2025-11-06_default_brainforge_weekly_sync_45abed50

Meeting Title: Default | Brainforge Weekly Sync Date: 2025-11-06 Meeting participants: Scratchpad Notetaker, Uttam Kumaran, Caitlyn Vaughn, Amber Lin, Mustafa Raja

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WEBVTT

1 00:01:34.180 ⇒ 00:01:35.520 Caitlyn Vaughn: Hello!

2 00:01:40.670 ⇒ 00:01:41.790 Uttam Kumaran: Hello!

3 00:01:42.960 ⇒ 00:01:46.690 Caitlyn Vaughn: Dog, I might be stuck in New York, and I’m shitting myself.

4 00:01:46.690 ⇒ 00:01:47.520 Uttam Kumaran: Why?

5 00:01:47.740 ⇒ 00:01:50.029 Caitlyn Vaughn: Because all the flights are getting canceled. All the airport.

6 00:01:50.030 ⇒ 00:01:52.030 Uttam Kumaran: No, no.

7 00:01:54.290 ⇒ 00:01:55.840 Uttam Kumaran: I have.

8 00:01:55.900 ⇒ 00:02:00.390 Caitlyn Vaughn: Two friends coming in today and tomorrow, and both of their flights have been canceled twice.

9 00:02:01.110 ⇒ 00:02:02.470 Uttam Kumaran: No way.

10 00:02:02.470 ⇒ 00:02:04.519 Caitlyn Vaughn: Yeah, I’m so nervous, actually.

11 00:02:04.520 ⇒ 00:02:07.009 Uttam Kumaran: I’m in… I’m in Boulder for, like.

12 00:02:07.010 ⇒ 00:02:08.259 Caitlyn Vaughn: Oh, no.

13 00:02:09.139 ⇒ 00:02:11.609 Uttam Kumaran: This is kind of chill. I’d stay here, though.

14 00:02:11.610 ⇒ 00:02:14.060 Caitlyn Vaughn: Honestly, same, yeah, I’m fine here.

15 00:02:14.190 ⇒ 00:02:16.520 Uttam Kumaran: That’s crazy, yeah.

16 00:02:16.520 ⇒ 00:02:17.899 Caitlyn Vaughn: Wait, what are you, in Boulder?

17 00:02:18.060 ⇒ 00:02:20.629 Uttam Kumaran: We’re just, like, hanging out, yeah, just like…

18 00:02:20.630 ⇒ 00:02:21.640 Caitlyn Vaughn: You and your girlfriend?

19 00:02:21.640 ⇒ 00:02:24.930 Uttam Kumaran: Yeah, just, like, wanted to get out of, Texas.

20 00:02:25.650 ⇒ 00:02:29.040 Uttam Kumaran: This is just, like, a little cute time. I have, like, one… we have some…

21 00:02:29.330 ⇒ 00:02:33.870 Uttam Kumaran: I’m trying not to make it a company event, but we do have some, like… I have some…

22 00:02:34.470 ⇒ 00:02:37.969 Uttam Kumaran: company friends that I want to see, also. She’s like.

23 00:02:38.190 ⇒ 00:02:46.100 Uttam Kumaran: you want… you said you were gonna hang out together. I’m like, these are, like, great… these are also great people, we should… they’ll… we should go get, like, a drink with them.

24 00:02:46.100 ⇒ 00:02:52.690 Caitlyn Vaughn: You’re so funny, oh my gosh. Also, I just realized I never responded to your text last week, and I’m so sorry!

25 00:02:52.690 ⇒ 00:02:53.690 Uttam Kumaran: We’re good!

26 00:02:53.690 ⇒ 00:02:55.550 Caitlyn Vaughn: It’s your birthday, and I forgot!

27 00:02:55.550 ⇒ 00:02:59.520 Uttam Kumaran: It’s okay. It was really, really fun. We should just do that again, honestly.

28 00:02:59.520 ⇒ 00:03:03.710 Caitlyn Vaughn: Okay, I’m super down. Honestly, we should host an event. I’m, like, very, very down to do that.

29 00:03:03.710 ⇒ 00:03:04.540 Uttam Kumaran: We should.

30 00:03:04.710 ⇒ 00:03:05.160 Caitlyn Vaughn: How’s my home?

31 00:03:05.160 ⇒ 00:03:07.130 Uttam Kumaran: Yeah, we…

32 00:03:07.590 ⇒ 00:03:08.640 Caitlyn Vaughn: How’s my goal?

33 00:03:08.820 ⇒ 00:03:09.780 Uttam Kumaran: Oh, it’s good, yeah.

34 00:03:10.280 ⇒ 00:03:12.359 Uttam Kumaran: He came for, like, an hour, and then he was like.

35 00:03:12.490 ⇒ 00:03:14.760 Uttam Kumaran: he… he’s just, I think, been…

36 00:03:14.760 ⇒ 00:03:24.780 Caitlyn Vaughn: every week he’s going to, like, a bachelor party, so he was like, I’m going to Ole Miss, and the weekend before, he was in Miami, and the weekend before, he was somewhere else. He’s really good.

37 00:03:24.780 ⇒ 00:03:30.670 Uttam Kumaran: No, we should, we should do something for all of, like, like, the best go-to-market people, like, go-to-market…

38 00:03:31.070 ⇒ 00:03:33.890 Uttam Kumaran: legit go-to-market SaaS people in Austin.

39 00:03:33.890 ⇒ 00:03:35.870 Caitlyn Vaughn: Which, there’s not gonna be much.

40 00:03:35.920 ⇒ 00:03:38.249 Uttam Kumaran: And default is way more famous.

41 00:03:38.600 ⇒ 00:03:41.900 Uttam Kumaran: Than it was last year, so you’ll get a great turnout.

42 00:03:43.160 ⇒ 00:03:44.240 Caitlyn Vaughn: Cooling down.

43 00:03:46.380 ⇒ 00:03:47.840 Uttam Kumaran: Okay, sick, we should.

44 00:03:48.420 ⇒ 00:03:52.300 Uttam Kumaran: When, I don’t know if… I don’t know when… I guess it’s up to you…

45 00:03:52.920 ⇒ 00:03:58.100 Uttam Kumaran: I mean, we should do it around when your stuff launches, but I feel like that’s gonna be in a bit.

46 00:03:58.900 ⇒ 00:04:00.220 Caitlyn Vaughn: It’s gonna be in March.

47 00:04:00.220 ⇒ 00:04:01.559 Uttam Kumaran: Okay, then we should do one early.

48 00:04:01.560 ⇒ 00:04:02.459 Caitlyn Vaughn: it’s March.

49 00:04:02.460 ⇒ 00:04:04.660 Uttam Kumaran: Maybe we should do one in January.

50 00:04:05.310 ⇒ 00:04:06.750 Uttam Kumaran: Okay, actually…

51 00:04:07.210 ⇒ 00:04:09.560 Caitlyn Vaughn: Yeah, maybe before.

52 00:04:09.560 ⇒ 00:04:12.480 Uttam Kumaran: Or we could do before, I guess, like… It’s gonna get…

53 00:04:13.400 ⇒ 00:04:14.929 Caitlyn Vaughn: Like, before we launch, I mean.

54 00:04:15.320 ⇒ 00:04:27.050 Uttam Kumaran: Oh, yeah, for sure. Well, I mean, there’s just so much time, like, you could do one then, and these are, like, pretty cheap to do. Most of, like, the planning here is just, like, getting a really good invite list.

55 00:04:27.250 ⇒ 00:04:29.249 Uttam Kumaran: And, like, blasting on socials, and then…

56 00:04:29.770 ⇒ 00:04:37.790 Uttam Kumaran: Like, if we can get that space that we had, or something where it’s just, like, a lot more chill, it’s like 20, 30 people, we’ll do a little bit of, like.

57 00:04:38.100 ⇒ 00:04:48.170 Uttam Kumaran: not a talk-at-you roundtable, but sort of like a talk-out-loud conversation. That went really well. We did one for a bunch of data people with one of our partner vendors, and it was, like.

58 00:04:48.520 ⇒ 00:04:58.239 Uttam Kumaran: like, we were up talking, and I was just, like, talking to people, like, what… oh, yeah, we met you, I met… what do you guys do at, like, Strava? Or, like, okay, how does this change? And it was really, really nice.

59 00:04:58.240 ⇒ 00:05:01.899 Caitlyn Vaughn: Hell yeah. And there’s, like, no one’s just, like, looking for jobs, or, like.

60 00:05:01.920 ⇒ 00:05:09.470 Uttam Kumaran: sort of, like, crappy happy hour crew. It’s, like, actual people, so… Oh, your own.

61 00:05:09.670 ⇒ 00:05:19.439 Caitlyn Vaughn: That’s so great. Also… Like, we’re, like, pretty sure we’re gonna go into a recession next year.

62 00:05:19.950 ⇒ 00:05:20.740 Caitlyn Vaughn: Wait, who’s.

63 00:05:20.740 ⇒ 00:05:22.899 Uttam Kumaran: Pretty sure. Oh, okay.

64 00:05:22.900 ⇒ 00:05:26.579 Caitlyn Vaughn: our company. So we’re gonna go raise another, like, 6 million.

65 00:05:27.450 ⇒ 00:05:29.440 Caitlyn Vaughn: Literally. Isn’t that crazy?

66 00:05:30.260 ⇒ 00:05:35.900 Uttam Kumaran: Oh, dude, I… well, this is… someone asked me, they were like, oh, are you guys hit by, like, the market? I was like.

67 00:05:36.200 ⇒ 00:05:42.100 Uttam Kumaran: I don’t… I mean, I personally don’t think… I used to be, like, really big on… in finance, but…

68 00:05:42.410 ⇒ 00:05:45.689 Uttam Kumaran: I don’t know, I’m like, well, we’re gonna keep trying no matter what the market is, but…

69 00:05:46.250 ⇒ 00:05:48.340 Uttam Kumaran: Also, we have SaaS companies that

70 00:05:48.670 ⇒ 00:05:51.730 Uttam Kumaran: And e-com, and, like, health, and so…

71 00:05:52.160 ⇒ 00:05:53.779 Caitlyn Vaughn: You’re diversified enough.

72 00:05:53.780 ⇒ 00:05:57.370 Uttam Kumaran: Even if e-coms are hit by tariffs, like.

73 00:05:57.590 ⇒ 00:06:02.179 Uttam Kumaran: we still have a bunch of SaaS clients, so it doesn’t really matter for us. Also, like.

74 00:06:03.450 ⇒ 00:06:11.899 Uttam Kumaran: We’re… it’s almost like we’re, like, kind of like guns for hire, so typically people, if they don’t… if they can’t find the right people, or they need something done urgently, is when we come in.

75 00:06:13.110 ⇒ 00:06:17.290 Caitlyn Vaughn: Oh, did you hear that Tasia moved to, San Francisco with his wife?

76 00:06:17.590 ⇒ 00:06:18.320 Uttam Kumaran: No.

77 00:06:18.780 ⇒ 00:06:21.080 Caitlyn Vaughn: He… they’re having a baby in, like.

78 00:06:21.080 ⇒ 00:06:22.620 Uttam Kumaran: Oh, really?

79 00:06:22.620 ⇒ 00:06:27.330 Caitlyn Vaughn: to SF, so if you have any contacts out there who, like, just texted me and asked for contacts.

80 00:06:27.330 ⇒ 00:06:33.829 Uttam Kumaran: I totally do, yeah, he should, yeah, can you put us in a group chat, and I will connect him with some really, really

81 00:06:34.060 ⇒ 00:06:34.940 Uttam Kumaran: Good people.

82 00:06:35.140 ⇒ 00:06:38.169 Caitlyn Vaughn: Yeah, for sure. TJ’s so cool, I love him.

83 00:06:38.170 ⇒ 00:06:43.329 Uttam Kumaran: I wish I chatted with him after. I haven’t talked to him… talked to him a little bit after that event we went to.

84 00:06:43.330 ⇒ 00:06:44.190 Caitlyn Vaughn: Oh, really?

85 00:06:44.190 ⇒ 00:06:44.650 Uttam Kumaran: Yeah.

86 00:06:44.650 ⇒ 00:06:46.469 Caitlyn Vaughn: love… I talk to Tasia, like, every…

87 00:06:46.470 ⇒ 00:06:48.879 Uttam Kumaran: But is he still running gun, or, like, what’s the deal?

88 00:06:49.240 ⇒ 00:06:53.629 Caitlyn Vaughn: He kind of stepped out of it.

89 00:06:53.800 ⇒ 00:06:59.779 Caitlyn Vaughn: I think he’s still involved, but, like, I think he hired someone else as CEO or something, I don’t know.

90 00:07:00.030 ⇒ 00:07:01.639 Uttam Kumaran: And he’s thinking about something new?

91 00:07:01.640 ⇒ 00:07:06.679 Caitlyn Vaughn: No, he was just cruising. He’s just having a baby. He, like, exited for, like.

