Meeting Title: Phoenix Project Weekly Sync Date: 2026-03-26 Meeting participants: Greg Stoutenburg, Scratchpad Notetaker, Nandika Jhunjhunwala, Caitlyn Vaughn, Mustafa Raja, Uttam Kumaran, Lev Katreczko
WEBVTT
1 00:01:21.750 ⇒ 00:01:22.850 Greg Stoutenburg: Hey, Nanda.
2 00:01:23.170 ⇒ 00:01:24.160 Nandika Jhunjhunwala: Hello?
3 00:01:24.970 ⇒ 00:01:30.509 Greg Stoutenburg: I got panicky there for a second, I’m like, I’m in the meeting by myself, did I screw up the link again?
4 00:01:32.240 ⇒ 00:01:37.030 Greg Stoutenburg: Did I put myself in my own meeting, and everyone else is in another one? Like, where’s Greg?
5 00:01:38.010 ⇒ 00:01:40.059 Greg Stoutenburg: I thought he was on time. Hey, Caitlin.
6 00:01:40.060 ⇒ 00:01:41.470 Caitlyn Vaughn: Hello!
7 00:01:41.830 ⇒ 00:01:49.260 Greg Stoutenburg: just laughing about my inability to consistently put meeting links in meetings. And when I was alone for a little while, I thought, oh no!
8 00:01:49.260 ⇒ 00:01:49.910 Caitlyn Vaughn: Like this.
9 00:01:49.910 ⇒ 00:01:52.800 Greg Stoutenburg: Did I click a link that’s separate from what I sent everyone else?
10 00:01:52.800 ⇒ 00:01:53.889 Caitlyn Vaughn: That’s so funny.
11 00:01:53.890 ⇒ 00:01:55.110 Greg Stoutenburg: Part of the way there, yeah.
12 00:01:55.710 ⇒ 00:01:58.090 Greg Stoutenburg: How’s your week going, everybody?
13 00:02:00.040 ⇒ 00:02:09.600 Caitlyn Vaughn: Good, it’s crazy. It’s always so much going on, but we’re getting kind of towards the end here, before launch, so thank goodness.
14 00:02:09.919 ⇒ 00:02:11.149 Greg Stoutenburg: Yep, yep.
15 00:02:11.150 ⇒ 00:02:12.119 Caitlyn Vaughn: How about you?
16 00:02:12.120 ⇒ 00:02:14.049 Greg Stoutenburg: Phoenix shall rise from the ashes.
17 00:02:14.050 ⇒ 00:02:15.080 Caitlyn Vaughn: Exactly.
18 00:02:15.080 ⇒ 00:02:24.680 Greg Stoutenburg: Yeah, good, yeah, you know, same deal, a lot going on. But, you know, it feels nice to have a, like, a normal work week and not drive across the country or anything.
19 00:02:24.680 ⇒ 00:02:25.280 Caitlyn Vaughn: like that.
20 00:02:25.280 ⇒ 00:02:28.729 Greg Stoutenburg: And then, like, try to also have a normal work week at the same time.
21 00:02:28.730 ⇒ 00:02:29.300 Caitlyn Vaughn: Yeah.
22 00:02:29.300 ⇒ 00:02:33.420 Greg Stoutenburg: You know, no more triaging from Slack, from 5.
23 00:02:34.250 ⇒ 00:02:36.020 Greg Stoutenburg: So, that’s good.
24 00:02:36.020 ⇒ 00:02:37.020 Caitlyn Vaughn: That’s so funny.
25 00:02:37.020 ⇒ 00:02:37.690 Greg Stoutenburg: Yeah.
26 00:02:37.920 ⇒ 00:02:41.660 Greg Stoutenburg: Yeah. Hey, Tom. Hey, Mustafa.
27 00:02:42.310 ⇒ 00:02:45.230 Uttam Kumaran: Hey, we were just, like, trying to get this latest
28 00:02:45.370 ⇒ 00:02:48.789 Uttam Kumaran: Data loaded, so we were just, like, finishing that up.
29 00:02:50.920 ⇒ 00:02:53.959 Caitlyn Vaughn: In one whole minute, you got it done, that was so fast.
30 00:02:53.960 ⇒ 00:02:54.350 Greg Stoutenburg: Very good.
31 00:02:54.350 ⇒ 00:02:57.250 Uttam Kumaran: Actually, like, yeah, maybe it was 6 minutes, but it was a…
32 00:02:57.250 ⇒ 00:02:57.910 Caitlyn Vaughn: Good.
33 00:02:58.090 ⇒ 00:03:02.459 Uttam Kumaran: It’s a lot. Nice. But we, like, tried to automate some stuff on our side to, like.
34 00:03:02.970 ⇒ 00:03:06.660 Uttam Kumaran: just facilitate it, because I know that’s just going to be the process, so yeah.
35 00:03:07.340 ⇒ 00:03:20.589 Uttam Kumaran: I think we’re good. Yeah, Greg, you can take it away. I think we mainly wanted to try to focus this meeting on some of the dashboards, like we talked about, but I also think, for Nandika’s sake, we have a lot of models also ready to start being used, so…
36 00:03:21.240 ⇒ 00:03:22.950 Uttam Kumaran: Made, like, a good amount of progress.
37 00:03:23.390 ⇒ 00:03:24.010 Caitlyn Vaughn: Cool.
38 00:03:24.220 ⇒ 00:03:48.880 Greg Stoutenburg: Yeah, yeah, so we’ve been doing, you know, we’ve been doing these weekly updates, and as we’re seeing where we think we can add the most value, we think, you know, let’s just kind of get through the updates and do those progress check-ins, but also spend this time when everybody’s blocked off some time in their calendar to review work in progress and talk about, you know, just the direction that things are going in, give feedback, and that sort of thing. So, we want to start to pivot in that direction starting now.
39 00:03:49.370 ⇒ 00:03:52.909 Greg Stoutenburg: So… Here it is, default weekly project review.
40 00:03:55.070 ⇒ 00:04:07.390 Greg Stoutenburg: I’ll skip that. So, weekly overview, we’ve got these dashboards that are, matching the raw data, not leading on equals, and we’ve got, customer reporting and enablement dashboards in internal review.
41 00:04:07.390 ⇒ 00:04:17.570 Greg Stoutenburg: We’ll look at a dashboard that is, just about done, you know, the data is in, and the charts are in place, and we’ll talk through it and solicit your feedback.
42 00:04:17.630 ⇒ 00:04:41.809 Greg Stoutenburg: For product analytics, the next step will be building on Nandika’s work of creating lots of charts from the various features that are available in Phoenix staging now. We’ll organize them to try to fit the pattern that we laid out in the SOW for this project, so we’ll focus them more explicitly on, like, these are the charts that show user activation, these are the charts that show retention, and so on, so that we, you know, make…
43 00:04:41.810 ⇒ 00:04:48.060 Greg Stoutenburg: Make that data more organized, identify where there are gaps in the reporting that’s been created, and then make a plan to fill those gaps.
