Meeting Title: Sales GTM | Standup Date: 2025-04-29 Meeting participants: Mariane Cequina, Luke Daque, Amber Lin, Ryan Brosas, Robert Tseng
WEBVTT
1 00:04:53.770 ⇒ 00:04:54.860 Amber Lin: Hi.
2 00:04:58.430 ⇒ 00:04:59.520 Luke Daque: Hello! Everyone.
3 00:05:00.590 ⇒ 00:05:11.050 Amber Lin: Hello, everyone! Let me open up our linear and check on our progress.
4 00:05:11.880 ⇒ 00:05:15.090 Amber Lin: So how was progress yesterday? Did everything go well?
5 00:05:22.730 ⇒ 00:05:23.900 Robert Tseng: Hey? Who are you talking to?
6 00:05:25.830 ⇒ 00:05:26.900 Robert Tseng: So it’s everyone.
7 00:05:26.900 ⇒ 00:05:34.579 Amber Lin: Yes, everyone I guess I’ll check one by one then. So, Marianne, how was the clay table.
8 00:05:37.530 ⇒ 00:05:43.540 Mariane Cequina: Oh, yeah, I actually send the link even in linear.
9 00:05:44.450 ⇒ 00:05:45.210 Amber Lin: Awesome.
10 00:05:45.210 ⇒ 00:05:55.849 Mariane Cequina: Oh, no, but I actually didn’t set up, though. There’s some sort of automations there or formula. It’s more in like the structure that I did. Yeah.
11 00:05:55.850 ⇒ 00:05:56.469 Amber Lin: That’s all we need.
12 00:05:56.470 ⇒ 00:05:57.190 Mariane Cequina: Okay.
13 00:05:59.275 ⇒ 00:06:09.170 Amber Lin: Let me. I’m gonna pull that up real quick and we can go look at it. Do you want to share screen? Because I still need to log in. I think you’re already logged in, so we’ll go look at that right.
14 00:06:11.070 ⇒ 00:06:11.790 Mariane Cequina: Okay.
15 00:06:15.060 ⇒ 00:06:15.680 Mariane Cequina: okay.
16 00:06:16.600 ⇒ 00:06:19.860 Mariane Cequina: Can you see it particularly evil?
17 00:06:20.360 ⇒ 00:06:24.170 Amber Lin: Loading for me. Yeah. Now I can see it.
18 00:06:24.760 ⇒ 00:06:52.960 Mariane Cequina: Okay, so this is what I did. For now, this is the 1st draft. So 1st is the imported imported profile, because I imagine it, that you guys are, of course, automating stuff. So here, imported profile, and that would there will be like a company name like the first, st like the full name, work, email, job, title, location. They can profile of that person, then scroll, row in clay. So this is actually like a.
19 00:06:54.610 ⇒ 00:07:01.420 Mariane Cequina: Like like this is something like it depends on what you’ve been looking for. If I
20 00:07:01.710 ⇒ 00:07:02.703 Mariane Cequina: if I
21 00:07:04.230 ⇒ 00:07:10.830 Mariane Cequina: remember correctly, it’s more like what? There’s like a score or something in clay where? Yeah.
22 00:07:11.540 ⇒ 00:07:33.390 Mariane Cequina: depending on what you need or what you’re looking for, something like that, and then we have the company domain, and then here will be like the automated for the comment, company summary, and then also company description, and then company Linkedin profile, and then industry country, and then the employee count.
23 00:07:33.880 ⇒ 00:07:35.050 Mariane Cequina: That’s what I have.
24 00:07:35.605 ⇒ 00:07:39.189 Amber Lin: Robert, what do you think is this what you’re looking for?
25 00:07:40.660 ⇒ 00:07:48.943 Robert Tseng: Respond on slide. I mean, I think it’s it’s a good start. I think that’s a good phase one. But yeah, I think that I? I called out that
26 00:07:49.830 ⇒ 00:07:55.580 Robert Tseng: yeah, like, we, we want to get to a point where it’s not just descriptions. These are just like static
27 00:07:55.970 ⇒ 00:07:58.649 Robert Tseng: deals we we want. I want. I want us like.
28 00:07:59.630 ⇒ 00:08:04.940 Robert Tseng: I want dynamic fields that are like able to be triggers. It’s like
29 00:08:05.220 ⇒ 00:08:09.730 Robert Tseng: this account had 5 new job openings on data in the past week.
30 00:08:09.870 ⇒ 00:08:26.089 Robert Tseng: That would be a trigger, or there’s you know, where they’re expand. They just there was a recent M and a with they either got acquired by key firm, or they they merged with another. You know, company like that stuff like that where.
