Meeting Title: Default | Brainforge Monthly Project Review Date: 2025-10-23 Meeting participants: Uttam Kumaran, Caitlyn Vaughn
WEBVTT
1 00:03:37.660 ⇒ 00:03:38.940 Caitlyn Vaughn: Hi!
2 00:03:39.940 ⇒ 00:03:40.990 Uttam Kumaran: Hello!
3 00:03:42.500 ⇒ 00:03:43.890 Caitlyn Vaughn: How’s it going?
4 00:03:43.890 ⇒ 00:03:44.760 Uttam Kumaran: Good.
5 00:03:44.960 ⇒ 00:03:46.039 Uttam Kumaran: How about you?
6 00:03:47.790 ⇒ 00:03:52.180 Caitlyn Vaughn: Good, I feel like I just have too many… Too much…
7 00:03:52.180 ⇒ 00:03:52.890 Uttam Kumaran: Come on.
8 00:03:52.890 ⇒ 00:03:55.010 Caitlyn Vaughn: Context with me going on.
9 00:03:55.410 ⇒ 00:03:56.040 Uttam Kumaran: Okay.
10 00:03:56.530 ⇒ 00:03:58.809 Caitlyn Vaughn: My brain might explode.
11 00:04:00.300 ⇒ 00:04:05.819 Uttam Kumaran: What’s the… I mean, I feel like the product is probably the most important stuff, right?
12 00:04:06.420 ⇒ 00:04:12.140 Caitlyn Vaughn: Yeah, but I just hired, a new… basically a new potential head of partnerships.
13 00:04:12.140 ⇒ 00:04:12.920 Uttam Kumaran: Yeah.
14 00:04:12.920 ⇒ 00:04:14.889 Caitlyn Vaughn: I’m just going full-time on product.
15 00:04:14.890 ⇒ 00:04:17.729 Uttam Kumaran: Like, I can’t keep doing both forever.
16 00:04:17.730 ⇒ 00:04:34.459 Caitlyn Vaughn: And so I was like, Nico, let me hire someone. And he was like, I don’t really think we need a full-time hire, but I met this woman named Aliza, and she’s so fucking awesome. She’s the one that was at, like, Rattle, LaunchDarkly, Datadoc, and she, like, built partnership programs all over there.
17 00:04:35.060 ⇒ 00:04:38.759 Caitlyn Vaughn: So, I managed to get her, like, part-time for now, and…
18 00:04:38.760 ⇒ 00:04:39.330 Uttam Kumaran: Yeah.
19 00:04:40.250 ⇒ 00:04:44.639 Caitlyn Vaughn: I’m like, great, you’re here, now you can just take over, but, like, I have to onboard her.
20 00:04:44.640 ⇒ 00:04:45.830 Uttam Kumaran: I know.
21 00:04:45.830 ⇒ 00:04:51.000 Caitlyn Vaughn: do all the intros and everything, and I’m like… she’s like, hey, are you gonna… I’m like, fuck me, I just have.
22 00:04:51.000 ⇒ 00:04:52.420 Uttam Kumaran: I know.
23 00:04:52.420 ⇒ 00:04:53.110 Caitlyn Vaughn: 50 parts.
24 00:04:53.110 ⇒ 00:05:05.269 Uttam Kumaran: It’s just, like, one week or two of just, like, helping people figure, and you’re like, yeah, just go figure it out. I will say, though, there’s a lot going on at your guys’ company, so it’s not surprised. Yeah.
25 00:05:05.270 ⇒ 00:05:06.269 Caitlyn Vaughn: Take some time.
26 00:05:06.270 ⇒ 00:05:07.130 Uttam Kumaran: Yeah.
27 00:05:07.130 ⇒ 00:05:17.390 Caitlyn Vaughn: 100%. But she’s, like, very self-sufficient, but, like, she’s just asking really normal questions that she shouldn’t be asking. And I’m like.
28 00:05:17.820 ⇒ 00:05:21.030 Caitlyn Vaughn: Anyways, also these Sigma guys are starting to piss me off.
29 00:05:21.170 ⇒ 00:05:22.010 Uttam Kumaran: Why?
30 00:05:22.600 ⇒ 00:05:25.110 Caitlyn Vaughn: They’re just so sales, you know?
31 00:05:25.520 ⇒ 00:05:35.530 Uttam Kumaran: I know, I’m surprised that you wanted to talk… call… call another vendor. I hate these guys, dude, it sucks, like, even, like… I mean, Greg is a friend of mine, but,
32 00:05:37.220 ⇒ 00:05:41.489 Uttam Kumaran: I don’t know, they just have no nuance. Like, no charm.
33 00:05:41.490 ⇒ 00:05:44.630 Caitlyn Vaughn: Yeah. I don’t know, Greg was, like, really cool.
34 00:05:44.630 ⇒ 00:05:55.970 Uttam Kumaran: Greg’s… Greg’s nice, but also, I’m the one that brought, like… I’m the facilitator here, so, like, of course, he’s not, like, putting it on, because he knows you guys already know the product, so…
35 00:05:55.970 ⇒ 00:05:57.779 Caitlyn Vaughn: Yeah, that’s true.
36 00:05:58.020 ⇒ 00:06:06.640 Caitlyn Vaughn: But Sigma, like, I really like the product, and they’re, like, nice and smart, and… likeable.
37 00:06:07.560 ⇒ 00:06:13.670 Caitlyn Vaughn: But at the end of the call, like, they gave me, like, an hour demo. After, like, a 30-minute…
38 00:06:13.830 ⇒ 00:06:17.120 Caitlyn Vaughn: what is it called? Like, a discovery call? Fuck that!
39 00:06:17.610 ⇒ 00:06:19.549 Caitlyn Vaughn: Show me your product. Like…
40 00:06:19.550 ⇒ 00:06:20.369 Uttam Kumaran: Yeah, yeah, I know.
41 00:06:20.370 ⇒ 00:06:24.929 Caitlyn Vaughn: Anyway, and then I got an hour call, and then at the end, he was like.
42 00:06:24.950 ⇒ 00:06:42.139 Caitlyn Vaughn: okay, well, let’s find some time with my director to, like, talk through… I’m like, no, like, you can email me. You can email me your questions. And I was like, we’ve covered everything. Like, you know how much I’ll pay for your product, so you can either give me that amount, or I’m gonna go with Omni. And he was like.
43 00:06:42.140 ⇒ 00:06:48.019 Caitlyn Vaughn: He was like, okay, but, like, I just don’t know if we can get that approved if we don’t get on… I’m like, I’m not getting on a call with you.
44 00:06:48.020 ⇒ 00:06:49.260 Caitlyn Vaughn: Like, you can email.
45 00:06:49.260 ⇒ 00:06:51.209 Uttam Kumaran: What’s… what’s this call today?
46 00:06:51.210 ⇒ 00:06:54.319 Caitlyn Vaughn: But this is different, this is for, like, technical.
47 00:06:54.320 ⇒ 00:06:56.279 Uttam Kumaran: Okay, okay. First of all architecture, yeah.
48 00:06:56.280 ⇒ 00:06:59.440 Caitlyn Vaughn: So I asked the,
49 00:07:00.210 ⇒ 00:07:04.580 Caitlyn Vaughn: Solutions Engineer a bunch of questions, and he was like, can we please have… can we just, like.
50 00:07:05.110 ⇒ 00:07:05.630 Uttam Kumaran: Yeah, yeah, yeah.
51 00:07:05.630 ⇒ 00:07:06.800 Caitlyn Vaughn: Like, fine.
52 00:07:06.800 ⇒ 00:07:10.049 Uttam Kumaran: Okay, I’ll try to make that meeting quick, then. I don’t think we’re gonna need…
53 00:07:10.230 ⇒ 00:07:13.850 Uttam Kumaran: Yeah, our… yeah, it’s only… it’s only 30 minutes. Okay, cool, yeah.
54 00:07:13.850 ⇒ 00:07:17.140 Caitlyn Vaughn: Yeah, and then I get fucking emails from them this morning.
55 00:07:17.610 ⇒ 00:07:19.890 Caitlyn Vaughn: Let me read this to you.
56 00:07:21.080 ⇒ 00:07:23.880 Uttam Kumaran: I just ghost. I just can’t.
57 00:07:24.200 ⇒ 00:07:33.090 Caitlyn Vaughn: Jack said, Hey Caitlin, looking forward to the chat. I saw Utam was added to the invite. I think it would be helpful to have your CTO on as well. Is Victor able to join?
58 00:07:33.660 ⇒ 00:07:36.170 Caitlyn Vaughn: Where did you get his name from, firstly?
59 00:07:36.490 ⇒ 00:07:38.170 Caitlyn Vaughn: Secondly, I…
60 00:07:38.170 ⇒ 00:07:39.569 Uttam Kumaran: Why does he need to be there?
61 00:07:39.830 ⇒ 00:07:52.229 Caitlyn Vaughn: And then the SE emailed me on the same train, Hey Caitlin, is your CTO able to make it to the call? We just want to ensure if he’s not able to, then Utom will be able to talk about the architecture design decision, so we can make a best plan forward.
62 00:07:52.530 ⇒ 00:07:55.040 Caitlyn Vaughn: I’m like, I’m about to cancel this.
63 00:07:56.830 ⇒ 00:08:00.080 Uttam Kumaran: Yeah, I’ll, yeah.
64 00:08:00.280 ⇒ 00:08:04.030 Uttam Kumaran: I’ll just make sure to… yeah. I don’t know why they’re tall…
65 00:08:04.170 ⇒ 00:08:06.160 Caitlyn Vaughn: Okay, whatever. Yeah.
66 00:08:06.310 ⇒ 00:08:13.469 Uttam Kumaran: And they’re… this is crazy, you know how loaded they are? Look, there’s 5… there’s, like, 5 fucking people from their company on this one call.
