Meeting Title: Default x BF | Product Analytics Kickoff Date: 2025-07-18 Meeting participants: Amber Lin, Uttam Kumaran, Ryan DeForest
WEBVTT
1 00:00:13.040 ⇒ 00:00:14.180 Uttam Kumaran: Hello!
2 00:00:15.340 ⇒ 00:00:16.520 Amber Lin: Bye.
3 00:00:16.760 ⇒ 00:00:17.967 Uttam Kumaran: Hi! Good morning!
4 00:00:18.370 ⇒ 00:00:26.289 Amber Lin: Good morning. I was just working on the insomnia cookies. I use Robert’s template. His templates are very helpful.
5 00:00:26.530 ⇒ 00:00:31.419 Uttam Kumaran: Oh, nice. Okay, yeah. I know. He gave like 0 information. I also have not had a chance to even read any of it.
6 00:00:32.549 ⇒ 00:00:39.119 Amber Lin: This is a bunch of cause. It’s discovery. So it’s just what are we gonna do for discovery? There’s not much.
7 00:00:39.760 ⇒ 00:00:41.310 Amber Lin: There’s not much information he can get.
8 00:00:41.310 ⇒ 00:00:46.099 Uttam Kumaran: I basically told him, like he should just have someone tied to the hip with him. And this whole thing.
9 00:00:46.430 ⇒ 00:00:49.763 Amber Lin: Yeah, I used his what is it?
10 00:00:50.500 ⇒ 00:00:54.509 Amber Lin: His A B testing template that he sent in the
11 00:00:55.145 ⇒ 00:01:07.104 Amber Lin: in the project management channel? And I and I made this is a bit long. I’m gonna ask him to review, because he said he did not have time today. But he also said, we’re starting next week. So I’m I was a bit concerned.
12 00:01:09.670 ⇒ 00:01:13.900 Amber Lin: Default right here.
13 00:01:14.570 ⇒ 00:01:21.970 Amber Lin: Okay? So the goal that we have for the next meeting is we wanna get Ryan’s
14 00:01:22.640 ⇒ 00:01:30.040 Amber Lin: feedback on the project management. Sorry not not that product. Analytics approach.
15 00:01:30.899 ⇒ 00:01:37.290 Amber Lin: You commented here that this isn’t necessary, are you? If you’re sure I’ll just delete that.
16 00:01:47.350 ⇒ 00:01:56.280 Uttam Kumaran: I just don’t think this is particularly like necessary. Right now, I do think that we’re gonna probably try to accomplish everything
17 00:01:57.080 ⇒ 00:02:03.240 Uttam Kumaran: straight in and amplitude.
18 00:02:04.120 ⇒ 00:02:06.489 Uttam Kumaran: Like, I think we will. Probably
19 00:02:07.660 ⇒ 00:02:13.679 Uttam Kumaran: we probably as part of the core data model strategy will probably be like our proposal for the next phase.
20 00:02:15.360 ⇒ 00:02:18.709 Uttam Kumaran: But I don’t think we’re gonna execute on that, like I just don’t think we’re out of time.
21 00:02:19.100 ⇒ 00:02:28.320 Amber Lin: Okay, so are we sorry? I’m trying to understand? Are we just going to do an audit for them and give them a roadmap? Or are we actually building it out.
22 00:02:31.140 ⇒ 00:02:40.381 Uttam Kumaran: Well, this, the the 3rd piece on there is is work, right? So we’re just not doing the data modeling like we, I I already? I so I sent you like
23 00:02:40.810 ⇒ 00:02:45.549 Uttam Kumaran: the list of things that Ryan needs right in the client default channel.
24 00:02:45.810 ⇒ 00:02:46.250 Uttam Kumaran: Hmm!
25 00:02:46.250 ⇒ 00:02:48.469 Amber Lin: Oh, okay, okay, wait. Let me see.
26 00:02:50.750 ⇒ 00:02:53.709 Uttam Kumaran: Yeah, there’s a document at the top which is like.
27 00:02:54.600 ⇒ 00:02:57.919 Uttam Kumaran: here’s all the things that Ryan is interested in.
28 00:02:59.590 ⇒ 00:03:01.899 Amber Lin: Oh, I’m so silly. Okay.
29 00:03:02.130 ⇒ 00:03:08.039 Uttam Kumaran: So these are the things that we’re going to work with. Like, we’re gonna I’m gonna basically
30 00:03:08.430 ⇒ 00:03:13.509 Uttam Kumaran: we need to use their existing tech stack to see how many of these we can accomplish, and then.
31 00:03:13.610 ⇒ 00:03:20.339 Uttam Kumaran: based on based on something anything else we can accomplish, we will
32 00:03:20.490 ⇒ 00:03:25.470 Uttam Kumaran: propose how we need to accomplish these. So this is the like initial set of goals.
33 00:03:25.470 ⇒ 00:03:26.070 Amber Lin: Hmm.
34 00:03:26.660 ⇒ 00:03:29.960 Uttam Kumaran: Like. It’s not all of these. It’s some of these.
35 00:03:30.701 ⇒ 00:03:36.120 Uttam Kumaran: So this is where I want to work with, like a Henry, probably on figuring out which ones of these we can tackle.
36 00:03:36.640 ⇒ 00:03:40.980 Uttam Kumaran: And then sort of deciding on what the initial roadmap is.
37 00:03:53.430 ⇒ 00:03:59.210 Amber Lin: Okay, so probably should.
38 00:04:00.990 ⇒ 00:04:04.032 Amber Lin: How? Okay, this is 3 months.
39 00:04:06.890 ⇒ 00:04:08.219 Amber Lin: So let me.
40 00:04:08.220 ⇒ 00:04:15.660 Uttam Kumaran: So the strategy work like yesterday, I’m just gonna sit in meetings with them and just help them like that’s gonna be loose.
41 00:04:17.339 ⇒ 00:04:24.729 Amber Lin: Yeah, I honestly did not think I need to be there, cause I my mind did not wrap around that I was doing. I like, and
42 00:04:25.210 ⇒ 00:04:26.019 Amber Lin: no, not this track.
43 00:04:26.020 ⇒ 00:04:31.540 Uttam Kumaran: A little bit for you to snow. I it was important for you to see both of those people.
44 00:04:31.540 ⇒ 00:04:32.040 Amber Lin: That! Oh, that!
45 00:04:32.040 ⇒ 00:04:32.470 Uttam Kumaran: That’s true.
46 00:04:32.470 ⇒ 00:04:35.040 Amber Lin: Like for that reason I wanted to be there.
47 00:04:35.340 ⇒ 00:04:37.190 Amber Lin: Yeah, that’s why I wanted you to be there.
48 00:04:37.190 ⇒ 00:04:37.950 Amber Lin: Okay, okay.
49 00:04:37.950 ⇒ 00:04:38.480 Uttam Kumaran: Yeah.
50 00:04:44.450 ⇒ 00:04:47.819 Amber Lin: Okay, phase one data architecture.
51 00:04:53.650 ⇒ 00:04:59.600 Amber Lin: Okay, core data models.
52 00:04:59.870 ⇒ 00:05:03.099 Amber Lin: Is this a bit closer to what it was before.
