Meeting Title: Default | Brainforge Weekly Sync Date: 2025-12-17 Meeting participants: Scratchpad Notetaker, Mustafa Raja, Uttam Kumaran, Caitlyn Vaughn
WEBVTT
1 00:01:52.690 ⇒ 00:01:53.670 Uttam Kumaran: Hey…
2 00:03:00.720 ⇒ 00:03:02.309 Mustafa Raja: Hey, can you hear me now?
3 00:03:02.800 ⇒ 00:03:04.029 Uttam Kumaran: Hey, yes, I can hear you.
4 00:03:04.510 ⇒ 00:03:08.320 Mustafa Raja: Oh, there’s something going on with my… headphones.
5 00:03:08.860 ⇒ 00:03:09.850 Uttam Kumaran: No problem.
6 00:03:12.430 ⇒ 00:03:13.790 Caitlyn Vaughn: Hello!
7 00:03:13.790 ⇒ 00:03:15.250 Uttam Kumaran: Hello, good morning.
8 00:03:15.250 ⇒ 00:03:17.359 Caitlyn Vaughn: Hey, how’s it going?
9 00:03:17.700 ⇒ 00:03:18.550 Uttam Kumaran: Good.
10 00:03:19.850 ⇒ 00:03:21.200 Uttam Kumaran: The usual.
11 00:03:21.600 ⇒ 00:03:23.039 Uttam Kumaran: Oh, hi, dog.
12 00:03:23.850 ⇒ 00:03:26.529 Caitlyn Vaughn: I know, you always have your dog in the background, so I told her…
13 00:03:26.530 ⇒ 00:03:30.819 Uttam Kumaran: Yeah, let’s go! Get in there, come on!
14 00:03:31.150 ⇒ 00:03:39.120 Caitlyn Vaughn: I, like, always take calls all over my house, and all over Nick’s house, and all over… like, wherever I am, I just, like, move around a lot.
15 00:03:39.120 ⇒ 00:03:39.550 Uttam Kumaran: Yeah.
16 00:03:39.550 ⇒ 00:03:43.780 Caitlyn Vaughn: time I get on with Nico, he’s like, why are you always somewhere different? I’m like, no.
17 00:03:43.780 ⇒ 00:03:44.350 Uttam Kumaran: I know.
18 00:03:44.350 ⇒ 00:03:47.750 Caitlyn Vaughn: Place, but like… Different angle.
19 00:03:47.750 ⇒ 00:03:59.620 Uttam Kumaran: You just need to, wherever you can get focused work done, you know? I don’t know, I feel like I’m, like, locked. This is nice, though, because it’s, like, this place has high ceilings, and it’s some depth, and so I don’t feel like…
20 00:03:59.740 ⇒ 00:04:04.860 Caitlyn Vaughn: I’m trapped here, but it is in my house every day, but…
21 00:04:05.020 ⇒ 00:04:11.319 Caitlyn Vaughn: I don’t know, like, how am I used to… I used to just work at, like, my apartment island, you know, for a long time. That sucked. Really?
22 00:04:11.320 ⇒ 00:04:19.279 Uttam Kumaran: Yeah, like, at Avenir, and then I would just walk to the coffee shop every day, like, for 4 hours. So at least I can, like, chill here.
23 00:04:19.630 ⇒ 00:04:22.160 Caitlyn Vaughn: Yeah, at least you have, like, your own space set up.
24 00:04:22.360 ⇒ 00:04:29.990 Uttam Kumaran: Yeah, yeah, it’s a mess, though. There’s stuff all over. There’s, like, books on the ground, and… but, like, above here?
25 00:04:34.210 ⇒ 00:04:35.080 Caitlyn Vaughn: Fucking Christmas.
26 00:04:35.080 ⇒ 00:04:38.549 Uttam Kumaran: Yeah, we look very, we look very rich and successful.
27 00:04:38.550 ⇒ 00:04:40.540 Caitlyn Vaughn: Yeah, that’s amazing. And then there’s, like.
28 00:04:40.540 ⇒ 00:04:47.840 Uttam Kumaran: There’s, like, mail, and the dog brought some toys in, and it’s like, yeah, but…
29 00:04:47.840 ⇒ 00:04:51.089 Caitlyn Vaughn: So funny. I do the same shit, everything is kind of everywhere.
30 00:04:52.450 ⇒ 00:04:53.520 Caitlyn Vaughn: Nice.
31 00:04:54.270 ⇒ 00:04:56.759 Caitlyn Vaughn: Well, we have a lot of shit.
32 00:04:56.760 ⇒ 00:04:57.590 Uttam Kumaran: Yes.
33 00:04:58.170 ⇒ 00:05:05.400 Caitlyn Vaughn: So yesterday, I held… Okay, wait, let me, let me back up.
34 00:05:05.510 ⇒ 00:05:10.720 Caitlyn Vaughn: So… I talked to Victor. Did I tell you about my conversation with him, or no?
35 00:05:12.200 ⇒ 00:05:18.399 Caitlyn Vaughn: Okay, so… last week, I finally called him, and I was like, yo!
36 00:05:18.930 ⇒ 00:05:20.260 Caitlyn Vaughn: We need…
37 00:05:20.530 ⇒ 00:05:25.400 Uttam Kumaran: Yeah. To be unblocked, like, this is ridiculous. And he was like…
38 00:05:26.250 ⇒ 00:05:32.999 Caitlyn Vaughn: I have so much shit to do, and this is such a low priority, and I don’t understand why this needs to get done, and I was like, okay.
39 00:05:33.250 ⇒ 00:05:38.709 Caitlyn Vaughn: well, I understand that. And he was like, the other thing is I don’t want to, like…
40 00:05:39.440 ⇒ 00:05:46.110 Caitlyn Vaughn: build all this out, and then have to rebuild it on Phoenix in, like, 2 months. Like, just add a bunch of tech debt.
41 00:05:46.300 ⇒ 00:05:52.830 Caitlyn Vaughn: And so, it was like, is there another way for us to do this? Like, even if it’s, like, manual monkey hours.
42 00:05:53.080 ⇒ 00:05:55.360 Caitlyn Vaughn: And I was like, I mean, yes.
43 00:05:56.090 ⇒ 00:06:13.230 Caitlyn Vaughn: it’s not ideal, but we can make it work for a few months. So I guess the consensus is Thomas is our ETL pipeline, and he’s going to be, like, once a week exporting all the data that we need, so at least we’ll have, like, a weekly freshness.
44 00:06:13.590 ⇒ 00:06:21.189 Caitlyn Vaughn: He was like, you can make him do it every day, but that feels kind of cruel, so maybe once a week is good. For now.
45 00:06:21.190 ⇒ 00:06:31.309 Uttam Kumaran: As soon as people start to get it once a week, their next ask is gonna be once a day. Yeah. But if we can get it once a week, that’s better than what’s going on now, you know?
46 00:06:31.310 ⇒ 00:06:32.540 Caitlyn Vaughn: Every few months.
47 00:06:32.540 ⇒ 00:06:37.389 Uttam Kumaran: Because I’m telling you, like, Demolati’s starting to build stuff, and the first thing people are gonna be like.
48 00:06:37.560 ⇒ 00:06:42.680 Uttam Kumaran: Oh, so we don’t have anything for, like, this month? And he’s gonna… he’s gonna ping and be like.
49 00:06:43.050 ⇒ 00:06:51.639 Uttam Kumaran: guys, did we not do any ETL? And then I’m gonna be like, yeah, we’re thinking about it. They’re still thinking about whether they want it or not.
50 00:06:53.610 ⇒ 00:06:54.490 Uttam Kumaran: So…
51 00:06:57.030 ⇒ 00:06:59.329 Caitlyn Vaughn: Yeah, it’s been hard to be…
52 00:06:59.630 ⇒ 00:07:14.360 Caitlyn Vaughn: what we’ve done is we’ve included some more people in the team now. So, I mean, everything is, like, roughly set up, and now it seems like we’re in a good place where we could start plugging in new sources.
53 00:07:14.360 ⇒ 00:07:19.879 Caitlyn Vaughn: So I used your data model to create a default-specific data model.
54 00:07:20.850 ⇒ 00:07:22.380 Uttam Kumaran: Yay! Yes.
55 00:07:22.650 ⇒ 00:07:40.069 Caitlyn Vaughn: Here’s Thomas. Here are the sources that we probably want. Like, we’re in Postgres, we’ll eventually move to ClickHouse, Hyperline, maybe Google… yeah, probably Google Analytics, Amplitude, QuickBooks, Salesforce Stripe. Laura, our Chief of Staff, she is…
56 00:07:40.620 ⇒ 00:07:46.099 Caitlyn Vaughn: going to need a lot of financial data. I think the plan is to move completely over from equals.
57 00:07:46.380 ⇒ 00:07:50.509 Caitlyn Vaughn: But if we want to get all of these sources in.
58 00:07:51.790 ⇒ 00:07:56.709 Caitlyn Vaughn: This would probably be a lot for Thomas to, like, export once a week, right?
59 00:07:57.170 ⇒ 00:08:02.579 Uttam Kumaran: Yeah, so our… our recommendation, which you listed, is, like, to go with a typical data ingestion tool.
60 00:08:03.020 ⇒ 00:08:04.630 Uttam Kumaran: I’m happy to send you…
61 00:08:04.750 ⇒ 00:08:15.820 Uttam Kumaran: we just did this for another client, where we basically showed, like, what the options are in the market. We typically either recommend Fivetran or another partner that’s called Polytomic.
62 00:08:16.530 ⇒ 00:08:18.800 Uttam Kumaran: They’re both, like, best in class.
63 00:08:19.390 ⇒ 00:08:21.620 Uttam Kumaran: Polyatomic is P-O-L-Y.
64 00:08:21.910 ⇒ 00:08:23.170 Uttam Kumaran: P-O-M-I-C.
65 00:08:25.750 ⇒ 00:08:26.770 Caitlyn Vaughn: Like that.
66 00:08:26.770 ⇒ 00:08:27.340 Uttam Kumaran: Yeah.
67 00:08:27.520 ⇒ 00:08:31.020 Uttam Kumaran: So I can send… we just did, like, a very elaborate memo on…
68 00:08:31.660 ⇒ 00:08:36.799 Uttam Kumaran: picking the best ETL tool, so I can send you… I’ll send you a version of that.
69 00:08:37.640 ⇒ 00:08:38.250 Caitlyn Vaughn: Great.
70 00:08:38.250 ⇒ 00:08:38.780 Uttam Kumaran: Yeah.
71 00:08:39.220 ⇒ 00:08:41.590 Caitlyn Vaughn: Okay, so probably for now…
72 00:08:42.070 ⇒ 00:08:50.200 Uttam Kumaran: Until we have Phoenix up, I’m assuming we’ll be a little bit more limited. Postgres, Hyperline, and Salesforce would solve a lot.
73 00:08:50.940 ⇒ 00:08:51.720 Caitlyn Vaughn: Okay.
74 00:08:53.000 ⇒ 00:08:54.320 Caitlyn Vaughn: out in Salesforce.
75 00:08:54.900 ⇒ 00:09:00.939 Uttam Kumaran: If… and what we can do for Thomas is, like, he… for Salesforce, he just needs to run, like, an export report.
76 00:09:01.810 ⇒ 00:09:08.149 Uttam Kumaran: And you can set that up in Salesforce, like, to be like, send me the CSV, or, like, he can go in and just click download.
77 00:09:08.370 ⇒ 00:09:12.819 Uttam Kumaran: Those three are, like, super, super critical.
78 00:09:12.820 ⇒ 00:09:13.420 Caitlyn Vaughn: Okay.
79 00:09:13.580 ⇒ 00:09:19.000 Uttam Kumaran: For… because that’s everything related to… to… active customers, and…
80 00:09:19.250 ⇒ 00:09:22.950 Uttam Kumaran: like, active deals, right? So, we got questions about
81 00:09:23.340 ⇒ 00:09:29.570 Uttam Kumaran: And so I’ll… I can reflect that in the spreadsheet, too, but that we got questions about deal cycle.
82 00:09:29.940 ⇒ 00:09:39.319 Uttam Kumaran: and of course, we’re getting questions about who are… what are our customers paying us, and then we’re also getting questions about who are our customers and, like, what are they doing in the application. Those three are, like.
83 00:09:39.670 ⇒ 00:09:40.750 Uttam Kumaran: B0.
84 00:09:40.890 ⇒ 00:09:47.280 Uttam Kumaran: Amplitude… Is gonna be post-Phoenix.
85 00:09:48.780 ⇒ 00:09:51.109 Uttam Kumaran: So, and, and also, like.
86 00:09:51.620 ⇒ 00:09:58.970 Uttam Kumaran: to tell you the truth, we may not even need to bring that into the warehouse. Like, you may be able to get a lot of it just within native reporting.
87 00:09:59.110 ⇒ 00:10:09.349 Uttam Kumaran: So that’s gonna be for us to, like, talk through, like, what data from amplitude do you want in the warehouse to combine? Or, for example, if you’re just looking at product funnels.
88 00:10:10.030 ⇒ 00:10:11.430 Uttam Kumaran: You don’t need to…
89 00:10:11.590 ⇒ 00:10:17.749 Uttam Kumaran: land that in here, you can just look at it in isolation. If you’re like, hey, we actually want to combine certain user events.
