Meeting Title: Uttam <> Patrick-Weekly Date: 2024-08-15 Meeting participants: Patrick Trainer, Uttam Kumaran
WEBVTT
1 00:00:17.530 ⇒ 00:00:18.810 Uttam Kumaran: Okay, we’re back.
2 00:00:18.810 ⇒ 00:00:19.590 Patrick Trainer: Yo.
3 00:00:22.040 ⇒ 00:00:23.110 Uttam Kumaran: So yeah.
4 00:00:23.200 ⇒ 00:00:26.390 Uttam Kumaran: we just finished like, creating
5 00:00:26.400 ⇒ 00:00:28.110 Uttam Kumaran: like a dummy rail.
6 00:00:28.710 ⇒ 00:00:29.530 Patrick Trainer: Oh, yeah.
7 00:00:29.530 ⇒ 00:00:39.930 Uttam Kumaran: One of the things I wanted to do, but Ryan knocked it out really quickly, which is great, and we’re becoming a lot flicker. On turning on these real instances. But basically we’re creating like a
8 00:00:40.870 ⇒ 00:00:44.849 Uttam Kumaran: public facing real, for like manufacturing where where we’ll have like.
9 00:00:45.440 ⇒ 00:00:48.480 Uttam Kumaran: we’ll have like 3 dashboards about manufacturing
10 00:00:50.220 ⇒ 00:01:04.790 Uttam Kumaran: like, or these are 5. Right now we’ll cut down to 3, and then it just makes our like call to action super great, because when I target manufacturing clients as part of the follow up, I’m gonna say, like, here’s like something that we could do for you.
11 00:01:06.600 ⇒ 00:01:08.129 Uttam Kumaran: and I’m going to send them that.
12 00:01:08.350 ⇒ 00:01:09.290 Uttam Kumaran: So
13 00:01:10.000 ⇒ 00:01:11.500 Uttam Kumaran: that should be like
14 00:01:11.530 ⇒ 00:01:15.860 Uttam Kumaran: that should be a lot better process than me, just being like we can
15 00:01:16.410 ⇒ 00:01:18.600 Uttam Kumaran: we? We can do something for you like.
16 00:01:18.600 ⇒ 00:01:20.410 Patrick Trainer: Shot of somebody else’s.
17 00:01:20.860 ⇒ 00:01:33.159 Uttam Kumaran: Yeah. And the other thing is, we can embed this in an iframe. So the next step is gonna be putting this under like a brain forged subdomain, like as an iframe, like wrapped with like some content.
18 00:01:33.160 ⇒ 00:01:33.720 Patrick Trainer: Right.
19 00:01:33.720 ⇒ 00:01:34.360 Uttam Kumaran: So.
20 00:01:34.360 ⇒ 00:01:35.520 Patrick Trainer: Yeah, that’s awesome.
21 00:01:36.050 ⇒ 00:01:36.670 Uttam Kumaran: Yeah.
22 00:01:36.670 ⇒ 00:01:37.270 Patrick Trainer: What a.
23 00:01:37.270 ⇒ 00:01:37.800 Uttam Kumaran: Been good.
24 00:01:37.800 ⇒ 00:01:40.700 Patrick Trainer: What’s the underlying
25 00:01:41.100 ⇒ 00:01:42.140 Patrick Trainer: data set.
26 00:01:42.740 ⇒ 00:01:44.790 Uttam Kumaran: It’s all coming from our internal snowflake.
27 00:01:46.310 ⇒ 00:01:58.849 Uttam Kumaran: cause we’re not like I like right now. I just had. I think Ryan created. This is getting the wrong number. Ryan just used Gpt to create like a dummy Csv that he loaded into Snowflake.
28 00:01:59.361 ⇒ 00:02:02.010 Uttam Kumaran: I think what would be cool?
29 00:02:03.337 ⇒ 00:02:05.129 Uttam Kumaran: Is probably to
30 00:02:05.960 ⇒ 00:02:09.469 Uttam Kumaran: like honestly, probably just to create a table with data going
31 00:02:12.860 ⇒ 00:02:16.119 Uttam Kumaran: between now and like 2025, or something like that
32 00:02:16.190 ⇒ 00:02:20.759 Uttam Kumaran: between like last 2 years and 2025 filtering it to today.
33 00:02:20.970 ⇒ 00:02:23.989 Uttam Kumaran: And that way it always has. Seems like it’s up to date with data.
34 00:02:25.910 ⇒ 00:02:29.389 Uttam Kumaran: and we just have like 3 simple data models or something like that.
35 00:02:29.390 ⇒ 00:02:35.520 Patrick Trainer: We can like. There’s like python libraries to you can use to create fake data.
36 00:02:35.950 ⇒ 00:02:36.460 Uttam Kumaran: Hmm.
37 00:02:36.460 ⇒ 00:02:37.810 Patrick Trainer: And like
38 00:02:38.100 ⇒ 00:02:39.890 Patrick Trainer: it’d be cool if we
39 00:02:39.950 ⇒ 00:02:43.999 Patrick Trainer: created a couple of tables of like a million rows or something like that.
40 00:02:44.000 ⇒ 00:02:44.750 Uttam Kumaran: Yeah.
41 00:02:44.750 ⇒ 00:02:46.279 Patrick Trainer: Really large set
42 00:02:48.020 ⇒ 00:02:50.629 Patrick Trainer: or well, a larger set.
43 00:02:51.120 ⇒ 00:02:54.470 Patrick Trainer: and then so it doesn’t look like, oh, there’s
44 00:02:54.500 ⇒ 00:02:58.220 Patrick Trainer: 10 orders like, just so it’s more like relatable to
45 00:02:58.910 ⇒ 00:03:02.009 Patrick Trainer: people, and then it also gives that opportunity to
46 00:03:02.670 ⇒ 00:03:05.169 Patrick Trainer: be able to pivot around everything.
47 00:03:05.560 ⇒ 00:03:06.470 Uttam Kumaran: Totally
48 00:03:09.100 ⇒ 00:03:11.419 Uttam Kumaran: no, you’re you’re like, completely on the ball.
49 00:03:11.680 ⇒ 00:03:13.620 Uttam Kumaran: So that’s what I think is going to be
50 00:03:14.790 ⇒ 00:03:17.524 Uttam Kumaran: next for this, which is great.
51 00:03:17.980 ⇒ 00:03:19.820 Patrick Trainer: See here!
52 00:03:21.000 ⇒ 00:03:23.463 Uttam Kumaran: Yeah, if you could, maybe you can send the python
53 00:03:23.980 ⇒ 00:03:26.650 Uttam Kumaran: dummy data generation thing if you have it.
54 00:03:27.300 ⇒ 00:03:32.610 Uttam Kumaran: I mean, I would have loved to have this data in like duck TV. So we don’t have to hit Snowflake. But
55 00:03:33.643 ⇒ 00:03:36.390 Uttam Kumaran: we don’t. They only connect the mother duck.
56 00:03:36.690 ⇒ 00:03:38.730 Uttam Kumaran: But I guess we could actually
57 00:03:39.250 ⇒ 00:03:42.080 Uttam Kumaran: have these connect to an S. 3
58 00:03:49.160 ⇒ 00:03:51.080 Patrick Trainer: Wait. So what’s the issue?
59 00:03:52.470 ⇒ 00:03:58.909 Uttam Kumaran: Well like Oh, I don’t see why we need to query our snowflake for this, to be honest, but I’m not sure how we can get around that.
60 00:03:59.260 ⇒ 00:04:01.009 Patrick Trainer: Yeah, no, we can just
61 00:04:01.400 ⇒ 00:04:04.819 Patrick Trainer: like in their in their demos. They just have a
62 00:04:06.030 ⇒ 00:04:07.920 Patrick Trainer: a Duckdb file.
63 00:04:08.390 ⇒ 00:04:12.110 Uttam Kumaran: Oh, yeah, in they have it in Github.
64 00:04:13.200 ⇒ 00:04:17.189 Uttam Kumaran: Oh, shit. Yeah, we should do. Okay. I didn’t. Fucking figure out how they did it.
65 00:04:17.190 ⇒ 00:04:17.860 Patrick Trainer: Yeah.
66 00:04:18.170 ⇒ 00:04:19.300 Patrick Trainer: yes.
67 00:04:19.660 ⇒ 00:04:23.829 Patrick Trainer: like it. If you do like the real init script. Oh, yeah.
68 00:04:23.830 ⇒ 00:04:25.289 Uttam Kumaran: We should just put that in here.
69 00:04:25.290 ⇒ 00:04:27.109 Patrick Trainer: Yeah, we can just drop it there.
70 00:04:29.060 ⇒ 00:04:32.180 Uttam Kumaran: I swear they didn’t have that when I looked.
71 00:04:35.540 ⇒ 00:04:37.309 Uttam Kumaran: See? Yeah, they changed it.
72 00:04:38.760 ⇒ 00:04:42.640 Uttam Kumaran: They changed it to like pull from this parquet or something.
73 00:04:43.020 ⇒ 00:04:47.559 Uttam Kumaran: But you’re right is like, we can just have it all in a Duckdb file in the repo.
74 00:04:47.700 ⇒ 00:04:49.890 Uttam Kumaran: I’m just gonna put that in my notes.
75 00:04:51.380 ⇒ 00:04:55.701 Uttam Kumaran: Yeah, can you send like the python thing? Cause then I’m gonna we’re we’ll we’ll
76 00:04:55.990 ⇒ 00:04:56.739 Patrick Trainer: Yeah, that’s what I’m doing.
77 00:04:56.740 ⇒ 00:04:58.939 Uttam Kumaran: We’ll wrap that into a little thing.
78 00:05:32.300 ⇒ 00:05:46.460 Uttam Kumaran: And then that way the Duckdb file will actually generate with values going yeah, with values going to like 2026, and then we will just filter that to just be less than today or less than equal to today. That way, it’s always like.
79 00:05:46.470 ⇒ 00:05:47.600 Uttam Kumaran: yeah.
80 00:05:48.820 ⇒ 00:05:58.239 Uttam Kumaran: which is dope. And the nice thing is we, we really only have to show the aggregated tables. We don’t have to show the constituent like data model. So it doesn’t have to be that fancy.
81 00:06:03.470 ⇒ 00:06:05.700 Uttam Kumaran: I mean, dude. You could probably run.
82 00:06:05.950 ⇒ 00:06:13.150 Uttam Kumaran: You could probably ask that Gpt to write, get use your Python Generation Library and give you the code to write the Duckdb file.