53 00:05:07.850 ⇒ 00:05:16.269 Uttam Kumaran: yeah, but I don’t. This is where, like, I don’t. I don’t exactly. I mean, it’s just gonna be hard for me, because it’s gonna be very technical.
54 00:05:16.965 ⇒ 00:05:17.430 Amber Lin: Okay.
55 00:05:17.430 ⇒ 00:05:27.530 Uttam Kumaran: I need to work. I need to work with Henry on this next phase. But what I guess what I’m trying to convey is that these Icp signals is what Ryan needs.
56 00:05:28.230 ⇒ 00:05:32.530 Uttam Kumaran: So Icp signals is like he wants his sales team to go tackle.
57 00:05:32.940 ⇒ 00:05:40.370 Uttam Kumaran: He wants a sales team to go find he like wants us to help them find which companies in their existing user base are using these things.
58 00:05:40.890 ⇒ 00:05:48.080 Uttam Kumaran: And so he can go tell a sales team to go like hit. Those people up.
59 00:05:49.010 ⇒ 00:05:49.710 Amber Lin: Hmm.
60 00:05:50.490 ⇒ 00:05:55.090 Uttam Kumaran: So this is like the goal for the initial product analytics.
61 00:05:55.240 ⇒ 00:06:02.830 Uttam Kumaran: So I don’t. I, in this meeting with like Ryan, just to like kind of give how I would like this to go. I don’t think we need to spend like
62 00:06:03.080 ⇒ 00:06:17.990 Uttam Kumaran: these guys work very, very quickly, so I don’t want to baby them. I just want to. Basically, I think you should just introduce yourself. I think we should say we’re we’re we’re this is, we’re starting this week on attacking those Icp signals.
63 00:06:18.840 ⇒ 00:06:19.430 Amber Lin: Hmm.
64 00:06:19.430 ⇒ 00:06:21.690 Uttam Kumaran: I think I want to confirm with him
65 00:06:21.990 ⇒ 00:06:38.489 Uttam Kumaran: that he wants. Where, like he wants this data like, does he want this like where he wants this to end up for a sales team. And what are the sales team? What are the tools the sales team are is are using? And then I want to lock in some sort of weekly cadence where we can meet with them.
66 00:06:39.940 ⇒ 00:06:40.720 Uttam Kumaran: Yeah.
67 00:06:52.160 ⇒ 00:06:55.070 Amber Lin: Well, this pre-meeting was very necessary.
68 00:06:56.530 ⇒ 00:06:57.410 Amber Lin: Oh.
69 00:06:57.410 ⇒ 00:06:59.470 Uttam Kumaran: Yeah, we should do more of these, because, yeah, otherwise.
70 00:06:59.470 ⇒ 00:07:00.010 Amber Lin: Yeah.
71 00:07:00.010 ⇒ 00:07:04.800 Uttam Kumaran: We’ll just every client is different. So this is where, like.
72 00:07:05.450 ⇒ 00:07:20.119 Uttam Kumaran: I don’t know. I I’m I’m particularly like, I’m always Gonna fight against every client having the same process which the Pmo office is gonna fight the other way against, which is fine because every client is different, and
73 00:07:20.580 ⇒ 00:07:24.060 Uttam Kumaran: if we treat every client the same. Then it’s like they’re they’re
74 00:07:24.320 ⇒ 00:07:29.899 Uttam Kumaran: they’re gonna just it’s like robotic, right? So every client has nuances like who they are, what they care about.
75 00:07:30.050 ⇒ 00:07:35.080 Uttam Kumaran: like what the company type is. And so I want us to kind of tailor it to that, to their
76 00:07:35.370 ⇒ 00:07:39.459 Uttam Kumaran: needs. Right? So, like some clients, need really slow. They need methodical. Some clients.
77 00:07:39.710 ⇒ 00:07:45.519 Uttam Kumaran: These guys are kind of savages like they’re good at their job. They move really fast. If we baby them, they’re gonna be like dude.
78 00:07:45.760 ⇒ 00:07:53.470 Uttam Kumaran: Who are these guys? They seem dumb. So that’s why that’s why, even yesterday you can tell, like, I was very locked in the whole meeting.
79 00:07:54.170 ⇒ 00:08:00.019 Uttam Kumaran: because they’re working on kind. That’s like kind of the probably the most advanced stuff that we’ve. We’ve been able to work on so far.
80 00:08:00.020 ⇒ 00:08:03.360 Amber Lin: Hmm, yeah, it’s very exciting.
81 00:08:03.850 ⇒ 00:08:09.549 Amber Lin: So let me go. Take a closer look at this.
82 00:08:10.610 ⇒ 00:08:13.980 Amber Lin: Okay, single confirmations.
83 00:08:26.930 ⇒ 00:08:27.830 Amber Lin: Okay?
84 00:08:29.010 ⇒ 00:08:31.170 Amber Lin: So he wants to look at
85 00:08:31.870 ⇒ 00:08:36.690 Amber Lin: Icps. With these Combos we might not get to all of them, but
86 00:08:36.919 ⇒ 00:08:40.839 Amber Lin: like, is there a priority ranking that he has.
87 00:08:41.950 ⇒ 00:08:47.400 Uttam Kumaran: That’s what I mean. I’m I’m that’s what we’re gonna talk about this meeting. So I can drive. I can drive the technical discussion in the meeting.
88 00:08:47.400 ⇒ 00:08:48.100 Amber Lin: Okay.
89 00:08:50.290 ⇒ 00:08:55.210 Uttam Kumaran: I think mainly I wanna make sure that we can get some recurring cadence with him.
90 00:08:55.930 ⇒ 00:08:59.530 Uttam Kumaran: We could confirm the tools that they that he uses.
91 00:09:00.200 ⇒ 00:09:02.740 Uttam Kumaran: and we can confirm if there’s any other priorities.
92 00:09:04.010 ⇒ 00:09:08.219 Uttam Kumaran: Hmm! That’s it. If the meeting ends early. Then we we can kill it. Yeah.
93 00:09:08.220 ⇒ 00:09:08.950 Amber Lin: Okay.
94 00:09:12.420 ⇒ 00:09:15.719 Amber Lin: Sounds good. I don’t.
95 00:09:16.270 ⇒ 00:09:18.360 Amber Lin: I don’t know if I like these.
96 00:09:24.760 ⇒ 00:09:33.149 Amber Lin: Okay, I will make this after we meet with him. I feel like I am building it off of assumptions.
97 00:09:33.870 ⇒ 00:09:37.909 Uttam Kumaran: Yeah. Also, like, I know, it’s mostly AI generated. So it’s not gonna be like.
98 00:09:37.910 ⇒ 00:09:38.680 Amber Lin: Yeah.
99 00:09:38.940 ⇒ 00:09:50.069 Uttam Kumaran: Accurate at all. So I think. Take take the meeting notes from this, and then I need to. I I don’t want to review this without having Henry or whoever’s gonna partner with me on this project.
100 00:09:50.070 ⇒ 00:09:50.440 Amber Lin: Yeah.
101 00:09:50.440 ⇒ 00:09:54.070 Uttam Kumaran: In the room, so I think, like we’ll have to.
102 00:09:54.070 ⇒ 00:09:56.110 Amber Lin: Meeting. We will invite Henry.