44 00:04:48.840 ⇒ 00:04:57.479 Greg Stoutenburg: On the data side, we’ll review the ARR dash with Ryan. Once he’s back, we understand he’s out now, but, once he’s back, we’ll review that with Ryan. We’ll roll the financial summary.
45 00:04:57.480 ⇒ 00:04:59.310 Uttam Kumaran: She’s back this week, right, Caitlin?
46 00:04:59.310 ⇒ 00:05:01.569 Caitlyn Vaughn: He’s back on Monday or Tuesday. Monday.
47 00:05:01.860 ⇒ 00:05:03.140 Caitlyn Vaughn: Yeah, next week.
48 00:05:03.420 ⇒ 00:05:08.330 Uttam Kumaran: Okay, I assume he’s gonna be, like, really jammed, but maybe I will… Try and just…
49 00:05:08.640 ⇒ 00:05:14.900 Uttam Kumaran: put time on his calendar? Or… is there any chance you could, like, see if you could snipe, like, 30 minutes right now?
50 00:05:15.050 ⇒ 00:05:16.059 Caitlyn Vaughn: Yeah, let me.
51 00:05:16.060 ⇒ 00:05:16.790 Uttam Kumaran: For us.
52 00:05:17.490 ⇒ 00:05:18.859 Uttam Kumaran: Yeah, and it’s like…
53 00:05:20.070 ⇒ 00:05:20.590 Caitlyn Vaughn: this…
54 00:05:20.590 ⇒ 00:05:24.249 Uttam Kumaran: group could be on, or, like, me, you, Nandika, Greg, that would be swell.
55 00:05:24.560 ⇒ 00:05:26.670 Caitlyn Vaughn: What day do you want it?
56 00:05:28.210 ⇒ 00:05:31.130 Uttam Kumaran: You can do Monday, I would love that.
57 00:05:31.990 ⇒ 00:05:34.299 Uttam Kumaran: I’m gonna be traveling a little bit on Tuesday.
58 00:05:34.690 ⇒ 00:05:39.510 Uttam Kumaran: But I can still call in, but Monday… Monday would be ideal, or… Yeah.
59 00:05:39.640 ⇒ 00:05:42.129 Caitlyn Vaughn: Can you do Monday at 12 Central?
60 00:05:42.580 ⇒ 00:05:43.270 Uttam Kumaran: Yeah.
61 00:05:44.650 ⇒ 00:05:45.570 Caitlyn Vaughn: Okay.
62 00:05:46.530 ⇒ 00:05:51.090 Caitlyn Vaughn: ARR… Dashboard review.
63 00:05:51.310 ⇒ 00:05:53.730 Uttam Kumaran: I’ll try to… we’ll try to make it tight.
64 00:05:54.470 ⇒ 00:05:55.460 Caitlyn Vaughn: 30 minutes?
65 00:05:55.460 ⇒ 00:05:56.500 Greg Stoutenburg: Cool, sounds good.
66 00:05:56.760 ⇒ 00:06:04.860 Caitlyn Vaughn: Okay, I’ll add you guys to it. Tom and Greg… Okay, cool.
67 00:06:06.220 ⇒ 00:06:07.150 Caitlyn Vaughn: Sniped.
68 00:06:07.730 ⇒ 00:06:12.039 Greg Stoutenburg: Sniped. Welcome back, hope your vacation was fun. I know.
69 00:06:12.040 ⇒ 00:06:12.859 Uttam Kumaran: it’s… I just, like…
70 00:06:12.860 ⇒ 00:06:13.390 Greg Stoutenburg: Bye.
71 00:06:13.390 ⇒ 00:06:15.209 Uttam Kumaran: I’m sure a lot of people are gonna be doing that, but…
72 00:06:15.210 ⇒ 00:06:15.850 Greg Stoutenburg: Of course.
73 00:06:16.170 ⇒ 00:06:19.479 Uttam Kumaran: This is blocked on our side to just be, like, we’re good, so…
74 00:06:19.610 ⇒ 00:06:31.680 Greg Stoutenburg: Yep, yeah, we want to get that over the line, so… Yep, key wins for Data Platform Analytics, V1 of Customer Reporting and Enablement Dashboard is done, so we’ll be able to see what users are utilizing in the product.
75 00:06:31.680 ⇒ 00:06:43.110 Greg Stoutenburg: PostDog’s been set up in Polytomic, so we’ll be able to send postdog data to Omni, and dbt modeling is started for customer success metrics and, Leb’s work on BizDev metrics.
76 00:06:44.570 ⇒ 00:06:48.910 Greg Stoutenburg: All right, Tom, do you want to take over here and walk through that dash?
77 00:06:49.830 ⇒ 00:06:52.939 Uttam Kumaran: Yes, I can do that.
78 00:06:55.020 ⇒ 00:06:56.140 Uttam Kumaran: Let me…
79 00:07:00.440 ⇒ 00:07:03.469 Uttam Kumaran: I was hoping we’d see some of this March data, but…
80 00:07:04.320 ⇒ 00:07:08.250 Uttam Kumaran: See if we maybe have to finish it out at the end of the meeting, but
81 00:07:09.850 ⇒ 00:07:24.110 Uttam Kumaran: Okay. So, it’s sort of a rough pass and a dash. I think I really, just this meeting, I want to get some more requirements, but I want to really probably show you guys more about, like, the topic that we developed, because that’s really…
82 00:07:24.300 ⇒ 00:07:30.209 Uttam Kumaran: more of the meat of the… of the challenge. So we have, like, a customer enablement daily.
83 00:07:30.260 ⇒ 00:07:49.079 Uttam Kumaran: topic, and so you can see everything from, the metric, the Salesforce-related details, the team, so we’re doing the join between Salesforce and, like, the product, team, and then you can actually go through all of the
84 00:07:49.290 ⇒ 00:07:54.020 Uttam Kumaran: you know, various product metrics. So, workflow creation,
85 00:07:54.170 ⇒ 00:08:00.779 Uttam Kumaran: Workflow creation, meetings booked, meeting minutes, tickets,
86 00:08:01.120 ⇒ 00:08:11.459 Uttam Kumaran: and then admin creation. So that were some of the initial requirements. And then this dashboard, I kind of want to wrap up today. We did…
87 00:08:11.660 ⇒ 00:08:13.910 Uttam Kumaran: Like, a first kind of, like, ugly pass.
88 00:08:14.270 ⇒ 00:08:17.179 Uttam Kumaran: Just try to rush something out, but…
89 00:08:17.910 ⇒ 00:08:22.889 Uttam Kumaran: I think we can do, like, we can actually put together something that’s, like, really beautiful. I just get some requirements.
90 00:08:23.090 ⇒ 00:08:26.680 Uttam Kumaran: So, you see, like, we have, like, total meeting duration, and then
91 00:08:26.820 ⇒ 00:08:34.280 Uttam Kumaran: I don’t know, Mustafa, on this call, if you want to see about if the data is loaded, but we have all of the… well, actually, some of it has, maybe some of it’s.
92 00:08:34.280 ⇒ 00:08:41.940 Mustafa Raja: Yeah, the two top charts, I need to see in the model, what’s happening with them. The data is loaded for the rest of them.