67 00:08:13.470 ⇒ 00:08:14.170 Caitlyn Vaughn: Yo…
68 00:08:15.190 ⇒ 00:08:22.110 Uttam Kumaran: John, Kevin, Tyler, and Jack. We have, we have all the white dude, Avengers.
69 00:08:22.250 ⇒ 00:08:24.450 Uttam Kumaran: On one call together.
70 00:08:24.450 ⇒ 00:08:28.000 Caitlyn Vaughn: We need to be bringing more people from our side so that we can all argue.
71 00:08:28.000 ⇒ 00:08:38.609 Uttam Kumaran: No! I’m down 5 on 1, I’m down. Let’s go. I don’t give a fuck. Bring 10 people. I’ll waste all your time.
72 00:08:38.610 ⇒ 00:08:42.100 Caitlyn Vaughn: I’m literally just not even gonna show up, I’m just gonna send you in.
73 00:08:42.100 ⇒ 00:08:47.339 Uttam Kumaran: I don’t mind either. Well, it’s just like, dude, what could they want? Like, I don’t know…
74 00:08:47.790 ⇒ 00:08:53.079 Uttam Kumaran: The problem with every tool is they think they’re, like, the star of the show.
75 00:08:53.220 ⇒ 00:08:53.730 Caitlyn Vaughn: Yeah.
76 00:08:53.730 ⇒ 00:09:09.869 Uttam Kumaran: And they forget that, like, your job is to get my data visualized so I can make decisions. My company is not, like, a number one sigma fan, like, I’m not… we hopefully don’t have to talk much about you if you get your job right, you know?
77 00:09:10.030 ⇒ 00:09:10.680 Uttam Kumaran: Assuming.
78 00:09:10.680 ⇒ 00:09:14.830 Caitlyn Vaughn: Everything’s good. We will never talk about you or talk to you ever again.
79 00:09:14.830 ⇒ 00:09:32.379 Uttam Kumaran: Yeah, it’s also, like, they’re not, like, a… their impact is on, like, you guys making decisions and understanding your product. Like, for example, your product is, like, really close to the revenue side, right? So, it is… it’s clear when you guys walk in, you’re like, hey, we can directly affect revenue. Like, for them.
80 00:09:32.920 ⇒ 00:09:50.040 Uttam Kumaran: it’s just… the whole data ecosystem and world is just filled with, like, this… these types of people. This is, like, my whole career is with these types of guys, who just, like, they think that their small piece of the pie is, like, everything. Like, the Mother Duck guys were great, because I always just send them, hey, like, we’re working with default.
81 00:09:50.250 ⇒ 00:09:54.420 Uttam Kumaran: they’re like, hey, we have two people that are on their account. I was like, what do you mean, on their account? I’m…
82 00:09:54.660 ⇒ 00:09:59.870 Uttam Kumaran: Nobody’s ever… no one’s ever talked to me about on their account, and I was like, don’t worry, don’t touch it.
83 00:10:00.070 ⇒ 00:10:00.800 Caitlyn Vaughn: Yeah.
84 00:10:00.800 ⇒ 00:10:01.679 Uttam Kumaran: We’re good, like…
85 00:10:01.680 ⇒ 00:10:02.040 Caitlyn Vaughn: Duh.
86 00:10:02.040 ⇒ 00:10:03.470 Uttam Kumaran: They don’t need anything.
87 00:10:03.740 ⇒ 00:10:04.770 Caitlyn Vaughn: Funny.
88 00:10:05.140 ⇒ 00:10:09.869 Caitlyn Vaughn: I was literally like, send me an email with the proposal. Smiley face.
89 00:10:11.660 ⇒ 00:10:12.210 Uttam Kumaran: Yeah, it’s funny.
90 00:10:12.210 ⇒ 00:10:16.759 Caitlyn Vaughn: I think they piled everybody on. I think the director’s on. If he’s on, I’m gonna, like, leave.
91 00:10:17.350 ⇒ 00:10:32.200 Uttam Kumaran: I hate… you know they’re gonna say, okay, guys, hey, I just want to say thank you so much for your time. Utah, it’s really nice to meet you. I was gonna be just a quick round of intros, just to reassess and reconfigure and realign.
92 00:10:32.720 ⇒ 00:10:39.510 Uttam Kumaran: and blah blah blah. Oh, where are you from? Oh, like, yeah, I love Austin, oh my god, I was just there for…
93 00:10:39.700 ⇒ 00:10:43.360 Uttam Kumaran: blah. It’s just gonna be, like, go 10 minutes of that.
94 00:10:44.330 ⇒ 00:10:48.110 Uttam Kumaran: And I was like, guys, it’s very simplified, we have… so, yeah. I think it’ll be good.
95 00:10:48.420 ⇒ 00:10:59.729 Caitlyn Vaughn: And at the end of my last call, they were like, hey, we just want to make sure, like, everyone has been looped on this call that needs to be, you know, for the decision making. And I’m like.
96 00:10:59.850 ⇒ 00:11:05.370 Caitlyn Vaughn: yeah, we’re an early-stage startup, and it’s… it’s me, babe. It’s fucking me, you know?
97 00:11:05.370 ⇒ 00:11:05.920 Uttam Kumaran: Yeah.
98 00:11:05.920 ⇒ 00:11:13.920 Caitlyn Vaughn: They were like, okay, well, has there been budget scope for this? I’m like, yeah, we raised $10 million. Fuck you.
99 00:11:13.920 ⇒ 00:11:26.770 Uttam Kumaran: No, dude, even Greg messed me that. He’s like, yeah, I just want to confirm, like, who’s gonna be setting everything up. I’m like, we set it all up. He’s like, but I just want to make sure, like, it… and I was like, what question are you asking me? Like, and I was like.
100 00:11:26.770 ⇒ 00:11:40.769 Uttam Kumaran: Kaylin is the head of product, so she… we’re… she’s our stakeholder, and then we’re starting to work with CS, we’re working on it, we will train people internally to start building on it, too. What questions, like, what do you ask? He’s like, oh, that’s all I needed. I was like.
101 00:11:40.900 ⇒ 00:11:45.280 Uttam Kumaran: And you needed to, like, ask me that question? Like, what? I don’t get it.
102 00:11:46.170 ⇒ 00:11:52.569 Caitlyn Vaughn: I’ll literally ask Victor questions, and he’ll answer, and then he’ll be like, can you please handle this yourself? And I’m like, yes, I can.
103 00:11:52.570 ⇒ 00:11:53.040 Uttam Kumaran: S.
104 00:11:54.070 ⇒ 00:11:59.739 Uttam Kumaran: Yes, that’s why, like, dude, we’re gonna build this and never hop on the phone, because it’s actually not, like, that hard.
105 00:11:59.740 ⇒ 00:12:00.130 Caitlyn Vaughn: Yeah.
106 00:12:00.130 ⇒ 00:12:02.420 Uttam Kumaran: It’s just, like, all of these competing interests.
107 00:12:02.600 ⇒ 00:12:07.460 Uttam Kumaran: Overcomplicate it, and… Like, yeah.
108 00:12:07.970 ⇒ 00:12:10.860 Uttam Kumaran: I don’t… I think for Victor, especially, I’m like.
109 00:12:11.150 ⇒ 00:12:18.240 Uttam Kumaran: I want to build your team something that can last, and that you guys don’t have to worry about coming in and being, oh, we picked this shit.
110 00:12:18.460 ⇒ 00:12:31.340 Uttam Kumaran: warehouse tool, or blah blah blah. And also, like, as soon as we have basic setup, like, if Thomas is gonna… I was gonna ask you today, too, if, like, Thomas is gonna end up being the sort of, like, the internal data person, then I’ll just train him up to, like.
111 00:12:31.510 ⇒ 00:12:33.839 Uttam Kumaran: start to do all the stuff we’re doing.
112 00:12:34.190 ⇒ 00:12:38.150 Uttam Kumaran: And that’ll be perfect. Like, I think he could learn a lot from us, and he could start to…
113 00:12:38.760 ⇒ 00:12:43.419 Uttam Kumaran: basically be the internal DE, if that’s kind of the direction, at least even part-time.
114 00:12:43.680 ⇒ 00:12:51.959 Caitlyn Vaughn: Yeah, so he’s, like, baby engineer, he just graduated from university, but he’s, like, our age. Yeah.
115 00:12:51.960 ⇒ 00:12:52.889 Uttam Kumaran: the other day.
116 00:12:52.890 ⇒ 00:12:53.730 Caitlyn Vaughn: You did?
117 00:12:53.870 ⇒ 00:12:57.309 Uttam Kumaran: Yeah, we chatted, we were chatting about, the GitHub stuff.
118 00:12:57.310 ⇒ 00:13:11.240 Caitlyn Vaughn: Okay, cool. Yeah, he’s, like, really smart. He was gonna be, like, a physician or something, and then he ended up hating it and quitting and going to, like, become an engineer. Yeah. Which is cool. And he’s, like, really good friends with Nico. Okay.
119 00:13:11.890 ⇒ 00:13:20.800 Caitlyn Vaughn: All of us, have started this bit where we all pretend like he’s Nico’s brother, and he’s like a Nepo baby, and so…
120 00:13:21.240 ⇒ 00:13:23.240 Caitlyn Vaughn: It’s, like, really quiet and shy, right?
121 00:13:23.240 ⇒ 00:13:24.200 Uttam Kumaran: Yes.
122 00:13:24.200 ⇒ 00:13:31.330 Caitlyn Vaughn: Thomas just getting promoted because he’s a fucking Nepo baby, and he’s just like, guys, we’re not related.