103 00:09:56.630 ⇒ 00:10:14.849 Uttam Kumaran: Yeah, well, we’ll we’ll meet internally with Henry. We’ll build out sort of he’ll. Henry can watch this meeting. We’ll build out the project plan. And then, yeah, like, I don’t. I? I’m really like Hopeful that this isn’t, too. These guys are really good. And so I don’t think it’s gonna be as much like project management
104 00:10:15.510 ⇒ 00:10:19.749 Uttam Kumaran: as some of our other clients. Actually, very, very much. Hope it’s not going to be
105 00:10:20.900 ⇒ 00:10:21.400 Amber Lin: Okay.
106 00:10:21.640 ⇒ 00:10:28.119 Uttam Kumaran: Cause. I can hop on, talk data all day with with them, and I think if Henry is here and you can sort of manage.
107 00:10:28.910 ⇒ 00:10:33.230 Uttam Kumaran: manage his, his shit on product analytics like we’re gonna be good, you know.
108 00:10:33.380 ⇒ 00:10:34.250 Amber Lin: Okay.
109 00:10:34.700 ⇒ 00:10:37.800 Uttam Kumaran: Yeah, what? How do you feel?
110 00:10:38.663 ⇒ 00:10:52.919 Amber Lin: I feel fine because I’m not like. I know that I have no clue. Well, barely a clue. What’s going on, but I like. It’s not required for me to know all of that for me to get this organized like. It’s different things. So I’m fine.
111 00:10:53.900 ⇒ 00:11:02.670 Uttam Kumaran: Yeah, I I think I I can give you the confidence that we don’t need everything figured out at this moment. I think what they’re going, what, what, where we’re going to
112 00:11:02.770 ⇒ 00:11:09.849 Uttam Kumaran: have to show. Our work is on our pace of development and like our aptitude. So I’m not gonna staff. Anyone on this that’s like
113 00:11:10.850 ⇒ 00:11:14.019 Uttam Kumaran: isn’t good. So it has to be like Henry
114 00:11:14.300 ⇒ 00:11:17.409 Uttam Kumaran: I mean, ideally, it’s Henry, or we’ll we’ll see what happens.
115 00:11:20.420 ⇒ 00:11:21.870 Uttam Kumaran: So but yeah.
116 00:11:22.450 ⇒ 00:11:26.339 Amber Lin: Yeah, okay, I mean 2 min, and then Ryan should be here.
117 00:11:26.870 ⇒ 00:11:29.350 Uttam Kumaran: Okay, oh, this is meaning, like, okay, cool. Yeah.
118 00:11:29.350 ⇒ 00:11:31.229 Amber Lin: Yeah, it’s just that we don’t need to hop.
119 00:11:33.750 ⇒ 00:11:34.790 Amber Lin: Oh.
120 00:11:41.760 ⇒ 00:11:46.489 Amber Lin: I guess generally it’s like a very high level
121 00:11:46.610 ⇒ 00:11:52.799 Amber Lin: overview of steps. We have the list of Icps. We’re gonna prioritize them today.
122 00:11:55.830 ⇒ 00:12:01.789 Uttam Kumaran: I mean, the thing is, he’s probably not going. We’re not gonna prior, like, I’m probably gonna make the decision with Henry, and let’s prioritize.
123 00:12:01.790 ⇒ 00:12:02.550 Amber Lin: Okay.
124 00:12:02.800 ⇒ 00:12:12.139 Uttam Kumaran: Cause. This is the thing of like these guys they want like a partner. So like a typical consultancy will be like, well, tell me the prioritization. I’m I know what these guys need.
125 00:12:12.320 ⇒ 00:12:17.670 Uttam Kumaran: I’ve been in their shoes before, so I know which one of these we can do and which will be
126 00:12:18.170 ⇒ 00:12:27.899 Uttam Kumaran: deserve to give their both spouse. I can give that unless Ryan, as I met, I met Ryan. And you basically was like, Here’s like a here’s about appears a bunch of them. You let me know what you think.
127 00:12:28.060 ⇒ 00:12:37.210 Amber Lin: Okay, great. So and then what do we do after that? Like as a high level overview.
128 00:12:40.600 ⇒ 00:12:47.010 Uttam Kumaran: Well, we need to just then, basically build a technical plan of like, how we’re gonna execute this.
129 00:12:50.340 ⇒ 00:12:53.280 Amber Lin: Execute discovery, experiments.
130 00:12:54.600 ⇒ 00:13:00.360 Uttam Kumaran: Well, the so the goal of this is to get Ryan these lists of people.
131 00:13:00.777 ⇒ 00:13:05.450 Uttam Kumaran: So we need to get him those these lists of people in a tool.
132 00:13:05.830 ⇒ 00:13:06.610 Amber Lin: Oh!
133 00:13:06.818 ⇒ 00:13:10.360 Uttam Kumaran: It’s got. This is gonna be way much too complicated to explain right now. So I think.
134 00:13:10.360 ⇒ 00:13:16.200 Amber Lin: Okay, no, I just. I just need to know. I feel like I didn’t understand what the purpose was, unless.
135 00:13:16.200 ⇒ 00:13:16.550 Uttam Kumaran: Yeah.
136 00:13:16.550 ⇒ 00:13:26.020 Amber Lin: Of icps, into the sales tools based on.
137 00:13:27.210 ⇒ 00:13:30.890 Amber Lin: Okay, okay.
138 00:13:30.890 ⇒ 00:13:31.750 Uttam Kumaran: It’s a
139 00:13:31.870 ⇒ 00:13:36.640 Uttam Kumaran: it’s, I think, when Henry’s here I can. I think it’ll be a little bit easier, and then you could meet with them.
140 00:13:36.640 ⇒ 00:13:37.300 Amber Lin: Okay.
141 00:13:37.510 ⇒ 00:13:38.210 Uttam Kumaran: Yeah.
142 00:13:38.940 ⇒ 00:13:39.670 Amber Lin: Okay.
143 00:13:55.700 ⇒ 00:14:01.700 Amber Lin: would we guess? Once we have the technical plan, we should review it.
144 00:14:03.216 ⇒ 00:14:04.570 Amber Lin: Where’s Ryan?
145 00:14:05.850 ⇒ 00:14:10.140 Amber Lin: That’s what IP, icps, okay.
146 00:14:41.060 ⇒ 00:14:45.969 Uttam Kumaran: Yeah, I think I’ll it’ll be a lot more clear like when when me you and Henry meet.
147 00:14:47.670 ⇒ 00:14:49.020 Uttam Kumaran: I’m sorry. It’s just all like
148 00:14:49.120 ⇒ 00:14:51.729 Uttam Kumaran: it will just take me an hour to explain. Product.
149 00:14:51.730 ⇒ 00:14:52.850 Uttam Kumaran: Oh, yeah, don’t worry.
150 00:14:52.850 ⇒ 00:14:53.180 Amber Lin: Oh!
151 00:14:53.180 ⇒ 00:14:54.090 Uttam Kumaran: That right now.
152 00:14:54.090 ⇒ 00:14:58.879 Amber Lin: Don’t, don’t, don’t need to. I just know I need to know what we’re even doing like.
153 00:14:58.880 ⇒ 00:14:59.580 Uttam Kumaran: Yeah.