83 00:06:14.130 ⇒ 00:06:16.179 Patrick Trainer: Yeah, I mean, it’s
84 00:06:16.350 ⇒ 00:06:17.560 Patrick Trainer: pretty much what I’m
85 00:06:18.160 ⇒ 00:06:19.610 Patrick Trainer: pretty much what I’m doing.
86 00:06:19.860 ⇒ 00:06:20.580 Patrick Trainer: Do you.
87 00:06:20.580 ⇒ 00:06:22.490 Uttam Kumaran: Is there a specific library for that.
88 00:06:22.490 ⇒ 00:06:23.850 Patrick Trainer: Yeah. It’s called Faker.
89 00:06:24.770 ⇒ 00:06:25.870 Uttam Kumaran: Okay. Take care.
90 00:06:25.870 ⇒ 00:06:27.469 Patrick Trainer: Yeah, but I’ve got
91 00:06:28.950 ⇒ 00:06:30.979 Patrick Trainer: I’ve got a script
92 00:06:31.200 ⇒ 00:06:32.060 Patrick Trainer: right now.
93 00:07:13.740 ⇒ 00:07:14.550 Uttam Kumaran: Nice.
94 00:07:38.340 ⇒ 00:07:42.140 Uttam Kumaran: Okay? Well, then, that’s that’ll I’ll that’s easy.
95 00:07:42.760 ⇒ 00:07:44.030 Patrick Trainer: Run this.
96 00:07:56.160 ⇒ 00:07:57.040 Patrick Trainer: that’s
97 00:08:27.430 ⇒ 00:08:28.940 Patrick Trainer: alright. Here, I’m gonna
98 00:08:30.270 ⇒ 00:08:32.590 Patrick Trainer: share the screen real quick.
99 00:08:34.520 ⇒ 00:08:35.829 Patrick Trainer: And then we can
100 00:08:38.020 ⇒ 00:08:40.119 Patrick Trainer: demo some data. So
101 00:08:40.970 ⇒ 00:08:41.980 Patrick Trainer: you see this.
102 00:08:43.080 ⇒ 00:08:43.850 Patrick Trainer: So basically.
103 00:08:44.186 ⇒ 00:08:46.539 Uttam Kumaran: Yeah, let me pull yours up. Okay.
104 00:08:55.360 ⇒ 00:08:57.810 Uttam Kumaran: can I like not pull your? Oh.
105 00:09:00.420 ⇒ 00:09:01.760 Uttam Kumaran: okay, yeah.
106 00:09:01.760 ⇒ 00:09:02.950 Patrick Trainer: Okay, cool.
107 00:09:03.130 ⇒ 00:09:03.990 Patrick Trainer: So
108 00:09:04.250 ⇒ 00:09:06.540 Patrick Trainer: like, we’ve got this Faker library
109 00:09:07.000 ⇒ 00:09:09.950 Patrick Trainer: and we initialize it with a seed.
110 00:09:09.990 ⇒ 00:09:13.069 Patrick Trainer: and the seed just allows you to like
111 00:09:13.960 ⇒ 00:09:18.060 Patrick Trainer: reproduce it, instead of having, like random stuff every single time.
112 00:09:18.406 ⇒ 00:09:19.100 Uttam Kumaran: Yeah. Yeah.
113 00:09:19.100 ⇒ 00:09:21.969 Patrick Trainer: And then here we just create and
114 00:09:22.190 ⇒ 00:09:29.390 Patrick Trainer: dates. And then here’s where we define, like the different products that we want faker to
115 00:09:29.490 ⇒ 00:09:30.430 Patrick Trainer: create.
116 00:09:30.640 ⇒ 00:09:34.870 Patrick Trainer: And so we got like different factories, regions, quality.
117 00:09:35.360 ⇒ 00:09:38.670 Patrick Trainer: And then we got like the start and end dates.
118 00:09:38.670 ⇒ 00:09:39.980 Uttam Kumaran: Nice.
119 00:09:40.290 ⇒ 00:09:42.559 Patrick Trainer: Due to like 26.
120 00:09:42.560 ⇒ 00:09:43.410 Uttam Kumaran: Sick.
121 00:09:43.500 ⇒ 00:09:45.899 Patrick Trainer: 2020 like, let’s do that
122 00:09:46.990 ⇒ 00:09:50.699 Patrick Trainer: and then, basically, we’re going through
123 00:09:50.970 ⇒ 00:09:53.130 Patrick Trainer: with all of the records.
124 00:09:54.870 ⇒ 00:09:57.640 Patrick Trainer: or however many records we want.
125 00:09:57.680 ⇒ 00:10:00.239 Patrick Trainer: It’s going to loop through and create
126 00:10:00.770 ⇒ 00:10:02.369 Patrick Trainer: like this object.
127 00:10:03.300 ⇒ 00:10:04.600 Patrick Trainer: And then
128 00:10:04.700 ⇒ 00:10:05.750 Patrick Trainer: it’s going to
129 00:10:06.230 ⇒ 00:10:08.270 Patrick Trainer: return like this list
130 00:10:08.520 ⇒ 00:10:09.980 Patrick Trainer: and then
131 00:10:10.340 ⇒ 00:10:13.129 Patrick Trainer: use Panda just to pandas just to get some
132 00:10:13.460 ⇒ 00:10:16.049 Patrick Trainer: metrics just to load into there.
133 00:10:16.280 ⇒ 00:10:18.969 Patrick Trainer: and then we can save it to a Csv.
134 00:10:19.240 ⇒ 00:10:22.870 Patrick Trainer: and then we can load this all into
135 00:10:22.880 ⇒ 00:10:24.210 Patrick Trainer: to duck. dB,
136 00:10:25.192 ⇒ 00:10:29.130 Patrick Trainer: and yeah, that’s it. So like we can
137 00:10:30.420 ⇒ 00:10:33.020 Patrick Trainer: like, let’s just run this with a
138 00:10:33.050 ⇒ 00:10:34.379 Patrick Trainer: 1,000 rows.
139 00:10:35.910 ⇒ 00:10:36.910 Patrick Trainer: cool. So
140 00:10:55.750 ⇒ 00:10:57.360 Patrick Trainer: look at that cool.
141 00:10:58.650 ⇒ 00:11:00.270 Uttam Kumaran: Nice. Okay.
142 00:11:00.270 ⇒ 00:11:02.540 Patrick Trainer: We have all this manufacturing data.
143 00:11:04.160 ⇒ 00:11:05.960 Uttam Kumaran: That’s why I’m not sick.
144 00:11:06.680 ⇒ 00:11:09.409 Patrick Trainer: And we have this Doc. dB, so let’s
145 00:11:11.420 ⇒ 00:11:13.539 Patrick Trainer: dot dB
146 00:11:14.790 ⇒ 00:11:15.650 Patrick Trainer: manufacturer.
147 00:11:15.650 ⇒ 00:11:18.910 Uttam Kumaran: I mean, dude. This is a real like. Try before you buy, you know.
148 00:11:18.910 ⇒ 00:11:19.700 Patrick Trainer: dB,
149 00:11:21.860 ⇒ 00:11:24.320 Patrick Trainer: and then dot tables.
150 00:11:24.970 ⇒ 00:11:26.500 Patrick Trainer: So select
151 00:11:27.200 ⇒ 00:11:28.740 Patrick Trainer: count one.
152 00:11:31.960 ⇒ 00:11:34.870 Patrick Trainer: Can you see this? I’m down in the terminal.
153 00:11:35.450 ⇒ 00:11:39.250 Patrick Trainer: So count on from manufacturing data.
154 00:11:39.800 ⇒ 00:11:40.760 Patrick Trainer: Aha.
155 00:11:46.510 ⇒ 00:11:47.964 Patrick Trainer: select
156 00:11:51.150 ⇒ 00:11:52.140 Patrick Trainer: they
157 00:11:54.440 ⇒ 00:11:55.310 Patrick Trainer: actually
158 00:11:55.430 ⇒ 00:11:56.290 Patrick Trainer: product
159 00:12:01.980 ⇒ 00:12:03.349 Patrick Trainer: And then
160 00:12:04.630 ⇒ 00:12:06.969 Patrick Trainer: let’s do some
161 00:12:07.130 ⇒ 00:12:08.070 Patrick Trainer: profit.
162 00:12:10.170 ⇒ 00:12:11.080 Patrick Trainer: It’s P.
163 00:12:14.550 ⇒ 00:12:15.460 Patrick Trainer: Bye.
164 00:12:15.910 ⇒ 00:12:16.750 Patrick Trainer: And
165 00:12:18.190 ⇒ 00:12:20.000 Patrick Trainer: yeah, nice.
166 00:12:22.810 ⇒ 00:12:24.050 Uttam Kumaran: Yeah. I mean, it’s great.
167 00:12:24.330 ⇒ 00:12:26.210 Uttam Kumaran: Okay, cool. That’s what we’re gonna do.
168 00:12:28.060 ⇒ 00:12:28.670 Patrick Trainer: And then.
169 00:12:28.670 ⇒ 00:12:31.842 Uttam Kumaran: Can you? Yeah. Can you just send me that?
170 00:12:33.050 ⇒ 00:12:34.750 Uttam Kumaran: that python file?
171 00:12:35.230 ⇒ 00:12:38.179 Uttam Kumaran: Yeah. Could have shot that into notion. So Ryan asked that
172 00:12:38.920 ⇒ 00:12:40.250 Uttam Kumaran: example.
173 00:12:41.403 ⇒ 00:12:42.469 Patrick Trainer: And then
174 00:12:42.810 ⇒ 00:12:43.969 Patrick Trainer: we can also do
175 00:12:44.060 ⇒ 00:12:45.380 Patrick Trainer: cool stuff like
176 00:12:46.570 ⇒ 00:12:47.970 Patrick Trainer: we can.
177 00:12:50.980 ⇒ 00:12:52.899 Patrick Trainer: We can like dump this to
178 00:12:56.900 ⇒ 00:12:58.810 Patrick Trainer: other file types
179 00:12:59.930 ⇒ 00:13:02.880 Patrick Trainer: like we can dump it to like parquet, or
180 00:13:05.050 ⇒ 00:13:06.940 Patrick Trainer: or really anything we want.
181 00:13:14.310 ⇒ 00:13:15.185 Patrick Trainer: Cool
182 00:13:16.990 ⇒ 00:13:19.648 Uttam Kumaran: That’s that from my side.
183 00:13:22.350 ⇒ 00:13:27.859 Uttam Kumaran: What else? We have like 3 open things that we’re in.
184 00:13:29.260 ⇒ 00:13:31.560 Uttam Kumaran: One is for Baylor health
185 00:13:34.210 ⇒ 00:13:35.849 Uttam Kumaran: for a snowflake
186 00:13:36.040 ⇒ 00:13:37.040 Uttam Kumaran: gig.