31 00:08:26.240 ⇒ 00:08:35.010 Amber Lin: Awesome would be a automation. So we’ll look at what automations we need and what we want to do right for the next step.
32 00:08:35.280 ⇒ 00:08:35.860 Robert Tseng: Yeah.
33 00:08:36.610 ⇒ 00:08:39.159 Luke Daque: That’s the enrichment part right?
34 00:08:39.530 ⇒ 00:08:41.550 Luke Daque: Yep, adding the yeah, I see that.
35 00:08:41.559 ⇒ 00:08:45.189 Amber Lin: Okay. Sounds good. Yeah. I think.
36 00:08:45.329 ⇒ 00:08:59.319 Amber Lin: Mary, I think this is a great foundation for us to move forward. And this also gave you a more, a better sense of how Clay works and what’s going on in there. So this will be really helpful, for down the road when we do more.
37 00:09:02.049 ⇒ 00:09:07.899 Amber Lin: So let’s go to Luke. So I just wanna
38 00:09:08.559 ⇒ 00:09:13.763 Amber Lin: give this meeting as fast as possible. Let’s go to look how it was yesterday.
39 00:09:14.649 ⇒ 00:09:16.609 Amber Lin: progress on your ticket.
40 00:09:17.550 ⇒ 00:09:38.570 Luke Daque: Yeah, to be honest, there’s like, not much progress, except that I did like a bit of research. And like how we can get the data from Linkedin to play without using their web hooks, because it’s pretty expensive. I mean, we, we need to add, like $200 per month. Just to use the web hooks. Yeah, I did update the ticket
41 00:09:38.910 ⇒ 00:09:47.599 Luke Daque: with several potential solutions we can use. I guess I’ll have to test
42 00:09:49.360 ⇒ 00:09:56.390 Luke Daque: to to test basically all those like options, like, maybe we can utilize zapier for that.
43 00:09:56.750 ⇒ 00:10:01.400 Luke Daque: or maybe any kind of linkedin scraper tools. If you already have.
44 00:10:02.360 ⇒ 00:10:07.590 Amber Lin: I see cool. Do you feel stuck on?
45 00:10:07.730 ⇒ 00:10:10.550 Amber Lin: Say more of the automation part?
46 00:10:12.770 ⇒ 00:10:20.180 Amber Lin: have you worked with automations before? Do if like if you want help. Do you know who you want to ask
47 00:10:20.510 ⇒ 00:10:22.819 Amber Lin: about for these things?
48 00:10:23.500 ⇒ 00:10:25.560 Luke Daque: Yeah, to be honest, I haven’t at
49 00:10:25.720 ⇒ 00:10:34.149 Luke Daque: tried doing automation before, but I believe there’s a lot of that that we can tap into like the AI team. Miguel and Casey are very like
50 00:10:34.500 ⇒ 00:10:38.240 Luke Daque: I’m experienced in like automations. And I believe even Ryan.
51 00:10:38.450 ⇒ 00:10:44.049 Luke Daque: Alright, Brian, you have like experience creating automation and stuff like that.
52 00:10:46.360 ⇒ 00:10:49.209 Ryan Brosas: Yeah, yeah, I have experience on that.
53 00:10:50.200 ⇒ 00:10:50.840 Luke Daque: Yeah.
54 00:10:51.010 ⇒ 00:10:54.269 Luke Daque: And just one question, though, are we going to be using
55 00:10:54.930 ⇒ 00:11:00.580 Luke Daque: like Utam’s Linkedin account, or some like, do we have a Linkedin account.
56 00:11:01.200 ⇒ 00:11:03.559 Luke Daque: Let me that we can use for this.
57 00:11:05.020 ⇒ 00:11:11.139 Amber Lin: Let’s do this for Robert. We have Robert’s account, and the company account.
58 00:11:15.070 ⇒ 00:11:18.090 Luke Daque: And which account are we going going to use
59 00:11:18.630 ⇒ 00:11:20.960 Luke Daque: for this one? I guess it’s a company account.
60 00:11:22.170 ⇒ 00:11:23.499 Amber Lin: Robert, what do you think.
61 00:11:25.580 ⇒ 00:11:28.310 Robert Tseng: We’ll probably use
62 00:11:31.690 ⇒ 00:11:35.909 Robert Tseng: and I don’t. I don’t think we would use the company account. We’d either be Utah or mine.
63 00:11:36.870 ⇒ 00:11:37.710 Luke Daque: Okay.