123 00:13:32.750 ⇒ 00:13:38.650 Caitlyn Vaughn: And the entire company’s on board now, so it’s like, it comes from every single person, it’s so funny.
124 00:13:38.650 ⇒ 00:13:41.970 Uttam Kumaran: That’s funny, yeah, he seems like a shy guy. He seems nice, though.
125 00:13:41.970 ⇒ 00:13:46.230 Caitlyn Vaughn: Yeah, yeah, we’re gone. Okay, that sounds good.
126 00:13:46.970 ⇒ 00:13:50.839 Caitlyn Vaughn: So the… what were we talking about? What were we gonna talk about?
127 00:13:50.840 ⇒ 00:13:55.470 Uttam Kumaran: Yeah, so there’s a couple things. So, one is I wanted to share.
128 00:13:56.130 ⇒ 00:13:59.669 Caitlyn Vaughn: Well, how… so, one thing I want to follow up on is…
129 00:14:00.010 ⇒ 00:14:07.740 Uttam Kumaran: This sort of, like, how we’re gonna start to save, the fields for every API.
130 00:14:08.380 ⇒ 00:14:11.359 Uttam Kumaran: I just wanted to flash you this, and then we’re gonna…
131 00:14:11.740 ⇒ 00:14:22.480 Uttam Kumaran: run through this for everything, which is just, like, here’s the field… this is… this is from People Data Labs, so this just gives you all the information on, like, what the field is.
132 00:14:23.210 ⇒ 00:14:30.149 Uttam Kumaran: Like, the description, and some information. We were gonna get this for every endpoint, for every…
133 00:14:30.260 ⇒ 00:14:33.410 Uttam Kumaran: Vendor, and then give you, like, kind of a master list, basically.
134 00:14:37.600 ⇒ 00:14:43.909 Caitlyn Vaughn: Perfect, yeah, that looks good. Can you go to the top so I can see the, yeah, short description requires iPhone…
135 00:14:44.310 ⇒ 00:14:45.889 Caitlyn Vaughn: What is required?
136 00:14:46.440 ⇒ 00:14:50.979 Uttam Kumaran: These are more about, like, the People Data Lab, like, pricing, I think.
137 00:14:51.640 ⇒ 00:14:54.979 Uttam Kumaran: So, like, depending on the plan you’re on, and depending on…
138 00:14:55.160 ⇒ 00:14:57.400 Uttam Kumaran: They have some other, like, pricing-related
139 00:14:58.130 ⇒ 00:15:08.439 Uttam Kumaran: like, this is more about the plan, I think, that they are on with them, and then this is another field… this is, like, something specific to People Data Labs, where there’s, like, a feature where you can search
140 00:15:08.720 ⇒ 00:15:12.009 Uttam Kumaran: Versus just, like… you can search for this versus, like.
141 00:15:12.430 ⇒ 00:15:17.899 Uttam Kumaran: pull it, like, again, some of the APIs are gonna have, like, slight differences.
142 00:15:18.120 ⇒ 00:15:23.729 Uttam Kumaran: like, some unique functionality, but the biggest thing is I at least want to get you every feel for every vendor in one place.
143 00:15:24.120 ⇒ 00:15:31.210 Caitlyn Vaughn: Yeah, that’s gonna be so nice, because the amount of times I get, like, from the team being like, are we gonna have this field? And I’m like.
144 00:15:31.780 ⇒ 00:15:35.329 Caitlyn Vaughn: I don’t know, we’ve got, like, 15 vendors, I’m sure one of them has it, but…
145 00:15:35.330 ⇒ 00:15:38.989 Uttam Kumaran: Okay, great, so then that’s… okay, so that’s really helpful. So what I’ll do is,
146 00:15:39.720 ⇒ 00:15:42.480 Uttam Kumaran: I will… I’ll just give you a master list of everything.
147 00:15:42.480 ⇒ 00:15:43.140 Caitlyn Vaughn: Yes.
148 00:15:43.140 ⇒ 00:15:45.469 Uttam Kumaran: One place. Yeah. And then.
149 00:15:45.470 ⇒ 00:15:48.070 Caitlyn Vaughn: And then also have the vendor on there, like the field.
150 00:15:48.070 ⇒ 00:15:48.930 Uttam Kumaran: Exactly.
151 00:15:49.070 ⇒ 00:15:49.750 Caitlyn Vaughn: Okay.
152 00:15:50.000 ⇒ 00:15:52.649 Uttam Kumaran: So yeah, what I’ll do is I’m gonna create
153 00:15:53.640 ⇒ 00:16:02.980 Uttam Kumaran: Yeah, I’m gonna decide what… depending on how many we have, if I… I’ll just put it all in one sheet or keep separate, but the other thing is, like, do you care about, like, if we should categorize these?
154 00:16:03.400 ⇒ 00:16:05.379 Uttam Kumaran: At all?
155 00:16:06.090 ⇒ 00:16:07.180 Caitlyn Vaughn: No, it’s fine.
156 00:16:07.180 ⇒ 00:16:13.999 Uttam Kumaran: Okay, because some of them will just be named differently, but again, like, you can just… it’ll be easy if you sort it by the field name.
157 00:16:14.000 ⇒ 00:16:16.009 Caitlyn Vaughn: To see all the ones, and then…
158 00:16:16.050 ⇒ 00:16:17.729 Uttam Kumaran: You can easily see, like, hey.
159 00:16:17.880 ⇒ 00:16:20.230 Uttam Kumaran: Five different vendors of employee churn rate.
160 00:16:20.660 ⇒ 00:16:21.790 Uttam Kumaran: Things like that.
161 00:16:21.790 ⇒ 00:16:25.340 Caitlyn Vaughn: Yeah, this looks great, this is exactly what I had in mind.
162 00:16:25.760 ⇒ 00:16:34.400 Uttam Kumaran: Okay, cool, so we’ll wrap this up. This was pretty quick. The other thing to… so we have, yeah, so we got you the ARR…
163 00:16:35.060 ⇒ 00:16:36.770 Uttam Kumaran: in Omni?
164 00:16:37.620 ⇒ 00:16:38.050 Caitlyn Vaughn: Mmm.
165 00:16:38.050 ⇒ 00:16:44.159 Uttam Kumaran: And then also the, what’s it called?
166 00:16:44.540 ⇒ 00:16:48.699 Uttam Kumaran: the workflow runs in Omni. I don’t know if you had a chance to…
167 00:16:50.910 ⇒ 00:16:55.660 Caitlyn Vaughn: I saw the message, but I haven’t looked at it yet. Also, it was down for me, like, 2 days ago.
168 00:16:56.000 ⇒ 00:16:57.520 Uttam Kumaran: Because AWS was, like.
169 00:16:57.520 ⇒ 00:16:58.350 Caitlyn Vaughn: Damn.
170 00:16:58.350 ⇒ 00:16:59.440 Uttam Kumaran: Yeah, the whole thing.
171 00:17:00.550 ⇒ 00:17:04.460 Uttam Kumaran: Which is just insane, like, I don’t even know… half the stuff was down, I was like.
172 00:17:04.990 ⇒ 00:17:05.349 Caitlyn Vaughn: It’s fun.
173 00:17:05.359 ⇒ 00:17:08.029 Uttam Kumaran: ruining, like, a ton of stuff, like, I…
174 00:17:08.329 ⇒ 00:17:11.319 Caitlyn Vaughn: Yeah, it was not… not a good time for anyone.
175 00:17:11.680 ⇒ 00:17:13.979 Uttam Kumaran: Yeah, were you guys, like, affected, your stuff?
176 00:17:14.930 ⇒ 00:17:19.310 Caitlyn Vaughn: Oh yeah, we were. We had a… Outages, and people were pissed.
177 00:17:19.740 ⇒ 00:17:24.559 Uttam Kumaran: Okay. Oh yeah, I guess it is tough, because nobody can book fucking meetings.
178 00:17:24.569 ⇒ 00:17:25.509 Caitlyn Vaughn: Is what?
179 00:17:25.510 ⇒ 00:17:29.380 Uttam Kumaran: Nobody can book meetings, and your guys’ stuff is Yep.
180 00:17:29.700 ⇒ 00:17:31.880 Caitlyn Vaughn: That was really convenient.
181 00:17:32.180 ⇒ 00:17:38.270 Uttam Kumaran: Okay. Yeah, so, I mean, the other stuff, like, we added before is, like, we added, like.
182 00:17:38.560 ⇒ 00:17:43.220 Uttam Kumaran: stuff about customers, about their funding rounds, and this is all stuff that I’ll…
183 00:17:43.410 ⇒ 00:17:55.720 Uttam Kumaran: I’ll… one thing I’ll share also after this is, like, my conversation with Deanna, so I’ll give you, like, what she asked for, and then you can get some thoughts on you and prioritization, but we put the workflow runs per month here.
184 00:17:56.330 ⇒ 00:18:04.580 Uttam Kumaran: And then, yeah, I mean, of course, we’re waiting on updated data, so…
185 00:18:04.580 ⇒ 00:18:04.980 Caitlyn Vaughn: Oh, yeah.
186 00:18:04.980 ⇒ 00:18:13.980 Uttam Kumaran: And then, similarly, Which, like, dude, all your numbers are really going, like, up in general. Like…
187 00:18:14.720 ⇒ 00:18:20.390 Uttam Kumaran: it’s… I’ve been with some clients where, like, this is usually, like, this is sometimes, like, not a good reveal.
188 00:18:20.390 ⇒ 00:18:21.419 Caitlyn Vaughn: Yeah, you’re like…
189 00:18:21.420 ⇒ 00:18:32.180 Uttam Kumaran: It’s not, like, a good best gender reveal, because sometimes the numbers aren’t going up, but for you guys, everything is, like, scaling really, really well, roughly. So, we haven’t done any intense analysis on anything, but…
190 00:18:32.360 ⇒ 00:18:35.080 Uttam Kumaran: Well…
191 00:18:35.120 ⇒ 00:18:46.359 Caitlyn Vaughn: We’re growing like crazy. We hit 1 million in ARR in, like, February. It took us 18 months to hit our first million, then 6 months to get to 2.