187 00:13:37.876 ⇒ 00:13:39.740 Uttam Kumaran: Which I might take.
188 00:13:40.407 ⇒ 00:13:41.570 Uttam Kumaran: What is it?
189 00:13:41.850 ⇒ 00:13:43.509 Uttam Kumaran: It’s for Baylor health.
190 00:13:44.100 ⇒ 00:13:45.009 Patrick Trainer: What about it?
191 00:13:45.808 ⇒ 00:13:49.239 Uttam Kumaran: Oh, no, I’m just talking about kind of like the opportunities that we have open.
192 00:13:49.680 ⇒ 00:13:50.670 Patrick Trainer: Oh, okay.
193 00:13:51.260 ⇒ 00:13:51.680 Uttam Kumaran: Yeah.
194 00:13:51.680 ⇒ 00:13:52.040 Patrick Trainer: Yeah, that’s.
195 00:13:52.040 ⇒ 00:13:52.520 Uttam Kumaran: So we want to.
196 00:13:52.520 ⇒ 00:13:53.919 Patrick Trainer: I missed the 1st part.
197 00:13:53.920 ⇒ 00:13:54.540 Uttam Kumaran: Oh, yeah.
198 00:13:54.540 ⇒ 00:13:55.839 Patrick Trainer: Of of what you’re saying.
199 00:13:56.110 ⇒ 00:14:02.897 Uttam Kumaran: No, we have one open for Baylor. Health. That like I might take on
200 00:14:03.500 ⇒ 00:14:04.750 Uttam Kumaran: We have
201 00:14:04.850 ⇒ 00:14:06.050 Uttam Kumaran: one
202 00:14:06.922 ⇒ 00:14:10.650 Uttam Kumaran: opportunity for this company called Hope Hydration.
203 00:14:11.310 ⇒ 00:14:11.950 Uttam Kumaran: That
204 00:14:12.080 ⇒ 00:14:14.510 Uttam Kumaran: they wanna actually a front end person.
205 00:14:14.600 ⇒ 00:14:39.639 Uttam Kumaran: But they have, like a bigger like data opportunity after that. So I’m actually like, I, I have a like. I cut a partnership with this firm that does that has, like engineers that are outside of our world like front and back, and like web app folks that we may, we’re gonna bring on one person from their team and kind of take some off the top and then ideally.
206 00:14:39.770 ⇒ 00:14:44.610 Uttam Kumaran: Well, once they’re at the point where they also need the data product like, we’ll come in with people from our side.
207 00:14:44.830 ⇒ 00:14:45.160 Patrick Trainer: Right.
208 00:14:45.563 ⇒ 00:14:48.789 Uttam Kumaran: And then there’s also an open opportunity for
209 00:14:48.950 ⇒ 00:14:52.829 Uttam Kumaran: this, like e-commerce, like coffee. Startup.
210 00:14:54.150 ⇒ 00:14:54.910 Patrick Trainer: Okay.
211 00:14:54.910 ⇒ 00:14:56.610 Uttam Kumaran: Kind of like comment here.
212 00:14:57.360 ⇒ 00:14:59.160 Uttam Kumaran: and they’re gonna need
213 00:15:00.730 ⇒ 00:15:08.399 Uttam Kumaran: And then they’re they’re just gonna need, like Stella, amount of hours, the toughest part, and that’s coming to Robert as well. The toughest part is that
214 00:15:08.470 ⇒ 00:15:10.859 Uttam Kumaran: anything less than 10 h
215 00:15:10.980 ⇒ 00:15:17.730 Uttam Kumaran: is anything like even anything less than 20 h is like really hard to do.
216 00:15:18.390 ⇒ 00:15:22.893 Uttam Kumaran: So I’m like trying to find out a way like make it economical.
217 00:15:23.240 ⇒ 00:15:25.609 Patrick Trainer: So those like weird problems of like
218 00:15:26.300 ⇒ 00:15:28.510 Patrick Trainer: the users like over
219 00:15:29.500 ⇒ 00:15:31.000 Patrick Trainer: expecting too much.
220 00:15:31.940 ⇒ 00:15:33.432 Uttam Kumaran: No, I know.
221 00:15:35.530 ⇒ 00:15:47.639 Uttam Kumaran: It’s just that we’re like, I get that like, we’re expensive, and that’s fine. But we’re just not in a position right now to be too choosy. So I’m kind of just taking on what we can do. But I also am like.
222 00:15:48.000 ⇒ 00:15:50.559 Uttam Kumaran: Hey, we only are working like 10 h.
223 00:15:50.858 ⇒ 00:15:59.910 Uttam Kumaran: So like we can’t like we can’t do a ton. But you know, I think now that we have like, if it’s just dbt for 10 h like you can get a decent amount done.
224 00:16:01.710 ⇒ 00:16:04.600 Uttam Kumaran: I will say, like the marketing side of stuff.
225 00:16:06.610 ⇒ 00:16:14.909 Uttam Kumaran: where, like we have a good like I now have the process down of like figuring out how to send emails out. I’m working on like how to also do
226 00:16:14.920 ⇒ 00:16:22.479 Uttam Kumaran: like Linkedin in an automated way I was. Gonna see if if once you click through Apollo, if, like any of that actually
227 00:16:23.103 ⇒ 00:16:26.730 Uttam Kumaran: seem interesting, if you’re like fuck the sales thing.
228 00:16:26.730 ⇒ 00:16:31.429 Patrick Trainer: Yeah, no, yeah, no, I’m I’m not in the like. Fuck the sales side area.
229 00:16:31.430 ⇒ 00:16:32.580 Uttam Kumaran: Okay. Yeah.
230 00:16:33.030 ⇒ 00:16:37.970 Uttam Kumaran: Well, it’s it’s just not like it’s just not like the most exhilarating thing. But.
231 00:16:37.970 ⇒ 00:16:40.600 Patrick Trainer: I mean, it’s it’s all good, I mean sales
232 00:16:40.990 ⇒ 00:16:42.760 Patrick Trainer: equates to money, and so.
233 00:16:42.760 ⇒ 00:16:44.030 Uttam Kumaran: Yeah, like.
234 00:16:44.370 ⇒ 00:16:46.250 Patrick Trainer: I can get over it, being boring.
235 00:16:46.470 ⇒ 00:16:47.290 Uttam Kumaran: Yeah.
236 00:16:53.190 ⇒ 00:16:56.300 Patrick Trainer: One thing that I was wondering was like about the
237 00:16:56.730 ⇒ 00:17:01.279 Patrick Trainer: How does Apollo like make sure they’re
238 00:17:02.680 ⇒ 00:17:07.000 Patrick Trainer: like the emails and whatnot that that you get from them are current.
239 00:17:07.119 ⇒ 00:17:08.220 Patrick Trainer: We’re good.
240 00:17:08.349 ⇒ 00:17:08.980 Patrick Trainer: or it’s like.
241 00:17:08.980 ⇒ 00:17:10.959 Uttam Kumaran: Good question, thanks for.
242 00:17:10.960 ⇒ 00:17:14.849 Patrick Trainer: Change either often, or they leave their job, or.
243 00:17:16.220 ⇒ 00:17:17.933 Uttam Kumaran: Yeah, I’m going to
244 00:17:20.349 ⇒ 00:17:24.921 Uttam Kumaran: Also. Did I show you that how their resumes turned out? I haven’t made yours yet. But
245 00:17:25.650 ⇒ 00:17:28.858 Uttam Kumaran: these are kind of like how they end up turning out.
246 00:17:29.630 ⇒ 00:17:31.140 Uttam Kumaran: this is like Jacobs.
247 00:17:32.950 ⇒ 00:17:35.089 Uttam Kumaran: it’s like very nicely styled
248 00:17:35.580 ⇒ 00:17:37.460 Uttam Kumaran: with like experiences.
249 00:17:37.460 ⇒ 00:17:38.199 Patrick Trainer: Oh, nice!
250 00:17:38.200 ⇒ 00:17:41.310 Uttam Kumaran: And then I basically just like kind of how I set profiles to people.
251 00:17:41.877 ⇒ 00:17:42.679 Uttam Kumaran: I need to.
252 00:17:42.680 ⇒ 00:17:43.839 Patrick Trainer: Is that something
253 00:17:44.430 ⇒ 00:17:48.179 Patrick Trainer: users are looking for like, who do you work with, or.
254 00:17:48.780 ⇒ 00:17:51.010 Uttam Kumaran: There. So when I when I like
255 00:17:51.820 ⇒ 00:18:00.080 Uttam Kumaran: previously, when I was talking to people, they’re like, I’m the one working so they’ll look me up on Linkedin, or I’ll send my resume. But actually, when they talk to Brainport, they’re like.
256 00:18:00.190 ⇒ 00:18:02.600 Uttam Kumaran: who would work our account
257 00:18:02.650 ⇒ 00:18:05.460 Uttam Kumaran: like? Who would you bring on? And I send them like.
258 00:18:05.480 ⇒ 00:18:14.709 Uttam Kumaran: here’s a couple of people that we have on our team, and I don’t want that to be. And like, for example, this is like a version of of what I got from
259 00:18:15.015 ⇒ 00:18:37.880 Uttam Kumaran: another like staffing firm that was like, Hey, do you guys need engineers? And they sent me this, which is like just like a little bit of a branded resume. But again, it’s like profiles of who we have on staff. So I would send like, here’s like your profile, like Patrick works for us as data engineer. Here’s another profile. Here’s another profile. And then, basically, they would just want to see like that. You know what your experience is. Right? Some places have
260 00:18:38.210 ⇒ 00:18:40.330 Uttam Kumaran: technical interviews, some places don’t
261 00:18:40.857 ⇒ 00:18:45.749 Uttam Kumaran: but, like the bigger firms will definitely do technical interviews smaller firms.
262 00:18:47.220 ⇒ 00:18:51.139 Uttam Kumaran: They may not have a luxury, or they don’t have a process for that.
263 00:18:52.310 ⇒ 00:18:53.650 Patrick Trainer: Wait, so it’d be
264 00:18:55.090 ⇒ 00:18:56.379 Patrick Trainer: so. These wouldn’t be
265 00:18:57.020 ⇒ 00:18:57.740 Patrick Trainer: direct
266 00:18:58.080 ⇒ 00:19:00.669 Patrick Trainer: like, who’s who’s who’s interviewing? Who.
267 00:19:01.610 ⇒ 00:19:05.499 Uttam Kumaran: No like. For example, if we’re going to work for like ut, Austin.
