Meeting Title: Phoenix Product Analytics Weekly Sync Date: 2026-01-29 Meeting participants: Mustafa Raja, Greg Stoutenburg, Demilade Agboola, Uttam Kumaran, Nandika Jhunjhunwala, Caitlyn Vaughn
WEBVTT
1 00:01:08.840 ⇒ 00:01:09.230 Nandika Jhunjhunwala: Hello!
2 00:01:09.230 ⇒ 00:01:10.010 Greg Stoutenburg: Yeah, Nautica.
3 00:01:10.350 ⇒ 00:01:11.310 Uttam Kumaran: Hello.
4 00:01:13.360 ⇒ 00:01:15.909 Nandika Jhunjhunwala: Sorry. Hello. Can you hear me?
5 00:01:16.380 ⇒ 00:01:17.569 Greg Stoutenburg: Yep, you’re fine.
6 00:01:17.570 ⇒ 00:01:18.400 Uttam Kumaran: Yes.
7 00:01:20.790 ⇒ 00:01:21.300 Nandika Jhunjhunwala: How’s it going?
8 00:01:21.300 ⇒ 00:01:22.370 Demilade Agboola: Caitlin, join you.
9 00:01:23.030 ⇒ 00:01:24.930 Nandika Jhunjhunwala: Yeah, she’s… she’s right here. I’m here.
10 00:01:24.930 ⇒ 00:01:25.699 Greg Stoutenburg: Oh, fantastic.
11 00:01:25.700 ⇒ 00:01:29.590 Demilade Agboola: I thought, maybe you have some kind of auto-tracker on with the camera?
12 00:01:29.590 ⇒ 00:01:34.690 Greg Stoutenburg: Because as soon as you started to speak, Nandica, it just, like, cut… it just zoomed in on you and cut Caitlin right out.
13 00:01:34.690 ⇒ 00:01:35.340 Nandika Jhunjhunwala: Oh, nice.
14 00:01:38.050 ⇒ 00:01:43.339 Nandika Jhunjhunwala: It’s a new laptop, so I… maybe this is, like, a pro quo tour. Yeah.
15 00:01:43.340 ⇒ 00:01:45.459 Greg Stoutenburg: Yeah, sort of an insulting camera, really.
16 00:01:49.800 ⇒ 00:01:51.350 Nandika Jhunjhunwala: Well, how’s it going, guys?
17 00:01:52.820 ⇒ 00:01:54.640 Demilade Agboola: Pretty good, pretty good. Very nice.
18 00:01:55.940 ⇒ 00:01:58.769 Demilade Agboola: Ready to hop into the presentation?
19 00:02:01.840 ⇒ 00:02:07.839 Demilade Agboola: Okay, so this is our presentation for this week, and how far we’ve… how far along we’ve come this week.
20 00:02:08.289 ⇒ 00:02:13.249 Demilade Agboola: So, high level… Overview of this week.
21 00:02:13.370 ⇒ 00:02:29.079 Demilade Agboola: from the product analytics workstream, which Greg is leading, we’re able to be able to align more on how to set up the instrumentation plan for Phoenix through Amplitude, in an iterative way, so that we can,
22 00:02:29.820 ⇒ 00:02:33.800 Demilade Agboola: get all the information that we need for Phoenix as we roll it out.
23 00:02:34.010 ⇒ 00:02:36.910 Demilade Agboola: And that ended up with us having, like.
24 00:02:37.850 ⇒ 00:02:44.750 Demilade Agboola: hardcore deliverables, like the Figma designs, as well as an updated Gantt chart.
25 00:02:45.250 ⇒ 00:02:53.709 Demilade Agboola: And on the data platform and analytics work stream, we’re able to make some progress on syncing up Mother Doc with dbt.
26 00:02:53.990 ⇒ 00:03:00.289 Demilade Agboola: As well as, syncing Salesforce data with Polytomic and integrating them together.
27 00:03:00.660 ⇒ 00:03:06.710 Demilade Agboola: And we’re also able to work on some ad hoc, analysis for… The default team.
28 00:03:07.670 ⇒ 00:03:16.380 Demilade Agboola: And so what would you like to do next? I think for the… on the product analytics side, we would, want to start to document the events and user properties.
29 00:03:16.540 ⇒ 00:03:23.360 Demilade Agboola: And for the data platform and analytics side, we would want to kick off GTM and revenue flow for LoRa.
30 00:03:23.570 ⇒ 00:03:29.380 Demilade Agboola: As well as start the process of, customer reporting and enablement for, Lauren.
31 00:03:29.760 ⇒ 00:03:33.389 Demilade Agboola: And so those are the two next things we will want to get into.
32 00:03:34.290 ⇒ 00:03:37.670 Caitlyn Vaughn: So just in terms of, like… With Lauren?
33 00:03:39.480 ⇒ 00:03:43.249 Demilade Agboola: Yes, we were able to meet with Lauren, about 2 weeks ago, 3 weeks ago.
34 00:03:44.140 ⇒ 00:03:48.020 Caitlyn Vaughn: Okay, so you did meet with her, and you guys are working on this actively?
35 00:03:48.250 ⇒ 00:03:51.840 Caitlyn Vaughn: Or you’re gonna, like, loop her back when it’s… more time.
36 00:03:51.840 ⇒ 00:03:55.060 Demilade Agboola: Yeah, so the plan is to work
37 00:03:55.260 ⇒ 00:04:06.670 Demilade Agboola: On this flow next, and then… because we’re able to get the metrics that she cared for and what she wants to see on her dash, so we’re able to use that to prioritize data sources necessary to generate that output.
38 00:04:06.790 ⇒ 00:04:19.960 Demilade Agboola: And then we will do that… we’ll build out the, like, V1 and give it… give her access to it, and then iteratively fine-tune it to the point where it’s, like, the most useful for her on a day-to-day basis.
39 00:04:21.079 ⇒ 00:04:25.349 Caitlyn Vaughn: Okay, and did Nico meet with you in that meeting with Lauren, too?
