Meeting Title: Sales-Automation-Contd Date: 2024-08-22 Meeting participants: Patrick Trainer, Abigail Zhao, Uttam Kumaran
WEBVTT
1 00:00:34.420 ⇒ 00:00:35.599 Uttam Kumaran: Hey! Good morning!
2 00:00:36.200 ⇒ 00:00:37.200 Abigail Zhao: Morning.
3 00:00:38.600 ⇒ 00:00:39.560 Uttam Kumaran: How’s everything?
4 00:00:39.840 ⇒ 00:00:40.849 Abigail Zhao: It’s good.
5 00:00:41.214 ⇒ 00:00:49.919 Abigail Zhao: I am officially moved into my apartment now, like the last few days, I was still living at home, but now I’m like officially here. So.
6 00:00:49.920 ⇒ 00:00:52.129 Uttam Kumaran: Oh, nice! It looks like the same background, though.
7 00:00:52.130 ⇒ 00:00:52.780 Abigail Zhao: Yeah.
8 00:00:55.060 ⇒ 00:00:57.009 Uttam Kumaran: I guess the door was on that side.
9 00:00:57.330 ⇒ 00:00:57.810 Abigail Zhao: Yeah.
10 00:00:57.810 ⇒ 00:00:59.265 Uttam Kumaran: Or yeah, whatever.
11 00:00:59.930 ⇒ 00:01:02.249 Abigail Zhao: Yeah, but it’s really
12 00:01:02.500 ⇒ 00:01:06.580 Abigail Zhao: very white here, a lot of just like bare white walls. So.
13 00:01:06.580 ⇒ 00:01:08.470 Uttam Kumaran: How? How’s your like area.
14 00:01:09.060 ⇒ 00:01:11.190 Abigail Zhao: It’s pretty good, I mean, like
15 00:01:11.310 ⇒ 00:01:20.110 Abigail Zhao: the General Sur, like surrounding area at my school, is like pretty widely known to be not safe, just because it’s like in downtown Los Angeles.
16 00:01:20.110 ⇒ 00:01:21.470 Uttam Kumaran: Yeah. That’s what I heard.
17 00:01:22.200 ⇒ 00:01:30.510 Abigail Zhao: Notoriously pretty dangerous, but I mean I feel pretty safe here, so hopefully, nothing happens.
18 00:01:32.400 ⇒ 00:01:33.640 Patrick Trainer: Feel like I missed something.
19 00:01:34.290 ⇒ 00:01:34.680 Abigail Zhao: No.
20 00:01:34.680 ⇒ 00:01:39.009 Uttam Kumaran: It’s just like I, I think. Usc. Campus is like right near
21 00:01:39.160 ⇒ 00:01:40.750 Uttam Kumaran: downtown la, which.
22 00:01:41.130 ⇒ 00:01:41.740 Uttam Kumaran: Like.
23 00:01:41.740 ⇒ 00:01:42.070 Patrick Trainer: No.
24 00:01:42.070 ⇒ 00:01:42.900 Uttam Kumaran: Really dangerous.
25 00:01:42.900 ⇒ 00:01:43.560 Abigail Zhao: Yeah.
26 00:01:44.750 ⇒ 00:01:47.020 Uttam Kumaran: But like, you guys have guards and stuff. I thought.
27 00:01:47.420 ⇒ 00:01:54.670 Abigail Zhao: Yeah, we do. But it’s a lot like different for people who live off campus. It’s like more
28 00:01:55.110 ⇒ 00:02:03.300 Abigail Zhao: like, yes, security. People are more centered like near campus, and it doesn’t really extend to where I’m living. But I think
29 00:02:03.420 ⇒ 00:02:05.200 Abigail Zhao: I’ll be fine. It’s the area.
30 00:02:05.200 ⇒ 00:02:06.170 Uttam Kumaran: Are there.
31 00:02:06.640 ⇒ 00:02:07.610 Abigail Zhao: A car? Oh, no.
32 00:02:07.610 ⇒ 00:02:08.270 Uttam Kumaran: Yeah.
33 00:02:08.270 ⇒ 00:02:09.160 Abigail Zhao: I don’t.
34 00:02:09.449 ⇒ 00:02:12.189 Uttam Kumaran: So you just uber or scooter, or walk.
35 00:02:12.190 ⇒ 00:02:17.869 Abigail Zhao: Yeah, it’s like, walk. It’s like a 5 to 10 min walk to get to campus. So it’s like, not super.
36 00:02:17.870 ⇒ 00:02:18.680 Uttam Kumaran: Oh, okay.
37 00:02:18.850 ⇒ 00:02:26.340 Abigail Zhao: Yeah, I know I have a lot of friends who it’ll take like half an hour for them to walk, so they like either like skateboard, or like bike or scooter.
38 00:02:26.340 ⇒ 00:02:28.730 Patrick Trainer: Skateboard. That’s the most like la.
39 00:02:33.090 ⇒ 00:02:34.200 Uttam Kumaran: Nice. Hell. Yeah.
40 00:02:34.500 ⇒ 00:02:35.160 Abigail Zhao: Yeah.
41 00:02:37.072 ⇒ 00:02:47.947 Uttam Kumaran: Cool. Well, I know it’s been a few days since we last chatted, but I made some good progress on just getting our
42 00:02:48.700 ⇒ 00:02:54.359 Uttam Kumaran: basically, how we’re going to be writing outbound email campaigns like into a pretty succinct process.
43 00:02:56.320 ⇒ 00:03:00.550 Uttam Kumaran: so I’m happy to, you know, share that. And then today I wanted to kind of go through.
44 00:03:02.210 ⇒ 00:03:08.139 Uttam Kumaran: I know on the to do. We have like buyer personas, and then some of the sales material stuff and then
45 00:03:08.300 ⇒ 00:03:10.266 Uttam Kumaran: scoring leads.
46 00:03:11.460 ⇒ 00:03:12.750 Uttam Kumaran: I think maybe.
47 00:03:13.260 ⇒ 00:03:18.539 Uttam Kumaran: Yeah, you want to talk about. I mean, I don’t know. We can go in anywhere, actually, all over the place.
48 00:03:18.540 ⇒ 00:03:19.470 Patrick Trainer: Talk about what?
49 00:03:19.750 ⇒ 00:03:21.289 Uttam Kumaran: Scoring leads. Yeah.
50 00:03:21.290 ⇒ 00:03:22.870 Patrick Trainer: Yeah, I’ve actually got that
51 00:03:23.010 ⇒ 00:03:24.549 Patrick Trainer: right up and ready to go.
52 00:03:24.550 ⇒ 00:03:26.890 Uttam Kumaran: Okay, sick and then cause. Then I can talk about.
53 00:03:27.170 ⇒ 00:03:32.050 Uttam Kumaran: I could talk about materials and personas, and I want to spend like as much time as we have today, like.
54 00:03:32.240 ⇒ 00:03:33.779 Uttam Kumaran: actually, all of us just in
55 00:03:33.830 ⇒ 00:03:36.480 Uttam Kumaran: in fig jam, just like putting stuff together.
56 00:03:39.670 ⇒ 00:03:40.460 Patrick Trainer: Cool.
57 00:03:40.660 ⇒ 00:03:42.319 Patrick Trainer: Alright. Yeah, let me
58 00:03:43.600 ⇒ 00:03:46.770 Patrick Trainer: share this. I hate how
59 00:03:47.000 ⇒ 00:03:50.029 Patrick Trainer: like, when you minimize like the
60 00:03:50.320 ⇒ 00:03:53.500 Patrick Trainer: zoom screen, like all of the controls, disappear.
61 00:03:53.890 ⇒ 00:03:58.279 Uttam Kumaran: Yeah, I I always bring it back, basically. But if you hit escape, it comes back.
62 00:04:01.350 ⇒ 00:04:02.220 Patrick Trainer: There’s
63 00:04:02.830 ⇒ 00:04:03.700 Patrick Trainer: eating.
64 00:04:06.520 ⇒ 00:04:08.059 Patrick Trainer: Oh, yeah, start share.
65 00:04:12.080 ⇒ 00:04:13.290 Patrick Trainer: Okay, cool.
66 00:04:13.770 ⇒ 00:04:14.870 Patrick Trainer: You can see this.
67 00:04:19.790 ⇒ 00:04:23.250 Patrick Trainer: Alright. So kind of like, how I have it
68 00:04:23.550 ⇒ 00:04:31.119 Patrick Trainer: is we’ve got like 2 scoring categories we’ve got like the fit score, and then engagement score so fit is like.
69 00:04:31.370 ⇒ 00:04:36.539 Patrick Trainer: who is the company like? What like are they in the right industry?
70 00:04:38.020 ⇒ 00:04:46.660 Patrick Trainer: those sorts of things like, do they have, like a sort of data, a data stack? Or are they established or less established
71 00:04:46.810 ⇒ 00:04:48.310 Patrick Trainer: things like that.
72 00:04:48.630 ⇒ 00:04:56.372 Patrick Trainer: and so the total lead score, like all of those, have like sub scores in them that either like
73 00:04:56.940 ⇒ 00:05:09.640 Patrick Trainer: these, all are additive, but we can add in like subtractive scores as well. But like that’s further down the line right now. All of these are just like additive.
74 00:05:09.930 ⇒ 00:05:14.673 Patrick Trainer: And then. So we sum up everything there, and it’s out of 200 points.
75 00:05:15.030 ⇒ 00:05:19.050 Patrick Trainer: And then, like the total lead score is just the summation of both
76 00:05:19.565 ⇒ 00:05:23.029 Patrick Trainer: and so going into like the fit score
77 00:05:23.960 ⇒ 00:05:30.339 Patrick Trainer: we have like our company size. And we’re gonna target that and add
78 00:05:30.750 ⇒ 00:05:36.909 Patrick Trainer: points based on based on that udum, you were saying like, we’re wanting to like
79 00:05:37.050 ⇒ 00:05:39.840 Patrick Trainer: target around like 10 million
80 00:05:40.277 ⇒ 00:05:47.820 Patrick Trainer: and so right here we’re looking at like 8 to 12. I just put that. That’s just kind of like a good range
81 00:05:48.167 ⇒ 00:05:51.769 Patrick Trainer: and then it kind of like goes down from there
82 00:05:51.840 ⇒ 00:05:53.602 Patrick Trainer: in terms of
83 00:05:54.750 ⇒ 00:05:58.220 Patrick Trainer: like, in terms of points. And so.
84 00:05:58.500 ⇒ 00:06:09.949 Patrick Trainer: yeah, I have like this, 8 to 12, because that’s our like target range. But then we also like, if we’re going over that like that’s going to be less points as well.
85 00:06:09.950 ⇒ 00:06:10.480 Uttam Kumaran: Because like.
86 00:06:10.480 ⇒ 00:06:12.620 Patrick Trainer: That’s starting to get like out.
87 00:06:12.980 ⇒ 00:06:13.450 Uttam Kumaran: Okay.
88 00:06:13.450 ⇒ 00:06:24.000 Patrick Trainer: Outside of the range, right? And so like these ranges are going to like, go above and like detract. But then it’s also on like the lower threshold as well.
89 00:06:24.000 ⇒ 00:06:24.700 Uttam Kumaran: 2.
90 00:06:26.167 ⇒ 00:06:29.902 Patrick Trainer: And so then we have, like our industry
91 00:06:30.480 ⇒ 00:06:36.118 Patrick Trainer: fit criteria. It’s like, so we’ve got in all of these like we can change through. So
92 00:06:36.670 ⇒ 00:06:39.270 Patrick Trainer: we’ve got like high data industries. So that’s like
93 00:06:39.370 ⇒ 00:06:47.099 Patrick Trainer: e-commerce sas. I know we don’t really want to look at sas but like Fintech. But basically people that are going to have like
94 00:06:47.290 ⇒ 00:06:52.240 Patrick Trainer: a lot of stuff flowing through, or a lot of data needs and a lot of definitely
95 00:06:52.900 ⇒ 00:06:54.770 Patrick Trainer: bits and bytes flowing through them.
96 00:06:55.275 ⇒ 00:06:57.600 Patrick Trainer: Just because there’s like more
97 00:06:58.740 ⇒ 00:07:02.430 Patrick Trainer: kind of like, not total addressable market, but like
98 00:07:02.530 ⇒ 00:07:04.380 Patrick Trainer: opportunity to
99 00:07:05.110 ⇒ 00:07:09.219 Patrick Trainer: offer more services. Right? That’s kind of how I’m thinking about it.
100 00:07:10.062 ⇒ 00:07:15.130 Patrick Trainer: And then we have like that medium data industry. So that’s like our manufacturing
101 00:07:15.520 ⇒ 00:07:16.840 Patrick Trainer: retail
102 00:07:17.350 ⇒ 00:07:22.500 Patrick Trainer: healthcare. I don’t really want to mess with, because it’s all a pain in the ass to
103 00:07:22.870 ⇒ 00:07:27.119 Patrick Trainer: to do. There’s all sorts of rules and regulations. And it
104 00:07:27.350 ⇒ 00:07:28.480 Patrick Trainer: usually they’re
105 00:07:28.650 ⇒ 00:07:29.989 Patrick Trainer: data needs or
106 00:07:30.090 ⇒ 00:07:30.635 Patrick Trainer: pretty
107 00:07:31.380 ⇒ 00:07:33.540 Patrick Trainer: just like terrible in general.
108 00:07:35.250 ⇒ 00:07:48.849 Patrick Trainer: and then like low data industries, so like, if we were to sell to like other traditional services or small local businesses. Sure, small local businesses like have data needs. But it’s just going to be like a huge lift.
109 00:07:49.240 ⇒ 00:07:49.820 Uttam Kumaran: Yeah.
110 00:07:49.820 ⇒ 00:07:51.560 Patrick Trainer: Them to like Step 0,
111 00:07:54.204 ⇒ 00:07:59.060 Patrick Trainer: and then like further in into the fit is like their infrastructure.
112 00:07:59.671 ⇒ 00:08:08.340 Patrick Trainer: Like, if they have nothing like, that’s a pretty good signal that like, Oh, we can help you right and like, go and get in there.
113 00:08:10.170 ⇒ 00:08:11.970 Patrick Trainer: And then, if they have, like
114 00:08:12.090 ⇒ 00:08:13.710 Patrick Trainer: the basic
115 00:08:13.780 ⇒ 00:08:19.800 Patrick Trainer: needs like, say, they have, like Google analytics set up or like shopify analytics like
116 00:08:20.040 ⇒ 00:08:30.349 Patrick Trainer: that’s, I think, an even better signal in the sense that it’s like they have something. And so it’s like we’d be able to like the
117 00:08:30.600 ⇒ 00:08:37.600 Patrick Trainer: time. To value would be a lot quicker than having like nothing at all right.
118 00:08:37.610 ⇒ 00:08:40.030 Patrick Trainer: Yeah, and so
119 00:08:40.169 ⇒ 00:08:50.080 Patrick Trainer: then, like looking to improve, and then advanced analytics like, they’ve already got it figured out so they probably wouldn’t need too much.
120 00:08:50.180 ⇒ 00:08:54.249 Patrick Trainer: or they would only like need a little bit of of services.
121 00:08:55.296 ⇒ 00:09:04.579 Patrick Trainer: And then we’ve got like our persona profile of kind of like, who’s who we’re talking to, or who this decision maker is.
122 00:09:04.940 ⇒ 00:09:20.190 Patrick Trainer: and so like the highest score here is going to be somebody that’s working with data directly and then it’s kind of like going back up into the hierarchy of of their roles. So like data, managers leads like
123 00:09:20.640 ⇒ 00:09:45.890 Patrick Trainer: those are like high leverage roles in within their organization. And kind of like they’re those like the champions of, like the tools that they want to use. And so they can. They have a lot of like influence, right? And then Vp, director, it analytics whatever we want to call it, like whatever that role is. That’d probably be best, because, like
124 00:09:46.070 ⇒ 00:09:52.250 Patrick Trainer: they can kind of like, they’re the ones with the amex and can can swipe that directly
125 00:09:52.370 ⇒ 00:09:54.070 Patrick Trainer: sea level.
126 00:09:54.100 ⇒ 00:09:55.433 Patrick Trainer: These people
127 00:09:56.580 ⇒ 00:10:03.279 Patrick Trainer: normally don’t know what they’re talking about. And and they’re they’re worried about like other things
128 00:10:04.027 ⇒ 00:10:07.930 Patrick Trainer: rather than like buying or procuring tools.
129 00:10:08.100 ⇒ 00:10:09.510 Patrick Trainer: and so
130 00:10:09.910 ⇒ 00:10:16.569 Patrick Trainer: like it’s good. But like I don’t it? I don’t think it’s like the best and then other roles like
131 00:10:17.020 ⇒ 00:10:21.438 Patrick Trainer: it’d be a stretch to try and get, but not impossible.
