Meeting Title: Default | Weekly Product Analytics Sync Date: 2025-09-12 Meeting participants: Uttam Kumaran, Scratchpad Notetaker, Rico Rejoso, vishalag, Henry Zhao, Caitlyn Vaughn
WEBVTT
1 00:02:44.650 ⇒ 00:02:46.090 Uttam Kumaran: There we go, hey guys.
2 00:02:49.050 ⇒ 00:02:50.030 Rico Rejoso: Hi, guys.
3 00:03:03.910 ⇒ 00:03:05.500 Henry Zhao: Morning, how’s it going, guys?
4 00:03:05.720 ⇒ 00:03:06.150 Uttam Kumaran: Ayy.
5 00:03:06.150 ⇒ 00:03:07.780 Caitlyn Vaughn: Good morning.
6 00:03:08.060 ⇒ 00:03:08.880 Uttam Kumaran: Mine.
7 00:03:11.330 ⇒ 00:03:13.239 Uttam Kumaran: Is that a real background, Kalen?
8 00:03:14.350 ⇒ 00:03:15.040 Caitlyn Vaughn: S?
9 00:03:15.690 ⇒ 00:03:16.890 Uttam Kumaran: Hey, listen!
10 00:03:17.170 ⇒ 00:03:19.669 Caitlyn Vaughn: It’s our office, it’s really messy.
11 00:03:19.670 ⇒ 00:03:20.989 Uttam Kumaran: Oh, that’s awesome!
12 00:03:20.990 ⇒ 00:03:22.759 Caitlyn Vaughn: But let’s fake about that.
13 00:03:24.090 ⇒ 00:03:25.660 Caitlyn Vaughn: So real.
14 00:03:25.660 ⇒ 00:03:26.620 Uttam Kumaran: Nice.
15 00:03:26.900 ⇒ 00:03:28.789 Henry Zhao: Is that in New York, or is that somewhere else?
16 00:03:28.790 ⇒ 00:03:34.750 Caitlyn Vaughn: Yeah, this is in New York. I’m just here for the week. I’m here, like, maybe one week a month.
17 00:03:35.640 ⇒ 00:03:36.939 Henry Zhao: Yeah, I was there a few weeks ago.
18 00:03:37.190 ⇒ 00:03:38.470 Caitlyn Vaughn: Oh, you were?
19 00:03:38.840 ⇒ 00:03:39.730 Henry Zhao: Yeah.
20 00:03:40.210 ⇒ 00:03:41.560 Caitlyn Vaughn: Wow, we miss you.
21 00:03:42.810 ⇒ 00:03:45.670 Uttam Kumaran: Did you stop by the office, Henry? Didn’t you guys were there?
22 00:03:45.880 ⇒ 00:03:47.690 Henry Zhao: Yep, Robert and I were both there.
23 00:03:48.200 ⇒ 00:03:49.989 Caitlyn Vaughn: Wait, you were? I didn’t even know that.
24 00:03:49.990 ⇒ 00:03:51.239 Uttam Kumaran: You miss them.
25 00:03:51.240 ⇒ 00:03:55.129 Caitlyn Vaughn: Yeah, apparently. We, just moved to a new office, though.
26 00:03:55.670 ⇒ 00:03:57.079 Henry Zhao: Oh, okay, we went to the old one, I think.
27 00:03:57.450 ⇒ 00:04:13.020 Caitlyn Vaughn: Yeah, yeah, yeah. The new one is, like, massive, and we probably fill up, like, a tenth of it, which is pretty cool, except for, like, nothing is in here yet. We have, like, one tiny couch with, like, a bunch of desks with, like, 10 chairs, so… we’ll get there.
28 00:04:13.120 ⇒ 00:04:14.000 Uttam Kumaran: Nice.
29 00:04:14.170 ⇒ 00:04:16.400 Henry Zhao: Yeah, hopefully the Wi-Fi works better for you guys also.
30 00:04:16.700 ⇒ 00:04:18.450 Caitlyn Vaughn: It doesn’t. Thanks for asking.
31 00:04:22.420 ⇒ 00:04:24.189 Uttam Kumaran: Henry, I’ll let you take it away.
32 00:04:24.640 ⇒ 00:04:30.380 Henry Zhao: Alright, and also, sorry in advance a little bit about my voice, I’m a little bit, under the weather, so I will…
33 00:04:30.490 ⇒ 00:04:32.619 Henry Zhao: do my best. Okay.
34 00:04:34.330 ⇒ 00:04:42.000 Henry Zhao: So, I wanted to talk about three things in today’s meeting. So, one, I wanted to give an update on where we are in terms of product analytics in Amplitude.
35 00:04:42.660 ⇒ 00:04:44.940 Henry Zhao: Let me share my screen real quick.
36 00:04:46.340 ⇒ 00:04:56.320 Henry Zhao: So one is give an update on where Michelle and I are in terms of product analytics and amplitude. Two is to have a little bit of discussion on what we need to align on in terms of internal definition.
37 00:04:56.900 ⇒ 00:05:03.250 Henry Zhao: Third, just having a quick definite, quick discussion also on CDP, and…
38 00:05:03.440 ⇒ 00:05:04.999 Henry Zhao: We can take it from there.
39 00:05:05.130 ⇒ 00:05:05.850 Henry Zhao: Okay?
40 00:05:07.790 ⇒ 00:05:15.279 Henry Zhao: So, you guys all saw the first draft of the product analytics dash that I shared last Friday.
41 00:05:15.450 ⇒ 00:05:22.900 Henry Zhao: And thank you guys for the feedback. That is exactly what it was, like, an initial draft. Like, that’s not what the final product is going to look like, hopefully.
42 00:05:23.160 ⇒ 00:05:27.480 Henry Zhao: But I wanted to at least get you guys’ feedback on, like, are we on the right track?
43 00:05:27.930 ⇒ 00:05:37.299 Henry Zhao: We do understand that some of the data is still not complete, or some of the data is not where the end state is going to be, because Michelle is still working hard to implement new events for us.
44 00:05:37.810 ⇒ 00:05:46.930 Henry Zhao: But we’ve already made a few changes on the feedback. I think the main issue right now is events are being duplicated, so whenever someone logs in, it’s firing twice.
45 00:05:47.210 ⇒ 00:05:52.000 Henry Zhao: So what we’re doing is, I’ve reached out to Amplitude support, as well as Mixed Panel support.
46 00:05:52.170 ⇒ 00:06:03.529 Henry Zhao: I mean, not mixed panel, segment support to figure out what’s going on, like, figure out whose issue it is, whether it’s segment sending it twice, or amplitude not deduplicating it properly.
47 00:06:04.170 ⇒ 00:06:10.089 Henry Zhao: Amplitude already responded, they asked for a screenshot, and I’ve sent that, so just waiting to hear back from them on that.
48 00:06:10.290 ⇒ 00:06:15.310 Henry Zhao: But that’s why the frequency charts are gonna be only 2, 4, 6, 8, etc.
49 00:06:15.430 ⇒ 00:06:20.500 Henry Zhao: So, right now, you can just interpret this as 1 is 449, 2 is 89,
50 00:06:20.890 ⇒ 00:06:24.169 Henry Zhao: And then 3 to 5 is 110, so on and so forth.
51 00:06:25.900 ⇒ 00:06:37.640 Henry Zhao: And anything that says last 30 days is actually pretty much last 7 days, because the first events were coming in pretty much 7 days ago, okay? So for, like, meetings booked, I’ve changed that to last 7 days.
52 00:06:38.100 ⇒ 00:06:42.220 Henry Zhao: And then now there’s a chart for inbound leads created per user.