40 00:04:25.880 ⇒ 00:04:26.600 Mustafa Raja: No.
41 00:04:27.340 ⇒ 00:04:38.600 Caitlyn Vaughn: No. Okay, I know he, like, mentioned that he wanted some things to do with it, so let me ping him and Sid. They’re kind of doing, like, a CS overhaul as well, so that might be…
42 00:04:38.750 ⇒ 00:04:42.409 Caitlyn Vaughn: good to get them, like, involved in this project, because I think that…
43 00:04:42.840 ⇒ 00:04:51.519 Caitlyn Vaughn: in the, like, revamping of CS for Phoenix, they have a lot of plans that could be very relevant for this, so… potential expansion on this.
44 00:04:55.290 ⇒ 00:04:58.200 Demilade Agboola: Sounds good. We’ll be looking forward to that as well.
45 00:04:59.490 ⇒ 00:05:02.310 Demilade Agboola: So in terms of, like.
46 00:05:03.300 ⇒ 00:05:13.620 Demilade Agboola: high level, or, like, low level, what we did this week, and key wins. So for, like, the dbt setup, we’re able to set up dbt Core, which is a local version of dbt.
47 00:05:13.880 ⇒ 00:05:20.089 Demilade Agboola: To mark the dock, and we’re using that to start to build out the flow of how things should start to run.
48 00:05:20.470 ⇒ 00:05:29.170 Demilade Agboola: And the idea is this would allow us to be able to scale up our transformations as the data starts to come in.
49 00:05:29.700 ⇒ 00:05:33.299 Demilade Agboola: Also, in terms of, like, the inbound performance table.
50 00:05:33.500 ⇒ 00:05:39.040 Demilade Agboola: We’re able to show and, give you the flow of the past month.
51 00:05:40.230 ⇒ 00:05:49.479 Demilade Agboola: How many visitors were able to visit, each domain, as well as how the conversion rate, in terms of how many submissions were made.
52 00:05:49.750 ⇒ 00:05:54.890 Demilade Agboola: For each of the domains, and this has been done and presented in a dashboard.
53 00:05:55.560 ⇒ 00:06:07.379 Demilade Agboola: And so this… the idea is this gives you the opportunity to be able to go, into conversations with each domain with some information of, like, effectiveness and possible ways in which you can consult for them.
54 00:06:07.940 ⇒ 00:06:13.479 Demilade Agboola: And also, we’re able to rerun the vendor analysis we’ve done previously.
55 00:06:13.660 ⇒ 00:06:19.319 Demilade Agboola: But seeing that we’ve, refined the company dataset, and things are in a better spot now.
56 00:06:19.550 ⇒ 00:06:21.649 Demilade Agboola: We decided to,
57 00:06:21.820 ⇒ 00:06:26.309 Demilade Agboola: We run that and provide that information with the better data that we have on ground.
58 00:06:26.870 ⇒ 00:06:34.710 Demilade Agboola: So the idea with that is we want to give you the best and most current, data so that you can have that on hand.
59 00:06:36.070 ⇒ 00:06:53.329 Caitlyn Vaughn: Okay, awesome. And then question around, I think for Mustafa, around the inbound performance table. So I just took, like, a quick look at it, and I feel like we may have the same issue of, like, data quality. I think you probably use SEMrush, right, which is…
60 00:06:53.330 ⇒ 00:06:53.730 Mustafa Raja: Yeah.
61 00:06:53.730 ⇒ 00:07:01.460 Caitlyn Vaughn: what I had run previous, but when I was looking through the, like, conversion percentages, some of them were, like, crazy.
62 00:07:01.460 ⇒ 00:07:02.470 Mustafa Raja: So…
63 00:07:02.470 ⇒ 00:07:10.000 Caitlyn Vaughn: I’m not sure what percentage is, like, very accurate versus, like, pretty skewed,
64 00:07:10.200 ⇒ 00:07:12.209 Caitlyn Vaughn: But just looking at that list, like.
65 00:07:13.140 ⇒ 00:07:24.139 Caitlyn Vaughn: the companies that I know well, it’s like, these numbers are so off. So I don’t know, like… I don’t know how helpful it is for us, or, like, maybe it still is, like, directionally helpful,
66 00:07:24.310 ⇒ 00:07:34.150 Caitlyn Vaughn: But we’re basically trying to figure out, like, when we move to this, you know, credit-based pricing model, and we’re, like, selling contracts to companies, like, taking a more…
67 00:07:34.470 ⇒ 00:07:43.809 Caitlyn Vaughn: consultative approach to, like, this is how many credits you should purchase from us, and here’s, like, why. And, like, even building a little calculator, like.
68 00:07:44.200 ⇒ 00:07:48.160 Caitlyn Vaughn: Seems like a good way for us to go with that, so… I don’t know if you have any…
69 00:07:48.160 ⇒ 00:07:49.230 Uttam Kumaran: I should have, like, an…
70 00:07:49.410 ⇒ 00:07:53.380 Uttam Kumaran: Yeah, one idea I had is, like, one, maybe you should have, like, a natural limit
71 00:07:53.510 ⇒ 00:08:02.190 Uttam Kumaran: like, whatever the typical benchmark is, like, I don’t know what… I haven’t seen the exact data set, but one idea is to do that. Second is…
72 00:08:02.330 ⇒ 00:08:07.840 Uttam Kumaran: For customers that, like, it’s sort of erroneous, we just go with the… whatever the benchmark is for their industry.
73 00:08:09.730 ⇒ 00:08:13.009 Uttam Kumaran: You know, that’s just gonna still be at least directional.
74 00:08:13.600 ⇒ 00:08:18.850 Caitlyn Vaughn: Interesting. Like, industry benchmarks for, like, website visit…
75 00:08:19.020 ⇒ 00:08:20.569 Uttam Kumaran: Yeah, yeah.
76 00:08:22.140 ⇒ 00:08:25.740 Uttam Kumaran: It’s kind of hard, because SEMrush… Sorry?
