Meeting Title: Signal Analysis and Product Analytics Sync Date: 2025-07-24 Meeting participants: Uttam Kumaran, Ryan DeForest, Mustafa Raja, Henry Zhao, Lev Katreczko
WEBVTT
1 00:01:02.780 ⇒ 00:01:03.620 Ryan DeForest: Yo.
2 00:01:05.960 ⇒ 00:01:06.860 Uttam Kumaran: Yo.
3 00:01:07.200 ⇒ 00:01:08.149 Ryan DeForest: What’s up? Dude.
4 00:01:08.880 ⇒ 00:01:09.860 Uttam Kumaran: How’s everything?
5 00:01:10.980 ⇒ 00:01:25.290 Ryan DeForest: It’s going. I apologize for the salesforce shit. It was like I tried to take out just buying one. I try to take out seats, and then Salesforce wasn’t letting me take it out, and then I took it out. Now it’s a refresh. I’ll I’ll just gonna buy some shit tomorrow.
6 00:01:26.040 ⇒ 00:01:26.970 Ryan DeForest: Figure out later.
7 00:01:27.250 ⇒ 00:01:28.199 Uttam Kumaran: That’s fine.
8 00:01:28.490 ⇒ 00:01:31.220 Uttam Kumaran: We got a bunch of other stuff done, so I think we’ll
9 00:01:31.670 ⇒ 00:01:33.850 Uttam Kumaran: we’ll have enough to kind of run through today.
10 00:01:34.160 ⇒ 00:01:35.260 Ryan DeForest: Let’s see, let’s see.
11 00:01:36.148 ⇒ 00:01:38.720 Uttam Kumaran: Cool. Should we wait for Lev.
12 00:01:39.719 ⇒ 00:01:43.151 Ryan DeForest: Let me just send him this real quick.
13 00:01:49.490 ⇒ 00:01:50.869 Ryan DeForest: Oh, there he is!
14 00:01:53.240 ⇒ 00:01:54.120 Uttam Kumaran: Perfect.
15 00:01:54.540 ⇒ 00:01:56.849 Uttam Kumaran: Hey? Everyone, hey? Love! Nice to meet you.
16 00:02:00.400 ⇒ 00:02:02.610 Uttam Kumaran: I assume you’re gonna say nice to meet you, too.
17 00:02:05.390 ⇒ 00:02:05.860 Ryan DeForest: Connecting.
18 00:02:05.860 ⇒ 00:02:08.050 Uttam Kumaran: Yeah, yeah.
19 00:02:08.190 ⇒ 00:02:09.459 Henry Zhao: Hey? Nice to meet you.
20 00:02:09.979 ⇒ 00:02:10.530 Ryan DeForest: Hey, Henny.
21 00:02:11.140 ⇒ 00:02:11.690 Lev Katreczko: Guys.
22 00:02:12.590 ⇒ 00:02:13.430 Uttam Kumaran: A
23 00:02:13.950 ⇒ 00:02:34.979 Uttam Kumaran: so you have. Yeah, folks on on our side. So Henry is sort of our product analytics Cdp expert. So it’s gonna be helping a lot with stuff on the amplitude side. Mustafa is gonna be helping with a bunch of stuff on the signal side, which is what we’re gonna focus. The 1st part of the meeting on could just jump into stuff. You guys.
24 00:02:35.710 ⇒ 00:02:39.110 Uttam Kumaran: we don’t have any small talk to get through.
25 00:02:39.940 ⇒ 00:02:52.879 Ryan DeForest: Yeah. Lev, I’m gonna start involving Lev on a lot of stuff. You could think of it like me with the crazy, stupid ideas. And Lev is the one that’s actually gonna like, put pen to paper and get shit done. I’m the user.
26 00:02:52.880 ⇒ 00:02:53.220 Uttam Kumaran: Okay.
27 00:02:53.220 ⇒ 00:03:02.250 Ryan DeForest: I’m the useless one. Live is a useful one here, so but you could just think of us like as a tag team for this, like not only like now, but in the future as well.
28 00:03:02.830 ⇒ 00:03:23.669 Uttam Kumaran: Okay, okay, awesome, great. So I think we wanted to spend this meeting in 2 ways. 1st part of this, I just wanted to talk through the signals. Work, and then the second I’ll let Henry kind of gives ask some questions about product analytics and the product. So let me just share
29 00:03:23.980 ⇒ 00:03:26.012 Uttam Kumaran: my screen here.
30 00:03:27.730 ⇒ 00:03:29.580 Uttam Kumaran: And I will.
31 00:03:30.121 ⇒ 00:03:32.088 Uttam Kumaran: I I’ll just send this in the
32 00:03:32.540 ⇒ 00:03:36.307 Uttam Kumaran: in the chat again here. So everyone has it. But basically
33 00:03:36.770 ⇒ 00:03:56.149 Uttam Kumaran: Ryan, we took sort of your 1st pass that like, Hey, we wanna start to target people based on signals and then create these like, really tailored lists. For sales, people to wake up and just have, like, really, the the prime leads to go after we basically started to break that down. So what you’ll see here in the 1st
34 00:03:56.330 ⇒ 00:03:59.250 Uttam Kumaran: section is just a list of signals that
35 00:03:59.682 ⇒ 00:04:01.698 Uttam Kumaran: we heard and that we were thinking of.
