Meeting Title: Sales-Automation-Weekly Date: 2024-08-27 Meeting participants: Uttam Kumaran, Patrick Trainer, Abigail Zhao
WEBVTT
1 00:00:25.420 ⇒ 00:00:26.320 Patrick Trainer: Hey!
2 00:00:30.800 ⇒ 00:00:32.400 Abigail Zhao: Hi! How are you?
3 00:00:32.580 ⇒ 00:00:34.150 Patrick Trainer: Doing good. How’s it going.
4 00:00:34.580 ⇒ 00:00:39.863 Abigail Zhao: Doing alright just started class yesterday. So getting back into the things.
5 00:00:40.270 ⇒ 00:00:40.900 Patrick Trainer: Yeah.
6 00:00:41.970 ⇒ 00:00:44.530 Patrick Trainer: it’s always like that was always like a funny.
7 00:00:44.970 ⇒ 00:00:46.869 Patrick Trainer: like kind of feeling. It’s like.
8 00:00:47.640 ⇒ 00:00:55.460 Patrick Trainer: I remember, especially like in middle and high school. It’s like not doing too much over the summer and then getting back into the class. It’s like.
9 00:00:55.720 ⇒ 00:00:57.711 Patrick Trainer: I’m not used to writing.
10 00:00:58.110 ⇒ 00:00:58.910 Abigail Zhao: Yeah.
11 00:00:58.910 ⇒ 00:00:59.540 Patrick Trainer: Yeah.
12 00:01:00.750 ⇒ 00:01:04.207 Abigail Zhao: Oh, close the black. It’s so bright. Okay.
13 00:01:10.150 ⇒ 00:01:11.909 Abigail Zhao: hopefully, that’s better.
14 00:01:13.520 ⇒ 00:01:15.629 Patrick Trainer: How many hours are you taking.
15 00:01:17.275 ⇒ 00:01:19.750 Abigail Zhao: This semester. I think it’s
16 00:01:20.120 ⇒ 00:01:21.850 Abigail Zhao: 16 HA week.
17 00:01:23.280 ⇒ 00:01:23.780 Abigail Zhao: Yeah.
18 00:01:24.126 ⇒ 00:01:24.820 Patrick Trainer: Solid load.
19 00:01:25.030 ⇒ 00:01:28.439 Abigail Zhao: Yeah, pretty standard. I feel, like, yeah.
20 00:01:28.600 ⇒ 00:01:30.899 Patrick Trainer: Anything over 18,
21 00:01:31.010 ⇒ 00:01:32.649 Patrick Trainer: or even 18 itself.
22 00:01:33.250 ⇒ 00:01:34.320 Patrick Trainer: I found, was like.
23 00:01:34.660 ⇒ 00:01:36.199 Patrick Trainer: I don’t know. It was just overload.
24 00:01:36.770 ⇒ 00:01:37.315 Abigail Zhao: Yeah.
25 00:01:43.900 ⇒ 00:01:44.669 Uttam Kumaran: Hey, guys.
26 00:01:45.370 ⇒ 00:01:46.019 Patrick Trainer: What’s up?
27 00:01:46.970 ⇒ 00:01:47.779 Abigail Zhao: Aye, yes, sir.
28 00:01:47.780 ⇒ 00:01:49.200 Patrick Trainer: You get some new pillows.
29 00:01:50.440 ⇒ 00:01:51.639 Uttam Kumaran: No! Why.
30 00:01:52.210 ⇒ 00:01:54.860 Patrick Trainer: No, I don’t. I don’t think I’ve seen those on the couch yet.
31 00:01:54.860 ⇒ 00:01:57.310 Uttam Kumaran: That’s been there that’s been there for like a week and a half.
32 00:01:57.550 ⇒ 00:02:00.540 Patrick Trainer: Oh, well, never mind, never mind.
33 00:02:00.540 ⇒ 00:02:02.889 Uttam Kumaran: That’s 5 bucks. That’s my buck. Now for that.
34 00:02:02.890 ⇒ 00:02:04.710 Patrick Trainer: Yeah, wait. Your what?
35 00:02:05.428 ⇒ 00:02:08.299 Uttam Kumaran: My buck now fell out from from school.
36 00:02:08.509 ⇒ 00:02:09.849 Patrick Trainer: Oh, okay. Okay.
37 00:02:14.840 ⇒ 00:02:22.074 Uttam Kumaran: Cool. I think we can get right into it. I think maybe we’ll start with
38 00:02:22.770 ⇒ 00:02:26.719 Uttam Kumaran: the Google sheet, Abigail, if you want to share, and maybe we can walk through
39 00:02:26.830 ⇒ 00:02:30.550 Uttam Kumaran: some comments I had. I don’t know, Pat, if you had a chance to look at that.
40 00:02:31.847 ⇒ 00:02:34.810 Patrick Trainer: Just like loosely. And also I
41 00:02:34.990 ⇒ 00:02:38.400 Patrick Trainer: probably won’t stay on this whole call, like I’m
42 00:02:38.550 ⇒ 00:02:47.399 Patrick Trainer: on a hotspot on my phone. Internet should be the the new modem, and everything should be delivered between 10 and 2. So
43 00:02:47.950 ⇒ 00:02:49.310 Patrick Trainer: after that I’ll be
44 00:02:49.600 ⇒ 00:02:51.200 Patrick Trainer: crushing away. But.
45 00:02:51.200 ⇒ 00:02:59.099 Uttam Kumaran: Okay, cool. Yeah. I think while you’re on, we could just kind of like, probably agree on like what we want to do this week, and then that should be it.
46 00:03:01.550 ⇒ 00:03:02.550 Abigail Zhao: You
47 00:03:05.000 ⇒ 00:03:06.659 Abigail Zhao: it be this one
48 00:03:07.520 ⇒ 00:03:08.700 Abigail Zhao: correct.
49 00:03:11.650 ⇒ 00:03:12.410 Uttam Kumaran: Cool.
50 00:03:13.840 ⇒ 00:03:21.619 Uttam Kumaran: so yeah, I think, it’s pretty self explanatory, based on the call we had last time. So the kind of the big changes I made is I just made source
51 00:03:21.690 ⇒ 00:03:24.729 Uttam Kumaran: like a dropdown. And then I added a
52 00:03:25.210 ⇒ 00:03:31.900 Uttam Kumaran: calling for like, Is it live yet? I guess my questions are really gonna be around
53 00:03:32.395 ⇒ 00:03:38.709 Uttam Kumaran: the areas where we can’t, where we don’t know the source yet. So event participation don’t really care about that.
54 00:03:38.920 ⇒ 00:03:39.330 Abigail Zhao: Yeah.
55 00:03:39.330 ⇒ 00:03:44.449 Uttam Kumaran: Content. And the growth stage is where I want to like. Figure out where that information comes from.
56 00:03:46.360 ⇒ 00:03:53.689 Uttam Kumaran: so content engagement. So download. I think the biggest thing is like I want to decide is that gonna come from
57 00:03:54.930 ⇒ 00:03:57.619 Uttam Kumaran: like post hog as like an event?
58 00:03:57.710 ⇒ 00:04:00.930 Uttam Kumaran: Or are we gonna do that? I.
59 00:04:00.930 ⇒ 00:04:07.489 Patrick Trainer: I would call it as like coming from Hubspot, or like Google Tag, tag manager, as like.
60 00:04:07.980 ⇒ 00:04:11.230 Patrick Trainer: that’s like a like a Utm code.
61 00:04:12.790 ⇒ 00:04:17.040 Uttam Kumaran: So the shared content on Twitter.
62 00:04:17.959 ⇒ 00:04:22.189 Uttam Kumaran: That’s gonna be a little bit harder. I’m not exactly sure. So subscribe to it.