154 00:14:59.580 ⇒ 00:15:05.079 Amber Lin: I think that was enough. We’re just getting the list of people based on Icps. That’s all I need.
155 00:15:07.730 ⇒ 00:15:08.390 Uttam Kumaran: Yes.
156 00:16:16.530 ⇒ 00:16:19.360 Uttam Kumaran: dang my Linkedin post got 40 likes.
157 00:16:19.750 ⇒ 00:16:20.920 Amber Lin: What?
158 00:16:22.880 ⇒ 00:16:33.489 Uttam Kumaran: They made me look like I’m some sort of influencer. I just. I’m glad, like I’m not like creative director. Otherwise I would be like so embarrassed, but I let them do whatever it works.
159 00:16:33.490 ⇒ 00:16:43.899 Amber Lin: I know I am. I’m still needing to cross that line of I feel cringe that this is like, not my voice. But I know you, just you just let them be.
160 00:16:43.900 ⇒ 00:16:49.470 Uttam Kumaran: It is. No, it is. It is partly my voice, like I do review everything that goes out. But also
161 00:16:49.840 ⇒ 00:16:53.469 Uttam Kumaran: our voice isn’t. What go isn’t what drives traffic.
162 00:16:53.750 ⇒ 00:16:56.849 Uttam Kumaran: So I think I realize there has to be some blend like.
163 00:16:57.600 ⇒ 00:17:02.339 Uttam Kumaran: What makes my, what makes us what makes my perspective unique?
164 00:17:03.080 ⇒ 00:17:04.800 Uttam Kumaran: Ryan is good at like
165 00:17:04.930 ⇒ 00:17:13.210 Uttam Kumaran: doing the cause, what it’s also about readability. It’s about the hook like. So we collaborate like, I will go review everything and make edits.
166 00:17:13.400 ⇒ 00:17:17.169 Uttam Kumaran: But the AI. This process is getting a lot better, you know.
167 00:17:17.170 ⇒ 00:17:17.940 Amber Lin: Yeah.
168 00:17:18.500 ⇒ 00:17:19.270 Uttam Kumaran: Yeah.
169 00:17:51.895 ⇒ 00:17:53.410 Amber Lin: Oh, Hi! Ryan.
170 00:17:53.570 ⇒ 00:17:54.920 Ryan DeForest: Good morning! Good morning!
171 00:17:55.220 ⇒ 00:17:56.879 Uttam Kumaran: Hey! How are you?
172 00:17:56.880 ⇒ 00:17:57.980 Ryan DeForest: Good yourself.
173 00:17:57.980 ⇒ 00:18:00.480 Uttam Kumaran: Good dude. How’s the kids?
174 00:18:00.480 ⇒ 00:18:02.299 Ryan DeForest: Sorry about that.
175 00:18:02.300 ⇒ 00:18:02.720 Uttam Kumaran: Oh!
176 00:18:02.720 ⇒ 00:18:07.310 Ryan DeForest: Just a just a classic Friday. Remote working, you know, or.
177 00:18:07.310 ⇒ 00:18:08.280 Uttam Kumaran: Summer, day.
178 00:18:08.280 ⇒ 00:18:15.060 Ryan DeForest: The 2 and a half year old does not want to help your days, your week finish strong at all. You know one of those just just those.
179 00:18:15.500 ⇒ 00:18:17.700 Uttam Kumaran: What’s his or her name?
180 00:18:17.700 ⇒ 00:18:19.170 Ryan DeForest: Her name is Peyton.
181 00:18:19.880 ⇒ 00:18:21.190 Uttam Kumaran: Paying, awesome.
182 00:18:21.190 ⇒ 00:18:23.829 Amber Lin: Wow! I have never heard that name before.
183 00:18:23.960 ⇒ 00:18:25.080 Ryan DeForest: Yeah, my.
184 00:18:25.610 ⇒ 00:18:38.360 Ryan DeForest: my! My wife came up with the idea to name our daughter pay. And I was like, Oh, that’s cool. It’s unique whatever. I’m down. And then she turned a year old and I found out she actually named it after like a care, a girl that was on one of her favorite shows growing up like, oh.
185 00:18:38.750 ⇒ 00:18:39.140 Ryan DeForest: so
186 00:18:39.140 ⇒ 00:18:45.069 Ryan DeForest: it’s not even so. It’s not. It’s not original whatsoever. But we’re we’re rolling with it now. At this point.
187 00:18:45.070 ⇒ 00:18:48.069 Uttam Kumaran: That’s great. How’s the week going.
188 00:18:48.280 ⇒ 00:18:51.670 Ryan DeForest: So far so good, just kind of getting stuff done as much as we can.
189 00:18:52.390 ⇒ 00:18:59.639 Uttam Kumaran: Okay, cool. Yeah. We had a great meeting with with Victor and Caitlin yesterday, just helping them on the event. Analytics
190 00:18:59.830 ⇒ 00:19:01.300 Uttam Kumaran: side of things.
191 00:19:02.000 ⇒ 00:19:09.180 Uttam Kumaran: I mean, yeah, I guess I just want to jump. Maybe Amber. I’ll let you give a brief introduction and maybe get a little bit of like how we want to run this meeting, and we can just jump right into stuff.
192 00:19:09.970 ⇒ 00:19:14.570 Amber Lin: Okay, well, this meeting, mostly, we just wanna
193 00:19:15.080 ⇒ 00:19:32.330 Amber Lin: just understand how you want this project to land for your sales team. What tools they’re using, and we want to confirm on a weekly cadence right here. So whatever it works for you, we’ll put it on the calendar and then just check. If there’s any additional priorities.
194 00:19:32.630 ⇒ 00:19:39.450 Ryan DeForest: Okay, yeah, I have. Yeah, let me. I’m pulling it up right now.
195 00:19:40.030 ⇒ 00:19:49.610 Ryan DeForest: I mean, if I could share my screen and show you what I’m thinking, basically like an idea. So I. So I brought this up. I shared with you that Doc, like basically signals to give us
196 00:19:50.308 ⇒ 00:19:54.340 Ryan DeForest: ideas of people to go after. And basically
197 00:19:55.270 ⇒ 00:20:01.570 Ryan DeForest: I’m challenging the idea that today’s day and age, the Plg model is broken
198 00:20:01.780 ⇒ 00:20:23.309 Ryan DeForest: for for today’s day and age. And what I mean by that is that right now plg is like people sign up for to use a platform, whether that be for free or for cheap or set, etc, and then based off usage. You target them appropriately on what what to do and go for there. So I’m actually challenging this, that based off this idea that it’s more or less broken. And I’m taking this.
199 00:20:23.590 ⇒ 00:20:31.149 Ryan DeForest: the buzzwordy Gtm engineering that we all know that we’ve all seen multiple times and taking this idea of signals
200 00:20:31.330 ⇒ 00:20:35.080 Ryan DeForest: like external signals and attribute it to the plg model.
201 00:20:35.510 ⇒ 00:20:39.120 Ryan DeForest: And what I mean by that is like we’re gonna have like.
202 00:20:39.520 ⇒ 00:20:43.289 Ryan DeForest: when we turn into this plg, you can see this. Okay, my figma.