90 00:10:18.850 ⇒ 00:10:23.319 Uttam Kumaran: with other data sources, then it’s worth this, but amplitude of the next.
91 00:10:23.760 ⇒ 00:10:29.350 Uttam Kumaran: And then I would say in the middle here is for you to tell me about
92 00:10:29.450 ⇒ 00:10:32.220 Uttam Kumaran: GA, Stripe, and QuickBooks.
93 00:10:33.200 ⇒ 00:10:42.479 Caitlyn Vaughn: Yeah, so I’ll start with Stripe and QuickBooks. So, Stripe is going to be our revenue processor.
94 00:10:42.710 ⇒ 00:10:44.829 Caitlyn Vaughn: for self-serve.
95 00:10:45.070 ⇒ 00:10:47.459 Caitlyn Vaughn: And we’ll hook into Hyperline.
96 00:10:47.740 ⇒ 00:10:51.249 Caitlyn Vaughn: And we’re gonna use it for metering for credits.
97 00:10:51.770 ⇒ 00:10:52.510 Uttam Kumaran: Okay.
98 00:10:52.900 ⇒ 00:11:01.389 Caitlyn Vaughn: So, there’s gonna be quite a few things in there that wouldn’t be in something like Hyperline. Like, Hyperline is just our CBQ versus
99 00:11:01.430 ⇒ 00:11:15.179 Caitlyn Vaughn: Stripe is, like, we can live see who has how many credits at what time, we’ll roll in entitlements, into Stripe through WorkOS. Like, basically, most of our billing is built around Stripe.
100 00:11:16.540 ⇒ 00:11:17.530 Uttam Kumaran: Okay, okay.
101 00:11:17.900 ⇒ 00:11:25.310 Caitlyn Vaughn: QuickBooks is obviously, like, our accounting and financial data, like, there’s a lot of information in here that’s just not gonna live anywhere else.
102 00:11:25.310 ⇒ 00:11:28.759 Uttam Kumaran: Yes. And then for Google Analytics.
103 00:11:28.760 ⇒ 00:11:31.830 Caitlyn Vaughn: This is where we do a lot of our, like.
104 00:11:32.640 ⇒ 00:11:39.390 Caitlyn Vaughn: web analytic data, like, our marketing data that we also don’t really host anywhere else, so…
105 00:11:39.520 ⇒ 00:11:44.390 Caitlyn Vaughn: I’m not really sure what exactly we would want to pull from here, but…
106 00:11:45.130 ⇒ 00:11:48.570 Caitlyn Vaughn: Thinking off the top of my head, probably, like.
107 00:11:49.740 ⇒ 00:12:06.350 Caitlyn Vaughn: like, web traffic data, although we might have that inside of ClickHouse. Well, web traffic data, like, page traffic data, like, key terms, it’s kind of like, SEMrush in a lot of ways, right? Google Analytics? Yeah, okay.
108 00:12:06.350 ⇒ 00:12:09.699 Uttam Kumaran: Okay, okay, great. So, like, really, like, top of funnel,
109 00:12:10.070 ⇒ 00:12:12.810 Uttam Kumaran: Like, yeah, page analytics. Okay, great.
110 00:12:12.810 ⇒ 00:12:14.850 Caitlyn Vaughn: Okay. Cool, that makes sense.
111 00:12:15.090 ⇒ 00:12:21.949 Caitlyn Vaughn: And then Plane is our, customer support tool that we do for, like, ticketing.
112 00:12:23.560 ⇒ 00:12:24.140 Uttam Kumaran: Okay.
113 00:12:24.690 ⇒ 00:12:34.690 Caitlyn Vaughn: So, I don’t know if we want that data in there, probably, of, like, we’ll probably want to see revenue, number of tickets per customer, you know, like…
114 00:12:35.190 ⇒ 00:12:42.509 Caitlyn Vaughn: bugs versus, quick fixes, how much time for each ticket, that kind of stuff. Yeah.
115 00:12:42.510 ⇒ 00:12:48.760 Uttam Kumaran: Well, that’s what I guess, like, I know you guys are interested in looking at the ticket data…
116 00:12:48.970 ⇒ 00:12:53.909 Uttam Kumaran: I feel like more than most SaaS folks are right now, so, like, how… if that’s, like.
117 00:12:54.420 ⇒ 00:12:57.189 Uttam Kumaran: If that’s, like, really critical, then I would move that up.
118 00:12:57.680 ⇒ 00:13:00.020 Uttam Kumaran: And, like, that literally may be him just running a…
119 00:13:00.950 ⇒ 00:13:05.410 Uttam Kumaran: Download every single ticket, and all the fields associated with it once a week.
120 00:13:05.910 ⇒ 00:13:06.730 Caitlyn Vaughn: Hmm.
121 00:13:07.270 ⇒ 00:13:08.260 Caitlyn Vaughn: Okay.
122 00:13:08.480 ⇒ 00:13:10.130 Caitlyn Vaughn: Possibly…
123 00:13:11.240 ⇒ 00:13:15.350 Uttam Kumaran: Like, if the export is easy, the thing is, like,
124 00:13:15.690 ⇒ 00:13:20.879 Uttam Kumaran: the Salesforce ex… like, I would… I would, of course, vote for the top 3 over… over everything.
125 00:13:21.600 ⇒ 00:13:26.719 Uttam Kumaran: If possible, then I would like to get plain and, and…
126 00:13:27.560 ⇒ 00:13:30.919 Uttam Kumaran: But right now, there’s nothing in Stripe, right?
127 00:13:30.920 ⇒ 00:13:31.720 Caitlyn Vaughn: No.
128 00:13:31.720 ⇒ 00:13:34.789 Uttam Kumaran: Okay, so then, yeah, then plain is probably the only one.
129 00:13:35.680 ⇒ 00:13:41.820 Uttam Kumaran: that I care about, and then it’s up to Laura if she wants… if she’s like, I can’t survive without the QuickBooks, then…
130 00:13:42.330 ⇒ 00:13:45.749 Uttam Kumaran: Then that’s… that’s probably also, like, the other thing where…
131 00:13:46.030 ⇒ 00:13:50.650 Uttam Kumaran: I would move it down, unless she’s like, I need this one piece.
132 00:13:50.970 ⇒ 00:13:56.960 Uttam Kumaran: And then I… we use, like, we use QuickBooks, so I know how to go export stuff, so I can tell Thomas where to go grab.
133 00:13:56.960 ⇒ 00:13:58.500 Caitlyn Vaughn: Like, if she’s like, I just need…
134 00:13:59.110 ⇒ 00:14:01.940 Uttam Kumaran: P&L data, or I need ledger data, or something.
135 00:14:01.940 ⇒ 00:14:02.869 Caitlyn Vaughn: Yeah.
136 00:14:04.840 ⇒ 00:14:08.530 Caitlyn Vaughn: Yeah, and we also… we do have equals right now, so…
137 00:14:08.530 ⇒ 00:14:09.180 Uttam Kumaran: Okay.
138 00:14:09.370 ⇒ 00:14:11.540 Caitlyn Vaughn: We can probably, like…
139 00:14:12.070 ⇒ 00:14:21.250 Caitlyn Vaughn: let that just live in equals, and then port it over, you know, once we’re done. So maybe I’ll move that over. Well, maybe I’ll ask Laura what.
140 00:14:21.250 ⇒ 00:14:21.580 Uttam Kumaran: Okay.
141 00:14:22.550 ⇒ 00:14:28.649 Caitlyn Vaughn: And then you said Amplitude can go after Salesforce, we want now… Google Analytics.
142 00:14:30.140 ⇒ 00:14:37.299 Uttam Kumaran: Probably can come post feedback. What is… what is… a prospect list is just, like… it’s just like… yeah, you mentioned this yesterday, right?
143 00:14:38.690 ⇒ 00:14:50.569 Caitlyn Vaughn: I don’t know if this is something we would include in here, but the thinking is we’re gonna have a list of, like, our… basically our TAM, right? All of the companies that our sales team wants.
144 00:14:50.570 ⇒ 00:14:51.999 Uttam Kumaran: Like, a literal CSV.
145 00:14:52.780 ⇒ 00:14:54.669 Caitlyn Vaughn: Yeah, I think so.
146 00:14:55.260 ⇒ 00:14:59.759 Uttam Kumaran: I guess, like, but talk to me about why that doesn’t end up… why they said don’t put that in Salesforce.
147 00:14:59.760 ⇒ 00:15:04.829 Caitlyn Vaughn: Yeah, they think it will, like, muddy the waters in Salesforce. Like, they only want, like.
148 00:15:04.830 ⇒ 00:15:05.850 Uttam Kumaran: Oh, active stuff.
149 00:15:05.850 ⇒ 00:15:12.920 Caitlyn Vaughn: customers in Salesforce. So I also won’t have, like, self-serve customers in Salesforce.
150 00:15:13.950 ⇒ 00:15:15.390 Caitlyn Vaughn: I’ll have them in Stripe.
151 00:15:17.090 ⇒ 00:15:18.050 Uttam Kumaran: Oh.
152 00:15:20.420 ⇒ 00:15:22.259 Caitlyn Vaughn: Unless they should go somewhere else.
153 00:15:25.870 ⇒ 00:15:29.839 Uttam Kumaran: up for debate. I guess, like… It’s tough.
154 00:15:31.900 ⇒ 00:15:38.240 Uttam Kumaran: You sh- I-I-I would typically… I get to muddy the waters, but in Salesforce, you can, like.
155 00:15:38.590 ⇒ 00:15:52.489 Uttam Kumaran: just be like, these are enterprise customers versus not… like, I feel like longer term, you are… at some point, someone is gonna make a decision probably to do everything in Salesforce, or whatever the CRM is. Like, there’s no reason for your customer data to be…
156 00:15:53.190 ⇒ 00:15:58.800 Uttam Kumaran: in multiple CRMs, and in this situation, you can consider the CSV another CRM, right? Like, this…
157 00:15:59.360 ⇒ 00:16:01.380 Uttam Kumaran: So… I feel like it…
158 00:16:01.610 ⇒ 00:16:10.419 Uttam Kumaran: if it’s a fight worth fighting now, then I would vote to centralize it all. If it’s not, then, like, eventually I feel like that’s probably…
159 00:16:11.040 ⇒ 00:16:13.270 Uttam Kumaran: Eventually, that’s probably where it’s gonna go.
160 00:16:14.340 ⇒ 00:16:15.150 Caitlyn Vaughn: Okay.
161 00:16:15.920 ⇒ 00:16:17.210 Caitlyn Vaughn: So…
162 00:16:17.400 ⇒ 00:16:28.639 Caitlyn Vaughn: I think, like, it makes sense to have paid customers in Salesforce. I mean, we’re gonna have a free tier, though. Like, do you think it makes sense for the free tier people to have an account created in Salesforce?
163 00:16:28.880 ⇒ 00:16:35.390 Uttam Kumaran: But I guess it’s like, account created in Salesforce just means, like, there’s a record there. Like, you can filter those out, you know?
164 00:16:35.390 ⇒ 00:16:36.170 Caitlyn Vaughn: Yeah.
165 00:16:36.450 ⇒ 00:16:37.750 Uttam Kumaran: Like, you can ta- you can…
166 00:16:37.930 ⇒ 00:16:41.119 Uttam Kumaran: Tag them with the plan they’re in, and then be like.
167 00:16:41.460 ⇒ 00:16:44.300 Uttam Kumaran: I mean, look, Muddy the Waters is tough because it’s like…
168 00:16:44.780 ⇒ 00:16:49.659 Uttam Kumaran: Are there plans to move them into… like, when you go try to sell the next…
169 00:16:49.940 ⇒ 00:16:53.639 Uttam Kumaran: move people up or move to enterprise? Are you interested in seeing, like.
170 00:16:53.810 ⇒ 00:16:56.969 Uttam Kumaran: When they joined, what they’ve done so far.
171 00:16:58.140 ⇒ 00:17:04.260 Uttam Kumaran: you know, could be relevant. I think… I think at your size, it doesn’t… it’s, like, not critical, but I feel like it will end up there.
172 00:17:04.810 ⇒ 00:17:10.359 Uttam Kumaran: Longer term. Like, on our side, this is something that we’ll… we will have to, like.
173 00:17:10.720 ⇒ 00:17:13.949 Uttam Kumaran: Mesh together and marry to create, like, a…
174 00:17:14.400 ⇒ 00:17:18.209 Uttam Kumaran: consolidated list of, like, leads, right? Both from…
175 00:17:18.369 ⇒ 00:17:23.069 Uttam Kumaran: the prospect list and from Salesforce, so it will result in, like, some…
176 00:17:23.200 ⇒ 00:17:25.280 Uttam Kumaran: Code that we write to marry that.
177 00:17:25.750 ⇒ 00:17:29.379 Uttam Kumaran: As a data person, I would prefer it to all come from Salesforce.
178 00:17:29.380 ⇒ 00:17:30.599 Caitlyn Vaughn: Yeah, I mean…
179 00:17:30.600 ⇒ 00:17:37.340 Uttam Kumaran: I’m not buying… I guess, like, if it… I’m not buying the Muddy the waters, because you can just filter those out, like, you don’t…
180 00:17:38.360 ⇒ 00:17:39.690 Caitlyn Vaughn: Yeah, right now…
181 00:17:39.690 ⇒ 00:17:42.899 Uttam Kumaran: It does… it will show that, like, there’s 100,000, but…
182 00:17:42.900 ⇒ 00:17:43.260 Caitlyn Vaughn: Yeah.