36 00:04:02.530 ⇒ 00:04:22.330 Uttam Kumaran: I think, I. Some of these are certainly like too specific. Some of these are kind of duplicates. Some of these are where it really affected. Some of these are probably like I don’t know, like everybody might have that. So I think one thing that could be helpful. Lev is, or or from you. Ryan is just to hear
37 00:04:23.070 ⇒ 00:04:52.939 Uttam Kumaran: kind of like what is what like stands out when I when you, when I kind of share this list. To kind of walk through this and like how this is set up. So we basically categorize a bunch of signals. These are signals, of course, on the leads themselves. We’ve we’ve said, like what tool we’re gonna use and sort of like the mechanism inside that tool. This allows us to start to add more signals. If we get signals from our product analytics from other sources, it’ll be easy. And then sort of feasibility is like me and Mustafa working on. Okay, like, how
38 00:04:53.320 ⇒ 00:05:11.480 Uttam Kumaran: how far are we from from getting to this? You know. Cause, for example, some of these are like, Hey, go check if they have long qualification forms on their demo booking. Right? Okay? So that involves like scraping their site. Probably doing some sort of, you know, using AI or something to basically identify that kind of like a medium
39 00:05:11.950 ⇒ 00:05:13.150 Uttam Kumaran: versus like
40 00:05:13.260 ⇒ 00:05:22.930 Uttam Kumaran: check. If they use lean data. Okay, that’s something that we can just use their stack and get that. So we sort of looked at it like efficiency and optimization
41 00:05:23.290 ⇒ 00:05:32.430 Uttam Kumaran: stuff on like growth and expansion, right? Like, based on like the amount of sales people. The types of tools are using market positioning and then team structure. And then
42 00:05:33.060 ⇒ 00:05:35.440 Uttam Kumaran: a lot of stuff on like, hey? Do they have
43 00:05:36.146 ⇒ 00:05:39.799 Uttam Kumaran: plg motions? What is their headcount growth?
44 00:05:40.446 ⇒ 00:05:55.629 Uttam Kumaran: So I think overall. Maybe I can just get your gut gut reaction to seeing this and then in the combination side. This is where I think the culmination of when we can get a like a little bit of a priority. On what important signals are
45 00:05:55.740 ⇒ 00:06:02.230 Uttam Kumaran: we can start to build some of those combinations so that we can drive towards like a 1st salesforce report?
46 00:06:02.848 ⇒ 00:06:05.619 Uttam Kumaran: So I’m gonna pause there and just get reaction.
47 00:06:08.840 ⇒ 00:06:31.720 Lev Katreczko: Yeah, I can jump in. So I think at like, 1st pass. Obviously, we’re we’re all where. Not all of these signals are either the most useful, or probably feasible, to scrape on a recurring basis so definitely seeing eye to eye. There. I am really interested in learning a little bit about the philosophy behind, like the signal combinations.
48 00:06:31.920 ⇒ 00:06:32.510 Uttam Kumaran: Okay.
49 00:06:32.510 ⇒ 00:06:50.649 Lev Katreczko: And then, yeah, zeroing in on. Let’s just say, for the time being, some of the ones that would going back to like just the the signal list some of the things that would be the easiest to operationalize like, you know. How. How would you recommend best practices for kind of
50 00:06:51.000 ⇒ 00:06:59.329 Lev Katreczko: building out some repeatable workflows that take some of the legwork away from the constant, like monitoring and list building associated with this stuff.
51 00:06:59.820 ⇒ 00:07:07.400 Uttam Kumaran: Yeah. So like, let me give you an example book. A meeting as like the primary Cta is a simple like, just have to scrape their page, and then we would
52 00:07:07.710 ⇒ 00:07:14.160 Uttam Kumaran: we could either run, rejects, or pass it to an like a cheap Llm. To basically find out is that does that exist?
53 00:07:14.772 ⇒ 00:07:18.479 Uttam Kumaran: Something like, do they have a long qualification form?
54 00:07:18.936 ⇒ 00:07:37.039 Uttam Kumaran: This is something where we either we’ll we will have to like basically either build a little bit of an of a web agent that can like find that, open it and like dissect those form fields if it’s not immediately apparent. But things like, Hey, like this is some examples of like company content mentioning lead scoring.
55 00:07:37.370 ⇒ 00:07:39.950 Uttam Kumaran: Is that really like that’s like, seems
56 00:07:40.080 ⇒ 00:07:52.009 Uttam Kumaran: it could either be too broad, or it also could attract companies that aren’t really in there. It’s really for the for something like that would be that paired with another more critical signal right so, and it’s it’d be an and
57 00:07:52.490 ⇒ 00:08:00.069 Uttam Kumaran: however, I do think like the thing that we found is really powerful would be like using their stack or
58 00:08:01.089 ⇒ 00:08:10.350 Uttam Kumaran: I forgot what the other main one, but basically like looking at their their tools, right? So if they’re using a comp competitive tools if they fit your Icp in terms of
59 00:08:10.500 ⇒ 00:08:17.059 Uttam Kumaran: sales team size and maybe there’s a growth in like the sales team that seems like a perfect combination.
60 00:08:17.210 ⇒ 00:08:21.859 Uttam Kumaran: So I guess if if nothing is like immediately apparent and like.
61 00:08:22.550 ⇒ 00:08:45.280 Uttam Kumaran: we don’t want to do this. And my, the way I would prioritize this is by going after the stuff. That’s the easiest feasibility. And the signal combinations as they stand today like these are just, I think, things that Ryan we took from your previous talk, so I wouldn’t take these as as final yet. I sort of wanted to just hear if there’s any sort of signal that’s particularly
62 00:08:45.490 ⇒ 00:08:57.110 Uttam Kumaran: either like really powerful, that you know, from your existing sales motions. Or if there’s like a wish list of stuff they’re like, Hey, is this possible? If not, then we will basically try to
63 00:08:57.410 ⇒ 00:09:03.370 Uttam Kumaran: make that jurisdiction ourselves, kind of looking at the feasibility and then propose a couple of combinations.
64 00:09:07.580 ⇒ 00:09:08.385 Ryan DeForest: Yeah.