132 00:10:22.120 ⇒ 00:10:28.950 Patrick Trainer: Given like all these other things like they’re they’re all going to like sway the sway, the bar there
133 00:10:29.410 ⇒ 00:10:35.010 Patrick Trainer: and then, like growth stages. I wrote this like to do of
134 00:10:35.050 ⇒ 00:10:40.870 Patrick Trainer: just like we need to align it to what we want, I know, like rapid growth can be
135 00:10:41.860 ⇒ 00:10:45.580 Patrick Trainer: can be kind of hairy. But
136 00:10:46.310 ⇒ 00:10:58.470 Patrick Trainer: there is like some value there in the sense that, like everything’s kind of on fire all the time, and everybody’s running around like wearing multiple hats, and they’re like
137 00:10:58.620 ⇒ 00:11:00.280 Patrick Trainer: they need the most help
138 00:11:00.857 ⇒ 00:11:04.679 Patrick Trainer: but then, like we have that like steady growth, early stage
139 00:11:04.940 ⇒ 00:11:15.350 Patrick Trainer: pre pre revenue probably isn’t the best, because they’re not looking to really spend too much money but like the other side of that is like
140 00:11:15.990 ⇒ 00:11:19.960 Patrick Trainer: they’re looking to like, get something started up yesterday.
141 00:11:20.200 ⇒ 00:11:20.740 Patrick Trainer: Yeah,
142 00:11:21.350 ⇒ 00:11:22.840 Patrick Trainer: mature and stable.
143 00:11:23.620 ⇒ 00:11:24.833 Patrick Trainer: It’s okay.
144 00:11:25.460 ⇒ 00:11:29.730 Patrick Trainer: I mean, that’s a solid like revenue stream. So like
145 00:11:29.740 ⇒ 00:11:36.415 Patrick Trainer: we wouldn’t have worries about like accounts receivable. And like that those sorts of things.
146 00:11:36.980 ⇒ 00:11:38.500 Patrick Trainer: But then again, like
147 00:11:38.720 ⇒ 00:11:43.159 Patrick Trainer: mature and stable companies like there’s normally like a lot of
148 00:11:43.650 ⇒ 00:11:45.060 Patrick Trainer: bureaucratic tape.
149 00:11:45.100 ⇒ 00:11:52.329 Patrick Trainer: and like other kind of like need to go up the command or command chain to to do this.
150 00:11:53.275 ⇒ 00:11:55.060 Patrick Trainer: Then going into.
151 00:11:55.210 ⇒ 00:11:59.501 Uttam Kumaran: I think maybe let’s let’s just like review. I just have like a ton of notes.
152 00:11:59.770 ⇒ 00:12:00.429 Patrick Trainer: Okay. Cool.
153 00:12:00.430 ⇒ 00:12:02.509 Uttam Kumaran: Maybe we should start. We just start with the
154 00:12:02.780 ⇒ 00:12:05.409 Uttam Kumaran: the what is this 1st section called.
155 00:12:06.920 ⇒ 00:12:07.960 Patrick Trainer: fit score.
156 00:12:08.130 ⇒ 00:12:25.559 Uttam Kumaran: Fit score. Okay, so fit score. Okay, cool. So I, yeah, I mean this, too. This is great. I think. There’s, of course, some like specifics like company size and things like that, where, you know, 10 million was just a number I threw out ideally that actually boils down to
157 00:12:25.770 ⇒ 00:12:30.344 Uttam Kumaran: like concrete, like maybe even more concrete. I think part of
158 00:12:31.010 ⇒ 00:12:35.886 Uttam Kumaran: And I have some of these notes on my end, too, so I can share them. I think part of
159 00:12:36.420 ⇒ 00:12:42.219 Uttam Kumaran: Each of these different criteria is also like our confidence level, like, I’m very confident about
160 00:12:42.330 ⇒ 00:12:49.520 Uttam Kumaran: a couple of the other sections versus the company size. However, I don’t want confidence to get in front of like executing, so
161 00:12:49.660 ⇒ 00:12:51.349 Uttam Kumaran: the thing, I’ll say, is like
162 00:12:51.910 ⇒ 00:12:53.280 Uttam Kumaran: we may adjust
163 00:12:53.510 ⇒ 00:12:57.050 Uttam Kumaran: the company size core faster than we adjust some other stuff.
164 00:12:57.170 ⇒ 00:13:07.401 Uttam Kumaran: But I think the way it’s set up now is actually great. Actually, what I’m thinking about in terms of company size. Is actually figuring out
165 00:13:07.910 ⇒ 00:13:13.250 Uttam Kumaran: percent of I percent of revenue to it. Budget
166 00:13:13.750 ⇒ 00:13:15.590 Uttam Kumaran: across industry.
167 00:13:15.740 ⇒ 00:13:27.659 Uttam Kumaran: Right? That’ll be the big thing is like, if we have a good sense of like, oh, like, for example, software companies spend a ton on it. Manufacturing companies, they spend a smaller percent of revenue. So
168 00:13:27.690 ⇒ 00:13:33.730 Uttam Kumaran: the clear math is like manufacturing companies get higher revenue for them to fit our target. We may take on
169 00:13:33.930 ⇒ 00:13:41.440 Uttam Kumaran: lower revenue software companies right? So there’s there’s some like next layer here. But I think 10 million. Let’s just let’s just start with that, because
170 00:13:41.470 ⇒ 00:13:46.700 Uttam Kumaran: that’s like this is gonna be the highest criteria. This is gonna be the highest filter out of stuff.
171 00:13:47.840 ⇒ 00:13:54.449 Patrick Trainer: Right right and like, if you like, there’s different weights, too, that, like we can attribute it.
172 00:13:54.450 ⇒ 00:13:58.849 Uttam Kumaran: Yeah, like, if we’re not confident, then we just lower the overall impact.
173 00:13:59.050 ⇒ 00:13:59.790 Patrick Trainer: Right?
174 00:13:59.930 ⇒ 00:14:01.390 Patrick Trainer: Right? Exactly.
175 00:14:06.730 ⇒ 00:14:10.590 Patrick Trainer: Okay, cool. So I’ll continue on here.
176 00:14:10.590 ⇒ 00:14:15.140 Uttam Kumaran: Well, I have like way more like way more you are. I can wait. I can wait till we go through everything.
177 00:14:15.140 ⇒ 00:14:16.150 Patrick Trainer: Okay. Okay.
178 00:14:16.260 ⇒ 00:14:17.040 Patrick Trainer: Sorry.
179 00:14:17.180 ⇒ 00:14:17.655 Patrick Trainer: Okay.
180 00:14:18.824 ⇒ 00:14:21.599 Uttam Kumaran: Like on on industry.
181 00:14:21.650 ⇒ 00:14:22.670 Uttam Kumaran: I think
182 00:14:23.340 ⇒ 00:14:32.099 Uttam Kumaran: high medium low is great, I think. Basically, what we’ll do for this is actually just take all the high level industries from Apollo and map them to one of these.
183 00:14:32.120 ⇒ 00:14:32.745 Uttam Kumaran: Yeah,
184 00:14:33.870 ⇒ 00:14:57.489 Uttam Kumaran: you know. And then this is actually, I think, the ones that are in the parentheses is actually just gonna be based on. This is, gonna be where we map to the industry we’re going after from a content side as well. So, for example, although, like you know, we like, for example, Sas Fintech, I think there’s definitely like there’s definitely high growth there. But do we have expertise or content associated. No.
185 00:14:57.500 ⇒ 00:14:59.490 Uttam Kumaran: So I think these will map.
186 00:14:59.630 ⇒ 00:15:02.670 Uttam Kumaran: These will take into account that as well.
187 00:15:04.030 ⇒ 00:15:14.379 Uttam Kumaran: I mean, really, again, I think the low data, the low data industries you have unlock. I think they’ll just be high data medium like that. I think that will just align well with
188 00:15:14.770 ⇒ 00:15:18.499 Uttam Kumaran: like we can’t. If we can’t go after Fintech. If, like none of us have
189 00:15:18.770 ⇒ 00:15:27.030 Uttam Kumaran: Fintech, it’s gonna be work meaning we can. But the the rate of success will likely be lower than going after places where we do have access.
190 00:15:27.030 ⇒ 00:15:27.790 Patrick Trainer: Right, right.
191 00:15:27.790 ⇒ 00:15:36.270 Uttam Kumaran: That’ll be like kind of going to this criteria. And again, that the whole thing is just based on our current mo right now, right. It’s not like where we want to shift into.
192 00:15:36.623 ⇒ 00:15:53.610 Patrick Trainer: Exactly, and part of that, too, like in in the engagement scores like you were saying like with Fintech and Sas like, do we have content? That’s geared towards them right now. No, but like a lot of that gets fit into the engagement score.
193 00:15:53.630 ⇒ 00:15:54.360 Uttam Kumaran: I see. Okay.
194 00:15:54.900 ⇒ 00:15:56.519 Patrick Trainer: And stow like
195 00:15:57.588 ⇒ 00:16:07.079 Patrick Trainer: well, you’ll see when we go through it. But like that, that’ll that’ll connect the dots. But like that’s kind of accounted for in the like other half
196 00:16:07.090 ⇒ 00:16:08.780 Patrick Trainer: of what the score.
197 00:16:09.030 ⇒ 00:16:10.780 Uttam Kumaran: Oh, I see. I see. I see. Okay. Okay.
198 00:16:10.780 ⇒ 00:16:11.360 Patrick Trainer: Yeah.
199 00:16:11.750 ⇒ 00:16:13.875 Uttam Kumaran: And then on the current data.
200 00:16:14.460 ⇒ 00:16:18.978 Uttam Kumaran: I think this is also really great. I actually think,
201 00:16:20.040 ⇒ 00:16:21.090 Uttam Kumaran: that
202 00:16:21.750 ⇒ 00:16:25.099 Uttam Kumaran: basic and some should be the highest.
203 00:16:25.570 ⇒ 00:16:31.910 Uttam Kumaran: The reason I say that is right. No formal is like Google sheets land.
204 00:16:31.910 ⇒ 00:16:32.370 Patrick Trainer: Right.
205 00:16:32.370 ⇒ 00:16:32.900 Uttam Kumaran: And.
206 00:16:32.900 ⇒ 00:16:37.360 Patrick Trainer: It is tough. Well, so basic basic is the highest right now. But like.
207 00:16:37.360 ⇒ 00:16:42.429 Uttam Kumaran: Yeah, like, basic in some meaning. It’s like we can. I can show you the gap from like.
208 00:16:43.880 ⇒ 00:16:55.619 Uttam Kumaran: having a shitty snowflake to having like Dbt and Bi, right, can I show you? Can I? Can I reasonably explain the gap? And then also there’s something to be said about like you’re not going to be in a normal, in a no
209 00:16:55.950 ⇒ 00:16:58.189 Uttam Kumaran: formal data, infrastructure land
210 00:16:58.230 ⇒ 00:17:00.329 Uttam Kumaran: without like
211 00:17:00.680 ⇒ 00:17:08.759 Uttam Kumaran: without, in like a hot, like a no formal data infrastructure in like a low data. Industry is like should be the worst
212 00:17:09.010 ⇒ 00:17:13.999 Uttam Kumaran: right right cause. They have no incentive. So that’s that’s what I would say in like my
213 00:17:14.150 ⇒ 00:17:25.270 Uttam Kumaran: in all of my like sales meetings is like. If they have something, then it’s easier to pitch them a dream. If they have nothing, then you’re almost explaining to them what the thing is, and then you also have to explain the dream.
214 00:17:25.270 ⇒ 00:17:25.700 Patrick Trainer: Right.
215 00:17:25.700 ⇒ 00:17:26.170 Uttam Kumaran: It’s just like.
216 00:17:26.170 ⇒ 00:17:26.540 Patrick Trainer: Right.
217 00:17:27.089 ⇒ 00:17:29.269 Uttam Kumaran: It’s not. If you think.
218 00:17:29.270 ⇒ 00:17:32.890 Patrick Trainer: Too like, who’s going to have nothing?
219 00:17:33.422 ⇒ 00:17:36.019 Patrick Trainer: Like it could be these low.
220 00:17:36.020 ⇒ 00:17:36.420 Uttam Kumaran: Exactly.
221 00:17:36.420 ⇒ 00:17:41.080 Patrick Trainer: Or below 3 million. And so it’s like we wouldn’t go after those leads.
222 00:17:41.080 ⇒ 00:17:41.880 Uttam Kumaran: Okay.
223 00:17:42.030 ⇒ 00:17:55.270 Uttam Kumaran: yeah, I just like, I think about just completely dogging that. Because also, I think of the one thing I’ll show. I’ll explain at the end of like this section is also like, what is our service like? What are the services we offer now?
224 00:17:55.490 ⇒ 00:17:56.580 Uttam Kumaran: And
225 00:17:56.740 ⇒ 00:18:03.600 Uttam Kumaran: like, even if we were to get a lead. That’s like no formal data for structure super low data
226 00:18:03.840 ⇒ 00:18:11.739 Uttam Kumaran: that probably. And they’re like below 3 million. Then there’s like, it’s like, I don’t want them to not even fit the bill at all.
227 00:18:11.940 ⇒ 00:18:25.200 Uttam Kumaran: Cut out right, and so that’ll be. The thing is like I. I haven’t done the math on the scores, but those will be like folks where it’s like they’re not going to pay. It’s a shit show and like the sale may not even happen. So it’s like
228 00:18:25.790 ⇒ 00:18:29.710 Uttam Kumaran: completely get rid of them. But I I feel like if they’re already.
229 00:18:29.760 ⇒ 00:18:32.630 Uttam Kumaran: I think what we’ll do is when we will run.
230 00:18:32.630 ⇒ 00:18:42.860 Patrick Trainer: They’re still the top of the funnel. And so like this, this is still the like the lead. We have all the other stages that like they would shift into of like
231 00:18:43.520 ⇒ 00:18:49.939 Patrick Trainer: the Mql. The Pql’s, yeah. And then, like sales accepted like, that’s where it’s going to be like.
232 00:18:50.130 ⇒ 00:18:50.640 Uttam Kumaran: Okay, that.
233 00:18:50.640 ⇒ 00:18:52.330 Patrick Trainer: Human in the loop of like.
234 00:18:52.380 ⇒ 00:18:58.370 Patrick Trainer: do we even want to talk to these guys? I see and it’d probably be like like what? You said, no.
235 00:18:59.660 ⇒ 00:19:00.190 Patrick Trainer: yeah.
236 00:19:00.190 ⇒ 00:19:01.739 Uttam Kumaran: So that makes it. This is all
237 00:19:03.340 ⇒ 00:19:04.510 Uttam Kumaran: involved with.
238 00:19:06.390 ⇒ 00:19:09.969 Uttam Kumaran: So then, on the decision maker.
239 00:19:10.150 ⇒ 00:19:12.055 Uttam Kumaran: I actually think
240 00:19:12.860 ⇒ 00:19:14.640 Uttam Kumaran: we should swap
241 00:19:15.460 ⇒ 00:19:18.200 Uttam Kumaran: the sea level
242 00:19:18.280 ⇒ 00:19:20.390 Uttam Kumaran: with the data manager.
243 00:19:22.240 ⇒ 00:19:30.129 Uttam Kumaran: for a couple of reasons. One is when, like one of the things that I realize in data is, it’s very hard to do like product like growth.
244 00:19:30.659 ⇒ 00:19:44.619 Uttam Kumaran: Meaning you kind of nailed it. But even just to continue to harp on that is like it’s very hard for someone low to come in and be like, Hey, I want to bring in like a hundred 1,000 a year data consultancy, right? But if the CEO is like.
245 00:19:44.740 ⇒ 00:19:47.910 Uttam Kumaran: Hey, I met with these guys. They said, we can do it. Can you talk to them?
246 00:19:48.150 ⇒ 00:19:56.140 Uttam Kumaran: Yeah, we may get some suspicion from the data folks, but then we’ll crush that right. If we get suspicion from the sea level, then it’s next
247 00:19:56.420 ⇒ 00:19:58.409 Uttam Kumaran: it’s next off the rep
248 00:19:58.780 ⇒ 00:20:04.399 Uttam Kumaran: and I don’t think the 5 Points. Really, I do still agree that the Vp is actually
249 00:20:04.570 ⇒ 00:20:08.580 Uttam Kumaran: way. Better because they’re they have decision making authority, and they have
250 00:20:08.700 ⇒ 00:20:12.669 Uttam Kumaran: some understanding. I do still think that sea level
251 00:20:13.250 ⇒ 00:20:14.770 Uttam Kumaran: like. For example.
252 00:20:14.900 ⇒ 00:20:22.510 Uttam Kumaran: I may have a 10 min call with a sea level person like out and about, and they’ll just loop they’ll be like, Hey, go talk to this person, make it happen.
253 00:20:22.700 ⇒ 00:20:27.799 Uttam Kumaran: and then it’s in motion already. Right? So I, the data manager lead, can’t do that.
254 00:20:27.880 ⇒ 00:20:30.350 Uttam Kumaran: And then I would almost. I would almost just like
255 00:20:30.830 ⇒ 00:20:34.359 Uttam Kumaran: kill other rules or like, I don’t know. I just like.
256 00:20:34.870 ⇒ 00:20:38.423 Uttam Kumaran: Yeah, I just think, like, I really like the sea level, because
257 00:20:38.920 ⇒ 00:20:49.840 Uttam Kumaran: not because they have a really good understanding. What’s going on, what we harp on, and we harp on this in our in our messaging and in our website is
258 00:20:50.666 ⇒ 00:20:53.249 Uttam Kumaran: Like results and speed
259 00:20:54.610 ⇒ 00:21:03.179 Uttam Kumaran: and like actual like action items that come out of it. And those are all music to their ears. The how is also what we’re good at. But that’s not like
260 00:21:03.260 ⇒ 00:21:07.139 Uttam Kumaran: we’re not a we’re not a company that outwardly we’re gonna share.