203 00:20:43.680 ⇒ 00:20:44.190 Uttam Kumaran: Yes.
204 00:20:44.630 ⇒ 00:21:06.519 Ryan DeForest: So we’re gonna turn when we once we turn this plg, and we’re like slamming it like we’re going after it. We’re just gonna have a shitload of leads come in and my sales team. There’s only gonna be like 2, maybe 3 of them, like by the end of the year. We don’t have like a direction or like a stra strategy around it. So I created this more or less. What I’m calling like a matrix like an ideal account profile matrix.
205 00:21:06.520 ⇒ 00:21:16.670 Ryan DeForest: where I’m I’m adding, instead of the Plg motion being just product usage, part of the equation. I’m adding the expansion potential based off external signals.
206 00:21:17.058 ⇒ 00:21:30.020 Ryan DeForest: So like, imagine like ad spend website, traffic is their team dispersed? How big is their team in general? Right? Like, do we see this as a technical sign up that requires a really long sales, cycle, etc, etc.
207 00:21:30.170 ⇒ 00:21:35.250 Ryan DeForest: That’s like the potential. And then based off, like the classic product usage for the people that sign up and use us
208 00:21:35.770 ⇒ 00:21:49.149 Ryan DeForest: puts us into specific quadrants, where, like, we don’t necessarily care about the folks here in the nurture niche nurture quadrant, but like we care about if they’re in one of these, because then our whole entire focus will be to get them into the top right?
209 00:21:50.448 ⇒ 00:22:14.320 Ryan DeForest: So. And this was just basically like an idea like a shower idea. I had lat on Monday, right like, this is like where and I and I’m basing this off the assumption that the that plg is not using the stuff that like this automated outbound blah blah! Blah like stuff that everyone has heard like a Bajillion. Times like Plg is not utilizing the same data when it should, when it should be.
210 00:22:14.320 ⇒ 00:22:27.603 Uttam Kumaran: Yeah, so what you would, what you would use during account based selling. And you have all these like things that you would research about account, and then you go after them. You kind of bring into that. It’s kind of a mix. Basically, it’s like a more
211 00:22:28.380 ⇒ 00:22:31.589 Uttam Kumaran: it’s a higher, enriched higher quality. Po like.
212 00:22:32.030 ⇒ 00:22:35.969 Uttam Kumaran: But plg, kind of implies like hands off approach.
213 00:22:36.360 ⇒ 00:22:42.580 Uttam Kumaran: or like a kind of like a product, roots itself, and then it kind of grows. And then you nurture it versus like.
214 00:22:42.840 ⇒ 00:22:47.570 Uttam Kumaran: Hey, we actually have existing clients. But we also, it’s kind of a little bit of a mix. Yeah.
215 00:22:47.800 ⇒ 00:23:03.729 Ryan DeForest: Yeah, yeah, so, and I think that’s kind of like where I would like our team to head. And like, I brought this up to Caitlin and Nico something like that. They’re super on board, of course, but like. But like that exact example you’re talking about, right is like this, this X-axis here, like your product. Your product is fantastic.
216 00:23:03.870 ⇒ 00:23:06.140 Ryan DeForest: Then this they should be way up here
217 00:23:06.410 ⇒ 00:23:15.729 Ryan DeForest: like on this side. But then, like you’re using, you’re missing the fact that maybe they have a sales team of 20 that says, 1st all around United States. So their routing is super important. So like we can then
218 00:23:15.900 ⇒ 00:23:35.670 Ryan DeForest: concentrate on getting them into routing into territories and stuff like that which is not even like part of like, like, it’s very hard for a product to push territories onto 2 team members. Right? So like, that’s where our sales team will then chime in like, Hey, look, I see you have 3 users routing like using us like, why don’t you look at Territories? Because it looks like your team is dispersed around United States right.
219 00:23:35.670 ⇒ 00:23:36.340 Uttam Kumaran: Yeah.
220 00:23:36.470 ⇒ 00:23:39.529 Ryan DeForest: So it’s kind of like combining the 2.
221 00:23:39.880 ⇒ 00:23:46.319 Ryan DeForest: because I like, I think that’s like the next, like frontier slash like next big wave. For like this plg like led
222 00:23:46.450 ⇒ 00:23:47.280 Ryan DeForest: model.
223 00:23:47.750 ⇒ 00:24:01.750 Uttam Kumaran: Yeah. So let me give me a couple of questions. So one is is, where do you expect your team to be? Sort of working and getting leads right now, because for my understanding, and you guys have post hoc, we also found you guys have amplitude with the tons of historic data.
224 00:24:01.960 ⇒ 00:24:06.803 Ryan DeForest: To be honest, we have a bunch of shit. There’s some shit there that I don’t. I don’t even know that we have to be honest.
225 00:24:06.990 ⇒ 00:24:22.130 Uttam Kumaran: Cool. We. We’ve done a lot of work in clay as well, like, probably to get some of these signals. But like, where would you like this to land for your team to triage and and get out like is this in a bi tool? Is this like in a hubspot, I guess like. Give me a sense of that.
226 00:24:22.590 ⇒ 00:24:25.420 Uttam Kumaran: Do you have any of you care either way?
227 00:24:25.420 ⇒ 00:24:29.919 Ryan DeForest: You could kind of call me a little crazy, but I would love it if it was like in salesforce.
228 00:24:30.330 ⇒ 00:24:31.010 Uttam Kumaran: Okay.
229 00:24:31.670 ⇒ 00:24:32.110 Ryan DeForest: And the.
230 00:24:32.110 ⇒ 00:24:34.350 Uttam Kumaran: You guys have existing salesforce instance.
231 00:24:34.350 ⇒ 00:24:34.799 Ryan DeForest: Oh, yeah.
232 00:24:35.096 ⇒ 00:24:36.280 Uttam Kumaran: Saw, yesterday. Yeah. Okay.
233 00:24:36.280 ⇒ 00:24:38.330 Ryan DeForest: Yeah, yeah, that’s probably that’s been like my biggest
234 00:24:38.880 ⇒ 00:24:42.890 Ryan DeForest: thing that I’ve been cleaning up and working on the last couple of weeks. Because, like
235 00:24:43.090 ⇒ 00:24:46.150 Ryan DeForest: and frankly like, I’m open to other avenues. But like.
236 00:24:46.150 ⇒ 00:24:57.869 Uttam Kumaran: I don’t mind. No, no, I actually would rather you be opinionated. And I and even in my company I don’t want tool sprawl. So if salesforce is the devil that you guys are using, then that’s where that’s where it’s gonna go.
237 00:24:57.870 ⇒ 00:24:59.170 Ryan DeForest: Yeah, yeah, it is.
238 00:24:59.170 ⇒ 00:25:04.960 Uttam Kumaran: Matter right? It’s it’s it’s just whichever one it’s. Actually, I think it’s more helpful to know that that’s where you needed to land.
239 00:25:04.960 ⇒ 00:25:08.239 Ryan DeForest: But but there’s 2 sides of this right? It’s like one of them is like.
