Meeting Title: Default | Brainforge Weekly Sync Date: 2025-10-30 Meeting participants: Amber Lin, Uttam Kumaran, Caitlyn Vaughn
WEBVTT
1 00:00:24.830 ⇒ 00:00:26.110 Uttam Kumaran: Hello?
2 00:00:27.410 ⇒ 00:00:28.580 Amber Lin: Hi!
3 00:00:30.400 ⇒ 00:00:31.589 Uttam Kumaran: How’s everything?
4 00:00:31.760 ⇒ 00:00:36.189 Amber Lin: I had a good call with Robert, so polishing off the slides, looks pretty good.
5 00:00:36.190 ⇒ 00:00:41.520 Uttam Kumaran: Yeah, one time, one thing we talked about yesterday was, like, okay, after soon we get out of stand-up, I feel like we have a lot of things going on.
6 00:00:41.520 ⇒ 00:00:42.290 Amber Lin: It’s just like…
7 00:00:42.290 ⇒ 00:00:45.289 Uttam Kumaran: People who need to do work together just work together.
8 00:00:45.290 ⇒ 00:00:46.030 Amber Lin: Yeah.
9 00:00:46.030 ⇒ 00:00:46.560 Uttam Kumaran: No.
10 00:00:47.760 ⇒ 00:00:49.210 Amber Lin: I think that’s a good model.
11 00:00:52.570 ⇒ 00:00:56.950 Amber Lin: What’s the purpose of this call? So I have some context.
12 00:00:56.950 ⇒ 00:01:16.090 Uttam Kumaran: This is just our weekly sync with Caitlin, so today I’m gonna be sharing some of our updates on People Data Labs. I’m gonna be talking about, like, where we’re going next with Omni, and, like, kind of, like, making sure that who else needs to be involved when we do a training, and then I kind of just want to spend the rest of the time getting answers from
13 00:01:16.180 ⇒ 00:01:17.550 Uttam Kumaran: Your pricing stuff.
14 00:01:17.930 ⇒ 00:01:21.880 Amber Lin: Okay, yeah, I just want to lock that down so I know actually what to test.
15 00:03:30.070 ⇒ 00:03:30.670 Caitlyn Vaughn: Hey, hey!
16 00:03:30.670 ⇒ 00:03:31.890 Uttam Kumaran: So…
17 00:03:32.070 ⇒ 00:03:32.860 Caitlyn Vaughn: Yay!
18 00:03:33.780 ⇒ 00:03:34.510 Caitlyn Vaughn: Duh!
19 00:03:34.510 ⇒ 00:03:35.490 Uttam Kumaran: everything.
20 00:03:35.680 ⇒ 00:03:37.160 Caitlyn Vaughn: Good, how are you?
21 00:03:37.160 ⇒ 00:03:38.230 Uttam Kumaran: Good.
22 00:03:38.230 ⇒ 00:03:39.920 Caitlyn Vaughn: Such a boug sweater.
23 00:03:40.300 ⇒ 00:03:41.360 Uttam Kumaran: Thank you.
24 00:03:41.840 ⇒ 00:03:44.359 Uttam Kumaran: I know, I decided to dress up today.
25 00:03:44.830 ⇒ 00:03:45.580 Caitlyn Vaughn: Really?
26 00:03:46.390 ⇒ 00:03:51.410 Uttam Kumaran: Yeah, you know, I just… I feel like I go back and forth, like, I’ll be wearing t-shirts.
27 00:03:51.700 ⇒ 00:03:59.359 Uttam Kumaran: And today, we have… I don’t know, I’m talking to you, like, we do have clients today, so I decided to dress up, you know, I think it’s just the right thing to do.
28 00:03:59.690 ⇒ 00:04:03.129 Caitlyn Vaughn: Yeah, I don’t think I’ve ever not seen you in a t-shirt, so this is so exciting.
29 00:04:03.130 ⇒ 00:04:11.090 Uttam Kumaran: Hey, I have other clothes. But, I don’t know, if you don’t leave the house, you know, you can get into the, work from home…
30 00:04:11.370 ⇒ 00:04:12.350 Uttam Kumaran: like…
31 00:04:12.350 ⇒ 00:04:13.990 Caitlyn Vaughn: I’m wearing pajamas right now.
32 00:04:13.990 ⇒ 00:04:25.559 Uttam Kumaran: Yeah, and you guys are, like, a true, like, tech startup, so it doesn’t matter for y’all. We have some other clients where I’m like, I should just maybe, like, put, like, a sweater on or something. And it’s cold here, so…
33 00:04:25.560 ⇒ 00:04:26.050 Caitlyn Vaughn: Yeah.
34 00:04:26.050 ⇒ 00:04:29.460 Uttam Kumaran: It’s like… Nice to wear something else.
35 00:04:29.640 ⇒ 00:04:33.760 Caitlyn Vaughn: Yeah, I’m going to New York on Sunday, and I, like.
36 00:04:34.010 ⇒ 00:04:36.709 Caitlyn Vaughn: Got all my winter stuff out.
37 00:04:37.920 ⇒ 00:04:44.990 Caitlyn Vaughn: And it’s just so hard, because I already don’t have winter stuff, and then I don’t have, like, nice winter stuff, you know?
38 00:04:45.220 ⇒ 00:04:52.560 Uttam Kumaran: You gotta go to, like, zara has really good stuff, and trying to think…
39 00:04:53.780 ⇒ 00:04:55.510 Uttam Kumaran: For, like, winter coats.
40 00:04:56.950 ⇒ 00:05:01.069 Caitlyn Vaughn: I have some stuff, but it’s like, I also don’t want to invest a bunch of money when I…
41 00:05:01.070 ⇒ 00:05:12.439 Uttam Kumaran: like, Aritzia has really good… like, my… I got my sister, like, a nice winter coat from Aritzia. Really? But yeah, it’s, yeah, Aritzia has, like, nice, like, business casual, like, those, like, long overcoats.
42 00:05:12.440 ⇒ 00:05:21.779 Caitlyn Vaughn: I know, that’s what you recommended to me 2 years ago, and that’s what my whole wardrobe is now. Yeah, let’s go! Where’s my referral code?
43 00:05:22.060 ⇒ 00:05:22.840 Caitlyn Vaughn: Oh, literally.
44 00:05:22.840 ⇒ 00:05:26.340 Uttam Kumaran: DC is good, there’s not, like, a dude version of that, like.
45 00:05:26.340 ⇒ 00:05:26.670 Caitlyn Vaughn: Is there.
46 00:05:26.670 ⇒ 00:05:28.219 Uttam Kumaran: Kinda sucks. Huh?
47 00:05:28.220 ⇒ 00:05:30.080 Caitlyn Vaughn: There’s no men’s clothes at Aritzia?
48 00:05:30.080 ⇒ 00:05:41.770 Uttam Kumaran: No, I don’t think so. Or if it was… like, yeah, if it was, I didn’t see… I mean, I just know Aritzia is really good for, like, women’s business… that’s, like, what everybody was wearing. Yeah, it’s all women’s, but that’s what everybody, when I was in New York, was like.
49 00:05:42.990 ⇒ 00:05:48.679 Uttam Kumaran: wearing, but yeah, like, for men, it’s so tough, like, it’s just, like, J.Crew, or, like…
50 00:05:48.680 ⇒ 00:05:49.910 Caitlyn Vaughn: Hard.
51 00:05:50.180 ⇒ 00:05:52.670 Uttam Kumaran: J. Cruise sucks, like, I don’t want to wear J. Crew.
52 00:05:52.670 ⇒ 00:05:54.580 Caitlyn Vaughn: Where’s your sweater from?
53 00:05:54.730 ⇒ 00:05:59.720 Uttam Kumaran: This is, this is… wait, where is this from?
54 00:05:59.720 ⇒ 00:06:01.440 Caitlyn Vaughn: JCPenney.
55 00:06:01.670 ⇒ 00:06:05.790 Uttam Kumaran: You’d be, oh, it’s, it’s, Abercrombie & Fitch.
56 00:06:05.790 ⇒ 00:06:06.470 Caitlyn Vaughn: Oh, that’s nice!
57 00:06:06.470 ⇒ 00:06:08.010 Uttam Kumaran: My dad bought it for me.
58 00:06:08.010 ⇒ 00:06:08.850 Caitlyn Vaughn: That’s boug!
59 00:06:08.850 ⇒ 00:06:22.280 Uttam Kumaran: He… my dad loves, like, going to… he likes going to Abercrombie & Fitch, American Eagle, J.Crew, he, like, loves those, like, that class of stores, I don’t know what you call that. Yeah, so he’ll be like, oh, I just found this, like.
60 00:06:22.420 ⇒ 00:06:29.839 Uttam Kumaran: sweater, like, do you want it? It’s, like, X percent off. I’m like, yeah, cool. So he gets… he buys me random stuff here and there.
61 00:06:30.050 ⇒ 00:06:31.900 Caitlyn Vaughn: Will you send me a picture of your dad?
62 00:06:32.740 ⇒ 00:06:37.439 Caitlyn Vaughn: Yes. I just wanna… I just wanna, like, picture it, you know?
63 00:06:37.440 ⇒ 00:06:42.789 Uttam Kumaran: Yes, yes, I will send you a picture of both of us today.
64 00:06:42.790 ⇒ 00:06:44.429 Caitlyn Vaughn: So, just your dad. No, I’m kidding.
