that may not have… may not be comfortable, like, using dashboards, but they just want to.

402 00:41:33.830 ⇒ 00:41:34.169 Caitlyn Vaughn: I’m a quick.

403 00:41:34.170 ⇒ 00:41:37.719 Uttam Kumaran: Ask the question, like, who’s our highest paying user, or, like.

404 00:41:37.930 ⇒ 00:41:45.239 Uttam Kumaran: quick lookups, and they have support for that, and they’re building, like, Slack stuff, so… I don’t know, overall, like.

405 00:41:46.290 ⇒ 00:41:48.669 Uttam Kumaran: Like, probably the best option here.

406 00:41:48.670 ⇒ 00:41:49.550 Caitlyn Vaughn: For Omni.

407 00:41:49.760 ⇒ 00:41:50.420 Uttam Kumaran: Yeah.

408 00:41:50.580 ⇒ 00:41:56.660 Caitlyn Vaughn: Yeah, yeah, yeah. Okay. Yeah, I think that makes sense. I checked out Sigma, and I do like Sigma as well.

409 00:41:56.660 ⇒ 00:41:57.280 Uttam Kumaran: Yeah.

410 00:41:57.280 ⇒ 00:42:02.480 Caitlyn Vaughn: But if you can get us a fat deal on Omni, I can definitely…

411 00:42:02.980 ⇒ 00:42:19.780 Caitlyn Vaughn: like, push everyone to go that way. I think I am, like, the sole driver of, like, the data thing at default. Like, I’m the person that cares the most about this, I’m realizing. But also, as soon as I started presenting the data that we.

412 00:42:19.780 ⇒ 00:42:20.360 Uttam Kumaran: Yeah.

413 00:42:20.360 ⇒ 00:42:22.640 Caitlyn Vaughn: All of a sudden, now, like…

414 00:42:23.100 ⇒ 00:42:23.580 Uttam Kumaran: Yes.

415 00:42:23.580 ⇒ 00:42:29.240 Caitlyn Vaughn: messaging me and being like, wow, this is so great, and I’m like, yeah, you know?

416 00:42:29.240 ⇒ 00:42:29.640 Uttam Kumaran: Yeah.

417 00:42:29.640 ⇒ 00:42:30.050 Caitlyn Vaughn: Like.

418 00:42:30.050 ⇒ 00:42:32.020 Uttam Kumaran: That’s what happens, which is great, you know?

419 00:42:32.020 ⇒ 00:42:32.500 Caitlyn Vaughn: PX.

420 00:42:32.500 ⇒ 00:42:33.420 Uttam Kumaran: Okay.

421 00:42:33.580 ⇒ 00:42:34.220 Uttam Kumaran: So…

422 00:42:34.220 ⇒ 00:42:46.590 Caitlyn Vaughn: This is, like, probably more of a case for this, but the other thing I’m thinking of is maybe this is, like, a good forcing function for us to, like, actually plug up… plug in the data directly to a tool like Omni, if we’re gonna, like, put spend on it.

423 00:42:46.910 ⇒ 00:42:47.610 Uttam Kumaran: Yeah.

424 00:42:47.780 ⇒ 00:42:51.500 Caitlyn Vaughn: Because as much as it’s fine to, like.

425 00:42:53.410 ⇒ 00:42:58.090 Uttam Kumaran: No, no, I mean, to support the… to support Deanna’s use case, like, we have to…

426 00:42:58.240 ⇒ 00:43:00.139 Uttam Kumaran: I have to figure that out.

427 00:43:00.300 ⇒ 00:43:07.709 Caitlyn Vaughn: Okay. Yeah. And maybe we can just do it on our side, like, have our team do it and hook it up, if that would make it better for us.

428 00:43:07.710 ⇒ 00:43:11.610 Uttam Kumaran: Yeah, if Thomas is still the right person, I could walk him through exactly, like.

429 00:43:12.270 ⇒ 00:43:12.900 Caitlyn Vaughn: What to do.

430 00:43:12.900 ⇒ 00:43:16.470 Uttam Kumaran: how to… what… how to shove that all into… into Mother Duck.

431 00:43:16.730 ⇒ 00:43:17.070 Caitlyn Vaughn: Pretty simple.

