Meeting Title: Sales GTM | Standup Date: 2025-05-05 Meeting participants: Mariane Cequina, Luke Daque, Amber Lin, Robert Tseng
WEBVTT
1 00:00:13.610 ⇒ 00:00:15.300 Amber Lin: Hello!
2 00:00:17.270 ⇒ 00:00:18.819 Luke Daque: Hi! Amber! How’s it going.
3 00:00:20.320 ⇒ 00:00:27.260 Amber Lin: Pretty good Mondays are kind of hard. I struggle so bad to get out of bed.
4 00:00:28.190 ⇒ 00:00:31.130 Luke Daque: Yeah, it’s always the case.
5 00:00:31.720 ⇒ 00:00:33.140 Amber Lin: Yeah, how are you?
6 00:00:33.620 ⇒ 00:00:37.930 Luke Daque: Yeah, I’m doing well, doing well.
7 00:00:39.800 ⇒ 00:00:42.600 Amber Lin: Is work busy like it’s.
8 00:00:43.420 ⇒ 00:00:44.870 Luke Daque: Is your password.
9 00:00:45.100 ⇒ 00:00:45.710 Amber Lin: Do you think?
10 00:00:45.710 ⇒ 00:00:47.426 Luke Daque: I think it’s it’s fine
11 00:00:48.010 ⇒ 00:00:55.547 Luke Daque: like I haven’t. I was like about to start working on like pool part stuff, but I remember we’re not supposed to.
12 00:00:55.890 ⇒ 00:01:01.972 Amber Lin: Canceled that meeting, too. It’s like no where. The less work we have to do the better.
13 00:01:03.181 ⇒ 00:01:08.959 Amber Lin: You’re on right now. What clients are you on or like? What products are you on.
14 00:01:09.990 ⇒ 00:01:12.300 Luke Daque: I guess. Matter more.
15 00:01:12.700 ⇒ 00:01:18.640 Luke Daque: Let’s see, let me check the everything should be in linear
16 00:01:19.570 ⇒ 00:01:22.810 Luke Daque: and like the sales thing like internal.
17 00:01:23.628 ⇒ 00:01:24.979 Luke Daque: Well, yeah, pool part.
18 00:01:24.980 ⇒ 00:01:27.089 Amber Lin: And there’s a data platform thing.
19 00:01:27.090 ⇒ 00:01:28.560 Luke Daque: The data platform. Yeah.
20 00:01:28.560 ⇒ 00:01:29.400 Amber Lin: Huh!
21 00:01:29.780 ⇒ 00:01:33.230 Luke Daque: And I think I’m also in urban stems. But I don’t have access yet, like.
22 00:01:33.230 ⇒ 00:01:39.429 Amber Lin: I know, I know, like there’s nothing we can do for now until we get that room map improved. So that’s.
23 00:01:39.430 ⇒ 00:01:42.519 Luke Daque: I’m just like reading all this stuff going on, you know.
24 00:01:42.520 ⇒ 00:01:42.880 Amber Lin: Yes.
25 00:01:42.880 ⇒ 00:01:48.560 Luke Daque: At least. But yeah, other than that. Yeah, that’s essentially it for me.
26 00:01:49.160 ⇒ 00:01:56.370 Amber Lin: I see it seems like, at least, for until we have that pool parts thing decided tomorrow, it’s a lot of
27 00:01:56.590 ⇒ 00:01:58.149 Amber Lin: the internal stuff in that.
28 00:01:58.150 ⇒ 00:02:01.630 Luke Daque: No, mostly here. Yeah. The platform in sales
29 00:02:01.870 ⇒ 00:02:04.210 Luke Daque: in a bit of matter more, I guess.
30 00:02:06.997 ⇒ 00:02:17.749 Amber Lin: let’s figure out this internal agent. Then I know, I was talking with Robert, I was like, okay, what do you want us to deliver by the end of this week, and let me just.
31 00:02:18.000 ⇒ 00:02:19.610 Luke Daque: For the sales thing.
32 00:02:19.610 ⇒ 00:02:24.930 Amber Lin: Yeah, for the sales. Go to market. Let me go pull up what he expected.
33 00:02:29.420 ⇒ 00:02:31.180 Amber Lin: Where is that?
34 00:02:32.390 ⇒ 00:02:33.350 Amber Lin: Oh.
35 00:02:37.360 ⇒ 00:02:42.429 Amber Lin: can you update me on the progress while I look for his message.
36 00:02:44.260 ⇒ 00:02:51.149 Luke Daque: Yeah, sure. So I guess, based on like the up, the other.
37 00:02:51.640 ⇒ 00:02:55.909 Luke Daque: What we discussed with Marion last Friday, I believe.
38 00:02:56.420 ⇒ 00:03:01.480 Luke Daque: was, yeah. Well, I was like also working on looking into
39 00:03:01.600 ⇒ 00:03:08.749 Luke Daque: how to enrich the data and stuff like that for for our sales stuff from clay for clay.
40 00:03:09.080 ⇒ 00:03:21.169 Luke Daque: and also looking for a way to integrate basically the Linkedin comments and stuff to to play. Basically.
41 00:03:21.880 ⇒ 00:03:27.570 Luke Daque: So yeah, I think we should be pretty much fine with the enrichment.
42 00:03:27.700 ⇒ 00:03:31.410 Amber Lin: But as to the integration from Linkedin to.
43 00:03:31.740 ⇒ 00:03:37.660 Luke Daque: Play that’s still like in process. I’m still like trying to figure out the the best way we can do that at the moment
44 00:03:38.380 ⇒ 00:03:39.220 Luke Daque: to in.
45 00:03:40.090 ⇒ 00:03:47.689 Amber Lin: I see. How long cause Robert essentially wants this lead list up and running
46 00:03:47.970 ⇒ 00:03:58.490 Amber Lin: by last week. But since we’re pretty over that deadline. So what do you think still needs to be done, or how and how long will it take to get this to the finish line?
47 00:04:02.170 ⇒ 00:04:03.850 Luke Daque: I don’t know, really.