192 00:18:46.690 ⇒ 00:18:47.070 Uttam Kumaran: Wow.
193 00:18:47.070 ⇒ 00:18:51.970 Caitlyn Vaughn: We’re trying to hit 3.3 million by the end of the year, and then 12 by the end of next year.
194 00:18:51.970 ⇒ 00:19:00.150 Uttam Kumaran: Yeah, so one thing I… one thing I want to show, and one thing this doesn’t show, is, like, I need to… we need to start getting the Hyperline data, and I’ll… I can even…
195 00:19:00.300 ⇒ 00:19:03.619 Uttam Kumaran: That’s a good thing, I should call Hyperline.
196 00:19:03.950 ⇒ 00:19:12.049 Uttam Kumaran: But I want to get snapshots of it every day so that I can show you the growth. Right now, we’re just doing a one-time snapshot of all your customers.
197 00:19:12.270 ⇒ 00:19:17.249 Uttam Kumaran: This is basically, like, showing you the, the ARR…
198 00:19:17.600 ⇒ 00:19:23.190 Uttam Kumaran: Per customer, and then what percentage of it is seats versus the flat fee?
199 00:19:23.320 ⇒ 00:19:24.390 Caitlyn Vaughn: Ugh.
200 00:19:24.660 ⇒ 00:19:26.790 Caitlyn Vaughn: That’s really interesting, actually.
201 00:19:26.790 ⇒ 00:19:31.910 Uttam Kumaran: Yeah, so, I mean, you guys, I would say, like.
202 00:19:32.690 ⇒ 00:19:36.969 Uttam Kumaran: You do have, like, some concentration, but not a lot.
203 00:19:37.200 ⇒ 00:19:44.600 Uttam Kumaran: I think probably what that shows is, like, there’s gonna be a lot of people here that should get moved here.
204 00:19:45.160 ⇒ 00:19:46.150 Caitlyn Vaughn: Like…
205 00:19:46.290 ⇒ 00:20:00.759 Uttam Kumaran: Right? And so, one of the things that I want to work with, like, CS on is, like, identifying who here, like… so some of these people, I want to see, like, who jumped from, like, who… who in… probably overlay this with, like, usage.
206 00:20:01.130 ⇒ 00:20:05.070 Uttam Kumaran: And start to look at, okay, whose usage indicates that they should be higher ARR?
207 00:20:05.280 ⇒ 00:20:05.880 Caitlyn Vaughn: God.
208 00:20:06.380 ⇒ 00:20:11.749 Uttam Kumaran: You know, but because you guys are… like, there are some good companies, like, towards the end here, but…
209 00:20:12.110 ⇒ 00:20:14.460 Uttam Kumaran: Of course, I think Deanna’s job is gonna be, like.
210 00:20:14.800 ⇒ 00:20:19.310 Uttam Kumaran: And these are all existing customers, right? So how do you move these people over to this side?
211 00:20:19.310 ⇒ 00:20:20.259 Caitlyn Vaughn: Right.
212 00:20:20.260 ⇒ 00:20:40.230 Uttam Kumaran: So part of that is gonna be, like, are we… did we undercharge them? Right, so it’s gonna be a bucket of that. Part of it is gonna be, like, hey, people are… should be using it, or they haven’t configured it right, right? So one of the things I talked to Dean about, about, like, did they configure their first workflow? Did they add their first admin? Did they configure their first trigger? Like, all those things.
213 00:20:40.230 ⇒ 00:20:43.460 Caitlyn Vaughn: So I feel like if she… if she gets her part right.
214 00:20:43.560 ⇒ 00:20:52.080 Uttam Kumaran: We should start to see, like, a ton of companies start to activate. Because these are, like… the benefit is you guys already sold these folks.
215 00:20:53.160 ⇒ 00:20:59.539 Uttam Kumaran: So, it’s not about new customer acquisition for this motion, this is about… this is all expansion.
216 00:21:00.900 ⇒ 00:21:07.380 Caitlyn Vaughn: It’s interesting, as I’m looking at this, a lot of our old customers, like our first customers, are on the left.
217 00:21:07.380 ⇒ 00:21:08.110 Uttam Kumaran: Yeah.
218 00:21:08.110 ⇒ 00:21:16.070 Caitlyn Vaughn: as we, like, continue to grow and expand, our ACV has gone up quite a bit, and we’ve been selling seat-based for routing and scheduling.
219 00:21:16.070 ⇒ 00:21:28.289 Uttam Kumaran: Yeah, well, you know why? One, I’m sure you guys are doing a better job of activating the customers within a shorter time frame. It’s, like, explaining the product better, like, coaxing them into getting it, and of course.
220 00:21:28.310 ⇒ 00:21:36.649 Uttam Kumaran: like, charging them the appropriate amount, but it doesn’t mean, like, these people… you did… I would say you’ve already got these people on the hook, and they’re paying.
221 00:21:36.680 ⇒ 00:21:41.699 Uttam Kumaran: So I really… this is, like, this is where it’s just, like, you’re sitting on this revenue.
222 00:21:41.890 ⇒ 00:21:57.460 Uttam Kumaran: And so it just takes, like, a nuanced conversation, a nuanced account management conversation, to make sure that, like, hey, some of these clients, for example, like, one of the things, even just in having this conversation, I want to overlay this for each client and their workflow run next to it.
223 00:21:57.460 ⇒ 00:21:58.230 Caitlyn Vaughn: So you could…
224 00:21:58.230 ⇒ 00:22:07.650 Uttam Kumaran: see, like, there are other people down here that are running a lot, but aren’t paying as much. Other people here that are paying a lot, but aren’t running as much. Both of those are problems, right?
225 00:22:08.830 ⇒ 00:22:12.439 Caitlyn Vaughn: How cool, that’s so cool. Wait, go up to the next row.
226 00:22:13.320 ⇒ 00:22:19.359 Caitlyn Vaughn: This chart on the right, workflow runs month over month, segmented by completion. What does that mean?
227 00:22:19.520 ⇒ 00:22:30.129 Uttam Kumaran: So, completion is, like, they filled out the… I think they ran through the entire workflow. I need to get you an example of, like, what is a non-completed workflow run.
228 00:22:30.920 ⇒ 00:22:32.300 Uttam Kumaran: Let me ask that.
229 00:22:32.680 ⇒ 00:22:34.490 Uttam Kumaran: I feel like there is a…
230 00:22:34.600 ⇒ 00:22:36.780 Uttam Kumaran: Yeah, I actually don’t have the definition.
231 00:22:37.990 ⇒ 00:22:39.370 Uttam Kumaran: We have to suffer.
232 00:22:42.060 ⇒ 00:22:42.940 Caitlyn Vaughn: Mustaf.
233 00:22:47.310 ⇒ 00:22:54.079 Uttam Kumaran: So, I think that’s helpful. I think I want to do, like, an overlay of that. And this is also, I think, where I kind of start to want to move
234 00:22:54.710 ⇒ 00:22:57.779 Uttam Kumaran: like… I mean, I think we can…
235 00:22:57.950 ⇒ 00:23:06.420 Uttam Kumaran: I want… one, I want to create, like, some type of concise dashboard after you decide on, like, where… which vendor to go with, but I think a…
236 00:23:06.570 ⇒ 00:23:13.150 Uttam Kumaran: Better… another use of our time is to start having us go after some of these, like, analyses.
237 00:23:13.460 ⇒ 00:23:14.090 Caitlyn Vaughn: Like…
238 00:23:14.090 ⇒ 00:23:15.220 Uttam Kumaran: these questions.
239 00:23:15.420 ⇒ 00:23:15.770 Caitlyn Vaughn: Yeah.
240 00:23:16.230 ⇒ 00:23:25.770 Caitlyn Vaughn: overlap would be really interesting. Even this, I’m about to go back into, like, PLG land and building and planning for it, and I’m gonna have to, like.
241 00:23:26.120 ⇒ 00:23:30.860 Caitlyn Vaughn: create an argument for the pricing structure that I’ve created, and this is actually such a.
242 00:23:30.860 ⇒ 00:23:31.510 Uttam Kumaran: Cool.
243 00:23:31.510 ⇒ 00:23:32.140 Caitlyn Vaughn: Nope.
244 00:23:33.150 ⇒ 00:23:49.700 Uttam Kumaran: Yeah, I mean, yeah, I would love to hear about even how you’re thinking of that, because I would want you to use, like, it would be great for you to use data to back it up. Like, we’ve done a lot of pricing analysis before in SaaS, and, like, for your analysis, I think it’d be helpful for you to show, like.
245 00:23:50.330 ⇒ 00:23:56.649 Uttam Kumaran: I mean, I’m sure you’re like, which features go to where, what type of usage? It’d be great for you to have a historical
246 00:23:57.250 ⇒ 00:23:58.400 Uttam Kumaran: basically, like.
247 00:23:58.750 ⇒ 00:24:05.170 Uttam Kumaran: where have, like, what is this current state? Like, where… what is our journey so far when you go propose the next one?
248 00:24:05.170 ⇒ 00:24:08.590 Caitlyn Vaughn: You know, because I think a lot of times in pricing conversations.
249 00:24:08.590 ⇒ 00:24:23.840 Uttam Kumaran: that I’ve been, you know, I’ve… in pricing conversations that I’ve been, sometimes just, like, people get in a room, like, we should charge 20 bucks, and they make a pricing decision, right? And I know that happens a lot, but for, like, while you have this data, like, I think you… you can’t tweak pricing, like, too often.