77 00:08:26.030 ⇒ 00:08:28.969 Caitlyn Vaughn: I said maybe that was a better way to go from the jump, Utah.
78 00:08:28.970 ⇒ 00:08:34.690 Uttam Kumaran: Well, like, for the best sites, SEMrush will have a good sense.
79 00:08:34.970 ⇒ 00:08:36.080 Uttam Kumaran: Yeah.
80 00:08:36.260 ⇒ 00:08:42.939 Uttam Kumaran: But… Also, like, again, if you’re talking to your customer, like, they’re more than likely gonna tell you
81 00:08:43.280 ⇒ 00:08:48.540 Uttam Kumaran: What it is, unless, like, It’s, like, so you just want to want to be in range, right?
82 00:08:50.380 ⇒ 00:08:53.480 Uttam Kumaran: So I’m thinking that, like, you at least have some type of max.
83 00:08:54.630 ⇒ 00:08:57.749 Uttam Kumaran: And then, maybe we just go with a… with a range.
84 00:08:58.690 ⇒ 00:09:05.639 Uttam Kumaran: Sometimes SEMrush will be accurate, though. Like, sometimes SEMrush will show that someone’s actually, like, really, really low or way higher than the industry.
85 00:09:06.130 ⇒ 00:09:06.760 Caitlyn Vaughn: Hmm.
86 00:09:06.760 ⇒ 00:09:08.970 Uttam Kumaran: It’s… So…
87 00:09:09.290 ⇒ 00:09:21.560 Caitlyn Vaughn: Yeah, but, like, even look at Bland here. Bland AI, they have, like, their Series D, they raise, like, $100 million in VC funding. You’re telling me they have 2,000 website visits a month? Absolutely not. You know?
88 00:09:21.560 ⇒ 00:09:22.099 Uttam Kumaran: Yeah, yeah.
89 00:09:22.650 ⇒ 00:09:23.840 Demilade Agboola: Yeah, huh.
90 00:09:24.070 ⇒ 00:09:30.169 Demilade Agboola: I think that that’s where the crazy numbers come from, which is, like, the total… Well, I think… I think 2%…
91 00:09:30.170 ⇒ 00:09:32.130 Uttam Kumaran: 5% seem about accurate.
92 00:09:32.640 ⇒ 00:09:34.850 Caitlyn Vaughn: Yeah. Like, I don’t know, Greg.
93 00:09:34.850 ⇒ 00:09:43.239 Uttam Kumaran: You tell me, I mean, when we talk about purchasing funnels, that’s usually the case, so I feel like it’s probably in line, like, less than 10%.
94 00:09:43.530 ⇒ 00:09:44.470 Caitlyn Vaughn: If not…
95 00:09:44.470 ⇒ 00:09:51.590 Greg Stoutenburg: Probably depends what the starting point is, but yeah, I mean, if we’re just talking SaaS, like, sign up to buying something, that’s a good number.
96 00:09:52.580 ⇒ 00:09:53.170 Caitlyn Vaughn: 2-5%.
97 00:09:53.170 ⇒ 00:09:53.940 Demilade Agboola: Yeah.
98 00:09:54.160 ⇒ 00:09:54.810 Greg Stoutenburg: Yeah.
99 00:09:55.280 ⇒ 00:10:01.649 Caitlyn Vaughn: Wow, here I was trying to really math this so hard, when you guys just had the answer the whole time.
100 00:10:03.520 ⇒ 00:10:06.279 Uttam Kumaran: Well, yeah.
101 00:10:07.880 ⇒ 00:10:08.240 Demilade Agboola: Yeah.
102 00:10:08.240 ⇒ 00:10:18.029 Caitlyn Vaughn: Okay, maybe we’ll just go with that benchmark then, 2 to 5. It’s interesting to have this data anyway, but that’s hilarious. I don’t know why I didn’t just think to fucking Google it.
103 00:10:19.130 ⇒ 00:10:23.799 Uttam Kumaran: There’s definitely benchmarks per type company, but yes, usually it’s 2 to 5 is, like, a good…
104 00:10:24.830 ⇒ 00:10:27.589 Greg Stoutenburg: I’ll send you a report on this, it has a lot of useful benchmarks. It’s a couple years.
105 00:10:27.590 ⇒ 00:10:29.740 Uttam Kumaran: Yeah, at least you can cite the report, yeah, yeah.
106 00:10:29.740 ⇒ 00:10:30.910 Greg Stoutenburg: Yeah, yeah.
107 00:10:30.910 ⇒ 00:10:31.990 Caitlyn Vaughn: Amazing.
108 00:10:32.540 ⇒ 00:10:33.869 Caitlyn Vaughn: Thanks, guys.
109 00:10:35.470 ⇒ 00:10:38.110 Demilade Agboola: Yeah, so, but in terms of just, like, the actual…
110 00:10:38.300 ⇒ 00:10:44.030 Demilade Agboola: dash, we have it built out, so you could just, like, play around with it. Give me one second.
111 00:10:45.510 ⇒ 00:10:56.080 Demilade Agboola: So you can just play around with it, kind of get a feel. I think it’s more directional than, necessarily, the most accurate, based off the data quality that we have.
112 00:10:56.250 ⇒ 00:11:00.690 Demilade Agboola: But you can just get an idea of what that looks like across different,
113 00:11:01.250 ⇒ 00:11:08.050 Demilade Agboola: Different industries. So, obviously, things above, say, 10% would just be… Huge question marks over them.
114 00:11:08.640 ⇒ 00:11:11.749 Demilade Agboola: So things like this, you will just be like, okay, maybe…
115 00:11:12.440 ⇒ 00:11:15.320 Demilade Agboola: That won’t necessarily be the best, source.
116 00:11:15.610 ⇒ 00:11:16.210 Demilade Agboola: An obvious.