65 00:09:10.440 ⇒ 00:09:20.269 Ryan DeForest: I mean to. To be frank, this is like scratching the surface right? So we don’t really know like how detail or deep you want to go into. But like like even one thing, that when I was looking through these, it’s
66 00:09:20.440 ⇒ 00:09:34.590 Ryan DeForest: one thing that was kind of missing, too, is like, when how does the actual form on the website like act slash work like, if you were to put your email address, name and a company does it? Say we’ll contact you? We’ll contact you later.
67 00:09:34.590 ⇒ 00:09:34.980 Uttam Kumaran: I see.
68 00:09:34.980 ⇒ 00:09:36.819 Ryan DeForest: Some type stuff like that. So like
69 00:09:37.110 ⇒ 00:09:47.470 Ryan DeForest: even like there could be like an angle of like a browser, less like a headless sort of agent running sort of vibes, but like, obviously, that’s like way deeper down the the rabbit hole.
70 00:09:47.470 ⇒ 00:09:53.030 Uttam Kumaran: But I guess that’s helpful to hear if you’re like, Hey, look! A crappy form on a website is like a great indicator.
71 00:09:53.150 ⇒ 00:09:56.409 Uttam Kumaran: Then that’s worth spending the time on
72 00:09:57.670 ⇒ 00:10:09.159 Uttam Kumaran: right or that. And that’s what like I I kind of want to hear is like there may be some things that we’ve listed as like difficult feasibility. But I want to hear that if the impact of that is like
73 00:10:09.320 ⇒ 00:10:17.470 Uttam Kumaran: it’s, and that’s also great, because it’s so specific, and it’s a great call out for your sales team to then bring up right? So if that’s
74 00:10:18.130 ⇒ 00:10:23.219 Uttam Kumaran: if that’s like an ex, that’s an example of something where I would, it’d be like, Okay, cool. That’s a priority like Crappy.
75 00:10:23.220 ⇒ 00:10:26.249 Ryan DeForest: Like, like, if like, if like, if Lev was in a
76 00:10:26.370 ⇒ 00:10:33.320 Ryan DeForest: target at a specific company, he will literally go on the website and put in like a fake email, fake phone number and see what happens right? And then he can talk to it.
77 00:10:33.870 ⇒ 00:10:34.660 Uttam Kumaran: Yeah.
78 00:10:34.660 ⇒ 00:10:47.450 Lev Katreczko: Yeah. So I I wanted to add a little piece here. It’s kind of related to the the topic of whether or not like an agent needs to be involved. Or at least one with like a little bit more complexity. So
79 00:10:47.720 ⇒ 00:10:58.970 Lev Katreczko: I would put a lot of these websites into 2 buckets. One of them, the ideal situation is, you can go on the website. You can use Claygen. Just ask, like, built with whatever it is.
80 00:10:58.970 ⇒ 00:10:59.560 Uttam Kumaran: Yeah.
81 00:10:59.560 ⇒ 00:11:06.180 Lev Katreczko: Pull the tool very easily. This is an example of a company where the tool that they’re using is more or less showing up
82 00:11:06.400 ⇒ 00:11:27.000 Lev Katreczko: on the homepage like the HTML of the homepage. Now, for whatever reason, that’s not always the case, especially for maybe more of an enterprise fit. There are situations where that code for the tool is not going to be accessible until you make it like pretty deep into the demo booking flow. Sometimes
83 00:11:27.260 ⇒ 00:11:31.660 Lev Katreczko: it’s not even available until after a form is submitted.
84 00:11:32.340 ⇒ 00:11:58.340 Lev Katreczko: this is kind of a nuance that Hubspot creates because Hubspot code can exist on websites that both have instant Hubspot scheduling where like a calendar, will pop up. But it’s hubspot code is also available on websites where they have manual scheduling, and those are like 2 completely different talk tracks to completely different combinations of pain, to discuss an outreach.
85 00:11:59.111 ⇒ 00:12:11.000 Lev Katreczko: And and that is a situation where it literally requires, like that level of manual research to to kind of get to the kernel there. So I know that was like a lot of information. But
86 00:12:11.000 ⇒ 00:12:12.409 Lev Katreczko: yeah, that makes sense.
87 00:12:12.830 ⇒ 00:12:34.850 Uttam Kumaran: I, I think, like the reason why I like this company their stack. Because we so for for our business, like we, we do a lot of data work and tools like snowflake and things. There’s no pixels for that work, right? So the way their stack works is they’re scraping off of job descriptions. So that’s something that is one layer d. But you’re right, like the particular hubspot product that’s being used is tougher.
88 00:12:35.050 ⇒ 00:12:47.699 Uttam Kumaran: What I mean. But again like this is where? Because you guys are narrowly focused on forms, there is something to go after previously. We’re just looking at, built with. But if there is like, Hey, open the forms, page.
89 00:12:47.860 ⇒ 00:12:53.090 Uttam Kumaran: and then take a snapshot of like what’s what’s being, you know, rendered
90 00:12:53.320 ⇒ 00:12:56.789 Uttam Kumaran: there’s opportunity there. But that’s helpful to hear that like.
91 00:12:56.980 ⇒ 00:12:59.340 Uttam Kumaran: okay, the forms is definitely something to focus on
92 00:12:59.845 ⇒ 00:13:05.419 Uttam Kumaran: because also other folks will be using a lot of these same signals. And we’re gonna get a lot of noise
93 00:13:05.530 ⇒ 00:13:09.950 Uttam Kumaran: from these. So this is just like the kind of like grand list.
94 00:13:10.090 ⇒ 00:13:13.420 Uttam Kumaran: I probably estimate we’ll cut this in half.
95 00:13:13.740 ⇒ 00:13:14.989 Ryan DeForest: Yeah, most likely.