64 00:11:37.850 ⇒ 00:11:38.315 Robert Tseng: Yeah.
65 00:11:41.500 ⇒ 00:11:50.270 Amber Lin: Cool, because probably it’s also we’ll want to get where they interacted with say, event, post, etcetera,
66 00:11:52.270 ⇒ 00:11:57.469 Amber Lin: Let’s see, cause if we’re if we’re stuck on the
67 00:11:58.850 ⇒ 00:12:09.049 Amber Lin: intake. So data ingestion, I don’t know how we can move to the next part, because if we don’t get the data, then we can’t enrich
68 00:12:09.160 ⇒ 00:12:11.140 Amber Lin: our leads. Based data.
69 00:12:11.430 ⇒ 00:12:14.820 Amber Lin: So I would say, we can. I mean.
70 00:12:15.110 ⇒ 00:12:21.280 Amber Lin: totally makes sense like, if I haven’t done automations before, I wouldn’t do this. So let’s say.
71 00:12:22.102 ⇒ 00:12:33.529 Amber Lin: can you work with Ryan or ask Casey or Miguel for their for their input.
72 00:12:33.770 ⇒ 00:12:40.559 Amber Lin: And then, essentially, we just wanna decide on, or at least not decide. But try
73 00:12:41.080 ⇒ 00:12:43.589 Amber Lin: at least one small thing.
74 00:12:44.034 ⇒ 00:12:47.709 Amber Lin: So maybe get their feedback on what might work the best.
75 00:12:47.850 ⇒ 00:12:59.230 Amber Lin: or at least in their opinion, or what might be the fastest to implement. And then we’ll just do a little experiment. We’ll do it. Based on the la event that Robert sent.
76 00:13:01.200 ⇒ 00:13:03.450 Luke Daque: Yeah, yeah, I agree, like, I’ll.
77 00:13:03.450 ⇒ 00:13:03.990 Amber Lin: Yeah.
78 00:13:04.150 ⇒ 00:13:12.179 Luke Daque: See what we can do for, like an Mvp. For this one. Maybe I can. I’ll I’ll tap with you Ryan on this
79 00:13:12.390 ⇒ 00:13:13.920 Luke Daque: and see what we can do.
80 00:13:14.270 ⇒ 00:13:22.170 Amber Lin: Yeah, let me go find that ticket, because we do have a little like project a small project dot
81 00:13:22.810 ⇒ 00:13:23.810 Amber Lin: here.
82 00:13:24.210 ⇒ 00:13:25.559 Amber Lin: Here it is.
83 00:13:26.030 ⇒ 00:13:30.420 Amber Lin: So we’ll do this.
84 00:13:31.321 ⇒ 00:13:52.509 Amber Lin: Feel free to ask Casey and Miguel. They’re still up. They will be up for a while, so feel free to just call like call them for 10 min and ask their opinion on that. And Ryan as well. I think you 2 can maybe collaborate on this one. I don’t know how much time you have.
85 00:13:53.240 ⇒ 00:13:56.429 Amber Lin: but it will be great if we just test
86 00:13:56.550 ⇒ 00:14:01.099 Amber Lin: a sample version a sample way and get an Mvp out.
87 00:14:03.600 ⇒ 00:14:07.629 Amber Lin: Because that will let us to know if it actually works or not.
88 00:14:08.220 ⇒ 00:14:09.480 Amber Lin: And
89 00:14:13.010 ⇒ 00:14:14.130 Amber Lin: awesome.
90 00:14:16.010 ⇒ 00:14:22.039 Amber Lin: I know you guys have a holiday on Thursday, so
91 00:14:23.180 ⇒ 00:14:33.099 Amber Lin: would love to see something by end of day tomorrow, so that Thursday you can just not think about work when it’s on holiday.
92 00:14:33.630 ⇒ 00:14:35.290 Amber Lin: Do you think that’s possible?
93 00:14:36.970 ⇒ 00:14:37.780 Luke Daque: Yeah, sure.
94 00:14:39.680 ⇒ 00:14:44.650 Amber Lin: Like it’s, it’s it’s okay. If it takes longer than that, I just don’t know how long it’s gonna take. Like.
95 00:14:44.650 ⇒ 00:14:52.479 Luke Daque: Yeah, we’ll try. We’ll see what we can do by tomorrow, and if, like, we don’t have anything by then, then we’ll we’ll definitely let you know.
96 00:14:52.660 ⇒ 00:14:54.330 Amber Lin: Okay, sounds good.