117 00:11:16.210 ⇒ 00:11:16.579 Caitlyn Vaughn: It’s gonna…
118 00:11:16.580 ⇒ 00:11:25.819 Demilade Agboola: for sure, 72% is, like, ridiculous, so… Right. Yeah. But the idea is, well, we do have this here, goal will be to…
119 00:11:26.090 ⇒ 00:11:30.850 Demilade Agboola: Keep… Iterating on, more reliable ways to benchmark
120 00:11:31.270 ⇒ 00:11:37.400 Demilade Agboola: What the visits are, and use those in our, analysis, so we can have some gauge of what’s going on.
121 00:11:38.220 ⇒ 00:11:46.950 Caitlyn Vaughn: Cool. Yeah, I think for now, Mustafa, if you would just, like, in this inbound performance, section, maybe the best thing for us to do is to just, like.
122 00:11:47.110 ⇒ 00:11:52.970 Caitlyn Vaughn: Not include companies above 10% or something, and then we can call it, you know, directionally correct for now.
123 00:11:53.180 ⇒ 00:11:53.870 Mustafa Raja: Okay.
124 00:11:54.290 ⇒ 00:11:54.890 Mustafa Raja: I’ll do that.
125 00:11:54.890 ⇒ 00:11:55.890 Caitlyn Vaughn: Thank you!
126 00:11:56.910 ⇒ 00:12:00.140 Demilade Agboola: Okay. Also, I know you mentioned something about range.
127 00:12:01.150 ⇒ 00:12:04.389 Demilade Agboola: potentially been an interesting way to, like, look at data.
128 00:12:04.390 ⇒ 00:12:05.060 Caitlyn Vaughn: Yeah.
129 00:12:05.370 ⇒ 00:12:07.800 Demilade Agboola: Kind of expand on what range you were trying to see.
130 00:12:08.300 ⇒ 00:12:25.270 Caitlyn Vaughn: Yeah, I think, you guys have already just given me the answer, which is, like, typically conversion ranges from 2% to 5%, so I was kind of looking for, like, the range, and I assume that a lot of that conversion is also, like.
131 00:12:25.640 ⇒ 00:12:35.620 Caitlyn Vaughn: website quality, if they have, you know, self-serve, like, a bunch of different factors that go into conversion. So, I was just, like, generally curious what…
132 00:12:35.860 ⇒ 00:12:39.700 Caitlyn Vaughn: Like, once again, just… Coming from, like, a…
133 00:12:39.960 ⇒ 00:12:49.820 Caitlyn Vaughn: consulting, you know, putting on my consulting hat if I was selling the product, like, I’d probably want to know this information and, like, be able to talk to it, just like you guys are doing with us.
134 00:12:51.630 ⇒ 00:13:01.450 Demilade Agboola: Yeah, I will say, like, if you have those sort of questions, Greg will probably be one of your best people to talk to about these sort of things, because he’s the product analytics man, so…
135 00:13:01.720 ⇒ 00:13:02.420 Caitlyn Vaughn: Hmm.
136 00:13:02.710 ⇒ 00:13:07.110 Caitlyn Vaughn: Amazing, yeah, instead of, pulling data anymore, I’ll just ask Greg.
137 00:13:07.110 ⇒ 00:13:13.390 Uttam Kumaran: There you go. That’s a… that’s honestly a good, like, yeah, that’s… I think… I agree. I’m the same way now.
138 00:13:13.810 ⇒ 00:13:14.770 Uttam Kumaran: I’ll set it up.
139 00:13:14.770 ⇒ 00:13:17.530 Demilade Agboola: Reggie-eyeing.
140 00:13:17.530 ⇒ 00:13:18.300 Caitlyn Vaughn: Yeah.
141 00:13:19.340 ⇒ 00:13:24.939 Greg Stoutenburg: I actually just… I just sent you the benchmark report that I mentioned a moment ago. As you know, like.
142 00:13:24.940 ⇒ 00:13:40.110 Greg Stoutenburg: the qualifications are all important, industry, company type, da-da-da-da, but, you know, the main thing is, like, yeah, 2-5% B2B, when it’s, like, a PLG motion, and then everything affects where you fall on that number, and, you know, that’s for the…
143 00:13:40.110 ⇒ 00:13:44.719 Greg Stoutenburg: That’s where the hard work is that we’ll, you know, that you’re doing and that we’ll collaborate on.
144 00:13:45.360 ⇒ 00:13:46.050 Caitlyn Vaughn: Cool.
145 00:13:50.220 ⇒ 00:13:53.800 Uttam Kumaran: I will say, like, if I say one thing, I feel like even…
146 00:13:54.170 ⇒ 00:14:07.940 Uttam Kumaran: the fact that your guys flow… like, we go to some companies, we see their inbound flows, it’s so bad that I do think you have some claim to, like, some modest gain, you could totally say. I think we’ll try to measure it more.
147 00:14:09.630 ⇒ 00:14:17.249 Uttam Kumaran: And ideally, like, the best thing is for sales folks, when you… when we implement, to ask them, like, do they have a sense of that, so that we can keep track?
148 00:14:17.450 ⇒ 00:14:22.039 Uttam Kumaran: But, like, I feel like you guys should totally claim some percent gain.
149 00:14:23.020 ⇒ 00:14:23.600 Uttam Kumaran: Yeah.
150 00:14:23.600 ⇒ 00:14:37.480 Caitlyn Vaughn: Yeah, no, we definitely do, and one of the reasons why we care about latency so much is because every additional second that you have to wait in between, like, form submit to scheduler is, like, an extra second potential for you to churn, right?
151 00:14:37.480 ⇒ 00:14:38.280 Uttam Kumaran: Yes.
152 00:14:38.280 ⇒ 00:14:49.290 Caitlyn Vaughn: Numbers around, like, decrease in churn, or, like, not falling through on submissions, associated with latency that we do talk about, but…
153 00:14:49.750 ⇒ 00:14:52.399 Caitlyn Vaughn: I don’t know, just on my own island over here, I guess.
154 00:14:54.910 ⇒ 00:15:02.210 Uttam Kumaran: That’s great. No, I also think there’s some report, probably, that explains some of that, like, what is the latency impact on inbound conversion?