96 00:13:15.200 ⇒ 00:13:16.750 Ryan DeForest: But in the form is like
97 00:13:17.280 ⇒ 00:13:40.289 Ryan DeForest: you could think of it almost like a starting point. Right? So like, if you go onto their website. And you see that they have Chili Piper. You’re like, Okay, automatically, I know I can go 10 different directions. Then I know that their sales team has grown 50% in the last 6 months. Chili Piper is dog shit with a scaling sales organization. Right? So like, now, you know, it’s like, okay, growing sales team. And they’re using Chili Piper. Now, I know exactly what I could target. And why? Right? So like.
98 00:13:40.290 ⇒ 00:13:40.720 Uttam Kumaran: Yeah.
99 00:13:40.720 ⇒ 00:13:44.389 Ryan DeForest: Like the form is a starting point for a whole. Another rabbit hole of like things that you can do.
100 00:13:44.650 ⇒ 00:13:50.349 Uttam Kumaran: Okay, okay, okay, so then, how yeah, sorry. Go ahead. Lev.
101 00:13:50.350 ⇒ 00:13:59.259 Lev Katreczko: Yeah, I I just wanted to add one more note, because it was brought up earlier. I would say a broader signal that I’d really like to 0 in on is
102 00:13:59.410 ⇒ 00:14:11.259 Lev Katreczko: related to evolving complexity of the sales team. I think that in certain respects like zeroing or like getting tunnel vision too much on this tool call out, could potentially be like
103 00:14:11.360 ⇒ 00:14:33.085 Lev Katreczko: a little bit of a fool’s errand, just because there are so many other reasons that default could be salient. And one thing that I’ve heard from experience sitting on a cold call on sales calls is that a lot of the companies that are like the most eager to take a call with default are those that either A have like really complex or disparate sales, teams,
104 00:14:33.430 ⇒ 00:14:49.949 Lev Katreczko: or B, and I guess, like related sales teams that are operating out of like many different regions, you know, kind of concurrently so with that in mind, I think it’d be a good direction to kind of angle great signals in general.
105 00:14:50.750 ⇒ 00:15:00.649 Uttam Kumaran: Okay, no, that’s really, really helpful. Okay, cool. Yeah. I think the tools and like, sort of like, how many people on your team? Those are like table stakes like we can get them. But
106 00:15:00.740 ⇒ 00:15:28.460 Uttam Kumaran: again, a lot of them will indicate qualification if we can. Then look at okay, cool distribution, like, do we see people in in multiple countries? Do we see like a rapid growth? Right? Those are the things that I think are the second level things that you can’t really. It’s that’s like what I want us to spend our time and and go after. And then we’ll see like I think, after. Let’s, I think our goal after this will be to basically, I think, given that I can probably cut this in half.
107 00:15:28.580 ⇒ 00:15:33.429 Uttam Kumaran: and then we’ll propose a couple of like signal combos that we wanna try to try to hit. Basically.
108 00:15:33.930 ⇒ 00:15:46.360 Ryan DeForest: Yeah. And that’s what we were kind of like alluding to. It’s like the the form on the website is kind of scratching the surface. You know what I mean like, that’s that’s like the starting point. And then we know, like how to really twist the knife if you will like, after we know that.
109 00:15:46.830 ⇒ 00:15:53.279 Uttam Kumaran: Yeah, one question that we had. And and I think Henry was probably asked, like, what what is the like graduation process
110 00:15:53.580 ⇒ 00:16:01.279 Uttam Kumaran: from just like people in the product like, is there like a pretty significant like upsell process, or like, How do you look at?
111 00:16:03.640 ⇒ 00:16:09.770 Uttam Kumaran: You know, both attracting new customers, but also like getting people existing on the platform to expand.
112 00:16:10.560 ⇒ 00:16:26.149 Ryan DeForest: Yeah, right now, I would say, it’s very minimal due to the nature of how the product is like, the only upsell you could do is like, if you have more users right? More scheduling seats. But in like 3, 2 to 3 months it’s gonna be a whole different ball game like, we’re gonna have people that are
113 00:16:26.696 ⇒ 00:16:32.710 Ryan DeForest: Chili piper customers and default customers right? Like they’re gonna like, cause we’re gonna have a product for like
114 00:16:32.810 ⇒ 00:16:58.469 Ryan DeForest: you can ab test your demo, sign up page while tracking the visitors and and deonymize deonization of like people visiting the site. And then your Chili piper form is still there, running right? So like in about 3 months, the upsells are gonna be crazy. And also happening very like frequently on their own, like a salesperson wouldn’t need to be involved. That’s why like this is super like important like. That’s why I gave you the that graph, you know, with the 4 quality.
115 00:16:58.470 ⇒ 00:16:58.920 Ryan DeForest: Yes.
116 00:16:58.920 ⇒ 00:17:18.630 Ryan DeForest: it’s like, we know that this person we know that they have a dispersed sales team, and we know that they have a very complex and long sales cycle. So we should upsell them on these 3 products right like that, like super specific there. And like, that’s where we’re headed. And that’s why, like once, if we get a good foundation with that in mind, then this would be like easy. Next steps.
117 00:17:19.050 ⇒ 00:17:35.420 Uttam Kumaran: So for this 1st sort of iteration, you know, I think when we talk about the product analytics, we’ll talk a little bit about like what is the longer roadmap there. But for this 1st iteration it’s not like super obvious that, like Upsell is the is the goal. It’s really for processing.
118 00:17:36.290 ⇒ 00:17:45.000 Uttam Kumaran: And this is another question I had is like, it’s for processing existing leads that are already in salesforce. And then, second, are we also going to be basically
119 00:17:45.170 ⇒ 00:17:48.800 Uttam Kumaran: hitting Apollo or something getting more into the pipeline.
120 00:17:49.970 ⇒ 00:17:55.099 Ryan DeForest: Yes and no. I mean really to be honest right now, we’re just kind of
121 00:17:56.710 ⇒ 00:18:03.199 Ryan DeForest: trying to stay afloat until the new product comes out right? For lack of better terms. So
122 00:18:03.804 ⇒ 00:18:12.189 Ryan DeForest: yeah, like, right now, like my, like, my head of Cs, all she does is she just looks at how many users are using the product? How many do they pay for? Okay.