268 00:19:05.690 ⇒ 00:19:08.800 Uttam Kumaran: and they’re like, and we come in as
269 00:19:09.390 ⇒ 00:19:14.170 Uttam Kumaran: engineers working for them. They may want to know that we’re all credentialed.
270 00:19:14.330 ⇒ 00:19:14.990 Patrick Trainer: Right.
271 00:19:15.280 ⇒ 00:19:16.970 Patrick Trainer: and so they they would get.
272 00:19:16.970 ⇒ 00:19:17.410 Uttam Kumaran: They!
273 00:19:17.410 ⇒ 00:19:18.580 Patrick Trainer: Technical interview.
274 00:19:18.850 ⇒ 00:19:23.440 Uttam Kumaran: They would have someone on their end who’s like technical. That’s like, met these guys. Basically.
275 00:19:23.650 ⇒ 00:19:24.550 Uttam Kumaran: yeah.
276 00:19:25.020 ⇒ 00:19:26.160 Patrick Trainer: Get us all doing
277 00:19:26.370 ⇒ 00:19:27.660 Patrick Trainer: some leak code.
278 00:19:28.240 ⇒ 00:19:37.140 Uttam Kumaran: No, I mean none of it has none. I would say none of it will be like coding related, or at least I will. I will be like, put the nicks on it unless it’s like
279 00:19:37.260 ⇒ 00:19:41.870 Uttam Kumaran: super important. For the most part, it’ll it’s all behavioral and like
280 00:19:41.980 ⇒ 00:19:43.580 Uttam Kumaran: broad technical stuff.
281 00:19:43.580 ⇒ 00:19:44.100 Patrick Trainer: Oh, right!
282 00:19:45.520 ⇒ 00:19:51.327 Uttam Kumaran: But again, this doesn’t happen at the smaller to mid size, but at the enterprise size,
283 00:19:52.720 ⇒ 00:20:01.630 Uttam Kumaran: the and the main reason they’re doing is because they get they get fucked by like outsource people basically coming in and being like we have like 10 people.
284 00:20:01.650 ⇒ 00:20:05.850 Uttam Kumaran: And they never meet them. And actually, I’m for our advantages that we
285 00:20:05.960 ⇒ 00:20:09.169 Uttam Kumaran: we have a lot of us citizens, senior talent.
286 00:20:09.170 ⇒ 00:20:09.620 Patrick Trainer: Right.
287 00:20:09.620 ⇒ 00:20:16.029 Uttam Kumaran: Not a lot of places have that and so so for me, I actually would prefer that like.
288 00:20:16.300 ⇒ 00:20:24.849 Uttam Kumaran: we’re like, Yeah, we have a face. You know, we’re we’re actually exist. But I send the profiles of sales thing because all of our profiles are stacked
289 00:20:25.000 ⇒ 00:20:25.500 Uttam Kumaran: so.
290 00:20:25.500 ⇒ 00:20:26.370 Patrick Trainer: Right, right.
291 00:20:26.370 ⇒ 00:20:27.940 Uttam Kumaran: I send it as like a
292 00:20:28.740 ⇒ 00:20:31.850 Uttam Kumaran: here’s like some of the people that we have, that you would get to work with.
293 00:20:31.850 ⇒ 00:20:32.440 Patrick Trainer: Right basically.
294 00:20:32.440 ⇒ 00:20:34.450 Uttam Kumaran: That’s great. Yeah, that makes sense.
295 00:20:36.946 ⇒ 00:20:38.373 Uttam Kumaran: I’ll show you
296 00:20:39.830 ⇒ 00:20:42.017 Uttam Kumaran: kind of like how the sale the
297 00:20:43.600 ⇒ 00:20:45.239 Uttam Kumaran: where the fuck is this?
298 00:20:47.450 ⇒ 00:20:50.979 Uttam Kumaran: Yeah. So I was working on this like kind of flow chart.
299 00:20:51.736 ⇒ 00:20:57.283 Uttam Kumaran: of like prospecting. And then instantly. And then like, kind of starting some content.
300 00:20:58.080 ⇒ 00:21:08.589 Uttam Kumaran: Basically, we have like, what are the inputs? So like, let’s say, you have a recruiter that we want to target. Let’s say you have a job posting that we want to target. Let’s say you have just like an a person at a company.
301 00:21:08.620 ⇒ 00:21:11.439 Uttam Kumaran: or you. Let’s just say you have a company that we want to target.
302 00:21:11.590 ⇒ 00:21:12.950 Uttam Kumaran: Let’s say you have.
303 00:21:13.100 ⇒ 00:21:20.680 Uttam Kumaran: You just know that you want to target these types of people in this industry, right? All those flows are things that basically, I want to have like.
304 00:21:20.740 ⇒ 00:21:25.529 Uttam Kumaran: how do we get in contact with them? This is the flow that I kind of was talking to you about, which is like.
305 00:21:25.580 ⇒ 00:21:36.999 Uttam Kumaran: based on a persona industry, right? For every industry and manufacturing, I think, is a perfect case, because we flush this whole thing out for manufacturing. So if you go to industries.
306 00:21:37.080 ⇒ 00:21:43.280 Uttam Kumaran: and then you go to manufacturing you’ll see that we have like.
307 00:21:43.560 ⇒ 00:21:45.080 Uttam Kumaran: Who are the titles.
308 00:21:45.720 ⇒ 00:21:47.700 Uttam Kumaran: What do they focus on? What do they care about.
309 00:21:48.080 ⇒ 00:21:48.429 Patrick Trainer: Okay.
310 00:21:48.430 ⇒ 00:21:57.079 Uttam Kumaran: And like. And then also, what is the revenue size for people that are going after number of employees? Do we have any geographical kind of considerations.
311 00:21:57.342 ⇒ 00:22:01.880 Uttam Kumaran: Like, what are they? What are technologies? They kind of use. What kind of growth stage there are?
312 00:22:02.304 ⇒ 00:22:05.429 Uttam Kumaran: What are the challenges you’re facing. So what’s like our persona?
313 00:22:05.830 ⇒ 00:22:08.989 Uttam Kumaran: And with that we really can begin to hone in on like.
314 00:22:09.000 ⇒ 00:22:25.800 Uttam Kumaran: okay, we need people who are like who we can’t go broadly, say every manufacturing company, because we could never get in contact with them. Instead, it’s like we want to target people that are that seem to be people that would bring us on because they’re dealing with one or more issues. And we can make the messaging super targeted.
315 00:22:25.800 ⇒ 00:22:26.230 Patrick Trainer: Right.
316 00:22:26.230 ⇒ 00:22:27.710 Uttam Kumaran: So every industry.
317 00:22:27.910 ⇒ 00:22:29.349 Patrick Trainer: Rep, or something like.
318 00:22:29.920 ⇒ 00:22:30.500 Uttam Kumaran: Exactly.
319 00:22:30.500 ⇒ 00:22:31.350 Patrick Trainer: In your time.
320 00:22:31.670 ⇒ 00:22:39.719 Uttam Kumaran: Yeah, so so everything is like targeted towards. And this will continue to become more honed in like, I think this is too broad as it is.
321 00:22:41.610 ⇒ 00:22:46.640 Uttam Kumaran: But and then so we’ll hone this in. And basically, we have that persona, and we have the industry.
322 00:22:46.750 ⇒ 00:22:54.809 Uttam Kumaran: So in Apollo, you actually can put in, and I’ll and I’ll show you can actually go put in these personas. And so what I’ve created is.
323 00:22:54.810 ⇒ 00:22:55.440 Patrick Trainer: I was.
324 00:22:55.440 ⇒ 00:22:56.360 Uttam Kumaran: The manufacturing.
325 00:22:56.360 ⇒ 00:22:57.260 Patrick Trainer: That. Yeah.
326 00:22:57.890 ⇒ 00:22:59.615 Uttam Kumaran: Yeah. And so
327 00:23:01.460 ⇒ 00:23:06.150 Uttam Kumaran: I think if you go into settings
328 00:23:09.820 ⇒ 00:23:12.450 Uttam Kumaran: prospecting personas.
329 00:23:13.038 ⇒ 00:23:15.880 Uttam Kumaran: I have this manufacturing Icp.
330 00:23:16.120 ⇒ 00:23:22.890 Uttam Kumaran: Icp is like initial customer, persona, ideal customer profile, something like that. All the titles.
331 00:23:23.090 ⇒ 00:23:27.009 Uttam Kumaran: the management levels, the industries, the location.
332 00:23:27.020 ⇒ 00:23:28.510 Uttam Kumaran: the employee, size.
333 00:23:28.720 ⇒ 00:23:31.040 Patrick Trainer: So you just like. So you don’t need to
334 00:23:31.640 ⇒ 00:23:33.629 Patrick Trainer: continually click all that.
335 00:23:33.630 ⇒ 00:23:35.109 Uttam Kumaran: Exactly, exactly.
336 00:23:35.110 ⇒ 00:23:36.300 Patrick Trainer: All into one. Okay.
337 00:23:36.300 ⇒ 00:23:37.310 Uttam Kumaran: Totally yep.
338 00:23:37.310 ⇒ 00:23:37.759 Patrick Trainer: That’s fantastic.
339 00:23:37.760 ⇒ 00:23:43.030 Uttam Kumaran: So I want to have. So it’s just. It’s just layers of filters like, is all this game is.
340 00:23:43.180 ⇒ 00:23:46.630 Uttam Kumaran: But then that way, whenever we’re looking at manufacturing.
341 00:23:47.150 ⇒ 00:24:05.660 Uttam Kumaran: we we each whoever’s prospecting can just plug plug in this like manufacturing Icp, so we we and in our in our world, like we. We’re targeting industries right now, a lot of people. Sometimes people target stack, they start, they target technologies like instead of targeting just manufacturing people. They may target
342 00:24:05.670 ⇒ 00:24:06.840 Uttam Kumaran: Texas
343 00:24:07.310 ⇒ 00:24:16.449 Uttam Kumaran: like, just Texas companies or Texas startups. Right? So you can either target by industry or you can target by like growth range.
344 00:24:16.900 ⇒ 00:24:29.550 Uttam Kumaran: For example, you could say, we, we go after people with like 500 plus employees. So there’s like different ways of skinning a lot of people ask me like, what do you? What’s your sales strategy before? It was like spray. And pray now it’s like we’re going after 3 industries
345 00:24:29.670 ⇒ 00:24:39.230 Uttam Kumaran: right? And we’re like, happy that we’re set in that. And then we’re like within those industries. Here are the here’s the profiles of people that we’re going after
346 00:24:42.180 ⇒ 00:24:50.470 Uttam Kumaran: And then that’s also different than like ideally, I would I would love to go after people that have thought about buying Snowflake, or who have Snowflake. I just haven’t found a way of like
347 00:24:50.700 ⇒ 00:24:59.759 Uttam Kumaran: isolating that. The one thing I am gonna do is that because we’re partnering with Robert, there’s a site called Built with. I’m sure you’ve heard of it.