93 00:08:42.299 ⇒ 00:08:43.079 Uttam Kumaran: Okay, okay.
94 00:08:43.249 ⇒ 00:08:44.329 Uttam Kumaran: So,
95 00:08:44.569 ⇒ 00:08:55.799 Uttam Kumaran: discount this area, but everything should be loaded until basically what we got from Thomas. So meeting duration, how many people booked a meeting, ticket submitted.
96 00:08:55.999 ⇒ 00:09:01.879 Uttam Kumaran: Created a workflow, We also can have information from Salesforce, which is, like.
97 00:09:02.149 ⇒ 00:09:09.689 Uttam Kumaran: Basically, like, the team, and then we’re looking at user growth, user growth month over month, year-over-year growth.
98 00:09:10.039 ⇒ 00:09:13.549 Uttam Kumaran: Workflows by trigger. So I guess, like.
99 00:09:14.089 ⇒ 00:09:24.489 Uttam Kumaran: seeing some of this data, like, what is the story we’re trying to tell, and then how can we kind of assist in, like, making that really obvious? I have some thoughts, but…
100 00:09:25.029 ⇒ 00:09:26.929 Uttam Kumaran: That’s kind of, like, what I would love to hear.
101 00:09:27.680 ⇒ 00:09:35.680 Caitlyn Vaughn: Yeah, this, customer reporting and enablement, this is probably most helpful for, like, Deanna and CS team, right?
102 00:09:36.880 ⇒ 00:09:39.359 Caitlyn Vaughn: That’s who this was requested by?
103 00:09:39.690 ⇒ 00:09:40.470 Uttam Kumaran: Yes.
104 00:09:40.470 ⇒ 00:09:48.320 Caitlyn Vaughn: Okay, so what I would probably do is defer to Deanna, now that she’s back, and get her take, because that’s…
105 00:09:48.830 ⇒ 00:09:59.580 Caitlyn Vaughn: going to be the person that’s either gonna say this is good or bad, or, like, what they need or not. I can look at this and say, it looks great, or we can add 5 things, but it won’t really change Deanna’s opinion.
106 00:09:59.860 ⇒ 00:10:00.530 Uttam Kumaran: Okay, okay.
107 00:10:00.530 ⇒ 00:10:01.970 Greg Stoutenburg: That’s data, okay.
108 00:10:01.970 ⇒ 00:10:04.300 Caitlyn Vaughn: Yeah. So this looks good.
109 00:10:04.300 ⇒ 00:10:20.210 Nandika Jhunjhunwala: Yeah, no, this looks great, thank you. I’ve been doing more customer support stuff this week, so, a lot of this is, like, stuff that I know the CSMs, like, would love to see. But one thing I wanted to flag, was we’re moving from, like, plane to pylon for support.
110 00:10:20.210 ⇒ 00:10:20.740 Caitlyn Vaughn: food.
111 00:10:21.060 ⇒ 00:10:31.790 Nandika Jhunjhunwala: So maybe that’s another data source you would have to ingest and model, unfortunately, so I just wanted to flag that, because I think we’re going to be retiring plain very soon, and all that data would be…
112 00:10:31.950 ⇒ 00:10:35.819 Nandika Jhunjhunwala: port it over to Elon, so that’s where, like, our tickets would live.
113 00:10:37.300 ⇒ 00:10:38.390 Caitlyn Vaughn: Good call out.
114 00:10:39.380 ⇒ 00:10:45.079 Uttam Kumaran: Okay, great. I even have some past notes from Deanna, so maybe I’ll… I…
115 00:10:45.200 ⇒ 00:10:52.060 Uttam Kumaran: We’ll… we’ll actually… my recommendation was just gonna be to kind of go with this, because this is actually what we were going to try to model
116 00:10:52.200 ⇒ 00:10:54.780 Uttam Kumaran: using, catalyst?
117 00:10:55.020 ⇒ 00:11:03.370 Uttam Kumaran: So… we mainly wanted to look at active users, feature adoption, seat utilization, license gap.
118 00:11:03.530 ⇒ 00:11:06.180 Uttam Kumaran: Meeting, workflow, usage, engagement.
119 00:11:06.540 ⇒ 00:11:07.429 Uttam Kumaran: And, like.
120 00:11:08.270 ⇒ 00:11:13.329 Uttam Kumaran: this is sort of, like, what the objective I discussed with her. Really, the high-level use case is, like.
121 00:11:13.600 ⇒ 00:11:23.940 Uttam Kumaran: Customer success people want to be able to filter to a specific client, see everything about that client in, like, one view. And then, broadly, we’ll have a section on, like, customer success in general.
122 00:11:24.160 ⇒ 00:11:34.539 Uttam Kumaran: So I do already have some notes on this, and some thoughts from last time I talked to Deanna, so I think we’ll run with this, and her, she’ll be the approver, so…
123 00:11:34.790 ⇒ 00:11:35.670 Caitlyn Vaughn: Okay.
124 00:11:35.670 ⇒ 00:11:36.620 Uttam Kumaran: We can run with this.
125 00:11:36.620 ⇒ 00:11:45.569 Caitlyn Vaughn: Should we do… I mean, if we’re gonna review the ARR dashboard, should we do… do you want to do a meeting right after that, and just meet with Deanna and make sure this looks good to her?
126 00:11:45.900 ⇒ 00:11:57.410 Uttam Kumaran: Yeah, yeah, that would be great. So what I want to do on our side is I’m going to implement what we talked… what we… what Deanna and I talked about a few months ago. Yeah. Because we actually have all the data.
127 00:11:57.510 ⇒ 00:12:06.500 Uttam Kumaran: And then, that way she’ll have something to reflect on, in terms of, like, what’s useful. So we’ll have a version of this, you know, by end of day tomorrow.
128 00:12:07.310 ⇒ 00:12:11.990 Uttam Kumaran: the ARR dashboard is totally ready for us to also get feedback on.
129 00:12:12.210 ⇒ 00:12:12.980 Uttam Kumaran: I shouldn’t.
130 00:12:12.980 ⇒ 00:12:14.220 Caitlyn Vaughn: Awesome.
131 00:12:14.970 ⇒ 00:12:22.829 Caitlyn Vaughn: Okay, I just sent that out for right after. We can just… Make Monday a… get-shit-done day.
132 00:12:22.830 ⇒ 00:12:23.400 Uttam Kumaran: Okay.
133 00:12:23.400 ⇒ 00:12:23.929 Greg Stoutenburg: We like that.
134 00:12:25.410 ⇒ 00:12:25.940 Greg Stoutenburg: Cool.
135 00:12:25.940 ⇒ 00:12:35.409 Uttam Kumaran: Yeah, now that, like, we’ve taken all of these from ingestion to modeling to, like, first-person dashboard, I feel like we’re in a really, really good spot to
136 00:12:35.670 ⇒ 00:12:38.070 Uttam Kumaran: Just get feedback on the dashboard and iterate on those.
137 00:12:38.640 ⇒ 00:12:44.239 Uttam Kumaran: And then Demi can continue on modeling the… the next piece.