123 00:18:12.190 ⇒ 00:18:12.550 Uttam Kumaran: Yes.
124 00:18:12.550 ⇒ 00:18:27.830 Ryan DeForest: 4 extra users you want to pay us like. That’s literally it, that’s all that and that’s why like this would be super helpful like from a bandwidth perspective. So that when we do get to that point where we have 15 different things we can upsell, we know the upsell them of to this team, 4 things and another team. 2 things right.
125 00:18:27.830 ⇒ 00:18:28.410 Uttam Kumaran: Yeah.
126 00:18:29.460 ⇒ 00:18:37.357 Lev Katreczko: Yeah, I wanted to jump in with another piece, just like, kind of tactically. Where I would like some of this to to kind of fall into. So
127 00:18:37.850 ⇒ 00:18:46.099 Lev Katreczko: as of right. Now we are just scratching the surface of sort of like automated, outbound, and more efficient prospecting.
128 00:18:46.220 ⇒ 00:19:14.089 Lev Katreczko: And I’d like that to be successful in a world where, like old, old school, Bdr outreach is also running. And hopefully, the 2 are kind of working in tandem. Right? So you know, list building and signal gathering for purposes of both automating, outbound mass, cold email campaigns and the like, but also packaging those signals up and assigning them to Bdrs, for you know, assisting and outreach on that side.
129 00:19:14.730 ⇒ 00:19:16.320 Uttam Kumaran: Cool. Okay, that’s perfect.
130 00:19:18.290 ⇒ 00:19:26.189 Uttam Kumaran: So for this initial you know, once we get access sales. So I assume there’s already existing sort of basket of leads. That’s what we’ll be taking as
131 00:19:26.360 ⇒ 00:19:27.670 Uttam Kumaran: sort of the first, st
132 00:19:28.230 ⇒ 00:19:33.849 Uttam Kumaran: you know things to sort of. Go find the signals for, and then start to build some of these lists.
133 00:19:35.640 ⇒ 00:19:37.630 Uttam Kumaran: Yep, okay. Yep, yep, cool.
134 00:19:38.270 ⇒ 00:19:41.199 Uttam Kumaran: Okay. So then, I mean, I don’t think I think probably by
135 00:19:41.360 ⇒ 00:19:49.190 Uttam Kumaran: Monday or so we should have like another paired down version of this, and then kind of hearing what I hear, I’ll we’ll propose some
136 00:19:49.340 ⇒ 00:19:56.199 Uttam Kumaran: kind of initial Combos to try to do the end to end again. That’ll be like pulling things out of salesforce.
137 00:19:56.410 ⇒ 00:20:01.970 Uttam Kumaran: running some of these signals through clay, and then ideally trying to get
138 00:20:02.770 ⇒ 00:20:08.624 Uttam Kumaran: get it back into salesforce, either as an attribute or some. So some sort of thing, I think.
139 00:20:09.730 ⇒ 00:20:26.059 Uttam Kumaran: I think we’re gonna we’re gonna we’re gonna be able to start just cranking on some of these signals and then depending on. If we, if we can get the salesforce thing set up, then that’ll work even we can work on. If you just have a couple of leads that you you guys know really well that we want to practice this on, we can.
140 00:20:26.360 ⇒ 00:20:31.609 Uttam Kumaran: I mean, yeah, we’re just gonna just gonna make up a couple and go after it. So
141 00:20:32.060 ⇒ 00:20:37.619 Uttam Kumaran: I mean, even on our side, we have a form. So that’s probably what I’d start to go check. Use us as a dummy for sure.
142 00:20:40.720 ⇒ 00:20:41.510 Uttam Kumaran: Cool.
143 00:20:42.360 ⇒ 00:20:47.130 Uttam Kumaran: Great, I think, Henry. Maybe I pass it to you. We could talk about product analytics.
144 00:20:47.430 ⇒ 00:20:50.289 Henry Zhao: Yeah, absolutely thanks. So
145 00:20:50.430 ⇒ 00:21:07.520 Henry Zhao: all all this looks good. But I think the next step that we want to talk about is product analytics. So as Utam already mentioned, about upselling product analytics, we wanna also look at the ability to do A B testing. We wanna look at how people are using the product? Which features do they find the most value out of?
146 00:21:08.316 ⇒ 00:21:14.710 Henry Zhao: You know, which Utms are producing the most engaged and most loyal and most easily retained users.
147 00:21:15.104 ⇒ 00:21:35.960 Henry Zhao: And so to do that one thing that I want to talk about is amplitude. So I went through, you guys amplitude instance, to look at kind of what you guys already have set up in there and just to get started. I wanted to know who was the one that set up amplitude and kind of what was the philosophy for the way it’s set up now, like, how are you guys using it? How is it set up.
148 00:21:36.740 ⇒ 00:21:48.940 Ryan DeForest: I would say the snippet was added somewhere about a year ago, and no one has logged in since. To be honest, I was shocked we. I was shocked. We even had it to be frank let alone for a year.
149 00:21:49.880 ⇒ 00:21:52.680 Henry Zhao: Are you guys paying for the full paid version right now? So
150 00:21:53.607 ⇒ 00:22:00.279 Henry Zhao: because of eventually, I think it would be nice if we could track all of the features. Right? Is that something you guys would be interested in
151 00:22:00.989 ⇒ 00:22:11.230 Henry Zhao: so anything from like clicking the notifications tab to, you know, dashboards, contacts, companies, basically, any action that can be taken. We would want to track.
152 00:22:11.380 ⇒ 00:22:12.960 Henry Zhao: or we can.