92 00:07:06.680 ⇒ 00:07:07.160 Uttam Kumaran: Oh, okay, okay.

93 00:07:07.160 ⇒ 00:07:07.730 Caitlyn Vaughn: or something.

94 00:07:08.030 ⇒ 00:07:24.629 Caitlyn Vaughn: Yeah. Yeah, I have some good people you should talk to, for sure. Okay, cool. Also, Amber, thank you so much for, like, your video and all of your, like, messages to me. I was, like, going deep on it last night, and I think I got, like, 1% of everything that you gave, but I’m, like, really excited to chat with you about it.

95 00:07:24.630 ⇒ 00:07:32.140 Amber Lin: I appreciate it. That tells me I can make it even clearer, because I just found a lot of things, and it didn’t… it was just…

96 00:07:32.680 ⇒ 00:07:40.050 Amber Lin: hit or miss, one here and one there, so I was hoping to talk to you, and then if I get how you think, I can probably make it cleaner so that…

97 00:07:40.050 ⇒ 00:07:40.700 Caitlyn Vaughn: Yeah!

98 00:07:40.700 ⇒ 00:07:42.019 Amber Lin: It guides you through.

99 00:07:42.020 ⇒ 00:07:58.490 Caitlyn Vaughn: Yeah, there’s, like, so much in there that I think I, like, really honed in on that, like, one section that I sent you a picture of that I was asking questions on. That’s… I probably spent, like, 2 hours on it last night. So I’m not sure if there’s, like, a ton outside of that that I should be looking at, but…

100 00:07:58.490 ⇒ 00:08:08.590 Uttam Kumaran: No, I guess today, really, what I was telling Amber is, like, we got to a point where I was like, okay, this is enough, just send it to Caitlin to see, like, which… what’s the most important for us to go more deeper on, and then…

101 00:08:09.180 ⇒ 00:08:14.709 Uttam Kumaran: I guess an outcome of this meeting is to think about, like, how… like, how… what…

102 00:08:14.860 ⇒ 00:08:22.779 Uttam Kumaran: drive towards, like, what decision you’re gonna make. Is it, like, hey, I want to take this to, like, the broader team? Is it like, okay, I want to just get these answers, and so I can…

103 00:08:22.880 ⇒ 00:08:34.079 Uttam Kumaran: help reshape that pricing document, so that’s probably what I want to leave with, is, like, what you need from us next. But I think I want to just make sure we have time today to just continue to just rip through

104 00:08:34.330 ⇒ 00:08:53.369 Uttam Kumaran: as many questions as possible, and then we can say, okay, certain things, like, we should… were decided on, or like, okay, these make sense, certain areas where, like, oh, we didn’t know that there was this much concentration, or we thought it was different. So, yeah, we can start with your questions, and then, yeah, towards the end, I’ll just sort of guide us to see, like, okay, what’s next?

105 00:08:53.710 ⇒ 00:08:56.230 Caitlyn Vaughn: Yeah, totally. So…

106 00:08:56.610 ⇒ 00:09:11.390 Caitlyn Vaughn: I probably should have, like, written questions before this, but… as I was going through this, so… I actually, like, whiteboarded it, it’s, like, right outside there. My, like, TLDR, the, like, things that I actually have written down is…

107 00:09:11.720 ⇒ 00:09:31.190 Caitlyn Vaughn: the first piece is, like, the revenue concentration, so I saw, like, our top 10 clients make up, like, 40% of our revenue, and then, like, the next 10 make up, like, 8, so on and so forth, so it seems like there’s this, like, distribution of revenue that’s, like, pretty heavy on both sides, and, like, not a lot in between.

108 00:09:32.450 ⇒ 00:09:34.320 Caitlyn Vaughn: Is that what you got, Amber?

109 00:09:35.650 ⇒ 00:09:44.140 Amber Lin: I… I know that on the… so on the left side, so there’s a lot of heavy, big clients. I don’t know if there’s a lot of…

110 00:09:44.230 ⇒ 00:09:56.590 Amber Lin: small clients, but there might be a lot of clients that’s not paying. I need to go in and check that. I can also… I can only say for certain that I know it’s concentrated on one side.

111 00:09:57.010 ⇒ 00:10:02.760 Caitlyn Vaughn: Yeah, so, I actually… here it is, this is the right doc.

112 00:10:02.860 ⇒ 00:10:05.410 Caitlyn Vaughn: So I actually went through…

113 00:10:05.690 ⇒ 00:10:18.310 Caitlyn Vaughn: Yesterday, or, like, sometime this week, and I looked at all of our, like, outstanding accounts, and it turns out we have $650,000 of uncollected revenue from our

114 00:10:18.550 ⇒ 00:10:22.810 Caitlyn Vaughn: Customers, and this is, like, revenue within Like, 9 months.

115 00:10:23.100 ⇒ 00:10:31.420 Caitlyn Vaughn: that have not been paid. So, we ended up, like, hiring an agency that’s going through, like, making sure they have or have not paid, like.

116 00:10:31.610 ⇒ 00:10:47.129 Caitlyn Vaughn: deleting duplicate quotes, and then, following up with people, because we, like, we weren’t following up, and we weren’t… there’s no punishment for not paying. Like, we weren’t cutting people off. So, there’s definitely, like, insights in here where I’m like, yeah, this is definitely reflecting…

117 00:10:47.410 ⇒ 00:10:53.179 Uttam Kumaran: Yeah, and you should totally also send them this, like, usage versus pricing thing.

118 00:10:53.640 ⇒ 00:10:55.979 Uttam Kumaran: While they’re in there, they can also look at

119 00:10:56.300 ⇒ 00:10:58.449 Uttam Kumaran: Who should we go have a res…

120 00:10:59.140 ⇒ 00:11:02.779 Uttam Kumaran: I mean, ideally a restructuring conversation with…

121 00:11:03.210 ⇒ 00:11:10.980 Uttam Kumaran: that’s, like, before March. You know, because some people, maybe you don’t want to, like, rock the boat on, but some people, it’s, like.

122 00:11:11.440 ⇒ 00:11:13.849 Uttam Kumaran: They should just be paying more, you know?

123 00:11:13.850 ⇒ 00:11:29.849 Caitlyn Vaughn: Yeah, that was another incident I got, which is, like, people who are paying us less are using it more, versus, like, in relative to how much they’re paying, versus people paying us more are, like, using it less. So, do you have, like, a list of companies that have higher usage and, like, aren’t paying us.

124 00:11:29.850 ⇒ 00:11:37.560 Amber Lin: Yeah, I should, let me find that for you. Also, you’re right in that here, this flat line is just people who are not paying.

125 00:11:38.390 ⇒ 00:11:39.200 Caitlyn Vaughn: Yeah.

126 00:11:39.200 ⇒ 00:11:41.240 Amber Lin: It reaches here, and then that’s just flat.

127 00:11:41.410 ⇒ 00:11:42.610 Caitlyn Vaughn: So nice.

128 00:11:42.610 ⇒ 00:11:47.240 Amber Lin: So there’s a lot of people who’s in the zero revenue section.

129 00:11:47.240 ⇒ 00:11:52.019 Uttam Kumaran: And Amber, this is zero revenue in terms of, like, there’s nothing coming from Hyperline for that.

130 00:11:54.260 ⇒ 00:11:55.060 Amber Lin: Pipeline?

131 00:11:55.060 ⇒ 00:11:57.510 Uttam Kumaran: But Hyperline is the subscription, like, where all the… Oh, okay.

132 00:11:58.020 ⇒ 00:11:59.170 Caitlyn Vaughn: Just the rubber, yeah.

133 00:11:59.170 ⇒ 00:12:03.219 Uttam Kumaran: Yeah, so I guess my question is, like, the zeros are, like, they don’t show up there at all?

134 00:12:03.920 ⇒ 00:12:10.240 Amber Lin: This is based on the spreadsheet that Mustafa made, so whatever is… There.

135 00:12:10.240 ⇒ 00:12:23.020 Caitlyn Vaughn: So, yeah, this is the exported data from Hyperline, which is all of the invoices, how much they owe us, and how much they’ve paid, and when they’re due. So she should have, like, a pretty accurate, up-to-date spreadsheet.

136 00:12:23.020 ⇒ 00:12:23.650 Uttam Kumaran: Okay.

137 00:12:24.550 ⇒ 00:12:35.090 Amber Lin: Yeah, and then, when you scroll down here a little bit, I did a section on over- and under-monetized.

138 00:12:35.740 ⇒ 00:12:41.809 Amber Lin: How I did this was that the people with high meetings and high subscriptions, because that’s the value they’re getting.

139 00:12:42.080 ⇒ 00:12:42.590 Caitlyn Vaughn: And…

140 00:12:42.590 ⇒ 00:12:49.939 Amber Lin: those with low ARR, relatively, so I did a Z score, so it’s relative. So…

141 00:12:51.220 ⇒ 00:12:59.570 Amber Lin: As we can see here, that’s default. I can match the names, I can go grab domain names for you.

142 00:13:00.320 ⇒ 00:13:10.080 Amber Lin: Ben… We can see here that there’s people whose let’s see… Where is their revenue?

143 00:13:10.510 ⇒ 00:13:11.830 Amber Lin: Yeah.

144 00:13:11.830 ⇒ 00:13:13.259 Caitlyn Vaughn: What is the z-score?

145 00:13:13.340 ⇒ 00:13:18.480 Amber Lin: Z scores, so essentially, it’s where they’re at in the distribution.

146 00:13:18.480 ⇒ 00:13:19.530 Caitlyn Vaughn: So…

147 00:13:19.820 ⇒ 00:13:25.770 Amber Lin: the higher… z-score is 0 and 1, so 1’s the highest, and you can see, like.

148 00:13:26.190 ⇒ 00:13:26.660 Caitlyn Vaughn: Okay.

149 00:13:26.660 ⇒ 00:13:28.530 Amber Lin: Like, negative 1 to 1, and so…

150 00:13:28.930 ⇒ 00:13:39.080 Amber Lin: it is, the less they use it, or the less… the smaller it is. So the less revenue, or the less usage. So here you can see people who have

151 00:13:39.260 ⇒ 00:13:45.410 Amber Lin: Sorry, it’s not negative 1 to 1, but it’s a long, like, the U, the bell-shaped curve.

152 00:13:45.880 ⇒ 00:13:47.050 Caitlyn Vaughn: Yeah, okay.

153 00:13:47.050 ⇒ 00:13:47.890 Amber Lin: higher end.

154 00:13:47.890 ⇒ 00:13:48.670 Caitlyn Vaughn: So…

155 00:13:48.670 ⇒ 00:13:51.289 Amber Lin: default, of course, uses it a lot, so you can.

156 00:13:51.290 ⇒ 00:13:51.760 Caitlyn Vaughn: See?

157 00:13:51.760 ⇒ 00:13:53.540 Amber Lin: It’s at 4.

158 00:13:53.960 ⇒ 00:13:57.069 Amber Lin: And then there’s people who use it

159 00:13:57.810 ⇒ 00:14:05.909 Amber Lin: pretty much very similar to default, but they only pay, I’ve only seen this much revenue, from them.

160 00:14:06.830 ⇒ 00:14:11.239 Amber Lin: And… so you can see here, the revenue mostly is between

161 00:14:11.350 ⇒ 00:14:21.189 Amber Lin: like, 5K to 10K, but they booked a lot of meetings, They have very little users.

162 00:14:21.360 ⇒ 00:14:28.009 Amber Lin: Relatively. And then, they have a lot of subscriptions.

163 00:14:28.170 ⇒ 00:14:36.020 Amber Lin: Which, if you can look at the people whose quote-unquote, under-monetized.

164 00:14:36.180 ⇒ 00:14:40.290 Amber Lin: they have… Very, very little subscriptions.

165 00:14:40.290 ⇒ 00:14:41.650 Caitlyn Vaughn: What is a subscription?

166 00:14:41.650 ⇒ 00:14:43.519 Amber Lin: No, sorry, submissions. Submissions.

167 00:14:43.520 ⇒ 00:14:44.779 Caitlyn Vaughn: -Oh, oh, oh.

168 00:14:45.120 ⇒ 00:14:46.400 Caitlyn Vaughn: Okay, yeah.

169 00:14:46.400 ⇒ 00:14:50.270 Amber Lin: So, that kind of gives you a context of how

170 00:14:51.610 ⇒ 00:14:55.949 Amber Lin: I… like, how some people are using it a lot, but we’re not…

171 00:14:55.970 ⇒ 00:15:00.809 Caitlyn Vaughn: Law from them, either from their platform fees, or either from.

172 00:15:00.810 ⇒ 00:15:01.990 Amber Lin: their seats.

173 00:15:02.340 ⇒ 00:15:07.720 Caitlyn Vaughn: Okay, cool. Yeah, this is really interesting. Probably the most helpful thing you could do here is to just connect.

174 00:15:07.720 ⇒ 00:15:11.030 Amber Lin: connect our team ID with, like, the actual account name.