53 00:06:43.330 ⇒ 00:06:52.360 Henry Zhao: Just to see, like, how many leads are coming in per user, which is now a mix of the meeting booked and form-submitted events that we added last week, thanks to Vishal.
54 00:06:53.400 ⇒ 00:06:58.229 Caitlyn Vaughn: And this is inbound leads created per user per day, or per week, or per month?
55 00:06:58.500 ⇒ 00:07:01.690 Henry Zhao: This is per day, but you can also come in here and make it.
56 00:07:01.690 ⇒ 00:07:04.700 Caitlyn Vaughn: Per week, per month. Since we only have one week of data, right now, I just…
57 00:07:05.200 ⇒ 00:07:07.160 Henry Zhao: Daily. Okay, cool.
58 00:07:07.480 ⇒ 00:07:09.640 Henry Zhao: Just give you one data point.
59 00:07:09.640 ⇒ 00:07:10.440 Caitlyn Vaughn: Amazing.
60 00:07:11.970 ⇒ 00:07:17.100 Henry Zhao: Yeah, and then it’ll be easy to come in here and just filter it by user, to break it down by user.
61 00:07:17.930 ⇒ 00:07:20.190 Henry Zhao: Whatever, whatever you guys want to do.
62 00:07:20.570 ⇒ 00:07:32.369 Henry Zhao: But at least we want to be on the right track, so that anything we want to add, or we want to fix, we do it right from the get-go, because a lot of this stuff can’t be backfilled. So we want to at least prioritize and make sure we’re
63 00:07:32.550 ⇒ 00:07:41.410 Henry Zhao: on the right track. And then the user activation funnel, we now have a new user onboarded. Did they log in? And then, did they do any sort of active event?
64 00:07:41.550 ⇒ 00:07:54.220 Henry Zhao: So, meeting booked, obviously, is not an active event, because that’s somebody else doing it. So, the next topic is I want to create a list of what are the active events. So, what are events where your users are actually doing something actively in the app?
65 00:07:54.420 ⇒ 00:08:05.119 Henry Zhao: Whether it’s, publishing a new work… Publishing a new workflow, creating a new meeting type, I think those are the types of things that we’re gonna call active events to look at engagement.
66 00:08:05.380 ⇒ 00:08:08.830 Caitlyn Vaughn: And then, did you say, data can’t be backfilled in here?
67 00:08:10.170 ⇒ 00:08:15.639 Henry Zhao: Yeah, anything in amplitude, like, once Visal implements, that gets tracked as of the time of implementation.
68 00:08:15.920 ⇒ 00:08:18.050 Caitlyn Vaughn: Oh man, okay, I know we had, like.
69 00:08:18.180 ⇒ 00:08:27.790 Caitlyn Vaughn: We had a lot of the events already spun up for the last few years, and we were, like, capturing some of that data. Are we able to, like, do anything with that, or no?
70 00:08:29.130 ⇒ 00:08:33.819 Henry Zhao: Just whatever we have in there, so…
71 00:08:35.350 ⇒ 00:08:41.029 Henry Zhao: And I think a lot of this might not be relevant anymore once you move to the new PLG, right? So I think…
72 00:08:41.620 ⇒ 00:08:52.840 Henry Zhao: a lot of this is very, very legacy, but we can take a look at what is still applicable. So maybe, Vishal, at our next call, we can go through the remaining unexpected events and just make sure that those are… are usable.
73 00:08:53.130 ⇒ 00:08:59.459 Uttam Kumaran: Yeah, there are some, like, backfill options in Amplitude. I think we can…
74 00:08:59.650 ⇒ 00:09:03.560 Uttam Kumaran: see if there’s, like, if there’s anything we could do.
75 00:09:03.840 ⇒ 00:09:08.920 Uttam Kumaran: I think I’ll probably talk to Michelle, see, like, if we have past events, and, like, if we can map those at all.
76 00:09:10.760 ⇒ 00:09:13.550 Uttam Kumaran: Yeah, even just to get pictures or something.
77 00:09:13.930 ⇒ 00:09:26.789 Caitlyn Vaughn: Yeah, because I think some of the value of these analytics is, like, as I’m planning for how we roll out PLG and, like, what levers we pull and what to, you know, like, gate versus keep, I think…
78 00:09:26.950 ⇒ 00:09:30.119 Caitlyn Vaughn: Like, some of the value of this is that we have that data of, like.
79 00:09:30.530 ⇒ 00:09:41.600 Caitlyn Vaughn: the way that people use PLG will be the same way that they’ve been using our current product today. Even though we’re doing this whole product overhaul, like, the first version of our product is still, like, basically our current product, right?
80 00:09:41.750 ⇒ 00:09:46.250 Caitlyn Vaughn: So if it’s possible, that would be great. Otherwise, not the end of the world.
81 00:09:46.250 ⇒ 00:09:52.070 Uttam Kumaran: Yeah, one thing we can do is, like, similar to how we’re sending events now, we can just, like.
82 00:09:52.230 ⇒ 00:10:00.410 Uttam Kumaran: create the synthetic events and fire them at past historical timestamps, right? So that’s something I think, Vishal, maybe we can talk about
83 00:10:00.510 ⇒ 00:10:01.740 Uttam Kumaran: where those…
84 00:10:01.900 ⇒ 00:10:16.650 Uttam Kumaran: past events are, or if we want to, like… for example, we have all the… we have a lot of data in Superbase, and for the product data, if we want to use that to create events, and then fire a bunch of them into Amplitude to, like, backfill, we could probably do that.
85 00:10:18.800 ⇒ 00:10:21.759 Uttam Kumaran: And then stitch them, and then so that way the product stitches them together.
86 00:10:22.600 ⇒ 00:10:25.530 Caitlyn Vaughn: Okay, and then another question,
87 00:10:25.690 ⇒ 00:10:37.390 Caitlyn Vaughn: I know that we had shared with you, like, I think a superbase instance, or, like, an exported amount of data. Should we be giving you, like, access to more real-time data?
88 00:10:37.390 ⇒ 00:10:38.120 Uttam Kumaran: Yeah, so I walked.
89 00:10:38.120 ⇒ 00:10:39.049 Caitlyn Vaughn: for you guys?
90 00:10:39.260 ⇒ 00:10:45.409 Uttam Kumaran: Yeah, so maybe I was gonna show that next. I guess, Henry, let me know if you’re… if you’re good on stuff from… from Amplitude, and…
91 00:10:46.020 ⇒ 00:10:47.060 Henry Zhao: Yeah, yeah.
92 00:10:47.060 ⇒ 00:10:52.159 Uttam Kumaran: Okay, cool. So let me just share, kind of, like, what we put together there, and then, sort of.
93 00:10:52.260 ⇒ 00:10:54.569 Uttam Kumaran: I want to give you a sense of, of, like.
94 00:10:54.740 ⇒ 00:11:04.629 Uttam Kumaran: what some of the data we got. So, this is, like, some of the stuff we got. Again, our data cuts off August 5th, or, like, August at some point.
95 00:11:04.730 ⇒ 00:11:23.649 Uttam Kumaran: But we were getting, like, you know, the teams, the members, meetings, queues, conversions, and then we were able to do some of these calculations. These are purely on the product outputs that I got. I also don’t… this is, like, everything. I think this is just what I modeled for, some of the measures.
96 00:11:23.650 ⇒ 00:11:33.109 Uttam Kumaran: If we can start to get a live feed of this, that would be great. Vishal, is this, like, this is… I think in the past you’ve mentioned this is in Supabase?
97 00:11:33.230 ⇒ 00:11:34.780 Uttam Kumaran: Like, this product data?
98 00:11:37.090 ⇒ 00:11:40.760 vishalag: Yeah, I mean, this is what I was talking about, yeah.