138 00:12:44.760 ⇒ 00:12:46.219 Uttam Kumaran: a lot of stuff for low, so…
139 00:12:46.400 ⇒ 00:12:53.509 Caitlyn Vaughn: Okay, cool. So, we have the financial dashboard done, we have the ARR dashboard done, the CS dashboard done, enough for.
140 00:12:53.510 ⇒ 00:12:54.270 Uttam Kumaran: Almost done.
141 00:12:54.480 ⇒ 00:12:56.170 Caitlyn Vaughn: Almost done.
142 00:12:56.410 ⇒ 00:12:58.420 Uttam Kumaran: Don’t give us too much credit.
143 00:12:59.560 ⇒ 00:13:00.770 Uttam Kumaran: Almost.
144 00:13:00.770 ⇒ 00:13:05.889 Caitlyn Vaughn: Appreciate the honesty. Okay, awesome. So then the…
145 00:13:06.440 ⇒ 00:13:12.489 Caitlyn Vaughn: The next would be to finish off those dashboards, and then move into the product dashboards.
146 00:13:13.880 ⇒ 00:13:15.700 Uttam Kumaran: Yeah, so…
147 00:13:16.360 ⇒ 00:13:23.930 Caitlyn Vaughn: Customer product activity. Oh, is that… customer product activity? That’s not the one that’s being worked on currently, though, right?
148 00:13:24.570 ⇒ 00:13:31.039 Uttam Kumaran: I don’t think this is gonna include… well, this is gonna include some activity, but it’s… it’s for the purpose of Deanna.
149 00:13:31.380 ⇒ 00:13:32.270 Uttam Kumaran: success.
150 00:13:32.270 ⇒ 00:13:32.760 Caitlyn Vaughn: The CS.
151 00:13:32.760 ⇒ 00:13:34.200 Uttam Kumaran: Yeah.
152 00:13:34.470 ⇒ 00:13:34.810 Caitlyn Vaughn: Okay.
153 00:13:34.810 ⇒ 00:13:36.380 Uttam Kumaran: Yes, I think, is a good… yeah.
154 00:13:37.530 ⇒ 00:13:43.629 Caitlyn Vaughn: Yeah, I think that sounds good, because we’ll probably need or want one that’s, like, more product-specific.
155 00:13:43.630 ⇒ 00:13:48.350 Uttam Kumaran: Yeah, so, like, for example, Lev was interested in, like, it’s more about, like.
156 00:13:48.660 ⇒ 00:13:51.180 Uttam Kumaran: Sort of some of the data of, like, workflow by trigger.
157 00:13:51.720 ⇒ 00:14:00.619 Uttam Kumaran: It’s a little bit similar, but it’s actually not as, specific about, like, when they onboarded versus how many users versus licenses.
158 00:14:00.620 ⇒ 00:14:01.120 Caitlyn Vaughn: Yeah.
159 00:14:01.120 ⇒ 00:14:04.679 Uttam Kumaran: Flagging that, like, someone’s paying, but they’re not, like, using the product.
160 00:14:05.000 ⇒ 00:14:05.499 Caitlyn Vaughn: Things like that.
161 00:14:05.530 ⇒ 00:14:08.530 Uttam Kumaran: Yeah, Lev, I said your name about, like, 3 times, and then you…
162 00:14:08.530 ⇒ 00:14:12.390 Greg Stoutenburg: But, like, on the last one, it’s like, yeah, three times.
163 00:14:12.390 ⇒ 00:14:20.250 Uttam Kumaran: I know you were here, and I was like, I was like, have I just been, like, talking about you? Nothing bad, but I don’t know, I just…
164 00:14:20.250 ⇒ 00:14:24.279 Lev Katreczko: My ears started ringing, and I just… GCAL.
165 00:14:25.240 ⇒ 00:14:27.700 Uttam Kumaran: No, that’s the… that’s from having AirPods in for too long.
166 00:14:28.400 ⇒ 00:14:30.620 Lev Katreczko: Yeah, too.
167 00:14:31.240 ⇒ 00:14:36.279 Uttam Kumaran: Right there with you. Yeah, so Dashboard 5 is the product activity.
168 00:14:36.470 ⇒ 00:14:36.870 Caitlyn Vaughn: Okay.
169 00:14:36.870 ⇒ 00:14:39.530 Uttam Kumaran: Stuff for Lev, and then we’re moving into marketing.
170 00:14:39.670 ⇒ 00:14:41.799 Caitlyn Vaughn: Cool. That sounds great. This looks good.
171 00:14:41.800 ⇒ 00:14:44.490 Uttam Kumaran: Actually, I… so I’m gonna push to see…
172 00:14:45.060 ⇒ 00:14:57.309 Uttam Kumaran: if we can… how much we can get done on that side, and then maybe we also call Lev on Monday? Or I… or maybe I can call Lev, now that we have this out, we can do that.
173 00:14:57.310 ⇒ 00:14:57.650 Caitlyn Vaughn: One sec.
174 00:14:57.650 ⇒ 00:15:01.789 Uttam Kumaran: on, like, what to produce there. We have a pretty… I think we’re pretty clear.
175 00:15:02.410 ⇒ 00:15:06.240 Uttam Kumaran: environments lab, but, yeah.
176 00:15:06.860 ⇒ 00:15:12.630 Caitlyn Vaughn: Okay, cool. Wait, Lev created a doc, right, for, what he wanted in the dashboard?
177 00:15:12.630 ⇒ 00:15:14.030 Uttam Kumaran: Yes. Okay. Yeah.
178 00:15:14.990 ⇒ 00:15:20.839 Caitlyn Vaughn: Okay, so is there any other, like, context that you guys are missing, or Lev, is there anything else you want to add before they, like, take a first pass at it?
179 00:15:21.420 ⇒ 00:15:33.509 Lev Katreczko: No, but I am also all ears if there are any dashboards that are, like, particularly nitpicky that you’d rather not do, because, like, I’m not married to every single one of them, and if we have to…
180 00:15:33.760 ⇒ 00:15:49.690 Lev Katreczko: had a few, like, totally cool with that. I can probably go ahead and do that myself also, but, like, I’m not entirely aware of the workload across all of them, so, like, if there’s one that’s, like, a total pain, and you want to just flag it, like, I’m… I’m cool with that too.
181 00:15:50.120 ⇒ 00:15:54.849 Uttam Kumaran: Yeah, so what we’re gonna do, just as usual, is we’re gonna drive towards creating a topic.
182 00:15:55.010 ⇒ 00:16:04.940 Uttam Kumaran: and then doing a first pass of, like, the dashboard, but what I’ll do is, as soon as the topic is ready, topic is, like, an Omni that you can start picking out metrics, I’ll just…
183 00:16:05.050 ⇒ 00:16:10.060 Uttam Kumaran: we’ll call you. Hopefully that’s tomorrow, if not Monday, and then…
184 00:16:10.880 ⇒ 00:16:17.970 Uttam Kumaran: Yeah, I think… so some of the… some of the pieces that I’m still not 100% on is just on sequences.
185 00:16:18.550 ⇒ 00:16:22.350 Uttam Kumaran: that I just have to look at, like, what the data is gonna be. Other than that.