175 00:15:11.030 ⇒ 00:15:15.259 Uttam Kumaran: Yeah, and then another thing that would… I think I would ask for is to do…

176 00:15:15.930 ⇒ 00:15:23.210 Uttam Kumaran: top 5, bottom 5, like, worst 5. So as much as possible, I think we want to have everybody, but…

177 00:15:23.360 ⇒ 00:15:28.820 Uttam Kumaran: like, I want to show which ones stand out as the most egregious, or, like, the people who are…

178 00:15:29.000 ⇒ 00:15:33.820 Uttam Kumaran: Using it the least, but paying the most, which is also… like, not great, right? So…

179 00:15:34.430 ⇒ 00:15:36.850 Uttam Kumaran: both of those categories, I think. I mean, it…

180 00:15:37.060 ⇒ 00:15:40.989 Uttam Kumaran: Those are just outliers. You can decide.

181 00:15:40.990 ⇒ 00:16:00.610 Caitlyn Vaughn: Yeah, honestly, so we’ve just recently started closing some larger accounts, which has been great. So the other thing that I can see with this, like, top 10, 40% thing is we’re… we’re starting to go upmarket, like, this is the beginning of us, so it’s a little bit expected for us to have, like.

182 00:16:00.610 ⇒ 00:16:10.039 Caitlyn Vaughn: that kind of distribution. Actually, I think it’s a good thing in the long term, it’s a bad thing for, like, the short term. Yeah. But I’m most curious about the, like.

183 00:16:10.210 ⇒ 00:16:13.409 Caitlyn Vaughn: under… monetized over…

184 00:16:14.020 ⇒ 00:16:29.049 Caitlyn Vaughn: search products. And I think when you guys build out the Omni dashboard for… for Deanna to go, like, follow up with these customers and have those conversations, that’s when this will, like, precipitate the most. But having that initial list would be really interesting and valuable for our team.

185 00:16:29.570 ⇒ 00:16:30.150 Amber Lin: Yeah.

186 00:16:30.830 ⇒ 00:16:31.880 Amber Lin: Totally.

187 00:16:34.190 ⇒ 00:16:39.149 Amber Lin: Yeah, the first time I did it, I had a domain name. I had a few revisions that kind of got lost along.

188 00:16:39.150 ⇒ 00:16:45.060 Caitlyn Vaughn: No, you’re all good. No, that’s so helpful, literally. You have, like… honed in.

189 00:16:45.060 ⇒ 00:16:48.499 Amber Lin: I appreciate it. That made me so motivated to do the analysis.

190 00:16:48.500 ⇒ 00:16:49.420 Caitlyn Vaughn: Okay, good.

191 00:16:49.420 ⇒ 00:16:56.869 Amber Lin: And then, I know we haven’t got to explore, this other pricing that.

192 00:16:57.750 ⇒ 00:17:03.329 Amber Lin: That you introduced. This one is more based on…

193 00:17:03.660 ⇒ 00:17:11.619 Amber Lin: I think ver- this is more vertical, because it’s, it’s based on functions versus what we have right now, just metrics.

194 00:17:11.829 ⇒ 00:17:16.219 Amber Lin: Blanket applied across everybody, just with different tiers.

195 00:17:16.990 ⇒ 00:17:20.590 Amber Lin: Do you guys just, explored if this is…

196 00:17:20.710 ⇒ 00:17:38.770 Amber Lin: viable. I tried to explore yesterday based on people’s… so the team’s main function, so some teams are more sales, some teams are more operational-based, and tried to explore it with the current relationship with revenue. I don’t see too much of a…

197 00:17:39.010 ⇒ 00:17:42.739 Amber Lin: Differentiation there, so do you think you guys are…

198 00:17:42.980 ⇒ 00:17:53.260 Amber Lin: At this stage where you should do vertical pricing, because I know when you mentioned Gong and HubSpot, they’re already very, very big when they introduced.

199 00:17:53.590 ⇒ 00:17:55.749 Amber Lin: This type of segmentation.

200 00:17:55.750 ⇒ 00:18:04.939 Caitlyn Vaughn: Yeah, that’s such a good point you brought up. So I actually asked the same exact question to our head of RevOps, who, like, proposed this pricing.

201 00:18:05.480 ⇒ 00:18:15.509 Caitlyn Vaughn: I think that there’s, like, a few things that go into it. The first is we’ve moved around our implementation process quite a bit, and right now, and for, like.

202 00:18:15.780 ⇒ 00:18:29.460 Caitlyn Vaughn: The recent times, we’ve had our forward-deployed engineers doing it, which is basically our engineering interns that are, like, starting out as baby engineers, and this is probably their first time doing anything client-facing, and

203 00:18:30.450 ⇒ 00:18:39.690 Caitlyn Vaughn: We’re actually switching as of next week to be more, like, partner-led implementation. We’re actually gonna go fully outsourcing, like, professional services.

204 00:18:39.690 ⇒ 00:18:40.140 Amber Lin: Hmm.

205 00:18:40.140 ⇒ 00:18:48.780 Caitlyn Vaughn: But I would say one of the downfalls to our current implementation process is the FDEs typically are, like.

206 00:18:49.130 ⇒ 00:18:56.899 Caitlyn Vaughn: here is your setup, here’s your one workflow, which is, like, form submission to, you know, X.

207 00:18:57.020 ⇒ 00:19:09.929 Caitlyn Vaughn: And they basically set up our main use case, but our platform could be very horizontal, like, there’s a lot of use cases, but they’re not actually pushing those use cases forward, or not following up with customers to expand horizontally.

208 00:19:09.930 ⇒ 00:19:20.589 Caitlyn Vaughn: So, there’s a lot of opportunity on our side to do it, but that’s actually a really, really good call-out. Like, I don’t know if we’re quite ready for the horizontal pricing yet, but it’s probably where we want to go eventually.

209 00:19:21.060 ⇒ 00:19:23.990 Amber Lin: With the vertical pricing, you mean? Like, by function?

210 00:19:24.310 ⇒ 00:19:26.860 Caitlyn Vaughn: Yes, by function, yeah.

211 00:19:26.860 ⇒ 00:19:27.360 Amber Lin: Awesome.

212 00:19:33.480 ⇒ 00:19:43.480 Caitlyn Vaughn: Let me write this down also. Amber, would you repeat what you said? You said most… the use cases were not correlated with.

213 00:19:44.000 ⇒ 00:19:58.810 Amber Lin: Yeah, so I did a analysis based on, I tagged teams by the most percentage of functions they’re in, either, say, sales or operations. It didn’t seem like it had a strong correlation with revenue.

214 00:19:59.240 ⇒ 00:20:06.769 Caitlyn Vaughn: Hmm, can you show me? Let me find that, because I just did that last night after we talked, so I don’t know if I put it… Oh, cool.

215 00:20:06.770 ⇒ 00:20:07.160 Amber Lin: yet.

216 00:20:07.160 ⇒ 00:20:08.149 Caitlyn Vaughn: Maybe you’re all good.

217 00:20:08.180 ⇒ 00:20:10.060 Amber Lin: Let me grab that.

218 00:20:17.830 ⇒ 00:20:23.439 Amber Lin: I mean, based on… I know you got inspired from Oh, and then HubSpot.

219 00:20:23.440 ⇒ 00:20:23.960 Caitlyn Vaughn: Nope.

220 00:20:23.960 ⇒ 00:20:29.979 Amber Lin: when they started to do that type of pricing, because they did start out similarly to you, they just had a flat.

221 00:20:30.830 ⇒ 00:20:35.040 Amber Lin: Like, a flat, horizontal plan, and then eventually a bait.

222 00:20:35.290 ⇒ 00:20:40.359 Amber Lin: had they separated out into different forks, but do you know when they did that?

223 00:20:41.340 ⇒ 00:20:44.919 Amber Lin: How much maturity, or how many consumers…

224 00:20:44.920 ⇒ 00:20:52.379 Caitlyn Vaughn: Not sure. I mean, it… it feels like for something like HubSpot and Gong, they’re now… they now have, like, so many SKUs and product offerings that it makes.

225 00:20:52.380 ⇒ 00:20:52.769 Amber Lin: a lot of sense.

226 00:20:52.770 ⇒ 00:20:59.409 Caitlyn Vaughn: But I’m sure, like, it takes a lot of time to get to that place, so I don’t know if it makes sense.

227 00:20:59.710 ⇒ 00:21:06.039 Caitlyn Vaughn: like, potentially, if you look at the pricing model that’s laid out, I feel like it does make sense, but then…

228 00:21:06.200 ⇒ 00:21:10.879 Caitlyn Vaughn: I don’t know how we would scale that down to self-service or up to, like…

229 00:21:11.280 ⇒ 00:21:14.150 Caitlyn Vaughn: How do we close a million dollar deal, you know?

230 00:21:15.010 ⇒ 00:21:15.540 Uttam Kumaran: Yeah.

231 00:21:22.570 ⇒ 00:21:27.460 Amber Lin: Yeah, let me… Put it down here.

232 00:21:28.020 ⇒ 00:21:29.400 Caitlyn Vaughn: Also, I did…

233 00:21:29.820 ⇒ 00:21:39.629 Caitlyn Vaughn: learn all of this stuff, all of these, like, stats-type stuff and charts in, like, high school and college, but it’s been such a long time since I’ve looked at this, so…

234 00:21:39.630 ⇒ 00:21:44.779 Amber Lin: Oh, sorry, see if I have to search up what the things mean as well.

235 00:21:45.270 ⇒ 00:21:47.020 Amber Lin: Oh, okay, that makes sense. I’ll try.

236 00:21:47.020 ⇒ 00:21:47.499 Caitlyn Vaughn: the level with it.

237 00:21:47.500 ⇒ 00:21:52.289 Amber Lin: And… I’ll try to put in as much explanation as needed, because that helps me understand.

238 00:21:52.290 ⇒ 00:21:59.550 Uttam Kumaran: Yeah, that’s also what, like, for us, it’s like, I… we kind of… these analyses go through phases, so usually our internal process

239 00:21:59.950 ⇒ 00:22:05.569 Uttam Kumaran: It’s like, we kind of go through a rough, like, what are we even trying to get at? And then it goes through, like, a first draft.

240 00:22:05.730 ⇒ 00:22:09.179 Uttam Kumaran: Then it goes through, like, okay, let’s get to the second draft, and then we put it into a deck.

241 00:22:09.870 ⇒ 00:22:13.380 Uttam Kumaran: you’re a unique client where I’m like, just get, like.

242 00:22:13.700 ⇒ 00:22:17.540 Uttam Kumaran: Because you guys are making decisions, like, very fast, so I’m like, just get, like…

243 00:22:17.540 ⇒ 00:22:19.250 Caitlyn Vaughn: Yeah. Something, so we can start…

244 00:22:19.250 ⇒ 00:22:25.150 Uttam Kumaran: conversating, and then we can totally help you produce a couple slides, or make it very polished to share with, like.

245 00:22:25.630 ⇒ 00:22:26.830 Uttam Kumaran: Everybody,

246 00:22:26.830 ⇒ 00:22:30.450 Caitlyn Vaughn: Yeah, I’ve already been sharing it, so… Okay, cool. It’s fine.

247 00:22:30.450 ⇒ 00:22:31.800 Uttam Kumaran: Right, alright, cool.

248 00:22:31.800 ⇒ 00:22:39.230 Caitlyn Vaughn: Like, z-score, I’m like, yeah, I remember the, like, you know, tail that I completed, I have no idea what that means.

249 00:22:43.950 ⇒ 00:22:44.550 Amber Lin: This is crazy.

250 00:22:45.810 ⇒ 00:22:50.189 Amber Lin: Boom… Okay, I’ll share screen here.

251 00:23:07.860 ⇒ 00:23:20.890 Amber Lin: So… This is the analysis I based on… function, so… I tagged… Team, based on…

252 00:23:21.140 ⇒ 00:23:26.150 Amber Lin: The highest percentage of… Functions.

253 00:23:26.320 ⇒ 00:23:33.679 Amber Lin: And then I essentially mapped that to revenue to see what what the difference is.

254 00:23:34.170 ⇒ 00:23:38.619 Amber Lin: So, I remember on the… on Omni, we also had

255 00:23:38.660 ⇒ 00:23:54.579 Amber Lin: this graph. We had this graph, and we know that most people are in sales, which is very understandable. It’s a mainly sales function, but when we look at the mean ARR, there’s not too big of a difference.

256 00:23:54.660 ⇒ 00:23:59.350 Amber Lin: Between these teams, especially, mainly between sales and…

257 00:23:59.610 ⇒ 00:24:04.809 Amber Lin: operations, because I don’t have enough data for marketing.

258 00:24:05.040 ⇒ 00:24:16.350 Amber Lin: And so… when I compared them, they don’t… there’s not a very statistically significant difference between different functions.

259 00:24:16.510 ⇒ 00:24:27.319 Amber Lin: So, either because it’s because we have too many people who are in sales, which then means that there’s not enough people in operations or marketing to justify having that partnership.