63 00:04:22.190 ⇒ 00:04:27.959 Patrick Trainer: The way I’m thinking about it is like you’re gonna have, or we’re gonna have a link
64 00:04:28.110 ⇒ 00:04:31.549 Patrick Trainer: like a specific link to that
65 00:04:31.830 ⇒ 00:04:33.310 Patrick Trainer: form of media like.
66 00:04:33.310 ⇒ 00:04:33.830 Uttam Kumaran: Okay.
67 00:04:33.830 ⇒ 00:04:39.229 Patrick Trainer: Content or post. And it’s basically like that, link’s gonna be embedded in some sort of
68 00:04:39.260 ⇒ 00:04:40.580 Patrick Trainer: call to action.
69 00:04:40.740 ⇒ 00:04:56.239 Patrick Trainer: And when people like go to that link like, you know how like in a URL, you have the path. And then you have like the path attributes. Yeah, there’s like the Utm, essentially like that’s going to be the key of where
70 00:04:56.920 ⇒ 00:04:59.000 Patrick Trainer: the source is.
71 00:04:59.000 ⇒ 00:04:59.410 Uttam Kumaran: Okay.
72 00:04:59.990 ⇒ 00:05:01.150 Patrick Trainer: And so.
73 00:05:01.310 ⇒ 00:05:02.150 Patrick Trainer: like
74 00:05:02.280 ⇒ 00:05:09.920 Patrick Trainer: again, we like, we can track that in Hubspot like that’s in their marketing cloud. Or we can set up
75 00:05:10.510 ⇒ 00:05:12.190 Patrick Trainer: Google tag manager.
76 00:05:12.734 ⇒ 00:05:14.349 Patrick Trainer: and do it there.
77 00:05:14.740 ⇒ 00:05:16.689 Patrick Trainer: or we can just create
78 00:05:18.510 ⇒ 00:05:21.100 Patrick Trainer: like unique links. For
79 00:05:21.430 ⇒ 00:05:22.750 Patrick Trainer: like each
80 00:05:23.360 ⇒ 00:05:25.339 Patrick Trainer: piece of content that we’re like
81 00:05:25.600 ⇒ 00:05:39.514 Patrick Trainer: publishing out and seeing so like if you go to brainforce.com slash Xyz and you’re and we’ve only sent that Xyz path to.
82 00:05:41.030 ⇒ 00:05:49.080 Patrick Trainer: I don’t know some channel to to you, and you go to it like we’ll know that you went to it because
83 00:05:49.220 ⇒ 00:05:51.560 Patrick Trainer: you’re the only one that has it right.
84 00:05:51.790 ⇒ 00:05:56.750 Uttam Kumaran: Yeah, I guess so. The kind of the thing I’m deciding is like we have.
85 00:05:57.080 ⇒ 00:06:03.270 Uttam Kumaran: We have the Newsletter subscription, which is beehive. We have reading the blog post which
86 00:06:03.900 ⇒ 00:06:06.030 Uttam Kumaran: we’ll have to try to get.
87 00:06:09.150 ⇒ 00:06:14.849 Uttam Kumaran: There’s something else like Apollo has the ability to scrape who’s on the site?
88 00:06:14.900 ⇒ 00:06:16.649 Uttam Kumaran: Maybe Hubspot does.
89 00:06:16.690 ⇒ 00:06:23.930 Uttam Kumaran: and then download like the white paper. You do have to provide your email to do that. So I feel like
90 00:06:24.570 ⇒ 00:06:27.510 Uttam Kumaran: that also will need some trigger.
91 00:06:31.180 ⇒ 00:06:40.080 Uttam Kumaran: yeah, it’s just it’s gonna be tough for us to to go and manage all the links. So something needs to be automated. Basically like, for example, it should be like.
92 00:06:40.700 ⇒ 00:06:47.770 Uttam Kumaran: if based wherever you download something you automatically get put somewhere right like that. Your email gets sent
93 00:06:47.800 ⇒ 00:06:52.659 Uttam Kumaran: to some trigger, and then you get put somewhere. So yeah, I guess I’ll
94 00:06:53.180 ⇒ 00:06:55.089 Uttam Kumaran: whatever the easiest way.
95 00:06:55.090 ⇒ 00:06:58.159 Patrick Trainer: Are you thinking of it in terms of like
96 00:06:59.050 ⇒ 00:07:05.730 Patrick Trainer: you click a link. And then by put somewhere, you mean like added into some sort of campaign.
97 00:07:05.950 ⇒ 00:07:12.109 Uttam Kumaran: Exactly. Yeah. Like, for example, yeah, cause if we, it’s it’s less. I actually care
98 00:07:12.150 ⇒ 00:07:16.370 Uttam Kumaran: way less about like how many people are doing it. And more. I more care about
99 00:07:16.720 ⇒ 00:07:20.140 Uttam Kumaran: like, do they automatically get put into an action?
100 00:07:20.340 ⇒ 00:07:21.210 Uttam Kumaran: Oh, yeah.
101 00:07:21.210 ⇒ 00:07:22.370 Patrick Trainer: Yeah, that makes sense.
102 00:07:22.660 ⇒ 00:07:24.469 Uttam Kumaran: So it’s more for
103 00:07:25.150 ⇒ 00:07:28.590 Uttam Kumaran: yeah, it’s more. For like, oh, they downloaded a thing cool.
104 00:07:28.630 ⇒ 00:07:37.670 Uttam Kumaran: They should automatically get sent like or like something should happen, whatever that is basically so whether that’s coming from
105 00:07:38.340 ⇒ 00:07:40.010 Uttam Kumaran: yeah.
106 00:07:40.150 ⇒ 00:07:47.229 Uttam Kumaran: I I think post hog like has the ability to tell us when events are fired. I do think
107 00:07:47.310 ⇒ 00:07:53.987 Uttam Kumaran: Hubspot does, although I haven’t decided on whether we want to do the marketing and the sales yet.
108 00:07:55.290 ⇒ 00:07:55.950 Uttam Kumaran: Or.
109 00:07:55.950 ⇒ 00:07:58.140 Patrick Trainer: Thing to think about is like.
110 00:07:58.760 ⇒ 00:08:00.520 Patrick Trainer: So we have, like
111 00:08:00.890 ⇒ 00:08:04.019 Patrick Trainer: the content, download the subscription
112 00:08:04.300 ⇒ 00:08:09.490 Patrick Trainer: and then shared social media, like all of that, is tied back to
113 00:08:09.960 ⇒ 00:08:11.880 Patrick Trainer: ultimately, like an email
114 00:08:12.460 ⇒ 00:08:22.719 Patrick Trainer: social media, maybe less so, but like readable like, so that it’s tied back to an email like, then it’d be, I mean, trivial to
115 00:08:23.050 ⇒ 00:08:25.419 Patrick Trainer: attribute it back to that email.
116 00:08:25.420 ⇒ 00:08:25.820 Uttam Kumaran: Yeah, but.
117 00:08:25.820 ⇒ 00:08:29.250 Patrick Trainer: Like in read a blog post. How would you know that
118 00:08:29.310 ⇒ 00:08:30.990 Patrick Trainer: Patrick at Brainforge
119 00:08:31.410 ⇒ 00:08:36.600 Patrick Trainer: read a blog post like it’s you’d really just see like
120 00:08:36.909 ⇒ 00:08:39.769 Patrick Trainer: someone in New Orleans, read a blog post.
121 00:08:39.770 ⇒ 00:08:49.390 Uttam Kumaran: So at the moment, we do have some like identity resolution on the site. Actually. So, I have, like an instantly pixel that’s on the site
122 00:08:49.580 ⇒ 00:08:52.420 Uttam Kumaran: that’s already kind of like telling us like
123 00:08:52.820 ⇒ 00:08:56.630 Uttam Kumaran: it’s giving us like decent info on like, Hey, this person
124 00:08:58.620 ⇒ 00:08:59.550 Uttam Kumaran: like.