48 00:04:04.330 ⇒ 00:04:05.599 Luke Daque: At the moment.
49 00:04:06.606 ⇒ 00:04:16.410 Amber Lin: let’s break it down. Then what step? What steps? Are we let me, pull up that diagram and then we can look at what steps we need.
50 00:04:16.899 ⇒ 00:04:17.889 Luke Daque: Yeah, sure.
51 00:04:18.130 ⇒ 00:04:21.710 Amber Lin: Yeah. What is it?
52 00:04:22.000 ⇒ 00:04:25.010 Amber Lin: Go to market? Right?
53 00:04:26.050 ⇒ 00:04:26.770 Amber Lin: Let me do it.
54 00:04:28.760 ⇒ 00:04:30.889 Amber Lin: Let me share.
55 00:04:31.510 ⇒ 00:04:36.470 Amber Lin: Yeah. Why don’t we go to that figma, doc that we sent the I sent the other day.
56 00:04:36.730 ⇒ 00:04:37.839 Luke Daque: Yeah, sure.
57 00:04:37.840 ⇒ 00:04:39.340 Amber Lin: Yeah, I will.
58 00:04:42.820 ⇒ 00:04:45.610 Amber Lin: I send it to you in DM, as well.
59 00:04:47.630 ⇒ 00:04:49.949 Amber Lin: So let’s go figure that out.
60 00:05:14.610 ⇒ 00:05:18.120 Amber Lin: Were you able to get on the figma, Doc, the fig jam.
61 00:05:18.808 ⇒ 00:05:20.600 Luke Daque: Still looking for a ticket.
62 00:05:22.129 ⇒ 00:05:26.019 Amber Lin: I sent it to our direct message as well. Just sent it to.
63 00:05:26.020 ⇒ 00:05:26.960 Luke Daque: Okay. Yeah.
64 00:05:28.960 ⇒ 00:05:30.339 Luke Daque: Yep. I meant.
65 00:05:30.780 ⇒ 00:05:35.970 Amber Lin: Okay, so new diagram on the bottom that makes more sense.
66 00:05:36.732 ⇒ 00:05:43.950 Amber Lin: So what part doesn’t makes doesn’t make sense. Yet a note.
67 00:05:44.330 ⇒ 00:05:45.899 Luke Daque: Yeah, this is.
68 00:05:46.380 ⇒ 00:05:54.809 Luke Daque: yeah, I think this looks fine. So this is based, the basically, what I’m working right now is the Hey reach post interaction to.
69 00:05:55.590 ⇒ 00:05:56.630 Amber Lin: Is this?
70 00:05:56.630 ⇒ 00:06:02.519 Luke Daque: Just like this is the very 1st step. And like, we can’t enrich any data if we don’t have
71 00:06:02.670 ⇒ 00:06:07.329 Luke Daque: data. So yeah, as long as so that’s what I’m trying to figure out. Like.
72 00:06:07.960 ⇒ 00:06:12.369 Luke Daque: be able to ingest data or like integrate data from.
73 00:06:12.370 ⇒ 00:06:14.040 Amber Lin: Oh, I see. Okay.
74 00:06:16.650 ⇒ 00:06:24.919 Amber Lin: we have this manual export. So I’m gonna give it a little. Oh, wow! That’s a very small thumbs up.
75 00:06:25.190 ⇒ 00:06:26.729 Amber Lin: Can that be bigger?
76 00:06:28.310 ⇒ 00:06:30.480 Amber Lin: So this is good, this part.
77 00:06:31.530 ⇒ 00:06:33.259 Luke Daque: It’s quite good.
78 00:06:36.180 ⇒ 00:06:42.070 Amber Lin: And is this enrichment to Apollo? Good.
79 00:06:42.981 ⇒ 00:06:54.229 Luke Daque: We all I did did some tests on the enrichment part, and I don’t think Apollo is the best way to do it. I was able to find a different way. But it
80 00:06:55.088 ⇒ 00:07:03.809 Luke Daque: it’s this. It’s just similar to what I shared last time. It was like a waterfall of 5 or 6 different ways to enrich
81 00:07:03.930 ⇒ 00:07:12.900 Luke Daque: data like the Linkedin URL, and like the company and stuff like that for all the emails.
82 00:07:13.130 ⇒ 00:07:16.400 Luke Daque: So we should, I think we should be fine with that.
83 00:07:16.780 ⇒ 00:07:23.769 Luke Daque: So yeah, should be fine. That step, although we might need to.
84 00:07:25.610 ⇒ 00:07:30.869 Luke Daque: Well, we can just monitor like how much we are spending on that just like every row would be.
85 00:07:31.310 ⇒ 00:07:38.289 Luke Daque: But there’s like cost associated with enrichment or enriching rules.
86 00:07:39.450 ⇒ 00:07:47.260 Amber Lin: I see sounds good. So this is something else we wanna do. But I can mark that little bar is done too.
87 00:07:47.540 ⇒ 00:07:48.800 Luke Daque: Sure. Yeah.
88 00:07:49.330 ⇒ 00:07:53.119 Luke Daque: Is there anybody like Marion and Ryan joining this.
89 00:07:53.873 ⇒ 00:07:59.106 Amber Lin: I don’t know they were invited, but I don’t know if they’re gonna join.
90 00:07:59.480 ⇒ 00:08:00.339 Luke Daque: So did they accept.
91 00:08:00.340 ⇒ 00:08:05.400 Amber Lin: The only one doing this partly. What? How was she helping you?
92 00:08:06.030 ⇒ 00:08:08.459 Amber Lin: Was she helping you on this at all?
93 00:08:12.570 ⇒ 00:08:20.020 Luke Daque: I don’t know if there’s anything that Marion did after like we discussed about this enrichment part. Hi, Marian.
94 00:08:20.020 ⇒ 00:08:21.329 Amber Lin: Hi Marianne.
95 00:08:21.330 ⇒ 00:08:23.910 Mariane Cequina: I am so sorry I forgot that we have.