250 00:24:24.120 ⇒ 00:24:30.379 Uttam Kumaran: And so, I think it’s important for you guys to try to leverage as much data as we’ve made available here to do that.
251 00:24:30.510 ⇒ 00:24:31.769 Caitlyn Vaughn: We can help with that.
252 00:24:32.140 ⇒ 00:24:36.359 Caitlyn Vaughn: Okay, cool, I can walk you through… let’s see…
253 00:24:39.800 ⇒ 00:24:41.820 Caitlyn Vaughn: Okay. This is, like…
254 00:24:42.600 ⇒ 00:24:58.760 Caitlyn Vaughn: This is interesting. Actually, I would love your perspective on this, just from, like, even you. So there’s basically, like, two ways we can do pricing, and me and our, like, head of RevOps have complete opposite perspectives on this.
255 00:24:59.060 ⇒ 00:25:02.560 Caitlyn Vaughn: So, he’s basically saying we should bundle things.
256 00:25:02.560 ⇒ 00:25:03.390 Uttam Kumaran: Brian?
257 00:25:03.390 ⇒ 00:25:04.300 Caitlyn Vaughn: Yeah, Ryan.
258 00:25:04.570 ⇒ 00:25:21.900 Caitlyn Vaughn: He’s basically saying we should bundle things and go for, like, a Gong sales approach, where it’s like, you could do the sales package, and it comes with these features, or the marketing package, and it comes with these. Which I like a lot, except for I just am not sure how that actually works for PLG and for, like, self-service.
259 00:25:22.200 ⇒ 00:25:28.619 Caitlyn Vaughn: If we want to do, like, a free account or something? I don’t know. I just haven’t really thought of it. And then…
260 00:25:28.870 ⇒ 00:25:47.590 Caitlyn Vaughn: The one that I just had created that I think scales really well is one free tier. It’ll come with, let’s say, like, 2 pages, 2 workflows, 3 saved tables, and 1 seat, and it’s free. And then, if you want to add anybody in, it’s $50 a month for a platform seat, 20 bucks a month for a member seat.
261 00:25:47.590 ⇒ 00:25:57.010 Caitlyn Vaughn: And then 25 for routing, and then you purchase credits and web intent. So it’s kind of like a choose-your-own-adventure. And then…
262 00:25:57.890 ⇒ 00:26:08.930 Caitlyn Vaughn: And then enterprise, like, you know, if you want discounts or any of that, it’ll go straight to sales, they can negotiate on their own. But, if we look at, like, how this scales up.
263 00:26:10.490 ⇒ 00:26:20.070 Caitlyn Vaughn: If we took a company that had, like, one admin seat and then two full member seats with routing and scheduling, the, like, total annual cost would be $1,900.
264 00:26:20.200 ⇒ 00:26:26.110 Caitlyn Vaughn: Right? Plus enrichment. So let’s say they spend 5K a year on enrichment. That’s like a…
265 00:26:26.510 ⇒ 00:26:44.659 Caitlyn Vaughn: 7K year account that we don’t have to support, right? Versus if we took Cherry, they have, like, 10 admin seats, 350 member seats with routing and scheduling, so their total, like, platform and seat count would be a quarter million dollars plus enrichment.
266 00:26:46.520 ⇒ 00:26:51.759 Caitlyn Vaughn: So, I feel like it scales really well, like, one of the problems we have with our current pricing is
267 00:26:51.910 ⇒ 00:27:02.280 Caitlyn Vaughn: The, like, barrier to entry is, like, $8,000 a year, which is really high, and then we’re undercharging most people on, like, the higher side of things.
268 00:27:03.240 ⇒ 00:27:09.679 Uttam Kumaran: Yeah, that’s exactly, like, what I’m writing down. Another thing I think that would be helpful, so I think maybe I’ll give you…
269 00:27:10.530 ⇒ 00:27:13.090 Uttam Kumaran: like… I… I think…
270 00:27:14.310 ⇒ 00:27:18.649 Uttam Kumaran: if I was to think about it from a user perspective, like, from… even from our company.
271 00:27:18.650 ⇒ 00:27:19.850 Caitlyn Vaughn: Hmm.
272 00:27:22.090 ⇒ 00:27:25.590 Uttam Kumaran: Like, I think the first one is actually… Pretty fair.
273 00:27:25.840 ⇒ 00:27:33.209 Uttam Kumaran: And then we would… we would definitely just move up to get the enrichment stuff, and then… then it moves into more of a usage-based.
274 00:27:34.790 ⇒ 00:27:44.979 Uttam Kumaran: I wonder if you guys are considering any type of, like, viewer licenses, or just, like, people to get, like, anything in the free tier, like, how to make it more beefier, like…
275 00:27:45.100 ⇒ 00:28:00.139 Uttam Kumaran: I wonder if you should start to give, like, free enrichment credits and things like that, because part of the thing I think you’re gonna have to do is, which I think is just in our industry, is, like, still people are… there’s still a world of people that are not familiar with
276 00:28:00.330 ⇒ 00:28:12.849 Uttam Kumaran: doing great enrichment, doing great routing. So I think part of your product is part of, like, what Clay is doing, which is, like, education. So giving some free credits in the free tier allows people to.
277 00:28:13.000 ⇒ 00:28:15.749 Caitlyn Vaughn: Understand the benefits of enrichment.
278 00:28:15.930 ⇒ 00:28:26.680 Uttam Kumaran: Right? Otherwise, the only people that will graduate to the middle are the people that know the benefits, right? Which I think… which I actually… this is where, like, I…
279 00:28:26.900 ⇒ 00:28:30.450 Uttam Kumaran: now that I’m in my world, in a lot of companies that
280 00:28:30.700 ⇒ 00:28:35.029 Uttam Kumaran: that, like, are very unsophisticated. People… a lot of people do not have a clue.
281 00:28:35.170 ⇒ 00:28:38.650 Uttam Kumaran: And so that’s why I’m al- I’m almost thinking, like, how can you…
282 00:28:39.130 ⇒ 00:28:42.470 Uttam Kumaran: how can you tease people more with the things that are in Pro?
283 00:28:42.470 ⇒ 00:28:45.480 Caitlyn Vaughn: Yeah. In almost, like, an educational sense.
284 00:28:45.480 ⇒ 00:28:53.239 Uttam Kumaran: Versus just assuming, like, everybody knows that enrichment is great, because I just don’t think that’s the case.
285 00:28:53.420 ⇒ 00:28:54.260 Caitlyn Vaughn: Yeah.
286 00:28:54.710 ⇒ 00:28:58.220 Caitlyn Vaughn: I think the other thing is I’m… I’m…
287 00:28:58.510 ⇒ 00:29:04.149 Caitlyn Vaughn: 100% sure that whatever pricing model we start with is not gonna be what we finish with, you know?
288 00:29:04.150 ⇒ 00:29:04.710 Uttam Kumaran: Yeah.
289 00:29:04.710 ⇒ 00:29:21.340 Caitlyn Vaughn: I, like, need some kind of thesis to begin, and then the benefit of a pricing model like this one is I could do exactly what you just said. Like, I can have a test group where I give them 100 credits, and, like, see what conversion is, and then I have a test group where I give them 0 credits, and then, you know? So…
290 00:29:22.640 ⇒ 00:29:32.849 Caitlyn Vaughn: I think, like, I’ve talked to a bunch of PLG people, and they’re basically like, keep it as simple as possible. Like, people trying your product will be the ones that, like.
291 00:29:34.020 ⇒ 00:29:38.319 Caitlyn Vaughn: Have you ever seen people that, like, go to, like, a McDonald’s, and they go to the… they’re like.
292 00:29:38.320 ⇒ 00:29:38.830 Uttam Kumaran: Yes.
293 00:29:38.830 ⇒ 00:29:40.829 Caitlyn Vaughn: Pull up to one of those.
294 00:29:40.830 ⇒ 00:29:47.059 Uttam Kumaran: They’re like, oh my god, I could get a Spray and a Dr. Pepper, like, mixed in. They’re like, how do I use this?
295 00:29:47.730 ⇒ 00:29:51.589 Uttam Kumaran: There’s two buttons, press the two buttons.
296 00:29:51.590 ⇒ 00:29:57.959 Caitlyn Vaughn: Or it’s like, check in here, text this number to, like, tell them you’re here, and they’re there, like, physically pressing the sign, you know?
297 00:29:57.960 ⇒ 00:29:58.889 Uttam Kumaran: Yeah, yeah, yeah.
298 00:29:58.890 ⇒ 00:30:02.000 Caitlyn Vaughn: texting them, those are, like, the people that are gonna come into the platform.
299 00:30:02.000 ⇒ 00:30:02.730 Uttam Kumaran: I agree.
300 00:30:03.030 ⇒ 00:30:12.220 Caitlyn Vaughn: So, I don’t know, I even think that there’s a world in which I don’t give out each of those SKUs. Like, maybe I have one test group with workflows, and one with tables, and like…
301 00:30:12.420 ⇒ 00:30:28.040 Caitlyn Vaughn: maybe they get 50 credits, you know? Like, there’s a bunch of different ways we could slice the pie, but my main concern here, I don’t think our goal is to make money off of, self-serve. Our goal is to, like, get more fish in the bucket, essentially, so that sales can go, like, shoot them.
302 00:30:28.860 ⇒ 00:30:29.470 Uttam Kumaran: Yeah.
303 00:30:32.630 ⇒ 00:30:36.890 Caitlyn Vaughn: So I’m just thinking of, like, how can we get people
304 00:30:37.000 ⇒ 00:30:41.760 Caitlyn Vaughn: How can we give enough value in a very simple way to get people to, like, stick around?