432 00:43:17.070 ⇒ 00:43:23.700 Uttam Kumaran: Simply, and then also, like, Mother Duck right now, I think we’re running for free. I don’t even know whether you’ll need to pay for them.

433 00:43:23.700 ⇒ 00:43:24.050 Caitlyn Vaughn: Okay.

434 00:43:24.050 ⇒ 00:43:30.829 Uttam Kumaran: If… and if you do, it’s 25 bucks a month, so it’s actually, like, dirt cheap, also.

435 00:43:32.250 ⇒ 00:43:36.649 Uttam Kumaran: Yeah, but also, again, I’m curious, even, like, in this process, I’m curious, like.

436 00:43:37.330 ⇒ 00:43:48.289 Uttam Kumaran: I mean, you’re also a friend, so I’m interested in even hearing, like, how you… how you’re deciding on, like, prices for vendors and stuff. Like, hearing… seeing something like this, is this helpful to, like, see it this way?

437 00:43:48.290 ⇒ 00:43:50.370 Caitlyn Vaughn: Yeah. Cause we also tr- we’re also, like, not…

438 00:43:50.730 ⇒ 00:43:54.269 Uttam Kumaran: I don’t… I don’t want to take… I don’t take… we don’t take money from any of the vendors.

439 00:43:54.700 ⇒ 00:43:56.430 Uttam Kumaran: And what… so what I do is I just…

440 00:43:56.540 ⇒ 00:43:58.589 Uttam Kumaran: Tell them to add it as another discount.

441 00:43:58.590 ⇒ 00:44:02.809 Caitlyn Vaughn: Yeah. So, like, some of these vendors will… yeah, some of these vendors will come to us and.

442 00:44:04.090 ⇒ 00:44:09.129 Uttam Kumaran: be like, oh, we can give you guys, like, some percent of the deal. I’m like, oh, so we’ll just further discount it.

443 00:44:09.680 ⇒ 00:44:20.880 Uttam Kumaran: our customers. And I think Omni has a pretty aggressive, like, they’re pretty aggressive right now on, like, both giving, like, discount, and I think they will actually put money towards

444 00:44:21.150 ⇒ 00:44:25.850 Uttam Kumaran: our fees to implement it, so I’m gonna try to get, like, as much as…

445 00:44:26.500 ⇒ 00:44:29.690 Caitlyn Vaughn: As possible. As much as I could get out from them. Yeah.

446 00:44:29.830 ⇒ 00:44:37.570 Caitlyn Vaughn: Okay. The only other thing that I’m thinking is we’re really just using this right now for, like, dashboarding, right? Like…

447 00:44:38.240 ⇒ 00:44:45.639 Caitlyn Vaughn: I’m sure there’s a ton of different use cases for tools like Omni, which is why it’s that expensive, that we’re, like, not using, right?

448 00:44:45.640 ⇒ 00:45:04.700 Uttam Kumaran: Yeah, so the biggest… the biggest way people… this tends to go is, like, for example, the customer health dashboard is probably going to be something you want to put in front of customers. You can then embed that into the product. So the… all of these tools will have… their money is really made a lot in enterprise, like, when you have, like, 100, a thousand people in BI tool.

449 00:45:04.700 ⇒ 00:45:05.330 Caitlyn Vaughn: Huh.

450 00:45:05.330 ⇒ 00:45:07.980 Uttam Kumaran: It’s all user-based, so the prices just rip.

451 00:45:07.980 ⇒ 00:45:11.009 Caitlyn Vaughn: And second, if you want to embed it into the product.

452 00:45:11.030 ⇒ 00:45:14.760 Uttam Kumaran: There’s, like, an added fee for, like, Embedded on me.

453 00:45:14.810 ⇒ 00:45:18.729 Caitlyn Vaughn: But it is an easy way, because… it is… it is something that you can consider.

454 00:45:18.860 ⇒ 00:45:23.239 Uttam Kumaran: But that’s, like, that is the other features. And then, out of all these.

455 00:45:23.680 ⇒ 00:45:29.900 Uttam Kumaran: Omni’s the only tool where I’ve seen significant AI… like, the AI capabilities are actually really, really solid.

456 00:45:30.200 ⇒ 00:45:38.689 Uttam Kumaran: Looker at Tableau, they’ve sort of been, like, left in the dirt. Power BI is, like, trash. Sigma is, like, okay. Real just came out with some stuff.