96 00:08:23.910 ⇒ 00:08:24.390 Amber Lin: Oh!
97 00:08:24.390 ⇒ 00:08:24.920 Amber Lin: Oh! Every day.
98 00:08:25.330 ⇒ 00:08:25.804 Mariane Cequina: There!
99 00:08:26.280 ⇒ 00:08:32.599 Amber Lin: I should have reminded you. Can you see my screen? We’re trying to figure out what we still need on the whole process.
100 00:08:32.870 ⇒ 00:08:33.720 Mariane Cequina: Okay.
101 00:08:34.020 ⇒ 00:08:34.690 Amber Lin: Yeah.
102 00:08:34.690 ⇒ 00:08:36.020 Mariane Cequina: Yeah, yeah, I can see it.
103 00:08:36.417 ⇒ 00:08:48.729 Amber Lin: Do you know, if, were you able to? Were you working on the clay enrichment? I just wanna make sure that we’re not working on the same, like duplicating our efforts.
104 00:08:49.210 ⇒ 00:08:56.050 Mariane Cequina: Yeah, yeah. Okay, actually, I need. There’s a learning curve for me in that enrichment
105 00:08:56.330 ⇒ 00:09:04.210 Mariane Cequina: leads. So with them actually sent a message in this, he said, that we actually have Apollo account.
106 00:09:05.260 ⇒ 00:09:11.220 Mariane Cequina: Yeah, he, he told me, like Apollo should be working. So should I try with Apollo first, st or
107 00:09:11.950 ⇒ 00:09:13.830 Mariane Cequina: directly to cloud.
108 00:09:13.830 ⇒ 00:09:29.360 Amber Lin: How do you guys wanna distribute like, how do you want to divide work? Because I just don’t want us to duplicate what we’re like. Because, Duke, you said, this is this should be working like, if we have. If we insert lead lead list.
109 00:09:29.620 ⇒ 00:09:34.790 Amber Lin: it gets enrich, enriched. Is that like? Is this process working already.
110 00:09:35.620 ⇒ 00:09:42.830 Luke Daque: Just based on the test that I did, which is like the 22 or 23 rows manual export.
111 00:09:43.565 ⇒ 00:09:47.129 Luke Daque: It should be working pretty fine, although not everything
112 00:09:47.440 ⇒ 00:09:50.919 Luke Daque: got enriched, but I I think we have most of them enriched.
113 00:09:51.670 ⇒ 00:09:55.280 Luke Daque: But it’s not using Apollo, though, because I did test Apollo.
114 00:09:55.280 ⇒ 00:09:56.030 Amber Lin: I see.
115 00:09:56.030 ⇒ 00:09:56.710 Luke Daque: In clay.
116 00:09:56.710 ⇒ 00:09:59.949 Amber Lin: We don’t need a polo. It’s fine like if we don’t need it.
117 00:10:00.590 ⇒ 00:10:09.039 Amber Lin: do we? Do we send that result somewhere because I would love for Robert to review and give us some feedback.
118 00:10:09.750 ⇒ 00:10:10.290 Amber Lin: I don’t.
119 00:10:10.290 ⇒ 00:10:12.629 Luke Daque: I think we sent it anywhere, but we can.
120 00:10:13.050 ⇒ 00:10:13.970 Amber Lin: Okay, we can.
121 00:10:13.970 ⇒ 00:10:17.940 Amber Lin: You wanted to send that now in the channel of link to the play table.
122 00:10:18.430 ⇒ 00:10:19.340 Luke Daque: Sure.
123 00:10:19.550 ⇒ 00:10:20.160 Amber Lin: Yeah.
124 00:10:20.660 ⇒ 00:10:24.770 Amber Lin: But just to yeah, let before I send, maybe I can share my screen so we can.
125 00:10:25.275 ⇒ 00:10:25.780 Amber Lin: Awesome.
126 00:10:26.030 ⇒ 00:10:27.559 Luke Daque: Let me see if this is working.
127 00:10:27.560 ⇒ 00:10:28.799 Luke Daque: Let’s look at it.
128 00:10:29.900 ⇒ 00:10:31.410 Mariane Cequina: Yeah, I also would like to.
129 00:10:31.530 ⇒ 00:10:32.210 Mariane Cequina: Oh.
130 00:10:32.550 ⇒ 00:10:33.210 Amber Lin: Oomph.
131 00:10:33.810 ⇒ 00:10:35.090 Mariane Cequina: Because it’s interesting.
132 00:10:36.220 ⇒ 00:10:36.730 Amber Lin: Yeah.
133 00:10:37.036 ⇒ 00:10:38.260 Luke Daque: You see my screen.
134 00:10:38.610 ⇒ 00:10:39.869 Mariane Cequina: Yeah, we can see it.
135 00:10:39.870 ⇒ 00:10:41.910 Amber Lin: Yes, yes, I can see it.
136 00:10:43.520 ⇒ 00:10:50.969 Luke Daque: So these are the names and emails that were manually imported. So basically, the list that.
137 00:10:52.089 ⇒ 00:10:53.539 Luke Daque: Robert sent in the chat.
138 00:10:53.540 ⇒ 00:10:54.150 Amber Lin: Okay.
139 00:10:54.380 ⇒ 00:10:58.350 Luke Daque: And then. So I tried different ways to enrich the data.
140 00:10:59.449 ⇒ 00:11:03.840 Luke Daque: I. This is Apollo, this part like from from this to
141 00:11:04.120 ⇒ 00:11:16.610 Luke Daque: everything. So I tried in enriching, like full name, Linkedin Company, domain, location, title and everything else. Just using Apollo, and you can see it if if we.
142 00:11:16.610 ⇒ 00:11:18.000 Amber Lin: Didn’t feel much.
143 00:11:19.158 ⇒ 00:11:21.350 Luke Daque: If you edit this.
144 00:11:21.710 ⇒ 00:11:28.550 Luke Daque: Wait. How do I? Oh, yeah, you can see it here. It’s oh, no, this is not a follow. This is.