240 00:25:08.350 ⇒ 00:25:32.670 Ryan DeForest: like, I want my team to be able to just like, Wake up in the morning log into salesforce and see like, have a report that they could refresh and see, here are the 20 accounts that you need to reach out to and send an email to, and why or like, they’re in a sequence right now, 2 of them replied. And we’re trying to get them up from activated to grow right like I just want them to like log in and like, be able to do like, take action. I’m open to like
241 00:25:33.330 ⇒ 00:25:45.200 Ryan DeForest: to moving it outside of that of of the salesforce side, but, like all of like, the source of truth, needs to happen within salesforce, for like from reporting like data structure lens, so like a lot.
242 00:25:45.200 ⇒ 00:25:53.839 Uttam Kumaran: So what what happens in between, like in between sourcing the data? You’re not. You don’t really have heavy opinions on, like if we use. If we, for example.
243 00:25:54.120 ⇒ 00:26:04.170 Uttam Kumaran: a common method here is like, okay, we we pull stuff out of salesforce. We enrich it in play. Or maybe we do manual enrichment in a data warehouse, and we route it back like.
244 00:26:04.170 ⇒ 00:26:06.009 Uttam Kumaran: yeah, you don’t realize what happens in the middle. Okay.
245 00:26:06.010 ⇒ 00:26:12.930 Ryan DeForest: No, I don’t care how the data gets there, or even how it’s like cleaned and and pushed in. That’s just like where
246 00:26:13.270 ⇒ 00:26:19.796 Ryan DeForest: I want to our my team to like, focus and work from and like, more or less, me to make reports from if that makes sense too.
247 00:26:20.580 ⇒ 00:26:25.830 Ryan DeForest: because then I could tie it to revenue. I could tie it to all that stuff and like lead source, and like close rates and all that shit.
248 00:26:26.370 ⇒ 00:26:37.079 Uttam Kumaran: No, I’m actually, I think it’s actually helpful to know that that’s the constraint. And are you? Are you currently reporting on salesforce like pipeline stuff in salesforce using salesforce. Okay.
249 00:26:37.080 ⇒ 00:26:40.030 Ryan DeForest: Yep, and I I say yes.
250 00:26:40.800 ⇒ 00:26:49.369 Ryan DeForest: like quickly, because it’s not really fully fleshed out. But like that’s obviously the the dream that we consistently like, are working towards.
251 00:26:49.750 ⇒ 00:26:56.580 Uttam Kumaran: Cool, and then in terms of like product usage metrics. Caitlin mentioned that it’s very light right now. That’s something, I think.
252 00:26:56.790 ⇒ 00:27:11.988 Uttam Kumaran: as a like precursor to us, getting you like, who’s using the product at what tiers, what we have to basically model that like, is there? Are you opinionated about the reporting side. There is that more of like a Caitlin world.
253 00:27:12.570 ⇒ 00:27:17.249 Uttam Kumaran: you know, if we were to build some rep, of course, if we were build reporting on product uses. I would
254 00:27:17.390 ⇒ 00:27:28.030 Uttam Kumaran: initially, we would just try to keep a lot of that in amplitude or in yeah, probably an amplitude or post hog. But of course, like, if we try to marry that with salesforce, we’ll need to do that
255 00:27:28.150 ⇒ 00:27:39.760 Uttam Kumaran: somewhere else, I guess. Like, if that’s your world, if you’re like, look, I’m only just focused on getting the sales thing up. Then we’re gonna work on that, anyways. But it’ll be a nice side effect of of this work stream.
256 00:27:39.760 ⇒ 00:27:41.900 Ryan DeForest: I mean to be honest where this is like
257 00:27:42.290 ⇒ 00:27:55.619 Ryan DeForest: couldn’t be like better timing, for that matter, like like this is all like transitioning by itself, right? And that’s why, like, I’m not really like. That’s why I’m not really pushing my team to get like number of seats and usage into salesforce right now, because, like shit’s gonna change in 6
258 00:27:55.620 ⇒ 00:27:56.040 Ryan DeForest: dance with
259 00:27:56.040 ⇒ 00:28:18.329 Ryan DeForest: you’ve seen so like for one like perfect timing in terms of that and 2 like. I’m fully on board to let you rock with it like what you think is best practice. I don’t have an opinion or a direction, I think. Me and Caitlin are more on the books where it’s like we give you like what our big ideas are. And then we let you guys rock it. You know what I mean from that perspective.
260 00:28:18.760 ⇒ 00:28:43.490 Uttam Kumaran: Okay. So I think, a couple of things. One, yeah, would be great to at least just get like 30 min on the calendar every week, and then ad hoc, we can expand that or whatever we’re big on slack. So you’re not gonna not hear from us. In between that time we’ll get everything into slack. Second thing is, would love to get access to salesforce. We have a brain forge, a default email address.
261 00:28:43.822 ⇒ 00:28:57.979 Uttam Kumaran: So if you can provision us, and we’ll send you this in in a note after. But if you can provision us a seat on salesforce that would be great. And then, yeah, I’m basically what I’m gonna do from your Icp signal sheet is, I’m kind of gonna create
262 00:28:58.050 ⇒ 00:29:06.389 Uttam Kumaran: a little bit of like what the ideal like enrichment model is going to be for a default user. And
263 00:29:06.490 ⇒ 00:29:09.379 Uttam Kumaran: that way again, a lot of these are
264 00:29:09.770 ⇒ 00:29:24.032 Uttam Kumaran: are things that you have in your product things that are coming from like a Linkedin. Things are coming from like maybe a clear bit or zoom info things that maybe are coming from like a Facebook ads, marketplace research, right?
265 00:29:24.380 ⇒ 00:29:25.579 Ryan DeForest: Tm, rush, stuff.
266 00:29:25.580 ⇒ 00:29:37.000 Uttam Kumaran: Yeah, it may come from. It may come from scraping. Their website may come from trustpilot. Right? So I’m gonna basically break that down. Our team will will. Basically, I’ll then show you cool. This is the way we’re gonna attack it.
267 00:29:37.420 ⇒ 00:29:43.139 Uttam Kumaran: And and then we’ll crush through that. I think again, my goal is for everything to end up in the salesforce
268 00:29:43.656 ⇒ 00:29:58.000 Uttam Kumaran: company or or person object, basically. And I assume you guys are using opportunities for stuff. And then, yeah, ideally, I once I give you those we can collaborate together on creating the filtered reports for for the teams and sort of
269 00:29:58.110 ⇒ 00:30:02.560 Uttam Kumaran: checking those out. I think it would be helpful to also
270 00:30:02.880 ⇒ 00:30:07.159 Uttam Kumaran: back tested a bit like, look at some of the clients that have succeeded.
271 00:30:07.300 ⇒ 00:30:12.630 Uttam Kumaran: Take a look at what properties we scrape for them, and then make sure, like, hey, had this person been low usage.
272 00:30:12.630 ⇒ 00:30:13.029 Ryan DeForest: Love it.
273 00:30:13.030 ⇒ 00:30:15.249 Uttam Kumaran: Would they have been flagged? Right?
274 00:30:15.250 ⇒ 00:30:15.870 Uttam Kumaran: Love it. That’s
275 00:30:15.870 ⇒ 00:30:25.020 Uttam Kumaran: sort of our like. Okay, what like is the process working? I I think, like, I’ll decide on like whether this is clay or a mix of something.