183 00:17:43.260 ⇒ 00:17:47.609 Uttam Kumaran: Just be, like, which are the ones that are active customers, or which are the ones that are in this revenue range?
184 00:17:47.610 ⇒ 00:17:47.950 Caitlyn Vaughn: Yeah.
185 00:17:47.950 ⇒ 00:17:51.979 Uttam Kumaran: I guess you’re right, if the prospect lists in literally every company.
186 00:17:52.650 ⇒ 00:17:56.649 Uttam Kumaran: then it’s kind of, like, that’s kind of, like, OD, you know?
187 00:17:57.300 ⇒ 00:18:00.160 Uttam Kumaran: Okay. Alright, I’m… I understand, yeah.
188 00:18:00.420 ⇒ 00:18:03.699 Caitlyn Vaughn: Because they could be a prospect and never respond to a.
189 00:18:03.700 ⇒ 00:18:04.150 Uttam Kumaran: Never.
190 00:18:04.150 ⇒ 00:18:05.950 Caitlyn Vaughn: Fucking email from us.
191 00:18:07.080 ⇒ 00:18:07.660 Uttam Kumaran: Yeah.
192 00:18:08.310 ⇒ 00:18:13.680 Caitlyn Vaughn: For 10 years. Like, they could just never, you know, which will be a good chunk of them, like, to be expected.
193 00:18:13.680 ⇒ 00:18:22.170 Uttam Kumaran: Yeah, I guess this is interesting. I wonder if, like, the CRM hygiene rules is, like, we only add them to the CRM once we’ve gotten a response.
194 00:18:24.080 ⇒ 00:18:29.940 Caitlyn Vaughn: Probably once they’ve, like, signed up for the product, or yeah, well, gotten a response, maybe.
195 00:18:31.150 ⇒ 00:18:32.160 Caitlyn Vaughn: Hmm.
196 00:18:33.490 ⇒ 00:18:42.340 Uttam Kumaran: I don’t know, maybe it’s… yeah, maybe a thought. I feel like… you’re right, though, you shouldn’t have every company in your, like, every company that exists in your CRM.
197 00:18:49.880 ⇒ 00:18:51.790 Uttam Kumaran: Yeah, this is interesting. Yeah, I’m not…
198 00:18:52.370 ⇒ 00:18:58.580 Uttam Kumaran: I mean, I don’t mind it the way it is now. I think there will be some middle ground, though, longer term.
199 00:19:01.740 ⇒ 00:19:07.989 Caitlyn Vaughn: the approvals for CRM to be… I’m Phoenix…
200 00:19:09.360 ⇒ 00:19:15.190 Caitlyn Vaughn: Okay, wait, let me just write this message. Can we talk about what you want the rules for CRM to become Phoenix?
201 00:19:15.740 ⇒ 00:19:22.109 Caitlyn Vaughn: When does a prospect or customer…
202 00:19:25.400 ⇒ 00:19:28.919 Uttam Kumaran: And you can indicate that the primary use case here is, like.
203 00:19:29.550 ⇒ 00:19:39.500 Uttam Kumaran: what type of reporting will we need, you know? Like, I think I agree the fact that we don’t want every single… we don’t want just, like, every company you thought of just to enter there.
204 00:19:39.630 ⇒ 00:19:47.090 Uttam Kumaran: But if someone is going to ask a question about, like, who are we in active conversations with, or who is in which part of the funnel.
205 00:19:48.550 ⇒ 00:19:53.459 Uttam Kumaran: Maybe it’s… maybe it’s, like, the moment they hit the first part of, like, whatever the…
206 00:19:53.660 ⇒ 00:19:57.969 Uttam Kumaran: opportunity journey is. Like, I don’t know exactly what the Salesforce funnel is now.
207 00:19:58.140 ⇒ 00:20:10.260 Uttam Kumaran: Or it’s like a… or it’s like a minimum number of touchpoints, or… and again, I do agree with, like, you guys are a small team, so you want to focus on less, so maybe it’s restrictive now.
208 00:20:10.470 ⇒ 00:20:15.110 Uttam Kumaran: But, like, for example, we have, like, a sail… a snowflake or someone
209 00:20:15.520 ⇒ 00:20:23.140 Uttam Kumaran: they just have, like, thousands of salespeople, right? So they’re just, like, entering in as much as possible, and they… they want to know, did we talk to these guys 10 years ago?
210 00:20:23.720 ⇒ 00:20:25.740 Uttam Kumaran: So there is a difference in, like.
211 00:20:26.690 ⇒ 00:20:38.749 Uttam Kumaran: And they use Salesforce, too, right? So there’s a difference in just, like, the tool will let you do it all. I think it is helpful to know, like, what qualifies the minimum requirements for a new CRM entry.
212 00:20:51.160 ⇒ 00:20:53.190 Caitlyn Vaughn: Okay, that’s interesting.
213 00:21:01.510 ⇒ 00:21:08.530 Caitlyn Vaughn: Okay, that makes sense. I’m gonna shoot that over to Ryan, because this is definitely his area.
214 00:21:09.140 ⇒ 00:21:10.490 Caitlyn Vaughn: And then…
215 00:21:12.870 ⇒ 00:21:19.849 Caitlyn Vaughn: Oh, oh, yeah. Okay, so the point, or the reason why this prospect data is interesting to our team.
216 00:21:20.190 ⇒ 00:21:34.350 Caitlyn Vaughn: is mainly because we want to be able to track attribution over time. So, if, you know, if we’ve reached out, if we’ve marketed to people over time, and we’ve sent them sales emails, and…
217 00:21:34.540 ⇒ 00:21:39.880 Caitlyn Vaughn: You know, did some certain sequence on them, and then in 2 years, they convert to.
218 00:21:40.330 ⇒ 00:21:40.790 Uttam Kumaran: Yes.
219 00:21:40.790 ⇒ 00:21:46.570 Caitlyn Vaughn: Like, we want to be able to track that. And then the other thing is…
220 00:21:47.930 ⇒ 00:21:58.979 Caitlyn Vaughn: With this prospect list, if people sign up for the product, and they’re, like, doing certain actions in the product, then we want those
221 00:21:59.060 ⇒ 00:22:15.040 Caitlyn Vaughn: prospects on our list to, like, trigger certain actions, like marketing flows, like, you know, let’s say this list of 40,000 prospects is, like, the cream of the crop. We know they would convert into, you know, six-figure plus contracts, whereas everyone else would just fill
222 00:22:18.270 ⇒ 00:22:31.680 Caitlyn Vaughn: filter in and, like, maybe be a 6K, and they’re on this list, then we want them, like, once they sign up and they hit, you know, add workflow, oh, you’ve maxed out, like, we want that to start on a marketing flow for them.
223 00:22:32.790 ⇒ 00:22:33.850 Uttam Kumaran: Okay, okay.
224 00:22:36.910 ⇒ 00:22:45.170 Uttam Kumaran: Yeah, makes sense. I mean, we need to basically start to… I mean, are all your, marketing or transaction emails coming out of Salesforce?
225 00:22:46.150 ⇒ 00:22:48.310 Uttam Kumaran: Are you using, like, Klaviyo or something?
226 00:22:48.620 ⇒ 00:22:51.900 Caitlyn Vaughn: I think we’re using Customer I.O.
227 00:22:51.900 ⇒ 00:22:52.950 Uttam Kumaran: Okay, great.
228 00:22:53.950 ⇒ 00:22:57.689 Caitlyn Vaughn: And then possibly Smart Leads. There might be, like, a few.
229 00:22:58.300 ⇒ 00:23:05.610 Uttam Kumaran: So basically, like, the way our other folks architect this is, like, we compute some stuff in the warehouse, and then we send it back into Customer I.O.
230 00:23:05.760 ⇒ 00:23:16.139 Uttam Kumaran: So, in Customer I.O, the person sending emails has things like, how many times has this person logged in? You can start to do sequences, like welcome sequences.
231 00:23:16.230 ⇒ 00:23:23.929 Uttam Kumaran: started the process of creating the first workflow, but didn’t finish its sequence, and then also, like, reactivation, so that… whoever is…
232 00:23:24.010 ⇒ 00:23:32.149 Uttam Kumaran: going to do these, like, email campaigns. Their one immediate ask for them is going to be, like, how do we get this, like.
233 00:23:32.230 ⇒ 00:23:45.699 Uttam Kumaran: usage data into the platform by which you’re sending, so that they can start building smart campaigns, right? So if they want to use it first onboards, and they’re in a certain category, we’re going to put them in this email flow. So there’s, like, a
234 00:23:45.750 ⇒ 00:23:56.719 Uttam Kumaran: first 1 day, 3-day, 5-day, like, drip. Additionally, if a… let’s say a bunch of customers sign up for free, but then they go 6… they go, like, 30 days without doing anything in the platform.
235 00:23:56.750 ⇒ 00:24:12.240 Uttam Kumaran: I need a dimension in Customer I.O. that says last login date, and then I’m gonna build a campaign that says, take everybody’s last login date was more than 30 days ago, and then send them an email, like, and do that, like, once a month. So that’s, like, all the reactivation
236 00:24:12.460 ⇒ 00:24:14.399 Uttam Kumaran: Flows and things like that.
237 00:24:14.400 ⇒ 00:24:15.110 Caitlyn Vaughn: That’s typically…
238 00:24:15.110 ⇒ 00:24:16.349 Uttam Kumaran: what they ask for.
239 00:24:19.190 ⇒ 00:24:20.390 Caitlyn Vaughn: Yeah.
240 00:24:21.090 ⇒ 00:24:24.990 Uttam Kumaran: I don’t know if anyone’s managing email right now, that’ll be what that person…
241 00:24:26.030 ⇒ 00:24:29.710 Uttam Kumaran: That’ll be what that person ends up caring about, for sure, in customer I.O.
242 00:24:31.320 ⇒ 00:24:35.329 Caitlyn Vaughn: Yes, we will have… so, Lev… you know Lev.
243 00:24:35.490 ⇒ 00:24:36.170 Uttam Kumaran: Yeah.
244 00:24:36.170 ⇒ 00:24:41.520 Caitlyn Vaughn: he’s, like, our BDR manager, and he will be in charge of, like, a lot of the,
245 00:24:42.630 ⇒ 00:24:55.949 Caitlyn Vaughn: like, growth hacking kind of stuff that way. I will be in charge of, like, product trigger growth, right? Like, within the product. How do we get people to expand so someone does this, doesn’t show up for…
246 00:24:56.270 ⇒ 00:25:09.549 Caitlyn Vaughn: you know, 30 days is hitting their usage limits, like, what do we do in the product to, like, help them expand and grow? And then Stan will be in charge of, like, the actual, like, marketing macro kind of stuff. Okay.
247 00:25:10.030 ⇒ 00:25:19.340 Caitlyn Vaughn: So, there’s a few stakeholders on our team that will care about that kind of stuff. My other ask is, I mean, we have… so I’ve, like, looped a bunch of people from…
248 00:25:22.620 ⇒ 00:25:38.650 Caitlyn Vaughn: from our team in on this. As I’m, like, thinking through this, I wonder what should or needs to be included in something like this versus, like, shouldn’t I be included? I don’t know if there’s, like, any pieces of data where, like, there’s no need for that to be part of the, like, data pipeline.
249 00:25:39.370 ⇒ 00:25:41.290 Uttam Kumaran: Oh, yeah, I mean,
250 00:25:43.230 ⇒ 00:25:51.020 Uttam Kumaran: I mean, ultimately, if someone is comfortable with the reporting they’re getting out of the tool.
251 00:25:51.330 ⇒ 00:26:03.390 Uttam Kumaran: and there is no other stakeholder that’s like, I need this combined, I don’t… we shouldn’t… we shouldn’t spend time bringing it in. Like, even the QuickBooks example, I want to understand, like, what it is that they need out of QuickBooks.
252 00:26:04.440 ⇒ 00:26:07.120 Uttam Kumaran: And if, like, truly what they’re looking for is, like.
253 00:26:07.440 ⇒ 00:26:11.510 Uttam Kumaran: a combined way to get it, or we can just help them build the report in QuickBooks.
254 00:26:11.700 ⇒ 00:26:29.629 Uttam Kumaran: Right? Like, the last thing you want us to do is, like, build… bring stuff in just to display what you could have already gotten out of the tool. The real alpha here is, like, we’re combining Postgres, Hyperline, Salesforce together, and then also, once Click House comes in, like, those have to be combined to tell you that, like.
255 00:26:29.890 ⇒ 00:26:37.069 Uttam Kumaran: end-to-end customer journey. But if there are pieces of this that we can get directly out of the tool, we will…
256 00:26:37.290 ⇒ 00:26:46.100 Uttam Kumaran: we will try to keep that there. For example, GA offers a lot of reporting, so we’re not… I don’t want to mimic everything that’s in Google Analytics.
257 00:26:46.910 ⇒ 00:26:58.189 Uttam Kumaran: So if someone’s like, I just want to pull traffic by day, that’s fine, just go to GA and do that. But if there is ways… if we want to look at traffic, and then new customer ads.
258 00:26:58.350 ⇒ 00:27:00.759 Uttam Kumaran: And then, like, segment by plan type.
259 00:27:01.080 ⇒ 00:27:06.110 Uttam Kumaran: we have to see that whole journey. You can’t do that in Google Analytics, right?