145 00:11:28.550 ⇒ 00:11:29.160 Amber Lin: I see.
146 00:11:29.160 ⇒ 00:11:31.300 Luke Daque: Clear bit. This is clear. Bit. Yeah.
147 00:11:31.460 ⇒ 00:11:31.860 Amber Lin: Okay.
148 00:11:31.860 ⇒ 00:11:35.240 Luke Daque: I tried using clear bit. And this is like what
149 00:11:35.460 ⇒ 00:11:37.689 Luke Daque: it was able to do. Basically, I just.
150 00:11:37.690 ⇒ 00:11:38.170 Amber Lin: Oh!
151 00:11:38.170 ⇒ 00:11:45.240 Luke Daque: Run, run it. Yeah. So it’s basically you can see here that the input is the work email.
152 00:11:45.340 ⇒ 00:11:47.600 Luke Daque: So it’s going to look for the
153 00:11:48.190 ⇒ 00:11:52.040 Luke Daque: it. Look for all the details like the full name. And Linkedin based on the
154 00:11:52.700 ⇒ 00:11:55.569 Luke Daque: work email. Which is this one over here?
155 00:11:56.430 ⇒ 00:11:57.290 Luke Daque: Right?
156 00:11:57.590 ⇒ 00:12:00.689 Luke Daque: It’s going to look for that, using clear bit.
157 00:12:01.190 ⇒ 00:12:04.500 Luke Daque: And, as you can see, it’s not that good.
158 00:12:04.720 ⇒ 00:12:08.060 Luke Daque: It only was able to find 3
159 00:12:08.180 ⇒ 00:12:13.300 Luke Daque: Linkedin Urls out of the 23 emails. And then, like.
160 00:12:13.300 ⇒ 00:12:13.950 Amber Lin: 18 months.
161 00:12:13.950 ⇒ 00:12:18.150 Luke Daque: Couple of full names and a couple of company related stuff.
162 00:12:18.260 ⇒ 00:12:25.769 Luke Daque: So it’s not really that impressive. And it’s pretty expensive in terms of tokens. It’s like 8 tokens per
163 00:12:26.610 ⇒ 00:12:29.789 Luke Daque: per row, and we only have
164 00:12:32.326 ⇒ 00:12:36.799 Luke Daque: like 10 k tokens right for our plan basically per month.
165 00:12:38.580 ⇒ 00:12:43.940 Luke Daque: So the more leads, we have the more tokens we’re gonna use. Basically.
166 00:12:43.940 ⇒ 00:12:45.220 Amber Lin: Yeah. Totally.
167 00:12:45.220 ⇒ 00:12:53.550 Luke Daque: So, yeah, so I tried a different way, like although this is like for each individual field or column.
168 00:12:53.710 ⇒ 00:13:01.210 Luke Daque: like for the Linkedin profile. For example, I tried this. Basically waterfall
169 00:13:01.440 ⇒ 00:13:08.607 Luke Daque: waterfalls just basically just means it’s gonna use. Yeah, different sources, like, oh,
170 00:13:09.150 ⇒ 00:13:09.890 Amber Lin: I’m sorry.
171 00:13:10.510 ⇒ 00:13:18.990 Amber Lin: so every time one of them don’t. Even if we don’t find any data from one of these waterfall steps, do we still get charged.
172 00:13:23.670 ⇒ 00:13:28.250 Amber Lin: It’s okay. If you don’t know, it’ll we’ll yeah. I don’t think
173 00:13:28.250 ⇒ 00:13:32.009 Amber Lin: like they can worry about the cost. Once we get this working.
174 00:13:32.390 ⇒ 00:13:33.010 Luke Daque: Yeah.
175 00:13:33.290 ⇒ 00:13:44.410 Luke Daque: so it’s basically, yeah, it’s waterfall. So it uses this first, st like companies, people jobs. I’m not sure what exactly this is. And like you can see here. It’s like, only solve for.
176 00:13:45.560 ⇒ 00:13:53.370 Luke Daque: Cool but for the like, once it gets these 4 it doesn’t like run the other than.
177 00:13:53.370 ⇒ 00:13:54.900 Amber Lin: Oh, okay.
178 00:13:54.900 ⇒ 00:14:01.859 Luke Daque: Protocols, but if it doesn’t find anything it runs the next one, and then, if it doesn’t, it runs the next one until it finds something.
179 00:14:02.090 ⇒ 00:14:04.200 Luke Daque: So with this.
180 00:14:04.200 ⇒ 00:14:05.440 Amber Lin: Hi Robert!
181 00:14:06.380 ⇒ 00:14:07.040 Robert Tseng: Hello!
182 00:14:07.040 ⇒ 00:14:07.950 Luke Daque: Yeah, everyone.
183 00:14:09.780 ⇒ 00:14:14.100 Luke Daque: So yeah, basically with this, we were able to find at least like
184 00:14:14.550 ⇒ 00:14:28.750 Luke Daque: a lot more more than like the 3 year bit that we tried in terms of like Linkedin URL, like, I guess, like 2, 3, 4, 5, 6, 7. So around like 18 out of the 23 or something.
185 00:14:29.138 ⇒ 00:14:45.050 Amber Lin: I see. Robert, my question cause Robert you mentioned it might be easier to go from Linkedin URL to their email. But my question is that, do we? Can we have their Linkedin URL from the start, like, how does.
186 00:14:45.380 ⇒ 00:14:47.490 Robert Tseng: How do you think that should work?
187 00:14:48.140 ⇒ 00:14:54.839 Robert Tseng: Yeah. Well, I guess, like the used case that I had described before was like from Linkedin.
188 00:14:55.300 ⇒ 00:14:56.390 Luke Daque: You’re.
189 00:14:56.840 ⇒ 00:15:11.570 Robert Tseng: You know I’m I’m sure you’ve all seen like those Linkedin posts that are just like comment for whatever like this thing, all those everyone who comments get. I’m sure they all get pulled into a lead list. And they all get hit with emails like, I think that’s pretty much what happens?