125 00:08:59.900 ⇒ 00:09:02.645 Uttam Kumaran: read this like, for example, if I look at
126 00:09:03.730 ⇒ 00:09:09.600 Uttam Kumaran: I got a web. If I look at website versus, yeah, I can tell that. Like, there’s people I actually have their name, and
127 00:09:09.740 ⇒ 00:09:20.300 Uttam Kumaran: who’s looking at the site? Sometimes I have their name, sometimes I don’t. So I think Apollo, and instantly have that. If Hubspot has that, then we’ll just use that basically.
128 00:09:20.300 ⇒ 00:09:20.980 Patrick Trainer: Okay.
129 00:09:21.300 ⇒ 00:09:23.339 Uttam Kumaran: As soon as someone looks at the site
130 00:09:23.460 ⇒ 00:09:25.140 Uttam Kumaran: they get put into something.
131 00:09:27.690 ⇒ 00:09:33.799 Uttam Kumaran: okay. So I’ll kind of again, like, I’ll kind of leave that to you to kind of decide on.
132 00:09:33.850 ⇒ 00:09:34.465 Uttam Kumaran: But
133 00:09:36.880 ⇒ 00:09:42.130 Uttam Kumaran: yeah, I. The the goal is just so that they get put into something.
134 00:09:42.130 ⇒ 00:09:45.179 Patrick Trainer: Yeah, yeah, no, that that makes sense. And that’s
135 00:09:45.360 ⇒ 00:09:46.940 Patrick Trainer: clear enough. And I mean.
136 00:09:47.400 ⇒ 00:09:49.280 Patrick Trainer: none of this is going to be like
137 00:09:49.510 ⇒ 00:09:50.470 Patrick Trainer: perfect.
138 00:09:50.870 ⇒ 00:09:52.179 Patrick Trainer: because it’s like
139 00:09:53.610 ⇒ 00:09:54.840 Patrick Trainer: or like.
140 00:09:55.320 ⇒ 00:09:58.419 Patrick Trainer: we’re not going to be able to resolve every single identity.
141 00:09:58.480 ⇒ 00:10:02.739 Patrick Trainer: And so it’s like the signal is the is the greatest
142 00:10:03.060 ⇒ 00:10:04.889 Patrick Trainer: like what we’re after.
143 00:10:04.890 ⇒ 00:10:05.969 Uttam Kumaran: Was that? And like.
144 00:10:05.970 ⇒ 00:10:07.580 Patrick Trainer: Perfect resolution.
145 00:10:07.580 ⇒ 00:10:11.589 Uttam Kumaran: Yeah, I assume we’re not gonna get everybody. But I mainly it’s like.
146 00:10:12.610 ⇒ 00:10:27.959 Uttam Kumaran: how fast can we get in contact with them from the moment of interaction is what we’re optimizing, for. The second thing will be measurement. And then the 3rd thing will be like, okay, how do we do this? Cheaper, faster, better? No.
147 00:10:28.190 ⇒ 00:10:30.800 Uttam Kumaran: and then so on the growth stage.
148 00:10:30.850 ⇒ 00:10:34.709 Uttam Kumaran: That also I think we need a we need a little bit more like
149 00:10:37.290 ⇒ 00:10:42.260 Uttam Kumaran: I mean, I don’t know. I think we should. We should just. My idea was like, do this in Apollo with like revenue. But
150 00:10:45.660 ⇒ 00:10:49.789 Uttam Kumaran: I don’t know. We we did already set some revenue goals. I don’t know.
151 00:10:50.170 ⇒ 00:10:53.810 Uttam Kumaran: I don’t know. If you know, if an Apollo we we can like look at anything like.
152 00:10:54.880 ⇒ 00:10:57.940 Patrick Trainer: There’s like there was a section for like
153 00:10:58.240 ⇒ 00:10:59.990 Patrick Trainer: recent fundraisers.
154 00:11:00.200 ⇒ 00:11:00.930 Uttam Kumaran: Okay.
155 00:11:01.468 ⇒ 00:11:04.159 Patrick Trainer: So like. That may be
156 00:11:04.850 ⇒ 00:11:06.720 Patrick Trainer: kind of like a proxy metric.
157 00:11:09.090 ⇒ 00:11:10.829 Patrick Trainer: or it could also be
158 00:11:15.790 ⇒ 00:11:20.030 Patrick Trainer: if they have, like a head count over time, and then we just like.
159 00:11:20.200 ⇒ 00:11:23.189 Patrick Trainer: look at how fast that’s growing, or
160 00:11:23.670 ⇒ 00:11:25.040 Patrick Trainer: could be even
161 00:11:25.390 ⇒ 00:11:27.089 Patrick Trainer: like a ratio of
162 00:11:28.030 ⇒ 00:11:30.470 Patrick Trainer: revenue to headcount
163 00:11:30.650 ⇒ 00:11:31.879 Patrick Trainer: something like that.
164 00:11:32.920 ⇒ 00:11:35.829 Uttam Kumaran: Yeah, let me just look. Let me just like kind of like, close.
165 00:11:35.830 ⇒ 00:11:38.320 Patrick Trainer: This growth is like subjective.
166 00:11:38.480 ⇒ 00:11:39.660 Patrick Trainer: it feels like.
167 00:11:41.320 ⇒ 00:11:42.570 Uttam Kumaran: Yeah. But then again, our.
168 00:11:42.570 ⇒ 00:11:46.739 Patrick Trainer: You would at least need, like some sort of time series to to.
169 00:11:46.740 ⇒ 00:11:47.280 Uttam Kumaran: Yeah.
170 00:11:47.280 ⇒ 00:11:48.040 Patrick Trainer: Off, of.
171 00:11:49.070 ⇒ 00:11:51.070 Uttam Kumaran: So they do have funding rounds.
172 00:11:52.790 ⇒ 00:11:56.120 Uttam Kumaran: So I think that that’s pretty
173 00:11:57.100 ⇒ 00:12:01.650 Uttam Kumaran: good, actually like, if we were to assign funding rounds to each of these.
174 00:12:01.650 ⇒ 00:12:03.160 Patrick Trainer: Look at A and B’s.
175 00:12:04.000 ⇒ 00:12:08.568 Uttam Kumaran: Yeah, they have funding rounds. They have
176 00:12:11.790 ⇒ 00:12:14.430 Patrick Trainer: Series C, and everything following is like
177 00:12:14.620 ⇒ 00:12:18.790 Patrick Trainer: when startups hit the brakes and try and become profitable.
178 00:12:18.940 ⇒ 00:12:22.049 Patrick Trainer: They start, they start thinking about their.
179 00:12:23.340 ⇒ 00:12:24.120 Uttam Kumaran: Yeah.
180 00:12:25.730 ⇒ 00:12:29.930 Uttam Kumaran: so they do have like signals which are all time series based.
181 00:12:30.130 ⇒ 00:12:31.713 Uttam Kumaran: And these are like,
182 00:12:37.810 ⇒ 00:12:40.000 Uttam Kumaran: office expansion.
183 00:12:40.570 ⇒ 00:12:48.312 Uttam Kumaran: new product, merger, recent funding rapid growth. So can, yeah, I guess. Abigail, can you put that in the
184 00:12:48.900 ⇒ 00:12:54.289 Uttam Kumaran: in column E for the growth stage? Just on one of them. You can just say, like, use Apollo signals.
185 00:12:54.690 ⇒ 00:12:55.410 Abigail Zhao: Okay.
186 00:12:56.600 ⇒ 00:12:57.690 Uttam Kumaran: And then
187 00:12:58.000 ⇒ 00:13:00.399 Uttam Kumaran: the other thing I want to do is
188 00:13:04.000 ⇒ 00:13:05.779 Uttam Kumaran: I want to assign
189 00:13:06.450 ⇒ 00:13:08.650 Uttam Kumaran: funding rounds for
190 00:13:11.390 ⇒ 00:13:14.890 Uttam Kumaran: early stage and like rapid growth.