155 00:15:02.600 ⇒ 00:15:03.350 Caitlyn Vaughn: Yeah.
156 00:15:03.790 ⇒ 00:15:04.380 Uttam Kumaran: Yes.
157 00:15:07.760 ⇒ 00:15:20.929 Greg Stoutenburg: Your island will soon have amplitude, which will help with some of those, you know, page view, page loading, times, time to value on this and that, how many minutes did your user spend here? That will… that will help. Yeah. You’re gonna get those answers.
158 00:15:21.480 ⇒ 00:15:23.699 Caitlyn Vaughn: Page load time on amplitude.
159 00:15:23.840 ⇒ 00:15:35.260 Greg Stoutenburg: Yeah, you can do… because you can look at, because you can get event data from the client side, you can look at the time between two events, and that gives you a measurement of duration.
160 00:15:35.660 ⇒ 00:15:37.740 Caitlyn Vaughn: Yeah, interesting.
161 00:15:37.740 ⇒ 00:15:52.889 Greg Stoutenburg: Yeah, so when we get to the point of building out, like, activation charts for you, we’ll be able to do things like look at the time from when a user clicked the sign up button, to when they, you know, performed some first event, and we’ll be able to make insights like, okay, what was the…
162 00:15:52.920 ⇒ 00:16:07.189 Greg Stoutenburg: what differences did we see in conversion rates when a user took, you know, this much time to achieve X versus if it took them longer? And, you know, and help you run experiments to improve those things?
163 00:16:07.840 ⇒ 00:16:14.509 Caitlyn Vaughn: Okay, cool. That also kind of segues… I guess I’ll let you continue when we get to the…
164 00:16:14.910 ⇒ 00:16:17.839 Caitlyn Vaughn: ETL project. I have some thoughts with Stripe.
165 00:16:18.660 ⇒ 00:16:23.729 Greg Stoutenburg: Yeah, sorry, I’m just slowing Demi down now. Keep going, Demi. I should just mute myself. It’s too tempting, I’m gonna mute myself.
166 00:16:23.730 ⇒ 00:16:24.349 Demilade Agboola: Love it.
167 00:16:24.600 ⇒ 00:16:26.389 Uttam Kumaran: It’s all good, it’s all good.
168 00:16:26.750 ⇒ 00:16:31.429 Demilade Agboola: Actually, it’s your turn to speak, so it’s all good. Perfect.
169 00:16:33.360 ⇒ 00:16:57.409 Greg Stoutenburg: Great. Okay, so, thanks for the workshop last week. It was really helpful to talk with you about what’s in place for the product now, and some of the things that you’re interested in measuring as we move to Phoenix. Nautica was super helpful, gave me a walkthrough of your designs in Figma the other day, as well as sharing the plan for, what your tiers and products will look like in this Notion document, PLG pricing and packaging.
170 00:16:57.510 ⇒ 00:17:05.689 Greg Stoutenburg: So, what we were able to do, Mousaf and I worked together, and he revised the Gantt chart for how we’ll do this implementation.
171 00:17:05.690 ⇒ 00:17:24.079 Greg Stoutenburg: Basically what we did is, because Phoenix isn’t ready to ship yet, what we did is kind of stretched out this initial period where we’re mapping the events that we’ll want to track that are shared in those designs with a tracking plan. And then the goal will be that as you’re shipping
172 00:17:24.079 ⇒ 00:17:29.729 Greg Stoutenburg: Phoenix in… in pieces, you said, right? Previously, you said things like that, it’ll start to ship in February.
173 00:17:29.870 ⇒ 00:17:42.339 Greg Stoutenburg: Right? Somewhere in there. So what we can do is try to do this, like, sort of a piece at a time. So as a piece goes up, we implement the code so that we get that event data going into amplitude, and we can do it in this sort of iterative way.
174 00:17:42.910 ⇒ 00:17:46.400 Caitlyn Vaughn: Okay, cool, yeah, I’m wondering…
175 00:17:46.970 ⇒ 00:17:50.649 Caitlyn Vaughn: I’m wondering if we could just, like, hook into our…
176 00:17:50.760 ⇒ 00:17:55.770 Caitlyn Vaughn: like, Phoenix repo, and as things get built, because we have to, like, specifically track
177 00:17:56.680 ⇒ 00:18:01.239 Caitlyn Vaughn: you know, events, right? Like, it’s not just, like, good out-of-the-box track things.
178 00:18:01.850 ⇒ 00:18:20.300 Greg Stoutenburg: Yeah, so there will be a couple of different pieces to it. So we’ll get… we’ll get some event data that’s… that’s sort of auto-tracked event data. That’s, like, user clicked a button, what was the button? Like, that’ll sort of apply broadly. Or user loaded a page, and it’ll tell us what page. So we’ll get some of that stuff close to for free.
179 00:18:20.300 ⇒ 00:18:25.600 Greg Stoutenburg: In the sense of, you know, engineering hours. But then there will be other things where, you know.
180 00:18:25.620 ⇒ 00:18:42.479 Greg Stoutenburg: where we’ll have much more focused goals in what we’re trying to measure. So some of the things, like, just mentioned a moment ago, right? Like, we’ll want to be pretty specific about what we’re measuring in your onboarding flow to make sure that you’ve got solid activation and get that PLG motion, running strong. So…
181 00:18:42.870 ⇒ 00:18:43.370 Caitlyn Vaughn: No.
182 00:18:43.370 ⇒ 00:19:00.600 Greg Stoutenburg: Yes, so if we can see… and this would be an ask from me then, any, anything that you can share on a timeline, or any documentation, anything that’s, like, in GitHub that shows issues moving forward to ship these pieces, then we can make sure to stay coordinated so that we’re, you know.
183 00:19:00.720 ⇒ 00:19:05.670 Greg Stoutenburg: right behind you. Something ships, we get the event data coming into amplitude from that piece.