99 00:11:40.760 ⇒ 00:11:41.380 Uttam Kumaran: Cool.
100 00:11:43.820 ⇒ 00:11:48.870 Uttam Kumaran: So that’s something that I think we should try to build a little bit of a live version, even
101 00:11:49.090 ⇒ 00:12:09.070 Uttam Kumaran: And that’s something I think I mentioned to Victor, but maybe, Vishal, if you’re the right person to work on it, I can send a thread about how we can start updating this live. So this is, like, a very simple BI tool that is free, that I just spun up. I’m gonna also give you another option that you can kind of look at that’s more similar to, like, Looker. Like, I don’t know, Caitlin, if you’ve heard of Omni?
102 00:12:09.240 ⇒ 00:12:10.929 Uttam Kumaran: They’re, like, a big…
103 00:12:11.260 ⇒ 00:12:17.039 Uttam Kumaran: BI tool. I should actually ask them. Sounds familiar, yeah. I should actually ask them if they’re using default. Let me do that also.
104 00:12:17.200 ⇒ 00:12:26.989 Uttam Kumaran: But, they are… it’s just a really nice product, and so I was gonna also set up a couple of dashboards there for you to poke around at.
105 00:12:26.990 ⇒ 00:12:40.259 Uttam Kumaran: So yeah, I think if we can start to circle around a live feed for that… Vishal, right now we’re using Mother Duck as the data warehouse, so we’ve just landed all the CSV export that I got from Victor there.
106 00:12:40.360 ⇒ 00:12:47.840 Uttam Kumaran: But we can pretty easily build an ETL pipeline from Subabase, or if this is an S3 or wherever, into…
107 00:12:48.460 ⇒ 00:12:49.270 Uttam Kumaran: Into there.
108 00:12:49.730 ⇒ 00:12:57.360 Caitlyn Vaughn: Okay, perfect. Let me talk to Victor about, I know that there was, like, when we had signed our initial contracts.
109 00:12:57.360 ⇒ 00:13:10.599 Caitlyn Vaughn: the scope of it was, like, not live data or production data, right? So let me just follow up with him and see if that’s something that we can do, because, I mean, at the end of the day, like, we need access to what is going on in our data on a day-to-day basis,
110 00:13:10.670 ⇒ 00:13:16.179 Caitlyn Vaughn: The other thing is, I have, like, an all-hands coming up next week, on Tuesday, so I would love to, like.
111 00:13:16.320 ⇒ 00:13:23.009 Caitlyn Vaughn: kind of package some of this data and be able to share it with the team. I feel like that’s a good opportunity for us to kind of, like, show what we’ve been working on.
112 00:13:23.010 ⇒ 00:13:30.010 Uttam Kumaran: Okay, so let me, I’m gonna… let me get you a view of something today that you could poke at, and then…
113 00:13:30.600 ⇒ 00:13:42.679 Uttam Kumaran: if you’re gonna do stuff over the weekend, let me know. Also, if you’re doing stuff on Monday, just hit me with questions, and then, yeah, that’s actually great. I want to hopefully get you some screens that you can add with some… with some views of stuff, so…
114 00:13:42.960 ⇒ 00:13:48.489 Caitlyn Vaughn: Yeah, it doesn’t need to be, like, real-time for that specifically, but if we could just get some, like, good,
115 00:13:48.750 ⇒ 00:13:53.810 Caitlyn Vaughn: new insights, as we’ve had Xero as a team, would probably be a cool thing for our team to see.
116 00:13:53.810 ⇒ 00:14:00.119 Uttam Kumaran: Okay, okay, cool. Okay, great. Alright, so let me… I’ll get you, like, sort of a view of that, and get you into something today that you can poke at.
117 00:14:00.340 ⇒ 00:14:01.450 Caitlyn Vaughn: Cool, thank you.
118 00:14:01.640 ⇒ 00:14:02.240 Uttam Kumaran: Okay.
119 00:14:03.180 ⇒ 00:14:08.420 Uttam Kumaran: Cool, so Henry, what’s next on, like, product analytics side for those dashboards?
120 00:14:10.350 ⇒ 00:14:24.260 Henry Zhao: So, next is, we have the next round of events that we want Vishal to, add to Amplitude, which is, like, the tab clicks on the… on the app. So, are they clicking on dashboard, are they clicking on meetings, are they clicking on whatever?
121 00:14:24.750 ⇒ 00:14:28.919 Henry Zhao: Because I think once they click on that, that at least shows us how they’re using the product.
122 00:14:29.060 ⇒ 00:14:41.520 Henry Zhao: But Vishal, I think you mentioned you might need some extra support, because this is a bit of a heavy lift. Do we want to talk about that now, in terms of, like, what we can do in terms of getting some extra engineering support to implement these things?
123 00:14:42.560 ⇒ 00:14:57.869 vishalag: Yeah, so I’m just letting Caitlin, you also know, I have already talked with Florida, so she mentioned either she or Soham are available for this, so I will be having a call with her today. I will be giving her the information as to what we need.
124 00:14:57.970 ⇒ 00:15:10.920 vishalag: So, first of all, when you say, like, I remember we need to have tablets, that’s, like, fairly straightforward. When you say session replays, and you mentioned that you want session ID also in the events, right? In the current
125 00:15:11.380 ⇒ 00:15:12.970 vishalag: events.
126 00:15:14.220 ⇒ 00:15:16.540 Caitlyn Vaughn: Yeah, that’s… Yeah, go ahead.
127 00:15:16.940 ⇒ 00:15:17.290 Henry Zhao: No, go ahead.
128 00:15:17.290 ⇒ 00:15:21.909 Caitlyn Vaughn: I think the session IDs would be…
129 00:15:22.100 ⇒ 00:15:29.139 Caitlyn Vaughn: Yes, helpful. I mean, we need to, like, be able to tie these, like, live events back to actual accounts, right?
130 00:15:31.370 ⇒ 00:15:36.260 vishalag: Yeah, I mean, like, if that’s the case, it’s fine, we can do it. The only thing is, it will take time, because…
131 00:15:36.370 ⇒ 00:15:51.040 vishalag: like, the session replays will be first supported on the UI, then all the session IDs needs to be supported on the API side also, like, everything will be sent as a header, it linked over to backend, and then we will set it up. So it’s not a issue, it will just take time, but, like.
132 00:15:51.040 ⇒ 00:15:57.109 vishalag: First, we will have to do the amplitude integration in the UI, so for that, I have Florida also on retail cases.
133 00:15:57.170 ⇒ 00:16:03.580 vishalag: So let’s see who comes in, and yeah, after that, we’ll see the session ID part being in the events.
134 00:16:04.240 ⇒ 00:16:07.920 Caitlyn Vaughn: Okay, as I’m thinking through this, just, like.
135 00:16:08.150 ⇒ 00:16:25.719 Caitlyn Vaughn: my first reaction to this, and maybe I’m gonna spend a little bit more time thinking about it, but in our, like, list of priorities, this is probably a little bit lower than, like, getting off the ground with Phoenix stuff, so if we can’t… if it’s, like, a big lift for us to pull, you said session IDs to, like, be able to, like, track back who is actually
136 00:16:27.350 ⇒ 00:16:40.989 Caitlyn Vaughn: using that session, or, like, where the data is actually coming from, I’m less concerned about that at the moment. If it’s something that, like, we can spin up pretty quickly, I think that’s fine, but let’s, like, lower the priority on that one.
137 00:16:42.120 ⇒ 00:16:47.530 vishalag: Cool. Alright, so I will talk it out with Florida. Florida’s basically doing the roadmap thing for Phoenix and all of it.