191 00:13:14.960 ⇒ 00:13:16.309 Uttam Kumaran: The other 2.
192 00:13:16.320 ⇒ 00:13:21.720 Uttam Kumaran: It’s okay. So you can just say you can just assign all these to Apollo.
193 00:13:22.710 ⇒ 00:13:26.590 Uttam Kumaran: and then in the you can call me. You can just put
194 00:13:28.354 ⇒ 00:13:36.030 Uttam Kumaran: or yeah, or or actually for the for the, you can just respond to the comment actually on growth stage, you can just say, use Apollo signals
195 00:13:36.480 ⇒ 00:13:39.650 Uttam Kumaran: and then also use Apollo funding rounds.
196 00:13:46.360 ⇒ 00:13:47.220 Uttam Kumaran: Okay.
197 00:13:50.537 ⇒ 00:13:56.789 Uttam Kumaran: okay, cool. Otherwise, I think this is in a good spot. I want to break this down
198 00:13:57.020 ⇒ 00:13:58.400 Uttam Kumaran: and
199 00:13:58.860 ⇒ 00:14:01.920 Uttam Kumaran: alongside, like other things that we want to try to
200 00:14:02.510 ⇒ 00:14:04.149 Uttam Kumaran: take care of this week.
201 00:14:05.410 ⇒ 00:14:05.950 Patrick Trainer: Yeah.
202 00:14:06.260 ⇒ 00:14:08.579 Patrick Trainer: ultimately, I think this is great.
203 00:14:08.750 ⇒ 00:14:10.505 Uttam Kumaran: Yeah, I think,
204 00:14:11.480 ⇒ 00:14:11.870 Patrick Trainer: Clear.
205 00:14:13.710 ⇒ 00:14:16.405 Uttam Kumaran: Yeah, I think the biggest thing I want to do is
206 00:14:17.800 ⇒ 00:14:19.870 Uttam Kumaran: one. Decide where we want to like.
207 00:14:20.050 ⇒ 00:14:21.289 Uttam Kumaran: track this stuff.
208 00:14:21.730 ⇒ 00:14:27.239 Uttam Kumaran: And and 2. I want to get to like the 1st proof of concept of like a loop here.
209 00:14:28.746 ⇒ 00:14:31.419 Uttam Kumaran: So let’s pick like.
210 00:14:31.560 ⇒ 00:14:35.729 Uttam Kumaran: I guess, Pat, let’s like, let’s just pick parts of this to do.
211 00:14:35.760 ⇒ 00:14:39.969 Uttam Kumaran: or like one of this, to try to like, hook up all the pieces with.
212 00:14:39.970 ⇒ 00:14:40.680 Patrick Trainer: Yeah.
213 00:14:41.677 ⇒ 00:14:44.709 Uttam Kumaran: Yeah, ideally, it’s cool that we already have. Like.
214 00:14:46.000 ⇒ 00:14:49.650 Uttam Kumaran: yeah, I think if you if you scroll up Abigail a little bit.
215 00:14:52.330 ⇒ 00:14:55.319 Uttam Kumaran: I mean ideally if we can get like
216 00:14:56.360 ⇒ 00:14:58.590 Uttam Kumaran: the Apollo, fit scores.
217 00:14:59.170 ⇒ 00:15:01.690 Patrick Trainer: Yeah, I think if yeah, if we do
218 00:15:02.121 ⇒ 00:15:07.840 Patrick Trainer: like fit score, obviously, or it seems obvious to me as like the the start
219 00:15:08.442 ⇒ 00:15:15.209 Patrick Trainer: and then, I think, like what gives us like the most signal here would probably be
220 00:15:15.710 ⇒ 00:15:18.989 Patrick Trainer: industry and decision maker
221 00:15:19.802 ⇒ 00:15:21.700 Patrick Trainer: like targeting that.
222 00:15:22.487 ⇒ 00:15:26.359 Patrick Trainer: So I think those 2 would be great to start
223 00:15:26.610 ⇒ 00:15:29.400 Patrick Trainer: or yeah to to just like.
224 00:15:29.620 ⇒ 00:15:30.839 Patrick Trainer: be step one.
225 00:15:31.010 ⇒ 00:15:32.419 Patrick Trainer: And then
226 00:15:32.500 ⇒ 00:15:34.540 Patrick Trainer: we just continue to knock out
227 00:15:34.730 ⇒ 00:15:36.940 Patrick Trainer: every subsequent subcategory.
228 00:15:37.220 ⇒ 00:15:42.076 Uttam Kumaran: So what does this look like in terms of execution like? Is this
229 00:15:42.810 ⇒ 00:15:47.509 Uttam Kumaran: Is this in? Is there like like, how do you actually do these points and things like that.
230 00:15:47.710 ⇒ 00:15:50.030 Patrick Trainer: So in
231 00:15:50.140 ⇒ 00:15:52.719 Patrick Trainer: Hubspot, like you set up
232 00:15:54.040 ⇒ 00:15:56.070 Patrick Trainer: kind of like these, different, like
233 00:15:57.820 ⇒ 00:16:03.900 Patrick Trainer: almost like workflows, like you know how in Apollo it has, like the motions, or
234 00:16:04.560 ⇒ 00:16:05.270 Patrick Trainer: something like.
235 00:16:05.270 ⇒ 00:16:05.650 Uttam Kumaran: Ways.
236 00:16:05.650 ⇒ 00:16:09.513 Patrick Trainer: It’s, it’s yeah, the plays. It’s really similar to that.
237 00:16:10.230 ⇒ 00:16:14.410 Patrick Trainer: and then you can like, link the workflows together
238 00:16:14.848 ⇒ 00:16:18.199 Patrick Trainer: based on like, it’s basically you have, like.
239 00:16:18.850 ⇒ 00:16:24.070 Patrick Trainer: a contact comes into the funnel, and that contact has attributes.
240 00:16:24.580 ⇒ 00:16:25.090 Uttam Kumaran: To it.
241 00:16:25.090 ⇒ 00:16:30.340 Patrick Trainer: And then, depending on those attributes, it goes down like a decision tree and then
242 00:16:30.700 ⇒ 00:16:32.660 Patrick Trainer: scores them that way.
243 00:16:33.360 ⇒ 00:16:37.909 Uttam Kumaran: So the score happens. So the attributes come from Apoll. The score happens in Hubspot.
244 00:16:37.910 ⇒ 00:16:38.540 Patrick Trainer: Correct.
245 00:16:39.280 ⇒ 00:16:43.979 Uttam Kumaran: Okay, cool. So the to break it down even further. The biggest thing
246 00:16:44.560 ⇒ 00:16:46.659 Uttam Kumaran: the thing we’ll do this week is
247 00:16:46.690 ⇒ 00:16:50.360 Uttam Kumaran: get company, size, and industry
248 00:16:50.620 ⇒ 00:16:52.140 Uttam Kumaran: into
249 00:16:52.320 ⇒ 00:16:54.550 Uttam Kumaran: get company, size and industry.
250 00:16:55.040 ⇒ 00:17:00.209 Uttam Kumaran: Like, I guess I’m trying to think about like what the actual steps are in Apollo. So you’re going to filter.
251 00:17:00.570 ⇒ 00:17:03.709 Uttam Kumaran: You’re gonna like, Create 3 lists, or like, how like.
252 00:17:04.520 ⇒ 00:17:06.379 Uttam Kumaran: we’re just talking out loud like.
253 00:17:06.380 ⇒ 00:17:07.280 Patrick Trainer: Yeah.