153 00:22:12.960 ⇒ 00:22:16.670 Ryan DeForest: Are. Are you saying right? Are you saying right now? We don’t have that plan that’s tracking.
154 00:22:17.180 ⇒ 00:22:22.900 Henry Zhao: I don’t believe so. So right now I’m looking at the live events to kind of just see what’s firing
155 00:22:24.710 ⇒ 00:22:34.260 Henry Zhao: And another question I had was. If these intercom events, the ones that start with intercom were set up by you guys, or those like out of the box amplitude events.
156 00:22:34.570 ⇒ 00:22:38.609 Ryan DeForest: I assume we don’t even use intercom for Comp. For support. So.
157 00:22:38.920 ⇒ 00:22:39.590 Henry Zhao: Okay.
158 00:22:39.590 ⇒ 00:22:40.360 Ryan DeForest: Probably that.
159 00:22:41.230 ⇒ 00:23:02.820 Henry Zhao: Gotcha. Okay? So we would want to eventually go in there and and kind of just look at all the events that are set up and figure out what are the events that we want to track? We can do 2 philosophies, right? One, we can just track the events that you guys really care about. So whether it’s we only care about new features, or I only care about the ones that are that we think are critical to upsells or maintaining users.
160 00:23:02.890 ⇒ 00:23:14.000 Henry Zhao: or we can track everything to say. We’ll have a full set, and eventually, if we want to look for insights that we never thought we want to look for, or we just want to have that data there. We can go that route as well.
161 00:23:16.950 ⇒ 00:23:21.390 Ryan DeForest: Well for one before I answer the question, who gave access to this? And the caitlin give access.
162 00:23:21.770 ⇒ 00:23:25.619 Uttam Kumaran: Caitlin got us access. Yeah, I think Nico set it up like.
163 00:23:25.890 ⇒ 00:23:29.942 Ryan DeForest: And Nicole has some logins. I prefer
164 00:23:31.560 ⇒ 00:23:35.619 Ryan DeForest: So what? I’m hesitant hesitating.
165 00:23:36.010 ⇒ 00:23:47.259 Ryan DeForest: it’s probably not the right word, but hesitating. Is that because this is all gonna change. I don’t want you to put in a shit ton of work for it to just kind of be manipulated and hold like literally the slug could be different, right and.
166 00:23:47.260 ⇒ 00:23:47.600 Henry Zhao: Yeah, yeah.
167 00:23:47.600 ⇒ 00:23:56.900 Ryan DeForest: Into once. So usage specifically, maybe not as important, but more like
168 00:23:57.720 ⇒ 00:24:02.509 Ryan DeForest: like. How many users are using the platform, and how many meetings are being scheduled right? Not
169 00:24:02.927 ⇒ 00:24:09.300 Ryan DeForest: in the future. Yes, we wanna know, like people are clicking what they’re using like that sort of vibes. But right now, like
170 00:24:09.760 ⇒ 00:24:11.999 Ryan DeForest: if you were. Just give me a snapshot of like.
171 00:24:12.840 ⇒ 00:24:19.210 Ryan DeForest: like ample market has is paying. It has like 5 people being routed right now per week.
172 00:24:19.630 ⇒ 00:24:24.679 Ryan DeForest: right, and they’re only paying us for 3 users. Obviously, that’s a no brainer for us to reach out to them.
173 00:24:25.430 ⇒ 00:24:32.910 Ryan DeForest: I would be. I would be more open to that right now, just for sake of not wasting time. If it’s just gonna change right
174 00:24:33.160 ⇒ 00:24:34.040 Ryan DeForest: in 6 years.
175 00:24:34.270 ⇒ 00:25:02.030 Henry Zhao: And it doesn’t matter if it’s going to change, I think, for now we can do the planning and structure and kind of just plan what that’s gonna look like to, no matter what the product looks like and figure out which route we want to go. And I think that’s valid to just figure out what are the important things that we want to track? Do we just care if they log in and use it for 5 seconds? Or do they need to have certain amount of activity for us to say that they use the product right? Whether it’s meetings or any anything else that you guys think is a set of proper events.
176 00:25:02.320 ⇒ 00:25:05.070 Henry Zhao: Plan it that way when the product changes.
177 00:25:05.470 ⇒ 00:25:08.720 Ryan DeForest: Yeah, where? Where would be a good spot for me to add, like the events that
178 00:25:08.860 ⇒ 00:25:14.330 Ryan DeForest: would be a good thing to track. And if you can’t track them. Now, then, I could figure out a way for you to be able to track them. Basically.
179 00:25:14.960 ⇒ 00:25:23.650 Henry Zhao: I think we can either have a quick meeting, or you know, if you want to just send a loom that highlights like in the product. What are the things that we want to track? We can just do it that way, whichever one you prefer.
180 00:25:24.150 ⇒ 00:25:29.609 Uttam Kumaran: Do we need? We need someone on the on the front end team, Henry, to add those events or like, what’s the process.
181 00:25:30.936 ⇒ 00:25:34.120 Henry Zhao: I’m not sure we can discuss that, I think, after we figure out what we want to track.
182 00:25:34.120 ⇒ 00:25:52.583 Uttam Kumaran: Okay, yeah. So, Ryan, these are like, for example, very simple. Is like sign up flow. Okay? What is like all the steps to sign up flow, and like, are people getting. And then for onboarding, right? So those are things that you’re gonna have some sort of that so separately, it’s like, yeah, how many users are just on right now. And what are like the core activities they’re doing?
183 00:25:54.390 ⇒ 00:26:12.870 Uttam Kumaran: the biggest reason is that as you guys start to. Yes, of course, go towards like consumption based. But you’re gonna want to start doing split testing. You’re gonna want to start doing product usage based marketing. Right? So you’re you’re proactively hitting people when they’re when they turned out of the onboarding or things like that, that. You’re gonna those are all. Gonna be
184 00:26:13.530 ⇒ 00:26:15.070 Uttam Kumaran: product analytics powered?