186 00:16:22.960 ⇒ 00:16:25.980 Uttam Kumaran: It’s… it’s fairly simple. It’s all accounts, contacts.
187 00:16:26.110 ⇒ 00:16:27.650 Uttam Kumaran: meetings.
188 00:16:27.800 ⇒ 00:16:28.530 Uttam Kumaran: tasks.
189 00:16:29.880 ⇒ 00:16:31.410 Caitlyn Vaughn: Okay, cool, that sounds good.
190 00:16:31.670 ⇒ 00:16:38.010 Lev Katreczko: Yeah, I will say real quick, sequences is actually probably one of the less important, pieces.
191 00:16:38.300 ⇒ 00:16:45.550 Lev Katreczko: like, task is definitely first-class citizen, and everything else… and I also anticipate it’s probably going to be the most annoying, so everything’.
192 00:16:45.550 ⇒ 00:16:46.120 Uttam Kumaran: draft stopers.
193 00:16:46.630 ⇒ 00:16:48.589 Uttam Kumaran: Contacts, accounts, yeah.
194 00:16:49.940 ⇒ 00:16:50.530 Lev Katreczko: Yep.
195 00:16:54.120 ⇒ 00:16:54.710 Uttam Kumaran: Okay.
196 00:16:57.090 ⇒ 00:17:00.640 Uttam Kumaran: So I feel pretty clear. I think probably Greg, I’ll probably ask…
197 00:17:00.830 ⇒ 00:17:03.900 Uttam Kumaran: Maybe get your help,
198 00:17:04.290 ⇒ 00:17:12.149 Uttam Kumaran: On… now that we have the post hoc data landing, what the models are gonna look like, so that would be great, and then…
199 00:17:13.609 ⇒ 00:17:22.040 Uttam Kumaran: Yeah, I feel like we’re chugging along. I think, Nandika, let me know, I wanted to… for this one, the topics are ready for the customer reporting and enablement, so I think
200 00:17:22.280 ⇒ 00:17:25.199 Uttam Kumaran: While we get feedback on the dashboard.
201 00:17:25.200 ⇒ 00:17:25.900 Nandika Jhunjhunwala: Nope.
202 00:17:25.900 ⇒ 00:17:29.559 Uttam Kumaran: I would also be great to get some feedback from you on the topics.
203 00:17:29.880 ⇒ 00:17:30.570 Uttam Kumaran: themselves.
204 00:17:32.700 ⇒ 00:17:41.379 Uttam Kumaran: just like, hey, can we add this metric, or can we form it a different way? And then also, we can use that opportunity, I can show you how to maybe make and push some of those changes.
205 00:17:42.030 ⇒ 00:17:48.810 Uttam Kumaran: I’m sure we’re not, like, expansive yet, so even for me, like, hey, can we change, can we add this metric, or can we create, like, a…
206 00:17:49.220 ⇒ 00:17:50.350 Uttam Kumaran: Something different.
207 00:17:50.770 ⇒ 00:17:53.110 Uttam Kumaran: So that way we’re getting feedback both at, like.
208 00:17:53.270 ⇒ 00:17:56.709 Uttam Kumaran: The dashboard formation level, as well as at the topic level.
209 00:17:57.800 ⇒ 00:18:07.580 Nandika Jhunjhunwala: Definitely. Going back to the CS dashboard, I don’t know if this is too much of a lift, or if it’s possible. I think what I see…
210 00:18:07.580 ⇒ 00:18:19.180 Nandika Jhunjhunwala: like, within customer success, when they have, like, a certain customer that they’re gonna talk to, they, like, love to see data on it. So, I was wondering, like, if at the dashboard level, you could have, like, a filter for, like.
211 00:18:19.180 ⇒ 00:18:23.299 Nandika Jhunjhunwala: The account level, and then those metrics populate for that account itself.
212 00:18:23.300 ⇒ 00:18:28.999 Nandika Jhunjhunwala: if that’s, like, a filter we could possibly add, I think that would be super helpful.
213 00:18:29.000 ⇒ 00:18:29.550 Uttam Kumaran: Yeah.
214 00:18:29.550 ⇒ 00:18:31.860 Nandika Jhunjhunwala: For the CS team.
215 00:18:31.860 ⇒ 00:18:32.410 Uttam Kumaran: Safa.
216 00:18:32.410 ⇒ 00:18:36.130 Nandika Jhunjhunwala: Yeah, so the CSMs can, you know, toggle in.
217 00:18:36.770 ⇒ 00:18:42.299 Mustafa Raja: Yeah, so the dashboard that we took a look at, it has that filter at the top.
218 00:18:42.580 ⇒ 00:18:44.259 Nandika Jhunjhunwala: Oh, nice, cool.
219 00:18:44.690 ⇒ 00:18:45.330 Nandika Jhunjhunwala: That’s crazy.
220 00:18:45.330 ⇒ 00:18:55.119 Uttam Kumaran: So there’s, like, there’s already an account name filter. I think one thing I’m sort of… I want to talk to Deanna about is, does she want something for… is there metrics where she’s looking, like, across the board?
221 00:18:55.120 ⇒ 00:18:55.500 Nandika Jhunjhunwala: Yeah.
222 00:18:55.500 ⇒ 00:18:58.650 Uttam Kumaran: at CS versus, like, a single account.
223 00:18:58.790 ⇒ 00:19:07.409 Uttam Kumaran: because the way I’ve, like, when I used to work, sort of, a lot of account management data, oftentimes, like, you may want to even just, like, send a version of this to the client.
224 00:19:07.540 ⇒ 00:19:09.570 Uttam Kumaran: Especially if the numbers are all looking good.
225 00:19:09.570 ⇒ 00:19:10.619 Nandika Jhunjhunwala: Yeah. It’s like…
226 00:19:10.620 ⇒ 00:19:18.409 Uttam Kumaran: hey, I want to export this as a PDF and send it. And this, Caitlin, naturally goes into, like, the customer-facing analytics work.
227 00:19:18.800 ⇒ 00:19:19.440 Uttam Kumaran: So…
228 00:19:19.440 ⇒ 00:19:19.830 Caitlyn Vaughn: Yeah.
229 00:19:19.830 ⇒ 00:19:23.990 Uttam Kumaran: piece that, like, I’m interested to hear about, like, kind of your thoughts there, but…
230 00:19:24.240 ⇒ 00:19:28.970 Uttam Kumaran: This account management flow and, like, using data to tell the story
231 00:19:29.680 ⇒ 00:19:32.940 Uttam Kumaran: Like, my recommendation is that that is what
232 00:19:33.160 ⇒ 00:19:41.089 Uttam Kumaran: like, kind of defines some of the data that you may push into the product. Because ultimately, some clients are going to care about certain things, they’re gonna be like, wow, I didn’t know, like.
233 00:19:41.300 ⇒ 00:19:45.170 Uttam Kumaran: I didn’t know, like, we have all these workflows, or I didn’t know that, like.