260 00:24:27.950 ⇒ 00:24:37.419 Amber Lin: pricing, or it just means that people use the functions equally, and in that case, then, we don’t need to separate it out for it.

261 00:24:39.590 ⇒ 00:24:41.130 Caitlyn Vaughn: So… hmm.

262 00:24:41.940 ⇒ 00:24:45.999 Caitlyn Vaughn: As I’m looking at mean ARR by dominant function.

263 00:24:46.700 ⇒ 00:24:53.319 Caitlyn Vaughn: So, is the ARR of the individual tied to, like, their… how much they’re paying for their company?

264 00:24:54.250 ⇒ 00:24:58.199 Amber Lin: I did it… I did it by team, so this is not by…

265 00:24:58.360 ⇒ 00:25:09.749 Amber Lin: individual, so I tagged teams by this team. So, essentially, I said, this team, I’ll call it sales, because it has… most of the people in that team are, are sales.

266 00:25:09.990 ⇒ 00:25:19.349 Caitlyn Vaughn: Okay, so there’s 10 people in an account, 8 of them are in sales, the other 2 are different, so you’re like, okay, this is a team that is primarily, like, a sales function.

267 00:25:20.260 ⇒ 00:25:24.209 Caitlyn Vaughn: Okay, and then you tag that to, like, how much is this account paying us?

268 00:25:24.210 ⇒ 00:25:24.940 Amber Lin: Yes.

269 00:25:25.230 ⇒ 00:25:27.879 Caitlyn Vaughn: Okay, cool, that’s really interesting.

270 00:25:28.290 ⇒ 00:25:40.329 Caitlyn Vaughn: Primarily sales, and it’s… it’s also interesting, because our primary, persona that we sell to is RevOps. So it’s sales operations, right? Which is technically the first two here, which is great.

271 00:25:40.780 ⇒ 00:25:46.279 Caitlyn Vaughn: But also, as I’m looking at this, I do feel like…

272 00:25:48.620 ⇒ 00:25:51.890 Caitlyn Vaughn: Thinking about the, like, function-based sales?

273 00:25:52.140 ⇒ 00:26:01.769 Caitlyn Vaughn: it seems like we have teams that are, like, tied to each of the three of those. So, like, potentially, we could be cross-selling, like, we should be cross-selling different use cases.

274 00:26:03.810 ⇒ 00:26:16.929 Amber Lin: Yeah, and then I think some of the members in there are also tagged as engineering. I’m not sure if it’s something that we did when we were enriching the data, or because they’re startups, and then they’re also just doing sales.

275 00:26:17.120 ⇒ 00:26:22.640 Caitlyn Vaughn: Yeah, I think… yeah, exactly. Since…

276 00:26:23.000 ⇒ 00:26:29.149 Caitlyn Vaughn: Our product is a little bit technical. We often have to have engineers set up at least, like, the form.

277 00:26:29.150 ⇒ 00:26:33.430 Amber Lin: like, SDKs in the beginning, so it makes sense that there’s a lot of engineers in here, too.

278 00:26:35.920 ⇒ 00:26:39.219 Caitlyn Vaughn: Cool, yeah, this is all making a lot of sense.

279 00:26:41.660 ⇒ 00:26:47.189 Amber Lin: Yeah, a lot of times I did this, and I was like, oh, it just mostly confirms Caitlin’s current guesses.

280 00:26:47.600 ⇒ 00:26:48.350 Caitlyn Vaughn: current beliefs.

281 00:26:48.350 ⇒ 00:26:49.160 Amber Lin: To have some data.

282 00:26:49.160 ⇒ 00:26:56.639 Uttam Kumaran: Yeah, but that’s also how you prove, again, like, I think the goal here is to prove the pricing, the new pricing structure will work.

283 00:26:56.700 ⇒ 00:26:58.280 Caitlyn Vaughn: - and then…

284 00:26:58.370 ⇒ 00:27:10.939 Uttam Kumaran: the additional work I… I told Amber is, like, if we can do backtesting. So once we agree on a pricing, we now have the mechanism to be like, if they were to pay this, how would that change the concentration?

285 00:27:11.170 ⇒ 00:27:13.980 Uttam Kumaran: Or change total revenue, things like that.

286 00:27:14.500 ⇒ 00:27:16.170 Caitlyn Vaughn: Wait, can you say that again?

287 00:27:16.170 ⇒ 00:27:20.559 Uttam Kumaran: Yeah, so I want… so you basically… I want to do a backtest of.

288 00:27:21.010 ⇒ 00:27:40.480 Uttam Kumaran: any new pricing that you propose. Like, if you were to learn from this, okay, we’re instead gonna bundle features in XYZ way, we’re gonna change it from $50amonthto$100 a month, and you get two admin seats, and yeah, it’s a plus 50, plus 20. I was like, okay, Amber, we can build a model that outputs

289 00:27:40.590 ⇒ 00:27:44.880 Uttam Kumaran: Like, basically any part of this analysis, given the new pricing scheme.

290 00:27:44.880 ⇒ 00:27:49.559 Caitlyn Vaughn: So, it’s based on whatever objective. Like, if you’re like, hey, I actually want to smooth out.

291 00:27:49.750 ⇒ 00:27:53.859 Uttam Kumaran: the curve, where I want, like, our highest paying customers to either

292 00:27:53.960 ⇒ 00:28:06.679 Uttam Kumaran: they’ll be paying us a lot, but, like, maybe the middle group ends up being a higher concentration. We should expect to see that, given the new scheme, right? Or if you’re like, hey, I actually want the highest paying customers.

293 00:28:06.680 ⇒ 00:28:18.810 Uttam Kumaran: They end up paying way more because they’re really price insensitive, and they should be paying, like, 5 times more. And we would… ideally, we would take this and run the test and produce

294 00:28:19.150 ⇒ 00:28:25.639 Uttam Kumaran: all or any of those graphs, again, given the new pricing. Because we’ll just basically estimate what their new ARR.

295 00:28:26.820 ⇒ 00:28:27.460 Uttam Kumaran: You know?

296 00:28:27.920 ⇒ 00:28:46.140 Caitlyn Vaughn: That’s so interesting. So you would take, like, our existing customer base and say, if they were on this pricing bundle, this is what the revenue would look like versus this one. Yeah. Okay, I would love to see that. That would be really, really interesting. And you did that, you know, you did that piecemeal for one or two, right? And that’s, like, what the power is.

297 00:28:46.140 ⇒ 00:28:51.869 Uttam Kumaran: One step further is to also understand, like, who gets negatively impacted.

298 00:28:52.080 ⇒ 00:29:08.020 Uttam Kumaran: And then it’s more of a… then everybody can be like, okay, we’re happy that we can go to those customers, and that they will be open to paying more, or paying less, and you… you kind of know what you’re walking into. And it also allows you to say, like, okay, let’s… can you run a test where

299 00:29:08.210 ⇒ 00:29:13.899 Uttam Kumaran: Like, for example, You know, my guess is that the a la carte options

300 00:29:14.120 ⇒ 00:29:22.069 Uttam Kumaran: maybe there’s more testing to be done on how much to price these, right? And so you can say, like, okay, let’s do an analysis where we

301 00:29:22.410 ⇒ 00:29:25.549 Uttam Kumaran: Rearrange this, or rechanged some of these, what is the impact?

302 00:29:25.770 ⇒ 00:29:29.090 Caitlyn Vaughn: And so we… I want to both show you, like, the total…

303 00:29:29.340 ⇒ 00:29:45.229 Uttam Kumaran: dollars amount, like, hey, if you change this, your new revenue will be here versus here, but it’s also very important to be like, which companies are paying that, right? Like, so you should see that the companies that are paying very little and getting a lot are now monetized effectively.

304 00:29:45.810 ⇒ 00:30:05.429 Uttam Kumaran: Additionally, you see that the companies that are paying the lion’s share also end up paying more, but, like, they’re… it’s more evenly spread out, versus, like, a 90-10, it’s more like 60-40, the top 20%, and the rest of the 80, right? So those are the things that, once… I guess my ask would be.

305 00:30:05.680 ⇒ 00:30:12.160 Uttam Kumaran: To… if we want to, we can run with… basically with this, but if you want to adjust this at all.

306 00:30:12.310 ⇒ 00:30:14.740 Uttam Kumaran: We could do that, and then we could run that test.

307 00:30:16.150 ⇒ 00:30:17.130 Caitlyn Vaughn: Okay.

308 00:30:17.700 ⇒ 00:30:24.210 Caitlyn Vaughn: That sounds good. We… we should, like, make some assumptions for talk to sales, based on, like, what we…

309 00:30:25.330 ⇒ 00:30:28.969 Caitlyn Vaughn: would maybe charge, and we can just make it up for now.

310 00:30:28.970 ⇒ 00:30:29.690 Uttam Kumaran: I agree.

311 00:30:29.690 ⇒ 00:30:32.100 Caitlyn Vaughn: Just to test it, but…

312 00:30:33.070 ⇒ 00:30:39.150 Caitlyn Vaughn: Honestly, I think talk-to-sales could just be, like, what the self-serve tier is, but at scale.

313 00:30:39.290 ⇒ 00:30:47.179 Caitlyn Vaughn: And then the only difference is that we’re adding in, like, managed services and implementation and support, right?

314 00:30:47.380 ⇒ 00:30:55.810 Caitlyn Vaughn: And, like, if you want to bring in 500 people, 500 seats, then that gives sales a lot of power to be able to discount heavily, right?

315 00:30:56.020 ⇒ 00:30:56.600 Uttam Kumaran: Yeah.

316 00:30:56.780 ⇒ 00:31:03.279 Uttam Kumaran: So, I guess I got one other question, like, are there people that are paying that would… end up…

317 00:31:03.480 ⇒ 00:31:07.049 Uttam Kumaran: just… Given their current usage, be free tier.

318 00:31:08.260 ⇒ 00:31:08.670 Caitlyn Vaughn: No.

319 00:31:08.670 ⇒ 00:31:09.190 Uttam Kumaran: liking.

320 00:31:09.400 ⇒ 00:31:13.860 Uttam Kumaran: Okay, so nobody in the platform right now should qualify for the… like.

321 00:31:14.790 ⇒ 00:31:17.009 Uttam Kumaran: Everyone will at least be at this level.

322 00:31:18.460 ⇒ 00:31:19.980 Caitlyn Vaughn: Yes, yeah.

323 00:31:19.980 ⇒ 00:31:21.280 Uttam Kumaran: Okay, so yeah.

324 00:31:21.280 ⇒ 00:31:21.980 Caitlyn Vaughn: Exactly.

325 00:31:21.980 ⇒ 00:31:24.969 Uttam Kumaran: I think it’s… I mean, we could do that, we could just basically say.

326 00:31:25.460 ⇒ 00:31:28.880 Uttam Kumaran: These two are the same from the revenue perspective, and…

327 00:31:29.470 ⇒ 00:31:32.830 Uttam Kumaran: I guess my question would be… what the…

328 00:31:33.400 ⇒ 00:31:36.300 Uttam Kumaran: Oh, so the lim… I guess the limits here…

329 00:31:36.780 ⇒ 00:31:43.379 Uttam Kumaran: are really the admin seat? Oh, okay. So, I guess talk to me about, like, when someone makes the transition.

330 00:31:43.830 ⇒ 00:31:46.420 Uttam Kumaran: Like, what are the… what’s the break?

331 00:31:46.790 ⇒ 00:31:58.279 Caitlyn Vaughn: The break is mainly around, support implementation and managed services. So, if a company comes in and they’re like, we need to, like, go through a DPA process, and we need

332 00:31:58.830 ⇒ 00:32:03.930 Caitlyn Vaughn: our security questionnaire, it’s like, okay, then you’re enterprise. Like, we’re not gonna go through this if you’re gonna pay us fucking…

333 00:32:03.940 ⇒ 00:32:06.110 Uttam Kumaran: $200 a month. Yeah, yeah, yeah, yeah, okay.

334 00:32:06.110 ⇒ 00:32:10.180 Caitlyn Vaughn: And let’s… we can just assume inside of here, like…

335 00:32:10.180 ⇒ 00:32:11.440 Uttam Kumaran: The same thing, then.

336 00:32:11.440 ⇒ 00:32:13.530 Caitlyn Vaughn: Let’s assume paid implementations

337 00:32:13.930 ⇒ 00:32:24.590 Caitlyn Vaughn: 5K, managed services, 5K, and Slack support is 5K. And the, like, support and managed services, let’s say, are, like, reoccurring annual.

338 00:32:25.610 ⇒ 00:32:33.209 Uttam Kumaran: Okay, great. So I guess to… yeah, if you want to note that down, Amber, so there’s two… so there’s one one-time 10K charge?