254 00:17:10.260 ⇒ 00:17:11.760 Patrick Trainer: So it’s
255 00:17:13.500 ⇒ 00:17:15.049 Patrick Trainer: I guess there, there could be.
256 00:17:15.050 ⇒ 00:17:19.190 Uttam Kumaran: But should we start with industry as the highest level filter, and then.
257 00:17:19.190 ⇒ 00:17:21.480 Patrick Trainer: That. That’s that’s what I think. Like.
258 00:17:22.359 ⇒ 00:17:29.110 Patrick Trainer: yeah. And and the the way to think about it, too, it’s like, if we’re going through Apollo like
259 00:17:29.330 ⇒ 00:17:34.789 Patrick Trainer: self clicking right and like self selecting. We’re pretty much already like
260 00:17:34.930 ⇒ 00:17:38.289 Patrick Trainer: we’re biasing the attributes that we’re that we’re looking for. So.
261 00:17:38.290 ⇒ 00:17:38.989 Uttam Kumaran: If you don’t know.
262 00:17:38.990 ⇒ 00:17:41.719 Patrick Trainer: Apollo and like filter down for
263 00:17:41.860 ⇒ 00:17:52.410 Patrick Trainer: 5 million dollar companies. Then it’s like everything in your top of the funnel is going to be 5 million. But if you’re just getting, if we’re if you open this up to like.
264 00:17:52.850 ⇒ 00:17:55.219 Patrick Trainer: who’s downloading the page.
265 00:17:55.250 ⇒ 00:17:58.780 Patrick Trainer: and they like a bunch of people do that.
266 00:17:58.780 ⇒ 00:18:00.329 Uttam Kumaran: Then we’re not gonna yeah.
267 00:18:00.330 ⇒ 00:18:17.290 Patrick Trainer: You’re not, gonna you’re gonna have to ask right? And then when they fill that out, that’s when, like the self filter and point attributes come. So it’s like you get that self selection that. But it’s like you’re. It’s biased because, like you’re choosing it.
268 00:18:17.300 ⇒ 00:18:21.960 Patrick Trainer: But then there’s that like that randomness that comes in that you? Then you’re
269 00:18:22.370 ⇒ 00:18:24.500 Patrick Trainer: scoring that randomness essentially.
270 00:18:24.500 ⇒ 00:18:25.060 Uttam Kumaran: Yeah.
271 00:18:27.430 ⇒ 00:18:28.410 Uttam Kumaran: okay.
272 00:18:28.940 ⇒ 00:18:33.140 Uttam Kumaran: so we do have an ability to do like personas
273 00:18:33.310 ⇒ 00:18:34.640 Uttam Kumaran: in
274 00:18:35.410 ⇒ 00:18:37.200 Uttam Kumaran: Apollo.
275 00:18:37.280 ⇒ 00:18:39.280 Uttam Kumaran: which is basically like.
276 00:18:39.900 ⇒ 00:18:41.940 Uttam Kumaran: you can filter
277 00:18:42.528 ⇒ 00:18:49.329 Uttam Kumaran: with the add a persona, you basically have the ability to do number of employees, location, industry and titles.
278 00:18:50.310 ⇒ 00:18:51.740 Uttam Kumaran: As a persona.
279 00:18:52.940 ⇒ 00:18:56.539 Uttam Kumaran: So I do. Maybe we start with persona. Which would
280 00:18:57.030 ⇒ 00:18:58.899 Uttam Kumaran: we? We? We create
281 00:19:04.400 ⇒ 00:19:06.070 Uttam Kumaran: industries
282 00:19:07.830 ⇒ 00:19:16.289 Uttam Kumaran: like, for example, right now, I have like a manufacturing persona, but it’s just like it kind of includes everything. So
283 00:19:16.560 ⇒ 00:19:23.450 Uttam Kumaran: maybe let’s let’s decide on just taking trying this with a high signal folks. So let’s do
284 00:19:23.730 ⇒ 00:19:25.660 Uttam Kumaran: all the decision makers.
285 00:19:25.940 ⇒ 00:19:27.259 Uttam Kumaran: And then
286 00:19:27.620 ⇒ 00:19:33.930 Uttam Kumaran: for like 8 to 12 million in like high data industries. And let’s just aim for that.
287 00:19:34.440 ⇒ 00:19:35.110 Patrick Trainer: Right.
288 00:19:36.120 ⇒ 00:19:44.160 Uttam Kumaran: And then so so the main cause I’m cause again, we’re gonna if you think about it, it’s like the cross right cross join. So we’re gonna have
289 00:19:44.909 ⇒ 00:19:47.759 Uttam Kumaran: like a ton of personas.
290 00:19:48.481 ⇒ 00:19:50.659 Uttam Kumaran: So I want to start with, like.
291 00:19:51.570 ⇒ 00:19:54.869 Uttam Kumaran: what we predict is like the highest intent one
292 00:19:55.340 ⇒ 00:19:57.410 Uttam Kumaran: for this like proof of concept
293 00:19:57.670 ⇒ 00:20:00.320 Uttam Kumaran: before starting to layer on
294 00:20:01.040 ⇒ 00:20:02.850 Uttam Kumaran: more combinations.
295 00:20:04.020 ⇒ 00:20:05.929 Uttam Kumaran: right? Or again, like.
296 00:20:05.930 ⇒ 00:20:09.060 Patrick Trainer: Like every contact’s going to have their own like
297 00:20:09.190 ⇒ 00:20:11.729 Patrick Trainer: unique persona. But
298 00:20:11.920 ⇒ 00:20:20.729 Patrick Trainer: what we’re looking for is to like group those personas into like the ideal customer. And that’s like that ideal customer profile.
299 00:20:21.492 ⇒ 00:20:22.940 Patrick Trainer: And it’s like.
300 00:20:23.290 ⇒ 00:20:28.629 Patrick Trainer: there’s like a propensity score of like, how much do they fit into these buckets.
301 00:20:28.750 ⇒ 00:20:30.690 Patrick Trainer: Yeah. And then, like
302 00:20:31.060 ⇒ 00:20:33.579 Patrick Trainer: of all of the people.
303 00:20:33.980 ⇒ 00:20:37.219 Patrick Trainer: the ones that are in these buckets, we go after those.
304 00:20:37.800 ⇒ 00:20:40.689 Uttam Kumaran: Oh, okay, so okay, that makes sense.
305 00:20:44.230 ⇒ 00:20:50.219 Uttam Kumaran: So I guess initially, let’s then we then the persona should just be the decision maker, because.
306 00:20:50.960 ⇒ 00:20:58.329 Uttam Kumaran: like, let’s like cause again. The thing I’m battling with is like, how do we avoid just exporting everybody in Apollo
307 00:20:58.350 ⇒ 00:20:59.549 Uttam Kumaran: so like.
308 00:20:59.550 ⇒ 00:21:05.159 Patrick Trainer: Yeah, I mean with with Apollo. Like, if you think like, Apollo is for
309 00:21:05.500 ⇒ 00:21:06.770 Patrick Trainer: signaled
310 00:21:06.880 ⇒ 00:21:12.950 Patrick Trainer: lead finding. So it’s like, it’s already kind of like whittled down.
311 00:21:13.020 ⇒ 00:21:19.129 Patrick Trainer: And so with that, I think it’d be fine just to like, pick and choose and export who we want.
312 00:21:19.520 ⇒ 00:21:20.620 Patrick Trainer: and then
313 00:21:20.750 ⇒ 00:21:24.019 Patrick Trainer: and once we have those exported like
314 00:21:24.310 ⇒ 00:21:27.477 Patrick Trainer: we still have all of the other
315 00:21:28.440 ⇒ 00:21:30.510 Patrick Trainer: like scoring metrics.
316 00:21:30.510 ⇒ 00:21:30.900 Uttam Kumaran: Yeah.