261 00:21:07.330 ⇒ 00:21:17.453 Uttam Kumaran: We’re gonna share more about like the results. We’re also going to share the how, and then we’ll share about us. But it’ll be in that order, because otherwise we become like,
262 00:21:17.940 ⇒ 00:21:21.920 Uttam Kumaran: if you become too data focused, you attract the folks on the bottom.
263 00:21:22.030 ⇒ 00:21:31.880 Uttam Kumaran: which is fine again. Not that about them, but they don’t have any decision making authority. So our goal is to attract the decision makers.
264 00:21:31.880 ⇒ 00:21:33.779 Patrick Trainer: Opportunity, too, for, like
265 00:21:33.810 ⇒ 00:21:36.030 Patrick Trainer: those in, I’m just playing devil’s advocate.
266 00:21:36.030 ⇒ 00:21:36.770 Uttam Kumaran: Yeah.
267 00:21:37.100 ⇒ 00:21:41.719 Patrick Trainer: For, like those other roles say like for us, just to do their job.
268 00:21:43.080 ⇒ 00:21:43.699 Uttam Kumaran: Yeah. Totally.
269 00:21:43.997 ⇒ 00:21:47.869 Patrick Trainer: There’s there’s a story of a guy that worked for at, and T.
270 00:21:47.960 ⇒ 00:21:49.140 Patrick Trainer: That
271 00:21:49.180 ⇒ 00:21:50.240 Patrick Trainer: he paid
272 00:21:50.310 ⇒ 00:21:56.290 Patrick Trainer: it. It was like, I mean, he made say, the $200,000 salary, and he would pay
273 00:21:56.760 ⇒ 00:22:01.869 Patrick Trainer: 20 grand to this consultancy outside like in India or China.
274 00:22:01.870 ⇒ 00:22:02.350 Uttam Kumaran: Yeah.
275 00:22:02.973 ⇒ 00:22:07.649 Patrick Trainer: And he would just pay them to do his job, and he would just chill
276 00:22:08.256 ⇒ 00:22:09.350 Patrick Trainer: very true.
277 00:22:09.450 ⇒ 00:22:10.100 Patrick Trainer: And
278 00:22:11.160 ⇒ 00:22:15.209 Patrick Trainer: yeah, I’ll I’ll send that article over like. That’s a. It’s a true story. It’s it’s.
279 00:22:15.210 ⇒ 00:22:15.610 Uttam Kumaran: Yeah.
280 00:22:15.610 ⇒ 00:22:16.210 Patrick Trainer: Really funny.
281 00:22:16.210 ⇒ 00:22:21.849 Uttam Kumaran: Then I would leave other rules, because then it’s like, if everything fits the bill and we don’t have one of those guys, then they still
282 00:22:22.250 ⇒ 00:22:25.790 Uttam Kumaran: can make it right. But in this sense I would.
283 00:22:25.790 ⇒ 00:22:28.639 Patrick Trainer: It’s a little ethically hairy on, on.
284 00:22:28.830 ⇒ 00:22:29.799 Uttam Kumaran: Yeah, on their.
285 00:22:29.800 ⇒ 00:22:30.860 Patrick Trainer: Sad, but.
286 00:22:33.100 ⇒ 00:22:39.636 Uttam Kumaran: Yeah, in this situation. Like, yeah, I think let’s re rank. Let’s rank C as the highest, and then
287 00:22:42.300 ⇒ 00:22:43.800 Uttam Kumaran: cause cause again.
288 00:22:43.830 ⇒ 00:22:50.710 Uttam Kumaran: if we get the odds of us getting on. The phone call with them is low, anyway, so we’ll get. We’ll get. We’ll get dogged on that.
289 00:22:50.870 ⇒ 00:22:56.280 Uttam Kumaran: But if I do get on the phone call with the CEO I’m getting. I’m booking a fucking meeting like
290 00:22:56.410 ⇒ 00:23:03.569 Uttam Kumaran: it’s gonna be easy, because it’s easy for me to be like, are you having issues? Getting basic numbers from your team
291 00:23:03.740 ⇒ 00:23:09.669 Uttam Kumaran: like are are things constantly down, and you can’t even understand your turn right like it. Just me. It’s just like
292 00:23:09.710 ⇒ 00:23:13.809 Uttam Kumaran: really digs into them. And then they’re like cool. Go talk to my head of analytics.
293 00:23:13.850 ⇒ 00:23:18.569 Uttam Kumaran: Then you already have the buying because the headline, analytics is like, Oh, they’re good.
294 00:23:18.730 ⇒ 00:23:23.889 Uttam Kumaran: Then it’s a done deal right. Otherwise it’s always a pitch up. Everything else is a pitch up
295 00:23:24.240 ⇒ 00:23:27.020 Uttam Kumaran: right? True kind of deal. So.
296 00:23:27.270 ⇒ 00:23:30.683 Patrick Trainer: And I think that’d be dependent, too, like
297 00:23:31.290 ⇒ 00:23:34.769 Patrick Trainer: on the size of the industry, especially.
298 00:23:34.770 ⇒ 00:23:35.219 Uttam Kumaran: For sure.
299 00:23:35.220 ⇒ 00:23:38.030 Patrick Trainer: The size of their company, too, like
300 00:23:38.390 ⇒ 00:23:39.460 Patrick Trainer: getting
301 00:23:39.700 ⇒ 00:23:41.899 Patrick Trainer: c-suite talk on a
302 00:23:42.480 ⇒ 00:23:49.150 Patrick Trainer: 3 million dollar company like, I think, would just be intrinsically easier than doing like a a.
303 00:23:49.150 ⇒ 00:23:49.719 Uttam Kumaran: Very true.
304 00:23:49.720 ⇒ 00:23:53.819 Patrick Trainer: Dollar company, because they just like they’re thinking about different things.
305 00:23:53.820 ⇒ 00:23:58.619 Uttam Kumaran: Yeah. Yeah. And I, I think again, on the come, the conversion rate from lead to meeting
306 00:23:58.810 ⇒ 00:24:00.209 Uttam Kumaran: that is going to be
307 00:24:00.430 ⇒ 00:24:01.210 Uttam Kumaran: like
308 00:24:01.650 ⇒ 00:24:10.410 Uttam Kumaran: or 10 times lower, right? So, although there’ll be higher math score like, will they? It won’t. It won’t happen at the same rate. So.
309 00:24:10.410 ⇒ 00:24:13.050 Patrick Trainer: Right? Right? Yeah. And it’s like
310 00:24:13.130 ⇒ 00:24:16.920 Patrick Trainer: every like, there’s so many different pathways that these leads can take.
311 00:24:17.404 ⇒ 00:24:28.729 Patrick Trainer: And like, have like these effectors on on scores. But then there’s also like, if you if you see like not all of these like total score.
312 00:24:29.200 ⇒ 00:24:31.779 Patrick Trainer: like points, not all of them are the same
313 00:24:31.860 ⇒ 00:24:34.099 Patrick Trainer: like some like like.
314 00:24:34.100 ⇒ 00:24:35.110 Uttam Kumaran: Some are 5, some are.
315 00:24:35.110 ⇒ 00:24:37.920 Patrick Trainer: Yeah, yeah, some are 5. Some are so like.
316 00:24:37.930 ⇒ 00:24:44.700 Patrick Trainer: we have different weights, basically or different opportunity for yeah, different weights based on like
317 00:24:44.940 ⇒ 00:24:47.939 Patrick Trainer: the category that things are falling into.
318 00:24:48.080 ⇒ 00:24:48.585 Patrick Trainer: Yeah.
319 00:24:51.040 ⇒ 00:24:51.570 Patrick Trainer: cool.
320 00:24:52.100 ⇒ 00:24:54.441 Uttam Kumaran: The last thing on growth.
321 00:24:55.410 ⇒ 00:24:59.090 Uttam Kumaran: I think this is a great way of putting it like I I think.
322 00:24:59.721 ⇒ 00:25:09.919 Uttam Kumaran: I’ll talk right after this about like how we actually map these to stuff, we can get out from Apollo. But, the only thing I would say is, one thing I learned is that
323 00:25:09.950 ⇒ 00:25:15.290 Uttam Kumaran: although you may need a some folks need a lot more help, it doesn’t mean that they have budget for it.
324 00:25:15.490 ⇒ 00:25:18.750 Uttam Kumaran: So that’s why my kind of afters.
325 00:25:19.010 ⇒ 00:25:20.310 Uttam Kumaran: Basically, it’s
326 00:25:20.570 ⇒ 00:25:27.269 Uttam Kumaran: working in startups. And I spend a lot of time with startups here in Austin. They have no fucking money for a few folks like us
327 00:25:29.430 ⇒ 00:25:33.290 Patrick Trainer: We’re a lot. We’re a hell of a lot cheaper than hiring a data engineer, though.
328 00:25:33.700 ⇒ 00:25:45.280 Uttam Kumaran: I know. But the problem is is like they’re not. They’re gonna they’re gonna they’d rather hire a data engineer and throw them into like a shit show than get us, because it’s just like a
329 00:25:45.480 ⇒ 00:25:49.380 Uttam Kumaran: it. It’s just like it is. I don’t know. I don’t know what the real like.
330 00:25:51.310 ⇒ 00:25:57.119 Uttam Kumaran: It’s also like, I want to be cautious of us going to work with the startup like, I actually.
331 00:25:57.120 ⇒ 00:25:57.840 Patrick Trainer: Yeah. That’s the.
332 00:25:57.840 ⇒ 00:26:01.569 Uttam Kumaran: I’m actually very. I’m actually very anti like.
333 00:26:02.710 ⇒ 00:26:10.289 Uttam Kumaran: I actually don’t even want to work with startups. Frankly, unless like a startup meaning like anyone below series.
334 00:26:10.350 ⇒ 00:26:19.030 Uttam Kumaran: See? I don’t even wanna touch, because it’s a nightmare, regardless like, even if they’re doing really great. It could be a nightmare.
335 00:26:19.030 ⇒ 00:26:19.440 Patrick Trainer: I mean.
336 00:26:19.440 ⇒ 00:26:19.950 Uttam Kumaran: And.
337 00:26:19.950 ⇒ 00:26:22.310 Patrick Trainer: Too like serious like, you don’t
338 00:26:22.440 ⇒ 00:26:26.359 Patrick Trainer: get a series C, unless you’re doing like 10 million in arr.
339 00:26:26.360 ⇒ 00:26:27.329 Uttam Kumaran: No, I agree.
340 00:26:27.330 ⇒ 00:26:27.670 Patrick Trainer: How.
341 00:26:27.670 ⇒ 00:26:30.209 Uttam Kumaran: So that’s so. That’s what I’m saying is that I think the
342 00:26:30.330 ⇒ 00:26:33.280 Uttam Kumaran: early stage pre-revenue should be like a 0
343 00:26:33.400 ⇒ 00:26:33.995 Uttam Kumaran: right
344 00:26:35.590 ⇒ 00:26:47.930 Uttam Kumaran: cause. There, it’s just not fun. It’s like, and it’s a like. Everything will be difficult, from the initial negotiation to the renegotiation, the amount of effort it will take from us.
345 00:26:48.466 ⇒ 00:26:54.569 Uttam Kumaran: Like the actual psychological burden of a client like that is so high that
346 00:26:54.950 ⇒ 00:26:58.220 Uttam Kumaran: I’m almost like, I don’t even wanna. And
347 00:26:58.280 ⇒ 00:27:03.155 Uttam Kumaran: you’re right that, like, maybe those people won’t even show up in the revenue categories.
348 00:27:03.690 ⇒ 00:27:05.150 Uttam Kumaran: I mean, I think, too, it’s like.
349 00:27:05.150 ⇒ 00:27:09.013 Patrick Trainer: This. This is very like our personal preference of like.
350 00:27:09.400 ⇒ 00:27:18.059 Uttam Kumaran: Well, it’s just not. It’s just not. It won’t be, as I know won’t be a successful client for us with our current offering. If I could offer a very like low touch.
351 00:27:18.190 ⇒ 00:27:19.190 Uttam Kumaran: cheap.
352 00:27:19.810 ⇒ 00:27:26.120 Uttam Kumaran: almost like a product, high service thing, then I would. I would be okay with taking them. But we’re very high touch.
353 00:27:26.210 ⇒ 00:27:31.220 Uttam Kumaran: very. You know. I we want to be very high, Bill, like.
354 00:27:32.570 ⇒ 00:27:32.900 Patrick Trainer: For fun.
355 00:27:32.900 ⇒ 00:27:34.770 Uttam Kumaran: That are very cost. Conscious
356 00:27:35.000 ⇒ 00:27:39.940 Uttam Kumaran: they’re gonna be. They’re gonna be caught in like equating our price with like.
357 00:27:40.170 ⇒ 00:27:42.329 Uttam Kumaran: how much does Github cost like.
358 00:27:42.330 ⇒ 00:27:42.840 Patrick Trainer: Yeah.
359 00:27:42.840 ⇒ 00:27:48.029 Uttam Kumaran: I want them to be like, we’re a drop in the bucket for the impact that we’re gonna do like. That’s
360 00:27:48.230 ⇒ 00:27:56.980 Uttam Kumaran: what is at what point does it change from like us, comparing other cost centers to us, comparing to like the impact we can make like, that’s what I’m trying to get to people.
361 00:27:57.274 ⇒ 00:27:59.830 Uttam Kumaran: And again I fucked this up for a year.
362 00:27:59.870 ⇒ 00:28:12.009 Uttam Kumaran: you know, like this problem about like who to go after cause. I constantly take calls and start, because that’s like my network. And it’s it’s like 98% let down.
363 00:28:13.340 ⇒ 00:28:25.719 Uttam Kumaran: And it let that happens a couple of ways, either. Like I I hop on a couple of calls. Things look great. And then I put a proposal they’re like, Oh, my God! I can’t afford like even 10 KA month. We were thinking like 4 KA month. I’m like, who on the planet.
364 00:28:26.080 ⇒ 00:28:26.799 Patrick Trainer: See ya.
365 00:28:26.800 ⇒ 00:28:33.320 Uttam Kumaran: What are you talking about? You’ve already spent 4 K. Just talking to me, and then and then so it’s just like
366 00:28:33.630 ⇒ 00:28:37.410 Uttam Kumaran: I I just done so many of them that I realized that this
367 00:28:38.040 ⇒ 00:28:39.260 Uttam Kumaran: I don’t know. It’s
368 00:28:39.470 ⇒ 00:28:49.499 Uttam Kumaran: that’s why I want to be hard headed on some things, because I think we just completely filter them out. But I know you. I know that like they will eventually get filtered out. But yeah.
369 00:28:49.500 ⇒ 00:28:52.530 Patrick Trainer: 4 K. Just to talk to people. Then I just say, just keep talking.
370 00:28:52.530 ⇒ 00:28:59.610 Uttam Kumaran: No, I mean dude, if we take like 10 meetings over like 2 months or 3 months, like what? And I have to like, prepare for those like.
371 00:28:59.610 ⇒ 00:29:00.620 Patrick Trainer: Yeah, yeah.
372 00:29:00.810 ⇒ 00:29:05.959 Uttam Kumaran: It’s like, that’s why. Also, when we talk about the stages and the time in between.
373 00:29:06.210 ⇒ 00:29:10.949 Uttam Kumaran: we’ll talk about, hey, like we’re trying to get things done like 2 meetings to like proposal.
374 00:29:11.210 ⇒ 00:29:19.509 Uttam Kumaran: you know, and I made the mistake of having conversations over months. Talk to this person, talk to this person. Can you help us with this? I’ve given free help?
375 00:29:19.740 ⇒ 00:29:23.339 Uttam Kumaran: It’s like again, I’m just like, fuck this up. So done.
376 00:29:23.340 ⇒ 00:29:24.230 Patrick Trainer: Right, right.
377 00:29:24.230 ⇒ 00:29:25.429 Uttam Kumaran: Year. So
378 00:29:26.504 ⇒ 00:29:30.729 Uttam Kumaran: yeah, but I would just downright early and then mature and stable. I kind of like.
379 00:29:30.910 ⇒ 00:29:34.089 Uttam Kumaran: because they pay bills and like they don’t blink.
380 00:29:34.090 ⇒ 00:29:34.480 Patrick Trainer: Right.
381 00:29:34.480 ⇒ 00:29:37.520 Uttam Kumaran: So I think I I think I got your.
382 00:29:37.570 ⇒ 00:29:39.867 Uttam Kumaran: I think where we’re gonna get
383 00:29:41.460 ⇒ 00:29:47.769 Uttam Kumaran: your your explanation for the mature and stable, and like these categories will actually come out from
384 00:29:47.800 ⇒ 00:29:49.380 Uttam Kumaran: B and CI think.
385 00:29:49.900 ⇒ 00:29:50.230 Patrick Trainer: It’s.
386 00:29:50.230 ⇒ 00:29:54.219 Uttam Kumaran: I think this is more about their aptitude, for, like our.
387 00:29:54.320 ⇒ 00:29:58.450 Uttam Kumaran: I think about more, is like their aptitude, their appetite for our like.
388 00:29:59.090 ⇒ 00:30:02.729 Uttam Kumaran: our amount of spend, and like our the sorts of problems that we solve.