234 00:19:45.340 ⇒ 00:19:51.770 Uttam Kumaran: we’re not utilizing our seats, you know? And then you’ll find out, like, okay, that’s probably what we should…
235 00:19:52.150 ⇒ 00:19:55.510 Uttam Kumaran: Bake into the product, or we should create an enterprise
236 00:19:56.280 ⇒ 00:20:03.520 Uttam Kumaran: view of. So, like, that’s how I’ve determined, when I built, sort of, some of these customer-facing analytics platforms.
237 00:20:03.670 ⇒ 00:20:11.090 Uttam Kumaran: It’s always, like, through account management, we find out, like, what they care about, and then that just sets the roadmap super, super easily.
238 00:20:11.090 ⇒ 00:20:14.779 Caitlyn Vaughn: I think there’s, like, a ton of unknowns right now for us, like.
239 00:20:14.870 ⇒ 00:20:26.839 Caitlyn Vaughn: I’m working on rebuilding pricing before we launch, so that we can, like, factor in tokens, and I’m trying to figure out, like, how much usage we should allow on each tier, and it’s like, at the end of the day, we could take a guess, but…
240 00:20:26.840 ⇒ 00:20:49.659 Caitlyn Vaughn: We’re not gonna know until, you know, we have people actually using the platform, we can see what usage looks like, where to kind of, like, draw the lines, and I think that’s probably true for most of the platform. So, the good news is, like, we’re rolling out the sales tiers first, right? So it’ll only be sales-led, and then we’ll migrate some customers over, so the PLG stuff won’t come until later, so we can, like, actually have some time to set everything up before that launches.
241 00:20:50.230 ⇒ 00:20:58.659 Uttam Kumaran: Okay. Yeah, I do think that, like, enterprise analytics is often a great offering at the enterprise level, just to continue to push more.
242 00:20:58.920 ⇒ 00:21:00.639 Caitlyn Vaughn: That’s true. Yeah.
243 00:21:00.640 ⇒ 00:21:06.029 Uttam Kumaran: So, like, when I was at Flowcode, like, that… because our product was sort of a QR code, we didn’t have…
244 00:21:06.120 ⇒ 00:21:23.560 Uttam Kumaran: you know, any other metrics. We didn’t have, like, a lot of data, but then when we go to the enterprise, they were interested in who’s scanning, when are they scanning, like, what about our portfolio of codes? So similarly, people may be interested in, like, all the workflows, how many meetings, my whole team, and then that naturally pushes you guys into, like.
245 00:21:23.610 ⇒ 00:21:29.210 Uttam Kumaran: Because the analytics is the… is really the sticky… one of the stickiest products, right?
246 00:21:29.520 ⇒ 00:21:37.500 Uttam Kumaran: And so, because you’re like, okay, I want to check in on how my team’s adopting default, you guys are also shifting into, like, more of a sales platform.
247 00:21:37.630 ⇒ 00:21:40.610 Uttam Kumaran: Where you want people to be logging in, so it kind of, like.
248 00:21:41.090 ⇒ 00:21:46.500 Uttam Kumaran: I’m very biased, because, like, I love this data, and it really helps you tell the story to the client.
249 00:21:46.750 ⇒ 00:21:47.140 Caitlyn Vaughn: Yeah.
250 00:21:47.140 ⇒ 00:21:49.500 Uttam Kumaran: It’s incredibly sticky to come back and look at it, you know?
251 00:21:49.820 ⇒ 00:21:58.389 Caitlyn Vaughn: We will have, like, metrics dashboards, or, like, pages on each SKU, or, like, each piece of the platform, like a micro one, but…
252 00:21:58.590 ⇒ 00:22:07.300 Caitlyn Vaughn: the goal would be to, like, roll out an actual version of analytics, you know, like a white-labeled Omni, eventually. Yeah, yeah.
253 00:22:07.430 ⇒ 00:22:12.100 Caitlyn Vaughn: So hopefully we can do, like, this and more, and maybe we templatize it, maybe we leave it open, but…
254 00:22:12.410 ⇒ 00:22:15.370 Caitlyn Vaughn: Anyways, for another day. That’s a good idea, actually.
255 00:22:16.260 ⇒ 00:22:29.119 Greg Stoutenburg: just when you were talking about surfacing information to folks who want to know how their client is doing, I just thought, like, oh, yeah, neat little thing. You could actually schedule a delivery for, like, to anyone to look just at particular clients, so, you know.
256 00:22:29.120 ⇒ 00:22:29.490 Caitlyn Vaughn: Mmm.
257 00:22:29.490 ⇒ 00:22:36.960 Greg Stoutenburg: Something like, someone gets, like, a daily update on, you know, here’s what your daily or weekly update, here’s what everyone who’s on your team is doing right now.
258 00:22:37.160 ⇒ 00:22:38.280 Greg Stoutenburg: Did you hear that?
259 00:22:38.650 ⇒ 00:22:40.800 Caitlyn Vaughn: That’s really interesting. We actually…
260 00:22:40.800 ⇒ 00:22:41.270 Greg Stoutenburg: that.
261 00:22:41.270 ⇒ 00:22:45.010 Caitlyn Vaughn: We do have some customers that have been asking for data, like.
262 00:22:45.010 ⇒ 00:22:45.580 Greg Stoutenburg: Yeah, cool.
263 00:22:45.580 ⇒ 00:22:46.240 Caitlyn Vaughn: Lee.
264 00:22:46.880 ⇒ 00:22:47.900 Greg Stoutenburg: Yeah, cool.
265 00:22:47.900 ⇒ 00:22:51.579 Uttam Kumaran: Do you know what, like, what in particular, or, like, what is the pain point?
266 00:22:51.950 ⇒ 00:22:59.610 Caitlyn Vaughn: Different clients, different things, like, there have been clients that have asked for, for, like.
267 00:22:59.640 ⇒ 00:23:03.409 Caitlyn Vaughn: Can we see a list of every single routing decision made?
268 00:23:03.410 ⇒ 00:23:19.690 Caitlyn Vaughn: Right? Because they want to see, they want to, like, make sure and verify that routing was split the way that they want, calculated correctly, or, like, can we see, total workflow runs? Because we want to, like, backlog and make sure, you know, the emails are passing through correctly, or whatever, you know, that kind of stuff.
269 00:23:19.690 ⇒ 00:23:31.039 Caitlyn Vaughn: Stuff that… I mean, candidly, it should be easier in our own platform to see, right? But since it’s not available in the current platform, like, we’ve been just manually sending data over, but…
270 00:23:31.340 ⇒ 00:23:32.080 Caitlyn Vaughn: Hopefully.
271 00:23:32.080 ⇒ 00:23:33.830 Uttam Kumaran: Format right now that you’re sending it?
272 00:23:34.350 ⇒ 00:23:35.710 Caitlyn Vaughn: like a CSV.
273 00:23:36.200 ⇒ 00:23:40.650 Uttam Kumaran: That’s why I think… I think what you’re gonna find, and maybe this is the conversation with Deanna.
274 00:23:40.940 ⇒ 00:23:44.930 Uttam Kumaran: They should, at minimum, screenshot whatever we’re gonna build.
275 00:23:44.930 ⇒ 00:23:45.490 Caitlyn Vaughn: Yeah.