97 00:14:55.392 ⇒ 00:14:59.539 Amber Lin: I will say, do that tomorrow, then.
98 00:15:03.480 ⇒ 00:15:13.879 Amber Lin: I can’t. I think we can mark the spike as done, because ultimately you did find out what ways we can do it with. And now we’re just testing out the different ways that
99 00:15:15.320 ⇒ 00:15:16.440 Amber Lin: we can.
100 00:15:18.360 ⇒ 00:15:19.760 Amber Lin: We can try.
101 00:15:20.750 ⇒ 00:15:32.739 Amber Lin: So let’s see, Robert, any progress on the ideal customer, profile framework, etcetera.
102 00:15:34.820 ⇒ 00:15:35.760 Robert Tseng: No.
103 00:15:36.200 ⇒ 00:15:38.409 Amber Lin: Okay, do you want with that.
104 00:15:39.470 ⇒ 00:15:40.520 Robert Tseng: I,
105 00:15:43.640 ⇒ 00:15:56.479 Robert Tseng: I basically will just pull over what I have from I mean sales navigator. I have all these lead lists already set up. I probably just reuse some of the stuff that I had there. So I just haven’t gotten around to doing it.
106 00:15:56.480 ⇒ 00:16:08.279 Amber Lin: Awesome. It just it doesn’t need to be perfect. We just want something to see what it will look like, even so just just copy and paste. It’s fine.
107 00:16:08.450 ⇒ 00:16:10.479 Amber Lin: We’ll refine, based on that.
108 00:16:11.970 ⇒ 00:16:12.700 Robert Tseng: Okay.
109 00:16:12.700 ⇒ 00:16:19.530 Amber Lin: Yeah, I don’t think this is too urgent, since we’re still figuring out
110 00:16:19.740 ⇒ 00:16:34.069 Amber Lin: our pipeline, I think this is more of a separate thing of, we’re getting all the target list personas, getting all the targets at companies or types of events. So this is part of the pro milestone. Knowing what
111 00:16:34.200 ⇒ 00:16:36.030 Amber Lin: areas we want to target.
112 00:16:36.780 ⇒ 00:16:37.849 Amber Lin: And
113 00:16:41.590 ⇒ 00:16:47.230 Amber Lin: right, let me see.
114 00:16:51.080 ⇒ 00:16:59.299 Amber Lin: that’s 1 last. Another thing. So I’m thinking about Marianne for for you.
115 00:17:01.760 ⇒ 00:17:06.099 Amber Lin: Maybe something we can do is to
116 00:17:07.770 ⇒ 00:17:14.150 Amber Lin: have you. Have you been able to look at the automations like what’s possible in play?
117 00:17:16.819 ⇒ 00:17:23.769 Mariane Cequina: But but those are. But those things are actually just the basic staff. Because I think Robert have different
118 00:17:23.889 ⇒ 00:17:32.539 Mariane Cequina: visual on this automation. So what I’ve seen is just the standard automating of lead generation.
119 00:17:33.340 ⇒ 00:17:34.050 Amber Lin: Hmm!
120 00:17:35.000 ⇒ 00:17:36.040 Amber Lin: I see
121 00:17:37.150 ⇒ 00:17:44.820 Mariane Cequina: Like importing importing files from Hubspot, for example. So that’s what I’ve seen.
122 00:17:45.350 ⇒ 00:17:46.450 Amber Lin: Oh.
123 00:17:47.550 ⇒ 00:17:54.054 Amber Lin: okay, I see. Why don’t we take this time, since we still have a few time, a few moments, and everybody’s here.
124 00:17:54.520 ⇒ 00:18:02.099 Amber Lin: We can. Now look at how we exactly wanna enrich the leads.
125 00:18:05.670 ⇒ 00:18:08.009 Luke Daque: Maybe we can start with manually
126 00:18:10.060 ⇒ 00:18:14.400 Luke Daque: importing Csv files, for now I think that’s what Tom also mentioned
127 00:18:17.700 ⇒ 00:18:21.640 Luke Daque: that way. We can get data in and then try doing the enrichment.
128 00:18:22.280 ⇒ 00:18:23.014 Luke Daque: There.
129 00:18:24.960 ⇒ 00:18:27.580 Amber Lin: What Csc files are you talking about? Like what.
130 00:18:27.580 ⇒ 00:18:34.200 Robert Tseng: I, just, I just send a Csv we can use that. That’s just naming emails. If I could get Linkedin and then.
131 00:18:35.860 ⇒ 00:18:45.440 Robert Tseng: oh, yeah, we just use that as like an example of like, how like a workflow walkthrough of how this gets uploaded into Barion’s table. How do we get Linkedin.