276 00:30:26.330 ⇒ 00:30:39.899 Uttam Kumaran: it’s Clay’s been really nice. So I may consider that also, because they have a lot of external scrapers. So ideally kind of the architecture is like, we want something that we can similar to the kind of the products that you guys are building except
277 00:30:39.900 ⇒ 00:30:59.228 Uttam Kumaran: way, more lighter version. It’s just like we want to continue to add signals, add new sources as you guys come up with, hey, like, it’d be interesting for us to get like their Google Maps reviews. Okay, like, that’s some like this is hypothetical. Okay, let’s add that to the the thing. And like, let’s map that to a salesforce field and let’s get that in. And then a new report gets created.
278 00:30:59.490 ⇒ 00:31:02.009 Ryan DeForest: There’s even going to be like a world like.
279 00:31:02.930 ⇒ 00:31:17.800 Ryan DeForest: I rather us like Po position this as like an ever growing, expanding thing as well, right? Because, like, there’s a world that once once this new version of defaults up and running, we’re taking snapshots of their Crm, and we’re capturing that right? So like we’ll be able to see like
280 00:31:18.100 ⇒ 00:31:28.229 Ryan DeForest: close rates like how big their deal sizes are and stuff like that like. Obviously, we won’t surface that information like publicly or Internet. But like, we can use that internally as like a signal, right? So like.
281 00:31:28.230 ⇒ 00:31:28.630 Uttam Kumaran: Exactly.
282 00:31:28.630 ⇒ 00:31:46.230 Ryan DeForest: Like we know that deal size. If there, if your deal size is over 50 k, then we know that that 80% of our customers use this product every single day. You know what I mean like the skew so like, that’s a whole. Another piece of the puzzle there that, like we need to keep this like open ended, that we could continue to add and refine and work and and go from there.
283 00:31:47.260 ⇒ 00:31:53.220 Uttam Kumaran: Yeah, I mean, and you know, I think the last probably potential thing is for you guys to consider using
284 00:31:53.380 ⇒ 00:32:03.930 Uttam Kumaran: some level of AI to do like lead prioritization or lead scoring. What you’ll find that’s complicated in salesforce, of course, is like all of these, you’re gonna have to be like.
285 00:32:04.170 ⇒ 00:32:23.520 Uttam Kumaran: you’re gonna have to kind of hard code filters to build these reports. One thing that we can start to maybe consider working on is like one or many like qualitative AI llm driven flags. Given all of the different properties that we know about this.
286 00:32:23.860 ⇒ 00:32:37.469 Uttam Kumaran: it should we flag this as like a higher, low priority. Right that way we we move from the limitation of like, oh, we need to come up with these signal combinations to something more, less like deterministic. It’s more like
287 00:32:37.510 ⇒ 00:33:05.020 Uttam Kumaran: these fit our profile. And then you can also get the reasoning right. So you mentioned, why is the reason? That’s a great way to use AI basically to take those properties of the customer and then provide you with provide the salesperson, hey? Here’s like a quick 2 line reason for why to prioritize and even like what to say, or like those types of things. So I think, looking at my past and given the technology that’s available. Now, that is something that, like, I think.
288 00:33:05.240 ⇒ 00:33:09.930 Uttam Kumaran: is totally valid, and would would honestly allow you to
289 00:33:10.090 ⇒ 00:33:22.290 Uttam Kumaran: ideally leapfrog like someone in the salesforce world having to create these reports, then going for something like this is a default certified high prior lead. And then all that’s the only filter you have. Right like something like that.
290 00:33:22.290 ⇒ 00:33:27.337 Ryan DeForest: Yep, yep, yep, I’m on board. I’m on board with whatever, with whatever direction I kind of give you like.
291 00:33:27.760 ⇒ 00:33:31.349 Ryan DeForest: My my word, my word, my word, vom high level idea.
292 00:33:31.350 ⇒ 00:33:32.709 Uttam Kumaran: No, no, no! Got it!
293 00:33:32.710 ⇒ 00:33:33.340 Ryan DeForest: Yeah, so.
294 00:33:33.340 ⇒ 00:33:33.700 Uttam Kumaran: So.
295 00:33:33.700 ⇒ 00:33:34.040 Ryan DeForest: I love.
296 00:33:34.040 ⇒ 00:33:41.220 Uttam Kumaran: If you could send me also that notion to that brain forge a default amber we can mention to follow up on that
297 00:33:42.450 ⇒ 00:33:44.130 Uttam Kumaran: And then what else?
298 00:33:45.090 ⇒ 00:33:57.540 Ryan DeForest: That’s it. Dude, those are the top those are. That’s the that’s the big thing top of mind. It could expand and grow, and I could have another random thought over a glass of whiskey like usual. But like, this is kind of like where I’m going, like, I like.
299 00:33:57.650 ⇒ 00:34:02.339 Ryan DeForest: like, I’m trying to scale this to a hundred 1 million with like 5 people, right like.
300 00:34:02.340 ⇒ 00:34:15.800 Uttam Kumaran: And then tell me what’s been what has been working so far like, I assume that the the reason you gave us sort of the signals is because you’re seeing that some of those are anecdotally working. But tell me, what’s the motion been so far and like what’s been working.
301 00:34:16.030 ⇒ 00:34:26.250 Ryan DeForest: Motion is just. People have been signing up for inbound like from an inbound form. They’re coming to us with a problem that they’re looking to solve. And like a lot of these signals are like, kind of
302 00:34:26.600 ⇒ 00:34:36.420 Ryan DeForest: have all been kind of like the same across the board, like like someone’s coming to us like, we know, for 10,000 people are coming to our website a month. But our conversion rates. We have no idea what the conversion rate is like.
303 00:34:36.900 ⇒ 00:34:41.110 Ryan DeForest: help us solve that like that sort of stuff. And then on the on the flip side is like.
304 00:34:41.219 ⇒ 00:34:45.560 Ryan DeForest: like, for example, dispersed sales team like people that are on Chili Piper, and stuff like that.
305 00:34:45.560 ⇒ 00:34:46.330 Uttam Kumaran: Yes.
306 00:34:46.330 ⇒ 00:34:51.380 Ryan DeForest: Chili pepper sucks for like a dispersed sales team, like managing a fast growing sales team. So like.
307 00:34:51.380 ⇒ 00:34:51.760 Uttam Kumaran: Yeah.
308 00:34:51.760 ⇒ 00:35:05.429 Ryan DeForest: Like, and we manage that super easy. So people come to us like, Look, we’re growing 10 x and sales and Chili pepper is falling behind. Right? So like, that’s why that’s a signal, too. So like these have all kind of come up like from either anecdotally from customers.
309 00:35:05.430 ⇒ 00:35:09.460 Uttam Kumaran: Are you guys targeting Chili piper customers like specifically.
310 00:35:09.460 ⇒ 00:35:16.180 Ryan DeForest: I would say, yes, we’re targeting them, but we’re kind of more or less like letting them come to us and hear through the grapevine like.
311 00:35:16.640 ⇒ 00:35:30.980 Ryan DeForest: We just signed a company called Owner for 50 K. And they literally had a contract on their desk from Chili Piper that they’re about to sign for 30 K. And they and they and they decided to check us out. And then in 2 weeks they signed for 50 K.