276 00:23:45.490 ⇒ 00:23:49.079 Uttam Kumaran: at maximum, you should just export this as a PDF.
277 00:23:49.640 ⇒ 00:23:50.749 Uttam Kumaran: It’s, it’s like…
278 00:23:51.010 ⇒ 00:23:58.039 Uttam Kumaran: Same, same, but I think it’s way more, like, professional, and, like, way more, like, oh yeah, let me go run that report for you.
279 00:23:58.040 ⇒ 00:23:58.630 Caitlyn Vaughn: Huh.
280 00:23:58.630 ⇒ 00:24:03.749 Uttam Kumaran: And then… and I think it’ll start building this, like, motion for the data anyways.
281 00:24:04.160 ⇒ 00:24:09.130 Uttam Kumaran: The jump from, like, that to the platform is, like, actually a lot neater.
282 00:24:09.330 ⇒ 00:24:09.770 Caitlyn Vaughn: Yeah.
283 00:24:09.770 ⇒ 00:24:14.279 Uttam Kumaran: I think even for the folks who are generating the CSCs, they’re probably, like, asking somebody to go pull something, probably.
284 00:24:15.250 ⇒ 00:24:15.650 Uttam Kumaran: So…
285 00:24:15.650 ⇒ 00:24:33.769 Caitlyn Vaughn: Yeah, I feel like if it’s high level, I can’t think of examples of customers that have asked for, like, high-level stuff, unless it’s in a renewal conversation of, like, you’ve been using, you know, 500,000 workflow runs per month, and, like, this is how many leads we’ve, you know, processed for you, kind of a thing, but…
286 00:24:34.010 ⇒ 00:24:48.770 Caitlyn Vaughn: all of the customer data requests that I can think of, I guess, pertain to more, like, granular, like, pieces of data, but it would be cool to do the, like, high-level… it would make us seem more, like, strategic and consultative.
287 00:24:49.180 ⇒ 00:24:56.100 Uttam Kumaran: Yeah, and again, like, the gap between that being sent from a person to, like, being able to log in.
288 00:24:56.290 ⇒ 00:24:56.730 Caitlyn Vaughn: is actually…
289 00:24:56.730 ⇒ 00:25:01.930 Uttam Kumaran: like, much more narrow. Yeah. So I think the leap from, like, CSV to, like, oh yeah, let me send you, like.
290 00:25:02.710 ⇒ 00:25:13.610 Uttam Kumaran: we’re rolling out Enterprise Analytics, we’re like, let me send you your account snapshot. And then the other thing, I talked to Deanna, and again, like, you want your… for the… for renewals, you want to come to the table with, like.
291 00:25:14.020 ⇒ 00:25:20.629 Uttam Kumaran: we… because otherwise, it’s… it’s, like, purely, like, hey, how is it going? We’d love to take more of your money. Like, it’s, like.
292 00:25:20.630 ⇒ 00:25:21.100 Caitlyn Vaughn: Yeah.
293 00:25:21.100 ⇒ 00:25:22.200 Uttam Kumaran: to reflect on something.
294 00:25:22.200 ⇒ 00:25:22.770 Caitlyn Vaughn: You know?
295 00:25:22.770 ⇒ 00:25:31.579 Uttam Kumaran: And, like, actually have a story, which is like, oh, like, look, because sometimes your buyer may not always be in the product or team, so…
296 00:25:31.580 ⇒ 00:25:32.040 Caitlyn Vaughn: Hmm.
297 00:25:32.040 ⇒ 00:25:39.500 Uttam Kumaran: hey, here’s your snapshot, you guys are doing an awesome job, like, you guys are hiring more, we think we can actually see this lift, like.
298 00:25:39.500 ⇒ 00:25:39.870 Caitlyn Vaughn: Yeah.
299 00:25:39.870 ⇒ 00:25:40.900 Uttam Kumaran: Let’s bump you up.
300 00:25:41.870 ⇒ 00:25:43.370 Caitlyn Vaughn: So it gives the…
301 00:25:43.370 ⇒ 00:25:45.669 Uttam Kumaran: It gives the renewal team something to work with.
302 00:25:45.670 ⇒ 00:25:48.099 Caitlyn Vaughn: Yeah, that would be a lot better than what we have today.
303 00:25:48.100 ⇒ 00:25:56.529 Uttam Kumaran: Well, I mean, you’re seeing great adoptions, it’s just gonna accelerate. I feel like it’s just gonna help, you know, so… Yeah.
304 00:25:56.860 ⇒ 00:25:59.859 Caitlyn Vaughn: We’re just making it up. Startup vibes.
305 00:26:01.770 ⇒ 00:26:13.789 Uttam Kumaran: We’re filling it in for you. Yeah, exactly. No, this is how wor- this is how it happens. I think as soon as you get the adoption, people that are paying you more, especially if it’s, like, a workflow product, oftentimes they want to see
306 00:26:14.060 ⇒ 00:26:14.880 Uttam Kumaran: that, because…
307 00:26:15.080 ⇒ 00:26:15.560 Caitlyn Vaughn: Yeah.
308 00:26:15.560 ⇒ 00:26:18.559 Uttam Kumaran: The day-to-day, it’s like, yeah, it’s working for us, but like.
309 00:26:18.760 ⇒ 00:26:25.700 Uttam Kumaran: who’s not adopting it, or, like, you guys are gonna start releasing, like, more product, so people are gonna be like.
310 00:26:25.840 ⇒ 00:26:29.849 Uttam Kumaran: oh yeah, we’re getting tons of value, like, I didn’t know that we weren’t using this other feature.
311 00:26:29.850 ⇒ 00:26:30.280 Caitlyn Vaughn: Yeah.
312 00:26:30.280 ⇒ 00:26:30.910 Uttam Kumaran: You know.
313 00:26:31.110 ⇒ 00:26:38.380 Caitlyn Vaughn: release a weekly NARC list of all the people who aren’t using the… the platform, like you do to us with Omni and Blobby.
314 00:26:38.380 ⇒ 00:26:46.169 Uttam Kumaran: Yes, yes, exactly. Hey, you pay… well, actually, you gotta phrase it like, these people could be so much more productive.
315 00:26:46.580 ⇒ 00:26:48.680 Uttam Kumaran: They were just using default to its full potential.
316 00:26:48.680 ⇒ 00:26:53.100 Caitlyn Vaughn: Don’t worry, I won’t use those exact words, but that’s what it will be. Sentiment.
317 00:26:54.270 ⇒ 00:27:03.459 Uttam Kumaran: No, you should see, I even… I told Greg, because Greg and I were talking about, like, scheduling, and I was like, dude, you can go into default and change your own workflow. Why are you asking people to…
318 00:27:03.460 ⇒ 00:27:04.080 Caitlyn Vaughn: Yeah. To change your…
319 00:27:04.080 ⇒ 00:27:04.470 Uttam Kumaran: counters.
320 00:27:04.470 ⇒ 00:27:05.299 Greg Stoutenburg: He’s like, oh my god.
321 00:27:05.300 ⇒ 00:27:08.389 Uttam Kumaran: I know. And I’m like, I’m like, come on, dude, like…
322 00:27:08.390 ⇒ 00:27:10.289 Caitlyn Vaughn: Get in there!