132 00:18:47.260 ⇒ 00:19:07.149 Robert Tseng: yeah. And then, like, there’s a few things that need to happen like once the Linkedin profiles flow in. I want to push the Linkedin list into. Hey, reach? I’ll do Linkedin messaging there all of these names also need to be kind of like created in Hubspot, like there’s got to be some sync to the Crm.
133 00:19:08.510 ⇒ 00:19:13.899 Robert Tseng: And then I guess all of their companies that kind of need to be.
134 00:19:14.840 ⇒ 00:19:19.110 Robert Tseng: But these are all leads. And then there’s accounts right? So there’s like a multi level
135 00:19:19.260 ⇒ 00:19:24.359 Robert Tseng: targeting here, like, even if I get connected to one person at a company.
136 00:19:24.640 ⇒ 00:19:27.090 Robert Tseng: I want our system to be able to
137 00:19:27.530 ⇒ 00:19:35.499 Robert Tseng: find who which company they work for, or like the other decision makers around them. And we’re having a more of an account based
138 00:19:36.190 ⇒ 00:19:43.159 Robert Tseng: targeting approach, like. I don’t only want to talk to one person if if I can.
139 00:19:44.230 ⇒ 00:19:44.840 Amber Lin: Hmm.
140 00:19:45.250 ⇒ 00:20:00.569 Amber Lin: so I think, based on that. A something we can work on Marion today is to fill that clay table with the sample data that Robert sent, and to try and fill in all the different
141 00:20:01.141 ⇒ 00:20:06.109 Amber Lin: all the different columns. Have you worked with Hubspot before Marianne?
142 00:20:07.640 ⇒ 00:20:10.480 Mariane Cequina: I actually just have limited knowledge on it.
143 00:20:10.980 ⇒ 00:20:11.370 Amber Lin: Okay.
144 00:20:11.370 ⇒ 00:20:17.300 Mariane Cequina: But can I? Can I have the? I think I missed the file for the Csv.
145 00:20:17.760 ⇒ 00:20:19.680 Robert Tseng: I just put it in the sales. Gtm.
146 00:20:20.071 ⇒ 00:20:24.379 Amber Lin: Copy the I’ll make a ticket and I’ll copy the link.
147 00:20:24.380 ⇒ 00:20:25.110 Mariane Cequina: Okay.
148 00:20:25.110 ⇒ 00:20:29.460 Amber Lin: To the message as well to do.
149 00:20:30.580 ⇒ 00:20:34.100 Luke Daque: Yeah, it looks like this has only name and email. So I guess we’ll have to.
150 00:20:34.100 ⇒ 00:20:34.760 Amber Lin: Yeah.
151 00:20:35.940 ⇒ 00:20:40.289 Luke Daque: There and make sure that the rest of the community.
152 00:20:42.650 ⇒ 00:20:52.539 Amber Lin: And do you know how to say connect clay rose to Hubspot Crm.
153 00:20:53.880 ⇒ 00:20:57.720 Mariane Cequina: What I’ve seen is just like importing files.
154 00:20:57.870 ⇒ 00:21:00.689 Mariane Cequina: but I think there’s actually just an option there as well.
155 00:21:01.188 ⇒ 00:21:03.490 Amber Lin: Robert, do you know anything about it?
156 00:21:04.526 ⇒ 00:21:07.240 Robert Tseng: No, but I feel like I could probably figure that out.
157 00:21:07.240 ⇒ 00:21:08.229 Amber Lin: Okay. So we’ll make.
158 00:21:08.230 ⇒ 00:21:13.959 Robert Tseng: There is a send message to Channel. There must be a send something to Hubspot.
159 00:21:15.290 ⇒ 00:21:15.850 Amber Lin: Hmm.
160 00:21:20.900 ⇒ 00:21:31.549 Amber Lin: right? So I think we can also make a ticket at discover play to hubspot connections, so we’ll
161 00:21:33.050 ⇒ 00:21:35.979 Amber Lin: try and discover that as well.
162 00:21:36.970 ⇒ 00:21:41.430 Luke Daque: Yeah, it looks like the native connection would be pro account.
163 00:21:41.890 ⇒ 00:21:44.060 Amber Lin: Let me share my screen.
164 00:21:46.096 ⇒ 00:21:47.679 Luke Daque: Can you see my screen.
165 00:21:47.680 ⇒ 00:21:48.230 Amber Lin: Yeah.