190 00:15:12.236 ⇒ 00:15:14.180 Robert Tseng: So I mean, that’s
191 00:15:14.390 ⇒ 00:15:24.499 Robert Tseng: I feel like that’s more likely. I I only got this email list because I actually attended this event. And I asked the organizer to send me
192 00:15:24.700 ⇒ 00:15:29.870 Robert Tseng: the list of emails which, if I already get that, then maybe
193 00:15:32.700 ⇒ 00:15:44.289 Robert Tseng: I mean sounds like it doesn’t take very long. I mean, it’s 20 people like I I could have just like copy pasted their names on Linkedin, and probably found them like I like I I would not.
194 00:15:44.610 ⇒ 00:15:46.630 Luke Daque: I wouldn’t do, I would not do.
195 00:15:46.960 ⇒ 00:15:51.720 Robert Tseng: I don’t know. I don’t know how much 8 tokens is per per person, but seems like I don’t know.
196 00:15:51.720 ⇒ 00:16:07.359 Amber Lin: Yeah, last time I also had to did some scraping for like Linkedin profiles. Sometimes it’s like a Google search. If we have their exact names, and you kind of know what the company they’re working for. You usually can just
197 00:16:07.460 ⇒ 00:16:15.279 Amber Lin: like Batch, search those. I I use a random python script, I think, using one of the Google Apis.
198 00:16:15.900 ⇒ 00:16:19.380 Amber Lin: and then it returned like a
199 00:16:19.960 ⇒ 00:16:27.490 Amber Lin: list of their, the Urls of their Linkedin profiles name.
200 00:16:27.490 ⇒ 00:16:36.990 Robert Tseng: Yeah, I think usually, if you just do like name and their organization, which is probably in the email, then you could probably find the Linkedin. That’d be my guess.
201 00:16:37.830 ⇒ 00:16:38.720 Amber Lin: Yeah.
202 00:16:42.310 ⇒ 00:16:47.440 Robert Tseng: And I’m I’m confused. I thought we were just using Apollo. I know we’re testing a bunch of and things, and
203 00:16:48.010 ⇒ 00:16:49.540 Robert Tseng: I don’t know like I.
204 00:16:51.110 ⇒ 00:16:55.109 Amber Lin: Luke said, Apollo doesn’t work that well.
205 00:16:55.520 ⇒ 00:16:56.070 Luke Daque: I see.
206 00:16:56.070 ⇒ 00:16:59.289 Luke Daque: Yeah, I think that’s my bad. I was actually
207 00:16:59.921 ⇒ 00:17:04.119 Luke Daque: using clearbit, not Apollo. And it was clear. But that wasn’t.
208 00:17:05.069 ⇒ 00:17:07.429 Amber Lin: Oh, okay. I see. Okay.
209 00:17:07.740 ⇒ 00:17:10.080 Luke Daque: I can try a follow up right now.
210 00:17:10.380 ⇒ 00:17:12.890 Luke Daque: for, like the 1st 10 rows and see what we can.
211 00:17:12.890 ⇒ 00:17:14.069 Luke Daque: Sure. Okay.
212 00:17:14.550 ⇒ 00:17:19.770 Amber Lin: Let’s try a polo, and then.
213 00:17:24.380 ⇒ 00:17:28.439 Amber Lin: were you? I’ll let you test that, and then we’ll see
214 00:17:32.748 ⇒ 00:17:35.610 Amber Lin: Marian. How was the progress with Hubspot?
215 00:17:38.210 ⇒ 00:17:41.210 Mariane Cequina: That integrating, because because the 1st
216 00:17:42.261 ⇒ 00:17:48.580 Mariane Cequina: initial plan for this is to enrichment right? And then, once it’s enriched, it will go to Hubspot.
217 00:17:50.830 ⇒ 00:17:57.950 Amber Lin: So essentially, hubspot and clay kind of runs they’re like
218 00:17:58.180 ⇒ 00:18:10.210 Amber Lin: in parallel. So club spot is where we have all our lead data, and sometimes we’ll have a list that will feed into clay. It gets enriched, and then it gets sent back to Hubspot. So
219 00:18:10.410 ⇒ 00:18:30.449 Amber Lin: how we send it back and forth. We’re probably gonna use a web hook and custom. Api. But I guess my questions was more of. Are you familiar with Hubspot now like, has it been enough time for you to get to know what this application does like? What, what, how do you navigate it like? How’s that progress.
220 00:18:30.980 ⇒ 00:18:46.669 Mariane Cequina: Yeah, it’s actually, it’s actually easy to use. But in terms of integrating it with lay, I’d say that we need a I think I think we talked about it last time that we need a higher subscription right.
221 00:18:47.880 ⇒ 00:18:49.010 Amber Lin: Or Hubspot.
222 00:18:49.630 ⇒ 00:18:56.679 Mariane Cequina: I mean for the clay, once we integrate it but one. I actually tried using the hubspot. It’s actually pretty easy to use.
223 00:18:56.680 ⇒ 00:19:09.620 Amber Lin: Okay, awesome. So you know how Hubspot works. I think for the integration, we’re gonna use a custom. Api, that doesn’t. It’s not a native play integration. So that would be
224 00:19:10.050 ⇒ 00:19:13.559 Amber Lin: getting the Api from Hubspot and then
225 00:19:13.800 ⇒ 00:19:21.849 Amber Lin: running a script or a web hook trigger that runs it. So we don’t have to upgrade to that like premium play plan.
226 00:19:22.050 ⇒ 00:19:22.740 Mariane Cequina: Okay.
227 00:19:23.090 ⇒ 00:19:24.010 Amber Lin: Yeah.
228 00:19:26.450 ⇒ 00:19:29.869 Luke Daque: So just to make sure, I understand.
229 00:19:30.130 ⇒ 00:19:37.099 Luke Daque: I understand there’s like 3 different sources that we have, right, like one would be directly from Linkedin
230 00:19:37.290 ⇒ 00:19:44.809 Luke Daque: comments and stuff events, and then another one that’s like manual, and then another one that’s coming from Hubspot right?