184 00:19:06.360 ⇒ 00:19:10.909 Caitlyn Vaughn: Okay, yeah, I guess that’s kind of going into my, like, main question here, which is…
185 00:19:11.530 ⇒ 00:19:28.370 Caitlyn Vaughn: if we are… I mean, everyone… every engineer is, like, basically building their project on their branch and then merging it back into, like, the main branch, right? So, if we hooked in the main branch today to Amplitude, could we start tracking events in there that are…
186 00:19:28.370 ⇒ 00:19:32.800 Caitlyn Vaughn: Like, on the products that are finished? Or should we wait until everything is merged in?
187 00:19:33.500 ⇒ 00:19:48.039 Greg Stoutenburg: We can start… so, a piece… when what we want to track is something that we’re going to ask for specifically. Like, there’s an event that can fire on this page, and we think it’s of value to us because it signifies this important action that the user is taking.
188 00:19:48.040 ⇒ 00:20:02.329 Greg Stoutenburg: That’s something that will go in that code. So when, so if something gets merged to main, and… which is to say, you know, the engineering team reviewed it, it got approved, and they merged it, then…
189 00:20:02.330 ⇒ 00:20:06.359 Greg Stoutenburg: We can go ahead and instrument amplitude on that piece of the product.
190 00:20:07.220 ⇒ 00:20:16.210 Greg Stoutenburg: And on that, implement amplitude just means put in the piece of code that’s going to track whatever event it is, so that we see that fire, in amplitude.
191 00:20:18.570 ⇒ 00:20:24.279 Caitlyn Vaughn: And we’re doing that through segment. We’re doing that through segment. That was gonna be my next question.
192 00:20:24.830 ⇒ 00:20:44.029 Caitlyn Vaughn: So, like, the only… I think, like, Greg and I were chatting, and the only thing that I wasn’t too sure about, and this is, like, a question for, like, our engineering team internally, is, like, some data might not be, like, in the database, or might not be, like, in Stripe or, like, other data sources that were…
193 00:20:44.030 ⇒ 00:20:59.250 Caitlyn Vaughn: bumping into amplitude. Some of it just might live in the UI, and in that case, I think we would still have to, like, write code to track that specific data. Like what? Like, I think some things are, like, in the UI, like, if some data, like, some action, and that’s not…
194 00:20:59.480 ⇒ 00:21:06.589 Caitlyn Vaughn: like, sent to our database. Oh! So that’s, like, I wasn’t sure if that was a limitation,
195 00:21:07.290 ⇒ 00:21:23.490 Caitlyn Vaughn: Of just using segment, too. That’s a good question. Like, if someone is actually interacting with the interface versus, like, when they hit next page, obviously, like, it toggles to a new page, so you know that they flipped the page, but… Yeah, okay. Yeah. That’s a you guys question.
196 00:21:24.000 ⇒ 00:21:27.080 Greg Stoutenburg: Yeah, sorry, I’m trying to get logged into your segment so I can show you what…
197 00:21:27.290 ⇒ 00:21:29.529 Greg Stoutenburg: what I see is being sent to Amplitude.
198 00:21:35.040 ⇒ 00:21:38.630 Caitlyn Vaughn: Let’s like you should talk to Michelle. Oh, okay, he would know better.
199 00:21:39.120 ⇒ 00:21:41.430 Caitlyn Vaughn: Because I think the…
200 00:21:42.110 ⇒ 00:21:49.650 Caitlyn Vaughn: the front-end engineers usually just, like, track whatever, like, we tell them to based on the plan. Oh, really? Because, like…
201 00:21:49.850 ⇒ 00:21:58.399 Caitlyn Vaughn: even resected. Because not everything is being modeled in the product database, and then you’re being sent. Yeah, that makes sense. Yeah.
202 00:21:58.730 ⇒ 00:22:03.129 Caitlyn Vaughn: be, like, data that you’re just putting in your database. Right, way too much data.
203 00:22:03.510 ⇒ 00:22:05.100 Caitlyn Vaughn: That’s a good point.
204 00:22:05.100 ⇒ 00:22:11.330 Greg Stoutenburg: Monica, I’m trying to do this faster so you don’t have to… you don’t have to read the buttons to me from the screen like last time.
205 00:22:11.340 ⇒ 00:22:12.329 Nandika Jhunjhunwala: No worries. Okay.
206 00:22:12.330 ⇒ 00:22:23.029 Greg Stoutenburg: Okay, so we… there’s a… there’s sort of minimal amount of information being sent from segment into amplitude.
207 00:22:23.610 ⇒ 00:22:25.849 Greg Stoutenburg: Yeah, I guess I don’t need to share it. It’s sort of like…
208 00:22:25.970 ⇒ 00:22:49.410 Greg Stoutenburg: 8 categories of events, and it’s useful information, but it’s not nearly at the level of granularity that’s going to be provided for by implementing pieces of amplitude code directly into the product, into the website. So we can keep using segment, but it won’t be… to really get that view that we were talking about a moment ago, you know, things like time on page.
209 00:22:49.410 ⇒ 00:23:01.629 Greg Stoutenburg: Segment won’t give us that information. It won’t give us durations, it won’t allow for us to really see in a close view way what the user’s experience looks like.
210 00:23:03.360 ⇒ 00:23:12.599 Greg Stoutenburg: So segment can stay a piece of the puzzle here, but we need to do more amplitude instrumentation throughout the product than just what we can get through segment.
211 00:23:13.080 ⇒ 00:23:24.979 Caitlyn Vaughn: Yeah, okay, that makes sense. It definitely, like, increases the… whenever you have to, like, pull in more people, it obviously increases the, like, resistance and complexity, so…
212 00:23:25.970 ⇒ 00:23:35.429 Caitlyn Vaughn: I would say just… I’m gonna have to, like, go talk to Victor and chat with our engineers, but for now, let’s focus on hooking up segment, and I will work on…
213 00:23:36.200 ⇒ 00:23:39.830 Caitlyn Vaughn: Prioritizing, like, actual code in front-end.