323 00:27:10.290 ⇒ 00:27:11.609 Greg Stoutenburg: Yeah, go use it.
324 00:27:12.600 ⇒ 00:27:14.850 Caitlyn Vaughn: Oh my god, incredible.
325 00:27:16.020 ⇒ 00:27:24.400 Uttam Kumaran: Okay, so we have… we have two meetings on Monday, we have… hopefully I get… have some feedback for Lev this week. I have a version of this dash.
326 00:27:24.580 ⇒ 00:27:27.489 Uttam Kumaran: I think, Nandika, we have the topics ready and merged.
327 00:27:27.940 ⇒ 00:27:32.720 Uttam Kumaran: Greg, is there any… was there anything else on the Prez that you had?
328 00:27:33.130 ⇒ 00:27:40.949 Greg Stoutenburg: The last thing would be… I mean, yeah, I’ll just throw up the last two, slides here. So, you know.
329 00:27:41.150 ⇒ 00:27:55.510 Greg Stoutenburg: key wins was just lots of new charts that are looking at usage. Thanks, Nanika, for all that work. Nanika and I talked about that on, I think, Monday. And then the last piece of that will be to review the dash with the next steps for organizing
330 00:27:55.510 ⇒ 00:28:11.179 Greg Stoutenburg: those into the dashboards that we really need for the project, and identifying any gaps and fixing them. So I… and I have a doc on that, I shared it in the team channel, and I can just flash it real quick in just a moment. And these are the things that are in progress, we just talked about this, what we’re going to review, and so on.
331 00:28:11.700 ⇒ 00:28:26.900 Greg Stoutenburg: Just to put that in front of you, and I won’t, like, read the whole thing, the main dashboards that we said we’d create as part of this project were one on user activation, user retention, user engagement, and then the product journey. And so,
332 00:28:26.900 ⇒ 00:28:47.830 Greg Stoutenburg: what I took a look at is which things are up now, and what’s needed to actually fill this in. So, so some of these… these are charts that exist already in posthog, and we’ll want to put them into this user activation dashboard, and then we would need to build these ones that don’t currently exist to add them to the dashboard. Can y’all see that? I don’t know.
333 00:28:47.830 ⇒ 00:28:48.770 Caitlyn Vaughn: Yeah, yeah.
334 00:28:48.770 ⇒ 00:28:50.310 Nandika Jhunjhunwala: That looks great.
335 00:28:50.310 ⇒ 00:29:04.039 Greg Stoutenburg: In Outline, and then the rest of the doc is just that for the other three dashboards. So, Nadica and I can meet to figure out how do we want to, how we want to tackle this, but that’s the stage of the product analytics work for right now.
336 00:29:04.870 ⇒ 00:29:06.770 Caitlyn Vaughn: Okay, awesome. Yep.
337 00:29:07.950 ⇒ 00:29:14.980 Caitlyn Vaughn: We may be pushing out some changes on SKUs that we thought were finished in the next, like, week or so.
338 00:29:14.980 ⇒ 00:29:18.219 Nandika Jhunjhunwala: Good. I was hoping so.
339 00:29:18.220 ⇒ 00:29:21.529 Caitlyn Vaughn: Yes, as were we. We were really hoping to redo everything last week.
340 00:29:22.110 ⇒ 00:29:29.939 Caitlyn Vaughn: But that should be, like, we shouldn’t be doing this more than the next week and a half.
341 00:29:30.060 ⇒ 00:29:34.480 Caitlyn Vaughn: Before the first version is… Coming out, so…
342 00:29:34.480 ⇒ 00:29:49.609 Greg Stoutenburg: Okay, cool. Yeah, let’s just say… yeah, let’s just be in communication about it, and if there’s anything we know, like, we’ve been fine just setting things up as it exists in staging and then distributing, but if there’s something that we know is gonna change, like, the next day or two days later, then we’d probably just pause on that.
343 00:29:49.610 ⇒ 00:29:49.940 Caitlyn Vaughn: Yeah.
344 00:29:49.940 ⇒ 00:29:50.700 Greg Stoutenburg: And come back.
345 00:29:50.700 ⇒ 00:29:52.329 Caitlyn Vaughn: Have you guys done routing yet?
346 00:29:53.380 ⇒ 00:29:55.030 Greg Stoutenburg: We have done…
347 00:29:55.460 ⇒ 00:29:57.549 Nandika Jhunjhunwala: It just got merged.
348 00:29:57.550 ⇒ 00:29:57.910 Greg Stoutenburg: Okay.
349 00:29:57.910 ⇒ 00:30:12.869 Nandika Jhunjhunwala: 2-3 days ago, I haven’t made insights, like, visualizations off of it, but I’ve instrumented routing and waiting for, like, workflows to roll in, and I’ll keep a tab on, like, what’s changing to make sure none of the events get messed up.
350 00:30:12.990 ⇒ 00:30:19.800 Caitlyn Vaughn: Okay, I would say pause on routing for now, because I think that’s getting the biggest, like, facelift. Okay.
351 00:30:19.800 ⇒ 00:30:20.299 Nandika Jhunjhunwala: It’s like one of our.
352 00:30:20.300 ⇒ 00:30:26.420 Caitlyn Vaughn: smaller SKUs, not workflows. Yeah, okay. Trying to think what else…
353 00:30:28.180 ⇒ 00:30:35.210 Caitlyn Vaughn: Tables should be good, website deanonymization should be good, pixels should be good.
354 00:30:36.560 ⇒ 00:30:44.889 Caitlyn Vaughn: Okay, yeah, I’ll keep updating you guys. I would just say, like, look out for routing for now, let’s just, like, pause on that front, on the instrumentation side, and, like, analytics, but…
355 00:30:44.890 ⇒ 00:30:45.620 Greg Stoutenburg: No routing code.
356 00:30:46.480 ⇒ 00:30:48.529 Caitlyn Vaughn: Another version in, like, a week or two.
357 00:30:48.820 ⇒ 00:30:49.290 Greg Stoutenburg: Cool.
358 00:30:50.430 ⇒ 00:30:51.130 Greg Stoutenburg: Alright.
359 00:30:51.850 ⇒ 00:30:52.759 Greg Stoutenburg: Well, that’s a wrap.
360 00:30:54.110 ⇒ 00:30:55.259 Greg Stoutenburg: Anything else? Anything else?
361 00:30:56.720 ⇒ 00:30:58.360 Greg Stoutenburg: Right? Cool.
362 00:30:58.740 ⇒ 00:30:59.470 Greg Stoutenburg: Go team!
363 00:31:00.100 ⇒ 00:31:00.810 Greg Stoutenburg: Good talk.
364 00:31:00.810 ⇒ 00:31:08.930 Caitlyn Vaughn: Thanks so much. Yeah, I’ll see you guys on Monday, and if you need anything in the meantime, let me know. Otherwise, have a great weekend.
365 00:31:08.930 ⇒ 00:31:11.010 Greg Stoutenburg: Have a good weekend. Alright, see y’all. Thanks. Bye.