138 00:16:47.530 ⇒ 00:16:48.010 Caitlyn Vaughn: True.
139 00:16:48.010 ⇒ 00:16:59.350 vishalag: sheet version for that, so I will let her know how much of a lift it is. If it’s, like, a max two days effort, then we will do it. Otherwise, I will let you know, and you can decide whether we want to do it now or later on.
140 00:16:59.820 ⇒ 00:17:05.119 Caitlyn Vaughn: Okay, cool, that sounds good. Yeah, me, Florida, and Sid are working on product roadmap stuff.
141 00:17:05.280 ⇒ 00:17:12.230 Caitlyn Vaughn: So, there’s obviously a lot on there. Like, every single thing we do is, like, what are we sacrificing to do it? So…
142 00:17:12.440 ⇒ 00:17:27.320 Uttam Kumaran: Yeah, I think as long as we can get the raw events, like, that’s… that would be ideal. Anything above that is just bonus, and then as we can start tracking that now, it’ll just start to build our backlog of data. So, yeah, I’m with you.
143 00:17:29.350 ⇒ 00:17:43.019 Henry Zhao: Yeah, and then in addition to session ID, I’d love session duration time, right? So, when they log in, when they either log off or become inactive, how long does that take? So we can figure out how much time people are spending in the app. Are they getting stuck? Or, you know, things like that.
144 00:17:43.300 ⇒ 00:17:44.570 vishalag: That might be…
145 00:17:44.850 ⇒ 00:17:50.329 vishalag: we have this session replay thing from PostHog. Do you think that will work? Because we already have it.
146 00:17:50.820 ⇒ 00:17:51.989 Henry Zhao: You just need to check, yeah.
147 00:17:52.000 ⇒ 00:17:54.649 Caitlyn Vaughn: We do have it, yeah, but we need to check that it’s working.
148 00:17:55.260 ⇒ 00:17:59.970 Uttam Kumaran: It’s also, like, recessions, right? Like, can we… can we do… can we infer that in amplitude? Like…
149 00:18:00.820 ⇒ 00:18:06.079 Uttam Kumaran: As soon as if an event ends, and then there’s no events after for some duration of time, that’s a session.
150 00:18:06.680 ⇒ 00:18:07.729 Henry Zhao: Yeah, we can look at that.
151 00:18:08.250 ⇒ 00:18:13.119 Uttam Kumaran: Yeah, and because that’s… that’s… I think we can just… if we can do that all on amplitude, I feel that’s better.
152 00:18:13.220 ⇒ 00:18:14.979 Henry Zhao: And how do you guys calculate it?
153 00:18:16.160 ⇒ 00:18:24.839 Uttam Kumaran: Because also, some people are not gonna, like, log in and log out, like… I don’t know, I don’t think I’ve ever logged out of any… unless I switch stuff, I never log out of anything anymore, right? So…
154 00:18:24.840 ⇒ 00:18:25.640 Caitlyn Vaughn: Yeah.
155 00:18:26.030 ⇒ 00:18:26.490 vishalag: Damn.
156 00:18:26.490 ⇒ 00:18:30.530 Henry Zhao: Whenever I re-access default, I have to re-login. Is it the same for you guys?
157 00:18:30.530 ⇒ 00:18:31.600 Uttam Kumaran: Alright, okay.
158 00:18:32.450 ⇒ 00:18:33.250 Caitlyn Vaughn: I do not have to.
159 00:18:34.040 ⇒ 00:18:35.412 vishalag: Yeah, I know.
160 00:18:38.220 ⇒ 00:18:40.610 Caitlyn Vaughn: We’ll push it to our CS team for you, Henry.
161 00:18:40.610 ⇒ 00:18:43.949 Uttam Kumaran: Yeah, so I’m gonna attack you.
162 00:18:46.630 ⇒ 00:18:47.350 Henry Zhao: Boom.
163 00:18:47.690 ⇒ 00:19:01.400 Henry Zhao: So then the next thing, we don’t have to define everything now, but I just wanted to say that before we finalize the reporting, we should at least also agree on important definitions. So, you know, Caitlin, you’ve asked for things like active users, total users.
164 00:19:02.000 ⇒ 00:19:12.569 Henry Zhao: I just want to make sure that we’re aligned on what counts as an active user. Does, like, somebody that just logs in count as an active user, or do they have to perform certain activities, like those active events I just mentioned?
165 00:19:13.210 ⇒ 00:19:21.940 Henry Zhao: Does just the team need to be active, or does, like, a specific person using default need to be active? So that’s, like, one…
166 00:19:22.300 ⇒ 00:19:29.729 Henry Zhao: And then for what is the total user? Is it just anyone that’s completed onboarding? Is it anyone that has, like, finalized the sales flow, right? So…
167 00:19:30.080 ⇒ 00:19:34.500 Henry Zhao: Or anyone that has logged in once, or downloaded the app, whatever that is, right?
168 00:19:34.780 ⇒ 00:19:41.500 Henry Zhao: And then, do we need to define whether it’s a team, or a company, or a customer, right? Like, there’s the individual person using it, and then there’s the company using it.
169 00:19:41.500 ⇒ 00:19:48.709 Uttam Kumaran: Yeah, maybe let’s just run this… let’s just run through this right now so we can get Kalen’s gut instinct. We’ll build it towards that, and then you’ll see…
170 00:19:48.990 ⇒ 00:19:51.659 Uttam Kumaran: The number’s low or high, you can change it.
171 00:19:52.980 ⇒ 00:19:53.430 Uttam Kumaran: Yeah.
172 00:19:53.430 ⇒ 00:20:04.870 Caitlyn Vaughn: Okay, so for an active user, I’m thinking we just go with the most simple version of this, which is, like, somebody who has logged in, probably in the last 30 days.
173 00:20:05.270 ⇒ 00:20:08.800 Henry Zhao: Okay. I like that.
174 00:20:08.800 ⇒ 00:20:10.759 Caitlyn Vaughn: Yeah, I mean, it’s interesting because…
175 00:20:10.920 ⇒ 00:20:14.779 Caitlyn Vaughn: the way that default is built right now is essentially, like.
176 00:20:15.000 ⇒ 00:20:23.839 Caitlyn Vaughn: The only people that have a strong reason to consistently come back and, like, log back into default for anything other than scheduling links is…
177 00:20:24.140 ⇒ 00:20:36.610 Caitlyn Vaughn: like, RevOps, or the people actually building workflows, right? Versus everybody else, it’s like, all you need is your link, and then you can’t even, you know, edit workflows, and right now you can’t add data into tables yourself, so…
178 00:20:37.210 ⇒ 00:20:41.099 Caitlyn Vaughn: Yeah, probably maybe last 30 days.
179 00:20:42.720 ⇒ 00:20:48.860 Henry Zhao: So if that’s how you define it, then I think we can also have churned users, which would be people that have logged in at least once, but more than 30 days ago.
180 00:20:49.260 ⇒ 00:20:52.709 Caitlyn Vaughn: Yeah, maybe inactive would probably be, like, more than 30 days.
181 00:20:53.250 ⇒ 00:21:08.080 Uttam Kumaran: I would use inactive, because churn is on the billing, so yeah, use inactive, and then typically, yeah, for active and inactive, there will be some users that are making modifications. There will be some people that log in, look at analytics. So I think login is a good place to start, and then…
182 00:21:08.560 ⇒ 00:21:10.410 Uttam Kumaran: It works towards something that is…
183 00:21:10.540 ⇒ 00:21:25.240 Uttam Kumaran: maybe product-specific, right? You want to start breaking down active users by product area. Like, how many active analytics users are there that have looked at a… that have opened an analytics page once in the last 30 days, right? So, I think this is fair for now.