339 00:32:33.350 ⇒ 00:32:37.609 Uttam Kumaran: And then there’s a 5K… Monthly recurring, so…

340 00:32:37.910 ⇒ 00:32:43.549 Uttam Kumaran: 60K… it’s basically 6… it’ll be 60… on the ARR side, it’ll be 60K plus whatever…

341 00:32:44.050 ⇒ 00:32:45.520 Uttam Kumaran: Gets charged for this.

342 00:32:45.520 ⇒ 00:32:49.609 Caitlyn Vaughn: Wait, sorry, it’s 10… let’s do 10K for implementation.

343 00:32:49.720 ⇒ 00:32:57.149 Caitlyn Vaughn: Great, and then… Yeah. Let’s do… let’s say, yeah, 10K is fine. Annual. 10K annual.

344 00:32:57.150 ⇒ 00:32:58.209 Uttam Kumaran: Oh, 10K, okay, okay.

345 00:32:58.210 ⇒ 00:33:01.730 Caitlyn Vaughn: Slack, and then, let’s say, 10K annual for…

346 00:33:02.200 ⇒ 00:33:05.889 Caitlyn Vaughn: Well, let’s say, let’s say 5K annual for paid managed services.

347 00:33:06.270 ⇒ 00:33:10.740 Uttam Kumaran: Okay. Okay, great. Look at that. That’s, like, probably a more realistic breakdown.

348 00:33:10.900 ⇒ 00:33:11.420 Uttam Kumaran: Yeah.

349 00:33:13.430 ⇒ 00:33:14.250 Uttam Kumaran: Cool.

350 00:33:14.250 ⇒ 00:33:15.739 Amber Lin: Presentation’s, like, one time.

351 00:33:16.070 ⇒ 00:33:20.140 Uttam Kumaran: Yeah, I would do one time… I mean, I would just… you could just spread it out, so basically…

352 00:33:21.640 ⇒ 00:33:32.109 Uttam Kumaran: it would be… it would just be… and again, we’re only… we’re mainly just focus on first-year revenue, so yeah, it’ll just be 15K divided by 12. That adds to MRR, and then…

353 00:33:32.290 ⇒ 00:33:34.620 Uttam Kumaran: Plus, whatever gets qualified by this.

354 00:33:35.750 ⇒ 00:33:39.629 Caitlyn Vaughn: And then also, with the Enterprise, we only do annual contracts.

355 00:33:39.820 ⇒ 00:33:41.030 Uttam Kumaran: Yeah, yeah, yeah, yeah.

356 00:33:42.080 ⇒ 00:33:51.649 Amber Lin: So some of the… some of the companies already are on annual contracts, would I assume that they are, enterprise?

357 00:33:52.020 ⇒ 00:33:58.550 Caitlyn Vaughn: So, technically, every single company that we have today is on an annual contract.

358 00:33:58.550 ⇒ 00:33:59.949 Amber Lin: I see, I see.

359 00:34:00.150 ⇒ 00:34:05.479 Caitlyn Vaughn: So even if they’re paying us monthly, or quarterly, or biannual billing, they’re still.

360 00:34:05.480 ⇒ 00:34:05.880 Uttam Kumaran: Yeah.

361 00:34:05.880 ⇒ 00:34:07.029 Caitlyn Vaughn: annual contract.

362 00:34:07.230 ⇒ 00:34:12.540 Amber Lin: I see. So when I do the calculation, should I assume a percentage of clients that will increase?

363 00:34:12.540 ⇒ 00:34:15.780 Uttam Kumaran: Yeah, I guess that’s also my question. Who in the current data set

364 00:34:16.250 ⇒ 00:34:22.510 Uttam Kumaran: would be in enterprise territory. Like, do you want us to just say the top… Some percent.

365 00:34:24.659 ⇒ 00:34:26.429 Caitlyn Vaughn: Or do you wanna… I would say…

366 00:34:26.830 ⇒ 00:34:31.550 Caitlyn Vaughn: Let’s say anybody who’s paying us more…

367 00:34:32.060 ⇒ 00:34:34.359 Caitlyn Vaughn: Let’s say anyone who has more than, like.

368 00:34:36.370 ⇒ 00:34:42.629 Caitlyn Vaughn: I wanna say… 40 seats? Maybe 30 seats would be Enterprise.

369 00:34:42.800 ⇒ 00:34:47.610 Uttam Kumaran: Okay. Yeah, Amber, can you pull that list, and then just… we can just send in Slack to Caitlin of who

370 00:34:48.350 ⇒ 00:34:51.070 Uttam Kumaran: Whenever, of who is more than 30 seats.

371 00:34:51.239 ⇒ 00:34:56.219 Uttam Kumaran: So let’s assume those are enterprise, assume everybody else on the list is self-serve, and then let’s run it, yeah.

372 00:34:56.969 ⇒ 00:34:58.339 Caitlyn Vaughn: Maths do that.

373 00:34:58.340 ⇒ 00:34:58.960 Amber Lin: Okay.

374 00:34:59.470 ⇒ 00:35:00.700 Caitlyn Vaughn: Mmm…

375 00:35:01.630 ⇒ 00:35:09.440 Uttam Kumaran: And then I think probably the biggest thing, Amber, is we’re not gonna know some of the things, like the API charges, so you’ll have to make some assumptions on, like.

376 00:35:10.990 ⇒ 00:35:13.960 Uttam Kumaran: the average company is gonna do XYZ.

377 00:35:14.780 ⇒ 00:35:17.029 Caitlyn Vaughn: and also usage. I mean.

378 00:35:17.030 ⇒ 00:35:17.590 Uttam Kumaran: Yeah.

379 00:35:17.590 ⇒ 00:35:20.190 Caitlyn Vaughn: Can we make some usage assumption? Like…

380 00:35:20.960 ⇒ 00:35:25.700 Caitlyn Vaughn: Let’s assume 50% of companies do,

381 00:35:26.790 ⇒ 00:35:31.089 Caitlyn Vaughn: Let’s assume 50% of companies do the, web intent.

382 00:35:31.360 ⇒ 00:35:36.209 Caitlyn Vaughn: Which is 1K a month, so 12K a year.

383 00:35:38.990 ⇒ 00:35:47.809 Caitlyn Vaughn: And then let’s assume that… Companies are using… Let’s see…

384 00:35:48.550 ⇒ 00:35:52.239 Caitlyn Vaughn: Trying to do, like, a seat per credit assumption.

385 00:35:52.720 ⇒ 00:35:57.590 Caitlyn Vaughn: Let’s just assume, like, every seat does a thousand credits a month or something.

386 00:36:03.250 ⇒ 00:36:12.520 Uttam Kumaran: Yeah, I won’t overcomplicate. Let’s start there, and then we can do ranges if we need later, Amber, but yeah, and then… okay, so we have the mem… so, seats is fine.

387 00:36:13.190 ⇒ 00:36:19.840 Uttam Kumaran: I guess it’ll be scheduling versus routing seat. I don’t think… I don’t know if we have that already, so we can just…

388 00:36:21.010 ⇒ 00:36:22.660 Uttam Kumaran: I guess, do you.

389 00:36:28.150 ⇒ 00:36:47.179 Amber Lin: Right now, I looked at it yesterday, because I was wondering about all the line items. So I have one that’s overall seats per flat fee, but we have a lot of individual line items, and sometimes they combine things. So there’s routing plus scheduling, so I don’t know how we’re going to separate that.

390 00:36:47.180 ⇒ 00:36:49.539 Uttam Kumaran: You’re airing out all the dirty laundry.

391 00:36:49.540 ⇒ 00:36:51.419 Caitlyn Vaughn: Yeah, Amber, stop.

392 00:36:51.420 ⇒ 00:36:52.500 Amber Lin: laundry thing.

393 00:36:52.500 ⇒ 00:36:54.720 Uttam Kumaran: I saw this, too, I was like, okay.

394 00:36:54.720 ⇒ 00:36:55.400 Caitlyn Vaughn: Look at that.

395 00:36:55.400 ⇒ 00:36:56.490 Uttam Kumaran: Talk about this, guys.

396 00:36:56.490 ⇒ 00:37:14.340 Caitlyn Vaughn: Yeah, okay, let’s talk about it. So, some of these are the old pricing model, some of them are the new pricing model, and then some of them are just sales, like, thrown in a crazy remix, and I actually am not quite sure why, but…

397 00:37:14.340 ⇒ 00:37:15.729 Amber Lin: Huh, I see.

398 00:37:16.100 ⇒ 00:37:16.700 Amber Lin: We just…

399 00:37:16.940 ⇒ 00:37:18.250 Caitlyn Vaughn: Freestyle.

400 00:37:18.250 ⇒ 00:37:27.850 Amber Lin: Let’s see. I mean, some of them have disappeared. Well, actually, this, because some of them are annual contracts that you can just actually just look at.

401 00:37:28.850 ⇒ 00:37:30.480 Amber Lin: look at before…

402 00:37:30.770 ⇒ 00:37:35.719 Uttam Kumaran: Yeah, but what you can tell is that most of them, they’re all mostly one time.

403 00:37:35.820 ⇒ 00:37:40.420 Uttam Kumaran: Except for, like, maybe, like, a couple of them, as you can see, that are growing.

404 00:37:40.990 ⇒ 00:37:42.660 Caitlyn Vaughn: Wait, what’s one time?

405 00:37:42.660 ⇒ 00:37:44.239 Uttam Kumaran: Like, meaning this blue one?

406 00:37:44.340 ⇒ 00:37:51.530 Uttam Kumaran: was probably, like, a contract someone, like, a name, and then, as you can tell, it gets spent over time, like…

407 00:37:51.530 ⇒ 00:37:52.160 Caitlyn Vaughn: Yeah, that’s…

408 00:37:52.160 ⇒ 00:37:53.010 Amber Lin: else?

409 00:37:53.010 ⇒ 00:37:56.830 Uttam Kumaran: No, like, no, like, new, like, new clients are getting.

410 00:37:57.530 ⇒ 00:38:00.199 Amber Lin: Kivan, this… whatever this blue one is, right?

411 00:38:00.200 ⇒ 00:38:02.470 Caitlyn Vaughn: Inbound platform fee?

412 00:38:03.220 ⇒ 00:38:08.429 Amber Lin: I think that’s what we started out with, and then there are just more plans that occurred.

413 00:38:08.430 ⇒ 00:38:12.229 Uttam Kumaran: But you can see, like, this pink one, maybe, could be one…

414 00:38:12.500 ⇒ 00:38:14.180 Caitlyn Vaughn: This new one…

415 00:38:14.600 ⇒ 00:38:18.639 Uttam Kumaran: I mean, this one seems… this blue one in the background seems like something.

416 00:38:19.090 ⇒ 00:38:23.979 Uttam Kumaran: But basically, I think one thing that could be helpful is, like, yeah, we should just map

417 00:38:24.260 ⇒ 00:38:31.250 Uttam Kumaran: Well, I guess now we have the… we have the plans, basically, so it doesn’t really matter. What we’re trying to do here is get an assumption of

418 00:38:31.590 ⇒ 00:38:38.950 Uttam Kumaran: Like, let’s say that they have 100 seats on the platform, what is our assumption for ratios between all the four seat types?

419 00:38:40.400 ⇒ 00:38:41.600 Caitlyn Vaughn: The four seat types.

420 00:38:41.600 ⇒ 00:38:46.130 Uttam Kumaran: Yeah, like, admin… what we have, admin routing… Or…

421 00:38:46.130 ⇒ 00:38:47.630 Caitlyn Vaughn: Admin member schedule.

422 00:38:47.630 ⇒ 00:38:48.050 Uttam Kumaran: Routing.

423 00:38:48.050 ⇒ 00:38:51.470 Caitlyn Vaughn: routing. Yeah, assumptions in… in what context?

424 00:38:51.470 ⇒ 00:38:56.580 Uttam Kumaran: In terms of, like, if, like, a Brain Forge was to have default, and we have 100 seats.

425 00:38:57.420 ⇒ 00:39:04.469 Uttam Kumaran: There’s typically gonna be… 2 admins, X amount of… Like, 60 members…

426 00:39:04.470 ⇒ 00:39:05.110 Amber Lin: Tensor.

427 00:39:05.110 ⇒ 00:39:05.720 Uttam Kumaran: Scheduling.

428 00:39:05.720 ⇒ 00:39:06.740 Amber Lin: member.

429 00:39:06.920 ⇒ 00:39:07.960 Caitlyn Vaughn: Yeah, you should have that.

430 00:39:07.960 ⇒ 00:39:12.260 Amber Lin: I have admin versus member, I don’t have scheduling and routing seats.

431 00:39:12.820 ⇒ 00:39:13.880 Caitlyn Vaughn: Hmm…

432 00:39:14.060 ⇒ 00:39:14.640 Uttam Kumaran: Okay.

433 00:39:14.960 ⇒ 00:39:22.070 Caitlyn Vaughn: Those are separate line items for us, like scheduling and routing seats, so they should… They shouldn’t accept that.