231 00:19:45.632 ⇒ 00:20:02.960 Amber Lin: Think, when Hubspot sends to sends to clay, it’ll also be in probably like a excel form. But we just need to figure out what the pathway to send it is. We figure out like the web, and back and forth would work.
232 00:20:03.960 ⇒ 00:20:10.989 Luke Daque: Yeah, if it’s coming from the Api, it’s probably gonna be in Json format or something. But that should be fine. It should still be
233 00:20:11.480 ⇒ 00:20:14.120 Luke Daque: showing us a table in clay, basically.
234 00:20:15.645 ⇒ 00:20:22.690 Amber Lin: So let’s say, for boy.
235 00:20:22.850 ⇒ 00:20:25.380 Amber Lin: Did Apollo work. Were you.
236 00:20:25.380 ⇒ 00:20:31.930 Luke Daque: Yeah, this is, it looks pretty good good, like, like most of the people also have, like all the details here. So.
237 00:20:32.370 ⇒ 00:20:33.720 Amber Lin: Oh, awesome!
238 00:20:33.720 ⇒ 00:20:38.280 Luke Daque: Yeah, there’s still like a couple that we weren’t able to find it wasn’t able to find. So.
239 00:20:38.280 ⇒ 00:20:41.809 Amber Lin: That’s a pretty good rate, like we have most of them.
240 00:20:42.558 ⇒ 00:20:47.190 Amber Lin: Okay, great. Let’s I’m gonna say, that’s good.
241 00:20:50.520 ⇒ 00:20:53.539 Robert Tseng: The token cost for that. I think it’s fine. Yeah.
242 00:20:53.940 ⇒ 00:21:01.660 Luke Daque: Doesn’t look like there’s anything like we’ve mentioned the token cost for some reason, so.
243 00:21:01.660 ⇒ 00:21:05.239 Amber Lin: Is it just? Is it connected to our Apollo account.
244 00:21:05.680 ⇒ 00:21:06.490 Luke Daque: Maybe that’s.
245 00:21:06.490 ⇒ 00:21:07.420 Amber Lin: Maybe it doesn’t give.
246 00:21:07.420 ⇒ 00:21:08.279 Robert Tseng: Yeah, that’s probably why.
247 00:21:08.690 ⇒ 00:21:09.669 Luke Daque: Yeah, that’s probably.
248 00:21:09.670 ⇒ 00:21:10.859 Amber Lin: This is our polo.
249 00:21:18.390 ⇒ 00:21:22.249 Luke Daque: Connections. Yeah, you were connected to Apollo. So maybe that’s why.
250 00:21:22.840 ⇒ 00:21:26.789 Amber Lin: Oh, okay, so we should check how much it costs, not polo.
251 00:21:28.500 ⇒ 00:21:30.730 Amber Lin: Check crossed.
252 00:21:40.590 ⇒ 00:21:46.379 Luke Daque: But yeah, it looks pretty good. I guess, for this. The ones that don’t have anything.
253 00:21:47.670 ⇒ 00:21:50.410 Amber Lin: Then we can run maybe another waterfall.
254 00:21:50.740 ⇒ 00:21:51.340 Luke Daque: How does?
255 00:21:51.340 ⇒ 00:21:51.870 Luke Daque: Yeah.
256 00:21:52.453 ⇒ 00:21:56.540 Amber Lin: Does the other method gets get those.
257 00:21:58.090 ⇒ 00:22:05.600 Luke Daque: Like, for this guy wasn’t able to get the Linking URL, but the waterfall was able to.
258 00:22:05.600 ⇒ 00:22:09.359 Amber Lin: Okay, what step in the waterfall got it? Okay? It was.
259 00:22:09.360 ⇒ 00:22:10.373 Luke Daque: This thing
260 00:22:10.880 ⇒ 00:22:11.970 Amber Lin: Great.
261 00:22:12.150 ⇒ 00:22:18.310 Amber Lin: What about for the other ones? Cause it seems like the 1st doesn’t get it get most anyways.
262 00:22:18.420 ⇒ 00:22:21.840 Amber Lin: So it seems like it’s like a.
263 00:22:22.790 ⇒ 00:22:26.750 Luke Daque: I think it’s this one margin.
264 00:22:26.980 ⇒ 00:22:32.701 Robert Tseng: I would say, it’s okay if we don’t get 100, like, I think Apollo gets us. What like 60% of the way there
265 00:22:32.930 ⇒ 00:22:33.480 Luke Daque: Oh, man!
266 00:22:33.480 ⇒ 00:22:37.489 Robert Tseng: I don’t know. We just have to have an acceptance criteria, and we can move on to the next step.
267 00:22:37.720 ⇒ 00:22:40.150 Robert Tseng: We can work about enriching it more later.
268 00:22:40.706 ⇒ 00:22:46.130 Amber Lin: I would say 60% is good enough, like, I think this kind of a this is a proof of concept. So
269 00:22:46.740 ⇒ 00:22:47.190 Amber Lin: all right.
270 00:22:47.190 ⇒ 00:22:47.620 Luke Daque: Nice.
271 00:22:47.620 ⇒ 00:23:04.130 Amber Lin: So let’s figure out the enrichment part we can consider done for now. My question is more of, if we had another Csv list. This would work again right? So this is a replicable process.
272 00:23:04.866 ⇒ 00:23:12.830 Luke Daque: Yeah, we just have to press this play button. I think there’s a way to automate this or some. I think I read that somewhere.
273 00:23:13.700 ⇒ 00:23:17.269 Amber Lin: So essentially to get our minimal viable product.
274 00:23:17.906 ⇒ 00:23:27.940 Amber Lin: We just need to get a list of people. So we’re now trying to figure out, how are we gonna get that list of people other than manual exports?
275 00:23:27.940 ⇒ 00:23:30.657 Robert Tseng: Yeah. So we’ve got the Csv use case down.