389 00:30:02.730 ⇒ 00:30:03.969 Patrick Trainer: Right right
390 00:30:04.190 ⇒ 00:30:09.320 Patrick Trainer: and and to like. I mean, obviously, these scores aren’t set in stone.
391 00:30:09.620 ⇒ 00:30:10.250 Uttam Kumaran: Yes.
392 00:30:10.250 ⇒ 00:30:16.820 Patrick Trainer: Kind of like more as a template to think about, which, I guess, is what we’re doing right now. But like we can have negative scores.
393 00:30:18.062 ⇒ 00:30:19.580 Patrick Trainer: and so like.
394 00:30:19.890 ⇒ 00:30:21.990 Patrick Trainer: if we like, these rapid
395 00:30:22.090 ⇒ 00:30:23.749 Patrick Trainer: growth phases like
396 00:30:24.040 ⇒ 00:30:26.779 Patrick Trainer: that could be a negative 5 apart from a 0.
397 00:30:27.300 ⇒ 00:30:27.950 Uttam Kumaran: Yeah.
398 00:30:27.950 ⇒ 00:30:31.049 Patrick Trainer: And it’s like it’s all going to fit to who
399 00:30:31.340 ⇒ 00:30:40.570 Patrick Trainer: we want and like. I mean, you have a gut instinct because you’ve been talking to these people for a while. And so it’s like we’ll.
400 00:30:40.980 ⇒ 00:30:43.820 Patrick Trainer: And it’s also like this is an iterative thing.
401 00:30:43.820 ⇒ 00:30:46.690 Uttam Kumaran: Sure for sure. Yeah, I just want to give you like the
402 00:30:47.330 ⇒ 00:30:50.470 Uttam Kumaran: I just want to give you the anecdotal on each of these, so that.
403 00:30:50.470 ⇒ 00:30:51.119 Patrick Trainer: Oh, yeah, for sure.
404 00:30:51.120 ⇒ 00:30:52.380 Uttam Kumaran: Kind of see? Because
405 00:30:53.190 ⇒ 00:30:57.989 Uttam Kumaran: yeah, cause I think that that’s what’s gonna help. Because I’m also mapping. Basically again, I have, like, maybe
406 00:30:58.660 ⇒ 00:31:08.960 Uttam Kumaran: I mean. There were times where I was talking like to people like 10 times a week, and so all those are just like ingrained on, like what was working what isn’t working. But okay, this makes this makes a ton of sense.
407 00:31:09.200 ⇒ 00:31:09.880 Patrick Trainer: Right.
408 00:31:10.490 ⇒ 00:31:19.099 Uttam Kumaran: I think the other thing overall is like, I want to make sure that for each of these they’re there. We can actually identify this in the score.
409 00:31:19.200 ⇒ 00:31:21.890 Uttam Kumaran: so we’ll find out whether some of this is like
410 00:31:22.921 ⇒ 00:31:27.799 Uttam Kumaran: we we do have some drivers that we can pull out of Apollo or pull out of Linkedin
411 00:31:27.900 ⇒ 00:31:29.830 Uttam Kumaran: right through some other research.
412 00:31:30.193 ⇒ 00:31:34.860 Uttam Kumaran: Or we’ll find out whether, hey, this is like, we want to get to this, but we don’t have the
413 00:31:35.010 ⇒ 00:31:42.040 Uttam Kumaran: the input data to do that, or it’s it would be harder. So that’s the only thing I want to make sure is that we don’t move forward with anything we can’t measure.
414 00:31:42.470 ⇒ 00:31:43.140 Patrick Trainer: Right.
415 00:31:43.520 ⇒ 00:31:44.409 Uttam Kumaran: Cause. This is what I have.
416 00:31:44.910 ⇒ 00:31:56.909 Uttam Kumaran: Segmentation people throw. This is just like what I dealt with before is that people throw things out, and then, like, we just don’t have the input data to do that. And then it becomes like, it becomes subjective immediately. Yeah, so.
417 00:31:56.910 ⇒ 00:31:57.800 Patrick Trainer: Yeah, true.
418 00:31:58.360 ⇒ 00:31:58.920 Uttam Kumaran: Cool.
419 00:31:58.920 ⇒ 00:31:59.350 Patrick Trainer: Yeah, exactly.
420 00:31:59.350 ⇒ 00:32:00.209 Uttam Kumaran: That makes sense.
421 00:32:02.000 ⇒ 00:32:18.350 Patrick Trainer: and then, so yeah, further on, we’ve got these like engagement scores, right? So we have. How do they interact with like our our website. So are they visiting the service page like of what we actually do? If we.
422 00:32:18.450 ⇒ 00:32:19.750 Patrick Trainer: I mean, when we
423 00:32:19.800 ⇒ 00:32:23.186 Patrick Trainer: right case studies like, are they viewing that?
424 00:32:23.750 ⇒ 00:32:28.971 Patrick Trainer: we don’t have like a pricing packaging page right now. I don’t. I don’t believe
425 00:32:29.542 ⇒ 00:32:30.210 Uttam Kumaran: But like looking.
426 00:32:30.210 ⇒ 00:32:35.539 Patrick Trainer: At that is is good signal, too. Because that that’s showing like they’re
427 00:32:36.030 ⇒ 00:32:51.839 Patrick Trainer: interest or like they’re they’re everything’s like a signal, right? And they’re kind of like signaling that they’re wanting to do this. Spending more than 3 min on a site. That’s also a good signal it. It could be like a a red herring but like
428 00:32:52.560 ⇒ 00:32:58.000 Patrick Trainer: like, when I’m viewing services or tools or whatnot like, I’m
429 00:32:58.860 ⇒ 00:33:02.590 Patrick Trainer: like, 2 min, 1 min, 2 min tops like, I’m
430 00:33:02.620 ⇒ 00:33:04.788 Patrick Trainer: see what I want. And then.
431 00:33:05.420 ⇒ 00:33:09.080 Patrick Trainer: if it’s not interesting, you’re not relevant like, you’re gone.
432 00:33:09.408 ⇒ 00:33:15.469 Patrick Trainer: Yeah, I feel like like 3 min, and we can adjust that to to 5 or whatever, but like.
433 00:33:15.490 ⇒ 00:33:16.969 Uttam Kumaran: This is this is amazing. This is.
434 00:33:16.970 ⇒ 00:33:42.939 Patrick Trainer: Doing that and so moving on, we got content engagement wrote this to do of, we actually need to write content and so like, if we have, like a like data strategy like, you see, this shit on Linkedin, like day in day out of like all these data influencers talking about like how to do things. That’s like the the top of the funnel, like.
435 00:33:43.380 ⇒ 00:33:46.799 Patrick Trainer: I mean, just getting people into the funnel at all. Like, that’s the.
436 00:33:46.800 ⇒ 00:33:47.190 Uttam Kumaran: Version, that.
437 00:33:47.190 ⇒ 00:33:49.640 Patrick Trainer: That’s that’s that’s the lead,
438 00:33:50.410 ⇒ 00:34:03.989 Patrick Trainer: and like, if we have blog posts like seeing that if they have a subscribe to Newsletter, I actually think that could be higher. I think that’s kind of interesting but it could also signal that, like.
439 00:34:04.200 ⇒ 00:34:12.800 Patrick Trainer: they kind of self serve already. And they’re just looking for kind of like tips, tips, and tricks, but don’t necessarily need
440 00:34:14.590 ⇒ 00:34:15.010 Uttam Kumaran: Your sister.
441 00:34:15.010 ⇒ 00:34:18.090 Patrick Trainer: It could. But like it’s all. It’s all dependent on
442 00:34:18.159 ⇒ 00:34:19.399 Patrick Trainer: anything else.
443 00:34:19.958 ⇒ 00:34:27.130 Patrick Trainer: or everything else. And then sharing content on social media. It’s I’m kind of iffy
444 00:34:27.139 ⇒ 00:34:32.320 Patrick Trainer: about that, but it could be huge, too, like it. It. There.
445 00:34:33.280 ⇒ 00:34:44.119 Patrick Trainer: I feel like that’s kind of like champion championing, championing. What we’re doing and then it’s also like.
446 00:34:45.010 ⇒ 00:34:49.379 Patrick Trainer: it’s more advertising, too. So there’s there’s potential there of like.
447 00:34:49.460 ⇒ 00:34:55.739 Patrick Trainer: do they have a network of like who we can go after and so like
448 00:34:55.760 ⇒ 00:35:01.740 Patrick Trainer: establishing some like a call or an email to them. But it would probably be worth it.
449 00:35:03.840 ⇒ 00:35:07.940 Patrick Trainer: Going in. Further, we have like email engagement. So
450 00:35:08.010 ⇒ 00:35:14.831 Patrick Trainer: we’re sending stuff to people day in, day out. Are they opening it? Are they clicking on links?
451 00:35:15.210 ⇒ 00:35:15.690 Uttam Kumaran: Yeah.
452 00:35:15.690 ⇒ 00:35:22.739 Patrick Trainer: Windows. These have like a ceiling. Because, like, if they’re just opening every single email, some people just like
453 00:35:23.350 ⇒ 00:35:28.909 Patrick Trainer: open emails to get the whole number off of their box like I do that
454 00:35:29.460 ⇒ 00:35:35.060 Patrick Trainer: and so which actually, I haven’t really thought about, maybe I shouldn’t do that
455 00:35:35.850 ⇒ 00:35:36.505 Patrick Trainer: But
456 00:35:37.470 ⇒ 00:35:40.650 Patrick Trainer: yeah. So these have like a ceiling of of
457 00:35:41.170 ⇒ 00:35:47.480 Patrick Trainer: how many emails they they do. And it’s also like, if they if they’re clicking on links 3 times like
458 00:35:48.220 ⇒ 00:35:53.440 Patrick Trainer: that’s enough to to say, like, Okay, it’s like, let’s talk. Let’s talk to this person.
459 00:35:55.330 ⇒ 00:35:59.799 Patrick Trainer: we’ve got this event participation. Maybe take this out like we’re not.
460 00:36:00.000 ⇒ 00:36:02.550 Patrick Trainer: I don’t think at the point to do events, but
461 00:36:03.240 ⇒ 00:36:03.899 Patrick Trainer: like
462 00:36:04.310 ⇒ 00:36:05.419 Patrick Trainer: we could go.
463 00:36:05.420 ⇒ 00:36:06.170 Uttam Kumaran: Think about yeah.
464 00:36:06.170 ⇒ 00:36:13.670 Patrick Trainer: Go to events and just like talk to people and say like, did they talk to me? 24.
465 00:36:13.670 ⇒ 00:36:17.359 Uttam Kumaran: No, and that’s the thing like again. I I’d rather like I
466 00:36:17.450 ⇒ 00:36:23.179 Uttam Kumaran: I used to go to a lot of these, and I just like don’t like going without an objective anymore, because
467 00:36:23.500 ⇒ 00:36:29.449 Uttam Kumaran: I’ve like heard everything for the most part, that there is to be said, and usually like one out of 10 events
468 00:36:30.120 ⇒ 00:36:32.959 Uttam Kumaran: I like meet someone that’s like kind of cool.
469 00:36:33.060 ⇒ 00:36:37.530 Uttam Kumaran: but like the amount of drinking, the amount of like small talk, the amount of like
470 00:36:37.650 ⇒ 00:36:40.980 Uttam Kumaran: people kind of like shitting on whatever you say. It’s like, kind of like.
471 00:36:41.120 ⇒ 00:36:51.930 Uttam Kumaran: just like emotionally, not like what I like to do. But if I if it’s like, go to this event and meet 10 people and like, get them in our Crm, yeah, I would total. I’d go right now, like.
472 00:36:52.220 ⇒ 00:37:04.600 Uttam Kumaran: if I had an objective. I would totally go before I’m just going to be like I’m do. I’m at this company. I’m doing this thing. I’m like, whatever if I’m going like as like a salesperson. Then it’s like a different story, right? You know.
473 00:37:04.870 ⇒ 00:37:07.870 Patrick Trainer: Have you met a guy, Noel Gomez?
474 00:37:08.180 ⇒ 00:37:10.220 Patrick Trainer: He runs data, coves.
475 00:37:10.940 ⇒ 00:37:12.870 Uttam Kumaran: No! Well, not.
476 00:37:12.870 ⇒ 00:37:15.470 Patrick Trainer: So data curves is like a A
477 00:37:15.660 ⇒ 00:37:28.760 Patrick Trainer: basically like Dbt cloud, but not dbt cloud. It’s like an analytics service. But it’s built into like Vs code that he’s doing so. I met him at
478 00:37:29.670 ⇒ 00:37:35.609 Patrick Trainer: actually, I think, the this the Snowflake Conference, and then saw him again here in New Orleans at.
479 00:37:35.610 ⇒ 00:37:36.070 Uttam Kumaran: Do you mean to you.
480 00:37:36.240 ⇒ 00:37:39.120 Patrick Trainer: And like, we’ve become like connected friends.
481 00:37:39.340 ⇒ 00:37:39.690 Uttam Kumaran: Yeah.
482 00:37:39.690 ⇒ 00:37:48.429 Patrick Trainer: But what he does when he’s walking around these conferences is like his T-shirt literally has a QR code on it.
483 00:37:48.430 ⇒ 00:37:49.490 Uttam Kumaran: Yeah, yeah, yeah.
484 00:37:49.834 ⇒ 00:38:01.530 Patrick Trainer: And and like, that’s his. That’s his capture. That’s his lead capture. That’s it’s also how he strikes up a conversation partially at how like I talked to him initially. I was like.
485 00:38:01.530 ⇒ 00:38:02.160 Uttam Kumaran: Yeah.
486 00:38:02.400 ⇒ 00:38:04.589 Patrick Trainer: What’s this QR code on your shirt?
487 00:38:04.870 ⇒ 00:38:07.630 Patrick Trainer: And and like we just kind of went
488 00:38:07.750 ⇒ 00:38:08.719 Patrick Trainer: from there.
489 00:38:09.160 ⇒ 00:38:14.229 Uttam Kumaran: No, I mean again, like I I just think like I have to do those things with like an objective
490 00:38:14.998 ⇒ 00:38:19.810 Uttam Kumaran: because then I’ll also make sure to go talk to certain people, or like I have a pitch in mind of like.
491 00:38:19.940 ⇒ 00:38:31.720 Uttam Kumaran: Oh, hey! We should connect next week. Here’s like my card. Or here’s a website. You just go sign up and like book a meeting like I just didn’t have any of that. But I’m I’m good at these. I’m good at these events.
492 00:38:32.020 ⇒ 00:38:34.639 Uttam Kumaran: It’s just like, very, very emotionally draining
493 00:38:35.490 ⇒ 00:38:38.179 Uttam Kumaran: for me, like, I’m not particularly like, Yeah.
494 00:38:38.540 ⇒ 00:38:48.369 Patrick Trainer: Yeah. And they do have like those parts that, like I in these events that I mean, I’ve never gone into. But they most
495 00:38:48.450 ⇒ 00:38:59.019 Patrick Trainer: usually have those like pods where you go in and, like you, schedule meetings with with people to talk to them like privately, like I had no business doing that. But
496 00:38:59.400 ⇒ 00:39:02.200 Uttam Kumaran: Yeah, like, now.
497 00:39:02.200 ⇒ 00:39:02.840 Patrick Trainer: Didn’t like.
498 00:39:02.840 ⇒ 00:39:17.210 Uttam Kumaran: I have no like ego anymore about any of this like. So I just gotta get put into meetings and like, have a directive, and I’ll go talk to people, because again, it’ll just turn off the part of my brain that’s like, look for validation.
499 00:39:17.320 ⇒ 00:39:21.129 Uttam Kumaran: which is like what you usually happens at these events. If I go and like
500 00:39:21.470 ⇒ 00:39:29.169 Uttam Kumaran: I’m doing this for Brainforge, like we go talk to Xyz. People were pitching. I don’t mind talking about a company it was. It was actually like going in with, just like.
501 00:39:30.330 ⇒ 00:39:31.999 Uttam Kumaran: Do you guys see, like
502 00:39:32.170 ⇒ 00:39:35.849 Uttam Kumaran: metric flow got bought by these people? And it’s like.
503 00:39:37.550 ⇒ 00:39:41.329 Uttam Kumaran: I don’t want to talk about shit. That doesn’t matter to anybody right.
504 00:39:41.330 ⇒ 00:39:41.710 Patrick Trainer: Right.
505 00:39:41.710 ⇒ 00:39:45.849 Uttam Kumaran: I don’t know, but anymore, I hope.
506 00:39:45.850 ⇒ 00:39:46.670 Patrick Trainer: The, the.
507 00:39:46.670 ⇒ 00:39:51.609 Uttam Kumaran: Yeah, it’s a lot of that dude. And it’s a lot of that from like low level people that like.
508 00:39:52.380 ⇒ 00:39:53.580 Uttam Kumaran: yeah, that’s fair.
509 00:39:53.580 ⇒ 00:39:55.960 Patrick Trainer: Yeah, and and then go into the
510 00:39:56.130 ⇒ 00:39:59.339 Patrick Trainer: what the parties where people are spending like
511 00:39:59.850 ⇒ 00:40:02.119 Patrick Trainer: 1 million bucks just to. I know.
512 00:40:02.120 ⇒ 00:40:06.210 Uttam Kumaran: Yeah, I know I’m just in New York is so much of this. It’s like.