184 00:21:25.240 ⇒ 00:21:25.710 Caitlyn Vaughn: costs.
185 00:21:27.950 ⇒ 00:21:37.819 Caitlyn Vaughn: Yeah, I could also see, like, the login piece, and then, like, if you’ve used the calendar, right? Like, if you’ve used the scheduling, like, you don’t necessarily need to log into the platform, but…
186 00:21:37.820 ⇒ 00:21:42.900 Uttam Kumaran: Yeah, I guess, like, that’s my question, like, do you find… do you feel like, that should count?
187 00:21:43.600 ⇒ 00:21:45.100 Caitlyn Vaughn: I mean, yes, but…
188 00:21:45.100 ⇒ 00:21:48.230 Uttam Kumaran: I don’t want to, like, overcomplicate it right now. Okay.
189 00:21:50.200 ⇒ 00:22:02.190 vishalag: I mean, does that count, let’s say, we have various events, like workflow published, workflow created, form created, form published, all of that has a created by updated by person ID. So if that person is…
190 00:22:02.190 ⇒ 00:22:10.239 vishalag: doing it, that’s also active, right? Like, that’s… that’s what I think it means. In the future, look at active by product area? Is that what that means?
191 00:22:11.930 ⇒ 00:22:25.000 Caitlyn Vaughn: Yeah, I think he’s, saying product area by, like, let’s look at routing. Like, is somebody active in the routing SKU? Have they been routed in the last 30 days? Have they maybe created a new routing queue? And then looking at
192 00:22:25.000 ⇒ 00:22:44.980 Caitlyn Vaughn: pages, like, have they interacted with a page? Have they run a test on a page? Have they changed a page? You know? But, like, once again, the way that our product is built now, if you’re a member, you actually can’t even see, like, workflows or, you know, a lot of the platform, so I feel like our definition of, like, active, if we did that, might be a little bit…
193 00:22:45.030 ⇒ 00:22:46.470 Caitlyn Vaughn: Difficult to track.
194 00:22:46.960 ⇒ 00:22:50.800 Uttam Kumaran: Yeah, yeah, okay, I agree. Yeah, right now, for example, you can be using
195 00:22:50.890 ⇒ 00:23:07.849 Uttam Kumaran: default, like, with four-year scheduling, but, like, you may not… you may not have an idea, right? So, like, what it… but I think that this will change on the new product. I think doing logins is a good place to start. It’ll naturally lead to, like, logins plus a certain type of product activity, and we can start to break that down.
196 00:23:08.380 ⇒ 00:23:11.670 Caitlyn Vaughn: And if you’re like me… Go ahead, Michelle.
197 00:23:11.670 ⇒ 00:23:18.809 vishalag: Yeah, I was going to say we can do one more thing. So, as soon as someone gets on the page, we do a validation call.
198 00:23:19.030 ⇒ 00:23:21.019 Uttam Kumaran: That is this person…
199 00:23:21.020 ⇒ 00:23:27.020 vishalag: token is valid or not. So, generally, we generate a token for 30 days, right? So.
200 00:23:27.970 ⇒ 00:23:30.909 vishalag: or I think 30 or 60 days, I don’t exactly remember, but…
201 00:23:31.370 ⇒ 00:23:50.299 vishalag: If we… we can make a new event type called user validation, okay? And if that user is getting validated, true, that means that that person has been active on the platform. Because you remember, Caitlin, in… in workflows, we have… look at the lead through a Slack message. Like, we… we send…
202 00:23:50.340 ⇒ 00:23:51.980 vishalag: Messages, right? Like.
203 00:23:52.040 ⇒ 00:24:04.950 vishalag: view lead in default, something like this. So that is also, you are trying to push the user into default, so we can do a new event for user validation, or user active check, and if it’s there, it’s there.
204 00:24:05.370 ⇒ 00:24:06.960 vishalag: Just a suggestion we can do.
205 00:24:07.690 ⇒ 00:24:09.780 vishalag: would look pretty… Excuse me.
206 00:24:10.060 ⇒ 00:24:11.230 Caitlyn Vaughn: Pretty easily.
207 00:24:11.900 ⇒ 00:24:12.480 vishalag: Yeah.
208 00:24:12.930 ⇒ 00:24:14.799 Caitlyn Vaughn: Okay, yeah,
209 00:24:14.850 ⇒ 00:24:34.070 Caitlyn Vaughn: let’s just do… let’s do login for now, and then I would really like to include that in, like, Phoenix and new builds, Vichelle, the, like, token piece of, like, being able to tag people. Because the other thing is, like, even if it’s a Slack notification of, like, new, lead, and default, if you’re not logged into default, you would still have to log in, right?
210 00:24:34.070 ⇒ 00:24:34.660 Uttam Kumaran: Yeah.
211 00:24:35.720 ⇒ 00:24:36.310 Caitlyn Vaughn: Okay.
212 00:24:36.580 ⇒ 00:24:39.560 Caitlyn Vaughn: Alright, we’ll do it 80-20 for now.
213 00:24:40.160 ⇒ 00:24:40.770 Uttam Kumaran: Boom.
214 00:24:41.850 ⇒ 00:24:49.040 Henry Zhao: And then, do we want to track somehow, like, if a meeting that was booked was actually… that was actually attended and occurred?
215 00:24:49.220 ⇒ 00:24:55.710 Henry Zhao: Because we should probably talk about that now, so that when we do want to add those things, we can have that tracking available.
216 00:24:55.710 ⇒ 00:25:02.600 Uttam Kumaran: Don’t know how you’re gonna get it, like, easily now. Yeah, I mean, like.
217 00:25:03.890 ⇒ 00:25:14.310 Uttam Kumaran: honestly, the easiest way is, like, if you’re in their HubSpot, or if you’re in their CRM, there’s usually an activity related to, like, meeting done. You can pull that in, but, like, I don’t know, I feel like it’s…
218 00:25:15.080 ⇒ 00:25:16.730 Uttam Kumaran: It’s not worth taking it, yeah.
219 00:25:16.730 ⇒ 00:25:25.969 Caitlyn Vaughn: Yeah, yeah, I mean, I think this is, like, a problem that every company wishes they could solve for, to be honest. I was,
220 00:25:26.350 ⇒ 00:25:35.670 Caitlyn Vaughn: optimistically hoping we had, like, sorted it out as an engineering community slash world, but, maybe… maybe next time I ask, someone will have figured it out.
221 00:25:35.670 ⇒ 00:25:39.679 Uttam Kumaran: No, you’ll have… you’ll end up with a default note-taker one day, and then you’ll solve the problem, right?
222 00:25:39.680 ⇒ 00:25:41.079 Caitlyn Vaughn: Wait, we have one!
223 00:25:41.080 ⇒ 00:25:48.070 Uttam Kumaran: Wait, no, no, no, like, not… meaning, like, it’s a default comp… like, you can add a default note-taker to your meetings?
224 00:25:48.070 ⇒ 00:25:51.079 Caitlyn Vaughn: Yeah, we basically have, like, a call recorder and a.
225 00:25:51.080 ⇒ 00:25:52.770 Uttam Kumaran: Oh, nice.
226 00:25:52.890 ⇒ 00:26:01.389 Uttam Kumaran: then yeah, then all your users should say… so you can say, hey, if you want to start seeing your meetings actually attended KPI, you need to install our
227 00:26:01.840 ⇒ 00:26:05.569 Uttam Kumaran: Our snooping tool to hear all your meetings.
228 00:26:05.570 ⇒ 00:26:06.350 Henry Zhao: Yes!