317 00:21:30.900 ⇒ 00:21:36.599 Patrick Trainer: Are going to affect the overall score like fit score is just like half of the equation.
318 00:21:36.600 ⇒ 00:21:41.189 Uttam Kumaran: No, totally. Yeah, I I totally. I totally agree with you in that.
319 00:21:41.390 ⇒ 00:21:43.949 Uttam Kumaran: We’re like, let’s let’s just
320 00:21:44.330 ⇒ 00:21:46.269 Uttam Kumaran: yeah. I see what you mean.
321 00:21:46.270 ⇒ 00:21:46.670 Patrick Trainer: Right.
322 00:21:46.670 ⇒ 00:21:47.590 Uttam Kumaran: Hi.
323 00:21:50.830 ⇒ 00:21:53.919 Patrick Trainer: Yeah. So like, I think part of it is like.
324 00:21:54.410 ⇒ 00:22:00.100 Patrick Trainer: we’ll hook it up and like, let’s just get running with it like not overthink it too much. And
325 00:22:00.520 ⇒ 00:22:04.079 Patrick Trainer: like, I think this is like, we’re this
326 00:22:04.210 ⇒ 00:22:08.070 Patrick Trainer: like spreadsheet. Right here. It’s we’re 80% of the way there.
327 00:22:08.070 ⇒ 00:22:08.490 Uttam Kumaran: Okay.
328 00:22:08.490 ⇒ 00:22:11.430 Patrick Trainer: And we just need to turn it on and then
329 00:22:12.700 ⇒ 00:22:16.189 Patrick Trainer: throw contacts into it, and then try and market towards them.
330 00:22:16.810 ⇒ 00:22:21.199 Uttam Kumaran: So so so I I just wanna be clear on like
331 00:22:21.540 ⇒ 00:22:24.480 Uttam Kumaran: separation of work. So you’re gonna take on
332 00:22:24.800 ⇒ 00:22:32.120 Uttam Kumaran: moving, creating the personas or basically moving people from Apollo to Hubspot, and then getting the scores there.
333 00:22:32.360 ⇒ 00:22:35.320 Patrick Trainer: Well, so I’ve got to set up the like.
334 00:22:35.450 ⇒ 00:22:37.420 Patrick Trainer: the scoring system
335 00:22:37.670 ⇒ 00:22:41.919 Patrick Trainer: in Hubspot. First.st Okay, like, get those workflows down.
336 00:22:42.740 ⇒ 00:22:43.640 Patrick Trainer: And then
337 00:22:44.190 ⇒ 00:22:45.790 Patrick Trainer: we can create
338 00:22:47.210 ⇒ 00:22:48.540 Patrick Trainer: like lists
339 00:22:48.810 ⇒ 00:22:49.740 Patrick Trainer: of
340 00:22:50.730 ⇒ 00:22:52.970 Patrick Trainer: potentials in Apollo.
341 00:22:53.180 ⇒ 00:22:58.519 Patrick Trainer: And then we can like. There’s an an Apollo integration with Hubspot that
342 00:22:59.200 ⇒ 00:23:00.750 Patrick Trainer: we can sync over.
343 00:23:02.300 ⇒ 00:23:09.560 Uttam Kumaran: So let’s put this up. Let’s put this up 3 ways, because I think now it’s like a little bit clear that there’s like 3 things. So one, I think there’s
344 00:23:09.610 ⇒ 00:23:10.930 Uttam Kumaran: basically
345 00:23:10.940 ⇒ 00:23:13.170 Uttam Kumaran: configuring Apollo
346 00:23:13.340 ⇒ 00:23:15.620 Uttam Kumaran: to decide on
347 00:23:16.330 ⇒ 00:23:23.469 Uttam Kumaran: who gets put into the list. Right? So so they kind of give you the background on our billing on Apollo 2. We have.
348 00:23:27.970 ⇒ 00:23:31.589 Uttam Kumaran: we have 24,000 export credits.
349 00:23:34.960 ⇒ 00:23:39.080 Uttam Kumaran: meaning. Those are all the people that we can export at the moment.
350 00:23:39.220 ⇒ 00:23:42.510 Uttam Kumaran: So that’s gonna be our like upward limit.
351 00:23:42.660 ⇒ 00:23:45.850 Uttam Kumaran: And for that reason is why we can’t
352 00:23:46.500 ⇒ 00:23:49.100 Uttam Kumaran: like. If you think about the far
353 00:23:50.470 ⇒ 00:23:59.969 Uttam Kumaran: part of like the pendulum. We can’t export everybody, because we just we don’t have enough credits. So instead, it’s like, I would rather, for the proof of concept.
354 00:24:00.200 ⇒ 00:24:03.180 Uttam Kumaran: have a higher filter in Apollo.
355 00:24:03.410 ⇒ 00:24:05.030 Uttam Kumaran: even though I know.
356 00:24:05.030 ⇒ 00:24:05.460 Patrick Trainer: Right.
357 00:24:05.460 ⇒ 00:24:10.520 Uttam Kumaran: Like it will bias the scores just to see this working, because then we’ll slow like.
358 00:24:10.520 ⇒ 00:24:11.560 Patrick Trainer: Yeah, I think that’s.
359 00:24:11.560 ⇒ 00:24:17.419 Uttam Kumaran: We’ll slowly let the filter go on, Apollo, and of course have the other
360 00:24:17.480 ⇒ 00:24:20.020 Uttam Kumaran: set of people come in
361 00:24:20.040 ⇒ 00:24:30.300 Uttam Kumaran: where we may, we may want to enrich them with Apollo, anyways, when they come in through another signal point. Right? So let’s maybe just think about
362 00:24:30.470 ⇒ 00:24:36.900 Uttam Kumaran: like for this initial test. Let’s aim for like a thousand people.
363 00:24:37.230 ⇒ 00:24:43.409 Uttam Kumaran: Right? A 1,000 emails that we want to go after. Let’s just like set that as like a broad goal.
364 00:24:44.640 ⇒ 00:24:50.300 Uttam Kumaran: and then I think the thing we will decide on is what set of filters
365 00:24:50.950 ⇒ 00:24:53.290 Uttam Kumaran: mix of using the
366 00:24:53.836 ⇒ 00:25:05.293 Uttam Kumaran: personas or using the signals, or whatever basically gets us to a thousand, right? We may filter 3 of these and basically have like 10,000 still. And then say, Okay, layer one on
367 00:25:05.630 ⇒ 00:25:06.080 Patrick Trainer: And she’s.
368 00:25:06.080 ⇒ 00:25:06.690 Uttam Kumaran: And then.
369 00:25:06.690 ⇒ 00:25:08.729 Patrick Trainer: The best 1,000, and then
370 00:25:08.970 ⇒ 00:25:10.750 Patrick Trainer: then we just kind of let it rip.
371 00:25:10.920 ⇒ 00:25:12.799 Uttam Kumaran: And then we let a rip. We save that
372 00:25:13.140 ⇒ 00:25:24.179 Uttam Kumaran: the second part of this is, once I get saved as a list. How does a list with the dimensionality, get pushed into Hubspot, get translated to a score.
373 00:25:24.760 ⇒ 00:25:29.070 Uttam Kumaran: And then the the last point is, how do we move
374 00:25:29.240 ⇒ 00:25:34.310 Uttam Kumaran: from Hubspot into instantly to basically
375 00:25:34.520 ⇒ 00:25:39.289 Uttam Kumaran: to do a couple of things. One is to not only send the email, but then have the touch back
376 00:25:39.360 ⇒ 00:25:42.000 Uttam Kumaran: when an open happens right? Right.
377 00:25:42.050 ⇒ 00:25:48.560 Uttam Kumaran: So I think here’s how we divvy it up. I think, Abigail, if you can take Apollo.