65 00:06:44.430 ⇒ 00:06:53.259 Uttam Kumaran: Just him? Okay, yeah, he just ran a… he just ran a half marathon, him and my sister. Yeah, they ran a half marathon in the Redwoods in California.
66 00:06:53.940 ⇒ 00:06:55.230 Uttam Kumaran: Like, last week.
67 00:06:55.380 ⇒ 00:06:56.659 Caitlyn Vaughn: How old is he?
68 00:06:56.660 ⇒ 00:06:59.889 Uttam Kumaran: is fifth… How is he, almost 60?
69 00:07:00.020 ⇒ 00:07:00.910 Uttam Kumaran: 50…
70 00:07:00.910 ⇒ 00:07:01.839 Caitlyn Vaughn: Why are you so young?
71 00:07:01.840 ⇒ 00:07:02.600 Uttam Kumaran: 11?
72 00:07:03.090 ⇒ 00:07:05.870 Uttam Kumaran: Yeah, I’m… well, I’m 20… 9.
73 00:07:06.640 ⇒ 00:07:07.540 Caitlyn Vaughn: You are?
74 00:07:07.890 ⇒ 00:07:09.049 Uttam Kumaran: Today’s my birthday!
75 00:07:09.440 ⇒ 00:07:10.470 Caitlyn Vaughn: Are you serious?
76 00:07:10.470 ⇒ 00:07:11.260 Uttam Kumaran: Yeah!
77 00:07:11.260 ⇒ 00:07:12.470 Caitlyn Vaughn: Happy birthday!
78 00:07:12.470 ⇒ 00:07:14.250 Uttam Kumaran: Thank you! What are you?
79 00:07:14.250 ⇒ 00:07:15.069 Caitlyn Vaughn: Are they?
80 00:07:15.070 ⇒ 00:07:17.339 Uttam Kumaran: Oh, I’m not- I’m gonna take the day off tomorrow.
81 00:07:17.340 ⇒ 00:07:18.540 Caitlyn Vaughn: Okay, good.
82 00:07:18.540 ⇒ 00:07:19.960 Uttam Kumaran: Are you in town today?
83 00:07:19.960 ⇒ 00:07:20.720 Caitlyn Vaughn: Yeah.
84 00:07:21.030 ⇒ 00:07:28.480 Uttam Kumaran: Do you wanna… I feel like I literally wrote… I have you down in a note, but I was gonna have some people come get wine later. You wanna join?
85 00:07:28.480 ⇒ 00:07:29.829 Caitlyn Vaughn: Mom, fuck you for not.
86 00:07:29.830 ⇒ 00:07:31.409 Uttam Kumaran: Sorry, dude, I feel.
87 00:07:31.410 ⇒ 00:07:32.839 Caitlyn Vaughn: I’ll be there.
88 00:07:32.840 ⇒ 00:07:45.839 Uttam Kumaran: Okay, great, I’ll send you a… I’ll send you a text. Yeah, no, I feel like I was gonna text you on Tuesday? I wrote down a note of everybody here, because I don’t… it’s just, like, all new friends and, like, and stuff, but I should text you. You should totally come and bring whoever.
89 00:07:45.840 ⇒ 00:07:53.650 Caitlyn Vaughn: Oh my god, that just reminds me, I created a list last night of people that I need to respond to via text. A list.
90 00:07:53.650 ⇒ 00:07:59.319 Uttam Kumaran: Oh, my gosh. That’s so bad. I am… Yeah, I’m really backed up.
91 00:07:59.420 ⇒ 00:08:06.209 Uttam Kumaran: Totally, so… But yeah, we’re going to, Cape Bottle Room, which is, like, near Target on 5th Street.
92 00:08:06.690 ⇒ 00:08:09.059 Caitlyn Vaughn: Cave Bottle Rick. Just text me.
93 00:08:09.060 ⇒ 00:08:10.740 Uttam Kumaran: Okay. Yeah, you should come.
94 00:08:10.740 ⇒ 00:08:11.990 Caitlyn Vaughn: Okay, I’ll try to come.
95 00:08:11.990 ⇒ 00:08:19.349 Uttam Kumaran: Okay, okay, dove. So today we wanted to talk through two things. So one, we finished up the PDL.
96 00:08:19.350 ⇒ 00:08:19.970 Caitlyn Vaughn: Oh, nice!
97 00:08:19.970 ⇒ 00:08:30.140 Uttam Kumaran: assessment, so… We both did the vendor… we did, like, kind of our normal thing, and…
98 00:08:30.750 ⇒ 00:08:34.500 Uttam Kumaran: we did… Looking at, people.
99 00:08:34.830 ⇒ 00:08:44.580 Uttam Kumaran: Still let me… Let me get this one… I sent it… in Zoom.
100 00:08:56.090 ⇒ 00:08:58.270 Uttam Kumaran: So, we found that it was…
101 00:08:58.840 ⇒ 00:09:02.940 Uttam Kumaran: Not doing a great job on companies, but at the bottom.
102 00:09:03.070 ⇒ 00:09:06.549 Uttam Kumaran: Of this, it does a pretty good job on people.
103 00:09:06.830 ⇒ 00:09:07.840 Caitlyn Vaughn: Really?
104 00:09:07.840 ⇒ 00:09:10.670 Uttam Kumaran: My name, but… Yeah.
105 00:09:11.420 ⇒ 00:09:13.320 Caitlyn Vaughn: That is so surprising.
106 00:09:14.490 ⇒ 00:09:18.089 Caitlyn Vaughn: Isn’t it… don’t people usually use PDL for companies?
107 00:09:18.280 ⇒ 00:09:23.929 Uttam Kumaran: I think people use PDL for companies, but the people data was, I think, A lot better.
108 00:09:23.980 ⇒ 00:09:24.750 Caitlyn Vaughn: Huh.
109 00:09:27.300 ⇒ 00:09:29.780 Uttam Kumaran: I guess one thing we’re gonna have to see is, like.
110 00:09:29.970 ⇒ 00:09:34.730 Uttam Kumaran: again, it’s a comparison, right? So I don’t know what… basically, we’re doing, like, a benchmark.
111 00:09:34.920 ⇒ 00:09:39.810 Uttam Kumaran: Of, like, so if everybody scores, like, around the 80, then that’s, like, probably a.
112 00:09:39.810 ⇒ 00:09:40.279 Caitlyn Vaughn: That’s gonna be.
113 00:09:40.280 ⇒ 00:09:41.010 Uttam Kumaran: guts.
114 00:09:41.010 ⇒ 00:09:42.839 Caitlyn Vaughn: Yeah, that’s for the curve.
115 00:09:43.100 ⇒ 00:09:50.550 Uttam Kumaran: I feel like… the recommendation piece, probably not as important. I would say one thing that we added… .
116 00:09:50.600 ⇒ 00:09:52.680 Caitlyn Vaughn: One thing that we added here…
117 00:09:53.700 ⇒ 00:09:55.530 Uttam Kumaran: was what’s unique?
118 00:09:55.710 ⇒ 00:10:03.269 Uttam Kumaran: And I think this is really, like, what is cool about PDL. Like, these are signals that,
119 00:10:03.750 ⇒ 00:10:11.880 Uttam Kumaran: are very hard to get otherwise. Like, I think typically people use, like, Trigify or, like, other random stuff to… to scrape this, but…
120 00:10:11.880 ⇒ 00:10:12.690 Caitlyn Vaughn: Hmm.
121 00:10:12.690 ⇒ 00:10:14.309 Uttam Kumaran: This is really, really nice.
122 00:10:14.660 ⇒ 00:10:18.750 Uttam Kumaran: So, that’s…
123 00:10:18.750 ⇒ 00:10:22.110 Caitlyn Vaughn: Are these the ones that we’re polling? Is this the list that I sent you?
124 00:10:22.770 ⇒ 00:10:26.920 Uttam Kumaran: What do you mean?
125 00:10:27.370 ⇒ 00:10:32.709 Caitlyn Vaughn: Hold on, let me look, because I initially had worked out, like, a… a deal…
126 00:10:33.010 ⇒ 00:10:39.780 Caitlyn Vaughn: with PDL for some, like, pretty random, specific signals.
127 00:10:40.420 ⇒ 00:10:46.850 Caitlyn Vaughn: By employee churn rate, employee count by month, employee count roll, employee growth rate, recent.
128 00:10:46.850 ⇒ 00:10:47.419 Uttam Kumaran: Yeah, yeah, yeah.
129 00:10:47.420 ⇒ 00:10:51.150 Caitlyn Vaughn: departures, hires, top employ metros, that’s what it is.
130 00:10:51.150 ⇒ 00:10:52.410 Uttam Kumaran: Yeah. Yeah, yeah, yeah.
131 00:10:52.410 ⇒ 00:10:59.290 Caitlyn Vaughn: Okay, yeah, then these are the… these are the fields that I have already negotiated with them. This is what we’re currently getting from them.
132 00:10:59.290 ⇒ 00:11:04.570 Uttam Kumaran: Cool. Yeah, so this is really, like, what is unique about these guys versus everything else.
133 00:11:04.570 ⇒ 00:11:08.140 Caitlyn Vaughn: Yeah, I think in our conversation, I was like, what is special about you?