348 00:24:59.760 ⇒ 00:25:00.480 Patrick Trainer: Yeah.
349 00:25:01.000 ⇒ 00:25:03.657 Uttam Kumaran: So basically in on build with
350 00:25:04.100 ⇒ 00:25:06.080 Patrick Trainer: Yeah, that’s like the tech stack thing.
351 00:25:06.080 ⇒ 00:25:11.140 Uttam Kumaran: Yeah. Like, for example, let’s type in real data.com.
352 00:25:15.910 ⇒ 00:25:17.960 Uttam Kumaran: you could tell everything that they used
353 00:25:17.990 ⇒ 00:25:25.589 Uttam Kumaran: ideally. We would go after. So this the coffee company that we’re thinking about going after is called Javi coffee.com.
354 00:25:25.690 ⇒ 00:25:28.979 Uttam Kumaran: So javi coffee.com.
355 00:25:30.990 ⇒ 00:25:34.950 Uttam Kumaran: and they use it. They use attentive and amplitude.
356 00:25:35.730 ⇒ 00:25:41.820 Uttam Kumaran: So the reason why this is important is, Robert is targeting amplitude and mix panel companies.
357 00:25:41.980 ⇒ 00:25:49.549 Uttam Kumaran: but our partner with him is our partnership with him is moving those companies and to explain them why they need to be using a data warehouse.
358 00:25:49.570 ⇒ 00:26:14.030 Uttam Kumaran: right? Because typically the reason people use amplitude is amplitude. A lot of people will just throw shopify into there and throw other stuff into there and just build on the amplitude reporting. But then they can never use. Like their application data, they can never combine other data could get more sophisticated reporting. So one of the things that I just had, like a fucking brain blast yesterday on was I want to
359 00:26:14.060 ⇒ 00:26:22.030 Uttam Kumaran: use built with to find amplitude companies within, within, like a specific Icp
360 00:26:22.060 ⇒ 00:26:27.089 Uttam Kumaran: to then target as like to then put together an email campaign about amplitude plus snowflake.
361 00:26:27.290 ⇒ 00:26:28.070 Patrick Trainer: Right.
362 00:26:28.490 ⇒ 00:26:37.820 Uttam Kumaran: So I would target e-commerce companies that are using amplitude that are like based somewhere and try to get that filtered. And then basically, the campaign would be like.
363 00:26:37.840 ⇒ 00:26:41.909 Uttam Kumaran: Hey, are using amplitude and shopify. And Clavio, like
364 00:26:42.010 ⇒ 00:26:44.350 Uttam Kumaran: we just did this for XYZ.
365 00:26:44.820 ⇒ 00:26:48.149 Uttam Kumaran: I’m wondering like, if we if you guys are having these same issues. Bam.
366 00:26:48.150 ⇒ 00:26:48.670 Patrick Trainer: Nice.
367 00:26:48.670 ⇒ 00:26:49.260 Uttam Kumaran: Right.
368 00:26:50.030 ⇒ 00:26:55.959 Uttam Kumaran: So that would be like another campaign that I basically want to start. I think I even noted it down
369 00:26:56.080 ⇒ 00:26:58.800 Uttam Kumaran: in this like sales materials, area, which is like.
370 00:27:00.125 ⇒ 00:27:02.860 Uttam Kumaran: yeah, amplitude for snowflake email series.
371 00:27:02.860 ⇒ 00:27:03.350 Patrick Trainer: Oh no!
372 00:27:04.750 ⇒ 00:27:09.469 Uttam Kumaran: The nice thing is we we worked on a proposal for Stella, which had, like a Y
373 00:27:09.580 ⇒ 00:27:11.319 Uttam Kumaran: data warehouse thing
374 00:27:11.340 ⇒ 00:27:20.410 Uttam Kumaran: for this coffee company. We’re working on another proposal together, too. I’m gonna I’ll use chat. Gbt, plug that in, get more and then basically begin to
375 00:27:20.630 ⇒ 00:27:24.290 Uttam Kumaran: begin to write this like amplitude for snowflake story.
376 00:27:24.620 ⇒ 00:27:25.545 Uttam Kumaran: Right?
377 00:27:27.670 ⇒ 00:27:28.530 Uttam Kumaran: so
378 00:27:29.920 ⇒ 00:27:30.910 Uttam Kumaran: so.
379 00:27:31.880 ⇒ 00:27:32.819 Patrick Trainer: Finding something.
380 00:27:34.850 ⇒ 00:27:35.709 Patrick Trainer: Where’d it go?
381 00:28:10.930 ⇒ 00:28:13.389 Patrick Trainer: I think there is a
382 00:28:17.300 ⇒ 00:28:19.910 Patrick Trainer: snowflake provider for
383 00:28:20.400 ⇒ 00:28:21.790 Patrick Trainer: built with.
384 00:28:23.820 ⇒ 00:28:24.730 Uttam Kumaran: Oh, really.
385 00:28:25.430 ⇒ 00:28:27.240 Patrick Trainer: Yeah, that’s what I’m looking at now.
386 00:28:27.240 ⇒ 00:28:34.019 Uttam Kumaran: But, like the thing is built with only skins at a website like Snowflake is never on their like.
387 00:28:34.610 ⇒ 00:28:37.269 Uttam Kumaran: Oh, you mean like plug into the Api.
388 00:28:37.270 ⇒ 00:28:38.610 Patrick Trainer: Like, yeah, you can
389 00:28:39.580 ⇒ 00:28:42.189 Patrick Trainer: query built with data directly.
390 00:28:42.750 ⇒ 00:28:46.649 Uttam Kumaran: Yeah. So one of the things that I think we would try to do is
391 00:28:48.210 ⇒ 00:28:49.540 Uttam Kumaran: basically
392 00:28:51.440 ⇒ 00:28:57.090 Uttam Kumaran: in relevance. There’s this thing called relevance AI that we’re using to basically automate some stuff
393 00:28:57.529 ⇒ 00:29:05.439 Uttam Kumaran: and they have a. So I just have to figure out like either Zapier or whatever to get like that initial list.
394 00:29:05.630 ⇒ 00:29:09.400 Uttam Kumaran: Run that through Apollo to get the contacts.
395 00:29:10.010 ⇒ 00:29:11.970 Uttam Kumaran: Put Paulo in me instantly.
396 00:29:12.110 ⇒ 00:29:13.860 Uttam Kumaran: and then turn on the instantly.
397 00:29:13.860 ⇒ 00:29:14.690 Patrick Trainer: Right.
398 00:29:17.820 ⇒ 00:29:21.430 Uttam Kumaran: So that’s kind of like what I was going through here was like, we have the persona.
399 00:29:21.740 ⇒ 00:29:30.389 Uttam Kumaran: Then we have, like the con enriched contacts that goes to a list. And then it goes to your question, which is, what do we do to verify? So there’s this thing called 1 million verifier.
400 00:29:31.208 ⇒ 00:29:39.560 Uttam Kumaran: Again. This is just after like months of like calling everybody I know. And Reddit, they’re like, Oh, use that email, use, use a million verify. I’m like.
401 00:29:39.560 ⇒ 00:29:40.140 Patrick Trainer: Right.
402 00:29:40.140 ⇒ 00:29:43.579 Uttam Kumaran: No dude. They’re like, Oh, how you gotta be using that. I’m like, okay.
403 00:29:43.630 ⇒ 00:29:51.130 Uttam Kumaran: 1 million verified just verifiers emails. There’s like 2 big companies that do this 1 million verifier and like one other company.
404 00:29:51.547 ⇒ 00:29:55.280 Uttam Kumaran: So what we would do is we would just plug into 1 million verifier.
405 00:29:55.390 ⇒ 00:30:00.709 Uttam Kumaran: and they give you like a ton of credits to verify emails. So we would.
406 00:30:01.130 ⇒ 00:30:02.810 Uttam Kumaran: we would basically have a
407 00:30:02.840 ⇒ 00:30:04.069 Uttam Kumaran: have a job. That
408 00:30:05.830 ⇒ 00:30:17.960 Uttam Kumaran: good question. I think these guys just like hit these emails and bounce them and try to measure. Because if your email account bounces a lot, you’ll realize you’re you’re hitting spam. You’re a spam account.
409 00:30:17.960 ⇒ 00:30:21.709 Patrick Trainer: Right? Yeah. And then you just get like blocked. So do they have, like
410 00:30:21.870 ⇒ 00:30:26.050 Patrick Trainer: an unlimited amount of like warm emails that they just.
411 00:30:26.050 ⇒ 00:30:26.510 Uttam Kumaran: For sure.
412 00:30:26.510 ⇒ 00:30:27.070 Patrick Trainer: Or.
413 00:30:27.070 ⇒ 00:30:28.759 Uttam Kumaran: For sure. Yeah.
414 00:30:28.820 ⇒ 00:30:30.570 Uttam Kumaran: probably definitely.
415 00:30:30.570 ⇒ 00:30:30.950 Patrick Trainer: That’s it.
416 00:30:30.950 ⇒ 00:30:32.729 Uttam Kumaran: Probably way dumber than you think.
417 00:30:34.320 ⇒ 00:30:39.000 Uttam Kumaran: So that’s why I need a zappy or something to do this, because I wanted to. I want to like, try to.
418 00:30:39.030 ⇒ 00:30:48.400 Uttam Kumaran: Yeah, these are like weaving into like all these fucking tools. That’s the challenge here that, like, you have to use a zapier or some sort of like thing to do this.
419 00:30:48.400 ⇒ 00:30:49.519 Patrick Trainer: Yeah, there’s glue.
420 00:30:49.520 ⇒ 00:30:53.329 Uttam Kumaran: This is. But this is like modern. This is kind of like how stuff is working right now.
421 00:30:53.640 ⇒ 00:30:54.350 Patrick Trainer: Right.
422 00:30:56.990 ⇒ 00:31:00.220 Patrick Trainer: Okay, cool. That’s that’s something good to think about.
423 00:31:00.960 ⇒ 00:31:08.817 Uttam Kumaran: So then, basically, what I would do is this meal go for 1 million verifier into instantly for emails. And then it would go into Linkedin for
424 00:31:09.460 ⇒ 00:31:11.370 Uttam Kumaran: also nudging them on Linkedin.