166 00:21:48.660 ⇒ 00:21:55.520 Luke Daque: So here, under under settings, connections like, if we look at Hotspot, for example.
167 00:21:59.370 ⇒ 00:22:00.110 Luke Daque: Alright.
168 00:22:05.350 ⇒ 00:22:07.520 Luke Daque: I think I found yeah, I think, in there. But
169 00:22:08.550 ⇒ 00:22:13.280 Luke Daque: oh, you need to add, for example, Spot.
170 00:22:13.590 ⇒ 00:22:16.749 Luke Daque: yeah, it needs the pro account. And that’s the
171 00:22:18.020 ⇒ 00:22:22.909 Luke Daque: that’s quite expensive, like $800 a month.
172 00:22:23.360 ⇒ 00:22:24.200 Amber Lin: Okay, so.
173 00:22:24.200 ⇒ 00:22:26.160 Luke Daque: This is their name, connecting.
174 00:22:27.090 ⇒ 00:22:34.549 Luke Daque: I’m not sure if there’s any other way like maybe using the zapier like like what what I like, added here.
175 00:22:36.840 ⇒ 00:22:41.940 Luke Daque: Into the Mdt. It’s fine, so we can probably use.
176 00:22:43.070 ⇒ 00:22:48.280 Luke Daque: like either Api based custom loaders or like me, to see.
177 00:22:48.280 ⇒ 00:22:48.700 Amber Lin: Wow!
178 00:22:48.700 ⇒ 00:22:50.919 Luke Daque: Information or something. But yeah, I’ll have to.
179 00:22:51.850 ⇒ 00:22:54.270 Luke Daque: The yeah, okay.
180 00:22:55.120 ⇒ 00:22:58.360 Amber Lin: I see, I see, I see.
181 00:22:58.830 ⇒ 00:23:02.360 Luke Daque: There’s even Linkedin, supposedly, but it’s still the same.
182 00:23:02.360 ⇒ 00:23:03.830 Amber Lin: Oh, wow!
183 00:23:03.830 ⇒ 00:23:04.520 Luke Daque: So.
184 00:23:05.160 ⇒ 00:23:06.014 Amber Lin: Okay.
185 00:23:06.870 ⇒ 00:23:14.090 Robert Tseng: That’s fine. I’ll I’ll just upgrade to pro now like 2.
186 00:23:17.270 ⇒ 00:23:19.029 Amber Lin: Oh, are we gonna pay for it?
187 00:23:19.190 ⇒ 00:23:20.709 Robert Tseng: Yeah, we’re we’re gonna pay for it.
188 00:23:20.710 ⇒ 00:23:28.400 Amber Lin: Okay. So if we pay for it, then I guess Luke, for our little project, we can just also just use the pro.
189 00:23:30.290 ⇒ 00:23:32.009 Luke Daque: Okay, we can test it out.
190 00:23:32.010 ⇒ 00:23:33.590 Amber Lin: Then we’ll get the linkedin.
191 00:23:34.020 ⇒ 00:23:35.389 Amber Lin: Okay? Awesome.
192 00:23:39.380 ⇒ 00:23:42.039 Amber Lin: Yeah. Money solves problems. Huh?
193 00:23:42.550 ⇒ 00:23:47.579 Robert Tseng: Yeah, I mean, I want us to like, move faster. I mean, okay, I guess it’s
194 00:23:48.210 ⇒ 00:23:50.609 Robert Tseng: Tom’s account on this.
195 00:24:07.090 ⇒ 00:24:08.749 Luke Daque: What’s the probe by this week?
196 00:24:11.900 ⇒ 00:24:14.870 Luke Daque: So, yeah, that’s good integrations.
197 00:24:37.920 ⇒ 00:24:44.180 Luke Daque: Yeah, it looks like for their hubspot integration. It has like syncing and managing.
198 00:24:45.150 ⇒ 00:25:04.909 Amber Lin: Okay, fantastic. So, and I guess part of what we want to do to fill out the clay table with a sample data that Robert sent. We can also add and add a column, or add a feature where you connect to Hubspot and I don’t know if you have the login details with hotspot.
199 00:25:05.200 ⇒ 00:25:06.609 Mariane Cequina: I don’t have for now.
200 00:25:06.610 ⇒ 00:25:09.879 Amber Lin: Okay. Robert, is it one password?
201 00:25:11.393 ⇒ 00:25:20.489 Robert Tseng: I didn’t set up hubspot, so I’ve actually never been in our Hubspot account. So I run everything out of notion. But I think, if we’re gonna do this.