134 00:11:08.550 ⇒ 00:11:11.349 Caitlyn Vaughn: oh, we have these, like, signals. I was like, that’s what I want. And he was like.
135 00:11:11.350 ⇒ 00:11:12.050 Uttam Kumaran: Yeah.
136 00:11:12.250 ⇒ 00:11:21.299 Uttam Kumaran: So that… this… I guess that’s my point, is, like, I think what you’ll see, and we’re… we’re working on, having all of the fields in one place, but…
137 00:11:21.300 ⇒ 00:11:25.810 Caitlyn Vaughn: I think it depends on, like, how the pro- I don’t know what the product is gonna be, like, whether…
138 00:11:25.910 ⇒ 00:11:32.900 Uttam Kumaran: You’re gonna be able to switch between 5, or, like, your people get enriched by 1, and this gets enriched by another.
139 00:11:33.110 ⇒ 00:11:37.660 Uttam Kumaran: But this is really, like, I think… super premium.
140 00:11:38.620 ⇒ 00:11:40.370 Uttam Kumaran: Where’d I go? .
141 00:11:40.790 ⇒ 00:11:43.280 Caitlyn Vaughn: Okay, so you think the people data is worth it?
142 00:11:44.060 ⇒ 00:11:44.750 Uttam Kumaran: Yeah.
143 00:11:45.350 ⇒ 00:11:46.110 Caitlyn Vaughn: Cool.
144 00:11:47.500 ⇒ 00:11:51.499 Uttam Kumaran: I mean, we’re gonna… we’ll continue on the next couple vendors, but yeah, it’s…
145 00:11:51.500 ⇒ 00:11:52.360 Caitlyn Vaughn: That’s right.
146 00:11:53.060 ⇒ 00:11:56.179 Uttam Kumaran: And this is something that, like, I don’t know if you guys are thinking about doing, like.
147 00:11:56.330 ⇒ 00:12:00.579 Uttam Kumaran: premium enrichment or whatever, but, like, this is really, really awesome stuff.
148 00:12:00.800 ⇒ 00:12:01.690 Caitlyn Vaughn: Yeah.
149 00:12:02.950 ⇒ 00:12:04.040 Caitlyn Vaughn: Yeah!
150 00:12:04.310 ⇒ 00:12:09.299 Caitlyn Vaughn: That’s definitely a potential. I think I’m gonna spend some time on pricing next week, so…
151 00:12:10.300 ⇒ 00:12:12.970 Caitlyn Vaughn: That is something we’ve talked about doing.
152 00:12:14.070 ⇒ 00:12:15.480 Caitlyn Vaughn: patient about…
153 00:12:17.450 ⇒ 00:12:18.000 Uttam Kumaran: Hey.
154 00:12:18.980 ⇒ 00:12:19.750 Caitlyn Vaughn: Besides.
155 00:12:20.630 ⇒ 00:12:22.410 Uttam Kumaran: Yeah.
156 00:12:22.800 ⇒ 00:12:29.469 Caitlyn Vaughn: Are there, like, multiple tiers of each API? So for, like, PDL, isn’t there, like…
157 00:12:30.570 ⇒ 00:12:36.239 Caitlyn Vaughn: Like, basic premium data, or basic person data, premium person data.
158 00:12:36.600 ⇒ 00:12:38.370 Uttam Kumaran: Yeah. Yeah.
159 00:12:38.970 ⇒ 00:12:43.339 Uttam Kumaran: There is, like, a list of… yeah, so I’ll share this,
160 00:12:51.480 ⇒ 00:12:55.869 Uttam Kumaran: Oh, yeah. So, yeah, so you get all these bass, and then there’s this, like.
161 00:12:57.210 ⇒ 00:13:01.040 Uttam Kumaran: All this is premium, and then there’s, like, all this comprehensive data.
162 00:13:01.560 ⇒ 00:13:06.610 Uttam Kumaran: so I just think it… It just depends.
163 00:13:06.820 ⇒ 00:13:14.550 Caitlyn Vaughn: Like, right now, we’re getting some from Premium and some from Comprehensive, but I think they just give it to us as part of the trial.
164 00:13:15.020 ⇒ 00:13:22.999 Uttam Kumaran: But, like, this, like, I don’t know, this is what’s unique about these guys from… compared to other folks, like, I think this… having this employee data.
165 00:13:23.010 ⇒ 00:13:26.610 Caitlyn Vaughn: is really rare, but the thing is, like, I don’t know…
166 00:13:26.610 ⇒ 00:13:32.509 Uttam Kumaran: In, like, a workflow setting, I’m not sure how people use it, like…
167 00:13:32.780 ⇒ 00:13:42.419 Uttam Kumaran: this is, like, useful if you’re doing, like, outbound campaigns. For example, in our business, I would say, cool, go after any company that’s, like, recently hired, like, a head of data.
168 00:13:43.310 ⇒ 00:13:46.489 Uttam Kumaran: But how would you think this gets leveraged in the default?
169 00:13:47.350 ⇒ 00:13:48.220 Uttam Kumaran: product.
170 00:13:48.220 ⇒ 00:13:59.700 Caitlyn Vaughn: Yeah, good question. So, we’re actually… we’re expanding. We’re, like… we started in inbound, but it was just to kind of get a foothold in the market. So, with the new product, our goal is to, like.
171 00:13:59.840 ⇒ 00:14:03.689 Caitlyn Vaughn: Become a go-to-market orchestration platform.
172 00:14:03.690 ⇒ 00:14:04.440 Uttam Kumaran: Okay.
173 00:14:04.440 ⇒ 00:14:05.070 Caitlyn Vaughn: Yeah.
174 00:14:05.400 ⇒ 00:14:07.110 Uttam Kumaran: So you’re gonna be able to go both ways, yeah.
175 00:14:07.110 ⇒ 00:14:09.619 Caitlyn Vaughn: Yeah, we should do outbound, yeah, everything.
176 00:14:09.620 ⇒ 00:14:14.789 Uttam Kumaran: Cool, then yeah, then these fields are super, super relevant. There’s not another tool that offers these.
177 00:14:15.440 ⇒ 00:14:16.179 Caitlyn Vaughn: Okay, awesome.
178 00:14:16.180 ⇒ 00:14:16.720 Uttam Kumaran: No.
179 00:14:17.550 ⇒ 00:14:22.509 Caitlyn Vaughn: Okay, we’re having, lunch with them next Wednesday, so trying to strike a deal.
180 00:14:22.670 ⇒ 00:14:28.819 Uttam Kumaran: Yeah, try to get… I would… in particular, I think anything around job postings and
181 00:14:29.230 ⇒ 00:14:33.990 Uttam Kumaran: any of the people are huge. Like, I think a lot of your companies
182 00:14:34.340 ⇒ 00:14:39.579 Uttam Kumaran: If they’re smart, will… like, all your clients will love this data, like, even for us.
183 00:14:39.700 ⇒ 00:14:43.379 Uttam Kumaran: like, these, these are great, like, I might call them, and I wanna… I wanna use them for something.
184 00:14:43.380 ⇒ 00:14:44.800 Caitlyn Vaughn: Okay.
185 00:14:44.800 ⇒ 00:14:51.609 Uttam Kumaran: Yeah, we’ll use them through default once you guys release it, but yeah. I was, like, looking at the data, I’m like, damn, we need some of this stuff.
186 00:14:51.610 ⇒ 00:14:52.889 Caitlyn Vaughn: Like, wait a minute…
187 00:14:52.890 ⇒ 00:14:53.670 Uttam Kumaran: Yeah.
188 00:14:54.650 ⇒ 00:14:56.180 Caitlyn Vaughn: Okay, cool! Yay!
189 00:14:57.070 ⇒ 00:15:10.720 Uttam Kumaran: And then, so next up, we also… so on terms of Omni, so a couple of things there. So one, we’re gonna… we’ll get the new, instance set up. I guess my question is, I wanted to do training for at least you…
190 00:15:10.850 ⇒ 00:15:13.770 Uttam Kumaran: Deanna, Thomas, but, like, who else…
191 00:15:13.920 ⇒ 00:15:18.479 Uttam Kumaran: internally needs training, and, like, what level? Meaning, like, there’s, like.
192 00:15:18.870 ⇒ 00:15:20.410 Uttam Kumaran: Okay, here’s just how to, like.
193 00:15:20.680 ⇒ 00:15:28.720 Uttam Kumaran: Use it, get access to stuff, here’s how to, like, build dashboards, and then probably, like, pomice, or maybe…
194 00:15:29.060 ⇒ 00:15:32.150 Uttam Kumaran: Victor or one other engineer, like, the people that can look at
195 00:15:32.350 ⇒ 00:15:35.479 Uttam Kumaran: like, we share them all the connection settings in config.
196 00:15:35.480 ⇒ 00:15:37.639 Caitlyn Vaughn: We can also do it broadly for, like.
197 00:15:37.640 ⇒ 00:15:38.780 Uttam Kumaran: the whole company.
198 00:15:39.810 ⇒ 00:15:48.149 Uttam Kumaran: and, like, be like, here’s an hour training on, like, how to use our flashy new BI tool to, like, do stuff. Yeah. But you… you let me know.
199 00:15:49.240 ⇒ 00:16:05.280 Caitlyn Vaughn: let me put some feelers out and see what people want to do. I don’t think that everybody will be interested in, like, doing this, but I think that we actually have a lot of people on the team who are pretty, like, scrappy and innovative, like, even some of the sales guys.