214 00:23:40.110 ⇒ 00:23:51.650 Caitlyn Vaughn: For the engineers, but I’m just not sure what that looks like, because talking about timelines, like, we’re just scrambling. Like, it’s not even a significant amount of time to add it, right? It’s just, like.
215 00:23:51.650 ⇒ 00:23:52.880 Greg Stoutenburg: No, it’s not.
216 00:23:52.880 ⇒ 00:23:53.980 Caitlyn Vaughn: Another thing to do.
217 00:23:54.450 ⇒ 00:24:08.379 Greg Stoutenburg: Yeah, yeah, it’s another task, but it’s like, drop this code here, to the place that we’ve identified we want to track it. So, and one of the nice things about doing this iteratively then, as pieces of Phoenix are coming out, is that,
218 00:24:08.380 ⇒ 00:24:31.739 Greg Stoutenburg: for… for the events that we want to track because we think that they’re especially important user flows, it’s not going to be everything. We’re going to be targeted about it, so it’s not like… it’s not like we’re going to be handing over, you know, like a 200-line or, you know, 1,000-line spreadsheet and say, you know, here, go through row by row and track all of these. It will be more limited than that, and we can do it a piece at a time.
219 00:24:32.610 ⇒ 00:24:42.750 Caitlyn Vaughn: Okay, so does that mean we would have to put, put a snippet of code on, like, each screen in our product? Or it’s, like, the screens that we care about tracking, each one individually?
220 00:24:42.960 ⇒ 00:24:51.649 Greg Stoutenburg: Yeah, it’s… we’ll go through the pieces where there’s something that we care to track, and we… we put in a code snippet that’ll help us track that.
221 00:24:52.170 ⇒ 00:24:53.989 Caitlyn Vaughn: Okay, so…
222 00:24:54.110 ⇒ 00:25:13.110 Caitlyn Vaughn: then I think the most helpful thing that you guys could do is, like, go through the product, figure out what we should prioritize in tracking, and then, like, send me extremely explicit, like, we need this code on this page, and, like, a list of it, and I can divvy it out to the engineers if it’s, like, a really quick, you know.
223 00:25:13.280 ⇒ 00:25:15.520 Caitlyn Vaughn: Changed,
224 00:25:15.880 ⇒ 00:25:24.990 Caitlyn Vaughn: Because maybe it would be better to be done before it’s, like, merged and finally through QA, so we just have to re-go through the process.
225 00:25:26.730 ⇒ 00:25:31.259 Uttam Kumaran: Do you guys do, like, a visual QA step, or what’s, like, can we get… can we just be…
226 00:25:31.620 ⇒ 00:25:33.619 Uttam Kumaran: at some point, maybe in QA?
227 00:25:34.190 ⇒ 00:25:36.440 Caitlyn Vaughn: Yeah, we’re doing QA right now.
228 00:25:37.800 ⇒ 00:25:41.379 Uttam Kumaran: But for the variety of, like, the feature branches, like, when does it happen?
229 00:25:42.310 ⇒ 00:25:43.580 Caitlyn Vaughn: What do you mean?
230 00:25:43.580 ⇒ 00:25:48.020 Uttam Kumaran: Like, you said people are shipping, like, features, sort of, as they finish pieces?
231 00:25:48.310 ⇒ 00:25:49.290 Caitlyn Vaughn: Yeah.
232 00:25:49.610 ⇒ 00:25:57.390 Uttam Kumaran: So, I guess, like, it’s just, as something’s ready, it just goes to, like, you to QA, or, like, is there… is there, like, a flow for the actual QA?
233 00:25:58.430 ⇒ 00:26:05.999 Caitlyn Vaughn: Yeah, so right now, engineers build their sections
234 00:26:07.250 ⇒ 00:26:15.420 Caitlyn Vaughn: they QA their branch with a few other engineers, and they merge it back, and the, like, full thing is QA’d, right?
235 00:26:15.720 ⇒ 00:26:17.790 Caitlyn Vaughn: That’s, like, roughly the flow.
236 00:26:18.110 ⇒ 00:26:23.540 Caitlyn Vaughn: So are you saying, like, you would be in the step of, like, the individual QA before it gets merged?
237 00:26:25.620 ⇒ 00:26:42.319 Uttam Kumaran: Yeah, like, if… so I guess we want to be after the step where all the elements in the page are, like, accurate. Like, I’m not gonna say this button shouldn’t be there, but if the button is there, I want to be like, okay, great, now that we’re confirmed the button is there, let’s put this snippet in.
238 00:26:42.650 ⇒ 00:26:47.619 Uttam Kumaran: They’re building off of, like, fixed designs, then we can also come in on the requirements side.
239 00:26:47.990 ⇒ 00:26:50.060 Uttam Kumaran: If it’s sort of variable.
240 00:26:50.480 ⇒ 00:26:56.060 Uttam Kumaran: then the best is just, like, before the thing gets merged, it needs to go through, like, product analytics QA.
241 00:26:56.490 ⇒ 00:26:57.640 Caitlyn Vaughn: Hmm…
242 00:26:58.380 ⇒ 00:26:58.960 Uttam Kumaran: Yeah.
243 00:27:01.130 ⇒ 00:27:01.830 Greg Stoutenburg: Okay.
244 00:27:01.830 ⇒ 00:27:02.300 Uttam Kumaran: Because what…
245 00:27:02.300 ⇒ 00:27:02.990 Greg Stoutenburg: I’m sitting.
246 00:27:03.290 ⇒ 00:27:04.250 Uttam Kumaran: Yeah, go ahead, go ahead.
247 00:27:04.480 ⇒ 00:27:08.120 Greg Stoutenburg: Yeah, I was just gonna say, my understanding is that the Figma designs are not…
248 00:27:08.620 ⇒ 00:27:13.690 Greg Stoutenburg: Fully… it’s not… they’re not 100% complete, but they’re… Pretty close.
249 00:27:15.070 ⇒ 00:27:18.970 Caitlyn Vaughn: Yeah, the Figman designs are, like, basically what is…
250 00:27:19.090 ⇒ 00:27:21.260 Caitlyn Vaughn: going to be produced. Yes, okay.