312 00:35:30.980 ⇒ 00:35:49.660 Uttam Kumaran: The only reason is like we started using this tool internally, and this will be something that we bring in is we’ve been using their stack. It’s they just it just literally scans job postings. And like the the Linkedin descriptions of people that work at the company to basically find out what tools the company is using.
313 00:35:49.660 ⇒ 00:35:51.479 Ryan DeForest: How much different is this than build with.
314 00:35:51.880 ⇒ 00:36:11.939 Uttam Kumaran: Built with. So the way built with works is, it looks. It looks at the the page, and it looks at like the the snippets in the page to figure out whether you’re using Hubspot and these things. It’s similar. But these guys they start with the job. For example, in my business. I’m looking for people with like different data tools that’s not gonna end up in your
315 00:36:12.110 ⇒ 00:36:19.590 Uttam Kumaran: website, you know, like whether you use snowflake or something. But you’re there’s going to be a data engineer job posting that where you’ve mentioned Snowflake.
316 00:36:19.830 ⇒ 00:36:24.979 Uttam Kumaran: That’s a great right? So like. But this is something perfectly where I I would be like cool. We’re gonna bring in
317 00:36:25.330 ⇒ 00:36:26.970 Uttam Kumaran: their staff for.
318 00:36:26.970 ⇒ 00:36:27.600 Ryan DeForest: Yep.
319 00:36:27.600 ⇒ 00:36:30.430 Uttam Kumaran: The competitive tool set right like field.
320 00:36:30.870 ⇒ 00:36:52.339 Ryan DeForest: Yeah, and that. And that’s and that’s 1 of the signals right? Like, that’s the bullet point is like competitors. So like Chili Piper revenue hero, if they’re only using hubspot forms or marketo forms, right? Like, it’s funny, because, like, we call them competitors just for a bullet point. But, like me and Nico are very strong that we don’t compete with them like we don’t lose to them so like that’s not.
321 00:36:52.340 ⇒ 00:36:52.740 Uttam Kumaran: Yeah.
322 00:36:52.740 ⇒ 00:36:53.510 Ryan DeForest: Petition.
323 00:36:53.510 ⇒ 00:36:54.399 Uttam Kumaran: Yeah, yeah, yeah, yeah.
324 00:36:54.400 ⇒ 00:36:57.410 Uttam Kumaran: But it’s an easy like, Rip and replace, like we just rip and replaced
325 00:36:57.800 ⇒ 00:37:09.719 Uttam Kumaran: somebody came up to us with revenue hero, and they’re paying revenue hero $9,000. And now they’re paying us almost 30,000 right? Like like, it’s not like a competition, because we we don’t really lose and hit right now, hopefully.
326 00:37:09.720 ⇒ 00:37:13.204 Ryan DeForest: Me to start hopefully, we do start losing so that we start learning more.
327 00:37:14.680 ⇒ 00:37:18.910 Ryan DeForest: but but like, that’s like a signal, right like, are they using somebody like that for sure?
328 00:37:18.910 ⇒ 00:37:20.309 Uttam Kumaran: Okay, okay.
329 00:37:21.390 ⇒ 00:37:26.639 Uttam Kumaran: okay, perfect. So I think that’s kind of all we need. I don’t know amber. Is there any other questions
330 00:37:26.990 ⇒ 00:37:28.150 Uttam Kumaran: on our side?
331 00:37:31.410 ⇒ 00:37:34.300 Amber Lin: I would love to grab a time on the calendar just right now.
332 00:37:34.300 ⇒ 00:37:39.170 Ryan DeForest: Yep, yeah, let’s do. If Wednesdays are probably the best, I’ll do like Wednesdays like
333 00:37:39.700 ⇒ 00:37:41.820 Ryan DeForest: 10 am. If that works 11 Am.
334 00:37:41.820 ⇒ 00:37:43.910 Amber Lin: NAMR ENES t.
335 00:37:44.080 ⇒ 00:37:45.449 Ryan DeForest: I am. Pst! Sorry.
336 00:37:45.450 ⇒ 00:37:53.950 Amber Lin: Oh, you’re in. Pst, okay, that works perfectly for me. So 11 Am. Pst, for 30 min each week.
337 00:37:53.950 ⇒ 00:37:54.779 Ryan DeForest: That works. Yep.
338 00:37:54.780 ⇒ 00:37:58.320 Amber Lin: Awesome. Okay, I will send that over to you.
339 00:37:58.590 ⇒ 00:38:02.349 Amber Lin: and then I will reach out afterwards for
340 00:38:02.560 ⇒ 00:38:09.560 Amber Lin: 2 things. So to add our email to the to salesforce, and also to send a notion Doc, over.
341 00:38:09.560 ⇒ 00:38:12.570 Ryan DeForest: Yeah, if you could just send that to me as a reminder, I’ll do that by today.
342 00:38:12.570 ⇒ 00:38:12.990 Amber Lin: Yeah.
343 00:38:12.990 ⇒ 00:38:18.080 Uttam Kumaran: And then I’m gonna I’ll ask Caitlin about like the other if there’s any floating tools otherwise, and try to get better.
344 00:38:18.080 ⇒ 00:38:21.679 Ryan DeForest: Dude. I just I just found out about one last last week. It’s it’s great.
345 00:38:21.680 ⇒ 00:38:22.629 Uttam Kumaran: Like the clap.
346 00:38:22.630 ⇒ 00:38:25.559 Ryan DeForest: It’s like new Ops guys scenario where you come in. And there’s like.
347 00:38:25.560 ⇒ 00:38:35.539 Uttam Kumaran: I was happy, though when I was in the office I was happy. Caitlin showed me amplitude. I was like dude. You guys have been tracking this for the lifetime of the company. This is perfect, and she’s like no one’s touching this. I’m like
348 00:38:35.720 ⇒ 00:38:38.340 Uttam Kumaran: at least someone put the snippet up at some point.
349 00:38:38.340 ⇒ 00:38:40.529 Ryan DeForest: The snippet is there? It exists somewhere.
350 00:38:41.580 ⇒ 00:38:48.995 Ryan DeForest: No, yeah, so perfect. Perfect. So I look forward to that meeting then next week, and I’ll get you folks access and send you that figma today?
351 00:38:49.730 ⇒ 00:38:51.689 Ryan DeForest: and then we could kind of see what’s up next week.
352 00:38:51.690 ⇒ 00:38:55.879 Uttam Kumaran: Am I good to sort of comment in there, and like, maybe use that to like. Do not. Okay.
353 00:38:55.880 ⇒ 00:38:56.880 Ryan DeForest: Oh, crazy. Yeah.
354 00:38:56.880 ⇒ 00:38:57.720 Uttam Kumaran: Okay. Okay.
355 00:38:58.130 ⇒ 00:38:58.660 Uttam Kumaran: Alright. Man.
356 00:38:58.660 ⇒ 00:39:00.920 Ryan DeForest: Awesome, perfect. I appreciate it. Team. Have a good weekend.
357 00:39:01.600 ⇒ 00:39:02.010 Amber Lin: That’s right.
358 00:39:02.010 ⇒ 00:39:03.280 Ryan DeForest: Okay? Bye.
359 00:39:03.470 ⇒ 00:39:04.190 Amber Lin: Bye.