200 00:16:05.280 ⇒ 00:16:15.649 Caitlyn Vaughn: I think would be interested in using this. So, let me just, like, post it out there, see who’s interested, and we can, like, find a time. Or maybe let’s find a time, and then people can come or not come.
201 00:16:15.650 ⇒ 00:16:17.189 Uttam Kumaran: Okay, yeah, you tell me.
202 00:16:17.630 ⇒ 00:16:19.440 Caitlyn Vaughn: Closer next week.
203 00:16:20.890 ⇒ 00:16:26.459 Uttam Kumaran: My week is always nasty, so whenever you think, I’ll move it or move stuff around.
204 00:16:26.640 ⇒ 00:16:32.280 Caitlyn Vaughn: Christ. What was I just gonna ask you?
205 00:16:32.640 ⇒ 00:16:37.029 Caitlyn Vaughn: Oh, is it gonna be completely set up by next Thursday, or no? Or do we need.
206 00:16:37.030 ⇒ 00:16:42.559 Uttam Kumaran: We probably need another, like… yeah, you’re right, we probably need another, like, two weeks at least.
207 00:16:42.770 ⇒ 00:16:45.849 Uttam Kumaran: So by next week, we’ll have, kind of, like, stuff migrated.
208 00:16:46.300 ⇒ 00:16:50.879 Uttam Kumaran: And then I would like to have a… I would like to just do a demo of, like.
209 00:16:51.170 ⇒ 00:16:52.510 Caitlyn Vaughn: what exists.
210 00:16:52.690 ⇒ 00:16:59.869 Uttam Kumaran: how to use the existing dashboards, and then how to create your own. And then I can also field questions from people on, like, what else they’d like to see.
211 00:16:59.990 ⇒ 00:17:05.510 Uttam Kumaran: So in 2 weeks, that’d be great. The week after, I mean, we’re sort of getting into Thanksgiving after…
212 00:17:05.710 ⇒ 00:17:06.340 Uttam Kumaran: In 3 weeks.
213 00:17:06.349 ⇒ 00:17:07.729 Caitlyn Vaughn: What about the 13th?
214 00:17:09.109 ⇒ 00:17:10.539 Uttam Kumaran: Yeah, we could do the 13th.
215 00:17:10.819 ⇒ 00:17:13.499 Caitlyn Vaughn: Do you wanna do it, like, on our session?
216 00:17:13.500 ⇒ 00:17:14.079 Uttam Kumaran: Yeah.
217 00:17:14.460 ⇒ 00:17:15.290 Caitlyn Vaughn: Okay.
218 00:17:16.470 ⇒ 00:17:20.319 Caitlyn Vaughn: do. Omni… can you title it, like, Omni Training Session?
219 00:17:20.329 ⇒ 00:17:21.089 Uttam Kumaran: Yes.
220 00:17:21.260 ⇒ 00:17:21.849 Caitlyn Vaughn: Okay.
221 00:17:28.089 ⇒ 00:17:39.240 Caitlyn Vaughn: And then I have a call with Thomas later today to update on decisions made, which is obviously Omni, primarily, but for us, doing the S3 to Mother Duck.
222 00:17:39.500 ⇒ 00:17:41.159 Uttam Kumaran: Yeah, I called… oh, I called him yesterday.
223 00:17:41.160 ⇒ 00:17:41.930 Caitlyn Vaughn: You did.
224 00:17:41.930 ⇒ 00:17:44.380 Uttam Kumaran: Yeah, so I actually need to send,
225 00:17:44.830 ⇒ 00:17:49.179 Uttam Kumaran: a Slack thread, just to get some confirmations from Victor on that.
226 00:17:50.540 ⇒ 00:17:56.680 Uttam Kumaran: So yeah, I can send that in Slack, I don’t know if… I feel like we covered a lot of it yesterday, in case I could save you a meeting, but…
227 00:17:57.570 ⇒ 00:17:59.480 Uttam Kumaran: Yeah.
228 00:17:59.680 ⇒ 00:18:01.390 Caitlyn Vaughn: So I talked to Victor about it.
229 00:18:01.390 ⇒ 00:18:01.990 Uttam Kumaran: Yeah.
230 00:18:02.410 ⇒ 00:18:09.880 Caitlyn Vaughn: And… I was trying to get, like, a decision out of him, but basically, he was like.
231 00:18:11.090 ⇒ 00:18:20.140 Caitlyn Vaughn: neither solution is, like, ideal, but there’s not, like, a better solution than the proposed. Like, there’s no, like, great solution for this problem, essentially.
232 00:18:20.140 ⇒ 00:18:31.979 Uttam Kumaran: Yeah, that’s why I just want to explain, like, what we’re trying to do short-term, is, like, I just need some way to get that into S3, so that I can put it into the dashboard. Like, Omni is not the data store, right? So it has to land somewhere.
233 00:18:31.980 ⇒ 00:18:32.310 Caitlyn Vaughn: Yeah.
234 00:18:32.310 ⇒ 00:18:41.630 Uttam Kumaran: Ideally, kind of the… I can also explain, like, typically how this goes is you have, like, an ETL tool that replicates data from multiple sources into
235 00:18:41.770 ⇒ 00:18:43.520 Uttam Kumaran: Mother Duck or S3.
236 00:18:43.920 ⇒ 00:18:49.510 Uttam Kumaran: We don’t have that many sources. We can go get… we can go get one, it won’t be that much, but…
237 00:18:49.800 ⇒ 00:18:53.650 Uttam Kumaran: I don’t know, like… You don’t have to, we don’t have to.
238 00:18:53.890 ⇒ 00:18:57.459 Uttam Kumaran: So, I can… I was gonna outline, sort of, those options.
239 00:18:58.460 ⇒ 00:19:06.990 Uttam Kumaran: ideally, again, like, when… if you were to invest in an ETL tool, I would push to have something that syncs from Hyperline daily, that syncs from…
240 00:19:07.240 ⇒ 00:19:13.139 Uttam Kumaran: Supabase, daily, and then… We can start to get the Salesforce data in there.
241 00:19:13.300 ⇒ 00:19:13.790 Caitlyn Vaughn: And then…
242 00:19:13.790 ⇒ 00:19:17.400 Uttam Kumaran: That becomes sort of the way that we move data around a default.
243 00:19:18.310 ⇒ 00:19:19.140 Caitlyn Vaughn: Okay.
244 00:19:20.980 ⇒ 00:19:22.939 Uttam Kumaran: I could write that out, and then…
245 00:19:24.030 ⇒ 00:19:34.079 Caitlyn Vaughn: Yeah, I’m gonna say… so, I talked to Victor about this a little bit, and he was like, I put, Thomas on this, and I was like, I know, but I just want some, like.
246 00:19:34.280 ⇒ 00:19:41.650 Caitlyn Vaughn: I want you to clarify, and you, like, answered my questions, like, go through Thomas, like, make him feel like this is his project, like, make him…
247 00:19:41.650 ⇒ 00:19:48.550 Uttam Kumaran: I did, but then what I did… I did yesterday, and he was like, oh, I don’t know what Victor thinks. I can tell he’s just, like, nervous, because he doesn’t know.
248 00:19:49.100 ⇒ 00:19:49.999 Uttam Kumaran: So, I mean.
249 00:19:50.000 ⇒ 00:19:50.390 Caitlyn Vaughn: guy.
250 00:19:50.390 ⇒ 00:20:04.410 Uttam Kumaran: This is where, like, I don’t want to steamroll, but I’m like, just do… you guys should just do this thing. He’s kind of like, oh, let me ask Victor. I’m like, okay, so I told… I told him what I’ll do is I’ll take our conversation, summarize it, and then… I want him to win. Like, I want Thomas, I want…
251 00:20:04.770 ⇒ 00:20:07.850 Uttam Kumaran: Doug Thomas suggested this, what do we do? So I can do that.
252 00:20:08.330 ⇒ 00:20:11.860 Caitlyn Vaughn: Okay, yeah, maybe let’s just get a Slack… Slack going.
253 00:20:11.860 ⇒ 00:20:12.910 Uttam Kumaran: Okay, okay.
254 00:20:12.910 ⇒ 00:20:14.869 Caitlyn Vaughn: Yeah, Victor Olivit.
255 00:20:15.470 ⇒ 00:20:16.130 Uttam Kumaran: Okay, cool.
256 00:20:18.430 ⇒ 00:20:20.080 Caitlyn Vaughn: Stop bothering me with this shit.
257 00:20:22.080 ⇒ 00:20:27.909 Uttam Kumaran: Okay, and then we want… and then I have Amber on the call here, because we want to talk about pricing stuff.
258 00:20:28.060 ⇒ 00:20:31.070 Caitlyn Vaughn: Yeah. What kind of pricing stuff?
259 00:20:31.210 ⇒ 00:20:32.080 Uttam Kumaran: Yes.
260 00:20:32.200 ⇒ 00:20:50.899 Uttam Kumaran: We poked at… Well, basically, I was like, look, we now finally have all of our usage data and the revenue data, at least a snapshot in one place. I kind of shared with Amber, like, our last conversation and our recent notion. I’m like, okay, we should… let’s think of a couple questions to support
261 00:20:51.080 ⇒ 00:20:57.199 Uttam Kumaran: Caitlin in, like, this decision. So, Amber, maybe I can pull up the…
262 00:20:57.520 ⇒ 00:21:00.679 Uttam Kumaran: Pricing, and then you can just let me know, like, what you were thinking.