202 00:25:21.320 ⇒ 00:25:25.149 Mariane Cequina: Yeah, if but if you we, if you currently use notion, I think
203 00:25:25.450 ⇒ 00:25:36.289 Mariane Cequina: I think it could be possible as well to integrate it, to play. Maybe maybe we can ask Casey, because he was the one that actually do a lot of automations
204 00:25:36.510 ⇒ 00:25:38.619 Mariane Cequina: in terms of that.
205 00:25:38.770 ⇒ 00:25:40.200 Mariane Cequina: About this insight.
206 00:25:40.440 ⇒ 00:25:40.940 Amber Lin: Bye.
207 00:25:41.190 ⇒ 00:25:41.530 Luke Daque: Yeah.
208 00:25:41.530 ⇒ 00:25:42.110 Mariane Cequina: One.
209 00:25:42.378 ⇒ 00:25:44.259 Amber Lin: What do we wanna just have some.
210 00:25:44.260 ⇒ 00:25:46.199 Mariane Cequina: But if you will be using Ops, I’m sorry.
211 00:25:46.200 ⇒ 00:25:47.710 Amber Lin: Go ahead! Go ahead!
212 00:25:48.160 ⇒ 00:25:52.480 Mariane Cequina: And if we will be using hubspot, I think it will be another payment.
213 00:25:53.580 ⇒ 00:25:54.569 Amber Lin: That’s true.
214 00:25:56.018 ⇒ 00:25:57.370 Amber Lin: Robert, what do you.
215 00:25:57.370 ⇒ 00:25:57.720 Robert Tseng: Yeah.
216 00:25:57.720 ⇒ 00:25:58.180 Amber Lin: We.
217 00:25:58.180 ⇒ 00:26:13.080 Robert Tseng: Well, I think slack as a Crm is not great. I mean not slack notion. So I mean we do. We? We should move, we should move into a real crm. So I think if we already have Hubspot, and that’s what your time set up. Then we should just use Hubspot.
218 00:26:13.610 ⇒ 00:26:15.909 Amber Lin: Okay. So Uton has a hexa account.
219 00:26:20.860 ⇒ 00:26:21.700 Luke Daque: Yeah. Maybe.
220 00:26:21.700 ⇒ 00:26:23.389 Luke Daque: He asked for the password.
221 00:26:35.410 ⇒ 00:26:45.980 Robert Tseng: Like I’m gonna keep using notion until we’re ready to switch over. But like I don’t, I don’t think notion will will work for all the stuff that we’re tracking notions too dependent on us, like
222 00:26:46.720 ⇒ 00:26:52.199 Robert Tseng: I have to go and drop notes on everything that I’m doing like it. Just the the triggers are not
223 00:26:52.640 ⇒ 00:26:55.549 Robert Tseng: not. It’s it’s not automated.
224 00:26:57.270 ⇒ 00:27:03.790 Amber Lin: Sounds good. I’ll tag Utam in our channel to ask for the Hubspot.
225 00:27:04.970 ⇒ 00:27:05.450 Amber Lin: Thank you.
226 00:27:05.450 ⇒ 00:27:08.030 Luke Daque: This is the Linkedin as well. Right? So we can.
227 00:27:08.040 ⇒ 00:27:12.039 Amber Lin: Yeah, that’s right. I think his Linkedin is in one pass.
228 00:27:13.510 ⇒ 00:27:13.865 Luke Daque: Oh!
229 00:27:14.596 ⇒ 00:27:17.609 Amber Lin: And I’ll but I’ll just let him know.
230 00:27:19.250 ⇒ 00:27:23.609 Luke Daque: Yeah, I don’t have access to him. I’ll ask him to share with me.
231 00:27:24.460 ⇒ 00:27:27.230 Luke Daque: But I believe we are. Ryan does, but I think.
232 00:27:29.540 ⇒ 00:27:31.340 Amber Lin: Hmm, yeah.
233 00:27:34.590 ⇒ 00:27:35.200 Luke Daque: Yeah.
234 00:27:41.230 ⇒ 00:27:42.610 Amber Lin: Sounds good
235 00:28:06.730 ⇒ 00:28:10.129 Luke Daque: Let me join to their slack channel. I guess maybe we can.
236 00:28:11.580 ⇒ 00:28:14.799 Luke Daque: Maybe we can have a demo or something.
237 00:28:15.200 ⇒ 00:28:16.100 Amber Lin: Hmm!
238 00:28:20.390 ⇒ 00:28:21.450 Amber Lin: Sounds good.