457 00:45:38.830 ⇒ 00:45:39.250 Caitlyn Vaughn: Huh.

458 00:45:39.250 ⇒ 00:45:44.979 Uttam Kumaran: But, like, I’m… I talked to the founder of Omni, like, the other week, and we’re interviewing them for some content.

459 00:45:44.980 ⇒ 00:45:45.420 Caitlyn Vaughn: Hmm.

460 00:45:45.420 ⇒ 00:45:50.930 Uttam Kumaran: Like, they’re, like, really think… it’s just a great product. They’re thinking about the AI stuff really well, so I think it’s a good bet.

461 00:45:51.360 ⇒ 00:45:59.599 Caitlyn Vaughn: Okay, wait, this is gonna get maybe more interesting. So, I also was looking at building out, like, a BI

462 00:45:59.960 ⇒ 00:46:01.590 Caitlyn Vaughn: tool for default, right?

463 00:46:01.590 ⇒ 00:46:02.060 Uttam Kumaran: Yeah.

464 00:46:02.060 ⇒ 00:46:06.039 Caitlyn Vaughn: Makes sense, especially in the, like, near term, to just do some, like, white labeling.

465 00:46:06.260 ⇒ 00:46:08.620 Uttam Kumaran: Yeah, that’s exactly this use case.

466 00:46:08.620 ⇒ 00:46:09.790 Caitlyn Vaughn: Oh, wow.

467 00:46:10.430 ⇒ 00:46:25.869 Uttam Kumaran: So if you’re… so, for example, if you… if you look at the customer health, or if you’re like, hey, there’s a pared-down version of this that we could just ship right to the product, basically, for your engineering team, all it is, is they just have to pass in, like, the customer ID,

468 00:46:26.040 ⇒ 00:46:35.429 Uttam Kumaran: the dashboard would get filtered for that customer, and you could just white-label everything, so that you would have, like, the customer would have no idea it’s Omni, and…

469 00:46:35.550 ⇒ 00:46:44.410 Uttam Kumaran: the white labeling features are really, really great. And then Omni would centralize the, like, authentication, right? And then…

470 00:46:44.730 ⇒ 00:46:49.910 Uttam Kumaran: Again, basically, now you can build You can build customer-facing dashboards.

471 00:46:50.020 ⇒ 00:46:52.350 Uttam Kumaran: Like, drag and drop versus, like.

472 00:46:53.310 ⇒ 00:46:56.499 Uttam Kumaran: Having, like, a full-stack team build it in the short term.

473 00:46:56.500 ⇒ 00:47:00.959 Caitlyn Vaughn: So, that’s another thing that I… like, that’s how we went at Flow Code, where I…

474 00:47:00.960 ⇒ 00:47:13.289 Uttam Kumaran: I got us… we were building a lot of stuff in Looker, and then I’m like, guys, it’s gonna take us, like, a year to build, like, a really sophisticated enterprise data product. I can build you that in 3 months off of existing Looker.

475 00:47:13.290 ⇒ 00:47:24.370 Uttam Kumaran: dashboards, and then we can ship that, because we were trying to raise money at the time, and, like, we… enterprise… enterprise data analytics was, like, a feature we needed to, like, get out to sell bigger contracts.

476 00:47:25.530 ⇒ 00:47:29.340 Uttam Kumaran: And so I was like, let’s just slap Looker in there, and then longer term, we can build it on our own.

477 00:47:30.340 ⇒ 00:47:33.910 Caitlyn Vaughn: This is fucking awesome. This is such a good idea.

478 00:47:34.280 ⇒ 00:47:38.549 Uttam Kumaran: So I don’t know how much time you, like, how much time did you earmark already for, like…

479 00:47:39.330 ⇒ 00:47:46.230 Uttam Kumaran: the data analytics. I mean, you showed me the mock-ups and stuff, but yeah, I think… I think…

480 00:47:47.030 ⇒ 00:47:56.199 Uttam Kumaran: This is gonna be cheaper than putting, like, an engineer or two on it, going through design, like, you could literally build the charts and have it show up in the product.