263 00:21:00.730 ⇒ 00:21:01.850 Amber Lin: Yeah, cool.
264 00:21:02.970 ⇒ 00:21:08.330 Amber Lin: Yeah, Caitlin, I saw the doc that you shared with me, Stereo Notion, so I went through,
265 00:21:08.710 ⇒ 00:21:16.430 Amber Lin: that document, and then I also went through our previous, say, revenue sheet, so I can’t… I understand how things are priced.
266 00:21:16.430 ⇒ 00:21:20.049 Caitlyn Vaughn: I just… I just have questions about how they’re going to be mapped.
267 00:21:20.320 ⇒ 00:21:21.230 Caitlyn Vaughn: Hmm.
268 00:21:21.690 ⇒ 00:21:24.879 Amber Lin: Because we need that to do the… to do the testing.
269 00:21:25.170 ⇒ 00:21:25.640 Caitlyn Vaughn: Okay.
270 00:21:25.640 ⇒ 00:21:31.210 Amber Lin: but overall, I think we’re gonna start off with, we want to know
271 00:21:31.380 ⇒ 00:21:49.779 Amber Lin: are things concentrated within a percentage of customers? Because that will allow us to focus on them, either give them better support, or if they’re using a lot, we might want to expand. We also want to look at the growth dynamics, and that will tell us about, okay.
272 00:21:50.610 ⇒ 00:21:54.430 Amber Lin: Are these, let’s see…
273 00:21:54.510 ⇒ 00:22:10.030 Amber Lin: Oh, sorry, for the first one, there’s also, like, can we see if there’s people using it a lot, but are not paying as much? So there will be opportunities to expand there. And then for the growth dynamics, it’s…
274 00:22:10.040 ⇒ 00:22:17.859 Amber Lin: Can we forecast based on people’s usage of if it’s, and then…
275 00:22:18.060 ⇒ 00:22:31.340 Amber Lin: So that helps us to do… forecast future revenue to see, okay, these people are growing, quite fast. We assume that it’s related to revenue, then we can see, okay, in the future, how much revenue is going to come in.
276 00:22:32.210 ⇒ 00:22:51.169 Amber Lin: And then the last one is just to make sure how efficient is the current pricing campaign? Maybe we can do some adjustments, because it’s based on our previous data, so we can suggest, oh, if we change this slight thing, what will impact, what will the impact be? We can make assumptions, and we can…
277 00:22:51.370 ⇒ 00:23:06.599 Amber Lin: build how we can have a few options, and that’s easier, than testing directly. It might not be as accurate because it’s not live market feedback, but it can tell us what might be, and we can test a few options here.
278 00:23:06.930 ⇒ 00:23:13.249 Caitlyn Vaughn: Okay, cool. I will also say, so the pricing model that I sent you is, like, my view of how
279 00:23:13.740 ⇒ 00:23:26.930 Caitlyn Vaughn: But we also had our head of RevOps come up with a pricing model, which is, like, based on, I can just show you here. It’s based on, like, groups.
280 00:23:28.870 ⇒ 00:23:30.010 Caitlyn Vaughn: Okay, here.
281 00:23:30.360 ⇒ 00:23:38.359 Caitlyn Vaughn: So… He’s, like, come up with this idea of basically packaging our product across
282 00:23:38.880 ⇒ 00:23:53.459 Caitlyn Vaughn: features for, like, a specific function. So, like, Relay is just our base one, and then you could also add on, like, a growth function, which has, like, UTMs and dashboarding and enriching records and da-da-da, and then you can add on Momentum, which focuses on
283 00:23:53.650 ⇒ 00:23:58.580 Caitlyn Vaughn: sales, and then you can add orchestration, which focuses on ops. So…
284 00:23:59.080 ⇒ 00:24:02.559 Caitlyn Vaughn: It’s, like, a completely different business model than…
285 00:24:02.780 ⇒ 00:24:09.040 Caitlyn Vaughn: the pricing model that I was thinking of. I do like a few things from here, which is, like.
286 00:24:09.500 ⇒ 00:24:11.180 Caitlyn Vaughn: I think that this…
287 00:24:11.570 ⇒ 00:24:23.240 Caitlyn Vaughn: type of packaging allows us to sell more, like, horizontally, and we can, like, cross-sell a bunch of products, but the only, like, hesitation I have for something like this is I don’t know how it would translate to PLG.
288 00:24:23.850 ⇒ 00:24:24.700 Caitlyn Vaughn: So…
289 00:24:24.700 ⇒ 00:24:25.400 Amber Lin: Hmm.
290 00:24:25.980 ⇒ 00:24:41.249 Caitlyn Vaughn: I don’t know, it’s something interesting to think about, like, companies like Gong do this, HubSpot does this, you know, and they have really figured out, like, a great pricing model that’s working well for them. So, as you guys are thinking through the pricing question, maybe…
291 00:24:41.300 ⇒ 00:24:49.500 Caitlyn Vaughn: maybe we can talk about, like, different potential pricing models, or, like, options, and we don’t need to go into, like, a ton of depth, but just, like.
292 00:24:50.020 ⇒ 00:24:53.850 Caitlyn Vaughn: Here’s, like, different ways that people do package their product.
293 00:24:54.120 ⇒ 00:25:01.420 Amber Lin: Yeah, totally. Would you be able to share that with me, or send a screenshot is also okay?
294 00:25:01.420 ⇒ 00:25:03.190 Caitlyn Vaughn: Yeah, yeah, I’ll send you a screenshot, that’s perfect.
295 00:25:03.190 ⇒ 00:25:03.830 Amber Lin: Okay.
296 00:25:09.350 ⇒ 00:25:13.390 Amber Lin: Because currently, I looked at your pricing model, and it maps
297 00:25:13.420 ⇒ 00:25:30.530 Amber Lin: decently to the… masks pretty well to what we have currently. It’s just, oh, people don’t have free seats anymore, and then they, pay by each additional seat, and then… so, I think we can do the backtest pretty well, based on yours. I think if we…
298 00:25:30.560 ⇒ 00:25:36.560 Amber Lin: But what’s proposed is also very interesting, and we’ll try to see if we can
299 00:25:37.800 ⇒ 00:25:48.330 Amber Lin: try to do a test with that as well, because I… I think you guys should consider what’s the possible different business models, because it might unlock something that we haven’t looked at before.
300 00:25:48.540 ⇒ 00:25:54.349 Caitlyn Vaughn: Yeah. I think also there’s this, like, interesting notion that I’ve been thinking of recently, which is…
301 00:25:54.600 ⇒ 00:26:04.769 Caitlyn Vaughn: Utami, you just asked earlier, you’re like, well, what are you gonna do with these signals? Because they’re for outbound, mainly. Yeah. We launched as, like, a very specific point solution.
302 00:26:04.770 ⇒ 00:26:22.780 Caitlyn Vaughn: And there’s… most products are point solutions, right? Like, Chili Piper, like Copy.ai, like, you know, most of them. And it’s easy to get traction in the market via being a point solution, but we want to become this, like, very horizontal product, where we have a whole suite, right? Like, Google.
303 00:26:23.000 ⇒ 00:26:35.809 Caitlyn Vaughn: And when I’m thinking of, like, one of the main reasons why we want to do that, it’s for pricing. Like, instead of being tied to, like, usage, or seat count, or, like, whatever on a point solution, we could…
304 00:26:35.810 ⇒ 00:26:46.509 Caitlyn Vaughn: create, like, a much more compelling pricing model that is like, okay, normally we would charge you, you know, $50,000 a year for scheduling, but if you do scheduling and routing, we’ll only charge you.
305 00:26:46.510 ⇒ 00:26:47.060 Uttam Kumaran: Yeah, yeah, yeah.
306 00:26:47.060 ⇒ 00:26:47.969 Caitlyn Vaughn: You know?
307 00:26:47.970 ⇒ 00:26:48.670 Uttam Kumaran: Yeah, yeah.
308 00:26:48.670 ⇒ 00:26:50.430 Caitlyn Vaughn: Which is clever, I think.
309 00:26:51.320 ⇒ 00:26:56.120 Uttam Kumaran: Yeah, it’s gonna be really hard to nail it until you see the usage.
310 00:26:56.120 ⇒ 00:26:58.110 Caitlyn Vaughn: And you guys can see how…
311 00:26:58.320 ⇒ 00:27:17.589 Uttam Kumaran: usage is impacting revenue, right? Like, because then, as soon… the basics is, like, if you know that, hey, you guys are clearly getting a lot ton of inbound, and we’re supporting that, let’s put a dollar value on that and make sure this is fair. Like, people will be fine, but I think it’s tough to know, like, what features people are using and how to price it.
312 00:27:17.750 ⇒ 00:27:30.859 Uttam Kumaran: and do kind of what’s common, which is, like, discounting, bundling, things like that. Right? But also, like, you want to save the discounting and bundling, I feel like, significantly for just the people that are going to pay a ton.
313 00:27:30.970 ⇒ 00:27:32.800 Uttam Kumaran: Right? Cause they’re gonna…
314 00:27:33.230 ⇒ 00:27:43.849 Uttam Kumaran: they’re the highest risk if they leave, but also biggest opportunity if they stay and sign a longer agreement. For the self-serve and things, I think kind of what we talked about last time, which is just, like.