276 00:23:30.960 ⇒ 00:23:31.630 Amber Lin: Yeah. Getting the.
277 00:23:31.630 ⇒ 00:23:35.709 Robert Tseng: But yeah, working through the Linkedin use case, I think, would be more interesting.
278 00:23:35.710 ⇒ 00:23:36.280 Amber Lin: Okay.
279 00:23:36.774 ⇒ 00:23:50.580 Amber Lin: for Linkedin. Right, Luke, how is the cause? We we know that we want to get, say, from a Linkedin post we use. Hey, reach? Right? Are we set on using? Hey, reach to get all the interaction data.
280 00:23:50.920 ⇒ 00:23:54.027 Luke Daque: Yeah, that’s what I’m still trying to figure out, because, like
281 00:23:54.940 ⇒ 00:24:00.219 Luke Daque: it looks like hair reach is like the other way around. It’s like from here reach to
282 00:24:00.550 ⇒ 00:24:05.429 Luke Daque: Linkedin cause like most of their Api. It’s like Post Api. So look at.
283 00:24:05.910 ⇒ 00:24:07.860 Luke Daque: So I think I’ll have to.
284 00:24:08.140 ⇒ 00:24:12.219 Luke Daque: I’m trying to find a different way, maybe through a web hook using a different
285 00:24:12.897 ⇒ 00:24:16.830 Luke Daque: or maybe Zapier, because we already have Zapier. But it will be
286 00:24:17.540 ⇒ 00:24:21.100 Luke Daque: might be using Linkedin’s Api itself. Oh.
287 00:24:21.100 ⇒ 00:24:46.410 Amber Lin: Yeah, my question is, say, we have a Linkedin post, right? Even if I click into a Linkedin post and I copy and paste like, control a, and get the whole content. We already have the content, like the content of the post we have, the people commented like their exact names on Linkedin. Do we need, hey? Reach? It was just something that tried to mitigate me. Do we even need, hey reach? Can we like sparse the.
288 00:24:46.660 ⇒ 00:24:47.130 Luke Daque: That’s.
289 00:24:47.130 ⇒ 00:24:48.510 Amber Lin: Copy and paste.
290 00:24:49.640 ⇒ 00:24:54.749 Luke Daque: I’m not sure if he reach, we can use her reach if using that, because
291 00:24:55.290 ⇒ 00:24:57.409 Amber Lin: You don’t have to use. Every like. These are just.
292 00:24:57.410 ⇒ 00:25:03.209 Luke Daque: We’ll have to use something else. Basically, that’s what I’m saying, because I don’t think we can use hey, reach
293 00:25:03.420 ⇒ 00:25:07.250 Luke Daque: from Linkedin to play, use and hearing.
294 00:25:08.620 ⇒ 00:25:15.649 Amber Lin: What if? What if, say I open a random Linkedin post? I copy all the content.
295 00:25:16.431 ⇒ 00:25:27.940 Amber Lin: That maybe includes the the links to their profiles, cause that that’s like everybody’s name is click is a clickable link, and then I send it to AI.
296 00:25:28.380 ⇒ 00:25:33.599 Amber Lin: Make it make a list for me, and then we get the same situation as the Csv.
297 00:25:33.930 ⇒ 00:25:35.319 Amber Lin: Do you think that would work.
298 00:25:37.550 ⇒ 00:25:44.589 Luke Daque: Yeah. But that’s that’s manual, a manual process that would probably fall into this bucket like the manual.
299 00:25:44.590 ⇒ 00:25:56.919 Amber Lin: If we, if it works right, if it works, we’ll just we’ll just like move that arrow into manual well, and then we’ll figure out how to automate that with maybe like an an I don’t know.
300 00:25:58.030 ⇒ 00:26:12.360 Luke Daque: Yeah, so basically, based just based on what I research, we can use Linkedin’s Api to get the comment. The events for a post like comments, likes and stuff like that to get the data. But yeah, that’s it.
301 00:26:13.240 ⇒ 00:26:18.970 Luke Daque: Yeah, that’s probably what we’re gonna do. If we need to automate it basically.
302 00:26:21.240 ⇒ 00:26:29.780 Amber Lin: Copy, and it’s call and make and make a list.
303 00:26:30.000 ⇒ 00:26:31.040 Amber Lin: Or
304 00:26:31.840 ⇒ 00:26:48.939 Amber Lin: I guess let’s just let’s do it manually for like the 1st run, and then let’s ask let’s ask Casey or Miguel how we can automate this source list.
305 00:26:48.940 ⇒ 00:26:49.320 Luke Daque: So.
306 00:26:49.320 ⇒ 00:26:51.050 Amber Lin: Play in an a n.
307 00:26:51.290 ⇒ 00:26:57.669 Luke Daque: So just to make sure I understand you. So like we go Tom’s profile, for example, and like
308 00:26:58.040 ⇒ 00:27:03.589 Luke Daque: look at all the posts that he made, for example, and then just copy paste in
309 00:27:05.200 ⇒ 00:27:10.570 Luke Daque: any like comments or or stuff from a specific.
310 00:27:10.837 ⇒ 00:27:15.119 Amber Lin: Let’s click on one of the posts. Yeah, let’s just click on the post that has
311 00:27:15.782 ⇒ 00:27:22.440 Amber Lin: maybe a few comments yeah.
312 00:27:24.870 ⇒ 00:27:26.700 Luke Daque: Yeah, so this one, there’s like.
313 00:27:27.010 ⇒ 00:27:27.350 Amber Lin: Okay.
314 00:27:27.350 ⇒ 00:27:28.199 Luke Daque: So I’d like.
315 00:27:28.720 ⇒ 00:27:30.260 Amber Lin: So.
316 00:27:30.260 ⇒ 00:27:31.710 Luke Daque: You’re saying, just like copy this.
317 00:27:32.972 ⇒ 00:27:37.560 Amber Lin: Let’s can you click on the post like, go into the post page.
318 00:27:39.571 ⇒ 00:27:46.240 Amber Lin: Not his profile. His like a post has. You can open up a post on a separate page, too.