425 00:31:11.730 ⇒ 00:31:17.999 Uttam Kumaran: So the way Linkedin works is linkedin would hit them with a message. Linkedin would 1st connect with them.
426 00:31:18.340 ⇒ 00:31:20.659 Uttam Kumaran: and then we would hit them with a message after.
427 00:31:20.660 ⇒ 00:31:21.350 Patrick Trainer: Right.
428 00:31:21.350 ⇒ 00:31:30.499 Uttam Kumaran: And for the at the at this point we would be using like my linkedin. But I actually have found that there’s
429 00:31:32.180 ⇒ 00:31:34.974 Uttam Kumaran: There’s like there’s a thing called
430 00:31:40.740 ⇒ 00:31:43.910 Uttam Kumaran: not this, not this.
431 00:31:44.250 ⇒ 00:31:45.670 Uttam Kumaran: It’s called.
432 00:31:48.410 ⇒ 00:31:50.189 Uttam Kumaran: is it hype fury?
433 00:31:59.060 ⇒ 00:32:02.373 Uttam Kumaran: Oh, this is for Twitter. So this is for messaging. It’s called
434 00:32:03.030 ⇒ 00:32:06.040 Uttam Kumaran: Think it’s called outreach, or
435 00:32:08.115 ⇒ 00:32:09.180 Uttam Kumaran: outreach
436 00:32:20.158 ⇒ 00:32:21.789 Uttam Kumaran: what is it called?
437 00:32:29.290 ⇒ 00:32:31.190 Uttam Kumaran: Oh, this is something else.
438 00:32:39.390 ⇒ 00:32:41.119 Uttam Kumaran: and to address it.
439 00:32:52.550 ⇒ 00:32:54.179 Uttam Kumaran: oh, hey, reach
440 00:32:54.610 ⇒ 00:32:56.550 Uttam Kumaran: yeah outreach.
441 00:32:59.960 ⇒ 00:33:04.609 Uttam Kumaran: So there’s this tool called, Hey reach where? Basically, I’m gonna put this into
442 00:33:05.960 ⇒ 00:33:23.600 Uttam Kumaran: basically what you could do is like I, you could connect, like my Linkedin, your Linkedin and Nico’s Linkedin. It would rotate through it, and then basically send people outbound messages. Everybody has told me, though, that Linkedin is like the killer right now.
443 00:33:25.260 ⇒ 00:33:27.989 Uttam Kumaran: like, people are telling me emails kind of dying.
444 00:33:28.490 ⇒ 00:33:33.599 Uttam Kumaran: And so a lot of the people in b 2 b sales are like Linkedin is like the go to right now.
445 00:33:33.600 ⇒ 00:33:34.150 Patrick Trainer: Yeah.
446 00:33:34.150 ⇒ 00:33:34.820 Uttam Kumaran: Yeah.
447 00:33:35.970 ⇒ 00:33:39.659 Patrick Trainer: Okay, yeah, that that makes sense. Because, like, I mean, if
448 00:33:41.250 ⇒ 00:33:43.369 Patrick Trainer: I mean email, there’s like a.
449 00:33:43.610 ⇒ 00:33:47.230 Patrick Trainer: it’s like a barrier to entry. Right? All fighting spam.
450 00:33:47.520 ⇒ 00:33:51.510 Patrick Trainer: Linkedin doesn’t really give a shit about that. And then the way that they’re
451 00:33:52.970 ⇒ 00:33:59.589 Patrick Trainer: like building a box around it or monetizing it is like they’re so they’re selling the like. The credits.
452 00:33:59.920 ⇒ 00:34:01.710 Patrick Trainer: the the in mail.
453 00:34:01.930 ⇒ 00:34:02.540 Patrick Trainer: Wow!
454 00:34:02.540 ⇒ 00:34:09.259 Uttam Kumaran: Yeah, so, so ideally like, kind of what my friend who works for work day. He’s a he’s a he’s like a
455 00:34:09.679 ⇒ 00:34:14.569 Uttam Kumaran: Sdr. There. He was like, you need to do both. And he he also said, you need to do 3.
456 00:34:14.679 ⇒ 00:34:21.980 Uttam Kumaran: It’s like the 3rd one, I’ll tell you is like I’m a little bit like that’ll be the biggest hurdle for me. But we’ll get there is like, he’s like, email them.
457 00:34:22.050 ⇒ 00:34:35.619 Uttam Kumaran: Put them on email series where you basically have like, Hey, we do. We do this, let me know. Hey, here’s a case. Study how we did this, hey? One more time, like, here’s a here’s like a real example of how we did it at the same time in the middle of that hit them with Linkedin.
458 00:34:35.650 ⇒ 00:34:38.560 Uttam Kumaran: because when you hit them on Linkedin, and you’ll say, Hey.
459 00:34:38.719 ⇒ 00:34:41.440 Uttam Kumaran: where I emailed you the other day.
460 00:34:41.750 ⇒ 00:34:44.150 Uttam Kumaran: Then they’ll go see that you emailed them
461 00:34:44.710 ⇒ 00:34:52.132 Uttam Kumaran: right? So you’re already in there with some connection. You’re not just doing one or the other. You’re doing both. The last thing he said was, then you call them.
462 00:34:54.110 ⇒ 00:34:57.110 Uttam Kumaran: and that’s why I was like, Bro, you need like.
463 00:34:57.660 ⇒ 00:35:05.170 Uttam Kumaran: I hope at that point I can hire somebody to fucking. Make that phone call. But then he’s like, Yeah, you call them being like, Hey, I’m calling from so and so like
464 00:35:05.720 ⇒ 00:35:07.630 Uttam Kumaran: calling about data. Let me.
465 00:35:07.630 ⇒ 00:35:08.530 Patrick Trainer: Yeah.
466 00:35:09.440 ⇒ 00:35:10.000 Patrick Trainer: Oh, yeah.
467 00:35:10.000 ⇒ 00:35:14.415 Uttam Kumaran: But see the but see, the thing is like, right now, we are at like
468 00:35:16.070 ⇒ 00:35:18.353 Uttam Kumaran: right now, we’re basically at
469 00:35:20.100 ⇒ 00:35:23.289 Uttam Kumaran: like, we just have manufacturing, running right now.
470 00:35:23.290 ⇒ 00:35:24.180 Patrick Trainer: Right.
471 00:35:24.180 ⇒ 00:35:26.901 Uttam Kumaran: Right. So basically, I want to have
472 00:35:31.420 ⇒ 00:35:32.719 Uttam Kumaran: I want to have.
473 00:35:33.920 ⇒ 00:35:35.509 Uttam Kumaran: And you have access to this.
474 00:35:36.420 ⇒ 00:35:37.200 Uttam Kumaran: I’m just.
475 00:35:37.370 ⇒ 00:35:38.590 Patrick Trainer: Is it figma.
476 00:35:38.590 ⇒ 00:35:41.369 Uttam Kumaran: I think, yeah, I’m just gonna share with you.
477 00:35:41.370 ⇒ 00:35:43.250 Patrick Trainer: Believe so.
478 00:35:48.976 ⇒ 00:35:50.710 Uttam Kumaran: I basically want to have
479 00:35:51.540 ⇒ 00:35:53.420 Uttam Kumaran: manufacturing.
480 00:35:59.000 ⇒ 00:36:00.530 Uttam Kumaran: advertising
481 00:36:09.680 ⇒ 00:36:11.799 Uttam Kumaran: Ecom or something like that.
482 00:36:11.970 ⇒ 00:36:14.419 Uttam Kumaran: And I want there to be
483 00:36:14.830 ⇒ 00:36:16.000 Uttam Kumaran: like.
484 00:36:16.810 ⇒ 00:36:19.290 Uttam Kumaran: And I wanted to be email.
485 00:36:20.270 ⇒ 00:36:21.980 Uttam Kumaran: I want there to be
486 00:36:25.320 ⇒ 00:36:26.480 Uttam Kumaran: Linkedin.
487 00:36:27.150 ⇒ 00:36:29.930 Uttam Kumaran: Right? So for every industry
488 00:36:30.110 ⇒ 00:36:31.450 Uttam Kumaran: we need to have
489 00:36:31.660 ⇒ 00:36:33.609 Uttam Kumaran: both of these as outbound.
490 00:36:33.720 ⇒ 00:36:37.449 Uttam Kumaran: And then the input is, gonna be like
491 00:36:37.630 ⇒ 00:36:39.179 Uttam Kumaran: that? Icp.
492 00:36:39.840 ⇒ 00:36:44.509 Uttam Kumaran: That’s gonna be what we know. It’s gonna be the Icp, it’s gonna be
493 00:36:44.600 ⇒ 00:36:45.710 Uttam Kumaran: like
494 00:36:45.830 ⇒ 00:36:47.850 Uttam Kumaran: what our messaging is.
495 00:36:48.630 ⇒ 00:36:50.080 Uttam Kumaran: And it’s gonna be
496 00:36:50.920 ⇒ 00:36:52.399 Uttam Kumaran: like, basically.
497 00:36:53.670 ⇒ 00:36:56.360 Uttam Kumaran: it’s gonna be case studies.
498 00:36:57.990 ⇒ 00:36:59.319 Uttam Kumaran: blog posts.
499 00:37:00.260 ⇒ 00:37:02.660 Uttam Kumaran: service documents.
500 00:37:04.740 ⇒ 00:37:15.190 Uttam Kumaran: Those are going to be all the the materials. And then all of these flow in. Basically to one of these. And this is all this is gonna this is like our. This is literally going to be like our.
501 00:37:15.690 ⇒ 00:37:18.060 Uttam Kumaran: that’s our. This is our like outbound
502 00:37:18.150 ⇒ 00:37:18.715 Uttam Kumaran: right?
503 00:37:21.110 ⇒ 00:37:24.599 Uttam Kumaran: right? So right now, we only have manufacturing
504 00:37:25.270 ⇒ 00:37:29.470 Uttam Kumaran: instantly going on. It’s a campaign. So this is gonna be our like, our
505 00:37:30.530 ⇒ 00:37:31.500 Uttam Kumaran: outbound.
506 00:37:31.500 ⇒ 00:37:32.290 Patrick Trainer: Okay.
507 00:37:32.660 ⇒ 00:37:33.290 Uttam Kumaran: Targeted after.
508 00:37:33.960 ⇒ 00:37:34.630 Patrick Trainer: Like
509 00:37:35.190 ⇒ 00:37:36.280 Patrick Trainer: for like
510 00:37:36.300 ⇒ 00:37:37.980 Patrick Trainer: focus in the next.
511 00:37:38.070 ⇒ 00:37:39.930 Patrick Trainer: like 2 weeks, are you thinking?