315 00:27:43.980 ⇒ 00:27:45.689 Uttam Kumaran: Making it simple.
316 00:27:46.430 ⇒ 00:27:56.779 Uttam Kumaran: And making sure that when people start their company, or they’re thinking about shit, like, we just need to… we have, like, cal.com, and we just need to figure some shit out.
317 00:27:56.780 ⇒ 00:27:57.280 Caitlyn Vaughn: Hmm.
318 00:27:57.280 ⇒ 00:28:07.049 Uttam Kumaran: they go with default, it’s the easiest, simplest, cheapest option, and that as they get wins, it’s a no-brainer for them to upgrade, right? They’re like, oh, yeah, totally upgrade.
319 00:28:07.050 ⇒ 00:28:09.640 Caitlyn Vaughn: Right? It’s similar to, like, other products we use, where.
320 00:28:09.640 ⇒ 00:28:13.080 Uttam Kumaran: You don’t think twice about, like, We need this, right?
321 00:28:13.080 ⇒ 00:28:14.199 Caitlyn Vaughn: Yeah.
322 00:28:14.420 ⇒ 00:28:15.360 Uttam Kumaran: So…
323 00:28:15.880 ⇒ 00:28:29.700 Uttam Kumaran: I think what we’re gonna need to find, and this is what we’ll do, like, once we… once the new product is there, and we can establish, like, amplitude and start to look at which types of users in different categories are using various features, it’ll give you a sense of, like, what to price gate
324 00:28:30.100 ⇒ 00:28:35.889 Uttam Kumaran: and what to give out for free, right? Because part of it is a narrative for your sales folks to be like, hey, you have all these features.
325 00:28:35.890 ⇒ 00:28:39.179 Caitlyn Vaughn: But there may only be 2 or 3 that are really the ones that…
326 00:28:39.180 ⇒ 00:28:42.720 Uttam Kumaran: Indicate an interest in driving to the next plan.
327 00:28:42.840 ⇒ 00:28:43.320 Caitlyn Vaughn: Yeah.
328 00:28:43.320 ⇒ 00:28:52.979 Uttam Kumaran: Right? And a couple other ones that you don’t want to gate, right? Because you don’t want to… for example, Slack doesn’t prevent you from adding everybody in, right?
329 00:28:53.260 ⇒ 00:29:08.450 Uttam Kumaran: And so… but they do say, if you’re free, then we’re gonna cut, like, your message history. Right. Which is like, there’s no… okay, I need, like, all my messages, you know? So, I don’t know, I think it’s helpful to take inspiration from some of those folks on how they’re thinking about pricing, especially
330 00:29:08.970 ⇒ 00:29:12.449 Uttam Kumaran: in a PLG, which they make it just super easy to start.
331 00:29:12.450 ⇒ 00:29:12.790 Caitlyn Vaughn: Yeah.
332 00:29:12.790 ⇒ 00:29:18.180 Uttam Kumaran: as you get wins, they then try to guide you towards the next plan, and it’s fair, you know?
333 00:29:18.880 ⇒ 00:29:25.510 Uttam Kumaran: But you’re also gonna get a lot of noise from the free people, and that’s just something that you want to make sure that… that doesn’t drown out, like.
334 00:29:26.140 ⇒ 00:29:31.640 Uttam Kumaran: the people who are actually willing to pay. Just because someone, like, doesn’t like your pricing doesn’t mean they’re, like, willing…
335 00:29:32.040 ⇒ 00:29:38.470 Uttam Kumaran: to pay, and so the people that actually are getting value from it, and can identify and see their ROI, they’ll totally move
336 00:29:38.660 ⇒ 00:29:39.750 Uttam Kumaran: and pricing.
337 00:29:40.000 ⇒ 00:29:43.029 Caitlyn Vaughn: Yeah. The other thing is we’re going upmarket, though. Like.
338 00:29:43.030 ⇒ 00:29:43.640 Uttam Kumaran: Yeah, yeah, yeah.
339 00:29:43.640 ⇒ 00:29:47.879 Caitlyn Vaughn: to go enterprise and not sell contracts below, like, 20K.
340 00:29:47.880 ⇒ 00:29:48.260 Uttam Kumaran: Yeah.
341 00:29:48.260 ⇒ 00:29:55.520 Caitlyn Vaughn: So, when I’m thinking about PLG, it’s, like, a very different way that I’ve built out PLG before.
342 00:29:55.520 ⇒ 00:29:55.950 Uttam Kumaran: Yeah.
343 00:29:55.950 ⇒ 00:29:58.800 Caitlyn Vaughn: You know, like, at copy, it was, like, freemium, and I…
344 00:29:58.800 ⇒ 00:29:59.900 Uttam Kumaran: Yeah, yeah, yeah.
345 00:29:59.900 ⇒ 00:30:06.670 Caitlyn Vaughn: out, and we got, like, millions of people pouring in, and that’s, like, not necessarily our goal here. Sure.
346 00:30:06.860 ⇒ 00:30:09.210 Caitlyn Vaughn: So it’s like… Yeah, so then, but…
347 00:30:09.210 ⇒ 00:30:16.440 Uttam Kumaran: Basically, what it is, it’s like, you want the people that go from… but, like, how do you think about the $50 tier, then, versus enterprise?
348 00:30:17.410 ⇒ 00:30:24.430 Caitlyn Vaughn: Well… I don’t know, like, the 50.
349 00:30:24.840 ⇒ 00:30:27.829 Caitlyn Vaughn: Like, if you look at the pricing page…
350 00:30:27.830 ⇒ 00:30:31.390 Uttam Kumaran: Oh, yeah, yeah, I see what you mean, I see what you mean. So it probably averages out, like.
351 00:30:32.990 ⇒ 00:30:36.219 Uttam Kumaran: Oh, yeah, close to, like, a couple hundred a month. Yeah, yeah, yeah.
352 00:30:36.220 ⇒ 00:30:38.169 Caitlyn Vaughn: Yeah, if you scroll to the bottom, I have, like.
353 00:30:38.170 ⇒ 00:31:02.079 Caitlyn Vaughn: here’s someone with 3 seats, and they’d be paying us, like, $1,900 a year, plus enrichment, versus, like, Cherry would be paying us a quarter million dollars plus enrichment. So I think, like, a lot of our revenue could be driven from enrichment, potentially, and it’s, like, another question. We could do premium enrichment and basic enrichment, and we could completely change the way that we structure this pricing again, you know?
354 00:31:02.080 ⇒ 00:31:02.770 Uttam Kumaran: Yeah.
355 00:31:02.870 ⇒ 00:31:06.359 Caitlyn Vaughn: So, I don’t know. I’m, like, plagued with too much optionality.
356 00:31:06.360 ⇒ 00:31:12.809 Uttam Kumaran: No, that’s the trouble here. I mean, that’s why… so one thing I think we want to set up a little bit of, like, a simple model where you can backtest pricing.
357 00:31:13.000 ⇒ 00:31:28.649 Uttam Kumaran: Right? And I think, Amber, it’s probably something we could easily do in, like, a Google Sheet. It doesn’t necessarily have to end up in Omni, but basically, I want to give… I want to give Caitlin just a simple way of, like, toggling features by different tiers of company, and then
358 00:31:28.770 ⇒ 00:31:35.219 Uttam Kumaran: You… she can do a simple… it could just basically affect, like, a simple model, which shows, like, okay.
359 00:31:35.300 ⇒ 00:31:49.259 Uttam Kumaran: by adding this feature, here’s the potential impact on which category. The other thing I think it’s helpful for us to do, and I don’t know, I assume you guys do, but I’m sure you guys have a really innate understanding of the… your competitors’ pricing, like, did you guys learn anything from looking…
360 00:31:49.480 ⇒ 00:31:53.029 Uttam Kumaran: at them, is there anything to learn from there? Like, that’s also something that…
361 00:31:53.260 ⇒ 00:31:56.859 Uttam Kumaran: I was gonna go basically do a canvas of everybody.
362 00:31:57.050 ⇒ 00:32:05.220 Caitlyn Vaughn: In this world. I don’t know, it’s like… Where we’re going… Unify.
363 00:32:06.050 ⇒ 00:32:10.050 Caitlyn Vaughn: go to market. Like, where we’re going, we don’t really…
364 00:32:10.590 ⇒ 00:32:21.040 Caitlyn Vaughn: there’s not, like, a lot of direct… like, Gong is probably a good one, right? If we go to pricing for UniFi. UniFi is a really interesting pricing model. This is their self-serve tier.
365 00:32:22.330 ⇒ 00:32:22.859 Caitlyn Vaughn: It’s so…
366 00:32:22.860 ⇒ 00:32:25.670 Uttam Kumaran: Oh, yeah, yeah, no, I know, it’s crazy expensive.
367 00:32:25.670 ⇒ 00:32:30.810 Caitlyn Vaughn: Yeah, it starts at $1,800 a month, but I think this is, like, probably more…
368 00:32:31.270 ⇒ 00:32:33.950 Caitlyn Vaughn: Of the route we want to take, versus.
369 00:32:33.950 ⇒ 00:32:34.290 Uttam Kumaran: Yeah.