513 00:40:06.740 ⇒ 00:40:07.390 Uttam Kumaran: I mean, yeah.
514 00:40:07.390 ⇒ 00:40:11.429 Patrick Trainer: Yeah, that’s I mean, that’s Las Vegas, too. I mean, that’s kind of their bread and butter.
515 00:40:12.330 ⇒ 00:40:14.379 Uttam Kumaran: I mean, cause there are a couple things in Austin.
516 00:40:14.380 ⇒ 00:40:15.010 Patrick Trainer: On!
517 00:40:15.610 ⇒ 00:40:18.629 Uttam Kumaran: I know it’s it’s good to go here and there. But again it
518 00:40:18.710 ⇒ 00:40:22.090 Uttam Kumaran: I don’t. I’m not particularly interested in that whole world of like
519 00:40:22.220 ⇒ 00:40:29.380 Uttam Kumaran: a lot of people will be like, Oh, do you know, like this person like this person from this company, or these people are raising this money? And like
520 00:40:29.550 ⇒ 00:40:30.500 Uttam Kumaran: right, I.
521 00:40:30.859 ⇒ 00:40:31.219 Patrick Trainer: Yeah.
522 00:40:31.220 ⇒ 00:40:31.690 Uttam Kumaran: Karen.
523 00:40:31.690 ⇒ 00:40:33.760 Patrick Trainer: It’s it’s just conjecture at that point.
524 00:40:33.760 ⇒ 00:40:34.760 Uttam Kumaran: Yeah, yeah.
525 00:40:36.230 ⇒ 00:40:40.510 Patrick Trainer: And then so we have like direct engagement which like.
526 00:40:40.560 ⇒ 00:40:52.410 Patrick Trainer: if they’re requesting consultation like that’s huge. And so like this direct engagement. It’s like this is the most heavily weighted, like subcategory of like how we have
527 00:40:52.810 ⇒ 00:40:54.119 Patrick Trainer: 75.2
528 00:40:55.590 ⇒ 00:40:57.050 Patrick Trainer: and so like.
529 00:40:57.270 ⇒ 00:41:10.550 Patrick Trainer: I mean, we don’t have a live chat, but we can set up a live chat like like, really quick and then, like the contact us form like, that’s huge as well, like that’s that’s them literally self selecting into it.
530 00:41:11.541 ⇒ 00:41:18.769 Patrick Trainer: And so then we have like these scoring thresholds of like, are they cold, warm?
531 00:41:18.990 ⇒ 00:41:23.700 Patrick Trainer: Do they actually work. And then, like sales qualified lead like that kind of.
532 00:41:25.110 ⇒ 00:41:30.840 Patrick Trainer: we can adjust these and like, obviously, we’ll need to like, adjust them to
533 00:41:31.550 ⇒ 00:41:32.490 Patrick Trainer: how we’re
534 00:41:32.970 ⇒ 00:41:36.070 Patrick Trainer: subjugating our our leads, but like
535 00:41:36.340 ⇒ 00:41:38.540 Patrick Trainer: keeping it simple, like, I think.
536 00:41:38.590 ⇒ 00:41:40.649 Patrick Trainer: like this works out pretty well.
537 00:41:42.440 ⇒ 00:41:53.129 Patrick Trainer: So I also have, like a flow of kind of like how this works. So top of the funnel comes in this new lead. We’re going to get their initial score based on like
538 00:41:53.350 ⇒ 00:41:59.850 Patrick Trainer: who it is, or what industry they are so like. This is all. How happening in Apollo?
539 00:42:00.278 ⇒ 00:42:10.719 Patrick Trainer: And then, like these are the rest of the workflows that are going to like further like tag, or like put on attributes to that lead. Can you also.
540 00:42:10.720 ⇒ 00:42:11.360 Uttam Kumaran: Yeah.
541 00:42:11.510 ⇒ 00:42:12.270 Uttam Kumaran: Yes.
542 00:42:12.560 ⇒ 00:42:13.560 Uttam Kumaran: Okay.
543 00:42:15.480 ⇒ 00:42:17.130 Patrick Trainer: And so
544 00:42:17.680 ⇒ 00:42:32.159 Patrick Trainer: we’ve got yeah getting that initial score if they’re less than 50 like. Yes, then no cold lead then we put them into like our nurturing phase and then
545 00:42:33.070 ⇒ 00:42:35.049 Patrick Trainer: goes on from there.
546 00:42:37.020 ⇒ 00:42:41.719 Patrick Trainer: Getting their score. If it’s more than 100, they’re warm
547 00:42:42.650 ⇒ 00:42:45.850 Patrick Trainer: target like, put them into our content.
548 00:42:45.960 ⇒ 00:42:48.289 Patrick Trainer: And then they have all that engagement.
549 00:42:48.877 ⇒ 00:42:53.789 Patrick Trainer: If they engage with that content, we’re gonna again like, recalculate that score.
550 00:42:54.358 ⇒ 00:42:58.741 Patrick Trainer: That’s gonna add them up to 50. They’re gonna loop through
551 00:42:59.440 ⇒ 00:43:01.130 Patrick Trainer: if they
552 00:43:01.400 ⇒ 00:43:04.839 Patrick Trainer: like. Then after that score, if they’re like.
553 00:43:05.360 ⇒ 00:43:07.949 Patrick Trainer: say, 1, 50 to 2, 50
554 00:43:09.090 ⇒ 00:43:11.779 Patrick Trainer: that tags them as an SQL.
555 00:43:11.810 ⇒ 00:43:15.540 Patrick Trainer: And then that’s when we’ll like assign
556 00:43:16.140 ⇒ 00:43:17.349 Patrick Trainer: like a content.
557 00:43:17.670 ⇒ 00:43:18.070 Uttam Kumaran: Yeah.
558 00:43:18.326 ⇒ 00:43:20.629 Patrick Trainer: Consultant, like one of us, will talk to him.
559 00:43:20.740 ⇒ 00:43:22.350 Patrick Trainer: and then
560 00:43:22.860 ⇒ 00:43:24.750 Patrick Trainer: like look for
561 00:43:24.800 ⇒ 00:43:29.270 Patrick Trainer: like actually making them a customer, and then also, like refining the model.
562 00:43:30.780 ⇒ 00:43:32.900 Patrick Trainer: that also goes into like.
563 00:43:33.050 ⇒ 00:43:34.820 Patrick Trainer: are they? Mql.
564 00:43:35.780 ⇒ 00:43:36.690 Patrick Trainer: we can
565 00:43:36.800 ⇒ 00:43:39.080 Patrick Trainer: outreach to them, call them.
566 00:43:39.220 ⇒ 00:43:43.689 Patrick Trainer: email them whatever, try and get them to engage with that content.
567 00:43:43.860 ⇒ 00:43:47.770 Patrick Trainer: And then, like the cycle just continues. So it’s like
568 00:43:47.800 ⇒ 00:43:49.970 Patrick Trainer: it’s it’s not like
569 00:43:50.330 ⇒ 00:43:51.520 Patrick Trainer: acyclic.
570 00:43:51.880 ⇒ 00:43:52.390 Uttam Kumaran: Yeah.
571 00:43:52.390 ⇒ 00:43:55.700 Patrick Trainer: Everything like Will has like this feedback loop
572 00:43:55.810 ⇒ 00:43:58.499 Patrick Trainer: that that goes back onto itself.
573 00:44:00.440 ⇒ 00:44:05.159 Uttam Kumaran: Okay, this is great. This makes a lot of sense. I think the big thing we’ll think about is like.
574 00:44:05.300 ⇒ 00:44:08.920 Uttam Kumaran: what’s an initial. I think 2 things. One is like
575 00:44:09.190 ⇒ 00:44:10.800 Uttam Kumaran: events happen.
576 00:44:10.920 ⇒ 00:44:14.780 Uttam Kumaran: They need. They get centralized into like probably Hubspot
577 00:44:15.030 ⇒ 00:44:17.529 Uttam Kumaran: and Ryan. And what’s great is like.
578 00:44:17.530 ⇒ 00:44:19.707 Patrick Trainer: Spot has all the powers to
579 00:44:20.070 ⇒ 00:44:21.790 Uttam Kumaran: Yeah, I think all the like.
580 00:44:21.790 ⇒ 00:44:23.880 Patrick Trainer: To make these workflows.
581 00:44:24.180 ⇒ 00:44:27.880 Uttam Kumaran: Yeah. And then a score happens. And then a play happens. Basically.
582 00:44:28.150 ⇒ 00:44:28.570 Patrick Trainer: Exactly.
583 00:44:28.570 ⇒ 00:44:29.430 Uttam Kumaran: Yeah,
584 00:44:30.460 ⇒ 00:44:34.909 Uttam Kumaran: so that makes a lot of sense. And I think for them, for the actual like.
585 00:44:35.190 ⇒ 00:44:41.940 Uttam Kumaran: what we action on in the next week is like, let’s think about what a proof of concept of this would be with what we have today.
586 00:44:41.940 ⇒ 00:44:44.410 Patrick Trainer: Yeah, there’s a whole like implementation
587 00:44:44.490 ⇒ 00:44:45.840 Patrick Trainer: side of this, too.
588 00:44:45.840 ⇒ 00:44:50.980 Uttam Kumaran: Yeah. So we think about a proof of concept, we’ll think about like an Mvp. And then we’ll
589 00:44:51.000 ⇒ 00:44:58.289 Uttam Kumaran: again. This will be like a product that we were the client of, and then we’ll we’ll continue to just have improvements that we want to make.
590 00:44:58.856 ⇒ 00:45:05.390 Uttam Kumaran: But this is sick, I mean again. This sort of lead scoring is like exactly what we prescribe for folks
591 00:45:05.630 ⇒ 00:45:06.410 Uttam Kumaran: like
592 00:45:06.840 ⇒ 00:45:17.189 Uttam Kumaran: for our clients. So it would be great for us to do this. And again, I think, with Apollo, with Hubspot, with instantly, with the tools we’re using, we’re not using anything. That’s a 2
593 00:45:17.810 ⇒ 00:45:21.920 Uttam Kumaran: everything is like pretty premiere. We should all be able to link it to Hubspot really easily.
594 00:45:21.920 ⇒ 00:45:22.630 Patrick Trainer: Right.
595 00:45:24.900 ⇒ 00:45:26.140 Uttam Kumaran: okay, perfect
596 00:45:26.320 ⇒ 00:45:27.000 Uttam Kumaran: cool.
597 00:45:30.110 ⇒ 00:45:37.589 Uttam Kumaran: So how do we want to? So I know I I kind of wanted to talk about. Oh, I guess we kind of walk through buyer personas and industries.
598 00:45:38.403 ⇒ 00:45:42.649 Uttam Kumaran: I think the biggest thing that I’ll share is like what I worked on
599 00:45:42.740 ⇒ 00:45:46.470 Uttam Kumaran: yesterday, which is, how do we kind of improve
600 00:45:46.690 ⇒ 00:45:54.769 Uttam Kumaran: our backlog of sales materials? And so kind of the way I think about content is like we’re going to have multiple channels for content.
601 00:45:54.830 ⇒ 00:45:56.199 Uttam Kumaran: We’re going to have
602 00:45:56.260 ⇒ 00:46:05.270 Uttam Kumaran: materials on the site. We’re going to have Linkedin posts, twitter posts. So like social media, we’re going to have an email newsletter
603 00:46:06.970 ⇒ 00:46:10.570 Uttam Kumaran: And so there’s just a lot of opportunity for people to find out about us.
604 00:46:11.308 ⇒ 00:46:20.880 Uttam Kumaran: All of that being said. We don’t have like a full time copywriter at the moment. We don’t have, like anyone who’s like typical, like social media management, which is like
605 00:46:21.480 ⇒ 00:46:23.890 Uttam Kumaran: content production. It’s like
606 00:46:24.450 ⇒ 00:46:29.720 Uttam Kumaran: like ideation production, like reviewing, getting illustrations done.
607 00:46:29.740 ⇒ 00:46:34.579 Uttam Kumaran: then scheduling for posting and then posting it right? That’s the usual content pipeline for
608 00:46:34.936 ⇒ 00:46:43.243 Uttam Kumaran: for this sort of stuff where, like, it’s got to be managed through some like easy to use process for us. So one is
609 00:46:43.990 ⇒ 00:46:55.510 Uttam Kumaran: basically, what we’ll do is we’ll we’re we’re gonna just have a backlog of ideas. That I’m kind of starting to populate, and I’ll share that I’m starting to populate in
610 00:46:56.430 ⇒ 00:46:57.950 Uttam Kumaran: in notion.
611 00:46:58.140 ⇒ 00:47:00.099 Uttam Kumaran: Let me just share this.
612 00:47:06.700 ⇒ 00:47:09.050 Uttam Kumaran: So if you go to
613 00:47:11.380 ⇒ 00:47:15.200 Uttam Kumaran: you go to documentation. Actually, this is Doc.
614 00:47:15.470 ⇒ 00:47:18.189 Uttam Kumaran: You go to sales materials.
615 00:47:18.260 ⇒ 00:47:24.939 Uttam Kumaran: You’ll see here that we actually have all of our materials. And we have a couple more views that’s like our website content. Pipeline.
616 00:47:25.030 ⇒ 00:47:26.409 Uttam Kumaran: we have our like
617 00:47:26.760 ⇒ 00:47:28.999 Uttam Kumaran: other like this is, this will be like.
618 00:47:29.980 ⇒ 00:47:34.043 Uttam Kumaran: I don’t know. We call this like outbound content. Pipeline,
619 00:47:34.670 ⇒ 00:47:37.139 Uttam Kumaran: or this is more like this is like blogs.
620 00:47:37.734 ⇒ 00:47:41.750 Uttam Kumaran: And this is more like sales materials for the actual sales process.
621 00:47:43.490 ⇒ 00:47:45.219 Uttam Kumaran: I’m actually gonna change.
622 00:47:45.640 ⇒ 00:47:54.669 Uttam Kumaran: Actually, I don’t know. I’m not a fan of the naming right now. But for example, like we will have an idea so that we have, like a bunch of ideas for things that
623 00:47:55.103 ⇒ 00:48:05.659 Uttam Kumaran: we want to write about. And so I want to be like, I was listening to something this morning. It’s basically like, I want to be like idea rich. And then like time, poor, meaning like
624 00:48:05.690 ⇒ 00:48:21.449 Uttam Kumaran: we should have like a hundred ideas that like and Pat, we’ve talked about this for a long time, like we just have a shitload of ideas here that we basically can pick from and turn into the whole thing. The problem is once an idea makes it into a stage, it starts to involve more work.
625 00:48:21.490 ⇒ 00:48:22.940 Uttam Kumaran: Right? So
626 00:48:23.380 ⇒ 00:48:29.889 Uttam Kumaran: at each of these stages, like, there’s some work to be done. And this is where actually, we’ll we’re going to start using like AI pretty heavily.
627 00:48:30.050 ⇒ 00:48:35.810 Uttam Kumaran: The little bit way AI is gonna help us is from getting to idea to like 80%,
628 00:48:35.860 ⇒ 00:48:39.199 Uttam Kumaran: the 20% of like reviewing it, adding our flavor.
629 00:48:39.779 ⇒ 00:48:52.020 Uttam Kumaran: and then adding illustrations, if we need like images, will be still manual, but for me the toughest part is is like I, staring at an empty page like this is really kind of tough
630 00:48:52.711 ⇒ 00:49:01.669 Uttam Kumaran: and instead, what’s easy is like if I was to like I can use a tool like aqua voice, or any sort of speech to text.
631 00:49:01.680 ⇒ 00:49:21.840 Uttam Kumaran: Talk about this for a couple of minutes. Throw it into AI. AI has a good. The AI relevance has a good understanding of like what our format is. Our brand voices like has links to do backlinking to other stuff. And then it gets 80% there. We then have 20% of work, and then we hand it off for illustration.
632 00:49:21.860 ⇒ 00:49:27.969 Uttam Kumaran: That’s going to be the way we do this and that that because otherwise we can’t spend. I want this to take
633 00:49:28.040 ⇒ 00:49:31.680 Uttam Kumaran: like half hour. I don’t want this to take 4 h
634 00:49:32.050 ⇒ 00:49:38.689 Uttam Kumaran: right? Right. And if we’re gonna if we’re gonna get any sort of consistency here, which is like at least one blog post
635 00:49:38.710 ⇒ 00:49:47.509 Uttam Kumaran: at least a couple of posts per week like that’s quickly got. It’s immediately it’s a full time job. And so that’s not our goal here. So
636 00:49:47.650 ⇒ 00:49:51.929 Uttam Kumaran: the one thing that I I worked on in a bit is like how to use
637 00:49:52.030 ⇒ 00:50:07.559 Uttam Kumaran: relevance in a couple of different ways, and it’s actually going to be very strong for us to use it. I’ve been using it for helping brainstorm email series, which is like, I give it an industry. I give it like a bunch of background. And then it like to generate.
638 00:50:07.560 ⇒ 00:50:22.880 Uttam Kumaran: based on like, actually, how we do emails, which is like, there’s a call to action. There’s kind of a hook. There’s like the second email has like a link to something on our side. It’s doing that really? Well. So it’s very similarly, we’ll have something where this, where at minimum, you need to give it a title. And like.