239 00:28:21.790 ⇒ 00:28:34.649 Amber Lin: I think that’s pretty pretty good for today of Marion to do this play table, and Luke and Ryan to work together on the sample project.
240 00:28:35.180 ⇒ 00:28:36.210 Amber Lin: I’ll stop.
241 00:28:37.280 ⇒ 00:28:37.880 Amber Lin: Sounds good.
242 00:28:37.880 ⇒ 00:28:41.057 Mariane Cequina: Should we start using the hubspot as well like
243 00:28:42.800 ⇒ 00:28:44.510 Amber Lin: Oh, setting up spot.
244 00:28:45.190 ⇒ 00:28:47.739 Mariane Cequina: Yeah, for the sample that we have right now.
245 00:28:48.120 ⇒ 00:28:51.479 Robert Tseng: Yeah, we should. That’ll be the 1st contacts going into Hubspot.
246 00:28:51.480 ⇒ 00:28:51.839 Amber Lin: I think.
247 00:28:51.840 ⇒ 00:29:01.090 Robert Tseng: I think our I think our web flow gets synced to Hubspot. So we have, but that we don’t really get many of these from there. So this will really be the 1st time we’re really using it.
248 00:29:01.420 ⇒ 00:29:02.100 Amber Lin: Sounds good.
249 00:29:02.100 ⇒ 00:29:02.600 Mariane Cequina: Okay.
250 00:29:04.790 ⇒ 00:29:10.249 Amber Lin: What do we need to set up hubspot like? What does this?
251 00:29:10.470 ⇒ 00:29:13.330 Amber Lin: What does this look like if we say it’s done.
252 00:29:16.270 ⇒ 00:29:25.140 Luke Daque: But I guess if we can import our Hubspot into clay like using this import object, or like, Yeah.
253 00:29:26.520 ⇒ 00:29:28.630 Luke Daque: using Hubspot’s Api and stuff.
254 00:29:33.240 ⇒ 00:29:35.220 Amber Lin: I see. So
255 00:29:36.840 ⇒ 00:29:55.929 Amber Lin: is this how it works. So we have a list of emails and names that Robert gave us. We import that into clay. Clay is automatically connected to Hotspot, and Hubspot is the one that does all the searching about the companies, etc, and then it feeds it back into the clay table. Is that how it works?
256 00:29:56.670 ⇒ 00:30:00.130 Mariane Cequina: I think the information will be coming from the Hubspot.
257 00:30:00.490 ⇒ 00:30:00.880 Amber Lin: Okay.
258 00:30:00.880 ⇒ 00:30:14.210 Mariane Cequina: And then it will be imported in clay and then in clay. This is just based on my knowledge. I could be wrong. I think Ryan has more knowledge in terms of clay, but I know is that here in clay it has like that, like you can, like.
259 00:30:14.690 ⇒ 00:30:24.619 Mariane Cequina: like, create like, add enrichment, for example, and then add like formula and trigger. That’s all I know, like you can add automation in place. So the automation will be.
260 00:30:24.620 ⇒ 00:30:25.449 Amber Lin: Coming from play.
261 00:30:25.450 ⇒ 00:30:27.650 Mariane Cequina: I think that’s how it works.
262 00:30:27.960 ⇒ 00:30:43.319 Amber Lin: Sounds good, I mean, Ryan. I’ll I’ll use you as a super force just to help wherever needed. We’ll be a team and we’ll work on this, and we’ll get this done by end of end of day, Wednesday. So you guys can have a Thursday off.
263 00:30:45.790 ⇒ 00:30:49.115 Mariane Cequina: Okay, can we ask also the
264 00:30:51.080 ⇒ 00:30:57.360 Mariane Cequina: AI team for some like advice? If they have, do they have a little bit of time here.
265 00:30:57.530 ⇒ 00:31:18.219 Amber Lin: Of course, whatever you need to ping ping, whoever text, whoever, if you need external help, if you want Utam to ask his friends for any experiences just tag him. He’ll be happy to help. All we want is just to move this project forward, and we’ll ask whoever we need for help.
266 00:31:19.290 ⇒ 00:31:20.010 Mariane Cequina: Okay.
267 00:31:20.470 ⇒ 00:31:24.460 Amber Lin: Yeah, okay, thank you guys, thank you.
268 00:31:24.460 ⇒ 00:31:25.559 Mariane Cequina: Oh, thank you!
269 00:31:25.560 ⇒ 00:31:26.490 Ryan Brosas: Hey, guys.
270 00:31:28.640 ⇒ 00:31:29.310 Luke Daque: Bye-bye.