370 00:32:34.290 ⇒ 00:32:37.259 Caitlyn Vaughn: Like, $50 a month, you know?
371 00:32:37.480 ⇒ 00:32:42.190 Uttam Kumaran: Well, like, let’s talk through, like, the example here, like, One, 1740…
372 00:32:42.340 ⇒ 00:32:44.520 Uttam Kumaran: It’s, like, such, like, a weird number.
373 00:32:44.670 ⇒ 00:32:46.770 Uttam Kumaran: Like, what… I don’t know what the,
374 00:32:47.970 ⇒ 00:32:54.410 Uttam Kumaran: I don’t know what the psychology is there, but I wanted to sign up for UniFi, too, and I was like, oh, they have no free tier, so, like, I can’t…
375 00:32:54.780 ⇒ 00:33:00.520 Uttam Kumaran: start. Yeah. I also think, like, I got recommended in Unified by other people who are, like, swear by it.
376 00:33:01.050 ⇒ 00:33:05.290 Uttam Kumaran: And so, like, I don’t know, it’s just tough for me to understand, like.
377 00:33:06.560 ⇒ 00:33:13.820 Uttam Kumaran: Maybe they all… see, but all of their things indicates that, like, they’re making the friction so high, so they want to talk to somebody.
378 00:33:14.110 ⇒ 00:33:19.729 Uttam Kumaran: So, like, I would say their motion is, like, talk to somebody no matter what.
379 00:33:20.380 ⇒ 00:33:25.179 Uttam Kumaran: Right. Which is tough, which is not exactly what I feel like you guys want to do.
380 00:33:25.450 ⇒ 00:33:26.260 Caitlyn Vaughn: Yup.
381 00:33:26.580 ⇒ 00:33:27.100 Caitlyn Vaughn: I…
382 00:33:27.100 ⇒ 00:33:27.610 Uttam Kumaran: I don’t know.
383 00:33:27.610 ⇒ 00:33:30.680 Caitlyn Vaughn: Their, like, conversion is from…
384 00:33:31.070 ⇒ 00:33:37.880 Caitlyn Vaughn: self-serve, like, who comes in self-serve versus, like, outbound? I actually think it’s higher than you would think for self-serve.
385 00:33:39.310 ⇒ 00:33:40.200 Uttam Kumaran: Oh, really?
386 00:33:40.200 ⇒ 00:33:40.870 Caitlyn Vaughn: Yeah.
387 00:33:42.110 ⇒ 00:33:44.479 Caitlyn Vaughn: Yeah, they just raised $40 million.
388 00:33:45.890 ⇒ 00:33:48.699 Uttam Kumaran: Yeah, no, I know, I know, like, yeah, it’s.
389 00:33:52.790 ⇒ 00:33:54.859 Caitlyn Vaughn: But then if we look at, like, Gong…
390 00:33:55.760 ⇒ 00:33:58.059 Uttam Kumaran: Yeah, Gong is just going the other way.
391 00:33:58.770 ⇒ 00:33:59.940 Caitlyn Vaughn: They’re doing bad.
392 00:33:59.940 ⇒ 00:34:05.080 Uttam Kumaran: Oh, no, I mean, they’re… I think their pricing is more tailored towards, like, Start for free, whatever.
393 00:34:07.280 ⇒ 00:34:09.010 Caitlyn Vaughn: But yeah, they don’t even give it.
394 00:34:09.179 ⇒ 00:34:11.199 Uttam Kumaran: Hit 1 to… hit 1 to 50, yeah.
395 00:34:14.620 ⇒ 00:34:17.470 Caitlyn Vaughn: That’s pretty cool, actually.
396 00:34:19.260 ⇒ 00:34:22.479 Caitlyn Vaughn: rap master. I’m giving it.
397 00:34:23.250 ⇒ 00:34:31.959 Caitlyn Vaughn: But yeah, they have, like, a pretty cool pricing model as well. It’s more like the one that Ryan proposed with the, you know, groupings of features.
398 00:34:33.800 ⇒ 00:34:39.040 Caitlyn Vaughn: But I don’t know, maybe it takes us, like, longer to get to something like that.
399 00:34:39.400 ⇒ 00:34:44.730 Caitlyn Vaughn: And I’m sure whatever pricing we come out with initially will change over time, but… I don’t know.
400 00:34:44.730 ⇒ 00:34:49.320 Uttam Kumaran: Well, I mean, you could… You could not offer a free tier to start.
401 00:34:50.650 ⇒ 00:34:52.499 Caitlyn Vaughn: And just do, like, a cheap tier.
402 00:34:52.710 ⇒ 00:34:53.300 Caitlyn Vaughn: Or no.
403 00:34:53.300 ⇒ 00:34:54.850 Uttam Kumaran: Just do the cheap one.
404 00:34:55.460 ⇒ 00:34:57.749 Uttam Kumaran: And then figure out what the free is after that.
405 00:34:58.130 ⇒ 00:35:08.639 Uttam Kumaran: Yeah. Because the free, the goal of the free is to get people to the paid, right? But without understanding which features in the Pro people love to use, it’s hard to, like.
406 00:35:08.780 ⇒ 00:35:14.539 Uttam Kumaran: build the free experience, right? Because your goal is, like, to get people free, get them hooked, and seeing
407 00:35:14.730 ⇒ 00:35:22.889 Uttam Kumaran: the success, and then they moved into Pro, versus, like, you don’t want people to just be able to use your free stuff and, like.
408 00:35:23.210 ⇒ 00:35:25.520 Uttam Kumaran: Sort of, like, chill, you know?
409 00:35:25.520 ⇒ 00:35:26.140 Caitlyn Vaughn: Hmm.
410 00:35:26.140 ⇒ 00:35:38.880 Uttam Kumaran: So… and it’s honestly, like, I don’t think it requires, like, quite any more, like, engineering work to just not do free. It’s probably the safer option to just do the
411 00:35:39.180 ⇒ 00:35:40.180 Uttam Kumaran: Pro.
412 00:35:40.430 ⇒ 00:35:44.479 Uttam Kumaran: Steve, who in your existing client base would
413 00:35:45.050 ⇒ 00:35:53.640 Uttam Kumaran: moved, like, you would bucket into that, and then what their impact would be, and then, like, kind of, like, that’s phase one. And then phase two.
414 00:35:53.750 ⇒ 00:35:55.200 Uttam Kumaran: You roll out free.
415 00:35:55.470 ⇒ 00:35:56.250 Caitlyn Vaughn: Yeah.
416 00:35:58.060 ⇒ 00:35:59.109 Caitlyn Vaughn: I don’t know.
417 00:35:59.320 ⇒ 00:35:59.870 Uttam Kumaran: I don’t know.
418 00:35:59.870 ⇒ 00:36:00.530 Caitlyn Vaughn: Yes.
419 00:36:00.530 ⇒ 00:36:01.150 Uttam Kumaran: Yeah.
420 00:36:01.150 ⇒ 00:36:06.839 Caitlyn Vaughn: someone tasked me with answering this question, I’m… I’m very stressed about it.
421 00:36:06.840 ⇒ 00:36:13.389 Uttam Kumaran: This is tough, I mean, look, so I want to give you data that at least solves the, like, what if we do this, what if we do that problem.
422 00:36:14.190 ⇒ 00:36:16.590 Uttam Kumaran: So, at least we can help you with that.
423 00:36:16.700 ⇒ 00:36:19.210 Uttam Kumaran: Yeah. In terms of, like, that price sensitivity?
424 00:36:19.210 ⇒ 00:36:20.020 Caitlyn Vaughn: Okay.
425 00:36:20.280 ⇒ 00:36:24.909 Uttam Kumaran: What’s the, like, timeline on, like, rap…
426 00:36:26.010 ⇒ 00:36:37.239 Caitlyn Vaughn: we’re gonna release PLG with Phase 2, like, our first customer-facing version of the product, so… I just had a conversation this morning with the team about timelines,
427 00:36:38.200 ⇒ 00:36:41.120 Caitlyn Vaughn: It’s looking like, hopefully, end of January.
428 00:36:41.120 ⇒ 00:36:41.720 Uttam Kumaran: Okay.
429 00:36:42.010 ⇒ 00:36:42.720 Uttam Kumaran: Cool.
430 00:36:42.720 ⇒ 00:36:43.140 Caitlyn Vaughn: Forcing.
431 00:36:43.140 ⇒ 00:36:43.770 Uttam Kumaran: Okay.
432 00:36:43.770 ⇒ 00:36:44.910 Caitlyn Vaughn: Maybe, like, end of February.
433 00:36:44.910 ⇒ 00:36:46.260 Uttam Kumaran: Yeah, yeah.
434 00:36:46.260 ⇒ 00:36:47.000 Caitlyn Vaughn: of March.
435 00:36:47.140 ⇒ 00:36:47.790 Uttam Kumaran: Yeah.
436 00:36:48.560 ⇒ 00:36:49.380 Uttam Kumaran: Okay.
437 00:36:50.790 ⇒ 00:37:02.800 Uttam Kumaran: Yeah, I mean, I, it’s so tough to say without the core usage data, but… but Amber, we are looking at, like, workflow runs, we’re looking at when users are added, we’re looking at subscription change events.