639 00:50:23.110 ⇒ 00:50:32.270 Uttam Kumaran: ideally, like just a brain dump of like a transcript where we’ve talked about it, it gets us 80% of the way there. So that’s going to be like our content, pipeline
640 00:50:32.830 ⇒ 00:50:35.380 Uttam Kumaran: on this side. So the one thing is, I think
641 00:50:35.400 ⇒ 00:50:36.809 Uttam Kumaran: you know, maybe
642 00:50:38.140 ⇒ 00:50:41.394 Uttam Kumaran: And again. I I know that anything that isn’t like
643 00:50:42.470 ⇒ 00:50:46.400 Uttam Kumaran: measured like really doesn’t get managed. So one thing I want to do is like
644 00:50:46.410 ⇒ 00:50:48.889 Uttam Kumaran: once a month, or maybe on Friday.
645 00:50:49.020 ⇒ 00:51:06.399 Uttam Kumaran: like every couple of Fridays, we can just all of us like throw in a bunch of ideas into here, and then that way we can cherry, pick. And then again, my goal is not like, I want everybody to be authors of these sorts of things, and like be able to publish it on their linkedin and stuff like that. So
646 00:51:06.821 ⇒ 00:51:18.760 Uttam Kumaran: but we’re not. Not. All of us are right, are writers, and also copywriting is a really difficult thing. It’s like a its own heart. So I want to make sure that people aren’t like.
647 00:51:18.950 ⇒ 00:51:39.619 Uttam Kumaran: oh, I have to write this article, and it’s like going to take me because I’ve had that happen to me before. I’m like, it’s 6, 7 h. The AI is getting so good, and I have a brand voice document about like how we talk. This will start getting better and better with like more natural voice. And then again, ideally gets 80% there, we put our flare on it and we ship it out. Yeah.
648 00:51:39.800 ⇒ 00:51:40.460 Patrick Trainer: Yeah.
649 00:51:41.680 ⇒ 00:51:44.578 Patrick Trainer: so you’re saying, you want to start a podcast.
650 00:51:47.110 ⇒ 00:51:51.329 Uttam Kumaran: I would. Well, what I was actually thinking about. And this is like, kind of around. My.
651 00:51:51.940 ⇒ 00:52:01.470 Uttam Kumaran: everything is about content, repurposing. And so what we’re what’s actually gonna happen is. And again, this is like where AI is actually gonna take care of all this is like, let’s say, we write
652 00:52:01.500 ⇒ 00:52:03.310 Uttam Kumaran: on this document
653 00:52:03.400 ⇒ 00:52:04.809 Uttam Kumaran: it gets published
654 00:52:04.960 ⇒ 00:52:08.359 Uttam Kumaran: on the blog. This will then get turned into
655 00:52:08.750 ⇒ 00:52:11.080 Uttam Kumaran: like a 3 post Linkedin series
656 00:52:11.220 ⇒ 00:52:14.889 Uttam Kumaran: like a twitter thread. It’ll get turned into
657 00:52:15.210 ⇒ 00:52:33.080 Uttam Kumaran: the other thing, I was think was like, Oh, it like either you should read it. And we turn into like a podcast that we just put out. Or I might just use 11 labs with your voice and then have that just get ripped. I was also thinking about basically publishing this to Youtube.
658 00:52:33.779 ⇒ 00:52:35.579 Uttam Kumaran: Again with a voiceover.
659 00:52:35.710 ⇒ 00:52:39.940 Uttam Kumaran: And then again, everything basically is like we write one thing, it needs to turn into like
660 00:52:40.150 ⇒ 00:52:41.510 Uttam Kumaran: 10 points of distribution.
661 00:52:41.510 ⇒ 00:52:42.290 Patrick Trainer: Yeah pivot.
662 00:52:42.290 ⇒ 00:52:43.170 Uttam Kumaran: Around.
663 00:52:43.430 ⇒ 00:52:44.854 Uttam Kumaran: Yeah. And so
664 00:52:46.260 ⇒ 00:52:49.130 Uttam Kumaran: there’s a ton of opportunity for us. I want to think
665 00:52:49.360 ⇒ 00:52:59.879 Uttam Kumaran: I don’t know. I’m going back and forth like I I think a lot about the the cost of goods right now is really high meaning like the cost it would take us to do. This is high, but
666 00:53:00.553 ⇒ 00:53:04.550 Uttam Kumaran: I don’t care too much about the production quality at the moment.
667 00:53:04.630 ⇒ 00:53:08.379 Uttam Kumaran: I care more about it getting out. However, like
668 00:53:08.510 ⇒ 00:53:22.510 Uttam Kumaran: we, we can’t even. It’s it’s already a taxi for me to even try to do one of these a week. So I have to use AI to help, you know. So so we’ll find some place in the middle where, like we’re publishing it. It’s getting better, and then we’ll polish up the production quality.
669 00:53:23.234 ⇒ 00:53:24.659 Uttam Kumaran: You know, down the line.
670 00:53:24.660 ⇒ 00:53:25.840 Patrick Trainer: Right, right.
671 00:53:28.330 ⇒ 00:53:30.969 Patrick Trainer: Cool. Yeah, no, that’s a really good point.
672 00:53:31.800 ⇒ 00:53:33.620 Uttam Kumaran: So this is kind of like the
673 00:53:34.380 ⇒ 00:53:41.949 Uttam Kumaran: the like content pipeline. And like again, we have a bunch of sales, materials and blog posts and service offerings that are sold yet to be written.
674 00:53:42.030 ⇒ 00:53:45.160 Uttam Kumaran: Probably I’ll just go on like a 2 day binge
675 00:53:45.200 ⇒ 00:53:47.580 Uttam Kumaran: like coffee binge with my white ollies.
676 00:53:47.870 ⇒ 00:53:49.660 Uttam Kumaran: It’s like, usually the way it goes.
677 00:53:50.544 ⇒ 00:53:55.895 Uttam Kumaran: So I’ll do that. And the other thing that I I’ll share with you guys is
678 00:54:00.990 ⇒ 00:54:03.850 Uttam Kumaran: is on relevance.
679 00:54:08.097 ⇒ 00:54:20.179 Uttam Kumaran: Yeah, I just like went to the library yesterday and was just like sitting on this for like all day and like figuring out like how to use relevance. And it’s actually really nice. I wrote this like, outreach copywriter, agent.
680 00:54:20.390 ⇒ 00:54:31.819 Uttam Kumaran: And basically what it does is it helps us create cold email series for outreach based on an industry I gave it. I worked on like a very good set of instructions which is like.
681 00:54:32.060 ⇒ 00:54:33.390 Uttam Kumaran: here’s like.
682 00:54:34.210 ⇒ 00:54:35.189 Uttam Kumaran: Here’s like
683 00:54:35.520 ⇒ 00:54:37.590 Uttam Kumaran: you’re the instructions about how to write.
684 00:54:37.690 ⇒ 00:54:42.840 Uttam Kumaran: Here’s basically like, kind of the things that we look for in like our email marketing.
685 00:54:42.860 ⇒ 00:54:46.239 Uttam Kumaran: Here are the key structures like an intro value prop
686 00:54:46.370 ⇒ 00:54:47.660 Uttam Kumaran: offer
687 00:54:47.810 ⇒ 00:54:50.830 Uttam Kumaran: an open-ended prompt. Here are some hooks.
688 00:54:51.320 ⇒ 00:54:52.560 Uttam Kumaran: you know, like.
689 00:54:52.560 ⇒ 00:54:53.290 Patrick Trainer: Right.
690 00:54:54.140 ⇒ 00:54:57.329 Uttam Kumaran: And then I also gave it like personalization. So I said.
691 00:54:57.420 ⇒ 00:55:06.049 Uttam Kumaran: make it personalized. But of course you’re not going to have it so just put placeholders because it instantly we can sub in those values programmatically.
692 00:55:06.050 ⇒ 00:55:06.470 Patrick Trainer: Right.
693 00:55:06.470 ⇒ 00:55:08.460 Uttam Kumaran: And then I was like.
694 00:55:11.000 ⇒ 00:55:18.440 Uttam Kumaran: I was like, look at our website and has access to like our website. I was like, look at our website for like things that we’ve done, if there’s anything worth mentioning.
695 00:55:18.480 ⇒ 00:55:19.520 Uttam Kumaran: mention it.
696 00:55:19.917 ⇒ 00:55:24.300 Uttam Kumaran: So it has it in this knowledge base. All the stuff we’ve currently published like, scraped everything.
697 00:55:25.990 ⇒ 00:55:37.504 Uttam Kumaran: and then I get a couple of more things about tone of voice things like that. And then I have a flow where basically, the next bit relevance is like, you have core instructions. But then you have a flow where it’s like it’s almost
698 00:55:37.810 ⇒ 00:55:39.570 Uttam Kumaran: You set your environment.
699 00:55:39.660 ⇒ 00:55:42.969 Uttam Kumaran: And then within the environment there can be more logical steps.
700 00:55:43.234 ⇒ 00:55:43.500 Patrick Trainer: That’s.
701 00:55:44.200 ⇒ 00:55:52.460 Uttam Kumaran: So like the starting point is like, Come up with a sequence of Google search. So you you give it like an industry and like anything you know about it.
702 00:55:52.590 ⇒ 00:56:00.410 Uttam Kumaran: It then Googles about the industry and about challenges with data. It finds a couple of links. It scrapes them.
703 00:56:00.500 ⇒ 00:56:07.160 Uttam Kumaran: It then basically like uses that data to then create the email series of which I have an output.
704 00:56:07.570 ⇒ 00:56:13.260 Uttam Kumaran: I have an output, 3 emails, 3 variations each. And and the subject.
705 00:56:14.690 ⇒ 00:56:29.730 Uttam Kumaran: actually, I need to remove this subject for the second and 3, rd but basically the components. And then you can say, Hey, I’m happy with this, or I’m not. If you’re happy, then it then goes and generates a Linkedin message that you should send. If you’re not happy, then there’s a couple of things it like looks
706 00:56:29.830 ⇒ 00:56:31.170 Uttam Kumaran: to revise.
707 00:56:31.330 ⇒ 00:56:34.099 Patrick Trainer: This would be fantastic, for like
708 00:56:34.760 ⇒ 00:56:35.950 Patrick Trainer: getting like
709 00:56:36.100 ⇒ 00:56:38.300 Patrick Trainer: sending out job applications.
710 00:56:38.860 ⇒ 00:56:40.120 Uttam Kumaran: That’s also true. Yeah.
711 00:56:40.410 ⇒ 00:56:40.969 Patrick Trainer: I mean.
712 00:56:40.970 ⇒ 00:56:42.070 Uttam Kumaran: This like we’re basically.
713 00:56:42.070 ⇒ 00:56:43.490 Patrick Trainer: You know, otherwise like.
714 00:56:43.490 ⇒ 00:56:44.199 Uttam Kumaran: No, I mean dude.
715 00:56:44.200 ⇒ 00:56:47.819 Patrick Trainer: Writing, write a cover, letter, tailor, my resume.
716 00:56:47.950 ⇒ 00:56:48.640 Patrick Trainer: find these.
717 00:56:48.640 ⇒ 00:56:49.569 Uttam Kumaran: Yeah, I think.
718 00:56:49.720 ⇒ 00:56:50.270 Patrick Trainer: Yeah.
719 00:56:50.270 ⇒ 00:56:56.190 Uttam Kumaran: The other. The other thing I would suggest is like, if you have any ideas for automations, also throw them in here because I like.
720 00:56:56.460 ⇒ 00:57:02.178 Uttam Kumaran: I’m just throwing everything about how to automate all the Bs and like, kind of the shit we do
721 00:57:02.780 ⇒ 00:57:05.829 Uttam Kumaran: But yeah, so basically, and I and I.
722 00:57:06.240 ⇒ 00:57:07.940 Uttam Kumaran: And then you can give it tools.
723 00:57:08.030 ⇒ 00:57:16.830 Uttam Kumaran: So it has, like a couple of tools about like usability, Google search. And then I did this. I tried it out yesterday. And it was like, really good.
724 00:57:16.880 ⇒ 00:57:27.810 Uttam Kumaran: So I was basically like, Hey, I want to create a series for the shipping logistics. Here’s like random notes I have on the subject I don’t know. I must have wrote a bunch of notes in notion at some point right, and it gave me all of it. It was like
725 00:57:28.400 ⇒ 00:57:31.889 Uttam Kumaran: it gave me these these messages email lines.
726 00:57:32.310 ⇒ 00:57:36.210 Uttam Kumaran: So I’m pretty decent. And the nice thing is I have variations. So I can kind of like
727 00:57:36.260 ⇒ 00:57:41.040 Uttam Kumaran: I don’t have to be like. That’s bad, that’s good. I’m like one of these will end up being good. It gave me 9 options.
728 00:57:41.110 ⇒ 00:57:46.419 Uttam Kumaran: and then I’m like, cool. It’s good, great! Here’s like, probably the Linkedin
729 00:57:46.450 ⇒ 00:57:48.260 Uttam Kumaran: outreach thing you should do.
730 00:57:48.600 ⇒ 00:57:49.959 Uttam Kumaran: And then, yeah, so like.
731 00:57:50.285 ⇒ 00:57:50.609 Patrick Trainer: Sweet!
732 00:57:50.610 ⇒ 00:57:54.439 Uttam Kumaran: It took me a it took me like maybe an hour or 2 to like, get to this point. But
733 00:57:55.120 ⇒ 00:57:59.758 Uttam Kumaran: it’s great. It’s just working like perfectly. So basically, what we’re gonna do is
734 00:58:00.470 ⇒ 00:58:03.442 Uttam Kumaran: we’re, gonna I’m gonna use this to basically also build
735 00:58:04.660 ⇒ 00:58:09.439 Uttam Kumaran: a copyright, a copywriter for our blog post. Given
736 00:58:09.560 ⇒ 00:58:24.339 Uttam Kumaran: a transcript relevant links, it will write in our voice the 80% of the blog, basically. And then we’ll take it from there. And then again, also, I’ll have it once you’re like cool. This is, this is great. It’ll generate, like
737 00:58:24.520 ⇒ 00:58:26.739 Uttam Kumaran: the Linkedin, the Twitter
738 00:58:26.980 ⇒ 00:58:34.089 Uttam Kumaran: ideas for videos, things like that. So this is like how we’ll kind of speed this whole process up doing this
739 00:58:34.180 ⇒ 00:58:39.528 Uttam Kumaran: I did this manually for manufacturing. It took me like 3 days right?
740 00:58:41.230 ⇒ 00:58:45.039 Uttam Kumaran: it took me 3 days across, like 4 weeks, basically to like figure out like.
741 00:58:45.040 ⇒ 00:58:45.850 Patrick Trainer: Yeah.
742 00:58:45.850 ⇒ 00:58:47.599 Uttam Kumaran: To to build that prompt
743 00:58:47.670 ⇒ 00:58:54.550 Uttam Kumaran: took me like a month right of like learning what makes a good email and like talking to everybody. But now that we have that.
744 00:58:54.640 ⇒ 00:58:57.349 Uttam Kumaran: it’s like it’s doing a great job right.
745 00:58:57.350 ⇒ 00:58:58.780 Patrick Trainer: Yeah, this is awesome.
746 00:58:59.050 ⇒ 00:59:00.180 Uttam Kumaran: So
747 00:59:01.260 ⇒ 00:59:05.553 Uttam Kumaran: that’s how we’re gonna kind of scale this up as fast as we can.
748 00:59:06.450 ⇒ 00:59:09.410 Uttam Kumaran: I think the big thing that I want to go through
749 00:59:09.650 ⇒ 00:59:15.200 Uttam Kumaran: today is like, how do we want to like kick off actual like takeaways for tasks?
750 00:59:16.263 ⇒ 00:59:17.450 Uttam Kumaran: And like.
751 00:59:18.000 ⇒ 00:59:21.969 Uttam Kumaran: I don’t know, how do we want to kind of get organized around like a proof of concept. Here.
752 00:59:22.340 ⇒ 00:59:23.029 Patrick Trainer: Right?
753 00:59:25.100 ⇒ 00:59:28.489 Patrick Trainer: I think it makes sense for me to. I mean, start with
754 00:59:29.170 ⇒ 00:59:34.140 Patrick Trainer: initial implementation of like lead scores, just because I’m kind of deep into it.
755 00:59:34.440 ⇒ 00:59:39.130 Uttam Kumaran: Yeah, why don’t you do you have it as part of your notes, or you want to hop on here, and we can just like work on it.
756 00:59:39.438 ⇒ 00:59:42.519 Patrick Trainer: Yeah, I’ve actually got it as part of my notes.
757 00:59:42.520 ⇒ 00:59:43.120 Uttam Kumaran: Okay. Cool.
758 00:59:44.380 ⇒ 00:59:45.840 Patrick Trainer: And so it’s kind of like
759 00:59:46.770 ⇒ 00:59:51.344 Patrick Trainer: pushed out into tasks already. So I can add that into
760 00:59:52.010 ⇒ 00:59:54.130 Patrick Trainer: I’m gonna I’ll track it in Github.
761 00:59:54.130 ⇒ 00:59:54.740 Uttam Kumaran: Okay.
762 00:59:55.870 ⇒ 00:59:56.859 Patrick Trainer: Do that.
763 01:00:09.490 ⇒ 01:00:11.920 Uttam Kumaran: Abigail, this is all kind of make sense.
764 01:00:12.580 ⇒ 01:00:13.240 Abigail Zhao: Yeah.