260 00:27:07.400 ⇒ 00:27:13.619 Uttam Kumaran: So that’s the real prioritization. The, like, pure play data teams will tell you, like, get everything in here, whatever, but, like.
261 00:27:13.680 ⇒ 00:27:27.520 Uttam Kumaran: I’m more interested in, like, the more stuff we add, the more stuff we have to maintain, and there is… for tools that have rich reporting, like Google Analytics, if someone is looking for Google Analytics data, I would rather work with them to configure a report there.
262 00:27:28.200 ⇒ 00:27:35.129 Uttam Kumaran: But if they’re looking at, like, hey, I want to look at traffic from this source, and how people converted, and what they did in the platform after.
263 00:27:35.290 ⇒ 00:27:35.740 Caitlyn Vaughn: Yeah.
264 00:27:35.740 ⇒ 00:27:42.339 Uttam Kumaran: okay, that’s GA plus, like, hyperline or amplitude plus the Postgres data, right? So…
265 00:27:42.340 ⇒ 00:27:46.319 Caitlyn Vaughn: Yeah, that is true, because attribution over time is an.
266 00:27:46.320 ⇒ 00:27:55.579 Uttam Kumaran: So it is use case by use case. Our team will do a good job at, like, on… we have to… we’ll understand, like, what is a reporting use case, and then be like, oh, I can get you that directly out of, like, GA.
267 00:27:55.580 ⇒ 00:27:56.639 Caitlyn Vaughn: Yeah, like, we don’t need.
268 00:27:56.640 ⇒ 00:28:07.609 Uttam Kumaran: There’s other, like, operational things, for example, a lot of folks, like, we do a lot of Shopify work, and I’m like, if you’re doing… just looking up an order, just go into Shopify and look up the order, like, you… that’s…
269 00:28:07.750 ⇒ 00:28:18.209 Uttam Kumaran: that’s great to do there, but if you’re, like, combining Shopify data with Amazon, and looking at, like, the traffic that came, like, that’s only something you can do in, like, a
270 00:28:18.490 ⇒ 00:28:19.649 Uttam Kumaran: This sort of setup.
271 00:28:19.890 ⇒ 00:28:22.060 Caitlyn Vaughn: Okay, cool.
272 00:28:22.180 ⇒ 00:28:25.499 Caitlyn Vaughn: Yeah, because I wonder for prospect data, if it’s, like…
273 00:28:26.550 ⇒ 00:28:30.760 Caitlyn Vaughn: even worth us having that in here, you know?
274 00:28:31.520 ⇒ 00:28:32.590 Caitlyn Vaughn: Like…
275 00:28:34.930 ⇒ 00:28:50.570 Caitlyn Vaughn: I guess, attribution over time, or like… I mean, really, the goal of every head of marketing is, like, what is the sequence that we need to do from a marketing perspective to, like, convert prospects the highest? And it’s like…
276 00:28:50.710 ⇒ 00:28:56.940 Caitlyn Vaughn: a mystery, right? You’re like, oh, do I need 3 email sequences, plus a cold call, plus a…
277 00:28:57.360 ⇒ 00:29:04.680 Caitlyn Vaughn: like, LinkedIn campaign plus a champion, like, it is so hard to figure that out. So maybe.
278 00:29:04.680 ⇒ 00:29:10.939 Uttam Kumaran: Well, you’re looking for, like, this is another thing that we’ve done, you look for, like, what is a golden event, or, like, set of events, and amplitude is what…
279 00:29:11.200 ⇒ 00:29:14.979 Uttam Kumaran: Amplitude is gonna solve… answer that for you in terms of product usage. Like.
280 00:29:14.980 ⇒ 00:29:15.410 Caitlyn Vaughn: Hmm.
281 00:29:15.410 ⇒ 00:29:25.220 Uttam Kumaran: what indications and product usage show higher LTV, higher, like, further higher product usage? Like, what indications show the propensity to do that?
282 00:29:25.470 ⇒ 00:29:31.950 Uttam Kumaran: That blown up is, like, what you’re talking about, which is, like, what’s the mix of the product usage, emails, like.
283 00:29:32.060 ⇒ 00:29:38.139 Uttam Kumaran: The… where they’re from, or the industry they’re in, that all capitalizes into, like, a great default customer.
284 00:29:38.870 ⇒ 00:29:54.620 Uttam Kumaran: answering that type of question is really going to be only possible, like, in the warehouse. Amplitude, though, I think you’re going to want to rely on initially, because you’re going to have very, very rich data on what your customers are doing, and using those as indicators for, like.
285 00:29:54.620 ⇒ 00:29:59.369 Uttam Kumaran: This customer is clearly an enterprise. They just… they signed up and they added, like, 30 people.
286 00:29:59.370 ⇒ 00:30:04.290 Uttam Kumaran: They send it for free, added 30 people, like, someone needs to get on the phone with them, right?
287 00:30:04.840 ⇒ 00:30:12.770 Uttam Kumaran: those are the… and then also, like, you’re gonna… you’re also gonna use Ample2 to find out, like, hey, our, like, sign-up page, there’s, like, tons of friction, like, there’s, like, huge drop-off.
288 00:30:13.010 ⇒ 00:30:13.400 Caitlyn Vaughn: Yeah.
289 00:30:13.400 ⇒ 00:30:19.060 Uttam Kumaran: what should we do there, right? So, there’s all these benchmarks, typically, on these, like, free…
290 00:30:19.200 ⇒ 00:30:37.250 Uttam Kumaran: like, sign up for an account, like, conversion rates, all of that is, like, what you’re going to want to make sure that you’re within the right ranges for. And then also, as more product people are growing default, and they’re releasing features, you want them to be able to report on whether the feature is getting adopted.
291 00:30:37.620 ⇒ 00:30:49.650 Uttam Kumaran: A good statistic is, like, 80% of features don’t get used as, like, a fair benchmark in our industry, which is such a shame, because a lot of work goes into that. And oftentimes.
292 00:30:49.660 ⇒ 00:30:58.850 Uttam Kumaran: in all the product companies I’ve worked at, the product managers never have the tools to, like, show whether their product is actually, like, working.
293 00:30:58.970 ⇒ 00:31:07.569 Uttam Kumaran: And this is what, like, sets, like, Google and these guys apart, is they have incredible, like, usage analytics on, like, hey, we released this product, and it’s, like.
294 00:31:07.670 ⇒ 00:31:15.639 Uttam Kumaran: we’re able to A-B test stuff and, like, start to grow the usage, versus, like, we shipped this, we, like, wanted this 6 months ago, I think, and, like.
295 00:31:16.190 ⇒ 00:31:19.390 Uttam Kumaran: I don’t know how it’s… I don’t know how it’s doing, right? So…
296 00:31:20.190 ⇒ 00:31:35.180 Uttam Kumaran: for your team members that are shipping new features, like, giving them the ability to actually go into Amplitude and see, like, okay, on day one, how many people are using it? On day 7, how many people are using it? How did the usage affect their, like, longer-term retention? So it’s all things that they’re gonna want, and, like, that’s just gonna compound the…
297 00:31:35.390 ⇒ 00:31:36.450 Uttam Kumaran: the product.
298 00:31:38.490 ⇒ 00:31:43.959 Uttam Kumaran: So the amplitude, I feel like we have to do after Phoenix, I think everybody’s gonna be like.
299 00:31:44.100 ⇒ 00:31:47.340 Uttam Kumaran: Oh, shit, we had, like, really limited visibility into, like.
300 00:31:47.960 ⇒ 00:31:55.909 Uttam Kumaran: Not exactly, like, did we create a workflow and stuff, but the transitions between those… those and, like, a ton of other events, you know?
301 00:31:55.910 ⇒ 00:31:56.530 Caitlyn Vaughn: Yeah.
302 00:31:56.680 ⇒ 00:31:57.320 Uttam Kumaran: Yeah.
303 00:31:57.730 ⇒ 00:32:03.199 Caitlyn Vaughn: Damn. Honestly, I didn’t even consider that, like, main part of this entire project.
304 00:32:03.200 ⇒ 00:32:11.420 Uttam Kumaran: I mean, it’s easy to just be like, oh, people are paying, people are doing workflows, but you may be sitting on just, like, a
305 00:32:11.570 ⇒ 00:32:13.910 Uttam Kumaran: Crazy, annoying, like, friction point.
306 00:32:13.910 ⇒ 00:32:16.919 Caitlyn Vaughn: And it’s all about, like, new feature release, right? Like…
307 00:32:16.950 ⇒ 00:32:23.640 Uttam Kumaran: How do we know… how do we release a new feature, see that it’s getting adopted, see that it’s leading to positive results?
308 00:32:23.640 ⇒ 00:32:24.090 Caitlyn Vaughn: Yeah.
309 00:32:24.230 ⇒ 00:32:32.749 Uttam Kumaran: Back to, like, when you guys go to get great product people, like, really hammer on, like, how they’re thinking about using data to drive their, like.
310 00:32:32.940 ⇒ 00:32:33.320 Caitlyn Vaughn: Yeah.
311 00:32:33.320 ⇒ 00:32:38.950 Uttam Kumaran: understanding of, like, product adoption. There’s a lot of people that can just come up with, like, oh, this is a cool feature, and, like, ship it.
312 00:32:39.130 ⇒ 00:32:39.500 Caitlyn Vaughn: Yeah.
313 00:32:40.530 ⇒ 00:32:55.969 Uttam Kumaran: Like, I feel like that’s, like, table stakes. Like, you guys are already great at that. You have some smart, like, go-to-market people, but it’s like, are these actually working, and are they leading to positive cloud outcomes? Like, are we seeing that the combination of these features, now people are like, I love this tool, let’s go enterprise, right?
314 00:32:55.970 ⇒ 00:32:56.320 Caitlyn Vaughn: Appreciate it.
315 00:32:56.320 ⇒ 00:32:57.020 Uttam Kumaran: like that.
316 00:32:57.210 ⇒ 00:32:58.860 Caitlyn Vaughn: Yeah. Totally.
317 00:32:59.240 ⇒ 00:33:00.360 Caitlyn Vaughn: Okay.
318 00:33:00.850 ⇒ 00:33:02.440 Caitlyn Vaughn: Yeah, that’s a good…
319 00:33:02.730 ⇒ 00:33:09.039 Uttam Kumaran: But we can only do that after the new product, or, like, at least until that’s in, like, basically staging, so you can start
320 00:33:09.200 ⇒ 00:33:10.959 Uttam Kumaran: instrumenting the events.
321 00:33:10.960 ⇒ 00:33:20.279 Caitlyn Vaughn: Yeah, because we’ll have to, like, move over to ClickHouse from Postgres, but then we’ll also have to, like, rebuild out all the events on Amplitude on ClickHouse, right?
322 00:33:21.710 ⇒ 00:33:26.120 Uttam Kumaran: Yeah. I mean, but it won’t be in Click House. It will be.
323 00:33:26.120 ⇒ 00:33:27.150 Caitlyn Vaughn: On the new year.
324 00:33:27.150 ⇒ 00:33:31.719 Uttam Kumaran: on the new, yeah, on the new application. So we’ll go through and basically
325 00:33:32.080 ⇒ 00:33:37.980 Uttam Kumaran: Work and say, like, here’s the naming conventions, here’s how the front-end team should go.
326 00:33:38.120 ⇒ 00:33:39.790 Uttam Kumaran: Crack, and then we, like.
327 00:33:40.680 ⇒ 00:33:46.850 Uttam Kumaran: I mean, again, the core thing to look at is, like, the sign-up flow, the payment flows, the…
328 00:33:47.000 ⇒ 00:33:50.920 Uttam Kumaran: Whatever the first, like, workflow creation, invite, like.
329 00:33:51.580 ⇒ 00:33:55.850 Uttam Kumaran: Pick, like, the 5 to 10, like, 4 core essential paths.
330 00:33:55.950 ⇒ 00:33:57.220 Uttam Kumaran: To, like, map out.
331 00:33:57.530 ⇒ 00:34:04.809 Caitlyn Vaughn: Yeah, like, if people do these things, then they’re… they’ve hit value, or, like, they have successfully onboarded.
332 00:34:05.120 ⇒ 00:34:06.159 Uttam Kumaran: Yeah, yeah.
333 00:34:06.620 ⇒ 00:34:12.290 Caitlyn Vaughn: Okay, cool. That makes sense. Okay, I feel good about that. I’m gonna get…
334 00:34:12.719 ⇒ 00:34:17.960 Caitlyn Vaughn: the lowdown from everyone, I just sent out that sheet, so they should start to fill that out.
335 00:34:17.960 ⇒ 00:34:18.550 Uttam Kumaran: Okay.
336 00:34:20.780 ⇒ 00:34:24.479 Caitlyn Vaughn: Next week is Christmas, so I’m off that whole week.
337 00:34:24.480 ⇒ 00:34:25.050 Uttam Kumaran: Gray.
338 00:34:25.050 ⇒ 00:34:29.119 Caitlyn Vaughn: Tomorrow’s my birthday.
339 00:34:29.310 ⇒ 00:34:32.710 Uttam Kumaran: Oh, really? Happy birthday! Wow, let’s go.
340 00:34:32.710 ⇒ 00:34:34.130 Caitlyn Vaughn: That’s the plan? I know.