185 00:26:15.270 ⇒ 00:26:17.320 Uttam Kumaran: Yeah, sort of flow. So yeah.
186 00:26:17.570 ⇒ 00:26:25.290 Ryan DeForest: Yeah, there’s there’s no argument there. But like, for example, I saw a screenshot of what the app is gonna look like, and the entire navigation bar on the left is completely different.
187 00:26:25.290 ⇒ 00:26:26.630 Ryan DeForest: Okay, yeah, right?
188 00:26:26.630 ⇒ 00:26:29.139 Ryan DeForest: Like, like. So it’s like a whole different, like.
189 00:26:29.350 ⇒ 00:26:41.219 Ryan DeForest: stylistically, though entirely different. You know what I mean, so like maybe there’s just some I’ll I’ll send over a list of events over in slack or something like that, and then you let me know like oh, I could easily find this with what we have now.
190 00:26:41.607 ⇒ 00:26:50.100 Ryan DeForest: Or you tell me like, look! There’s no way I could. I need this access, or I need this script, or I need something else on there. And then we could kind of go from there. I think.
191 00:26:51.010 ⇒ 00:27:00.099 Henry Zhao: Yeah. And then I think the next step is deciding what properties you guys want each of the events to have? Right? So if somebody logs on. Do you want to know what country they’re logging in from? Do you want to know what device they’re using?
192 00:27:00.402 ⇒ 00:27:06.689 Henry Zhao: Do you want to know how the age of the user right like, how are they a new user? Or have they been here for 2 years? Whatever it may be?
193 00:27:07.099 ⇒ 00:27:13.319 Henry Zhao: I think that’s the next step. So we can have robust data. And do these types of product analytics? Looking at numbers.
194 00:27:15.310 ⇒ 00:27:17.430 Ryan DeForest: Yep. Yeah. I’ll send over a list.
195 00:27:17.660 ⇒ 00:27:27.749 Henry Zhao: Yeah. And then with the new product. And then the last stage would be like, Do you guys want to run a B tests? Do you guys want care about? You know the utms of like where these users came from? So.
196 00:27:27.750 ⇒ 00:27:35.649 Uttam Kumaran: Yeah. So that’s a big thing we were talking today is like we want to see, like of the engaged users, for example, like what source they’re coming from.
197 00:27:35.830 ⇒ 00:27:57.300 Uttam Kumaran: so that you can go double down. So it’s not only just like Cac, but you’re kind of looking at the usage and the Ltv. Based on the source. Right? Similarly, for disengaged users like, Are you getting a lot of shitty users from like one source? And yeah, maybe like, it looked really good on paper, because the Cac. Was really good. But all those users turned in like a month right? That’s the sort of
198 00:27:57.730 ⇒ 00:27:59.629 Uttam Kumaran: stuff that we want to try to enable.
199 00:27:59.920 ⇒ 00:28:14.680 Henry Zhao: Yeah, like, I’ve had companies where you know, their Facebook Cpm was really, really low. They got a lot of new users from Facebook campaigns. But they were just signing on. They weren’t using the product. And they all turned really quickly. So these are the things we want to identify hopefully with product analytics. Down, down the road.
200 00:28:14.970 ⇒ 00:28:17.250 Ryan DeForest: Yeah, no. All for it.
201 00:28:17.650 ⇒ 00:28:23.120 Uttam Kumaran: And then, Ryan, how involved is like. So I was gonna I was mentioning we should just run that same thing by Caitlin, like.
202 00:28:23.830 ⇒ 00:28:26.923 Uttam Kumaran: maybe I’ll just whatever we share with you.
203 00:28:27.610 ⇒ 00:28:30.429 Uttam Kumaran: We just ask her to see what events or like. What do you think.
204 00:28:31.910 ⇒ 00:28:42.640 Ryan DeForest: Yeah, I mean, or just start a thread with me and her like me. Honestly, me and her are usually on the same wavelength, no matter what so she’ll have the same answer as me. But yeah wouldn’t hurt.
205 00:28:43.190 ⇒ 00:28:45.979 Uttam Kumaran: And then, yeah, for the new product. Maybe. Let’s
206 00:28:46.390 ⇒ 00:28:55.069 Uttam Kumaran: yeah. I guess we should. We can talk Henry on like, if we need to, whatever, if that’s in a staging instance, or whatever having them implement the events where we need them.
207 00:28:55.180 ⇒ 00:29:05.832 Uttam Kumaran: Certainly, I think you know whether that’s in 2 months. I think there’s some stuff we can sit on and start to report on today, like, I don’t think implementing the events is like monstrous task.
208 00:29:06.120 ⇒ 00:29:11.739 Ryan DeForest: And, to be honest, to be honest, you could like I’ll lean on you from from that perspective. But I just wanna make sure.
209 00:29:11.740 ⇒ 00:29:15.780 Uttam Kumaran: I’ll tell you. If you’re we’re about to burn a bunch of hours, I will tell you. Yeah.
210 00:29:15.890 ⇒ 00:29:21.409 Uttam Kumaran: yeah. But like we’re sitting on a bunch of stuff I think we want to show. And when when I met Caitlin she’s like
211 00:29:21.750 ⇒ 00:29:35.290 Uttam Kumaran: dude we’re not tracking like anything I was like. My heart like dropped. I’m like we should definitely be looking at who’s on the product and stuff. So yeah, I think we can get that enabled okay, cool.
212 00:29:35.490 ⇒ 00:29:37.359 Ryan DeForest: Is that that is definitely something that’s
213 00:29:38.160 ⇒ 00:29:42.359 Ryan DeForest: priority for me. But, like priority, as soon as, like the.
214 00:29:42.360 ⇒ 00:29:43.110 Uttam Kumaran: Yeah.