512 00:37:39.960 ⇒ 00:37:41.439 Patrick Trainer: Just outbound
513 00:37:41.640 ⇒ 00:37:43.240 Patrick Trainer: like? Just so. So we
514 00:37:43.330 ⇒ 00:37:46.109 Patrick Trainer: like build tools for outbound? Or
515 00:37:49.010 ⇒ 00:37:50.420 Patrick Trainer: what are your thoughts on that.
516 00:37:51.130 ⇒ 00:37:58.100 Uttam Kumaran: Yeah, so kind of like, here’s like the different growth strategies that we have. So we have like, we have.
517 00:37:58.650 ⇒ 00:38:05.429 Uttam Kumaran: we have like stuff that’s like direct growth, which is like SEO, the website. And like landing pages.
518 00:38:07.210 ⇒ 00:38:21.560 Uttam Kumaran: that’s gonna be. People are coming direct to us like you talk to someone on a coffee shop. Someone is like, I wonder what Patrick’s doing these days. And then because they see you liked a linkedin post, they saw you post something that’s gonna be all of our content and like
519 00:38:21.620 ⇒ 00:38:23.960 Uttam Kumaran: basically us. But this is gonna be like.
520 00:38:24.410 ⇒ 00:38:27.959 Uttam Kumaran: non direct, like, I don’t know how to say this. But this is like.
521 00:38:29.190 ⇒ 00:38:32.489 Uttam Kumaran: this isn’t targeted outbound. This is just like.
522 00:38:33.330 ⇒ 00:38:36.809 Uttam Kumaran: I think it’s like, organic.
523 00:38:37.740 ⇒ 00:38:38.800 Uttam Kumaran: Yeah.
524 00:38:39.660 ⇒ 00:38:44.830 Uttam Kumaran: this is like organic. But I also, I do think this doesn’t mean that we don’t put any effort into it right?
525 00:38:45.340 ⇒ 00:38:47.340 Uttam Kumaran: That’ll it does mean that like
526 00:38:47.430 ⇒ 00:38:51.050 Uttam Kumaran: not only landing pages, but like, I want there to also be
527 00:38:51.270 ⇒ 00:38:53.050 Uttam Kumaran: like, this is where
528 00:38:53.140 ⇒ 00:38:57.039 Uttam Kumaran: area, the content really matters because there’s gonna be like
529 00:38:58.470 ⇒ 00:39:00.570 Uttam Kumaran: linkedin threads.
530 00:39:00.860 ⇒ 00:39:09.720 Uttam Kumaran: I just saw they they released Linkedin videos. We’re gonna do that at I’m we’re going balls. The fucking wall on this one
531 00:39:10.759 ⇒ 00:39:13.159 Uttam Kumaran: linkedin threads, twitter threads.
532 00:39:13.791 ⇒ 00:39:26.319 Uttam Kumaran: I at the in the Sauna the other day I installed Tiktok for the 1st time, and I created us an account cause I I looked through like who was posting with the data engineering hashtag, 5 videos. Nobody.
533 00:39:26.320 ⇒ 00:39:27.030 Patrick Trainer: Really.
534 00:39:27.180 ⇒ 00:39:30.789 Uttam Kumaran: And the one guy posting is posting like shitty content.
535 00:39:32.350 ⇒ 00:39:35.580 Uttam Kumaran: And it’s like, and he’s got like a hundred 1,000 views just talking about data.
536 00:39:35.660 ⇒ 00:39:49.119 Uttam Kumaran: And I’m like, Oh, perfect. Yeah. But see? Like I’m I’m talking about like where this flywheel like needs to go, not where I don’t know how long. It’s gonna take us to get there. But the thing I’m saying is that, like our ability to go from industry
537 00:39:49.320 ⇒ 00:39:50.909 Uttam Kumaran: to the materials
538 00:39:50.920 ⇒ 00:39:52.770 Uttam Kumaran: to the outbound channel
539 00:39:53.060 ⇒ 00:39:59.509 Uttam Kumaran: and do that across multiple industries, across multiple channels. You can see that this took this took
540 00:39:59.620 ⇒ 00:40:06.609 Uttam Kumaran: like right now, just to do manufacturing. Get the messaging down. An email has taken about 2 months.
541 00:40:06.610 ⇒ 00:40:07.240 Patrick Trainer: Right.
542 00:40:07.240 ⇒ 00:40:09.539 Uttam Kumaran: The next one needs to take
543 00:40:10.030 ⇒ 00:40:11.599 Uttam Kumaran: like 2 weeks.
544 00:40:11.600 ⇒ 00:40:12.380 Patrick Trainer: Right.
545 00:40:12.380 ⇒ 00:40:14.979 Uttam Kumaran: Because not only do we need to activate multiple.
546 00:40:15.060 ⇒ 00:40:26.039 Uttam Kumaran: we need to activate multiple and then constantly and be improving. So every iteration of like the activation of a new outbound channel. For a new industry needs to take
547 00:40:26.260 ⇒ 00:40:28.240 Uttam Kumaran: half the time as the previous one.
548 00:40:28.240 ⇒ 00:40:28.980 Patrick Trainer: Right.
549 00:40:29.360 ⇒ 00:40:39.190 Uttam Kumaran: And we need to be have. We need to have multiple campaigns running at all times. Because you can expect a conversion rate for us to be like 1%, I would think
550 00:40:39.740 ⇒ 00:40:45.349 Uttam Kumaran: is is gonna be. And if a conversion rates 1%, that means like one out of a hundred leads.
551 00:40:46.020 ⇒ 00:40:53.485 Uttam Kumaran: and for us, for us to get a hundred leads. It’s not even. It’s like we need to prospect so much right? Right?
552 00:40:54.340 ⇒ 00:41:01.720 Uttam Kumaran: and so you can quickly see how like, if it takes us 2 months just to launch one industry on 1 1 channel.
553 00:41:02.020 ⇒ 00:41:03.060 Uttam Kumaran: we’re fucked.
554 00:41:03.060 ⇒ 00:41:04.650 Patrick Trainer: Yeah, yeah, it’s because then.
555 00:41:04.650 ⇒ 00:41:05.230 Uttam Kumaran: Yeah.
556 00:41:05.230 ⇒ 00:41:06.599 Patrick Trainer: There’s such a lag.
557 00:41:06.970 ⇒ 00:41:10.970 Uttam Kumaran: There’s such a lag, and we never hit that number, you know.
558 00:41:10.970 ⇒ 00:41:11.800 Patrick Trainer: Right.
559 00:41:11.800 ⇒ 00:41:14.590 Uttam Kumaran: We never hit the outbound number. The nice thing is
560 00:41:14.990 ⇒ 00:41:18.880 Uttam Kumaran: like, I’ve basically figured out the stack on how to do this
561 00:41:18.920 ⇒ 00:41:30.729 Uttam Kumaran: basically figured out, and we have budget like I I mean, like, we don’t have budget. But like I’ve been spending money on the tools and between Zapier Apollo instantly.
562 00:41:30.880 ⇒ 00:41:38.779 Uttam Kumaran: and like we between Zapier, Apollo, and instantly. And then the AI tools. We’re good.
563 00:41:38.960 ⇒ 00:41:39.520 Patrick Trainer: Right.
564 00:41:39.520 ⇒ 00:41:47.560 Uttam Kumaran: Ideally, we can. Once we get some luck and we get money in, we’ll pick. We’ll use some of these better tools. But we basically
565 00:41:47.910 ⇒ 00:41:49.360 Uttam Kumaran: have it in front of us.
566 00:41:49.360 ⇒ 00:41:50.280 Patrick Trainer: Right.
567 00:41:51.170 ⇒ 00:41:56.250 Uttam Kumaran: Right? So the the big things is like, we need to define the Icp for the next few industries
568 00:41:56.300 ⇒ 00:41:57.769 Uttam Kumaran: we need to.
569 00:41:58.190 ⇒ 00:42:02.960 Uttam Kumaran: For each of these there’s like just a shit load of to do. So. That’s why I’m trying to think about how I can
570 00:42:03.100 ⇒ 00:42:05.092 Uttam Kumaran: divvy some stuff up
571 00:42:07.640 ⇒ 00:42:09.511 Uttam Kumaran: like. I don’t know
572 00:42:13.270 ⇒ 00:42:21.770 Uttam Kumaran: but I think it’s I think I’m I’m glad to walk you through it, because I think it’s helpful to see the whole picture, and to see that like this is a compounding like.
573 00:42:22.790 ⇒ 00:42:24.889 Uttam Kumaran: I mean, this is just like we’re building like
574 00:42:24.920 ⇒ 00:42:27.099 Uttam Kumaran: we’re building like a sales arm.
575 00:42:27.100 ⇒ 00:42:28.070 Patrick Trainer: Right, yeah.
576 00:42:28.070 ⇒ 00:42:28.950 Uttam Kumaran: From scratch.
577 00:42:28.950 ⇒ 00:42:29.910 Patrick Trainer: Quite literally.
578 00:42:30.630 ⇒ 00:42:31.370 Patrick Trainer: I’m
579 00:42:32.300 ⇒ 00:42:33.679 Patrick Trainer: clicking through
580 00:42:34.000 ⇒ 00:42:36.920 Patrick Trainer: the the built with stuff. And like
581 00:42:37.680 ⇒ 00:42:40.440 Patrick Trainer: this, seems like there’s a lot of signal.
582 00:42:41.230 ⇒ 00:42:47.279 Uttam Kumaran: So. So that’s what I’m saying is like, what? What will we look to find? Signal that they have Snowflake, or they need snowflake.
583 00:42:49.330 ⇒ 00:42:50.360 Uttam Kumaran: you know.
584 00:42:50.360 ⇒ 00:42:51.150 Patrick Trainer: Right.
585 00:42:51.520 ⇒ 00:42:52.610 Patrick Trainer: yeah, like.
586 00:42:53.010 ⇒ 00:42:55.249 Uttam Kumaran: What associate, what adjacent tools.
587 00:42:55.250 ⇒ 00:42:58.379 Patrick Trainer: Right, right? Right that that was the word I was looking for.
588 00:43:04.980 ⇒ 00:43:07.819 Patrick Trainer: I’m wondering if you, if you think about it.
589 00:43:07.930 ⇒ 00:43:11.909 Patrick Trainer: not necessarily of like, oh, this person has mixed pan, I think.
590 00:43:12.300 ⇒ 00:43:15.570 Patrick Trainer: looking at like people with mixed panel, and then trying to convert
591 00:43:15.750 ⇒ 00:43:17.540 Patrick Trainer: like, that’s 1 thing
592 00:43:17.580 ⇒ 00:43:24.290 Patrick Trainer: like, I think that’s a good idea. It is difficult to get people to switch things, but it’s neither here nor there. I’m thinking.