341 00:34:36.330 ⇒ 00:34:43.789 Caitlyn Vaughn: I have a hair appointment in the morning, and then Nick booked us massages at 2, and then I have a writing lesson in the evening, so…
342 00:34:44.690 ⇒ 00:34:45.540 Uttam Kumaran: Perfect day.
343 00:34:45.540 ⇒ 00:34:46.620 Caitlyn Vaughn: I know.
344 00:34:46.620 ⇒ 00:34:47.400 Uttam Kumaran: Perfect day.
345 00:34:47.409 ⇒ 00:34:54.119 Caitlyn Vaughn: I would like to, bully some of my friends into playing Catan, maybe this weekend, so let me know if you want to play.
346 00:34:54.120 ⇒ 00:34:58.490 Uttam Kumaran: I would love to play, yeah, let me… oh, actually, I’m gonna be in the Bay Area.
347 00:34:58.490 ⇒ 00:35:01.210 Caitlyn Vaughn: Dude, Chungen, everybody is leaving on Friday!
348 00:35:01.370 ⇒ 00:35:10.119 Uttam Kumaran: I’m good, I’m here for Christmas, but Robert and his wife, my business partner, they’re in SF, so I was like, we should all go meet up.
349 00:35:10.120 ⇒ 00:35:10.710 Caitlyn Vaughn: Yeah.
350 00:35:10.930 ⇒ 00:35:11.939 Uttam Kumaran: And, yeah, so…
351 00:35:11.940 ⇒ 00:35:16.270 Caitlyn Vaughn: I think I’m rescheduling my birthday for June 18th, so keep it open.
352 00:35:16.500 ⇒ 00:35:20.569 Uttam Kumaran: Okay, wait, what? Oh, 6 months? 6-month birthday, okay.
353 00:35:20.570 ⇒ 00:35:22.300 Caitlyn Vaughn: Yeah, it’s my half birthday.
354 00:35:22.660 ⇒ 00:35:32.250 Uttam Kumaran: Yeah, usually I’m, like, I celebrate quarter, half, and real birthday. So whenever I, like, need something, I’m like, let’s go get ice cream, like, it’s my quarter birthday.
355 00:35:32.250 ⇒ 00:35:32.630 Caitlyn Vaughn: Yeah.
356 00:35:32.630 ⇒ 00:35:34.050 Uttam Kumaran: Celebrate.
357 00:35:34.120 ⇒ 00:35:45.219 Caitlyn Vaughn: Wait, that’s so funny. In college, all my roommates and I would celebrate every holiday and half holiday. So we’d be like, it’s half Easter, it’s half Christmas, it’s half.
358 00:35:45.220 ⇒ 00:35:46.220 Uttam Kumaran: Yeah, I guess so.
359 00:35:46.220 ⇒ 00:35:46.990 Caitlyn Vaughn: It’s a day.
360 00:35:46.990 ⇒ 00:36:01.539 Uttam Kumaran: there’s these months where, like, nothing happens, and, like, you always can fill that. Like, I was… I was talking… I was like, what’s the next holiday? Like, I guess February, and then there’s, like, yeah, April. I’m like, we’re not gonna decorate or anything until then, like… Yeah.
361 00:36:01.540 ⇒ 00:36:05.519 Caitlyn Vaughn: Hell no. No, it’s half… it’s half Thanksgiving.
362 00:36:05.520 ⇒ 00:36:07.190 Uttam Kumaran: Yeah, yeah, exactly.
363 00:36:08.130 ⇒ 00:36:14.270 Caitlyn Vaughn: I’m dead. Okay, amazing. And then, Mustafa, how is the testing going?
364 00:36:14.890 ⇒ 00:36:31.979 Mustafa Raja: Yeah, so, I have the comparison doc ready for all of them, apart from the harmonic one. I’m still waiting on the data. I’m going to also start a document, so we have, better understanding on how to look into
365 00:36:31.980 ⇒ 00:36:35.540 Mustafa Raja: The data fields, the sheets that I… that we have.
366 00:36:36.010 ⇒ 00:36:38.210 Mustafa Raja: Yeah.
367 00:36:38.370 ⇒ 00:36:39.720 Mustafa Raja: That’s pretty much it.
368 00:36:40.660 ⇒ 00:36:48.370 Caitlyn Vaughn: Okay, amazing. I think we have, like, a handful of… Vendors still to test, right?
369 00:36:51.100 ⇒ 00:36:52.100 Mustafa Raja: No, I think just…
370 00:36:52.100 ⇒ 00:36:52.890 Uttam Kumaran: Harmonic.
371 00:36:53.020 ⇒ 00:36:53.780 Uttam Kumaran: Right?
372 00:36:55.040 ⇒ 00:36:58.909 Mustafa Raja: Yeah, because we canceled the ocean and the other one, right?
373 00:36:59.500 ⇒ 00:37:01.820 Caitlyn Vaughn: Did we test Apollo in Clearbit?
374 00:37:01.820 ⇒ 00:37:05.079 Mustafa Raja: Yeah, yeah, yeah, we did.
375 00:37:05.670 ⇒ 00:37:06.620 Caitlyn Vaughn: Let’s go.
376 00:37:09.090 ⇒ 00:37:18.710 Caitlyn Vaughn: Docs… your bit… your guys’ document, database is a little crazy. You guys gonna get full.
377 00:37:18.710 ⇒ 00:37:19.240 Uttam Kumaran: Yes.
378 00:37:20.140 ⇒ 00:37:21.400 Uttam Kumaran: Yes, we do.
379 00:37:23.400 ⇒ 00:37:36.649 Caitlyn Vaughn: No funding data. Also, all of these reports, look so aggressive. Every time I look through them, it’s like, critical, do not implement! This is a disaster! And so…
380 00:37:39.930 ⇒ 00:37:50.080 Caitlyn Vaughn: I’ve, like, had a lot of people ask for them, words from it. I’m like, here you go. And they’re still kind of aggressive, but I kind of like it, because I want them to feel bad about their data, you know?
381 00:37:50.580 ⇒ 00:37:57.700 Uttam Kumaran: That’s funny. Yeah, I mean, you guys have high… I mean, it’s going into the product, which is, like, a lot different use case than we’re used to.
382 00:37:57.900 ⇒ 00:38:03.769 Uttam Kumaran: But… Yeah, I mean, if the… if the data sucks, and the latency sucks, you’re…
383 00:38:04.380 ⇒ 00:38:04.890 Mustafa Raja: It’s okay.
384 00:38:04.890 ⇒ 00:38:06.310 Uttam Kumaran: direct reflection on y’all, so…
385 00:38:06.310 ⇒ 00:38:14.690 Mustafa Raja: Yeah, Clearbit had, pretty good company data, but the people, they don’t have much about that.
386 00:38:14.900 ⇒ 00:38:16.229 Caitlyn Vaughn: Really? Was bad?
387 00:38:16.230 ⇒ 00:38:21.230 Mustafa Raja: Yeah, the coverage is pretty bad, and then the fields that they are offering, they’re not offering much.
388 00:38:22.550 ⇒ 00:38:26.510 Caitlyn Vaughn: Good to know, and they don’t have any funding data?
389 00:38:28.210 ⇒ 00:38:28.900 Mustafa Raja: Yeah.
390 00:38:29.840 ⇒ 00:38:30.760 Caitlyn Vaughn: What?
391 00:38:31.770 ⇒ 00:38:37.170 Caitlyn Vaughn: Field coverage. Full name… oh, wow. They really shit the bed.
392 00:38:38.580 ⇒ 00:38:41.520 Caitlyn Vaughn: Well, good thing they’re closing in a few years.
393 00:38:42.660 ⇒ 00:38:43.480 Uttam Kumaran: Shift.
394 00:38:44.410 ⇒ 00:38:45.730 Caitlyn Vaughn: Are they really?
395 00:38:46.040 ⇒ 00:38:49.319 Caitlyn Vaughn: Yeah, they were purchased by HubSpot.
396 00:38:49.680 ⇒ 00:38:53.819 Uttam Kumaran: And they… actually, maybe not their core.
397 00:38:54.150 ⇒ 00:38:57.230 Caitlyn Vaughn: But they’re in… they’re web intent data.
398 00:38:58.330 ⇒ 00:38:59.149 Caitlyn Vaughn: They don’t sell it.
399 00:38:59.150 ⇒ 00:39:00.250 Uttam Kumaran: Oh, okay.
400 00:39:01.320 ⇒ 00:39:03.930 Caitlyn Vaughn: But it’s fine, because we have a 3-year contract.
401 00:39:06.950 ⇒ 00:39:10.889 Caitlyn Vaughn: Okay, cool, so Harmonic is the only other one.
402 00:39:11.600 ⇒ 00:39:15.079 Caitlyn Vaughn: Oh, actually, he might have emailed me back. Let me look on that right now.
403 00:39:15.350 ⇒ 00:39:16.110 Mustafa Raja: Okay.
404 00:39:17.170 ⇒ 00:39:20.430 Mustafa Raja: Yeah, once I have that, I’ll, I’ll get that ready.
405 00:39:30.350 ⇒ 00:39:32.429 Caitlyn Vaughn: He did!
406 00:39:32.700 ⇒ 00:39:39.629 Caitlyn Vaughn: The sample is split into 7 JSON files with 50 companies each. Well, that’s fucking annoying. Okay.
407 00:39:40.160 ⇒ 00:39:46.850 Caitlyn Vaughn: Alright, I’m forwarding this to you right now. Forward… Snap.
408 00:39:47.350 ⇒ 00:39:49.159 Caitlyn Vaughn: Is it just Mustafa?
409 00:39:50.870 ⇒ 00:39:54.009 Uttam Kumaran: Mustafa.raja, R-A-J.
410 00:39:54.320 ⇒ 00:39:55.750 Caitlyn Vaughn: R-H-A-A.
411 00:39:56.180 ⇒ 00:40:01.580 Caitlyn Vaughn: Happy Forge… dot com… .co.ai?
412 00:40:02.120 ⇒ 00:40:02.670 Uttam Kumaran: ai.
413 00:40:03.470 ⇒ 00:40:06.380 Caitlyn Vaughn: Nice, you guys, that the AI…
414 00:40:07.020 ⇒ 00:40:09.440 Uttam Kumaran: See, that was all my foresight from 3 years ago.
415 00:40:09.620 ⇒ 00:40:10.989 Caitlyn Vaughn: Hey, that’s pretty good.
416 00:40:12.940 ⇒ 00:40:16.150 Caitlyn Vaughn: Okay… missing domain.
417 00:40:16.580 ⇒ 00:40:19.229 Caitlyn Vaughn: What is I-N-L-I-F?
418 00:40:24.940 ⇒ 00:40:26.150 Uttam Kumaran: Where do you see that?
419 00:40:37.490 ⇒ 00:40:38.240 Caitlyn Vaughn: Missing…
420 00:40:38.240 ⇒ 00:40:39.810 Uttam Kumaran: I-N-L-I-F.
421 00:40:41.220 ⇒ 00:40:42.809 Mustafa Raja: This could be a company.
422 00:40:44.520 ⇒ 00:40:45.210 Uttam Kumaran: Oh.
423 00:40:45.520 ⇒ 00:40:46.350 Caitlyn Vaughn: What does this mean?
424 00:40:46.350 ⇒ 00:40:47.470 Mustafa Raja: Yeah, because we.
425 00:40:47.470 ⇒ 00:40:52.310 Uttam Kumaran: Oh, INLIF Limited is missing, they’re don’.
426 00:40:52.310 ⇒ 00:40:53.160 Mustafa Raja: I mean?
427 00:40:53.400 ⇒ 00:40:54.170 Mustafa Raja: Yeah.
428 00:40:54.770 ⇒ 00:40:55.730 Caitlyn Vaughn: Beautiful.
429 00:40:57.030 ⇒ 00:41:00.190 Uttam Kumaran: The sample is split into 7 JSON files.
430 00:41:02.710 ⇒ 00:41:06.999 Uttam Kumaran: Oh, we provided them with this. Okay, okay, cool. Yeah, we can… we should just work with this.
431 00:41:07.130 ⇒ 00:41:07.690 Mustafa Raja: Yeah.
432 00:41:08.710 ⇒ 00:41:14.399 Caitlyn Vaughn: Guys, why is there duplicate domains? We need to adjust all of our test data.
433 00:41:14.400 ⇒ 00:41:15.430 Mustafa Raja: Yeah, so…
434 00:41:18.230 ⇒ 00:41:20.540 Uttam Kumaran: I mean, Wix.com is a company.
435 00:41:22.590 ⇒ 00:41:24.390 Caitlyn Vaughn: It’s not unsupported.
436 00:41:24.890 ⇒ 00:41:27.480 Uttam Kumaran: Yeah, why is this unsupported? They are, like, a company.
437 00:41:28.200 ⇒ 00:41:28.599 Caitlyn Vaughn: What is this?
438 00:41:28.600 ⇒ 00:41:29.430 Uttam Kumaran: But I guess Harmonica.
439 00:41:29.430 ⇒ 00:41:32.200 Mustafa Raja: You might not have any data on that, or something.
440 00:41:33.710 ⇒ 00:41:37.920 Mustafa Raja: Yeah, they say the website provider platforms around Enrich.
441 00:41:38.570 ⇒ 00:41:39.690 Uttam Kumaran: Yeah, okay.
442 00:41:41.880 ⇒ 00:41:42.640 Caitlyn Vaughn: Cool.