378 00:25:48.980 ⇒ 00:25:53.849 Uttam Kumaran: And this is gonna require probably some like watching of the Apollo
379 00:25:53.920 ⇒ 00:25:57.789 Uttam Kumaran: like videos and learnings. The nice thing is
380 00:25:57.840 ⇒ 00:25:59.399 Uttam Kumaran: the only big
381 00:25:59.660 ⇒ 00:26:06.167 Uttam Kumaran: screw up you can do an Apollo. It’s like, if you hit export you’ll use a bunch of credits. But until then
382 00:26:06.600 ⇒ 00:26:16.019 Uttam Kumaran: you really can just keep clicking around and like click all the filters. And Apollo will tell you how many people you filter to you. You kind of poked around Apollo
383 00:26:16.160 ⇒ 00:26:17.919 Uttam Kumaran: last week or week before, right?
384 00:26:19.160 ⇒ 00:26:22.980 Uttam Kumaran: So the the big area you’ll be living in is like search
385 00:26:23.570 ⇒ 00:26:25.840 Uttam Kumaran: is like that search thing on the left.
386 00:26:26.257 ⇒ 00:26:28.280 Uttam Kumaran: And then the other thing is.
387 00:26:29.160 ⇒ 00:26:34.239 Uttam Kumaran: I think we could. Yeah, you can set up personas. But I think personas are basically just like
388 00:26:34.440 ⇒ 00:26:42.079 Uttam Kumaran: saved filters. Maybe we just start with the search for now and then we’ll kind of lead into personas. So again, your goal will be
389 00:26:42.400 ⇒ 00:26:50.110 Uttam Kumaran: understanding, like super in depth how we filter, and then how we get to our 1st 1,000 people based on
390 00:26:50.280 ⇒ 00:27:00.635 Uttam Kumaran: this like in fit score list. So again, it’s using the signals. Anything there you’ll basically like, just understand what all the filters are.
391 00:27:01.080 ⇒ 00:27:04.350 Uttam Kumaran: and even Apollo has scores.
392 00:27:05.153 ⇒ 00:27:08.186 Uttam Kumaran: So I’ll just let you go play around with that.
393 00:27:08.550 ⇒ 00:27:15.410 Uttam Kumaran: I think your job will be end to end searching for people on Apollo and getting them to a list. I think the second part.
394 00:27:15.600 ⇒ 00:27:24.840 Uttam Kumaran: your part is once again to the list. How do you use automation, or zapier, or whatever to move that list into Hubspot.
395 00:27:25.000 ⇒ 00:27:26.260 Uttam Kumaran: To then
396 00:27:26.350 ⇒ 00:27:29.530 Uttam Kumaran: get a score based on the attributes.
397 00:27:29.790 ⇒ 00:27:30.490 Patrick Trainer: Right.
398 00:27:30.490 ⇒ 00:27:42.520 Uttam Kumaran: And then basically owning like just owning how Hubspot works. So like, what we’ll expect is basically because I haven’t even clicked in the Hubspot, I’ll basically just like, Look for like, what? How does that whole thing work?
399 00:27:43.500 ⇒ 00:27:48.619 Uttam Kumaran: The 3rd thing that I will own is, I have the content ready for the emails.
400 00:27:49.660 ⇒ 00:27:51.580 Uttam Kumaran: The fake rill ready?
401 00:27:52.341 ⇒ 00:27:54.750 Uttam Kumaran: And I’m gonna go ahead and
402 00:27:55.060 ⇒ 00:27:56.709 Uttam Kumaran: just set up
403 00:27:57.285 ⇒ 00:28:01.964 Uttam Kumaran: like a couple of instantly campaigns that are ready to go.
404 00:28:03.380 ⇒ 00:28:06.311 Uttam Kumaran: I’m not. Gonna I’m not gonna know.
405 00:28:08.270 ⇒ 00:28:12.689 Uttam Kumaran: The thing is, I don’t know yet what the industries we’re going after are.
406 00:28:12.930 ⇒ 00:28:16.680 Uttam Kumaran: So there’s gonna be part of the emails that I can’t write
407 00:28:20.030 ⇒ 00:28:22.969 Uttam Kumaran: But that’s okay. I just want to like, get past that.
408 00:28:23.300 ⇒ 00:28:23.630 Patrick Trainer: Yeah.
409 00:28:23.630 ⇒ 00:28:25.440 Uttam Kumaran: The nice thing about instantly is
410 00:28:25.470 ⇒ 00:28:28.110 Uttam Kumaran: that you could personalize it. So basically.
411 00:28:28.340 ⇒ 00:28:35.080 Uttam Kumaran: if we’re going after one industry, 2 sentences can be personalized to that industry that’ll come in as a placeholder, and then the rest will
412 00:28:35.427 ⇒ 00:28:47.219 Uttam Kumaran: be like static. So I’m going to go ahead and have the instantly stuff figured out, and I’m going to go figure out how to move from Hubspot to instantly and from instantly back to Hubspot.
413 00:28:47.630 ⇒ 00:28:48.920 Uttam Kumaran: So let me own
414 00:28:49.250 ⇒ 00:28:51.380 Uttam Kumaran: the far end of the outreach.
415 00:28:52.140 ⇒ 00:28:55.400 Uttam Kumaran: and then let’s try to get back together
416 00:28:56.140 ⇒ 00:28:59.639 Uttam Kumaran: like on Thursday, and just like chat through like what we’re looking at.
417 00:29:00.238 ⇒ 00:29:04.420 Uttam Kumaran: I think a good like stretch goal this week is to get
418 00:29:04.870 ⇒ 00:29:06.180 Uttam Kumaran: some emails out
419 00:29:06.945 ⇒ 00:29:10.059 Uttam Kumaran: or at least turn on the campaign
420 00:29:10.120 ⇒ 00:29:11.599 Uttam Kumaran: to send on Monday.
421 00:29:12.090 ⇒ 00:29:15.219 Uttam Kumaran: I think that’s like a nice stretch goal that we aim for.
422 00:29:17.390 ⇒ 00:29:21.370 Uttam Kumaran: There’s a lot of unknowns, I think the biggest unknown big hubspot, frankly.
423 00:29:21.710 ⇒ 00:29:24.790 Uttam Kumaran: But I do think that like that’s totally
424 00:29:26.710 ⇒ 00:29:29.010 Uttam Kumaran: I think it’s possible. So.
425 00:29:29.010 ⇒ 00:29:29.950 Patrick Trainer: Yeah, I do, too.
426 00:29:30.330 ⇒ 00:29:35.809 Uttam Kumaran: Let’s do that. You have a bunch of tools at your disposal. You have Zapier.
427 00:29:35.930 ⇒ 00:29:40.840 Uttam Kumaran: you have Chat Gbt, Claude, we can sign up for make.
428 00:29:41.170 ⇒ 00:29:44.650 Uttam Kumaran: So like, whatever we need to do to to get it to work.
429 00:29:47.190 ⇒ 00:29:48.660 Uttam Kumaran: And then let’s just like
430 00:29:48.740 ⇒ 00:29:50.284 Uttam Kumaran: push towards that.
431 00:29:50.950 ⇒ 00:29:54.659 Uttam Kumaran: I’m gonna be working on this stuff like today and tomorrow, like pretty heavily. So
432 00:29:54.860 ⇒ 00:29:55.420 Uttam Kumaran: Tweet,
433 00:29:56.200 ⇒ 00:29:56.760 Uttam Kumaran: yeah.
434 00:29:56.760 ⇒ 00:29:57.660 Patrick Trainer: Sounds good.
435 00:29:59.050 ⇒ 00:30:02.699 Uttam Kumaran: Okay. Cool. I guess, Abigail. Any questions.
436 00:30:03.585 ⇒ 00:30:09.110 Abigail Zhao: I think I’m good, am I? Basically just aiming for, like the highest score like for each
437 00:30:09.510 ⇒ 00:30:11.950 Abigail Zhao: category, or like subcategory, like.