481 00:47:56.300 ⇒ 00:47:59.249 Caitlyn Vaughn: I think, yeah, we talked about,

482 00:47:59.450 ⇒ 00:48:11.209 Caitlyn Vaughn: we were talking about this, Nico and I, and he was just like, yeah, we should just, like, white label it for now, because it’s something we should have, but it’s… you could spend, like, a lot, a lot of time building it, it’s hard to build, right?

483 00:48:11.210 ⇒ 00:48:12.690 Uttam Kumaran: Yeah, it’s hella hard to build.

484 00:48:12.690 ⇒ 00:48:14.820 Caitlyn Vaughn: Yeah, let’s just, like, set it up quickly.

485 00:48:15.440 ⇒ 00:48:16.190 Uttam Kumaran: Yeah.

486 00:48:16.470 ⇒ 00:48:31.719 Uttam Kumaran: So that’s something, like, I also… I could get… I don’t think you… I think you should… we should just use it internally for now. You should see… the… also, the thing is, the product is really, really good looking. Like, you can do all types of crazy charts and shit. It’s… it’s like…

487 00:48:32.490 ⇒ 00:48:35.440 Uttam Kumaran: It would be really solid to put in front of your customers.

488 00:48:35.440 ⇒ 00:48:37.610 Caitlyn Vaughn: Especially your enterprise customers, I think.

489 00:48:37.860 ⇒ 00:48:42.919 Uttam Kumaran: they may expect something like this. And it gives your sales team, like, hey, we now…

490 00:48:43.710 ⇒ 00:48:49.909 Uttam Kumaran: we now offer enterprise data analytics, like, you know, or… and then you can do things like CSV exports of, like.

491 00:48:50.250 ⇒ 00:48:52.449 Uttam Kumaran: Default usage, like…

492 00:48:52.910 ⇒ 00:49:09.049 Uttam Kumaran: basically, you get… you just give all that functionality to your customers, and instead of building yourself, and then what we did at Flowcode is we built a… we built the free and, like, the pro version, and then the enterprise version, we white-labeled Looker. Because the free version was, like.

493 00:49:09.190 ⇒ 00:49:10.570 Uttam Kumaran: Simple charts.

494 00:49:10.890 ⇒ 00:49:18.999 Uttam Kumaran: And then the Enterprise one… because the way it will work is they will charge you per enterprise user.

495 00:49:19.300 ⇒ 00:49:23.670 Uttam Kumaran: And so, we had way more free and pro users than enterprise.

496 00:49:23.960 ⇒ 00:49:27.929 Uttam Kumaran: And I could get you what the pricing is, but we were like, let’s just… we’re gonna just give…

497 00:49:28.300 ⇒ 00:49:35.020 Uttam Kumaran: The embedded use case the white-label use case to enterprise folks, And then build our own…

498 00:49:35.310 ⇒ 00:49:37.860 Uttam Kumaran: Like, the free analytics, simply.

499 00:49:39.180 ⇒ 00:49:41.380 Uttam Kumaran: It’s just, like, 3 or 4 charts for them.

500 00:49:41.490 ⇒ 00:49:42.139 Caitlyn Vaughn: Yeah, that’s.

501 00:49:42.140 ⇒ 00:49:43.460 Uttam Kumaran: You know? Yeah.

502 00:49:43.460 ⇒ 00:49:48.109 Caitlyn Vaughn: Okay, cool. Would you intro us to the Omni team?

503 00:49:48.420 ⇒ 00:49:49.150 Uttam Kumaran: Yeah.

504 00:49:50.380 ⇒ 00:49:55.329 Caitlyn Vaughn: I’ll probably do a little bit of research with, like, OmniLooker and Sigma, just to, like.

505 00:49:56.340 ⇒ 00:49:57.130 Uttam Kumaran: You should, yeah.

506 00:49:57.130 ⇒ 00:49:59.800 Caitlyn Vaughn: sure I can speak to them.

507 00:50:00.520 ⇒ 00:50:01.090 Caitlyn Vaughn: With a.