434 00:39:22.070 ⇒ 00:39:23.630 Uttam Kumaran: Out of… that… would that come out of…

435 00:39:23.980 ⇒ 00:39:27.590 Uttam Kumaran: Admins, or would that come out of, like, the members.

436 00:39:28.590 ⇒ 00:39:35.340 Caitlyn Vaughn: So we’re actually… Like, the way that the platform is currently set up.

437 00:39:36.490 ⇒ 00:39:42.590 Caitlyn Vaughn: The only difference between admin and members is that admins…

438 00:39:42.970 ⇒ 00:39:49.660 Caitlyn Vaughn: Can see workflows and, like, forms and stuff, versus members can only get their booking link.

439 00:39:51.450 ⇒ 00:39:56.460 Uttam Kumaran: So I guess, would you say, like, in this new mechanism, you’re releasing scheduling and routing as an ability for

440 00:39:56.930 ⇒ 00:40:02.869 Uttam Kumaran: Certain members do… Like, manage more things.

441 00:40:02.990 ⇒ 00:40:08.520 Uttam Kumaran: Or is it for… there to be less admins? I guess is that… is the question.

442 00:40:09.370 ⇒ 00:40:14.630 Caitlyn Vaughn: We haven’t fully sliced up that decision and product yet, but I would say…

443 00:40:15.670 ⇒ 00:40:24.450 Uttam Kumaran: We’ll have a ratio of admins to members. I guess what I’m trying to do now is think about… we have… we’ll have four types, and so what is the…

444 00:40:24.890 ⇒ 00:40:27.680 Uttam Kumaran: now we have four, kind of, four ratios, right? So there’s…

445 00:40:28.270 ⇒ 00:40:34.099 Uttam Kumaran: For every one admin, there’s… 4 members, one routing, one scheduling.

446 00:40:34.790 ⇒ 00:40:37.970 Uttam Kumaran: in an ideal world. Again, it’s just all assumptions.

447 00:40:40.130 ⇒ 00:40:44.430 Caitlyn Vaughn: I mean, I mean, I don’t know, it doesn’t really… it doesn’t really matter… I guess we don’t… it’s like…

448 00:40:44.430 ⇒ 00:40:52.999 Uttam Kumaran: I guess, Amber, what I would say is, like, these are all, like, 20, 10, 25. You can probably just flat assume 20 bucks.

449 00:40:54.670 ⇒ 00:40:56.019 Uttam Kumaran: And then we can just, like…

450 00:40:56.600 ⇒ 00:41:02.769 Uttam Kumaran: it… I guess it doesn’t necessarily matter unless… I- and also, like,

451 00:41:02.900 ⇒ 00:41:07.510 Uttam Kumaran: Yeah. I guess we should just assume all members are gonna be, on average, 20 bucks.

452 00:41:08.460 ⇒ 00:41:14.540 Uttam Kumaran: Regardless of the seat type, and just, let’s use our existing ratio.

453 00:41:15.260 ⇒ 00:41:16.210 Amber Lin: Cool. Yeah.

454 00:41:18.960 ⇒ 00:41:21.939 Caitlyn Vaughn: ADK, yeah. There’s so many, like, question marks in this.

455 00:41:21.940 ⇒ 00:41:22.430 Uttam Kumaran: I would just…

456 00:41:22.430 ⇒ 00:41:24.569 Caitlyn Vaughn: I’ll say, like, use your best.

457 00:41:24.570 ⇒ 00:41:26.119 Uttam Kumaran: gut feeling, like…

458 00:41:26.390 ⇒ 00:41:31.879 Caitlyn Vaughn: let’s say 10… 5% of users are admins, or something, like, you can make it up.

459 00:41:33.930 ⇒ 00:41:46.649 Caitlyn Vaughn: Oh, I know what else I was gonna ask. So, Amber, the other thing that I pulled out of your doc that I thought was pretty interesting was, like, the strongest correlation to revenue was meetings booked.

460 00:41:46.900 ⇒ 00:41:52.189 Caitlyn Vaughn: Those seem to scale, like, the strongest together. So, if I’m thinking of, like.

461 00:41:53.340 ⇒ 00:41:57.819 Caitlyn Vaughn: how I would build out a pricing model using those two things, rather than, like.

462 00:41:58.520 ⇒ 00:42:07.530 Caitlyn Vaughn: seats and revenue? I don’t know if, like… can you think of, like, a strong use case in which we would price based on meetings booked versus, like.

463 00:42:07.790 ⇒ 00:42:09.189 Caitlyn Vaughn: everything else.

464 00:42:09.850 ⇒ 00:42:10.480 Caitlyn Vaughn: That’s my first.

465 00:42:10.480 ⇒ 00:42:28.749 Amber Lin: When I looked it up, I think that’s essentially outcome-based pricing. It leans more towards that, because that’s… meetings is what people want to book, and one step further would be, oh, how many leads you get, but that’s too far, and then we can’t really track that. I think we could…

466 00:42:29.190 ⇒ 00:42:46.549 Amber Lin: like, a softer way to introduce is to have tiers based on meetings. So we say, if you book past a certain amount of meetings, then you should upgrade, because obviously, you are getting a lot of value, and you are using it, and you’re getting more leads. So I think that’s something that we can

467 00:42:46.720 ⇒ 00:42:57.930 Amber Lin: start with. I think if we do per meeting, that could feel a bit more… like, that could feel like a… like the customer might not feel very good about, oh, for.

468 00:42:58.640 ⇒ 00:43:00.010 Amber Lin: book, I have to pay more.

469 00:43:00.250 ⇒ 00:43:00.880 Caitlyn Vaughn: Right.

470 00:43:00.880 ⇒ 00:43:03.819 Amber Lin: Tier-based would be… would be a good approach.

471 00:43:06.090 ⇒ 00:43:06.440 Caitlyn Vaughn: Okay.

472 00:43:06.440 ⇒ 00:43:14.030 Uttam Kumaran: Yeah, like, we did… we thought about this pricing, because we are… we have a customer that we built an AI agent for that we…

473 00:43:14.140 ⇒ 00:43:17.540 Uttam Kumaran: We’re trying to do, like, usage-based pricing.

474 00:43:17.540 ⇒ 00:43:20.460 Caitlyn Vaughn: But we decided instead to treat it, like, kind of like a.

475 00:43:20.460 ⇒ 00:43:29.509 Uttam Kumaran: the advice we got was treated like a phone plan. Like, give them tier caps. Like, up to 500 meetings, up to 5,000 meetings. I mean, of course.

476 00:43:29.940 ⇒ 00:43:34.180 Uttam Kumaran: It depends on, like, what… in my business, one meeting.

477 00:43:34.400 ⇒ 00:43:36.579 Uttam Kumaran: It can be very high value.

478 00:43:36.580 ⇒ 00:43:38.700 Caitlyn Vaughn: In some other businesses, they’re just like.

479 00:43:38.780 ⇒ 00:43:43.020 Uttam Kumaran: Turning through, but instead, what we did is saying, okay, you get

480 00:43:43.310 ⇒ 00:43:54.490 Uttam Kumaran: In this example, it’d be like, you get 1,000 meetings, and then it’s at this price per meeting, the next 1,000 is at this price per meeting, the next… or the next 1,000 to 5,000, 5,000 and up.

481 00:43:54.730 ⇒ 00:43:58.770 Uttam Kumaran: That way, you’re not, like… disincentivized.

482 00:43:59.610 ⇒ 00:44:10.370 Uttam Kumaran: you don’t get, like, incrementally billed, you’re just more, like, in these tiers, so that’s one thing that’s possible. Another thing you can do is just, like, kind of start with a flat cap. You can see, like, your first 5,000 meetings are free.

483 00:44:10.370 ⇒ 00:44:12.580 Caitlyn Vaughn: And then after this, you go to a certain amount.

484 00:44:14.020 ⇒ 00:44:21.109 Uttam Kumaran: I mean, you’re… you guys are sell… you guys are selling…

485 00:44:21.320 ⇒ 00:44:27.320 Uttam Kumaran: primarily to other high-vol… you want to sell to other high-volume, high-meeting organizations, like.

486 00:44:27.800 ⇒ 00:44:35.400 Uttam Kumaran: for example, we’re not… we’re not… we’re not ICPs because our… we just don’t have as much meetings booked, right? And so that’s…

487 00:44:35.690 ⇒ 00:44:47.660 Uttam Kumaran: if to think about it the other way, I would say that’s… that could be an approach. Like, you basically figure out what those tiers are. Your first 1,000 meetings a month are free, and then after that, it’s some cents per meeting.

488 00:44:48.180 ⇒ 00:44:52.720 Uttam Kumaran: Or it’s another fee. Could be… another angle.

489 00:44:53.310 ⇒ 00:45:01.659 Caitlyn Vaughn: Okay, that’s interesting, I hadn’t considered that yet. Amber, it would be interesting to see some more data around meetings booked.

490 00:45:02.080 ⇒ 00:45:19.779 Caitlyn Vaughn: I know we have a little bit in the Omni as well, but if you could just, like, maybe aggregate it into one place, that is an interesting thought. The only other thing that I’m thinking, though, is if we were going to go for, like, the function-based pricing, and we were doing meetings booked.

491 00:45:20.040 ⇒ 00:45:32.230 Caitlyn Vaughn: I would be worried that it would inhibit our expansion into, like, other use cases. You know, it would, like, really tie us to sales versus to, like, marketing and ops and so on and so forth. You know what I mean?

492 00:45:34.040 ⇒ 00:45:35.029 Amber Lin: I see.

493 00:45:35.230 ⇒ 00:45:47.179 Amber Lin: You’re right. Let me… let me explore a bit further on that. I did remember of… like, there’s pros and cons of tying it to a very specific outcome metric.

494 00:45:47.180 ⇒ 00:45:47.680 Caitlyn Vaughn: different steps.

495 00:45:47.680 ⇒ 00:45:58.040 Amber Lin: you’re limited to that outcome. How strongly or how reason do you plan to expand to different functions? I know we’re adding different features and stuff.

496 00:45:58.740 ⇒ 00:46:03.170 Caitlyn Vaughn: Yeah, so… I’m gonna say, like, our…

497 00:46:03.470 ⇒ 00:46:18.790 Caitlyn Vaughn: our primary user will remain as RevOps for, like, the foreseeable future, but what we’re actually trying to do as a product is build everything from, like, the very early stages of RevOps, like.

498 00:46:18.890 ⇒ 00:46:22.140 Amber Lin: Being able to calculate your TAM and figure out.

499 00:46:22.260 ⇒ 00:46:31.319 Caitlyn Vaughn: like, for example, orgs have 50 reps across the United States, each one has a state, and, like, half of them are failing, so they fire half of them, but it’s like…

500 00:46:31.440 ⇒ 00:46:40.720 Caitlyn Vaughn: well, the opportunity isn’t really even across all the territories, but since you’re, like, a RevOps person, you don’t really know how to, like, properly calculate territories, and, like.

501 00:46:41.330 ⇒ 00:46:52.249 Caitlyn Vaughn: ACV and everything, then it’s hard for you to say that, like, reps are doing good or bad, right? So, like, getting all the way into, like, the planning parts of,

502 00:46:52.550 ⇒ 00:47:05.260 Caitlyn Vaughn: of sales and marketing, all the way into, like, HR software, where we have teams, we sync into Salesforce. If you fire somebody, it, like, reroutes all of the, queues, all the way down to, like.

503 00:47:05.260 ⇒ 00:47:14.989 Caitlyn Vaughn: dashboarding and reporting. So we’ll have, like, a BI tool on top of our tool where you can, like, see all of your information laid out. So it’s, like, the full picture of

504 00:47:15.350 ⇒ 00:47:24.210 Caitlyn Vaughn: go to market. So I would say, like, RevOps is still the person we’re selling to, but it should start appealing to, like, a broader market in the next, like, one year.

505 00:47:25.330 ⇒ 00:47:40.939 Amber Lin: I see. Would they still care about the meetings book? So, meaning, would there still be a lot of salespeople using it and wanting to book meetings, and the other functions are sort of hovering around that to support that main function of booking meetings?

506 00:47:41.400 ⇒ 00:47:45.459 Caitlyn Vaughn: That’s a really good question. I’m, like, looking at the packaging,

507 00:47:45.680 ⇒ 00:47:48.380 Caitlyn Vaughn: sheet that I had sent over, where it’s like.

508 00:47:48.640 ⇒ 00:47:55.780 Caitlyn Vaughn: Out of the box, you get Relay, which is, like, form fill, routing, and scheduling, and then Growth, which is, like, UTMs, dashboards.

509 00:47:56.250 ⇒ 00:48:08.630 Caitlyn Vaughn: upsell Marketo records, enrich records, add Zoom webinar, which, I mean, really, I guess that all goes still back into, like, did we book meetings? Right?

510 00:48:09.560 ⇒ 00:48:13.109 Caitlyn Vaughn: But even more so, like, if I was a marketing person.