305 00:30:43.580 ⇒ 00:30:50.590 Uttam Kumaran: Yeah, I mean, I think one thing that we could look at is for the customers that came on in the last, like, 2-3 months.
306 00:30:50.800 ⇒ 00:31:01.089 Uttam Kumaran: I could try to see if we can do something quick that just shows, like, what features were they using, and maybe it gives you some indication of, like, what features to bundle
307 00:31:01.190 ⇒ 00:31:03.620 Uttam Kumaran: with, like, a more PLG motion.
308 00:31:03.900 ⇒ 00:31:06.649 Uttam Kumaran: I think ha- I think definitely…
309 00:31:06.910 ⇒ 00:31:20.489 Uttam Kumaran: you should have some principles in mind when deciding the pricing, like, having that goal of, like, we’re willing to take a hit on these early customers in order to upsell them. The mathematical equation there is…
310 00:31:20.680 ⇒ 00:31:21.850 Uttam Kumaran: the LTV.
311 00:31:22.020 ⇒ 00:31:24.139 Uttam Kumaran: Right? So, for example.
312 00:31:24.300 ⇒ 00:31:28.810 Uttam Kumaran: and this is something that… I think there’s two things that I want to help produce. One thing is, like.
313 00:31:29.010 ⇒ 00:31:36.099 Uttam Kumaran: you… if you have some assumption of, like, hey, of… if we have 100 people that sign up for PLG, 5 will convert.
314 00:31:36.260 ⇒ 00:31:40.169 Uttam Kumaran: or 10 will con- 10 will convert to this, and 1 will convert to Enterprise.
315 00:31:40.170 ⇒ 00:31:43.250 Caitlyn Vaughn: you can show, like, the LTV.
316 00:31:43.250 ⇒ 00:31:56.049 Uttam Kumaran: And you could say, hey, for every 100 of those, we may lose 10 bucks, but in a long tail, we’re gonna gain that, and here’s the time it will take us to realize that. That’s the math equation here.
317 00:31:56.050 ⇒ 00:31:57.440 Caitlyn Vaughn: Yeah.
318 00:31:57.460 ⇒ 00:32:04.619 Uttam Kumaran: You know, so one thing that could be helpful is also to look at your largest clients, look at how long it took them to get to their current point.
319 00:32:05.360 ⇒ 00:32:17.579 Uttam Kumaran: and then factor in some sense, okay, we’re faster, our sales are better, we can get people to that point in X amount of time, and that’s… that’s, like, how long it’ll take for you to start to realize that, like.
320 00:32:17.740 ⇒ 00:32:21.760 Uttam Kumaran: 0 to the 200K, right? So that… so…
321 00:32:22.100 ⇒ 00:32:35.060 Uttam Kumaran: for example, that may be 2 years, that may be 1 year, but there is some natural time it will take someone to graduate that, and so that’s, like, your period… that’s kind of the period where you’re spending on that person.
322 00:32:35.290 ⇒ 00:32:53.110 Uttam Kumaran: So I think that’s the kind of initial equation to understand. The second thing I want to do is I want to take your scheme and run, like, an impact analysis on, like, the existing customers. So you have the seats, you have the things, we can just show, hey, like, given that those people.
323 00:32:53.290 ⇒ 00:32:57.100 Uttam Kumaran: What are, like, what are their old prices? What are their new prices?
324 00:32:57.210 ⇒ 00:33:08.119 Uttam Kumaran: Because part of your problem is you’re going to have to think about, is this a grandfather situation? Is this a complete renegotiation? Or do you want to leave those conversations and just test on new people?
325 00:33:08.480 ⇒ 00:33:09.430 Caitlyn Vaughn: Yeah.
326 00:33:09.430 ⇒ 00:33:15.779 Uttam Kumaran: So… I’m with what the other folks said, is, like, start very narrow.
327 00:33:16.030 ⇒ 00:33:27.220 Uttam Kumaran: But I think you have a lot of the data to do some of this, like, for data to back up some of these decisions. Yeah. Also, that way, next time you make another adjustment, you have some, like, artifact on, like.
328 00:33:27.580 ⇒ 00:33:32.920 Uttam Kumaran: What was our mindset here? So in case scenario changes, you can understand what to tweak.
329 00:33:33.190 ⇒ 00:33:41.459 Caitlyn Vaughn: It will become a lot more obvious once we have the live product data, like, amplitude data hooked up, and we can see, like…
330 00:33:41.460 ⇒ 00:33:42.349 Uttam Kumaran: Yeah, yeah, for sure.
331 00:33:42.350 ⇒ 00:33:43.840 Caitlyn Vaughn: Events of, you know, how people.
332 00:33:43.840 ⇒ 00:33:46.610 Uttam Kumaran: You’re in a tough spot, because you have to decide before that a little bit.
333 00:33:46.610 ⇒ 00:33:49.670 Caitlyn Vaughn: I’m just… winging it.
334 00:33:49.670 ⇒ 00:34:00.709 Uttam Kumaran: Yeah, so that’s the thing, I think… but I think that’s fine. I think, like, you have to make a decision one way or another on that. I’m with them, though, in that keep it simple.
335 00:34:01.510 ⇒ 00:34:19.679 Uttam Kumaran: in order to really… because you guys are… I’m sure you’re going to do a bigger announcement, everything. You want to have as many people come in that day and convert within whatever, like, period of conversion, like, whether it’s 3 months or whatever, as humanly possible. Because you don’t have many shots on goal, so I also agree, it’s like, keep it very simple.
336 00:34:20.230 ⇒ 00:34:25.219 Uttam Kumaran: find what you can afford to give away, and give it… and give it. Yeah.
337 00:34:25.290 ⇒ 00:34:33.289 Caitlyn Vaughn: So right now, I can show you this, I don’t know if I’ve showed you this before. This is our cost of acquiring a new customer.
338 00:34:34.060 ⇒ 00:34:42.299 Caitlyn Vaughn: So this is over time, and it’s not updated, but this was, like, in April, it was, like, 9,600. May, it was…
339 00:34:42.570 ⇒ 00:34:43.709 Uttam Kumaran: Yeah, yeah, yeah, yeah.
340 00:34:43.719 ⇒ 00:34:51.939 Caitlyn Vaughn: So now it’s, it’s nearly, like… we’re basically in the red on any customer under, like, 15K.
341 00:34:52.120 ⇒ 00:34:52.800 Uttam Kumaran: K.
342 00:34:53.080 ⇒ 00:34:54.869 Caitlyn Vaughn: Which is most of our customers.
343 00:34:54.870 ⇒ 00:34:55.990 Uttam Kumaran: Yeah, it’s brutal.
344 00:34:55.989 ⇒ 00:34:57.490 Caitlyn Vaughn: Yeah. 50K annual.
345 00:34:57.850 ⇒ 00:34:58.470 Caitlyn Vaughn: Yeah.
346 00:34:58.680 ⇒ 00:34:59.320 Uttam Kumaran: Okay.
347 00:35:00.030 ⇒ 00:35:05.000 Caitlyn Vaughn: But, like, year two, we’re in the green on them, and we’re… we have, like, a very high,
348 00:35:05.690 ⇒ 00:35:07.329 Caitlyn Vaughn: like, retention rate, so…
349 00:35:07.330 ⇒ 00:35:07.720 Uttam Kumaran: Yeah.
350 00:35:08.310 ⇒ 00:35:27.290 Caitlyn Vaughn: It’s to be expected, this is just kind of how it works in tech, but we are pretty far in the red. So the, like, SaaS, or the self-service model will be to, like, reduce all of our costs, and a lot of that is around, the time it takes to, like, actual… actually onboard somebody after we acquire them as well.
351 00:35:28.990 ⇒ 00:35:44.160 Uttam Kumaran: Yeah, so part of, like, I think there’s the two phases to that is, like, one, you have existing customers that need to move up. I think one thing I’ll show today is, like, that’s the Deanna equation, right? Like, all the customers that she needs to nurture and move up fast, and then
352 00:35:44.450 ⇒ 00:35:49.200 Uttam Kumaran: Naturally try to find a way to filter the free people into that category.
353 00:35:49.200 ⇒ 00:35:49.600 Caitlyn Vaughn: Yeah.
354 00:35:49.600 ⇒ 00:35:51.960 Uttam Kumaran: is the next challenge. But I don’t know, I just feel like…
355 00:35:52.410 ⇒ 00:36:07.939 Uttam Kumaran: I think… now, as a user, like, being able, like, being able to do a couple of things like enrichment, like, do the things that you guys are powerful of, time box it, or, like, give it away for some free period, because I think that’s, like, what people get hooked on.
356 00:36:08.250 ⇒ 00:36:08.660 Uttam Kumaran: Really?
357 00:36:08.900 ⇒ 00:36:09.740 Uttam Kumaran: Yeah.
358 00:36:09.860 ⇒ 00:36:11.839 Caitlyn Vaughn: Like, the users and stuff, like…
359 00:36:13.210 ⇒ 00:36:19.140 Uttam Kumaran: if the product works really well, they’re gonna add more users, right? Like, so, giving away the users
360 00:36:19.830 ⇒ 00:36:27.849 Uttam Kumaran: is one thing, like, 5 admin, 10 whatever, but I think the enrichment piece is, for me, is, like.
361 00:36:28.370 ⇒ 00:36:36.920 Uttam Kumaran: I want to test that as a free user. I want to show that, oh my god, like, we can have… bundle all that into one place, and even if I get that for 30 days.
362 00:36:37.170 ⇒ 00:36:42.929 Uttam Kumaran: it’s like, I’m gonna turn… after 30 days, you’re gonna say, hey, you need to turn it off or upgrade. I’m like, keep it on, it’s working.
363 00:36:42.930 ⇒ 00:36:43.460 Caitlyn Vaughn: Yeah.