508 00:50:01.090 ⇒ 00:50:08.689 Uttam Kumaran: You should also get them to, you should get them to use default as a, as a kicker for you guys signing. I asked…

509 00:50:08.690 ⇒ 00:50:09.150 Caitlyn Vaughn: Hmm…

510 00:50:09.150 ⇒ 00:50:11.700 Uttam Kumaran: I asked them what they were using, and let me,

511 00:50:12.770 ⇒ 00:50:17.050 Uttam Kumaran: Let me see what, my friend Greg said. He said they’re,

512 00:50:19.530 ⇒ 00:50:22.129 Uttam Kumaran: I think they’re… oh, yeah, they’re using Revenue Hero.

513 00:50:22.480 ⇒ 00:50:23.030 Caitlyn Vaughn: Paul.

514 00:50:23.160 ⇒ 00:50:24.220 Caitlyn Vaughn: Trash.

515 00:50:24.580 ⇒ 00:50:31.230 Uttam Kumaran: Yeah, so I said you should use default, they’re way better. He said, We’ve been using Revenue Hero.

516 00:50:33.510 ⇒ 00:50:37.960 Uttam Kumaran: And yeah, you should basically say, like, we need Revenue Hero to get this deal over the law.

517 00:50:38.260 ⇒ 00:50:41.430 Uttam Kumaran: Or we need default to get the deal over the line, yeah.

518 00:50:41.430 ⇒ 00:50:43.160 Caitlyn Vaughn: Okay, I’m down. That’s great.

519 00:50:44.890 ⇒ 00:50:45.900 Uttam Kumaran: So…

520 00:50:46.190 ⇒ 00:50:46.520 Caitlyn Vaughn: Perfect.

521 00:50:46.520 ⇒ 00:50:50.580 Uttam Kumaran: Okay, so yeah, I can, I’ll get that.

522 00:50:51.300 ⇒ 00:50:53.559 Caitlyn Vaughn: Will you send me that, like, sheet that you showed me?

523 00:50:53.560 ⇒ 00:50:54.780 Uttam Kumaran: Yes, yes.

524 00:50:54.900 ⇒ 00:50:56.049 Caitlyn Vaughn: That’s so helpful.

525 00:50:56.590 ⇒ 00:50:58.849 Uttam Kumaran: Thank you, yeah, I’m trying to… we’re just trying to, like…

526 00:50:59.500 ⇒ 00:51:07.000 Uttam Kumaran: Because we go talk to all these guys, and they get pricing, and I’m like, okay, I want to just make it really easy, and talking to software vendors sucks.

527 00:51:07.230 ⇒ 00:51:12.110 Uttam Kumaran: sucks. Like, and I don’t want our customers to, like, get scammed.

528 00:51:12.390 ⇒ 00:51:12.870 Caitlyn Vaughn: Yeah.

529 00:51:13.550 ⇒ 00:51:14.750 Caitlyn Vaughn: Yeah, every time I.

530 00:51:14.750 ⇒ 00:51:15.869 Uttam Kumaran: It’s really high pressure.

531 00:51:15.870 ⇒ 00:51:17.160 Caitlyn Vaughn: We’re, like…

532 00:51:17.160 ⇒ 00:51:28.209 Uttam Kumaran: a new software to look at it, and it’s… there’s no pricing on it, and you have to talk to sales. I’m like, fuck this, I’m out. I know, it’s high pressure, and I… I don’t know, you’re right, like, they bully you, and I don’t like… I don’t like talking to them.

533 00:51:28.210 ⇒ 00:51:28.980 Caitlyn Vaughn: Yeah.

534 00:51:28.980 ⇒ 00:51:36.899 Uttam Kumaran: But I have to talk… I’ve talked to a lot of them, and so, like, I want to take that burden on for our, like, clients, basically, if we can, you know.

535 00:51:36.900 ⇒ 00:51:42.689 Caitlyn Vaughn: It’s better when you, like, don’t have skin in the game to, like, push back on bullies, because when it’s you, you’re just like, I don’t know.

536 00:51:42.690 ⇒ 00:51:47.600 Uttam Kumaran: For me, I’m like, look, we gotta make a deal for our customers, so, like, you’re not getting any customers if you don’t…

537 00:51:47.600 ⇒ 00:51:48.140 Caitlyn Vaughn: Yeah.

538 00:51:48.140 ⇒ 00:51:51.930 Uttam Kumaran: And so I… but again, it’s fun because, like, I just pit them all against each other.

539 00:51:51.930 ⇒ 00:51:52.380 Caitlyn Vaughn: Gosh.