229 00:26:06.350 ⇒ 00:26:06.929 Uttam Kumaran: That’s not.
230 00:26:07.340 ⇒ 00:26:12.160 Henry Zhao: But you could also, when the meeting starts, turn off the snooping tool, and then that’s also a signal that the meeting happened.
231 00:26:13.020 ⇒ 00:26:14.140 Caitlyn Vaughn: Yeah!
232 00:26:14.560 ⇒ 00:26:16.179 Caitlyn Vaughn: Wait, that is true.
233 00:26:16.180 ⇒ 00:26:19.809 Uttam Kumaran: But then you have to offer some AI insights, like, that’s a good product.
234 00:26:20.200 ⇒ 00:26:21.740 Caitlyn Vaughn: We do have AI insights.
235 00:26:22.070 ⇒ 00:26:27.200 Uttam Kumaran: Oh, no, no, no, but, like, on top of the… on top of the meeting, like, what was talked about in the meeting?
236 00:26:27.200 ⇒ 00:26:28.210 Caitlyn Vaughn: We do have that.
237 00:26:28.210 ⇒ 00:26:35.929 Uttam Kumaran: Oh, really? No way! Wait, what the hell? I’m gonna stop talking, man. You guys are way farther than what I see.
238 00:26:35.930 ⇒ 00:26:40.209 Caitlyn Vaughn: So funny. I think that a lot of people don’t know that we have that.
239 00:26:41.180 ⇒ 00:26:41.980 vishalag: Never rolled it up.
240 00:26:41.980 ⇒ 00:26:42.410 Uttam Kumaran: Hold on.
241 00:26:42.410 ⇒ 00:26:46.870 vishalag: Like, we rolled it out only to 3-4 customers, and the cost was so high.
242 00:26:47.080 ⇒ 00:26:47.859 Caitlyn Vaughn: Oh, really?
243 00:26:48.490 ⇒ 00:26:49.250 Uttam Kumaran: Thanks, Luke.
244 00:26:49.250 ⇒ 00:26:55.569 vishalag: 1 per user per hour. So, like, if, let’s say, 4 people are there, $4 per hour.
245 00:26:56.000 ⇒ 00:27:06.590 Caitlyn Vaughn: What? Wait, that’s so crazy. Okay, I also had a meeting recently with someone else, like a partner, and she was like, it’d be so great if you guys had, like, call recording. I was like, we do. And she was like.
246 00:27:06.990 ⇒ 00:27:14.919 Caitlyn Vaughn: are you serious? I was like, yeah! So I just totally gaslit her, as well. I didn’t realize that only 4 people had it.
247 00:27:17.040 ⇒ 00:27:18.010 Caitlyn Vaughn: Totally.
248 00:27:18.870 ⇒ 00:27:22.500 Uttam Kumaran: Team customer… company customer…
249 00:27:22.500 ⇒ 00:27:28.850 Henry Zhao: Total user, so when we look at what percentage of active users are active, what would you define as, like, a total user?
250 00:27:29.920 ⇒ 00:27:34.670 Caitlyn Vaughn: Can I get some more context on this? Like, where did this come from, total user?
251 00:27:35.290 ⇒ 00:27:38.919 Henry Zhao: I think it was one of your questions, was like, what percentage of our total users are active?
252 00:27:39.150 ⇒ 00:27:48.540 Caitlyn Vaughn: Okay. What percentage of people who have created an account on default have come and logged in in the last 30 days?
253 00:27:49.690 ⇒ 00:27:56.740 Henry Zhao: Right, so people have created an account, basically. Do they need to create complete onboarding, or, like, if they just went through step one, for example, and didn’t finish?
254 00:27:57.510 ⇒ 00:28:07.890 Caitlyn Vaughn: I would say it probably needs to complete onboarding. They probably need to at least, like, authenticate their calendar, I’m assuming? Like, that’s part of the onboarding, right?
255 00:28:08.140 ⇒ 00:28:17.029 Uttam Kumaran: But I would have those as steps, Henry, like, I would have just, like, this person’s created account, and then these many, like, the waterfall of, like, these many people.
256 00:28:18.210 ⇒ 00:28:19.050 Caitlyn Vaughn: After onboarding.
257 00:28:19.290 ⇒ 00:28:25.960 Uttam Kumaran: Yeah, like, got through onboarding, and then we can go deeper onto onboarding. But I think those are, like, 3 segments, basically.
258 00:28:27.030 ⇒ 00:28:32.220 Henry Zhao: Yeah, but just what would be the denominator when you’re looking at percentage active users?
259 00:28:32.220 ⇒ 00:28:33.360 Uttam Kumaran: Yeah, yeah.
260 00:28:33.710 ⇒ 00:28:46.600 Caitlyn Vaughn: Yeah, I think the other thing is, like, at the moment, since we’re, like, once again, still fully sales-led, the only people that are getting invited are people that, like, need a seat, so I could imagine the…
261 00:28:46.910 ⇒ 00:28:49.360 Caitlyn Vaughn: Like, onboarding to be pretty…
262 00:28:49.670 ⇒ 00:28:58.470 Caitlyn Vaughn: pretty high, versus, like, PLG, this will matter a lot more. Like, how many people got through onboarding, what step did they get stuck on, like, what’s unclear, you know? Like, less concerned about that at the moment.
263 00:28:58.790 ⇒ 00:28:59.400 Uttam Kumaran: Yeah.
264 00:29:00.970 ⇒ 00:29:03.830 vishalag: And, I think you should… We’ll be honest.
265 00:29:03.830 ⇒ 00:29:06.309 Henry Zhao: Sorry, go ahead, Vishal.
266 00:29:07.600 ⇒ 00:29:10.700 vishalag: I’m saying you remove the authenticate calendar thing, it’s optional, so, like…
267 00:29:10.700 ⇒ 00:29:12.390 Caitlyn Vaughn: Yeah, okay.
268 00:29:12.560 ⇒ 00:29:13.709 vishalag: Yeah, I cannot agree on it.
269 00:29:17.980 ⇒ 00:29:25.120 Henry Zhao: Okay, then do we want to have some terms and term… terminology in terms of what is, like, an individual user’s versus, like, a team or a company?
270 00:29:25.400 ⇒ 00:29:30.380 Uttam Kumaran: Yeah, right now there is teams, there is members, right?
271 00:29:30.610 ⇒ 00:29:31.990 Henry Zhao: Team to the members, right?
272 00:29:36.550 ⇒ 00:29:44.079 Uttam Kumaran: And then… companies, and do you guys have… in your new product, you guys have… you have a… you have a concept of, like, company org hierarchy?
273 00:29:44.600 ⇒ 00:29:47.080 Caitlyn Vaughn: Yes, so…
274 00:29:47.400 ⇒ 00:29:54.879 Caitlyn Vaughn: I think we… we’ve decided to go with WorkOS, for, like, the permissions type thing, so what we’ll have in the…
275 00:29:55.030 ⇒ 00:30:01.689 Caitlyn Vaughn: new platform is, like, org, and then on the user level, admin and member.
276 00:30:01.980 ⇒ 00:30:07.830 Caitlyn Vaughn: But I think right now we do have admin and member. You do have admin and member. Yeah, we do.
277 00:30:07.830 ⇒ 00:30:10.139 Uttam Kumaran: Permission role is not really, like, hierarchy.
278 00:30:10.510 ⇒ 00:30:16.420 Caitlyn Vaughn: Yeah, I think, like, for P0, I don’t think we’ll have that rolled out yet.
279 00:30:16.530 ⇒ 00:30:21.269 Caitlyn Vaughn: But we will in, like, fast follow, so let’s just discount that for now.