438 00:37:03.030 ⇒ 00:37:07.600 Uttam Kumaran: So, ideally, as part of, like, this initial thing, and I think we could probably get
439 00:37:07.650 ⇒ 00:37:24.869 Uttam Kumaran: a first version of this next week, is I want to just share the link between usage and your revenue to date. So, like, who are the most expensive… who are the highest paying customers? How are… is their revenue tied to their usage of the platform? Who’s… who’s growing?
440 00:37:25.120 ⇒ 00:37:30.500 Uttam Kumaran: And then certainly, like, Who’s underpaying right now, based on how much they’re doing.
441 00:37:31.420 ⇒ 00:37:35.789 Uttam Kumaran: We have users added, workflows, submissions, and meetings to work with.
442 00:37:38.700 ⇒ 00:37:40.200 Uttam Kumaran: And then of C-types.
443 00:37:40.650 ⇒ 00:37:41.190 Caitlyn Vaughn: Yeah.
444 00:37:41.190 ⇒ 00:37:41.880 Uttam Kumaran: Also.
445 00:37:42.280 ⇒ 00:37:43.270 Caitlyn Vaughn: That’s right.
446 00:37:43.620 ⇒ 00:37:46.379 Uttam Kumaran: So the other thing that I’m not sure, until I get the,
447 00:37:46.640 ⇒ 00:37:51.449 Uttam Kumaran: the SuperBase data. I don’t know yet whether the team is tracking
448 00:37:51.790 ⇒ 00:37:55.979 Uttam Kumaran: like, change events. Like, when did a billing event
449 00:37:56.130 ⇒ 00:38:04.089 Uttam Kumaran: happen, or when did an upgrade happen? If they don’t, then we’ll have to start snapshotting, basically. Because you want to see incrementally, like.
450 00:38:04.260 ⇒ 00:38:10.780 Uttam Kumaran: User got added, user got added, user got added, user got added, user got changed, right? Like, how many people are switching roles?
451 00:38:10.930 ⇒ 00:38:18.440 Uttam Kumaran: how often are this… are… yeah, like, you want to see that movement, and so that’s one thing that… until I see, like, the Supabass…
452 00:38:19.350 ⇒ 00:38:22.830 Uttam Kumaran: data, I can then recommend, like, hey, can you guys start tracking, like.
453 00:38:23.480 ⇒ 00:38:25.230 Uttam Kumaran: role change events, basically.
454 00:38:25.230 ⇒ 00:38:26.990 Caitlyn Vaughn: Yeah, like when new seats were added.
455 00:38:26.990 ⇒ 00:38:32.490 Uttam Kumaran: Because usually, you don’t… it’s not like… it’s not necessary for anything apart from analytics to track
456 00:38:32.810 ⇒ 00:38:36.430 Uttam Kumaran: Seat change events, because you’d just be like, this person is now this.
457 00:38:36.490 ⇒ 00:38:37.500 Caitlyn Vaughn: Yeah.
458 00:38:37.560 ⇒ 00:38:51.150 Uttam Kumaran: But for us, it’s very important to see, like, how… how much time in between, like, Deanna asked me, like, how much time in between they… they start, between their first workflow draft, their workflow submitted, and their first submission.
459 00:38:51.150 ⇒ 00:38:53.089 Caitlyn Vaughn: How fast between…
460 00:38:53.090 ⇒ 00:38:56.110 Uttam Kumaran: their start, and then they’re adding users. All of that timing.
461 00:38:56.110 ⇒ 00:38:56.670 Caitlyn Vaughn: Yeah.
462 00:38:56.670 ⇒ 00:39:04.660 Uttam Kumaran: is… she mentioned is, like, what shows the adoption rate. So, like, if it’s slow, then someone from default needs to call them and be like, what’s good?
463 00:39:04.850 ⇒ 00:39:05.610 Uttam Kumaran: Yeah. You know?
464 00:39:05.650 ⇒ 00:39:06.710 Caitlyn Vaughn: Yeah.
465 00:39:07.170 ⇒ 00:39:08.110 Uttam Kumaran: So, yeah.
466 00:39:09.090 ⇒ 00:39:16.690 Caitlyn Vaughn: And we’re talking about also no longer doing implementations, or, like, only doing them for certain clients.
467 00:39:16.690 ⇒ 00:39:19.029 Uttam Kumaran: Yeah, I mean, why don’t you just go through partners for all that?
468 00:39:19.240 ⇒ 00:39:24.080 Caitlyn Vaughn: I literally suggested it, so we’ll see. Okay. Yeah, you should. Hopefully, yeah.
469 00:39:24.080 ⇒ 00:39:31.050 Uttam Kumaran: It’s not like a… it’s not like an extremely complicated… I mean, I don’t know, the new… maybe the new product, but, like, you should totally go through partners, because
470 00:39:31.480 ⇒ 00:39:37.810 Uttam Kumaran: Yeah, I don’t know, I mean, maybe there’s… you take a hit on the… On the… whatever. Yeah, but…
471 00:39:38.170 ⇒ 00:39:39.370 Uttam Kumaran: Yeah. It’s worth it.
472 00:39:39.370 ⇒ 00:39:42.670 Caitlyn Vaughn: We don’t want to keep, like, hiring more people for this.
473 00:39:42.670 ⇒ 00:39:43.340 Uttam Kumaran: Yeah.
474 00:39:44.090 ⇒ 00:39:47.599 Caitlyn Vaughn: Yeah. This is the whole reason why we launched partnerships, actually.
475 00:39:47.600 ⇒ 00:39:48.330 Uttam Kumaran: Yeah.
476 00:39:48.740 ⇒ 00:39:51.750 Uttam Kumaran: And then, so, a couple things, like, I think we… we’re gonna go out…
477 00:39:51.860 ⇒ 00:39:57.339 Uttam Kumaran: After here is, like, we wanted to look at Revenue and usage concentration.
478 00:39:57.650 ⇒ 00:40:02.650 Uttam Kumaran: So, we’re gonna put graphs that are, like, who are the top 10, 20, 50,
479 00:40:03.090 ⇒ 00:40:04.979 Uttam Kumaran: By usage and revenue.
480 00:40:05.130 ⇒ 00:40:06.540 Uttam Kumaran: And we can share that.
481 00:40:06.540 ⇒ 00:40:07.440 Caitlyn Vaughn: Mmm.
482 00:40:07.440 ⇒ 00:40:11.780 Uttam Kumaran: We wanted to show, like, basically growth by feature usage.
483 00:40:11.920 ⇒ 00:40:16.959 Uttam Kumaran: So seeing, like, Who’s using stuff the most versus the least.
484 00:40:18.770 ⇒ 00:40:27.279 Uttam Kumaran: And again, naturally, if we do a V1 of this, it’ll make the case for amplitude and stuff even clearer, you know? I’m like, hey, we should start tracking more stuff.
485 00:40:27.440 ⇒ 00:40:33.240 Uttam Kumaran: And then we talked a little bit about, like, pricing. So, looking at, like,
486 00:40:34.120 ⇒ 00:40:37.720 Uttam Kumaran: the impact of the pricing scheme. Okay, like, what if…
487 00:40:38.040 ⇒ 00:40:47.249 Uttam Kumaran: Based on, like, our current customer, what are they currently paying? If they are to adopt the new pricing, what will they pay? Having that literally in just, like, a
488 00:40:47.400 ⇒ 00:40:49.100 Uttam Kumaran: side-by-side Excel.
489 00:40:49.230 ⇒ 00:40:53.710 Uttam Kumaran: So you can see the difference, and then, like, at least start to simulate, you know, what that is.
490 00:40:53.950 ⇒ 00:40:54.770 Caitlyn Vaughn: Yeah.
491 00:40:55.090 ⇒ 00:40:58.229 Caitlyn Vaughn: Okay, cool. Did you guys share this stock with me?
492 00:40:58.430 ⇒ 00:41:03.210 Uttam Kumaran: I did not. That is on me, I should have yesterday, but I was… Oh, you’re.
493 00:41:03.210 ⇒ 00:41:05.490 Caitlyn Vaughn: It’s late, I’ll share with me, so I can see it too.
494 00:41:05.490 ⇒ 00:41:07.289 Uttam Kumaran: Yeah, I’ll send it to you today, yeah.
495 00:41:07.440 ⇒ 00:41:14.210 Caitlyn Vaughn: Okay, I need to jump to another call, but this looks good. What can I help with? What do you need?
496 00:41:14.850 ⇒ 00:41:15.510 Uttam Kumaran: Yeah, so…
497 00:41:15.510 ⇒ 00:41:17.690 Amber Lin: Send me the screenshot that you showed earlier?
498 00:41:17.690 ⇒ 00:41:18.899 Uttam Kumaran: It’s in Slack.
499 00:41:19.150 ⇒ 00:41:26.229 Amber Lin: Oh, okay. I don’t think I’m in the channel yet, where I will find it. No, I think… no, I think I am. I just need to grab it.
500 00:41:26.230 ⇒ 00:41:26.610 Caitlyn Vaughn: Okay.
501 00:41:26.610 ⇒ 00:41:28.189 Amber Lin: Perfect. Thank you so much.
502 00:41:28.190 ⇒ 00:41:32.249 Caitlyn Vaughn: Yeah, amazing. Okay, good to see you guys. Utam, tell me your birthday deeds.
503 00:41:32.250 ⇒ 00:41:35.680 Uttam Kumaran: I will, I will, I will. Thank you. Okay, bye. Okay, bye.