438 00:30:12.130 ⇒ 00:30:15.929 Uttam Kumaran: Yeah, I think the I think the biggest thing to drive towards is like.
439 00:30:16.200 ⇒ 00:30:22.070 Uttam Kumaran: how do you get the filters in a way to get a thousand of like the highest score folks.
440 00:30:22.460 ⇒ 00:30:24.949 Uttam Kumaran: And then basically owning
441 00:30:25.110 ⇒ 00:30:27.419 Uttam Kumaran: like how those
442 00:30:27.490 ⇒ 00:30:29.740 Uttam Kumaran: a thousand people get into a list.
443 00:30:29.790 ⇒ 00:30:34.510 Uttam Kumaran: And like how basically, how that whole process works in Apollo. What we’ll do
444 00:30:34.690 ⇒ 00:30:39.259 Uttam Kumaran: after that, I believe, is start to expand those filters.
445 00:30:39.808 ⇒ 00:30:44.710 Uttam Kumaran: And then we’ll also move to the disengagement side of things. The engagement side we’re not going to have.
446 00:30:45.000 ⇒ 00:30:46.883 Uttam Kumaran: We’re not going to know
447 00:30:47.760 ⇒ 00:30:50.300 Uttam Kumaran: like their score until we enrich them.
448 00:30:52.120 ⇒ 00:30:54.000 Uttam Kumaran: But the engagement
449 00:30:54.230 ⇒ 00:30:59.970 Uttam Kumaran: those people will get put into something in Hubspot. So the fit score there’ll be some.
450 00:31:00.080 ⇒ 00:31:10.499 Uttam Kumaran: Basically. What we’ll do is like we have a budget of like 24,000, some portion of it will get allocated towards like using the filters. Some portion will get allocated towards
451 00:31:10.610 ⇒ 00:31:13.839 Uttam Kumaran: people coming in the pipeline like organically.
452 00:31:14.130 ⇒ 00:31:19.940 Uttam Kumaran: Does that make sense fit score is like we’re going out into the market finding people.
453 00:31:20.090 ⇒ 00:31:23.749 Uttam Kumaran: The engagement stuff is people coming to us.
454 00:31:24.140 ⇒ 00:31:29.199 Uttam Kumaran: We can’t burn all of our credits on the 5th score, because those are people that like
455 00:31:30.040 ⇒ 00:31:40.909 Uttam Kumaran: for the most part, are don’t have in. There’s there’s actually only 50% of the way there. The engagement is another 50%. Ideally, you have engagement and a good fit, and then you’re like a great target.
456 00:31:41.395 ⇒ 00:31:44.080 Uttam Kumaran: And that’ll show up in the score. So
457 00:31:44.150 ⇒ 00:31:49.789 Uttam Kumaran: our goal now is to just aim for a thousand people that we want to move into a list, move into Hubspot, move into instantly.
458 00:31:50.730 ⇒ 00:31:52.239 Uttam Kumaran: Once we’re good on that.
459 00:31:52.490 ⇒ 00:31:58.900 Uttam Kumaran: we will have some sort of drip going on, basically a hundred to 500 people a week.
460 00:31:59.300 ⇒ 00:32:04.000 Uttam Kumaran: Go through your filter and get moved into Hubspot automatically.
461 00:32:04.200 ⇒ 00:32:09.170 Uttam Kumaran: Right that way, we’ll have a budget of like, okay, we have 24,000 credits for the rest of the year.
462 00:32:09.320 ⇒ 00:32:10.939 Uttam Kumaran: If we move
463 00:32:11.270 ⇒ 00:32:13.009 Uttam Kumaran: X amount per week
464 00:32:13.090 ⇒ 00:32:15.060 Uttam Kumaran: through just the fit score.
465 00:32:15.510 ⇒ 00:32:19.850 Uttam Kumaran: like, here’s kind of us getting to our budget range by the end of the year.
466 00:32:19.890 ⇒ 00:32:22.399 Uttam Kumaran: so some portion will move through.
467 00:32:22.430 ⇒ 00:32:28.199 Uttam Kumaran: We’ll just automatically go through your filter. If new people show up, they’ll get automatically put into campaigns.
468 00:32:28.450 ⇒ 00:32:33.030 Uttam Kumaran: The Pro. The thing we can expect is that these will have the lowest conversion rates.
469 00:32:33.680 ⇒ 00:32:45.349 Uttam Kumaran: So some portion to some of this will happen automatically. The second thing we’ll go focus on once that’s starting to happen, and people are automatically getting put into campaigns. We will then move on to the engagement stuff.
470 00:32:45.490 ⇒ 00:32:47.640 Uttam Kumaran: the engagement stuff you can expect
471 00:32:47.790 ⇒ 00:32:51.059 Uttam Kumaran: to have higher conversion rates, but much lower volume.
472 00:32:51.210 ⇒ 00:32:58.659 Uttam Kumaran: So we’ll and then, as we get our content up, the ratio will change. This is getting a little bit, Meta. I’m just kind of thinking through this for the 1st time.
473 00:32:58.670 ⇒ 00:33:00.389 Uttam Kumaran: But that’s like
474 00:33:01.190 ⇒ 00:33:03.190 Uttam Kumaran: kind of like, how we’re gonna do things.
475 00:33:03.190 ⇒ 00:33:03.850 Patrick Trainer: Right.
476 00:33:04.570 ⇒ 00:33:05.240 Abigail Zhao: Sounds good.
477 00:33:05.430 ⇒ 00:33:14.350 Uttam Kumaran: Because the fit, the the people that come through Apollo, we can rapture that up and down as we want. Right? You can just remove filters and get more people in. But we’re gonna burn credits
478 00:33:14.400 ⇒ 00:33:19.409 Uttam Kumaran: the engagement score. It’s up to me on like the
479 00:33:20.389 ⇒ 00:33:22.220 Uttam Kumaran: content and the
480 00:33:22.230 ⇒ 00:33:25.489 Uttam Kumaran: like schmoozing side to get people to come to the site
481 00:33:25.600 ⇒ 00:33:28.599 Uttam Kumaran: right? And so that’ll be. My next job is.
482 00:33:29.520 ⇒ 00:33:47.700 Uttam Kumaran: once we have the fit people coming through the fit reliably. Then it’ll be like cool. Our goal is to move. Now. We have, like 10 people a week coming through engagement. It needs to get 100 people a week, 1,000 people a week, right? And that that’ll all be tasked towards through work on the social side.
483 00:33:49.220 ⇒ 00:33:50.980 Uttam Kumaran: so that’s like the
484 00:33:51.430 ⇒ 00:33:55.119 Uttam Kumaran: big brain idea here, basically, yeah, yeah.
485 00:33:57.180 ⇒ 00:34:12.880 Uttam Kumaran: cool. Okay, so I’ll send this meeting notes after this, and then I’ll throw something on for Thursday. And then we could also chat again. Friday, I think, do spending, like every 2 days or so is has been nice for me. Gives me, like.
486 00:34:12.969 ⇒ 00:34:16.670 Uttam Kumaran: you know, a day, a day and a half to like really quick. Try to push through things.
487 00:34:17.453 ⇒ 00:34:19.797 Uttam Kumaran: and then we’ll just talk, talk on slack
488 00:34:20.380 ⇒ 00:34:21.070 Uttam Kumaran: cool
489 00:34:21.300 ⇒ 00:34:22.110 Uttam Kumaran: cool.
490 00:34:22.900 ⇒ 00:34:23.620 Patrick Trainer: Alright.
491 00:34:23.940 ⇒ 00:34:24.850 Patrick Trainer: See you all, then.
492 00:34:24.850 ⇒ 00:34:26.429 Uttam Kumaran: Alright, great thanks. Guys.