280 00:30:21.520 ⇒ 00:30:25.490 Uttam Kumaran: Like, are there gonna be sub-teams and stuff, or is it just… is, like, team the smallest…
281 00:30:25.660 ⇒ 00:30:26.330 Caitlyn Vaughn: supplies.
282 00:30:26.490 ⇒ 00:30:29.309 Uttam Kumaran: Like, organizational unit of, like, a group of people.
283 00:30:29.510 ⇒ 00:30:31.690 Caitlyn Vaughn: Yeah, it’s just gonna be team.
284 00:30:31.900 ⇒ 00:30:32.500 Uttam Kumaran: Okay.
285 00:30:32.570 ⇒ 00:30:34.260 Caitlyn Vaughn: And we’re B2B, so…
286 00:30:34.850 ⇒ 00:30:36.060 Uttam Kumaran: Okay, okay.
287 00:30:37.260 ⇒ 00:30:42.769 Henry Zhao: Okay, so we’ll have team, which Michelle is team ID in Amplitude, member, which hopefully that’s user ID.
288 00:30:42.770 ⇒ 00:30:44.179 Uttam Kumaran: Yes. And then lead…
289 00:30:44.180 ⇒ 00:30:50.840 Henry Zhao: is what I’m calling the lead ID that Michelle added for us, which is just a distinct email that booked a meeting or submitted a form.
290 00:30:52.490 ⇒ 00:30:56.010 Henry Zhao: Okay, so we’re gonna go by email as, like, the distinct identifier. That’s how you…
291 00:30:56.450 ⇒ 00:30:59.130 Henry Zhao: Like, the lead ID is just the email hash.
292 00:30:59.130 ⇒ 00:31:03.329 vishalag: We are not exposing the email right now, the lead ID is…
293 00:31:03.680 ⇒ 00:31:07.760 Henry Zhao: You’re not exposing it, but if two of them have the same email, it’s the same lead ID, right?
294 00:31:08.100 ⇒ 00:31:08.800 vishalag: Yeah.
295 00:31:11.140 ⇒ 00:31:16.730 Henry Zhao: Okay, and then, what would you consider a power user? Because one of your questions was, how many power users do we have? Like, do you want to.
296 00:31:18.810 ⇒ 00:31:21.299 Caitlyn Vaughn: That’s a good question. Let’s say, like.
297 00:31:22.180 ⇒ 00:31:25.770 Caitlyn Vaughn: Probably logging in once a week for…
298 00:31:28.210 ⇒ 00:31:30.849 Caitlyn Vaughn: I don’t know, let’s say 4 weeks in a row?
299 00:31:31.620 ⇒ 00:31:32.280 Henry Zhao: Okay.
300 00:31:34.460 ⇒ 00:31:40.810 Henry Zhao: The only thing I think about that is if there’s, like, a holiday, And, like, nobody works… is a…
301 00:31:41.000 ⇒ 00:31:44.689 Henry Zhao: Power user. Maybe we want to do, like, 4 out of the 5 weeks?
302 00:31:44.850 ⇒ 00:31:46.019 Henry Zhao: Or something, I don’t know.
303 00:31:46.430 ⇒ 00:31:55.239 Uttam Kumaran: I think this is fine. I think see what the number is. If it’s zero, then we’ll expand it. If it’s really high, then we’ll thin it.
304 00:31:55.240 ⇒ 00:31:56.090 Caitlyn Vaughn: Yeah.
305 00:31:56.450 ⇒ 00:32:03.159 Uttam Kumaran: Yeah, logging in is probably the best proxy, like, instead of, like, looking at specific events.
306 00:32:03.350 ⇒ 00:32:06.790 Caitlyn Vaughn: And then, yeah, for power user in… this is where…
307 00:32:07.000 ⇒ 00:32:13.830 Uttam Kumaran: like, Caitlin, for the next… for the next product, we want to start mapping power users to, like, revenue.
308 00:32:13.950 ⇒ 00:32:24.120 Uttam Kumaran: Or, like, the fact that this person is using a bunch of features that makes them stickier, right? And so, basically, your goal is to, like, move people from total to active to power.
309 00:32:24.240 ⇒ 00:32:27.669 Uttam Kumaran: You know, and then… and then dissect that, so this is…
310 00:32:28.400 ⇒ 00:32:29.080 Uttam Kumaran: now.
311 00:32:29.920 ⇒ 00:32:34.960 Caitlyn Vaughn: Yeah, the thinking for, like, PLG and new products, essentially, there’s, like.
312 00:32:35.220 ⇒ 00:32:52.620 Caitlyn Vaughn: three things we want people to do, and it’s all under the usage category. The first is, like, use tokens, like, AI tokens. The second is to use credits across our platform, and the third is, like, usage and SKUs. So, like, using more SKUs. Okay. So that’s, like, a theme that will…
313 00:32:52.930 ⇒ 00:32:56.470 Caitlyn Vaughn: Probably rely on pretty heavily, like, with new product.
314 00:32:56.850 ⇒ 00:33:01.679 Uttam Kumaran: Okay, cool. Do you guys have, like, a doc on, like, KPIs for the next product anywhere?
315 00:33:02.100 ⇒ 00:33:03.670 Caitlyn Vaughn: Yeah, Tom, all right here.
316 00:33:03.670 ⇒ 00:33:09.859 Uttam Kumaran: Okay, I will… then we should write you one. Then, Henry, one thing we can do as part of this is let’s have, like, a…
317 00:33:10.030 ⇒ 00:33:19.889 Uttam Kumaran: current state, and then what is the proposed future state for some of these, based on the new product build. So, what Kate mentioned about, sort of tokens and SKUs.
318 00:33:20.100 ⇒ 00:33:22.799 Uttam Kumaran: Just so we have it written somewhere, Anna Kaylee.
319 00:33:22.800 ⇒ 00:33:23.380 Caitlyn Vaughn: Shop.
320 00:33:23.760 ⇒ 00:33:28.540 Caitlyn Vaughn: I think we may have, like, a usage doc somewhere. Let me send you, like… there’s been just so much written in general.
321 00:33:28.540 ⇒ 00:33:29.100 Uttam Kumaran: Oh, really sad.
322 00:33:29.100 ⇒ 00:33:39.869 Caitlyn Vaughn: through. I’m also, like, starting to hash the actual, like, PLG stuff. I worked on pricing and packaging this week, so I’ll have some more, like, stuff written down for you soon.
323 00:33:39.870 ⇒ 00:33:40.750 Uttam Kumaran: Okay, okay.
324 00:33:42.400 ⇒ 00:33:49.110 Henry Zhao: Okay, and the last thing is a small one, is just what do we count as a workflow being completed? Is it when it’s published, set up, or successfully run the first time?
325 00:33:51.990 ⇒ 00:33:59.949 Caitlyn Vaughn: I’m gonna say published is gonna be our best unit for metric. Unit of metric right now.
326 00:34:01.870 ⇒ 00:34:04.769 Uttam Kumaran: We’ll see the data on… on workflow runs.
327 00:34:05.210 ⇒ 00:34:05.870 Uttam Kumaran: As well.
328 00:34:07.490 ⇒ 00:34:08.080 Henry Zhao: Yeah.
329 00:34:08.800 ⇒ 00:34:10.610 Caitlyn Vaughn: Yeah, let’s just stick with that for now.
330 00:34:10.739 ⇒ 00:34:11.309 Uttam Kumaran: Okay.
331 00:34:13.949 ⇒ 00:34:24.569 Henry Zhao: Thank you. We can go over these again next Friday, just to see if our gut instinct has changed, and I’ll try to look at some of the data with these numbers, and we’ll have two weeks of data next week, so…
332 00:34:24.570 ⇒ 00:34:26.610 Caitlyn Vaughn: Yay!