443 00:41:47.120 ⇒ 00:41:51.279 Caitlyn Vaughn: They split it into 7 files. I don’t even see a single file, though.
444 00:41:57.170 ⇒ 00:41:58.639 Caitlyn Vaughn: Should download it.
445 00:42:01.880 ⇒ 00:42:07.040 Caitlyn Vaughn: Okay, anyways, I think that’s the last one, and then…
446 00:42:08.200 ⇒ 00:42:13.549 Caitlyn Vaughn: Your master sheet, that’s in Google Sheets, right? Pretty sure I’ve asked for it, like, 3 times.
447 00:42:14.290 ⇒ 00:42:15.670 Caitlyn Vaughn: And you’ve sent it to me.
448 00:42:15.670 ⇒ 00:42:18.550 Uttam Kumaran: So I’m gonna work on that, and then I’m also gonna…
449 00:42:18.840 ⇒ 00:42:24.689 Uttam Kumaran: I can work on a larger, like, architecture diagram with the stuff that you mentioned, too.
450 00:42:25.320 ⇒ 00:42:30.050 Uttam Kumaran: and… what’s helpful… what else is helpful for, like, planning? I mean, we can go…
451 00:42:30.470 ⇒ 00:42:41.599 Uttam Kumaran: further, like, I mean, it just depends on, like, what you guys… what you need to kind of tell the story and, like, show the vision. Like, we can do, like, a Gantt chart or something to kind of show broader
452 00:42:41.930 ⇒ 00:42:45.799 Uttam Kumaran: like, what the different phases are. We’re now getting… this type of work is actually…
453 00:42:46.230 ⇒ 00:42:54.630 Uttam Kumaran: this is what we do, like, every day, which is, like, establishing ETL, landing data into the warehouse, doing the data modeling, and then making in BI.
454 00:42:54.810 ⇒ 00:42:56.690 Uttam Kumaran: We went a little bit…
455 00:42:57.420 ⇒ 00:43:10.260 Uttam Kumaran: I mean, we can go backwards, but we did… we drove towards BI, and then we’re, like, coming back and doing it, which is good, like, I think you’re now seeing the… the vision, so I can put together, like, what generally the…
456 00:43:10.910 ⇒ 00:43:14.389 Uttam Kumaran: Like, timelines on some of this could look like, and just sort of put a plan together.
457 00:43:14.860 ⇒ 00:43:21.200 Caitlyn Vaughn: Okay, yeah, that sounds good. I feel like you guys are never the blocker on timelines, though, it’s always us.
458 00:43:21.530 ⇒ 00:43:22.510 Uttam Kumaran: Yeah, so…
459 00:43:22.510 ⇒ 00:43:24.710 Caitlyn Vaughn: Put a timeline together, and then…
460 00:43:25.190 ⇒ 00:43:28.410 Caitlyn Vaughn: Like, it just could not happen on our side, you know?
461 00:43:28.410 ⇒ 00:43:34.340 Uttam Kumaran: Okay, so then I think mainly the… I mean, the best way to think about it is really that diagram is, like, we’re just going left to right.
462 00:43:35.010 ⇒ 00:43:44.099 Uttam Kumaran: And what I’ll help outline is, like, what are the data marts that we’re driving towards? But really, this is going to be driven by Demolade working directly with stakeholders to, like.
463 00:43:44.100 ⇒ 00:43:44.460 Caitlyn Vaughn: Yeah.
464 00:43:44.460 ⇒ 00:43:50.819 Uttam Kumaran: what they need. So one thing I told him, he just… he was gonna join, he… I think he had a migraine, so he stepped out.
465 00:43:51.520 ⇒ 00:44:01.180 Uttam Kumaran: But I told him, like, yo, go… go build a relationship with Stan, with Lev, and, like, Ryan, and just see what they need, but then I’ll… we’ll bring it back to you to be like.
466 00:44:01.390 ⇒ 00:44:02.480 Uttam Kumaran: what’s important?
467 00:44:02.700 ⇒ 00:44:03.110 Caitlyn Vaughn: Yeah.
468 00:44:03.400 ⇒ 00:44:06.680 Uttam Kumaran: You know? Because I told them that, like, look, everybody’s super busy.
469 00:44:07.070 ⇒ 00:44:12.699 Uttam Kumaran: Caitlin knows somewhat of what everybody needs. They may not even really know what they need.
470 00:44:13.230 ⇒ 00:44:22.709 Uttam Kumaran: So go, like, meet them and put a face to a name and understand, like, what their goals are for reporting for their thing, and then we’ll come back and qualify to you on, like, priority.
471 00:44:23.840 ⇒ 00:44:27.170 Uttam Kumaran: You know, so I think that should take a little bit of a load off of, like.
472 00:44:27.320 ⇒ 00:44:33.500 Uttam Kumaran: you having to be the intermediary, like, we’ll go fish, and then be like, wait, what are… which ones do we want to tackle?
473 00:44:33.710 ⇒ 00:44:40.010 Uttam Kumaran: I do think that most of the answers, most of the questions that I heard in the last call are going to be answered by those top 3 sources, though.
474 00:44:40.220 ⇒ 00:44:45.700 Caitlyn Vaughn: Okay, cool. Also, the best person for you guys to connect with is gonna be Laura.
475 00:44:46.260 ⇒ 00:44:48.120 Caitlyn Vaughn: Okay, great, I told…
476 00:44:48.620 ⇒ 00:44:54.190 Uttam Kumaran: I told… I told him to also chat with her. So, her, Stan, Ryan, Lev…
477 00:44:55.130 ⇒ 00:44:58.180 Uttam Kumaran: Yeah, I basically said call those four people. Yeah, okay, great.
478 00:44:58.180 ⇒ 00:45:09.590 Caitlyn Vaughn: Yeah, Laura, she’s, like, kind of PMing the getting together of, like, all of the fields that everyone needs, or, like, wants to see all the data. Okay.
479 00:45:09.830 ⇒ 00:45:14.410 Caitlyn Vaughn: For end result, and then… She’s also just, like…
480 00:45:14.680 ⇒ 00:45:17.250 Caitlyn Vaughn: really on top of everything, so she’s, like, the.
481 00:45:17.250 ⇒ 00:45:17.610 Uttam Kumaran: Okay.
482 00:45:17.610 ⇒ 00:45:29.040 Caitlyn Vaughn: Right now, and she’s, like, newer, so she’s, like, starting to get into different work streams, so if you could actually capture her now, you might, like, get a little bit more influence across the company, which could be helpful.
483 00:45:29.180 ⇒ 00:45:31.670 Uttam Kumaran: Cool. What else was I gonna say?
484 00:45:34.740 ⇒ 00:45:37.419 Caitlyn Vaughn: I forget, it’s left me, but .
485 00:45:37.650 ⇒ 00:45:41.679 Uttam Kumaran: And then when we get into Amplitude World, we’re bringing on, like, a really, really awesome…
486 00:45:41.830 ⇒ 00:45:49.250 Uttam Kumaran: Amplitude product analytics guy. I actually… where I hired him, I met him on the product… on the Amplitude Slack channel.
487 00:45:49.720 ⇒ 00:46:06.100 Uttam Kumaran: Which is where I found a… I found a lot of our team members in other companies’ Slack channels. Yeah. But yeah, he’s great, and he’s working on, another client of ours, Readme. They’re, like, a docs website. We’re doing product analytics for them. Awesome, awesome, dude, so…
488 00:46:06.320 ⇒ 00:46:19.219 Uttam Kumaran: I’ll… I basically, like, as soon as we kind of get into that world, like, he’s just so great at, like, setting up amplitude and basically starting to drive towards, like, you guys getting weekly understanding of, like, what people are doing in the platform, so…
489 00:46:19.860 ⇒ 00:46:20.440 Uttam Kumaran: Yeah.
490 00:46:20.610 ⇒ 00:46:28.399 Caitlyn Vaughn: Okay, fuck yeah. Yay, I’m so excited! I’m excited to, like, have all this together, and to, like, have it set up, and be done with it, also.
491 00:46:28.400 ⇒ 00:46:38.909 Uttam Kumaran: Yeah, yeah, and that’s what also, kind of, I want to see where we end up at the end of next quarter, like, whether people are getting reporting, whether… I mean, for a lot of companies, they just don’t have
492 00:46:39.150 ⇒ 00:46:51.470 Uttam Kumaran: a lot of the types of folks that you have who can fish themselves, so we’re, like… we do, like, business… we do, like, weekly… we’re, like, putting together decks and, like, doing, like, consulting-style, like, analysis, like, here’s what happened in your company this week.
493 00:46:51.910 ⇒ 00:46:59.820 Uttam Kumaran: I don’t think that that… maybe in, like, specific areas, like, the pricing one is an example where we’re like, we just need help on pricing, like.
494 00:47:00.300 ⇒ 00:47:05.130 Uttam Kumaran: Let’s do a two-week sprint on that, like, that’s a good way to use us. Or if there’s, like.
495 00:47:05.620 ⇒ 00:47:09.880 Uttam Kumaran: Really complicated pieces that we need to, like, measure, and… but, like.
496 00:47:10.020 ⇒ 00:47:18.990 Uttam Kumaran: I like those, because I think, like, the presentation style of a deck where you, like, walk through, like, here’s the problem, here’s how we, like, walk through, and it’s a great artifact to sort of keep.
497 00:47:18.990 ⇒ 00:47:20.529 Caitlyn Vaughn: Yeah. Like, the pricing stuff.
498 00:47:20.530 ⇒ 00:47:32.130 Uttam Kumaran: that’s a good way to use us too, but my goal with Tradeem a lot is I’m like, get everyone, like, interested in using Omni, and get them to start fishing themselves, and starting to build, like, a backlog of things people want.
499 00:47:32.130 ⇒ 00:47:35.370 Caitlyn Vaughn: And then also that I want… I told him the fir… I said, like.
500 00:47:35.370 ⇒ 00:47:37.589 Uttam Kumaran: hammer the AI feature there, like…
501 00:47:38.090 ⇒ 00:47:41.260 Uttam Kumaran: Really try to make sure that that’s working so people can just, like.
502 00:47:41.570 ⇒ 00:47:47.339 Uttam Kumaran: the moment someone asks a data question, you should literally be like, did you try the Omni AI chat, you know?
503 00:47:47.340 ⇒ 00:48:02.730 Caitlyn Vaughn: Yeah, yeah, yeah. Okay, cool. What actually also might be interesting as, like, a quick sprint, I don’t know if this is too fast of a turnaround. On Friday, I’m working on, like, cost of credits.
504 00:48:03.490 ⇒ 00:48:04.280 Uttam Kumaran: Hmm.
505 00:48:04.280 ⇒ 00:48:12.930 Caitlyn Vaughn: So, I have some, like, initial thesis, but, I mean, we have the cost of all of our providers, right? Like, how much they’re charging us.
506 00:48:13.800 ⇒ 00:48:31.180 Caitlyn Vaughn: So there’s, like, a few different ways that we could break down cost of credit on our side, because I created the pricing and packaging, right? But I just have, kind of, placeholders on number of credits, and we’ll have, like, a few different tiers for self-serve, but it’s mainly just, like, number of credits.
507 00:48:31.180 ⇒ 00:48:32.490 Uttam Kumaran: Okay.
508 00:48:32.980 ⇒ 00:48:34.790 Caitlyn Vaughn: But we could either do, like.
509 00:48:35.010 ⇒ 00:48:48.290 Caitlyn Vaughn: a credit is a penny, and, like, you know, we’re passing off the cost, or… the interesting… interesting thing is, that was, like, my initial thesis, and then I looked at, like, Clay, and UniFi, and a few other tools, and, like.
510 00:48:48.450 ⇒ 00:48:54.049 Caitlyn Vaughn: They’re all charging between 3.5 and 80 cents per credit.
511 00:48:54.250 ⇒ 00:49:01.669 Uttam Kumaran: No, they’re raking it in. Yeah, like, in Cursor and a lot of these companies, they’re just wrapping OpenAI, and then they’re just…
512 00:49:01.890 ⇒ 00:49:05.009 Uttam Kumaran: breaking it in, so… Really? Yeah.
513 00:49:05.270 ⇒ 00:49:10.879 Uttam Kumaran: like… I feel like you should charge for it.
514 00:49:11.160 ⇒ 00:49:12.000 Uttam Kumaran: Like…
515 00:49:12.000 ⇒ 00:49:23.959 Caitlyn Vaughn: Clearbit is charging us an eighth of a penny per API call. In Clay, they’re charging 3 credits per API call on Clearbit.
516 00:49:23.960 ⇒ 00:49:28.960 Uttam Kumaran: And guess what the… guess… guess how big the volume discount play is probably getting?
517 00:49:30.000 ⇒ 00:49:31.320 Uttam Kumaran: You know what I mean?
518 00:49:31.320 ⇒ 00:49:31.730 Caitlyn Vaughn: Yeah.
519 00:49:31.730 ⇒ 00:49:43.619 Uttam Kumaran: They’re probably getting… it’s probably, like, 1 16th of a… it’s probably, like, 50 to 100 times… to 100% lower than the discount you guys are getting, because they’re probably sending out the most…
520 00:49:43.780 ⇒ 00:49:48.989 Uttam Kumaran: traffic into Clearbit, And… labor doesn’t care, you know?