511 00:48:13.230 ⇒ 00:48:15.630 Caitlyn Vaughn: I probably would care less about…

512 00:48:15.820 ⇒ 00:48:19.780 Caitlyn Vaughn: meetings booked, and I would care more about, like, MQLs, right?

513 00:48:20.960 ⇒ 00:48:26.450 Caitlyn Vaughn: So… I would say each of the roles are differently aligned on…

514 00:48:27.580 ⇒ 00:48:34.180 Caitlyn Vaughn: What they probably care the most about, but the company probably cares the most about meetings booked.

515 00:48:34.310 ⇒ 00:48:35.780 Caitlyn Vaughn: Sorry, that was a lot of words.

516 00:48:36.590 ⇒ 00:48:37.280 Uttam Kumaran: I agree.

517 00:48:38.940 ⇒ 00:48:47.359 Amber Lin: So I can also try to explore what other metrics we can… that’s related to the outcomes of the different departments.

518 00:48:48.070 ⇒ 00:48:49.610 Amber Lin: Bat Cook University.

519 00:48:49.810 ⇒ 00:48:58.260 Amber Lin: Good point to start, and I’ll give you more info on the meetings book, because if we… if we do book by tier, then it matters less if we

520 00:48:58.460 ⇒ 00:49:04.779 Amber Lin: We don’t have to completely transition to meeting books, it could just be an add-on from the.

521 00:49:04.780 ⇒ 00:49:05.230 Caitlyn Vaughn: Hmm.

522 00:49:05.230 ⇒ 00:49:11.359 Amber Lin: To just make sure they don’t… we don’t leak in value if someone books a lot of meetings, and we completely don’t look at it.

523 00:49:13.070 ⇒ 00:49:16.339 Uttam Kumaran: It’s more like, once they start booking a lot of meetings.

524 00:49:16.510 ⇒ 00:49:21.200 Uttam Kumaran: Like, what are the odds that they, like, that, like.

525 00:49:21.400 ⇒ 00:49:26.039 Uttam Kumaran: After 5,000 meetings, or whatever it is, that they start getting billed, and they’re like.

526 00:49:26.170 ⇒ 00:49:35.169 Uttam Kumaran: F this, like, they’re happy, right? So, it’s sort of like, yeah, I don’t know. I mean, you’re… at the moment, you’re not going to charge at all, right?

527 00:49:35.300 ⇒ 00:49:37.719 Caitlyn Vaughn: for that, and so I feel like…

528 00:49:37.720 ⇒ 00:49:40.529 Uttam Kumaran: The easiest way is to just start with a super high cap.

529 00:49:40.770 ⇒ 00:49:46.550 Uttam Kumaran: Like, we could look at, historically, what the most amount of meetings booked in the month has been.

530 00:49:47.080 ⇒ 00:49:49.740 Uttam Kumaran: Like, whatever, and propose a cap that’s, like.

531 00:49:50.360 ⇒ 00:49:56.049 Uttam Kumaran: Yo, if you’re actually booking, like, this many meetings through our tool, like, okay, we’re gonna charge a slight usage fee.

532 00:49:56.180 ⇒ 00:49:58.780 Uttam Kumaran: Sort of like a data plan. Like…

533 00:49:58.920 ⇒ 00:50:12.730 Uttam Kumaran: You know? And we do… and you do it in a way where some companies are gonna start with you and be like, oh yeah, we’re never gonna hit that. Some companies are gonna be like, okay, maybe we could hit that, but it’s still something we would… they probably don’t even know how many meetings are getting booked today.

534 00:50:12.820 ⇒ 00:50:14.700 Caitlyn Vaughn: So you can start there, and then you can…

535 00:50:14.700 ⇒ 00:50:17.210 Uttam Kumaran: I would say it’s always easier to then bring that.

536 00:50:17.600 ⇒ 00:50:18.660 Uttam Kumaran: lower.

537 00:50:18.660 ⇒ 00:50:20.769 Caitlyn Vaughn: You know? Like, lower over time.

538 00:50:20.770 ⇒ 00:50:21.400 Uttam Kumaran: Yeah.

539 00:50:23.120 ⇒ 00:50:39.670 Caitlyn Vaughn: That’s a good point. Yeah, I feel like, Amber, you’re onto something that is, like, we haven’t seen before. I wonder if there’s, like, a better, like, a more, like, an indirect, clever way that we can price this to where it feels, like, less outcomes-based? Because I don’t know…

540 00:50:39.950 ⇒ 00:50:43.719 Caitlyn Vaughn: if outcomes is necessarily the thing that I want to peg to, but it’s like…

541 00:50:45.900 ⇒ 00:50:54.729 Caitlyn Vaughn: I don’t know, let me… let me mull it over more. The second question I had for you is, you had another really interesting insight, which was…

542 00:50:54.870 ⇒ 00:51:09.079 Caitlyn Vaughn: like, we basically have about 200 customers, right? And our top 100 customers are only contributing to, like, 32% of our… or maybe 37% of our, like, total usage for workflows.

543 00:51:10.650 ⇒ 00:51:19.249 Amber Lin: Yes, so the number of workflows essentially are pretty flat and even across.

544 00:51:21.480 ⇒ 00:51:28.299 Caitlyn Vaughn: Which is… Interesting, and slightly perplexing to me.

545 00:51:29.520 ⇒ 00:51:40.990 Caitlyn Vaughn: Because… That is assuming that the bottom, basically 100 of our customers are doing 62% of our

546 00:51:41.290 ⇒ 00:51:51.930 Caitlyn Vaughn: workflow usage, and… Basically, like, we’re calculating this number based on form fills, essentially, right?

547 00:51:51.930 ⇒ 00:52:05.219 Amber Lin: So submissions is essentially workflow runs, right? And this green line is the number of workflows, so the different types of workflows that they have. So…

548 00:52:05.430 ⇒ 00:52:06.830 Amber Lin: I think there’s…

549 00:52:07.350 ⇒ 00:52:16.639 Amber Lin: like, there’s a quick spike, and then a lot of people just have a few. Like, I check the data, and usually workflows, people have around, say, like.

550 00:52:16.810 ⇒ 00:52:26.140 Amber Lin: 2 to 8, around that range. It doesn’t really differ. Nobody really has, like, 60 types of workflows. And I do think

551 00:52:26.620 ⇒ 00:52:32.410 Amber Lin: When you want more workflows, are you trying to encourage more experimentation?

552 00:52:32.700 ⇒ 00:52:41.569 Amber Lin: Because workflows are just the way they do it. Maybe they booked a lot of meetings or did a lot of submissions through just one or two workflows, and they did.

553 00:52:41.570 ⇒ 00:52:42.550 Caitlyn Vaughn: Okay.

554 00:52:42.940 ⇒ 00:52:47.589 Caitlyn Vaughn: So this line is the number of workflows, not number of workflow runs.

555 00:52:47.760 ⇒ 00:52:51.609 Amber Lin: Yeah, the number of workflow runs would be submissions, so this…

556 00:52:51.610 ⇒ 00:52:59.369 Caitlyn Vaughn: Oh… Oh, okay. Wait, that’s so different then.

557 00:53:00.490 ⇒ 00:53:03.040 Caitlyn Vaughn: So, submissions…

558 00:53:03.810 ⇒ 00:53:08.560 Caitlyn Vaughn: Okay, can we change the way that we talk about this, just because it’s gonna, like, trip me up?

559 00:53:08.560 ⇒ 00:53:09.360 Amber Lin: I agree.

560 00:53:09.360 ⇒ 00:53:18.049 Caitlyn Vaughn: Instead of submissions, can we start calling them workflow runs, and can we differentiate workflows as, like, the number of workflows?

561 00:53:18.050 ⇒ 00:53:18.660 Amber Lin: Okay.

562 00:53:19.180 ⇒ 00:53:36.200 Caitlyn Vaughn: Okay, wait, that makes so much more sense. So the number of workflows doesn’t scale past a certain amount. So this is what I was trying to get into. Like, as I’m looking at PLG, or, like, self-serve, and us getting into that, what are the triggers that we should set for people as…

563 00:53:36.480 ⇒ 00:53:41.910 Caitlyn Vaughn: like… We should give them…

564 00:53:42.390 ⇒ 00:53:48.780 Caitlyn Vaughn: This amount of this one thing for them to hit enough value to convert and, like, want to upgrade, but, like.

565 00:53:49.140 ⇒ 00:53:57.019 Caitlyn Vaughn: like, get some value, but not get all of the value kind of a thing. So would that be, like, creating one workflow? Like, allowing them to create one workflow?

566 00:53:58.880 ⇒ 00:54:04.939 Amber Lin: I would like to look into the threshold of when people bump from one… one…

567 00:54:05.560 ⇒ 00:54:22.770 Amber Lin: amount of meetings… workflows to another amount, like, the next tier amount of workflows. I do think there’s a… there is a bump, because you see, you know, some people don’t use it at all, and then there’s, like, a group of people who have… who use it.

568 00:54:24.470 ⇒ 00:54:44.200 Amber Lin: a certain amount, and I do think what you have right now, unlimited, for the paid tier, it does make sense, because it… I don’t think it makes sense to segment workflows any further if usage is decently flat, they’re not going to bump it up, but I can find where the cutoff is. It might not be one workflow, but I do think what you have currently.

569 00:54:45.200 ⇒ 00:54:50.990 Caitlyn Vaughn: Okay, this is still really interesting. The submissions for our top 100 companies are 57% thin.

570 00:54:51.090 ⇒ 00:54:57.220 Caitlyn Vaughn: Not 37, which is still, like, I would expect it to be so much higher, since they’re paying us the most, you know?

571 00:54:58.480 ⇒ 00:55:00.220 Amber Lin: Yeah, and submissions…

572 00:55:00.220 ⇒ 00:55:05.330 Uttam Kumaran: But that just means, like, you’re… yeah, I guess it… It’s,

573 00:55:06.000 ⇒ 00:55:15.660 Uttam Kumaran: It could be because they don’t succeed on the platform, or maybe they just, like, your sales team did a great job on pitching them, and they’re just getting you low usage, right? It’s not exactly related to…

574 00:55:16.000 ⇒ 00:55:23.989 Uttam Kumaran: I mean, what you hope is that the, again, like, true value that they’re getting exceeds what they’re paying you by, like, the time, right?

575 00:55:24.440 ⇒ 00:55:38.249 Uttam Kumaran: this is, like, to Deanna, what I was talking to her about is, like, many of these cases may just be, like, it didn’t get set up well, or whoever set it up didn’t explain it internally well. There’s probably some low-hanging fruit there to kind of move some of those people who are paying a lot to get their usage up, too.

576 00:55:38.600 ⇒ 00:55:39.400 Caitlyn Vaughn: Right, like…

577 00:55:39.400 ⇒ 00:55:42.580 Uttam Kumaran: That should… that, I would say, is not an ideal customer.

578 00:55:43.480 ⇒ 00:55:59.539 Caitlyn Vaughn: Yeah. Yeah, I think that goes back to the whole, like, our FDEs are building one workflow for customers and, like, not expanding out, so if they could use some templates for other use cases and, like, push those, you know, to customers, I bet that number would expand quite a bit.

579 00:56:01.120 ⇒ 00:56:02.180 Caitlyn Vaughn: Okay.

580 00:56:02.410 ⇒ 00:56:04.779 Caitlyn Vaughn: Okay, this is really clear.

581 00:56:05.620 ⇒ 00:56:07.770 Caitlyn Vaughn: This was super helpful. I think…

582 00:56:08.610 ⇒ 00:56:20.889 Caitlyn Vaughn: going forward, what I would love more clarity on is, like, who the actual customers are, domain-wise, or, like, company name-wise, of the list of the under-monetized over usage.

583 00:56:21.100 ⇒ 00:56:33.810 Caitlyn Vaughn: And then probably some more clarity around, like, meetings booked, or how we can approach pricing in, like, possibly a more clever way.

584 00:56:34.100 ⇒ 00:56:42.890 Caitlyn Vaughn: To set ourselves up, if we’re not gonna do the, like, function-based pricing yet, if we could set ourselves up to, like, in a future have function-based pricing?

585 00:56:45.000 ⇒ 00:56:52.259 Caitlyn Vaughn: And then… what is the lever that we should, like, what is the threshold we should give to free users?

586 00:56:52.370 ⇒ 00:56:59.330 Caitlyn Vaughn: For them to upgrade, whether that’s, like, number of workflows, whether that’s number of meetings booked,

587 00:57:01.300 ⇒ 00:57:02.929 Caitlyn Vaughn: Yeah, let’s start there.

588 00:57:05.490 ⇒ 00:57:06.150 Amber Lin: Gotcha.

589 00:57:08.450 ⇒ 00:57:11.819 Caitlyn Vaughn: And then as for Omni, have you guys talked to Victor?

590 00:57:12.840 ⇒ 00:57:18.410 Uttam Kumaran: No, I’m going back and forth with him in Slack, so I feel like we should probably get somewhere this week.