319 00:27:47.110 ⇒ 00:27:47.690 Amber Lin: Okay.
320 00:27:47.690 ⇒ 00:27:48.300 Luke Daque: Exactly.
321 00:27:48.300 ⇒ 00:27:49.690 Amber Lin: It’s slow.
322 00:27:51.742 ⇒ 00:27:57.050 Amber Lin: Let me check. Let me open my linkedin. I’ll figure that out. Let me show you guys
323 00:27:57.820 ⇒ 00:28:03.090 Amber Lin: so I’ll share screen as well.
324 00:28:04.140 ⇒ 00:28:04.720 Luke Daque: Excuse me.
325 00:28:04.720 ⇒ 00:28:05.739 Amber Lin: Oh, from them.
326 00:28:05.740 ⇒ 00:28:08.490 Luke Daque: So we can copy the reactions as well. I guess.
327 00:28:09.320 ⇒ 00:28:09.840 Amber Lin: Oh!
328 00:28:09.840 ⇒ 00:28:10.669 Luke Daque: That’s what she.
329 00:28:10.670 ⇒ 00:28:14.739 Amber Lin: Alright. I I wasn’t. I wasn’t looking at. Let me go! Check what you were showing.
330 00:28:17.370 ⇒ 00:28:18.760 Amber Lin: Where is it?
331 00:28:20.180 ⇒ 00:28:25.539 Amber Lin: Oh, oh, dang! I have to run soon.
332 00:28:26.900 ⇒ 00:28:28.360 Amber Lin: Yeah.
333 00:28:28.360 ⇒ 00:28:29.930 Robert Tseng: Yeah, I’m gonna I’m gonna jump off.
334 00:28:30.150 ⇒ 00:28:34.009 Amber Lin: Yeah, great. So this this works right?
335 00:28:34.750 ⇒ 00:28:40.400 Amber Lin: Like, if we copy that, then you can open there like, if you click on one.
336 00:28:40.650 ⇒ 00:28:42.789 Amber Lin: you go to their profile right.
337 00:28:43.320 ⇒ 00:28:44.030 Luke Daque: Yeah.
338 00:28:44.030 ⇒ 00:28:45.500 Amber Lin: Yeah, that’s all we need.
339 00:28:45.780 ⇒ 00:28:47.399 Amber Lin: That’s all we need.
340 00:28:47.920 ⇒ 00:28:57.480 Luke Daque: Yeah, we can do that. But you’ll have to do that like for each post. We’ll probably have to copy it all the comments and stuff every like once in a while, because there might be new
341 00:28:57.630 ⇒ 00:28:59.810 Luke Daque: people that light.
342 00:28:59.810 ⇒ 00:29:08.339 Amber Lin: And then we figure, then we can go figure out the automation, know how to do that. But now we know that we can just copy and paste, and they’ll give us the links.
343 00:29:08.340 ⇒ 00:29:10.501 Luke Daque: Yeah. And that’s what I was like,
344 00:29:11.814 ⇒ 00:29:15.909 Luke Daque: looking through like the Linkedin Api, so we can
345 00:29:16.420 ⇒ 00:29:18.880 Luke Daque: yeah, get like effects and stuff.
346 00:29:19.550 ⇒ 00:29:20.310 Amber Lin: Okay.
347 00:29:21.430 ⇒ 00:29:28.020 Mariane Cequina: Also would like to add, just in case in clay, that we can actually use the score.
348 00:29:28.440 ⇒ 00:29:28.980 Luke Daque: All right.
349 00:29:28.980 ⇒ 00:29:43.519 Mariane Cequina: Yeah, so we can automatically prioritize contacts. As far as I know, I don’t know much about it, but I think I’ve read that we, it could like the contacts update could be pulled from Linkedin, for example, how like
350 00:29:44.160 ⇒ 00:29:47.150 Mariane Cequina: like the changes like, it’s more like the interaction
351 00:29:47.660 ⇒ 00:29:52.289 Mariane Cequina: smart like that. So we will know how to prioritize something.
352 00:29:53.280 ⇒ 00:29:56.219 Mariane Cequina: So I think it’s important to explore that one as well.
353 00:29:56.470 ⇒ 00:30:00.309 Amber Lin: Would you like to ex take on that and explore that.
354 00:30:01.020 ⇒ 00:30:02.430 Mariane Cequina: Okay, I’ll try to do that.
355 00:30:02.740 ⇒ 00:30:07.949 Amber Lin: Why don’t we find one of our let’s find one of our webinar
356 00:30:08.390 ⇒ 00:30:23.219 Amber Lin: post that has a lot of like comments from other people. And then let’s make a playlist based off that. And then Marianne can explore. Hey? How do we prioritize different contents con comments based on the
357 00:30:23.660 ⇒ 00:30:26.440 Amber Lin: based on like scoring and stuff.
358 00:30:27.220 ⇒ 00:30:27.940 Mariane Cequina: Okay.
359 00:30:31.360 ⇒ 00:30:34.320 Amber Lin: Okay, yeah. Okay, this is great.
360 00:30:34.924 ⇒ 00:30:49.139 Amber Lin: Score comments. Yeah. Let Marianne let Luke know what you might need. Say, like, do you need the exact wording of the comments. Say, what? What is the exact thing that we need? And then we’ll figure out like
361 00:30:49.690 ⇒ 00:30:53.740 Amber Lin: what needs to go in that list that we feed into play.
362 00:30:54.180 ⇒ 00:30:56.530 Mariane Cequina: Okay, got it?
363 00:30:58.410 ⇒ 00:31:00.702 Amber Lin: Okay, bye, guys, I need to jump.
364 00:31:01.030 ⇒ 00:31:02.689 Luke Daque: Sounds good. See you, we’ll back.
365 00:31:02.690 ⇒ 00:31:03.089 Amber Lin: All right.
366 00:31:03.090 ⇒ 00:31:04.440 Mariane Cequina: Thank you. Bye-bye.