364 00:36:43.460 ⇒ 00:36:44.200 Uttam Kumaran: You know?
365 00:36:44.200 ⇒ 00:36:47.119 Caitlyn Vaughn: Yeah, that’s so true. I have, like, a…
366 00:36:47.390 ⇒ 00:36:51.660 Caitlyn Vaughn: a beef with, like, trial-based, I feel like.
367 00:36:51.660 ⇒ 00:36:52.300 Uttam Kumaran: Okay.
368 00:36:52.990 ⇒ 00:36:54.740 Caitlyn Vaughn: Because they’ve kind of pulled a rug.
369 00:36:55.020 ⇒ 00:37:02.360 Caitlyn Vaughn: Yeah, I’m okay, though, with, like, giving away 100 credits, and you have 100 credits for free, forever, right?
370 00:37:02.360 ⇒ 00:37:02.960 Uttam Kumaran: Yeah.
371 00:37:02.960 ⇒ 00:37:12.229 Caitlyn Vaughn: And then you use them in whatever time, but the only reason I’m against, like, a trial, at least to begin, is because it’s, like, a lot more work on the support side.
372 00:37:12.230 ⇒ 00:37:12.740 Uttam Kumaran: Oh, okay.
373 00:37:12.740 ⇒ 00:37:22.050 Caitlyn Vaughn: Oh, no, I, like, I want to… I really wanted to use that, can you, like, you know, redo my… can you reset? It’s a lot. Like, the amount of entitlement from free users is crazy.
374 00:37:22.050 ⇒ 00:37:24.269 Uttam Kumaran: Yes, yes, I agree, I agree.
375 00:37:26.550 ⇒ 00:37:35.289 Uttam Kumaran: Interesting, that’s sick. That’s a good… this is a… it’s an awesome case study, like, yeah, you guys… yeah, I think… I’m kind of with them, keep it simple.
376 00:37:35.440 ⇒ 00:37:44.240 Uttam Kumaran: Also, what a fucking cool company. Yeah, and what… sick job, dude, this is great, like, you… I would say, like, I was involved in pricing…
377 00:37:44.750 ⇒ 00:37:49.970 Uttam Kumaran: twice, and… and at Flow Code, we did multiple iterations, it was very fun, but, like.
378 00:37:50.290 ⇒ 00:37:58.430 Uttam Kumaran: very complicated, and a lot of, like, people… like, you guys, I think, are less political. That company was, like, incredibly toxic, and, like.
379 00:37:58.820 ⇒ 00:38:02.799 Uttam Kumaran: yeah, like, you have to really defend, like, what you… and I was, of course.
380 00:38:02.860 ⇒ 00:38:07.449 Uttam Kumaran: I’m the data person, and I’m like, yo, you guys are just throw… you’re, like, 50 bucks on… you guys…
381 00:38:07.500 ⇒ 00:38:12.529 Uttam Kumaran: you have to… how… you can’t just say numbers. You can’t be like, oh, like, QR,
382 00:38:12.530 ⇒ 00:38:30.350 Uttam Kumaran: QR Tiger is 10 bucks, so we should just be $9. It’s like, dude, are you stupid or dumb? Like, can we… can we please, like, just use a little bit of data here? And they… we had free pro… we had free data for more than a year, we had all this great, rich data, like, we weren’t making, like, that many product changes in parallel, so…
383 00:38:30.350 ⇒ 00:38:30.860 Caitlyn Vaughn: Yeah.
384 00:38:30.860 ⇒ 00:38:32.320 Uttam Kumaran: Yeah, it’s.
385 00:38:32.320 ⇒ 00:38:33.490 Caitlyn Vaughn: That’s hilarious.
386 00:38:34.300 ⇒ 00:38:43.219 Caitlyn Vaughn: Yeah, it’s such a cool job, and, like, such a cool company. And Nico’s literally like, can you just decide on pricing? There’s, like, been 10 pricing models suggested. He’s like, I need you to figure it out.
387 00:38:43.220 ⇒ 00:38:52.260 Uttam Kumaran: No, that’s the thing, so that’s why I think… I think the way you’re gonna get a lot of, conviction yourself and the support is if you have, like, some data around it.
388 00:38:52.430 ⇒ 00:38:59.689 Caitlyn Vaughn: Yeah, okay, cool. I’m gonna… so I’m in New York the week after next. I’m probably gonna put together some, like.
389 00:39:00.930 ⇒ 00:39:08.439 Caitlyn Vaughn: more thought-through pricing. I’m gonna just, like, try to write out every pricing model that might make sense, and then kind of go down the line.
390 00:39:08.440 ⇒ 00:39:10.679 Uttam Kumaran: I have a great book on pricing, too.
391 00:39:10.680 ⇒ 00:39:11.170 Caitlyn Vaughn: Really?
392 00:39:11.170 ⇒ 00:39:13.999 Uttam Kumaran: I think… I don’t know if you’re, like, a big business book reader.
393 00:39:14.000 ⇒ 00:39:14.690 Caitlyn Vaughn: Yeah, I am.
394 00:39:14.690 ⇒ 00:39:19.580 Uttam Kumaran: Sort of like, okay, this is sort of like the canonical book on pricing.
395 00:39:19.580 ⇒ 00:39:20.069 Caitlyn Vaughn: What is it called?
396 00:39:20.070 ⇒ 00:39:25.690 Uttam Kumaran: let’s see, it’s been a while. I wanna… I’ll read it again, too.
397 00:39:28.530 ⇒ 00:39:31.790 Uttam Kumaran: Let’s see…
398 00:39:36.910 ⇒ 00:39:39.339 Uttam Kumaran: Hold on, let me find it to my cart somewhere.
399 00:39:41.270 ⇒ 00:39:45.439 Caitlyn Vaughn: I have, like, 20 unread Slack messages and, like, a thousand unread emails.
400 00:39:58.230 ⇒ 00:40:00.150 Uttam Kumaran: What is it?
401 00:40:17.980 ⇒ 00:40:19.920 Uttam Kumaran: Yeah, this is it.
402 00:40:20.370 ⇒ 00:40:21.550 Uttam Kumaran: Yes!
403 00:40:34.350 ⇒ 00:40:38.210 Uttam Kumaran: Here, I’ll send it to you in, back.
404 00:40:40.950 ⇒ 00:40:43.120 Caitlyn Vaughn: Make that 21 messages.
405 00:40:45.860 ⇒ 00:40:52.320 Uttam Kumaran: Yeah, there’s a podcast with this guy that I listened to, and I was like, oh my god. This was, like, when I was…
406 00:40:52.500 ⇒ 00:40:55.219 Uttam Kumaran: Deciding on pricing for prequel.
407 00:40:55.670 ⇒ 00:41:13.360 Uttam Kumaran: and I was like, holy shit, this is, like, gold. I, like, need to read this. And he literally analyzed… he analyzed, like, pricing schemes of, like, the most famous companies and, like, created an entire thesis around it, and it’s just fucking great. Like, if you’re thinking about pricing, this is probably, like, what you need to read.
408 00:41:13.360 ⇒ 00:41:14.420 Caitlyn Vaughn: So helpful.
409 00:41:17.020 ⇒ 00:41:26.290 Uttam Kumaran: Yeah, then you could be like, guys, I’m the only one here that read a fucking book on pricing. Who here read a book on pricing to prepare for this meeting?
410 00:41:26.290 ⇒ 00:41:30.159 Caitlyn Vaughn: That’s so funny. I’m like, according to Medhaven remedies…
411 00:41:30.160 ⇒ 00:41:33.059 Uttam Kumaran: Yeah, yeah, yeah, oh, according to my…
412 00:41:33.060 ⇒ 00:41:34.480 Caitlyn Vaughn: According to my data.
413 00:41:36.140 ⇒ 00:41:42.509 Uttam Kumaran: Okay, I have one thing, one more thing to share, sorry, I just need, like, probably one more minute.
414 00:41:43.130 ⇒ 00:41:47.479 Caitlyn Vaughn: Dashboard… So…
415 00:41:47.530 ⇒ 00:41:51.530 Uttam Kumaran: I met with Deanna,
416 00:41:51.970 ⇒ 00:42:04.250 Uttam Kumaran: we talk through, like, what she needs in Catalyst, and everything kind of seems pretty valid. She just needs, like, indications of, like, if a customer has set up, like, stuff through onboarding.
417 00:42:04.410 ⇒ 00:42:09.759 Uttam Kumaran: Users that are missing mapping? Do they have queues setups? Do they have event types?
418 00:42:10.070 ⇒ 00:42:14.209 Uttam Kumaran: time to their first inbound lead? Did they connect their calendar? So those are all things that I can…
419 00:42:14.400 ⇒ 00:42:28.630 Uttam Kumaran: I can get en route to her pretty easily. Yeah. And then she kind of wants a little bit about, like, sort of CS adoption, which is actually… we have a lot of it there, so it’ll just be creating a new dashboard and with a filter, for the most part, and a couple more things.
420 00:42:30.120 ⇒ 00:42:37.420 Uttam Kumaran: And then, like, feature usage we can’t do. Time and product, opt to see if we have anything basic in amplitude that I can start to show.
421 00:42:37.700 ⇒ 00:42:38.540 Uttam Kumaran: And then…
422 00:42:38.540 ⇒ 00:42:39.849 Caitlyn Vaughn: That’s posthog.
423 00:42:40.810 ⇒ 00:42:43.029 Uttam Kumaran: Okay, then maybe I’ll,
424 00:42:43.710 ⇒ 00:42:49.279 Uttam Kumaran: Yeah, I can ask both. Is Thomas a good person to ping? For, like, random stuff like that? Or no?
425 00:42:49.660 ⇒ 00:42:50.389 Caitlyn Vaughn: I could.