215 00:29:43.110 ⇒ 00:29:46.490 Ryan DeForest: Set up right. It’s like I don’t. I don’t want to do all this stuff, and then.
216 00:29:46.490 ⇒ 00:29:46.920 Uttam Kumaran: Yes.
217 00:29:46.920 ⇒ 00:29:48.540 Ryan DeForest: Just happens to be changed up.
218 00:29:49.080 ⇒ 00:29:49.630 Uttam Kumaran: Okay.
219 00:29:49.630 ⇒ 00:29:50.260 Henry Zhao: Yeah.
220 00:29:51.360 ⇒ 00:29:55.219 Uttam Kumaran: Okay, cool. So I think we have next steps, anything else
221 00:29:55.510 ⇒ 00:30:00.049 Uttam Kumaran: that we wanted to cover. And then, yeah, love, if you have any like
222 00:30:00.400 ⇒ 00:30:15.359 Uttam Kumaran: any ideas or anything that you’re thinking about, or you guys tried on either outbound or that. You guys are trying, you know, when, when you’re when you’re directly hitting people, let me know, just like anecdotally. And that way they’ll kind of give me some context on, like how some of these
223 00:30:15.480 ⇒ 00:30:18.759 Uttam Kumaran: conversations are going and like how the sales motion is today.
224 00:30:19.770 ⇒ 00:30:23.440 Lev Katreczko: Yeah, that all that, all sounds good. I think I might sit on this and
225 00:30:24.870 ⇒ 00:30:30.990 Lev Katreczko: yeah, think about something to write you up. And yeah, maybe maybe I can hit you with an email like before the end of the week. There.
226 00:30:31.130 ⇒ 00:30:35.629 Uttam Kumaran: Yeah, cool. Yeah, we’re we’re in this sort of world pretty heavy. So
227 00:30:35.930 ⇒ 00:30:47.969 Uttam Kumaran: any crack ideas or things you’re thinking about, we want to try to do the the narrower like, you know, especially when I when you hear like going after forms and stuff like that’s a really narrow use case, which is great versus like.
228 00:30:48.110 ⇒ 00:30:53.319 Uttam Kumaran: do they have salespeople? Do they? Using hubspot like we’re gonna get so many? It’s gonna be super
229 00:30:53.620 ⇒ 00:30:55.269 Uttam Kumaran: heavy like false flags.
230 00:30:55.430 ⇒ 00:31:01.759 Uttam Kumaran: So really narrow things, and that that’s like where Mustafa and I will work on, you know, building whatever agents we need to
231 00:31:01.890 ⇒ 00:31:03.760 Uttam Kumaran: to go isolate that information. So.
232 00:31:04.790 ⇒ 00:31:22.019 Lev Katreczko: Cool. Yeah, I mean, just top of mind right now is like one of the things that I’ve been sorry. Keep locking my computer. One of the things that that I’ve been working towards implementing is this sort of like living and breathing, prospecting and qualification list, where
233 00:31:22.170 ⇒ 00:31:34.360 Lev Katreczko: we can kind of conditionally route a company to, you know, one of the few journeys based on whatever is the most relevant to them. So obviously a longer conversation, but definitely like an end goal of mine.
234 00:31:34.840 ⇒ 00:31:35.440 Uttam Kumaran: Cool.
235 00:31:35.620 ⇒ 00:31:38.940 Uttam Kumaran: I mean, I, the way I basically described it to Henry. I’m like.
236 00:31:39.380 ⇒ 00:31:56.559 Uttam Kumaran: you’re you’re you’re you’re releasing. Your product is like a light version of like, what you guys are trying to do for yourselves here, you know. So I thought, thought that was kind of unique. I was like, yeah, it’s kind of it’s kind of a weird meta thing. But cause, Henry’s like, Wait, are we doing this for their product or for them like, no, this is for them, but they’re releasing kind of like a more
237 00:31:56.960 ⇒ 00:31:59.322 Uttam Kumaran: chill version of this in the product.
238 00:31:59.930 ⇒ 00:32:03.119 Ryan DeForest: I mean, there’s gonna be a world where like you can sign up for like
239 00:32:03.680 ⇒ 00:32:09.439 Ryan DeForest: to use Chili Piper, a feature of ours, and it’s like a Chili piper ripper in place, and it’s free. Right? Yeah.
240 00:32:09.440 ⇒ 00:32:09.910 Uttam Kumaran: Yeah.
241 00:32:09.910 ⇒ 00:32:17.949 Ryan DeForest: That that’s the world we’re going to get to. It’s like, and it’s like, gonna be a no brainer for people it’s like. But then what else can we do to make them pay us some money right like? That’s the biggest thing.
242 00:32:17.950 ⇒ 00:32:25.030 Uttam Kumaran: No, the flexibility, I mean, I I’m speaking to Vic a bunch, and the flexibility in the routing, and like all the enrichment stuff, is like huge. There’s not a product
243 00:32:25.210 ⇒ 00:32:29.770 Uttam Kumaran: out there where you can do that. So yep, cool. Okay.
244 00:32:29.770 ⇒ 00:32:43.180 Ryan DeForest: Yeah. So we got some to do items. If I don’t get I I have to get you guys salesforce access. I’ll do that by tomorrow, and then I’ll shoot over by end of day if you don’t hear with me end of day tomorrow, for, like some events, just yell at me, and then I could do that too.
245 00:32:43.550 ⇒ 00:32:45.070 Uttam Kumaran: Okay, perfect.
246 00:32:45.430 ⇒ 00:32:46.930 Henry Zhao: Okay. Thank you guys.
247 00:32:47.220 ⇒ 00:32:48.349 Ryan DeForest: Awesome. Thank you.
248 00:32:48.930 ⇒ 00:32:49.850 Uttam Kumaran: Talk to you soon.