593 00:43:25.190 ⇒ 00:43:26.500 Patrick Trainer: looking at
594 00:43:27.910 ⇒ 00:43:29.780 Patrick Trainer: either, like companies that
595 00:43:30.800 ⇒ 00:43:32.310 Patrick Trainer: traditionally.
596 00:43:32.650 ⇒ 00:43:33.800 Patrick Trainer: are like
597 00:43:34.160 ⇒ 00:43:37.049 Patrick Trainer: versed in analytics or companies that
598 00:43:37.580 ⇒ 00:43:38.889 Patrick Trainer: would want
599 00:43:39.650 ⇒ 00:43:41.130 Patrick Trainer: analytics, right?
600 00:43:41.540 ⇒ 00:43:42.290 Patrick Trainer: That
601 00:43:43.220 ⇒ 00:43:51.089 Patrick Trainer: aren’t using some of the technologies that we’re seeing. I think, like with the absence of it. So if we see like.
602 00:43:51.220 ⇒ 00:43:56.550 Patrick Trainer: say, like a I’m thinking of like a shopify store, right and shopify has their own
603 00:43:56.720 ⇒ 00:43:59.290 Patrick Trainer: kind of like internal analytics.
604 00:43:59.990 ⇒ 00:44:03.740 Patrick Trainer: But then, what’s the next step above? Like.
605 00:44:03.740 ⇒ 00:44:04.160 Uttam Kumaran: Yeah.
606 00:44:04.160 ⇒ 00:44:08.610 Patrick Trainer: By analytics that’s being able to pivot around the same.
607 00:44:08.720 ⇒ 00:44:09.620 Patrick Trainer: So
608 00:44:10.120 ⇒ 00:44:11.950 Patrick Trainer: if we see that, like.
609 00:44:13.430 ⇒ 00:44:15.190 Patrick Trainer: like, everybody has like
610 00:44:15.880 ⇒ 00:44:20.529 Patrick Trainer: Google analytics, or if they have like
611 00:44:21.850 ⇒ 00:44:23.820 Patrick Trainer: something else. But then not
612 00:44:24.920 ⇒ 00:44:26.810 Patrick Trainer: this one technology.
613 00:44:27.890 ⇒ 00:44:29.510 Patrick Trainer: That might be some signal.
614 00:44:31.750 ⇒ 00:44:36.899 Uttam Kumaran: Exactly so I’m trying to think is like the the reason I amplitude is nice, because
615 00:44:38.870 ⇒ 00:44:42.379 Uttam Kumaran: it means that you’ve installed like this like next layer tool.
616 00:44:43.110 ⇒ 00:44:48.230 Uttam Kumaran: so I would probably mix. I would mix Bill with amplitude with a certain employee count
617 00:44:48.720 ⇒ 00:44:52.949 Uttam Kumaran: in order to see, like a size of like the opportunity.
618 00:44:53.830 ⇒ 00:44:59.129 Patrick Trainer: Yeah, like, I mean, there’s amplitude. There’s segment
619 00:44:59.430 ⇒ 00:45:00.490 Patrick Trainer: mix panel.
620 00:45:03.030 ⇒ 00:45:04.780 Patrick Trainer: I’m at post hogs, one of them.
621 00:45:37.610 ⇒ 00:45:39.779 Patrick Trainer: yeah, it’s like you can even see
622 00:45:39.870 ⇒ 00:45:41.960 Patrick Trainer: in in, built with like
623 00:45:42.330 ⇒ 00:45:44.149 Patrick Trainer: shopify websites
624 00:45:44.440 ⇒ 00:45:48.489 Patrick Trainer: that spend over a hundred dollars a month on tech.
625 00:45:50.130 ⇒ 00:45:50.929 Uttam Kumaran: Oh, really.
626 00:45:50.930 ⇒ 00:45:54.909 Patrick Trainer: Yeah, yeah, there’s like, there’s a lot here.
627 00:45:55.350 ⇒ 00:45:56.550 Uttam Kumaran: In the Api.
628 00:45:57.380 ⇒ 00:46:00.060 Patrick Trainer: I mean, just in their like. They have like.
629 00:46:00.600 ⇒ 00:46:02.550 Uttam Kumaran: Oh, so I would just look up, shopify.
630 00:46:04.480 ⇒ 00:46:05.840 Patrick Trainer: Yeah, like, here, I’ll
631 00:46:05.890 ⇒ 00:46:07.299 Patrick Trainer: put my screen on.
632 00:46:09.910 ⇒ 00:46:10.870 Patrick Trainer: You see that.
633 00:46:13.260 ⇒ 00:46:15.430 Patrick Trainer: Yeah, so like, these are just
634 00:46:15.470 ⇒ 00:46:19.179 Patrick Trainer: shopify people that have it like instant pot.
635 00:46:20.740 ⇒ 00:46:23.129 Patrick Trainer: whatever that is. But.
636 00:46:23.130 ⇒ 00:46:23.870 Uttam Kumaran: Oh!
637 00:46:23.870 ⇒ 00:46:27.120 Patrick Trainer: See like their usage, and all of
638 00:46:28.000 ⇒ 00:46:30.510 Patrick Trainer: all of that. So like here you got like.
639 00:46:32.530 ⇒ 00:46:34.510 Patrick Trainer: and you can get these lead lists.
640 00:46:34.740 ⇒ 00:46:37.000 Patrick Trainer: and if we go into like e-commerce.
641 00:46:37.880 ⇒ 00:46:39.589 Patrick Trainer: let’s look at like.
642 00:46:41.460 ⇒ 00:46:45.359 Uttam Kumaran: So I wonder if we should just look at shopify plus amplitude.
643 00:46:47.270 ⇒ 00:46:49.009 Patrick Trainer: Yeah, let’s do that.
644 00:46:49.620 ⇒ 00:46:51.010 Patrick Trainer: And then
645 00:46:57.330 ⇒ 00:46:58.469 Patrick Trainer: let’s shop.
646 00:46:59.720 ⇒ 00:47:01.619 Patrick Trainer: Okay, that’s e-commerce.
647 00:47:02.580 ⇒ 00:47:05.750 Patrick Trainer: So let’s amplitude
648 00:47:12.380 ⇒ 00:47:13.840 Patrick Trainer: live. And okay.
649 00:47:18.120 ⇒ 00:47:20.579 Patrick Trainer: yeah, 382,000.
650 00:47:21.470 ⇒ 00:47:22.070 Patrick Trainer: And for.
651 00:47:22.070 ⇒ 00:47:22.760 Uttam Kumaran: Both
652 00:47:23.070 ⇒ 00:47:24.529 Uttam Kumaran: oh, adjust their platoon.
653 00:47:25.100 ⇒ 00:47:27.090 Patrick Trainer: Yeah, this is, this is just amplitude.
654 00:47:27.670 ⇒ 00:47:29.430 Patrick Trainer: And then
655 00:47:31.870 ⇒ 00:47:35.010 Patrick Trainer: what we’d be then able to do
656 00:47:36.050 ⇒ 00:47:40.430 Patrick Trainer: is, Look, I think we’d have to like join these
657 00:47:41.960 ⇒ 00:47:47.500 Patrick Trainer: on ourselves or break down your list by set verticals like, okay, there’s verticals
658 00:47:48.090 ⇒ 00:47:51.889 Patrick Trainer: like, if we join different sets together.
659 00:47:59.520 ⇒ 00:48:00.640 Patrick Trainer: Yeah, like.
660 00:48:01.310 ⇒ 00:48:03.530 Patrick Trainer: here’s like websites that
661 00:48:04.090 ⇒ 00:48:06.670 Patrick Trainer: recently added amplitude.
662 00:48:07.890 ⇒ 00:48:09.390 Patrick Trainer: and then we can
663 00:48:12.740 ⇒ 00:48:13.400 Patrick Trainer: like.
664 00:48:13.550 ⇒ 00:48:16.990 Patrick Trainer: Let’s let’s go to the Swanson vitamins.
665 00:48:30.770 ⇒ 00:48:31.979 Patrick Trainer: Wonder what else there
666 00:48:32.540 ⇒ 00:48:33.470 Patrick Trainer: be a detail?
667 00:48:39.830 ⇒ 00:48:41.000 Patrick Trainer: then.
668 00:48:42.610 ⇒ 00:48:44.000 Patrick Trainer: what other
669 00:48:44.790 ⇒ 00:48:47.110 Patrick Trainer: tech did they use? Okay, there.
670 00:48:48.580 ⇒ 00:48:49.370 Patrick Trainer: want some
671 00:48:49.670 ⇒ 00:48:50.490 Patrick Trainer: health
672 00:48:54.370 ⇒ 00:48:55.970 Patrick Trainer: And so let’s go here.
673 00:49:06.030 ⇒ 00:49:11.949 Patrick Trainer: Okay? And so these are all Swanson. Here’s Swanson vitamins. Looks like they’re using
674 00:49:12.750 ⇒ 00:49:13.860 Patrick Trainer: mix panel
675 00:49:14.900 ⇒ 00:49:16.210 Patrick Trainer: core metrics.
676 00:49:16.910 ⇒ 00:49:18.250 Patrick Trainer: Yeah, a ton of
677 00:49:19.690 ⇒ 00:49:20.850 Patrick Trainer: clavia.
678 00:49:22.530 ⇒ 00:49:23.950 Patrick Trainer: And these are all pretty
679 00:49:24.010 ⇒ 00:49:25.203 Patrick Trainer: standard. But
680 00:50:05.520 ⇒ 00:50:09.700 Uttam Kumaran: Yeah, okay, so yeah, I mean, that’s why I wanna try to use something like this.
681 00:50:09.970 ⇒ 00:50:12.519 Patrick Trainer: Yeah. And so then we’d be able to.
682 00:50:15.580 ⇒ 00:50:17.710 Patrick Trainer: I mean, I guess, just like link this back.
683 00:50:19.410 ⇒ 00:50:20.110 Patrick Trainer: Each other.
684 00:50:21.650 ⇒ 00:50:22.960 Patrick Trainer: This is.
685 00:50:40.160 ⇒ 00:50:47.510 Uttam Kumaran: Yeah, I mean, I would just basically pick the companies that we want. Put them into Apollo, find the people in there that are relevant. And then.
686 00:50:47.900 ⇒ 00:50:48.730 Uttam Kumaran: yeah.
687 00:50:48.730 ⇒ 00:50:52.589 Patrick Trainer: Right. I mean, it’s even got these contacts here, too.