521 00:49:49.290 ⇒ 00:49:49.680 Caitlyn Vaughn: Yeah.
522 00:49:49.680 ⇒ 00:49:51.629 Uttam Kumaran: And yeah, Clay’s raking it in.
523 00:49:51.830 ⇒ 00:49:55.319 Caitlyn Vaughn: But the plan’s charging 3 credits, which is 7… so, like.
524 00:49:55.320 ⇒ 00:49:55.780 Uttam Kumaran: Yes.
525 00:49:55.780 ⇒ 00:50:01.150 Caitlyn Vaughn: 23 cents per call, and they’re charging us an eighth of a penny?
526 00:50:02.690 ⇒ 00:50:08.320 Uttam Kumaran: No, that’s what I’m saying, like, when you said that, I’m like, make some money on… make some money here.
527 00:50:08.320 ⇒ 00:50:08.970 Caitlyn Vaughn: Crazy.
528 00:50:08.970 ⇒ 00:50:17.179 Uttam Kumaran: I also… I also think people are… people look at the subscription fee different than the credits. You know, there’s some psychology there, where it’s like…
529 00:50:17.690 ⇒ 00:50:20.409 Uttam Kumaran: I claim that the subscription price is a lot.
530 00:50:20.520 ⇒ 00:50:23.030 Uttam Kumaran: But once you’re in, and you’re just credits, like, nobody says.
531 00:50:23.030 ⇒ 00:50:23.420 Caitlyn Vaughn: looking.
532 00:50:23.420 ⇒ 00:50:24.710 Uttam Kumaran: At the credit amounts.
533 00:50:24.710 ⇒ 00:50:25.360 Caitlyn Vaughn: Yeah.
534 00:50:25.360 ⇒ 00:50:26.809 Uttam Kumaran: You know, so…
535 00:50:27.090 ⇒ 00:50:35.710 Uttam Kumaran: I feel like it is a bigger deal to, like, think through that, and just, like… but also, again, if you want to advertise that, like, hey, we are…
536 00:50:35.830 ⇒ 00:50:41.699 Uttam Kumaran: our connectors are X percent lower than, like, if you were to do this in clay or whatever.
537 00:50:42.390 ⇒ 00:50:48.309 Uttam Kumaran: I still think at least you should charge Double whatever price you’re getting.
538 00:50:48.310 ⇒ 00:50:48.930 Caitlyn Vaughn: Yeah.
539 00:50:49.440 ⇒ 00:50:51.990 Caitlyn Vaughn: But again, it’s worth, it’s worth us seeing, like, we could go…
540 00:50:52.630 ⇒ 00:50:59.479 Uttam Kumaran: if I were to handle this, I would go tell us to go look at clay, like, do a canvas of everything that’s being offered in Clay.
541 00:50:59.830 ⇒ 00:51:03.279 Uttam Kumaran: I know HubSpot also does some of this.
542 00:51:03.390 ⇒ 00:51:04.230 Uttam Kumaran: I know.
543 00:51:04.840 ⇒ 00:51:10.589 Uttam Kumaran: I feel like they’re reselling some credits, or, like, they have some type of reseller fee on some enrichment sources.
544 00:51:10.840 ⇒ 00:51:16.470 Caitlyn Vaughn: Did I? Yeah, I wonder if it would be, like, worth our time… I think it might be.
545 00:51:18.010 ⇒ 00:51:20.559 Uttam Kumaran: I would just tell Amber to do, like, a few days on it.
546 00:51:20.970 ⇒ 00:51:21.590 Caitlyn Vaughn: Yeah.
547 00:51:21.590 ⇒ 00:51:23.240 Uttam Kumaran: Spend a couple hours and, like.
548 00:51:24.250 ⇒ 00:51:27.780 Uttam Kumaran: Put some preliminary thing together for you to look at and be like, okay, let’s…
549 00:51:28.390 ⇒ 00:51:34.919 Uttam Kumaran: go deeper. At least if it’s easier for our team to go, like, find out all those answers, like, we can go do that.
550 00:51:34.920 ⇒ 00:51:35.900 Caitlyn Vaughn: Yeah.
551 00:51:36.780 ⇒ 00:51:46.009 Caitlyn Vaughn: I know, I’m honestly, like, I’m wondering… I’m supposed to have this chat with my Chief of staff on Friday about it, and we’re gonna, like, back into some number.
552 00:51:46.850 ⇒ 00:51:51.960 Caitlyn Vaughn: So, I feel like it might be too quick of a turnaround, like, I might just, like, dump some time into it myself.
553 00:51:51.960 ⇒ 00:51:55.560 Uttam Kumaran: What’s the, what’s the, like, urgency.
554 00:51:56.870 ⇒ 00:51:58.239 Uttam Kumaran: That’s how I do it.
555 00:51:58.240 ⇒ 00:52:05.690 Caitlyn Vaughn: Before Christmas? No, you know, it’s actually not. There’s actually not the sense of urgency, it’s more like people are waiting on…
556 00:52:06.110 ⇒ 00:52:14.370 Caitlyn Vaughn: I guess, okay, the urgency is that the financial modeling is hinging on what we’re going to charge per credit, unlike.
557 00:52:14.370 ⇒ 00:52:14.940 Uttam Kumaran: Okay.
558 00:52:15.360 ⇒ 00:52:23.870 Caitlyn Vaughn: We are doing a lot of the financial modeling for Phoenix vs. Vanilla, and we actually just created a new model where we split out the cost.
559 00:52:24.070 ⇒ 00:52:25.930 Caitlyn Vaughn: Between the two, because…
560 00:52:26.430 ⇒ 00:52:35.919 Caitlyn Vaughn: some of our costs were kind of bleeding in already to… to Phoenix. I mean, we’ve spent, let’s say, $2 million on R&D and building already on Phoenix.
561 00:52:36.470 ⇒ 00:52:40.899 Caitlyn Vaughn: So… We need to prove that.
562 00:52:41.420 ⇒ 00:52:42.940 Caitlyn Vaughn: You know, it’s gonna be.
563 00:52:42.940 ⇒ 00:52:43.330 Uttam Kumaran: Yeah.
564 00:52:43.330 ⇒ 00:52:44.240 Caitlyn Vaughn: Squeeze.
565 00:52:47.130 ⇒ 00:52:50.249 Uttam Kumaran: I mean, maybe continue with the Friday thing, and then…
566 00:52:50.430 ⇒ 00:52:52.700 Uttam Kumaran: I’ll take that and pass it to Amber.
567 00:52:52.700 ⇒ 00:52:53.669 Caitlyn Vaughn: Okay. But, like…
568 00:52:53.670 ⇒ 00:52:57.930 Uttam Kumaran: to, like, fill out some edges, or I’ll leave some comments in there about, like.
569 00:52:58.450 ⇒ 00:53:05.670 Uttam Kumaran: maybe we can add more clarity from other… because, again, I feel like a lot of your conversations are probably, like, what’s Clay doing, what’s UniFi doing?
570 00:53:05.880 ⇒ 00:53:07.690 Caitlyn Vaughn: Yeah. Like, what are other…
571 00:53:08.030 ⇒ 00:53:21.219 Uttam Kumaran: like, kind of, like, these go-to-market tools doing in terms of upcharge. And then also, how are they segmenting upcharge? Because there’s probably a reason why Clay has some stuff at 3 versus 1, and, like, how are they organizing?
572 00:53:21.390 ⇒ 00:53:23.230 Caitlyn Vaughn: Maybe you just kinda, like.
573 00:53:23.440 ⇒ 00:53:27.339 Uttam Kumaran: We can just see what they’re doing and, like, follow their lead a little bit, you know?
574 00:53:29.440 ⇒ 00:53:36.270 Caitlyn Vaughn: Okay, wait, let me show you this. Actually, maybe I will pass this over to Amber, and I don’t need, like, a super deep dive.
575 00:53:36.270 ⇒ 00:53:36.870 Uttam Kumaran: Yeah.
576 00:53:37.620 ⇒ 00:53:53.400 Caitlyn Vaughn: But it is interesting, and I… I don’t think that I figured it out, but I do feel like there’s an answer, you know? So I started on some, like, preliminary cost of credit, and this was the initial thesis that I started with, right? That we should have one cent per
577 00:53:53.640 ⇒ 00:54:06.260 Caitlyn Vaughn: credit, and just charge, but I actually don’t think that anymore. But, okay, so here’s Clay and UniFi. So, the Clay cost for credit is 7.5, UniFi is 3.5, right?
578 00:54:06.350 ⇒ 00:54:13.680 Caitlyn Vaughn: But, with Clay’s starter plan, they give you 2,000 credits, and with UniFi’s starter plan, they give you 50,000 credits.
579 00:54:13.790 ⇒ 00:54:33.089 Caitlyn Vaughn: And I was thinking about it more, and really, like, the way that you would use Clay is you’d upload a list and then enrich the list, right? Versus for UniFi, the way that they’re using enrichment is mainly through web traffic, right? Like, somebody visits the website, they reveal that person, and that uses credits.
580 00:54:33.290 ⇒ 00:54:36.519 Caitlyn Vaughn: So it’s, like, flipped on, and you can’t turn it off.
581 00:54:37.070 ⇒ 00:54:37.670 Caitlyn Vaughn: So…
582 00:54:37.670 ⇒ 00:54:38.670 Uttam Kumaran: I understand.
583 00:54:38.670 ⇒ 00:54:41.679 Caitlyn Vaughn: Like, the difference in, like, initial credit?
584 00:54:41.860 ⇒ 00:54:45.239 Caitlyn Vaughn: Like, giving you these initial credits.
585 00:54:46.200 ⇒ 00:54:52.189 Uttam Kumaran: But, like, you can… the thing is, you can stay, play, starter plan, and just spend more on credits.
586 00:54:52.690 ⇒ 00:54:55.820 Uttam Kumaran: And then, basically, it’s, like…
587 00:54:56.010 ⇒ 00:55:13.479 Uttam Kumaran: it’s… for me, I made… I… we had did this, and I’m like, guys, if we’re gonna spend that much on credits anyways, I’m just gonna move to the pro plan. Yeah, because they layer a couple of things on, but for me, I’m like, the per-credit pricing just changes when you go to Pro. So I hear you on UniFi. Like, do you… which one do you think you’re closer to? Like, UniFi?
588 00:55:13.480 ⇒ 00:55:15.890 Caitlyn Vaughn: In terms of, like, the mechanism? We do both. We do both.
589 00:55:15.890 ⇒ 00:55:17.410 Uttam Kumaran: Yeah, yeah, yeah, yeah, yeah.
590 00:55:17.590 ⇒ 00:55:18.940 Caitlyn Vaughn: So…
591 00:55:19.060 ⇒ 00:55:28.850 Caitlyn Vaughn: I don’t know, like, as I’m thinking through pricing, I don’t know what makes sense. I do think we should do, like, a cost discount, you know? Like…
592 00:55:29.050 ⇒ 00:55:29.710 Caitlyn Vaughn: the more.
593 00:55:29.710 ⇒ 00:55:35.599 Uttam Kumaran: Like, ultimately, you’re not gonna turn it off. They wouldn’t run out, because then, like, what would happen, you know?
594 00:55:36.350 ⇒ 00:55:37.409 Caitlyn Vaughn: What do you mean?
595 00:55:37.410 ⇒ 00:55:40.770 Uttam Kumaran: Like, let’s say they just blow through your credits, like, are you gonna…
596 00:55:40.880 ⇒ 00:55:42.920 Uttam Kumaran: Shut the form down, or like…
597 00:55:43.220 ⇒ 00:55:43.920 Caitlyn Vaughn: Yeah?
598 00:55:44.530 ⇒ 00:55:46.170 Uttam Kumaran: Oh, really? Okay.
599 00:55:46.480 ⇒ 00:55:48.280 Caitlyn Vaughn: Yeah, so…
600 00:55:48.530 ⇒ 00:55:57.369 Caitlyn Vaughn: we’ve decided that we’ll be, like, we’ll allow people to purchase credits, and we’ll give notifications on, like, if it’s about to run out, right?
601 00:55:57.820 ⇒ 00:55:59.030 Caitlyn Vaughn: But…
602 00:56:00.300 ⇒ 00:56:10.909 Caitlyn Vaughn: Yeah, like, for this, it starts at 800, but then for, like, the custom sales plan, it starts at $50K.
603 00:56:11.770 ⇒ 00:56:17.150 Caitlyn Vaughn: So, we’re obviously closer to, like, the UniFi pricing, like, UniFi starts at $17.50.
604 00:56:17.740 ⇒ 00:56:28.070 Caitlyn Vaughn: Right? Like, we are going to be Clay-esque in a lot of ways, but one thing I do not want to compete with Clay on is I don’t want to, like, undercut their pricing. Like, we’re.
605 00:56:28.070 ⇒ 00:56:32.780 Uttam Kumaran: Yeah. Totally different segment, actually. Like, we’re trying to sell the enterprise, we’re not trying to sell the, like.
606 00:56:32.780 ⇒ 00:56:34.440 Caitlyn Vaughn: GTMEs who are, like.
607 00:56:34.440 ⇒ 00:56:35.239 Uttam Kumaran: Yeah, yeah, yeah, yeah.
608 00:56:35.240 ⇒ 00:56:37.550 Caitlyn Vaughn: your org money, like, fuck that. We want you to.