540 00:51:52.380 ⇒ 00:51:55.579 Uttam Kumaran: to get the best deals. So I’m… it’s nice, because I… I’m like a…

541 00:51:55.830 ⇒ 00:52:07.750 Uttam Kumaran: kind of like a third… kind of like a broker, but this is what I did. I didn’t want to take any fees from these guys, because what I didn’t like about consultants, too, is, like, they’ll be paid by, like, Omni to just shill Omni, and I don’t want…

542 00:52:08.050 ⇒ 00:52:11.170 Uttam Kumaran: I don’t think that’s, like, where our business is gonna get made in, like.

543 00:52:11.170 ⇒ 00:52:11.890 Caitlyn Vaughn: Yeah.

544 00:52:12.050 ⇒ 00:52:12.960 Uttam Kumaran: Being, like.

545 00:52:13.090 ⇒ 00:52:19.950 Uttam Kumaran: Omni over everything. I’m like, look, it depends. Like, if you don’t have budget, like, there’s really cheap versions. If you want embedded, there’s different… so, like.

546 00:52:20.260 ⇒ 00:52:27.400 Caitlyn Vaughn: Wait, yeah, wait, talk to me about Rel really quick. So, if our main use case is just dashboarding, why would we not use a tool like Reel?

547 00:52:28.270 ⇒ 00:52:34.120 Uttam Kumaran: The visualization options are, like, pretty limited, and the,

548 00:52:34.390 ⇒ 00:52:38.169 Uttam Kumaran: Like, it’s… it’s gonna be hard for non…

549 00:52:38.560 ⇒ 00:52:46.730 Uttam Kumaran: tech users to develop Unreal. Like, that’s what we found. Like, you have to do a lot of, like, writing a lot of code to develop.

550 00:52:46.730 ⇒ 00:52:47.250 Caitlyn Vaughn: Okay.

551 00:52:47.250 ⇒ 00:52:48.469 Uttam Kumaran: Unreal.

552 00:52:48.580 ⇒ 00:52:51.510 Uttam Kumaran: But it is… Pretty cheap. It’s…

553 00:52:51.510 ⇒ 00:52:52.020 Caitlyn Vaughn: God.

554 00:52:52.020 ⇒ 00:52:53.640 Uttam Kumaran: Like, 3K for the year.

555 00:52:53.770 ⇒ 00:52:57.170 Uttam Kumaran: But I don’t know, again, like, it’s clear that you’re gonna have, like.

556 00:52:57.410 ⇒ 00:53:02.299 Uttam Kumaran: Product manager use cases, customer service use cases.

557 00:53:02.470 ⇒ 00:53:03.040 Caitlyn Vaughn: And you’re gonna…

558 00:53:03.040 ⇒ 00:53:10.739 Uttam Kumaran: And you’re going to want to set, like, for example, in Omni, you can set things like, here’s our goal, versus, like, here’s what we’re achieving, and then alerts based on that.

559 00:53:10.740 ⇒ 00:53:12.719 Caitlyn Vaughn: It’s just like a robust BI.

560 00:53:13.220 ⇒ 00:53:14.410 Uttam Kumaran: platform.

561 00:53:14.410 ⇒ 00:53:18.309 Caitlyn Vaughn: Yeah. Real is good, and their pricing is really competitive.

562 00:53:18.310 ⇒ 00:53:31.579 Uttam Kumaran: But I… we’ve implemented real a bunch, too, and I’ve implemented more for customers that are, like, not… they’re just, like, looking at the dashboard, where you’re gonna… you have a lot of smart people at your company that are gonna want to build their own stuff.

563 00:53:31.780 ⇒ 00:53:40.509 Caitlyn Vaughn: For sure. Potentially, but I think for now, I think it might be okay if it’s just you guys, like, building for us.

564 00:53:41.260 ⇒ 00:53:46.880 Uttam Kumaran: Yeah, but I guess what I’m saying is, like, you’re gonna have to sign an annual deal with any of them.

565 00:53:47.160 ⇒ 00:53:47.930 Caitlyn Vaughn: Yeah.

566 00:53:47.930 ⇒ 00:53:53.989 Uttam Kumaran: And if you… If you’re gonna ditch it later, it’s just, like, it is an investment.