591 00:57:20.040 ⇒ 00:57:20.880 Caitlyn Vaughn: Okay.

592 00:57:22.630 ⇒ 00:57:23.840 Uttam Kumaran: Why, what do you think?

593 00:57:24.070 ⇒ 00:57:33.070 Uttam Kumaran: Yeah, we, we… I guess, Mustafa, we just moved over the dashboard, and then, I was gonna… I need to just double-check it, and then run it by you, and then we’re gonna…

594 00:57:33.330 ⇒ 00:57:35.909 Uttam Kumaran: Invite everybody to the noob thing.

595 00:57:36.450 ⇒ 00:57:40.720 Uttam Kumaran: get everybody off the old thing, I can… done that in Slack.

596 00:57:40.920 ⇒ 00:57:45.019 Uttam Kumaran: Or give you a blurb to send. And then,

597 00:57:46.330 ⇒ 00:57:51.199 Uttam Kumaran: Yeah, basically, gonna aim to run, like, a little bit of an Omni walkthrough next week.

598 00:57:51.650 ⇒ 00:57:52.620 Caitlyn Vaughn: The S3…

599 00:57:52.620 ⇒ 00:57:56.970 Uttam Kumaran: the S… the S3 stuff, yeah, we’ll… I think we’re still just going back and forth, and…

600 00:57:57.380 ⇒ 00:57:58.990 Uttam Kumaran: We’re close to an answer.

601 00:57:59.470 ⇒ 00:58:07.219 Caitlyn Vaughn: Okay, Victor’s, I’ve been, like, pushing him on this, and he’s just being so weird about, like, having our product data live.

602 00:58:07.460 ⇒ 00:58:12.880 Caitlyn Vaughn: duplicatively in another database. He’s, like, worried about the…

603 00:58:13.200 ⇒ 00:58:13.680 Uttam Kumaran: Yeah.

604 00:58:13.680 ⇒ 00:58:14.620 Caitlyn Vaughn: for, like, open.

605 00:58:14.620 ⇒ 00:58:15.159 Uttam Kumaran: I guess, like.

606 00:58:15.160 ⇒ 00:58:15.910 Caitlyn Vaughn: anymore.

607 00:58:16.090 ⇒ 00:58:19.580 Uttam Kumaran: Yeah, I guess what I’m… One is just, like…

608 00:58:20.380 ⇒ 00:58:24.140 Uttam Kumaran: we could… we just… he could keep it all in Supabase.

609 00:58:24.800 ⇒ 00:58:36.270 Uttam Kumaran: But then I have to quit… I just have… yeah, I just want to… I can kind of just walk them through kind of the ergonomics. We can do it any other way, but we have to have the data somewhere that I can pull for reporting. It’s not…

610 00:58:36.490 ⇒ 00:58:47.079 Uttam Kumaran: ideal to pull this straight from Postgres. At minimum, I just need to drop it into S3, and then we can… don’t have to store it anywhere else. It can be under the same access controls.

611 00:58:47.720 ⇒ 00:58:50.999 Uttam Kumaran: This is not, like, an uncommon pattern for analytics.

612 00:58:51.000 ⇒ 00:58:52.040 Caitlyn Vaughn: I know, yeah.

613 00:58:53.170 ⇒ 00:58:54.880 Caitlyn Vaughn: I don’t know why he’s so weird about it.

614 00:58:55.100 ⇒ 00:58:56.480 Uttam Kumaran: It’s fine, I mean, it’s worth, it’s worthwhile.

615 00:58:56.480 ⇒ 00:58:58.619 Caitlyn Vaughn: weird about S3. I don’t know why.

616 00:58:58.620 ⇒ 00:58:59.569 Uttam Kumaran: Oh, really? Okay.

617 00:58:59.570 ⇒ 00:59:02.279 Caitlyn Vaughn: Yeah, he’s, like, beefing with it pretty hard, so…

618 00:59:02.280 ⇒ 00:59:10.270 Uttam Kumaran: Oh, okay, alright. Well, I told him to answer… ask me any… this is all we do, so I’ll tell them everything that we do for other clients, and, like, what the best pa…

619 00:59:10.290 ⇒ 00:59:25.269 Uttam Kumaran: practices, and yeah, I mean, if you… it’s actually good, like, to have high constraints on security, but to, like, an end, you know? Like, if I can just answer the questions, like, in terms of the data process, the DPA, like, you guys aren’t using this to

620 00:59:25.520 ⇒ 00:59:35.609 Uttam Kumaran: process client data for your service. It’s for operational use cases, this type of analytics, so you don’t… yeah, for now. So, if Omni ends up being into the thing.

621 00:59:35.720 ⇒ 00:59:42.860 Uttam Kumaran: then yeah, we can go through that process. But for now, we haven’t had any client that’s ever had to adjust BPAs because we’re

622 00:59:43.390 ⇒ 00:59:51.770 Uttam Kumaran: moving customer data through this. The only things you have to maintain is that we have data governance and audit trails, and I said, you just tell me how heavy you want.

623 00:59:52.080 ⇒ 00:59:54.249 Uttam Kumaran: To go there, and we’ll do that, so…

624 00:59:55.110 ⇒ 01:00:02.580 Caitlyn Vaughn: Yeah, take what you would build for Amazon, and then duplicate it for default, because for some reason, our CTO is crazy with this.

625 01:00:02.580 ⇒ 01:00:03.870 Uttam Kumaran: No, I mean, look, you know.

626 01:00:03.870 ⇒ 01:00:13.000 Caitlyn Vaughn: He’s the genius, so… No, he’s really clever with it, but he’s like, yeah, I just need to, like, do some research on it, because I don’t want to, like, trust anyone else’s word on it. I’m like, okay, then.

627 01:00:13.000 ⇒ 01:00:14.480 Uttam Kumaran: No, he should, yeah, he should totally…

628 01:00:14.480 ⇒ 01:00:15.090 Caitlyn Vaughn: Who’s gonna do it.

629 01:00:15.090 ⇒ 01:00:20.290 Uttam Kumaran: Copy-paste everything I said and send it to chat your team, like, is this guy spoofing me? I don’t want it.

630 01:00:20.290 ⇒ 01:00:27.570 Caitlyn Vaughn: go to GPT research, I’m like, go get a Victor, like, come up with some answer, though. Yeah, yeah, okay.

631 01:00:27.570 ⇒ 01:00:45.859 Caitlyn Vaughn: So I’ll push on more on that, and then how is the… I don’t know if you… you guys sent me the PDL, research that you did, but then I lost it. Will you, like, resend it? I think this was Mustafa, in the Brainforge chat, and did we make any progress on doing any other providers?

632 01:00:46.210 ⇒ 01:00:50.560 Uttam Kumaran: We kind of switched to spending time on this, and then Deanna stuff.

633 01:00:50.830 ⇒ 01:00:52.709 Caitlyn Vaughn: We can.

634 01:00:52.710 ⇒ 01:00:53.730 Uttam Kumaran: to another vendor.

635 01:00:54.290 ⇒ 01:00:55.100 Caitlyn Vaughn: Okay.

636 01:00:55.950 ⇒ 01:00:58.779 Caitlyn Vaughn: I feel like we just need to, like, up your hours, TVH.

637 01:01:00.050 ⇒ 01:01:05.199 Uttam Kumaran: Yeah, I’ve kind of, like, we could probably have capacity to kind of do two… Things at a time.

638 01:01:06.460 ⇒ 01:01:13.660 Uttam Kumaran: We can do 3, but the 3 work streams here are gonna be… Just company-wide reporting.

639 01:01:14.100 ⇒ 01:01:17.819 Uttam Kumaran: Deanna’s Catalyst thing, and then analysis.

640 01:01:19.040 ⇒ 01:01:25.429 Caitlyn Vaughn: Okay. Because we also have this… well, this pricing is, like, wrapping up, though. Hopefully, we can.

641 01:01:25.430 ⇒ 01:01:26.030 Uttam Kumaran: Yeah, yeah.

642 01:01:26.030 ⇒ 01:01:26.490 Caitlyn Vaughn: Yeah, soon.

643 01:01:26.490 ⇒ 01:01:35.429 Uttam Kumaran: I mean, and then… and then what, basically, the next kind of things, and we can do a little roadmap, is, like, once the… once Phoenix is in some type of staging environment for us to do…

644 01:01:35.640 ⇒ 01:01:37.240 Uttam Kumaran: It’s important to…

645 01:01:37.240 ⇒ 01:01:39.739 Caitlyn Vaughn: Amplitude, or whatever, and to get…

646 01:01:39.740 ⇒ 01:01:40.950 Uttam Kumaran: analytics there.

647 01:01:42.120 ⇒ 01:01:48.179 Uttam Kumaran: And then we want to kind of move into doing, like, omni-training for folks and get people to use the dashboards.

648 01:01:48.390 ⇒ 01:01:56.500 Caitlyn Vaughn: Okay, cool. Well, hopefully Vic gets his shit together this week so that we can be ready for Omni Training next week. Okay.

649 01:01:57.020 ⇒ 01:02:00.649 Caitlyn Vaughn: And then… what was I gonna say?

650 01:02:00.820 ⇒ 01:02:09.170 Caitlyn Vaughn: Oh, so… I think we’re gonna end up rolling out some parts of the platform early for customers.

651 01:02:09.170 ⇒ 01:02:09.570 Uttam Kumaran: Cool.

652 01:02:09.570 ⇒ 01:02:21.269 Caitlyn Vaughn: Including Pages, which is, like, our basically white-labeled version of Vercel, where people can create, like, form pages, like, pricing pages and stuff.

653 01:02:21.520 ⇒ 01:02:30.479 Caitlyn Vaughn: And then… We are gonna roll out… what’s what we’re rolling out?

654 01:02:31.080 ⇒ 01:02:33.460 Caitlyn Vaughn: That’s the tables, I don’t know, tables.

655 01:02:34.450 ⇒ 01:02:43.749 Caitlyn Vaughn: I forget what else we’re rolling out, but anyways, we’re gonna roll out two things before, but I would assume that we’re really not gonna launch Phoenix until, like, March 1st at the earliest.

656 01:02:44.120 ⇒ 01:02:45.040 Uttam Kumaran: Okay, okay.

657 01:02:45.170 ⇒ 01:02:46.359 Caitlyn Vaughn: So we have some time.

658 01:02:46.890 ⇒ 01:02:52.149 Uttam Kumaran: Yeah, I mean, even as soon as you, like, you think, like, okay, new features are coming, I would love us to just…

659 01:02:52.310 ⇒ 01:02:54.430 Uttam Kumaran: Make sure Amplitude is, like, set up.

660 01:02:54.810 ⇒ 01:02:56.760 Uttam Kumaran: And we’re collecting events.

661 01:02:57.010 ⇒ 01:03:03.850 Caitlyn Vaughn: Okay, the, like, tables and backend is starting to get stood up, but it doesn’t matter because there’s no actual data, like…

662 01:03:04.190 ⇒ 01:03:05.659 Caitlyn Vaughn: Flowing into it, yet.

663 01:03:06.920 ⇒ 01:03:10.529 Caitlyn Vaughn: Okay, cool. Thank you guys so much, you guys are the friggin’ G.

664 01:03:11.310 ⇒ 01:03:17.220 Uttam Kumaran: I’m very happy, this is great, yeah, Amber’s crushing it. Yeah, this is great. This is, like, the real, like…

665 01:03:17.830 ⇒ 01:03:22.439 Caitlyn Vaughn: the best data work is, like, this stuff, you know? So I’m very jealous that you get to do this type of thing.

666 01:03:22.610 ⇒ 01:03:32.580 Caitlyn Vaughn: I just get to listen. I read through it on Monday, like, I looked at it and I was just like, I’m gonna need so many stimmies to process this. And then last night, I was like, I’m ready.

667 01:03:32.890 ⇒ 01:03:38.509 Caitlyn Vaughn: And I, like, got onto the whiteboard, and everyone was talking to me. I was like, don’t talk to me, and they were like, okay.

668 01:03:40.160 ⇒ 01:03:43.559 Amber Lin: I had a lot of fun doing it. It was… it’s very interesting.

669 01:03:43.910 ⇒ 01:03:47.510 Caitlyn Vaughn: It is, I feel like you know all of our secrets now, Amber.

670 01:03:47.510 ⇒ 01:03:49.350 Uttam Kumaran: That’s funny.

671 01:03:49.890 ⇒ 01:03:55.909 Caitlyn Vaughn: I’m dead. Alright, I will chat to you guys soon. If Victor doesn’t respond today, ping me and I’ll bug him more.

672 01:03:55.910 ⇒ 01:03:56.750 Uttam Kumaran: Okay, okay.

673 01:03:57.010 ⇒ 01:03:57.620 Caitlyn Vaughn: Okay. Alright.

674 01:03:57.620 ⇒ 01:03:59.040 Uttam Kumaran: See you guys later. Bye.

675 01:03:59.040 ⇒ 01:03:59.690 Mustafa Raja: And…