333 00:34:27.090 ⇒ 00:34:35.880 Uttam Kumaran: I’m so excited! I’ll DM you today, as soon as I get, like, a dashboard for you to start to look at, and then, yeah, tell me how I can help for Tuesday. Like…
334 00:34:35.889 ⇒ 00:34:36.469 Caitlyn Vaughn: Okay.
335 00:34:36.469 ⇒ 00:34:38.489 Uttam Kumaran: I’ll have to get used to it as much as you need.
336 00:34:39.120 ⇒ 00:34:43.769 Caitlyn Vaughn: Okay, cool, and also thank you, tell Brian, thank you for submitting that to Partnerships.
337 00:34:43.770 ⇒ 00:34:44.400 Uttam Kumaran: Oh, yeah.
338 00:34:44.400 ⇒ 00:34:45.900 Caitlyn Vaughn: this week, let’s go!
339 00:34:45.900 ⇒ 00:34:55.670 Uttam Kumaran: You know, I gotta ping some more people to try to get more in today. And Henry, you should think about applying for the raffle.
340 00:34:55.679 ⇒ 00:34:57.449 Caitlyn Vaughn: Yeah, Heather, you do it too.
341 00:34:57.450 ⇒ 00:35:01.059 Uttam Kumaran: Yeah, I sent it, I sent it in our company channel.
342 00:35:01.060 ⇒ 00:35:01.850 Caitlyn Vaughn: You did?
343 00:35:01.850 ⇒ 00:35:02.979 Uttam Kumaran: Yeah, yeah, yeah.
344 00:35:02.980 ⇒ 00:35:04.100 Caitlyn Vaughn: That’s go.
345 00:35:04.420 ⇒ 00:35:05.210 Uttam Kumaran: Yeah, you should be.
346 00:35:05.210 ⇒ 00:35:08.169 Caitlyn Vaughn: I will pay you money. Do it.
347 00:35:08.610 ⇒ 00:35:09.160 Henry Zhao: Okay.
348 00:35:12.010 ⇒ 00:35:16.679 Caitlyn Vaughn: We actually needed it, like, what Ryanville? I needed, so it was crazy.
349 00:35:16.680 ⇒ 00:35:17.780 Uttam Kumaran: Really?
350 00:35:17.780 ⇒ 00:35:39.819 Uttam Kumaran: And we were like, I was like, oh, this is actually perfect, like, I was asking about this last week. So, it’s actually great. There’s so much more we could do. And then, you know, his first question was, when are they adding more enrichment opportunities? I said, don’t you… you hold your breath, like… It was so funny, that was, like, his first jump, was like, yeah, I wish they would add way more enrichment sources.
351 00:35:39.820 ⇒ 00:35:40.600 Caitlyn Vaughn: Yeah, let’s.
352 00:35:40.600 ⇒ 00:35:48.249 Uttam Kumaran: He’s on the marketing side, so he doesn’t know, like, the work that we’re doing, so… Yeah, totally. Yeah, and we… I think we added in.
353 00:35:48.250 ⇒ 00:36:01.360 Caitlyn Vaughn: like, was, an abstract, and, like, a couple of others, and I didn’t realize we made them public, but in these videos, I’ve seen partners, like, using all of those enrichment nodes, and I’m like, oh, I don’t know if we’re supposed to have that rolled out, but cheers.
354 00:36:01.360 ⇒ 00:36:03.590 Uttam Kumaran: Yeah, nice, nice.
355 00:36:03.590 ⇒ 00:36:05.250 Caitlyn Vaughn: Might as well, we’re paying for it, right?
356 00:36:05.250 ⇒ 00:36:15.780 Uttam Kumaran: Yeah, yeah, we’re gonna start to use it. I think we’re gonna… I think we’re gonna start to use the People Data Labs. I don’t know… oh, also, what… what happened… I don’t know what Mustafa, what ended up happening with that API key thing, but…
357 00:36:16.330 ⇒ 00:36:18.179 Uttam Kumaran: Amazing. Like, we’re just gonna use that.
358 00:36:18.820 ⇒ 00:36:33.830 Caitlyn Vaughn: Gosh darn it, I wish I had this information before. Also, I haven’t looked through the sheet that, was sent over for, like, the testing, because that’s actually something that I’ve needed to do. Actually, this might be an area that you guys could for sure help in.
359 00:36:34.090 ⇒ 00:36:43.230 Caitlyn Vaughn: So I have this list of data providers that I’ve, like, negotiated with, gone through procurement, DPA, like, had them all approved, but I haven’t actually tested any of them yet.
360 00:36:43.230 ⇒ 00:36:52.760 Uttam Kumaran: We’re… we’re writing this doc for you right now. Really? We’re writing… I told… we’re writing a, like, a documentation on all the testing we did across all of them.
361 00:36:55.140 ⇒ 00:37:07.980 Uttam Kumaran: This is so great. It’s not done… I don’t know, I haven’t gone to read it yet, but… because we ran through it, I was like, we need to save all of that, and then in our data platform, I want to put all the providers, so yeah, I’ll hand that to you, and then…
362 00:37:08.900 ⇒ 00:37:18.980 Caitlyn Vaughn: Okay, cool. I think for the People Data Labs, the other thing that I did when I was negotiating these contracts is, like, try to get more diversity of what data we’re getting.
363 00:37:19.030 ⇒ 00:37:38.340 Caitlyn Vaughn: So for, like, PDL and all these brands, I, like, pulled specific fields. So we have the company API, we don’t have the person API, and then we have, like, a bunch of specific, like, like, signal kind of based data. If it would be helpful for me to get people API access for PDL, I can try.
364 00:37:38.800 ⇒ 00:37:39.330 Uttam Kumaran: Okay.
365 00:37:39.330 ⇒ 00:37:39.830 Caitlyn Vaughn: And let you know.
366 00:37:39.830 ⇒ 00:37:50.839 Uttam Kumaran: I’ll tell you, like, Mustafa went through the whole thing, so I’ll have him list out what he found. For Lev, and for Ryan, we’re using it to look at sales team growth, and the amount of people on the sales team, and, like, roles.
367 00:37:51.590 ⇒ 00:37:57.040 Uttam Kumaran: I’ll tell you what we found, and then we’ll have examples of, like, companies we… because right now we’re…
368 00:37:57.170 ⇒ 00:38:06.840 Uttam Kumaran: we are backtesting this on what Lev told us is, like, Tier 1, Tier 2, Tier 3, this, like, stuff, so we have a great example of, like, the types of data that we’re getting back, so…
369 00:38:07.840 ⇒ 00:38:12.249 Uttam Kumaran: Yeah, we’re gonna toss that all into Notion, and yeah, you can tell us, like, what you think.
370 00:38:12.970 ⇒ 00:38:21.020 Caitlyn Vaughn: Cool, this is so helpful. When we get closer to actually rolling out Data Marketplace and all that, I’m gonna have you guys, like, run tests on it, too, if you’re okay with it.
371 00:38:21.020 ⇒ 00:38:22.010 Uttam Kumaran: Okay, cool.
372 00:38:22.680 ⇒ 00:38:25.579 Caitlyn Vaughn: Sick! Okay, thank you guys for everything!
373 00:38:25.780 ⇒ 00:38:26.920 Caitlyn Vaughn: Very helpful.
374 00:38:27.230 ⇒ 00:38:28.379 Uttam Kumaran: Thank you. Talk to you soon.
375 00:38:28.380 ⇒ 00:38:29.400 Caitlyn Vaughn: See you guys later.
376 00:38:29.400 ⇒ 00:38:29.980 Uttam Kumaran: Right.