567 00:53:53.990 ⇒ 00:53:55.130 Caitlyn Vaughn: set up on the right one.

568 00:53:55.130 ⇒ 00:54:04.010 Uttam Kumaran: you just want to do this once, like, you don’t want to have to talk about… like, we’re… we have a lot of customers who have been on Looker for, like, 6-7 years, and they’re like, can we switch? And I’m like.

569 00:54:04.170 ⇒ 00:54:04.510 Caitlyn Vaughn: No.

570 00:54:04.510 ⇒ 00:54:07.349 Uttam Kumaran: Dude, this is a huge project, so…

571 00:54:07.850 ⇒ 00:54:10.409 Uttam Kumaran: This is just a decision you just don’t… don’t want to…

572 00:54:11.140 ⇒ 00:54:13.179 Uttam Kumaran: You just don’t want to make twice, you know?

573 00:54:13.900 ⇒ 00:54:24.339 Caitlyn Vaughn: Also, like, if we’re gonna rely on you guys for… like, if we went with Rel, and we were gonna rely on you guys to build everything out, and it was, like, pretty manual, we’re still paying you, like, $2.50 an hour or something, right?

574 00:54:24.340 ⇒ 00:54:41.590 Uttam Kumaran: So that’s the thing, is like, I want… that’s where, like, I don’t… we could still do that, of course, but I want you guys to be able to build certain stuff. Like, I want to equip Deanna with, like, if she needs to make a modification to the dashboard, here’s how she can go do that. Yeah. And you guys are gonna grow, and you’re gonna have more people that are gonna…

575 00:54:41.760 ⇒ 00:54:43.400 Uttam Kumaran: Want to do stuff, so…

576 00:54:44.180 ⇒ 00:54:44.790 Uttam Kumaran: Yeah.

577 00:54:45.160 ⇒ 00:54:50.050 Caitlyn Vaughn: Yeah, you guys are gonna have to teach us a little… class, an omni class soon.

578 00:54:50.050 ⇒ 00:54:52.899 Uttam Kumaran: Yes, yeah, and their support is really good, and…

579 00:54:52.900 ⇒ 00:54:53.310 Caitlyn Vaughn: It’s so.

580 00:54:53.310 ⇒ 00:55:00.370 Uttam Kumaran: Yeah. It is kind of hard, but once you get the hang of it, it’s really good, and they have just so many visualization options, and…

581 00:55:00.650 ⇒ 00:55:11.340 Uttam Kumaran: it’s a really good platform, and basically what happened in BI is all the smart people from all those companies, like, went to Omni in the last, like, 3-4 years. So the product is really good, but…

582 00:55:11.340 ⇒ 00:55:12.160 Caitlyn Vaughn: Okay, a lot better.

583 00:55:12.800 ⇒ 00:55:13.590 Uttam Kumaran: Yeah.

584 00:55:13.890 ⇒ 00:55:20.319 Caitlyn Vaughn: Okay, sick. That sounds good. If you could intro me, that would be great. I’m gonna do a little,

585 00:55:20.520 ⇒ 00:55:23.479 Caitlyn Vaughn: schmoozing on the default side to get this over.

586 00:55:23.480 ⇒ 00:55:27.199 Uttam Kumaran: S… Yes, definitely. Okay, cool.

587 00:55:27.200 ⇒ 00:55:34.409 Caitlyn Vaughn: And then for the testing, if you could get me the results for Owler today, we can probably chat about it later today, because we have that.

588 00:55:34.410 ⇒ 00:55:34.810 Uttam Kumaran: Okay.

589 00:55:34.940 ⇒ 00:55:35.830 Caitlyn Vaughn: with them.

590 00:55:36.170 ⇒ 00:55:37.729 Uttam Kumaran: Okay, perfect.

591 00:55:38.100 ⇒ 00:55:40.199 Caitlyn Vaughn: Sick! Thanks, Tom!

592 00:55:40.200 ⇒ 00:55:41.760 Uttam Kumaran: Thank you. Talk to you soon.

593 00:55:41.760 ⇒ 00:55:42.450 Caitlyn Vaughn: Bye.

594 00:55:42.650 ⇒ 00:55:43.070 Uttam Kumaran: Bye.

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