765 01:00:16.850 ⇒ 01:00:20.650 Uttam Kumaran: I think hopefully, after today we can all kind of take a little piece of it, and like
766 01:00:21.570 ⇒ 01:00:22.600 Uttam Kumaran: governments around.
767 01:00:23.080 ⇒ 01:00:23.780 Patrick Trainer: Right?
768 01:00:24.340 ⇒ 01:00:29.049 Patrick Trainer: Yeah. So I mean, what are the like? The big rocks here we’ve got like.
769 01:00:29.600 ⇒ 01:00:31.719 Patrick Trainer: actual like funnel stuff.
770 01:00:31.830 ⇒ 01:00:33.230 Patrick Trainer: We have content.
771 01:00:34.147 ⇒ 01:00:35.939 Patrick Trainer: Like actual, like
772 01:00:36.270 ⇒ 01:00:38.999 Patrick Trainer: meat and potatoes, or words on page
773 01:00:39.665 ⇒ 01:00:41.480 Patrick Trainer: and then the
774 01:00:41.780 ⇒ 01:00:44.460 Patrick Trainer: other side of it is
775 01:00:48.180 ⇒ 01:00:49.269 Patrick Trainer: There’s another one.
776 01:00:51.100 ⇒ 01:00:53.040 Uttam Kumaran: Have like the systems.
777 01:00:54.320 ⇒ 01:01:00.250 Uttam Kumaran: I mean, basically, there’s like a couple of things. There’s 1 like, can we connect instantly to Hubspot, Apollo to Hubspot.
778 01:01:00.250 ⇒ 01:01:02.119 Patrick Trainer: Oh, yeah, there’s like integrations.
779 01:01:02.480 ⇒ 01:01:03.969 Uttam Kumaran: Yeah. And then
780 01:01:04.330 ⇒ 01:01:07.690 Uttam Kumaran: I’m happy to take. I think I’ll just take the content.
781 01:01:07.800 ⇒ 01:01:08.920 Uttam Kumaran: because.
782 01:01:08.920 ⇒ 01:01:09.339 Patrick Trainer: Yeah, let me.
783 01:01:09.340 ⇒ 01:01:16.679 Uttam Kumaran: I think I’m just like I’m just gonna have a backlog of stuff that like we can start to push out because that’s also pressing.
784 01:01:16.940 ⇒ 01:01:24.439 Uttam Kumaran: But again, this has been really helpful, because if I know that the workflows, and like the integration between Hubspot Apollo instantly.
785 01:01:24.810 ⇒ 01:01:26.300 Uttam Kumaran: and then we also have, like
786 01:01:26.550 ⇒ 01:01:30.989 Uttam Kumaran: we can use. I I install buffer, which is like a content management platform
787 01:01:31.100 ⇒ 01:01:41.140 Uttam Kumaran: like as long as I know that that’s all getting worked on. It’ll give me actually a lot of head space to go, because whenever I like, get into this world, I I just spend like 6 h like hopping between like 10 things.
788 01:01:41.270 ⇒ 01:01:41.920 Uttam Kumaran: So instead of.
789 01:01:41.920 ⇒ 01:01:43.349 Patrick Trainer: We also need to nail down.
790 01:01:43.350 ⇒ 01:01:43.890 Uttam Kumaran: Almost like.
791 01:01:43.890 ⇒ 01:01:45.640 Patrick Trainer: The the scores themselves.
792 01:01:45.640 ⇒ 01:01:46.280 Uttam Kumaran: Yeah.
793 01:01:47.980 ⇒ 01:01:48.960 Patrick Trainer: So
794 01:01:49.190 ⇒ 01:01:51.149 Patrick Trainer: I guess, like like takeaways.
795 01:01:51.822 ⇒ 01:01:54.079 Patrick Trainer: Like Abigail, if you want to think about.
796 01:01:54.080 ⇒ 01:01:55.040 Uttam Kumaran: Sharing pad.
797 01:01:55.280 ⇒ 01:01:55.910 Patrick Trainer: No.
798 01:01:56.240 ⇒ 01:01:56.690 Uttam Kumaran: Okay.
799 01:01:56.690 ⇒ 01:01:58.070 Patrick Trainer: Should I be.
800 01:01:58.295 ⇒ 01:02:00.554 Uttam Kumaran: I just. I didn’t know if you were gonna share
801 01:02:00.780 ⇒ 01:02:02.530 Patrick Trainer: Oh, yeah, I know.
802 01:02:02.530 ⇒ 01:02:03.350 Uttam Kumaran: You are right, Helen.
803 01:02:03.350 ⇒ 01:02:04.500 Patrick Trainer: I’m thinking about like next.
804 01:02:04.500 ⇒ 01:02:05.679 Uttam Kumaran: Okay. Okay. Okay. Okay. Cool.
805 01:02:06.048 ⇒ 01:02:07.890 Patrick Trainer: I was just like if
806 01:02:09.350 ⇒ 01:02:13.930 Patrick Trainer: sorry, like nailing down the actual scores, and like
807 01:02:16.640 ⇒ 01:02:17.680 Patrick Trainer: of the.
808 01:02:18.500 ⇒ 01:02:22.089 Uttam Kumaran: You. Just you go to robot mode for a sec.
809 01:02:24.270 ⇒ 01:02:25.130 Uttam Kumaran: You’re back.
810 01:02:26.240 ⇒ 01:02:30.189 Patrick Trainer: Okay, yeah. Just it just told me what was my post. What was I.
811 01:02:30.470 ⇒ 01:02:32.150 Uttam Kumaran: Yeah, I don’t know. It’s like.
812 01:02:34.720 ⇒ 01:02:38.219 Patrick Trainer: I was, I was saying, Abigail, if you wanna
813 01:02:38.960 ⇒ 01:02:42.839 Patrick Trainer: get down like the points for each like subcategory
814 01:02:42.940 ⇒ 01:02:44.379 Patrick Trainer: of like
815 01:02:45.220 ⇒ 01:02:57.430 Patrick Trainer: how much is like talking to a CEO worth and then like that and then I can take like implementation of and like integration of the different tools.
816 01:02:57.450 ⇒ 01:03:02.699 Patrick Trainer: And so we can then like, have that feedback loop, or or that connection there
817 01:03:02.720 ⇒ 01:03:04.030 Patrick Trainer: and then
818 01:03:04.380 ⇒ 01:03:06.820 Patrick Trainer: Udamill grab content.
819 01:03:07.220 ⇒ 01:03:08.640 Patrick Trainer: and all that.
820 01:03:08.950 ⇒ 01:03:17.130 Uttam Kumaran: Okay in terms of like where each of these live. So for points for each category, do you want to just do that in like a
821 01:03:17.960 ⇒ 01:03:19.269 Uttam Kumaran: was she.
822 01:03:19.770 ⇒ 01:03:20.659 Patrick Trainer: Yeah, I don’t like that.
823 01:03:20.660 ⇒ 01:03:21.040 Uttam Kumaran: It’s.
824 01:03:21.040 ⇒ 01:03:24.819 Patrick Trainer: Sounds easiest like, I’ll just drop that entire
825 01:03:25.060 ⇒ 01:03:25.760 Patrick Trainer: like
826 01:03:26.600 ⇒ 01:03:27.530 Patrick Trainer: document.
827 01:03:28.134 ⇒ 01:03:30.809 Patrick Trainer: In Google Sheet. And then we can just
828 01:03:30.920 ⇒ 01:03:33.450 Patrick Trainer: edit change as as we need.
829 01:03:34.290 ⇒ 01:03:41.739 Uttam Kumaran: Yeah. So I think, Abigail, I think the the kind of way to probably think about is like, let’s just have a Google sheet where it’s like category.
830 01:03:41.940 ⇒ 01:03:43.909 Uttam Kumaran: and then maybe the
831 01:03:44.560 ⇒ 01:03:46.750 Uttam Kumaran: value and the score.
832 01:03:47.180 ⇒ 01:03:50.410 Uttam Kumaran: And then like also to have a category definition.
833 01:03:50.770 ⇒ 01:03:53.270 Uttam Kumaran: And then that way, like we can, basically.
834 01:03:54.090 ⇒ 01:04:11.039 Uttam Kumaran: we’ll be using. And then the other things. I’m sorry. I’m just gonna talk out loud. But the biggest things is like we have. We have category. We have value. We have score, we have definition. The other things I want to look at is like the source of the of the
835 01:04:11.410 ⇒ 01:04:12.649 Uttam Kumaran: of the
836 01:04:13.170 ⇒ 01:04:18.519 Uttam Kumaran: like data? Basically, for example, if we’re looking at, did they click on a website?
837 01:04:18.570 ⇒ 01:04:21.349 Uttam Kumaran: Where is that coming from? Like, where is that event coming from?
838 01:04:21.540 ⇒ 01:04:27.240 Uttam Kumaran: Right? This is exactly how we would do a data dictionary for a client which is like they clicked on a website. Is that coming from?
839 01:04:27.300 ⇒ 01:04:32.629 Uttam Kumaran: Or is it coming from Beehive, where a newsletter set up is that coming from post hog
840 01:04:32.760 ⇒ 01:04:38.930 Uttam Kumaran: like that’s those are things that I want to iron out, because then we’ll basically, that’ll be like the actual.
841 01:04:39.090 ⇒ 01:04:41.959 Uttam Kumaran: Yeah, we’ll we’ll find out what’s hard versus what’s easier.
842 01:04:41.970 ⇒ 01:04:45.119 Uttam Kumaran: So category values for definition and source
843 01:04:46.110 ⇒ 01:04:51.458 Uttam Kumaran: pat on the implementation side, there’s a lot to implement. So yeah, I don’t know if you’re thinking about
844 01:04:52.110 ⇒ 01:04:55.040 Uttam Kumaran: like a hierarchy. The other thing I wanna
845 01:04:55.130 ⇒ 01:04:56.699 Uttam Kumaran: try to do is like.
846 01:04:56.700 ⇒ 01:05:00.199 Patrick Trainer: Gonna start with Hubspot, just because that like, that’s the
847 01:05:00.420 ⇒ 01:05:02.230 Patrick Trainer: that’s like the nexus right?
848 01:05:02.560 ⇒ 01:05:03.170 Uttam Kumaran: Yeah.
849 01:05:03.540 ⇒ 01:05:12.950 Uttam Kumaran: So maybe maybe think about Hubspot and Apollo together. Yeah, if you end up like merging, because Apollo is going to be all the source for leads the primary source for leads.
850 01:05:13.030 ⇒ 01:05:15.219 Uttam Kumaran: Hubspot is gonna be the nexus, too.
851 01:05:15.220 ⇒ 01:05:15.730 Patrick Trainer: Yeah.
852 01:05:15.730 ⇒ 01:05:17.786 Uttam Kumaran: Yeah, yeah, okay, exactly.
853 01:05:20.080 ⇒ 01:05:24.610 Uttam Kumaran: Okay, perfect. So you can take Hubspot. The other thing I’m thinking about is like.
854 01:05:24.840 ⇒ 01:05:29.990 Uttam Kumaran: you know, I I keep looking at on config jam, the the list of like
855 01:05:30.490 ⇒ 01:05:33.709 Uttam Kumaran: the legend which is like we have systems, workflows.
856 01:05:33.940 ⇒ 01:05:36.919 Uttam Kumaran: the workflows. I kind of want to.
857 01:05:37.010 ⇒ 01:05:43.740 Uttam Kumaran: I just think we’re going to end up with so many connecting systems that I’m trying to think about the best way to like document
858 01:05:44.400 ⇒ 01:05:47.022 Uttam Kumaran: the workflows like, do you think?
859 01:05:49.530 ⇒ 01:05:55.560 Patrick Trainer: I don’t. I don’t know the best way to keep that under control, like I’m thinking back to.
860 01:05:55.690 ⇒ 01:06:00.690 Patrick Trainer: I mean, like the 1st startup that I was at like. It turns into this like.
861 01:06:01.340 ⇒ 01:06:02.870 Uttam Kumaran: No, I don’t want to just document it. One.
862 01:06:02.870 ⇒ 01:06:03.300 Patrick Trainer: Does, but.
863 01:06:03.300 ⇒ 01:06:05.880 Uttam Kumaran: Because it’s like a yeah, I just more like.
864 01:06:06.620 ⇒ 01:06:11.299 Uttam Kumaran: I’m I’m even trying to think about what the benefit would be of even like documenting at this point, but.
865 01:06:11.880 ⇒ 01:06:23.310 Patrick Trainer: It’d be best for like our future selves when we try and like change it, because otherwise we’re gonna have to go back and like, try and decipher the
866 01:06:23.340 ⇒ 01:06:24.780 Patrick Trainer: hieroglyphics.
867 01:06:25.300 ⇒ 01:06:27.980 Uttam Kumaran: Yeah, the reason being is like, for example.
868 01:06:31.660 ⇒ 01:06:34.629 Uttam Kumaran: yeah, I mean, I guess, like, what I’m gonna do is like.
869 01:06:34.740 ⇒ 01:06:40.879 Uttam Kumaran: I’m just gonna start a notion database with a couple of these objects because we already have the documents in notion.
870 01:06:40.990 ⇒ 01:06:50.540 Uttam Kumaran: Right? I’m already gonna I’m gonna start a tools database, because I want to put like, basically all the tools we use as a company and then back like to the docs. And then
871 01:06:50.570 ⇒ 01:06:54.969 Uttam Kumaran: we’ll just start adding workflows, which is like, makes it be generic. For now, which is like
872 01:06:55.210 ⇒ 01:06:57.559 Uttam Kumaran: moving, leads from Apollo to this, and then
873 01:06:57.580 ⇒ 01:07:01.440 Uttam Kumaran: we just shove anything we know into there. And that way we know
874 01:07:01.900 ⇒ 01:07:06.020 Uttam Kumaran: that workflow is associated with these tools, or if we go to the tool, we know the workflows associated with it.
875 01:07:06.020 ⇒ 01:07:07.260 Patrick Trainer: I see? Yeah.
876 01:07:07.750 ⇒ 01:07:12.370 Uttam Kumaran: Yeah, so that’s it. Again, this is gonna be immediately stale.
877 01:07:12.410 ⇒ 01:07:16.200 Uttam Kumaran: But like at least it’s stale somewhere versus like stale in the ether.
878 01:07:16.310 ⇒ 01:07:17.615 Uttam Kumaran: so I will.
879 01:07:18.730 ⇒ 01:07:21.839 Uttam Kumaran: I’m like big in Notion Land this week, so I will own that
880 01:07:22.310 ⇒ 01:07:23.040 Uttam Kumaran: cool.
881 01:07:27.510 ⇒ 01:07:28.040 Patrick Trainer: And then.
882 01:07:28.040 ⇒ 01:07:28.700 Uttam Kumaran: Okay. Cool.
883 01:07:28.700 ⇒ 01:07:32.840 Patrick Trainer: I’ll I’ll create that Google, Doc, and then just drop it in the Channel.
884 01:07:33.560 ⇒ 01:07:36.159 Uttam Kumaran: The one thing we didn’t talk about is goals.
885 01:07:37.390 ⇒ 01:07:40.830 Uttam Kumaran: I think I’m gonna take that on which is just like
886 01:07:41.910 ⇒ 01:07:45.849 Uttam Kumaran: I’m just gonna talk about. I want to set some high level goals of like
887 01:07:45.990 ⇒ 01:07:48.990 Uttam Kumaran: the company goals, and we can back that into
888 01:07:49.500 ⇒ 01:07:49.990 Uttam Kumaran: the
889 01:07:51.450 ⇒ 01:07:53.240 Uttam Kumaran: It’s almost like the
890 01:07:54.280 ⇒ 01:07:58.560 Uttam Kumaran: events, or like the touch points or whatever which, just like
891 01:07:58.970 ⇒ 01:08:00.170 Uttam Kumaran: we need
892 01:08:00.450 ⇒ 01:08:02.470 Uttam Kumaran: this, many emails sent.
893 01:08:02.600 ⇒ 01:08:05.829 Uttam Kumaran: We need this many email. We need this, many meetings booked
894 01:08:06.290 ⇒ 01:08:10.839 Uttam Kumaran: with our student conversion rate. To get that many meetings booked we need this many.
895 01:08:11.354 ⇒ 01:08:15.145 Uttam Kumaran: Each of the each of the conversion points from lead to
896 01:08:15.490 ⇒ 01:08:16.640 Patrick Trainer: Right, yeah.
897 01:08:16.640 ⇒ 01:08:21.490 Uttam Kumaran: So let’s just try. Let’s just start with something basic which is like lead meeting
898 01:08:21.680 ⇒ 01:08:22.810 Uttam Kumaran: closed.
899 01:08:23.050 ⇒ 01:08:24.210 Uttam Kumaran: And then
900 01:08:24.319 ⇒ 01:08:30.089 Uttam Kumaran: we’ll have assumed conversion rates. And then, if we need to, we’ll, we’ll eventually break down between lead and meeting.
901 01:08:30.340 ⇒ 01:08:31.020 Patrick Trainer: Right.
902 01:08:31.029 ⇒ 01:08:33.109 Uttam Kumaran: And we’ll have conversion rates like.
903 01:08:33.459 ⇒ 01:08:38.499 Uttam Kumaran: and those will be like very conservative to start. And then that way again. I want it back into volume.