609 00:56:37.550 ⇒ 00:56:38.510 Uttam Kumaran: Yeah, yeah, yeah.
610 00:56:38.510 ⇒ 00:56:40.869 Caitlyn Vaughn: CS as a premium vendor.
611 00:56:41.660 ⇒ 00:56:45.350 Uttam Kumaran: So that’s how I feel like UniFi, that’s, like, their… that’s their kind of lane, right?
612 00:56:46.690 ⇒ 00:56:50.169 Uttam Kumaran: they’ve sort of carved that in the sort of GTM automation world.
613 00:56:50.170 ⇒ 00:56:50.870 Caitlyn Vaughn: Yeah.
614 00:56:52.140 ⇒ 00:56:58.080 Caitlyn Vaughn: Yeah, so I don’t know. It’d probably be good to look at a few other examples, and I’m not…
615 00:56:58.270 ⇒ 00:57:01.689 Caitlyn Vaughn: I should probably think through, like, who else we want to look at?
616 00:57:02.800 ⇒ 00:57:06.740 Caitlyn Vaughn: I don’t know. I also don’t think it’s, like, that difficult.
617 00:57:06.870 ⇒ 00:57:08.519 Caitlyn Vaughn: To understand the, like.
618 00:57:08.910 ⇒ 00:57:18.910 Caitlyn Vaughn: the thesis of, like, why they’re including a certain number of credits, but it is interesting on the vendor cost for clay, especially. I’m like, why are you charging 3 fucking credits for this?
619 00:57:22.690 ⇒ 00:57:28.980 Uttam Kumaran: Yeah, I mean, it may be a mix of, like, look, people know Clearbit the best, like, I wonder what their Apollo cost is?
620 00:57:30.320 ⇒ 00:57:31.150 Uttam Kumaran: and, like.
621 00:57:33.630 ⇒ 00:57:40.229 Uttam Kumaran: I mean, they’re only doing increments of 1, right? So they have, like, 1, 2, 3, 4, like, I don’t remember what… I haven’t looked in a sec, but…
622 00:57:40.230 ⇒ 00:57:41.190 Caitlyn Vaughn: Per play.
623 00:57:41.440 ⇒ 00:57:42.080 Uttam Kumaran: path.
624 00:57:42.500 ⇒ 00:57:44.609 Caitlyn Vaughn: But each credit is 7 cents.
625 00:57:45.500 ⇒ 00:57:49.659 Uttam Kumaran: I know, I know, but it’s like… it’s like when you go to Dave & Buster’s, you know, you’re not really, like…
626 00:57:50.320 ⇒ 00:57:55.429 Uttam Kumaran: for the user, it’s sort of like 1 credit, 3 credits, right? They can do the conversion.
627 00:57:55.970 ⇒ 00:58:05.220 Uttam Kumaran: But I guess I’m wondering, like, why they labeled something 3 versus 1. Like, is it because of frequency? Is it truly because their cost of it is higher?
628 00:58:05.710 ⇒ 00:58:08.709 Uttam Kumaran: Or maybe it’s sort of neither, and they’re, like, kind of just, like.
629 00:58:09.840 ⇒ 00:58:10.770 Caitlyn Vaughn: Value?
630 00:58:11.230 ⇒ 00:58:12.450 Uttam Kumaran: Yeah, value.
631 00:58:13.900 ⇒ 00:58:18.069 Caitlyn Vaughn: I don’t know. I was, like, a little bit baffled looking at that.
632 00:58:18.400 ⇒ 00:58:19.000 Caitlyn Vaughn: But…
633 00:58:19.000 ⇒ 00:58:21.069 Uttam Kumaran: I think they’re breaking it in off the.
634 00:58:21.070 ⇒ 00:58:23.129 Caitlyn Vaughn: No, they for sure are. Yeah.
635 00:58:23.130 ⇒ 00:58:29.179 Uttam Kumaran: Yeah, it’s like, you’re just buying the thing and just art is complete… but again, the users are not going to Clearbit, they’re going to clay, so…
636 00:58:29.180 ⇒ 00:58:31.219 Caitlyn Vaughn: I know, but also, like…
637 00:58:31.220 ⇒ 00:58:39.260 Uttam Kumaran: Some people are there or don’t know what things cost. They don’t know what enrichment providers cost, and Clay sort of sets the floor at this, and they’re like, okay, cool, let’s get it.
638 00:58:39.260 ⇒ 00:58:45.290 Caitlyn Vaughn: We should just charge. Like, people are not gonna care once it’s in, but then they will care for, like…
639 00:58:45.490 ⇒ 00:58:46.330 Caitlyn Vaughn: If it’s…
640 00:58:46.330 ⇒ 00:58:59.620 Uttam Kumaran: Offer the discount later, like, if they move up, offer, like, a huge discount. You can always discount from… sort of like what happens with pricing. You start here, and then you’re like, oh, but then we’ll give you, like, 80% off. But if you start down here, and then there’s, like, no…
641 00:58:59.620 ⇒ 00:59:00.740 Caitlyn Vaughn: Raise that!
642 00:59:00.740 ⇒ 00:59:05.459 Uttam Kumaran: There’s no room for your salespeople to sort of offer… yeah, and you can’t really raise it either.
643 00:59:05.460 ⇒ 00:59:13.730 Caitlyn Vaughn: Yeah… I think our, like, average cost per credit is about 4 cents, but I just can’t fathom, like…
644 00:59:14.010 ⇒ 00:59:20.389 Caitlyn Vaughn: charging 4 cents for Clearbit. Like, are you fucking kidding me? It’s literally, like, not even one penny.
645 00:59:21.620 ⇒ 00:59:23.080 Uttam Kumaran: I know, I…
646 00:59:23.080 ⇒ 00:59:24.440 Caitlyn Vaughn: It feels like…
647 00:59:24.440 ⇒ 00:59:27.310 Uttam Kumaran: You’re a very… you’re a very nice person, Russ. I don’t know.
648 00:59:27.310 ⇒ 00:59:29.320 Caitlyn Vaughn: We need to be less technical and more…
649 00:59:29.320 ⇒ 00:59:39.139 Uttam Kumaran: It’s not… I guess it’s… there’s a… there is ethics, yeah, but it’s… you guys are… you guys are running a business, like, look, if they’re coming to you, and the customer’s willing to pay that, and they’re getting the value, it’s like…
650 00:59:40.130 ⇒ 00:59:45.010 Uttam Kumaran: You know, it’s… it is kind of egregious, but… Like…
651 00:59:45.120 ⇒ 00:59:57.740 Uttam Kumaran: guess what? Like, like, Apple makes… Apple makes, like, almost, like, 80-90% on this phone. They make it for, like, 100… 100 bucks, they sell it for, like, 1,200, like, it’s ridiculous.
652 00:59:58.030 ⇒ 00:59:59.439 Uttam Kumaran: Yeah, it’s insane.
653 00:59:59.950 ⇒ 01:00:01.949 Uttam Kumaran: And so, what are you gonna do, you know?
654 01:00:03.270 ⇒ 01:00:04.890 Caitlyn Vaughn: I literally don’t know. That’s…
655 01:00:04.890 ⇒ 01:00:08.440 Uttam Kumaran: Okay, well, I’ll help you buy it out, so…
656 01:00:08.490 ⇒ 01:00:12.670 Caitlyn Vaughn: I need to be more of a shark, you know? God bless America.
657 01:00:12.770 ⇒ 01:00:27.119 Uttam Kumaran: I just… I think… I don’t know, you guys are, yeah, I don’t know, I feel like you got… this is a fun space to be in because of this sort of pricing, but I don’t know, I feel like you want to have a thesis that you can share with your customers on why. Why are things the way they are?
658 01:00:27.240 ⇒ 01:00:30.060 Uttam Kumaran: And then I think people will pay, you know? They’re getting value.
659 01:00:30.690 ⇒ 01:00:31.410 Caitlyn Vaughn: Yeah.
660 01:00:32.270 ⇒ 01:00:41.849 Caitlyn Vaughn: Get Amber on it! Get Amber on it! But, yeah, we have this… this convo on Friday, so maybe if she… I don’t know, like, what your.
661 01:00:41.850 ⇒ 01:00:43.929 Uttam Kumaran: I’ll ask her, I’ll ask her today.
662 01:00:44.130 ⇒ 01:00:50.880 Caitlyn Vaughn: If she wants to, like, spend a few hours today or tomorrow on it, otherwise, like, it’s fine.
663 01:00:51.270 ⇒ 01:00:52.659 Uttam Kumaran: Okay, okay, I will.
664 01:00:52.790 ⇒ 01:00:54.080 Caitlyn Vaughn: OMG, okay, I’m excited.
665 01:00:55.860 ⇒ 01:01:02.260 Uttam Kumaran: And then I’ll follow up with a couple more things, and then I need to… we’re following up with a contract amendment to, like, move the stuff to, like.
666 01:01:02.420 ⇒ 01:01:02.980 Caitlyn Vaughn: Yeah!
667 01:01:02.980 ⇒ 01:01:04.100 Uttam Kumaran: For auto-renew, so…
668 01:01:04.100 ⇒ 01:01:06.829 Caitlyn Vaughn: Can we just be forever? Forever and ever?
669 01:01:06.830 ⇒ 01:01:18.959 Uttam Kumaran: That’s what I told them. This is the first… this is the… we’re basically changing a bunch of contracts next quarter to, like, get more adult, but basically, we’ll sign an amendment that’s just, like, on auto-renew, so…
670 01:01:18.960 ⇒ 01:01:33.960 Caitlyn Vaughn: Yeah, totally. Because it’s like, I don’t want to keep doing this every 3 months. Like, we’re gonna need you for, like, at least 6 months, and I’m gonna keep using you until we don’t, you know, have any more needs. But it seems like, for the foreseeable future, like.
671 01:01:34.140 ⇒ 01:01:42.919 Caitlyn Vaughn: we’re still gonna be relying on you, so… I don’t know. And then… but then I’m like, I don’t want to sign a 6-month contract. I don’t know. I feel weird about a non-.
672 01:01:42.920 ⇒ 01:01:48.709 Uttam Kumaran: But there’s a 14-day out on everything, and, like, it’s not like you can just say, like, stop working, so…
673 01:01:48.710 ⇒ 01:01:49.109 Caitlyn Vaughn: Well then.
674 01:01:49.110 ⇒ 01:01:55.789 Uttam Kumaran: So really, the contract… yeah, the contract for us is so I can just… I can forecast, and so, yeah, we don’t have to sign…
675 01:01:56.090 ⇒ 01:02:02.920 Uttam Kumaran: Month to month. So we can sign a year, and then just do auto-renew, and then just leave it at that. And at any moment, you can cancel and redo it. It’s like…
676 01:02:02.980 ⇒ 01:02:03.500 Caitlyn Vaughn: Okay.
677 01:02:03.970 ⇒ 01:02:06.909 Caitlyn Vaughn: Yeah, I think that probably makes the most sense, but…
678 01:02:06.910 ⇒ 01:02:07.440 Uttam Kumaran: Okay.
679 01:02:07.860 ⇒ 01:02:09.270 Caitlyn Vaughn: So much shit to do.
680 01:02:09.660 ⇒ 01:02:10.389 Uttam Kumaran: There’s a lot today.
681 01:02:10.390 ⇒ 01:02:11.200 Caitlyn Vaughn: Have a day.
682 01:02:13.250 ⇒ 01:02:15.720 Uttam Kumaran: I know you’re starting to…
683 01:02:15.720 ⇒ 01:02:16.260 Caitlyn Vaughn: You are…
684 01:02:16.260 ⇒ 01:02:21.229 Uttam Kumaran: You’re starting Yeah, you’re starting to see the amount of work, so… it’s great. Now I’m pumped.
685 01:02:21.480 ⇒ 01:02:29.929 Caitlyn Vaughn: No, I’m pumped too. I’m so glad that we have you guys. This is, like, amazing. We have, like, 15 people on a data team without, you know, hiring 50 people.
686 01:02:30.130 ⇒ 01:02:35.010 Uttam Kumaran: Yeah, data people are very expensive, and they’re not always great.
687 01:02:35.630 ⇒ 01:02:42.439 Uttam Kumaran: Put it that way, so… Yeah. Like, the people we try to bring on are really sharks, like, they’re really, really good. Like, come in and just, like.
688 01:02:42.690 ⇒ 01:02:47.390 Uttam Kumaran: yo, just, like, go in there and figure it out. Like, Demolati’s the best. You’ll love working with them, like…
689 01:02:47.390 ⇒ 01:02:48.820 Caitlyn Vaughn: Yeah, he’s great.
690 01:02:48.860 ⇒ 01:02:49.650 Uttam Kumaran: Yeah.
691 01:02:49.930 ⇒ 01:02:50.350 Caitlyn Vaughn: Cool team.
692 01:02:50.350 ⇒ 01:02:52.999 Uttam Kumaran: Okay, cool. I’ll let Amber know, I’ll let you know.
693 01:02:53.240 ⇒ 01:02:55.139 Caitlyn Vaughn: Amazing. Keep me posted.
694 01:02:55.140 ⇒ 01:02:56.770 Uttam Kumaran: Alright, thank you, happy birthday!
695 01:02:56.770 ⇒ 01:02:58.010 Caitlyn Vaughn: Thank you, bye.
696 01:02:58.010 ⇒ 01:02:59.250 Uttam Kumaran: Okay, bye.