426 00:42:50.390 ⇒ 00:42:52.120 Uttam Kumaran: Betting you, of course, but…
427 00:42:53.220 ⇒ 00:43:00.659 Caitlyn Vaughn: Yes, you can definitely ping Thomas. I actually don’t know if Thomas has access to Post Hog. Let me see…
428 00:43:00.890 ⇒ 00:43:03.890 Caitlyn Vaughn: Sorry, continue. I’ll look at this while you’re doing that. Okay.
429 00:43:04.480 ⇒ 00:43:08.559 Uttam Kumaran: And then she kind of, like, wants to look at, like,
430 00:43:08.800 ⇒ 00:43:14.079 Uttam Kumaran: The time they created their workspace, the time… and the time in between that and, like, their first lead.
431 00:43:14.370 ⇒ 00:43:19.970 Uttam Kumaran: And then, basically, I told… at the end, she’s like, okay, after that, I’ll kind of create some type of product help score.
432 00:43:20.260 ⇒ 00:43:28.100 Uttam Kumaran: So, like, it’s really in line with, I think, a lot of the stuff we’ve already talked about. I think probably…
433 00:43:29.640 ⇒ 00:43:46.490 Uttam Kumaran: I’m really… I’m really excited to do this, because we can finally show you, like, product usage in association with the money, and I think she’s, like, clearly really skilled at Catalyst, and, like, creating that. So, if I can get her this data.
434 00:43:46.610 ⇒ 00:43:51.989 Uttam Kumaran: I think she’ll be in the clear. I don’t think we’re gonna… I don’t think we’re necessarily gonna, like, build…
435 00:43:52.100 ⇒ 00:43:54.809 Uttam Kumaran: dashboards for all of this. I think the only…
436 00:43:55.450 ⇒ 00:44:00.629 Uttam Kumaran: thing I may build is just some type of CS adoption dashboard where you can filter to one client.
437 00:44:00.850 ⇒ 00:44:11.090 Uttam Kumaran: these things. Actually, a lot of this, she’s gonna end up doing in Catalyst, which I think is great. So that was the one thing I wanted to hash with her. I don’t want to redo it in Omni, since it’s mainly just her, so…
438 00:44:11.090 ⇒ 00:44:14.330 Caitlyn Vaughn: Okay, cool. Yay, she’s so smart.
439 00:44:14.330 ⇒ 00:44:23.849 Uttam Kumaran: Yeah, she’s really good, and that Catalyst product is good, like, I was like, oh, damn, you could do all this, because we haven’t… I’ve used Gainsight before in a couple things, but yeah, I’ve never heard of that product, that’s really great.
440 00:44:23.850 ⇒ 00:44:25.799 Caitlyn Vaughn: She worked at Catalyst before.
441 00:44:25.800 ⇒ 00:44:32.419 Uttam Kumaran: Oh, really? Oh, okay, alright, cool. Because she was explaining, I’m like, Dave, you really, like, you’re like a power user on this thing, so…
442 00:44:32.590 ⇒ 00:44:34.969 Caitlyn Vaughn: Yeah, she was. She was there for, like, 6 years.
443 00:44:35.150 ⇒ 00:44:42.410 Uttam Kumaran: Oh, sick, okay. Okay, cool. Yeah, then she’s the one. So yeah, I’m gonna… I’m gonna try to get her this stuff, and then, yeah, if you feel like if…
444 00:44:42.510 ⇒ 00:44:45.080 Uttam Kumaran: if Thomas is a good person to sort of, like.
445 00:44:45.390 ⇒ 00:44:48.739 Uttam Kumaran: Do stuff with us, because there’s a lot of work here.
446 00:44:48.990 ⇒ 00:44:49.450 Caitlyn Vaughn: Yeah.
447 00:44:49.450 ⇒ 00:45:00.149 Uttam Kumaran: I would… I would love to just start to loop him in and maybe give him some couple things to take on. Do you, like… is that, like, a… is he, like, slammed with stuff, or, like, what’s his, like, day… what is he working on? Random stuff?
448 00:45:00.150 ⇒ 00:45:17.219 Caitlyn Vaughn: So… he was… he’s… I think he’s still an intern, technically. We may have given him a full-time offer. Whenever he’s ready, we’re gonna give him a full-time offer. But he started out as a forward-deployed engineer, so he’s basically, like, doing implementation, and, like.
449 00:45:17.580 ⇒ 00:45:32.620 Caitlyn Vaughn: technical customer support, and then he is showing high potential as a company for moving into engineering. So, we’re basically… we started giving him, like, random projects, and he’s been, like, crushing them. He’s been doing really, really good, and just, like.
450 00:45:32.620 ⇒ 00:45:33.130 Uttam Kumaran: So…
451 00:45:33.640 ⇒ 00:45:37.030 Caitlyn Vaughn: in the office 7 days a week, obsessed kind of a thing. So…
452 00:45:37.030 ⇒ 00:45:41.040 Uttam Kumaran: I would like to put a vote in to have him help us with some data stuff.
453 00:45:41.040 ⇒ 00:45:41.370 Caitlyn Vaughn: Yeah.
454 00:45:41.370 ⇒ 00:45:46.300 Uttam Kumaran: because we do… I do kind of, like, some of this stuff is gonna get to the point where I need, like, a technical.
455 00:45:46.300 ⇒ 00:45:48.379 Caitlyn Vaughn: counterpart internally, and I know we didn’t…
456 00:45:48.380 ⇒ 00:45:51.360 Uttam Kumaran: We worked a little bit with the crew, but they’re slammed.
457 00:45:51.740 ⇒ 00:45:59.899 Uttam Kumaran: So, like, I would like to put my vote for him being that for us, if he has… if he is currently sort of, like, floating around.
458 00:45:59.900 ⇒ 00:46:05.940 Caitlyn Vaughn: Yeah, so he’s, like, my… I was like, you are now… I cut him long ago, I was like, you’re now a data engineer, and you’re gonna.
459 00:46:05.940 ⇒ 00:46:06.540 Uttam Kumaran: Sick.
460 00:46:06.540 ⇒ 00:46:10.860 Caitlyn Vaughn: data engineer, and he was like, okay, cool, I’m really excited about it. I was like, great.
461 00:46:10.860 ⇒ 00:46:13.519 Uttam Kumaran: Okay, cool, because, yeah, I told him to do a bunch of stuff, and I was like.
462 00:46:13.660 ⇒ 00:46:21.679 Uttam Kumaran: I don’t know if he knows a thing about what I’m saying, but I’m gonna make him… I’m gonna give him all the words so that he can go to Victor and then say a bunch of words, and then…
463 00:46:21.990 ⇒ 00:46:25.550 Uttam Kumaran: Maybe it buys him some… some good… goodwill, but…
464 00:46:25.550 ⇒ 00:46:35.770 Caitlyn Vaughn: Yeah. Yeah, I would say he’s your guy, but I would look at him as somebody that you’re gonna have to teach. But he’s really smart, so quick, but you’re gonna have to, like, explain a lot to him.
465 00:46:35.910 ⇒ 00:46:41.720 Uttam Kumaran: Okay, that’s fine. Yeah, I don’t mind. I just want us… it helps us keep moving things along, too.
466 00:46:41.930 ⇒ 00:46:52.150 Uttam Kumaran: Okay, cool. So then I’ll assume… I’ll send him a message, and yeah, we… we meet every day, and we talk at least, like, 10-15 minutes about you guys, so if… I’ll… I’ll see if I can start to do something…
467 00:46:52.150 ⇒ 00:46:55.559 Caitlyn Vaughn: Huh? No, no, no, our team. Oh. Crane Forge.
468 00:46:55.560 ⇒ 00:47:01.130 Uttam Kumaran: So I’ll see if we meet, no, I mean, I would like to… yeah, me and him could become friends, for sure.
469 00:47:01.130 ⇒ 00:47:02.180 Caitlyn Vaughn: opportunities.
470 00:47:02.180 ⇒ 00:47:03.100 Uttam Kumaran: Tom.
471 00:47:03.590 ⇒ 00:47:17.530 Uttam Kumaran: I’ll, I’ll see if I can maybe grab some recurring time with him, and then some of these tasks I would like him to take on, because I want someone internally… God forbid you, decide to part ways with us, I want you guys to have somebody, that knows how to do all this stuff, so…
472 00:47:17.530 ⇒ 00:47:20.760 Caitlyn Vaughn: Actually, you suck! The last 6 months is big!
473 00:47:20.760 ⇒ 00:47:27.889 Uttam Kumaran: No, so I just… but also, I… like, I don’t want to be… this all… for all of our clients, I’m like, don’t want to just…
474 00:47:28.080 ⇒ 00:47:35.989 Uttam Kumaran: do everything, and then, like, whatever, we’re the gatekeepers for all this, so I actually think… and he’s on the inside, so he…
475 00:47:36.150 ⇒ 00:47:38.749 Uttam Kumaran: We’ll know all… a lot of these questions we have, so…
476 00:47:38.920 ⇒ 00:47:42.880 Caitlyn Vaughn: Totally, yeah. And if he doesn’t, it’s good that you’re asking them, because he’ll learn.
477 00:47:43.350 ⇒ 00:47:43.880 Uttam Kumaran: Cool.
478 00:47:44.630 ⇒ 00:47:45.140 Caitlyn Vaughn: Cool.
479 00:47:45.140 ⇒ 00:47:50.249 Uttam Kumaran: Okay, great, thank you for the time. I’ll send you some pings, and then Yessi and the Sigma call.
480 00:47:50.250 ⇒ 00:47:51.480 Caitlyn Vaughn: See you in an hour.
481 00:47:51.480 ⇒ 00:47:52.620 Uttam Kumaran: Okay, alright, bye.