904 01:08:39.309 ⇒ 01:08:54.009 Uttam Kumaran: Right? I want to back into like having people need to come to the website and click for it to like, do like right? And like, okay, then that means we need. If we’re getting on average, the way it’ll end up working is like it’ll all go back to the content outbound, which is like, okay, if on average.
905 01:08:54.119 ⇒ 01:09:03.109 Uttam Kumaran: we get a thousand impressions per post, 2 people click, then we need to do this. Many posts per week, or the post need to grow right? And then it’s clear.
906 01:09:03.319 ⇒ 01:09:04.349 Uttam Kumaran: That’s it.
907 01:09:04.929 ⇒ 01:09:06.569 Uttam Kumaran: Yeah, okay, sick.
908 01:09:08.249 ⇒ 01:09:11.959 Uttam Kumaran: Okay. Shall we? Maybe chat on like
909 01:09:13.309 ⇒ 01:09:15.489 Uttam Kumaran: Monday, like Monday afternoon?
910 01:09:15.490 ⇒ 01:09:16.780 Patrick Trainer: Yeah. Monday, Tuesday.
911 01:09:17.960 ⇒ 01:09:21.840 Uttam Kumaran: Let’s see, Monday afternoon. I’ll just put 30 min on that gives
912 01:09:22.410 ⇒ 01:09:25.919 Uttam Kumaran: like today tomorrow, and kind of like Monday during the day
913 01:09:25.960 ⇒ 01:09:30.159 Uttam Kumaran: to kind of like touch stuff. And then let’s like, maybe we can.
914 01:09:30.580 ⇒ 01:09:40.868 Uttam Kumaran: Based on that. We could book another chat. I mean ideally, next week it would be one. I I have a goal to just like have a big backlog of content ready
915 01:09:41.620 ⇒ 01:09:43.609 Uttam Kumaran: across the primary sources.
916 01:09:43.680 ⇒ 01:09:47.359 Uttam Kumaran: And then I think ideally, it would be cool if we can
917 01:09:47.960 ⇒ 01:09:51.000 Uttam Kumaran: do some sort of test here. Yeah.
918 01:09:51.720 ⇒ 01:09:57.180 Uttam Kumaran: like. And I don’t know. I have to think about what a very basically, I want to see that the systems are talking to each other.
919 01:09:57.870 ⇒ 01:10:02.699 Uttam Kumaran: test of both test of the content outbound and people coming to the site, and then also test of
920 01:10:03.070 ⇒ 01:10:04.739 Uttam Kumaran: us sending email
921 01:10:05.240 ⇒ 01:10:10.330 Uttam Kumaran: and then people opening it. A score happens. Maybe something happens in Hubspot like that.
922 01:10:10.330 ⇒ 01:10:10.879 Patrick Trainer: Yeah, like.
923 01:10:10.880 ⇒ 01:10:13.520 Uttam Kumaran: Back year turns. When I say, we see that, then it’s like.
924 01:10:13.690 ⇒ 01:10:20.919 Uttam Kumaran: Yeah, yeah, either to ourselves. Or again, we’re sending emails every day. So we’ll just. We’ll just launch another one. And then.
925 01:10:21.330 ⇒ 01:10:23.869 Uttam Kumaran: like once we see those gears turning, it’ll be
926 01:10:23.890 ⇒ 01:10:26.960 Uttam Kumaran: really clear to layer stuff on. So this is dope. And then
927 01:10:27.070 ⇒ 01:10:29.280 Uttam Kumaran: I haven’t. I have a
928 01:10:29.440 ⇒ 01:10:43.222 Uttam Kumaran: one of the guys who’s working with us, Miguel. He’s doing automations for us. He’s like, very, very well versed in like Crm automation and stuff. So I think I might have him. I might see if he can join on Monday or chat with us next week.
929 01:10:44.110 ⇒ 01:10:46.750 Uttam Kumaran: once. You’re like, kind of in the weeds there, because.
930 01:10:47.357 ⇒ 01:10:50.759 Uttam Kumaran: yeah, he’s helping us on some automation stuff. So.
931 01:10:50.990 ⇒ 01:10:51.560 Patrick Trainer: Sweet.
932 01:10:53.210 ⇒ 01:10:55.870 Uttam Kumaran: Okay, do I will send
933 01:10:56.472 ⇒ 01:11:01.120 Uttam Kumaran: this meeting. Well, it’s just end up in notion if the thing is working.
934 01:11:01.140 ⇒ 01:11:04.840 Uttam Kumaran: and then I’ll send out the to do’s and
935 01:11:05.368 ⇒ 01:11:09.259 Uttam Kumaran: slack. And then, Abigail, if you want to just create a Google sheet and then share it
936 01:11:09.550 ⇒ 01:11:10.860 Uttam Kumaran: in slack.
937 01:11:11.620 ⇒ 01:11:17.209 Uttam Kumaran: Then, yeah, we’ll just touch base on that on Monday. Then, Pat, if you want to. Also send
938 01:11:18.360 ⇒ 01:11:21.239 Uttam Kumaran: that. Github, docs are a real asset. Yeah.
939 01:11:21.240 ⇒ 01:11:22.010 Patrick Trainer: Yeah.
940 01:11:26.620 ⇒ 01:11:28.999 Uttam Kumaran: Cool product. A meeting, as usual.
941 01:11:29.210 ⇒ 01:11:30.290 Patrick Trainer: Yeah, 2, for 2.
942 01:11:30.290 ⇒ 01:11:32.619 Uttam Kumaran: Check out, check out relevance. If you guys.
943 01:11:33.220 ⇒ 01:11:34.909 Patrick Trainer: Yeah, that’s that’s pretty cool.
944 01:11:35.370 ⇒ 01:11:36.649 Uttam Kumaran: Yeah, it’s like.
945 01:11:37.340 ⇒ 01:11:40.539 Uttam Kumaran: I was like, holy shit. This is like working really well, like.
946 01:11:42.280 ⇒ 01:11:44.010 Uttam Kumaran: So it’s it’s gonna get better.
947 01:11:44.380 ⇒ 01:11:47.569 Patrick Trainer: Abigail, what’s the like? The talk in
948 01:11:47.680 ⇒ 01:11:49.689 Patrick Trainer: like university. Now? Yeah.
949 01:11:49.690 ⇒ 01:11:50.710 Uttam Kumaran: The same question.
950 01:11:50.710 ⇒ 01:11:51.950 Patrick Trainer: 80, in.
951 01:11:51.950 ⇒ 01:11:56.240 Abigail Zhao: Oh, okay, like the whole like. Meta now, is like
952 01:11:56.270 ⇒ 01:12:08.390 Abigail Zhao: all Cs majors, like they don’t know how to code anymore. Because, like AI like, does it all for them like not a single like person studying computer science like actually knows how to code anymore.
953 01:12:08.390 ⇒ 01:12:09.470 Patrick Trainer: Oh, nice!
954 01:12:09.750 ⇒ 01:12:10.549 Abigail Zhao: But yeah.
955 01:12:10.550 ⇒ 01:12:22.170 Uttam Kumaran: But then, like, what like, what does that actually mean? Like, I mean one like, yeah. If I had it, I’d just be cheating on everything. And so I get that that’s happening. But I mean, it’s not like that that wasn’t happening before, anyway.
956 01:12:22.170 ⇒ 01:12:22.889 Abigail Zhao: Yeah, it’s like.
957 01:12:22.890 ⇒ 01:12:24.419 Patrick Trainer: Copy, paste of Stack, Overflow.
958 01:12:24.420 ⇒ 01:12:30.900 Uttam Kumaran: Yeah, it’s like, I don’t know a lot of that stuff was happening. I still think, like, if you’re smart, you’re smart. I think the thing is is like
959 01:12:31.440 ⇒ 01:12:37.029 Uttam Kumaran: you’re gonna be able to see like how we’re using it in this company. And like you could ease like all this, most of the shit we’re doing is free.
960 01:12:37.120 ⇒ 01:12:54.160 Uttam Kumaran: You could easily, like, have hook up relevance to your email like, I could have automated my entire fucking like existence back then, basically so before they because this stuff about relevance, they don’t, they’re never there. It’s gonna take another year to like, kind of like, understand, this is even happening.
961 01:12:54.609 ⇒ 01:13:01.070 Uttam Kumaran: So I don’t know. You should fuck around and try stuff with that. Well, I don’t even know what you would use it for, but like.
962 01:13:01.070 ⇒ 01:13:02.880 Patrick Trainer: I see I see it as like
963 01:13:03.610 ⇒ 01:13:08.610 Patrick Trainer: it. It’s like a supplement, or it’s a tool like I I think of.
964 01:13:08.740 ⇒ 01:13:14.539 Patrick Trainer: like using a chat, Gpt or Claude, or whatever, as like akin to
965 01:13:14.600 ⇒ 01:13:17.600 Patrick Trainer: syntax, highlighting, or autocomplete.
966 01:13:18.020 ⇒ 01:13:19.840 Uttam Kumaran: Oh, same, I mean, it’s just like.
967 01:13:20.110 ⇒ 01:13:23.509 Uttam Kumaran: yeah, there’s just things where I’m like, I could have spent 4 h
968 01:13:23.590 ⇒ 01:13:24.890 Uttam Kumaran: and done this.
969 01:13:25.150 ⇒ 01:13:29.350 Uttam Kumaran: or like thought through this. And I’m like, I just wish I could talk to the expert right now.
970 01:13:29.350 ⇒ 01:13:30.060 Patrick Trainer: Right.
971 01:13:30.230 ⇒ 01:13:37.330 Patrick Trainer: Athar was saying that, like his professors, make them like he’s he’s doing everything in like c plus plus
972 01:13:38.113 ⇒ 01:13:38.806 Patrick Trainer: and
973 01:13:40.150 ⇒ 01:13:42.750 Patrick Trainer: his professors are making them like. Write it out
974 01:13:43.060 ⇒ 01:13:44.390 Patrick Trainer: pencil and paper.
975 01:13:45.130 ⇒ 01:13:50.380 Uttam Kumaran: That’s tough. But I mean dude. That’s how coding interviews are like whiteboard. So
976 01:13:50.872 ⇒ 01:13:55.730 Uttam Kumaran: I still think, once you get on a job, it’s not. There’s not gonna be like. It’s hard to hide.
977 01:13:57.045 ⇒ 01:13:57.400 Abigail Zhao: Right.
978 01:13:58.000 ⇒ 01:13:58.680 Patrick Trainer: Right.
979 01:13:59.170 ⇒ 01:14:06.920 Uttam Kumaran: I mean, it’s if you’re in a job, and everybody’s kind of taking it, then that’s you’re probably chilling. But if you’re in a job like if you come work here, and you’re like
980 01:14:07.100 ⇒ 01:14:09.979 Uttam Kumaran: like, I don’t know. It’s just so easy to sniff.
981 01:14:10.540 ⇒ 01:14:13.159 Uttam Kumaran: So if people know cause like, I’ll have just a
982 01:14:13.240 ⇒ 01:14:21.850 Uttam Kumaran: casual conversation about like what we’re doing, so I don’t know. I still think it. I think it’s good for like, if you’re if you want to get out of tests and homework, but
983 01:14:22.500 ⇒ 01:14:27.559 Uttam Kumaran: like rubber still is gonna beat the road. When you want to make money, I feel like, you know, yeah.
984 01:14:28.520 ⇒ 01:14:36.899 Abigail Zhao: Yeah, I’d say right now, like in school, they just like. Still, all view like any form of AI like they just automatically deem as like cheating. So.
985 01:14:37.110 ⇒ 01:14:37.810 Uttam Kumaran: Yeah.
986 01:14:37.810 ⇒ 01:14:39.400 Patrick Trainer: Yeah, makes sense.
987 01:14:40.110 ⇒ 01:14:46.309 Uttam Kumaran: I guess, like I would just use it to maybe speed up like, for example, if you had questions like Google isn’t cutting it.
988 01:14:46.530 ⇒ 01:14:48.570 Uttam Kumaran: I would use that instead of Google.
989 01:14:48.710 ⇒ 01:14:51.829 Uttam Kumaran: But then you do need to have some self discipline to not like.
990 01:14:52.130 ⇒ 01:14:52.630 Abigail Zhao: Yeah.
991 01:14:53.130 ⇒ 01:14:54.790 Uttam Kumaran: Here’s the fucking problem.
992 01:14:54.970 ⇒ 01:15:00.479 Uttam Kumaran: Answer it. I mean, like, maybe once in a while, if you’re like, I gotta go to the bar like I can’t do this right now, but
993 01:15:00.950 ⇒ 01:15:03.530 Uttam Kumaran: like not all the time right. I don’t know.
994 01:15:03.950 ⇒ 01:15:04.399 Abigail Zhao: I mean, yeah.
995 01:15:04.400 ⇒ 01:15:05.280 Uttam Kumaran: Yeah, definitely.
996 01:15:05.700 ⇒ 01:15:08.360 Abigail Zhao: Very widely used, but.
997 01:15:08.360 ⇒ 01:15:09.010 Patrick Trainer: Yeah.
998 01:15:09.260 ⇒ 01:15:12.832 Abigail Zhao: It’s hard to like catch, too, like, I know, a lot of
999 01:15:13.370 ⇒ 01:15:19.689 Abigail Zhao: professors like they they claim like they have like software or whatever they can like detect it. But it’s like.
1000 01:15:19.690 ⇒ 01:15:34.509 Uttam Kumaran: They don’t work right now. Yeah. And also, unless you’re dumb. And you’re literally like, solve this problem. And you just like, take copy paste. You’re actually a moron instead. It’s like dude. If you spend 10 min just thought about like a really good prompt
1001 01:15:34.640 ⇒ 01:15:37.649 Uttam Kumaran: like, you’re not gonna be able to tell at all.
1002 01:15:38.080 ⇒ 01:15:38.480 Abigail Zhao: Oh, my!
1003 01:15:38.480 ⇒ 01:15:39.270 Uttam Kumaran: I like.
1004 01:15:39.270 ⇒ 01:15:40.030 Abigail Zhao: Good.
1005 01:15:40.390 ⇒ 01:15:54.070 Abigail Zhao: I I had a friend in a Cs class who, like works as like a learning assistant, and like one of the like homework responses she found once was like someone accidentally submitted like their chat, Gprompt, along with, like the.
1006 01:15:56.110 ⇒ 01:15:57.090 Abigail Zhao: Insane.
1007 01:15:57.090 ⇒ 01:16:01.419 Uttam Kumaran: No, I know it’s cause again, it’s like this is like lowest denominator stuff like.
1008 01:16:01.420 ⇒ 01:16:02.010 Abigail Zhao: Yeah.
1009 01:16:02.010 ⇒ 01:16:03.370 Uttam Kumaran: In school, and like
1010 01:16:03.570 ⇒ 01:16:12.529 Uttam Kumaran: that, that the baseline for school is like you just get into school like for a job. It’s like a little bit higher, but you’d be surprised at like some companies without, like.
1011 01:16:12.950 ⇒ 01:16:18.349 Uttam Kumaran: Again, you have a lot of people. It’s like people just fish for like the lowest effort
1012 01:16:18.560 ⇒ 01:16:27.899 Uttam Kumaran: thing. But at this company we actually enjoy doing that like where I’m like hugging AI like we need to be doing that. I don’t want to be working this hard like.
1013 01:16:27.900 ⇒ 01:16:28.280 Abigail Zhao: Get up!
1014 01:16:28.280 ⇒ 01:16:32.170 Uttam Kumaran: So it’s like an opposite thing. But I get a lot of companies like they
1015 01:16:33.280 ⇒ 01:16:49.320 Uttam Kumaran: they just have people who like don’t care at all. And so they just go like as low as possible, like low quality. But that’s why I think you know, those companies are always going to have a hard time like here. I really was like, I want to use AI from the jump everywhere, on everything.
1016 01:16:49.390 ⇒ 01:16:59.222 Uttam Kumaran: And ideally, it frees us time to just work on the best things, or like just like work, a little less like that’s like, I’m not really afraid to say that.
1017 01:16:59.640 ⇒ 01:17:00.810 Uttam Kumaran: whether that’s like.
1018 01:17:01.160 ⇒ 01:17:05.169 Uttam Kumaran: yeah, I mean, I was like, I’m not like a normal person, or like, I’m not like a corp corporate.
1019 01:17:05.170 ⇒ 01:17:05.710 Patrick Trainer: So I see.
1020 01:17:05.710 ⇒ 01:17:06.660 Uttam Kumaran: Lord, type, person.
1021 01:17:06.660 ⇒ 01:17:07.470 Patrick Trainer: Arkansas.
1022 01:17:07.960 ⇒ 01:17:09.700 Uttam Kumaran: Yeah, I don’t know.
1023 01:17:10.840 ⇒ 01:17:12.330 Uttam Kumaran: Cool. That’s interesting.
1024 01:17:13.970 ⇒ 01:17:20.530 Uttam Kumaran: Okay, cool. Alright. Well, I’ll send a couple of things to slack. And then, yeah, I think we’ll all probably chat tomorrow on the team meeting, and then
1025 01:17:20.580 ⇒ 01:17:21.819 Uttam Kumaran: time on Monday afternoon.
1026 01:17:21.950 ⇒ 01:17:22.830 Uttam Kumaran: Sounds good
1027 01:17:23.320 ⇒ 01:17:24.429 Uttam Kumaran: thanks, guys, too.