251 00:27:21.260 ⇒ 00:27:21.850 Greg Stoutenburg: Yeah.
252 00:27:23.400 ⇒ 00:27:28.900 Caitlyn Vaughn: I mean, yeah, I can send you guys, like, our main branch, and you can see what is finished, but…
253 00:27:30.560 ⇒ 00:27:33.129 Caitlyn Vaughn: It’s obviously, like, not public yet.
254 00:27:33.870 ⇒ 00:27:34.450 Uttam Kumaran: Yeah.
255 00:27:37.440 ⇒ 00:27:41.610 Uttam Kumaran: I mean, we can send instructions on just, like, how we would implement the event.
256 00:27:42.500 ⇒ 00:27:45.409 Uttam Kumaran: And then maybe we just see, like, where they… where we want to fit in.
257 00:27:46.110 ⇒ 00:27:54.540 Caitlyn Vaughn: Yeah, like, the only thing I’m thinking, the only issue is, like, we have to know what events we want to track before we ask them, right?
258 00:27:55.100 ⇒ 00:28:00.329 Uttam Kumaran: Yeah, so that’s why I think, Greg, you have a good… like, you have the designs, right? So if we know the designs…
259 00:28:01.330 ⇒ 00:28:05.400 Uttam Kumaran: Or if we can see the visual preview of the feature.
260 00:28:06.000 ⇒ 00:28:12.359 Uttam Kumaran: If they’re… if they differ from the designs, then we can immediately tell you which… what events to… to track.
261 00:28:12.810 ⇒ 00:28:16.100 Caitlyn Vaughn: Yeah, the designs are, like, the final designs.
262 00:28:16.380 ⇒ 00:28:24.169 Greg Stoutenburg: Yeah, and okay, they are the panel signs. Okay, great. Yeah, so that’s… that’s the step we’re on now, and so now that I have access to those, I’m working on the tracking plan.
263 00:28:24.810 ⇒ 00:28:28.879 Caitlyn Vaughn: Cool. Yeah, as soon as we have that, it would probably be a lot easier for us to make an ask.
264 00:28:29.240 ⇒ 00:28:29.790 Greg Stoutenburg: Yeah.
265 00:28:31.130 ⇒ 00:28:32.550 Greg Stoutenburg: Definitely. Understood.
266 00:28:32.980 ⇒ 00:28:33.700 Caitlyn Vaughn: Fifth.
267 00:28:33.990 ⇒ 00:28:35.920 Greg Stoutenburg: Yep, so that’s where we’re at with that.
268 00:28:37.340 ⇒ 00:28:37.970 Caitlyn Vaughn: Amazing.
269 00:28:37.970 ⇒ 00:28:40.170 Greg Stoutenburg: I think maybe the Gantt chart is next?
270 00:28:41.330 ⇒ 00:28:43.490 Demilade Agboola: Yes, the Gantt charts is next.
271 00:28:43.490 ⇒ 00:28:43.980 Mustafa Raja: Yeah.
272 00:28:43.980 ⇒ 00:28:51.840 Greg Stoutenburg: Yeah, okay. Yeah, go for it, Mustafa. We can probably be sort of brief and just highlight what the change is, since we mostly just talked through a lot of this.
273 00:28:52.110 ⇒ 00:28:54.130 Mustafa Raja: Demadi, can I share my screen?
274 00:28:54.930 ⇒ 00:28:55.949 Mustafa Raja: Yeah, thank you.
275 00:28:55.950 ⇒ 00:28:57.039 Demilade Agboola: Go for it.
276 00:28:58.160 ⇒ 00:29:11.159 Mustafa Raja: Okay, so, we have stretched out the timeline, to sort of support the, iterative, implementation of Amplitude. So next week, what we are doing is we, we will be…
277 00:29:11.190 ⇒ 00:29:19.520 Mustafa Raja: Tracking, or sorry, we will be, documenting the events and user properties, and then, we will be coming up with.
278 00:29:19.520 ⇒ 00:29:42.430 Mustafa Raja: a master event, tracking template, and, we will be discussing funnel definitions with default team, and then once we are, 2 weeks in February, we will start, with an amplitude organization setup, and then we will have three weeks of, property tracking, and event tracking, and,
279 00:29:42.430 ⇒ 00:30:01.759 Mustafa Raja: So, with that, in the third week, we will, start, dashboard building in parallel, and then, we’ll have, a few weeks of dashboard building and ad hoc, reporting needs, and then documentation handoff, and then, in the last phase.
280 00:30:01.950 ⇒ 00:30:09.610 Mustafa Raja: In April, we will, do a training session and hand off to default payment plan next steps.
281 00:30:09.760 ⇒ 00:30:13.390 Mustafa Raja: So, this is our, plan for product analytics.
282 00:30:14.350 ⇒ 00:30:22.039 Caitlyn Vaughn: Okay, amazing, this all looks good. What is the, like, final end date of this? Like, March or April?
283 00:30:22.680 ⇒ 00:30:24.519 Mustafa Raja: The goal is to get…
284 00:30:24.520 ⇒ 00:30:27.350 Greg Stoutenburg: comfortably handed over before May.
285 00:30:27.910 ⇒ 00:30:29.250 Caitlyn Vaughn: Okay. Amazing.
286 00:30:31.860 ⇒ 00:30:33.069 Mustafa Raja: Yeah, that’s pretty much it.
287 00:30:39.320 ⇒ 00:30:41.089 Demilade Agboola: That’s still continuing.
288 00:30:43.480 ⇒ 00:30:50.130 Demilade Agboola: The slideshow and presentation… Okay,
289 00:30:54.230 ⇒ 00:31:03.899 Demilade Agboola: Alright, so, as mentioned before, one of the things we also did was just, like, the new requests to handle the inbound performance, and that was done.
290 00:31:04.870 ⇒ 00:31:09.870 Demilade Agboola: And, like, I think just finally, like, risks and, mitigation to those risks.
291 00:31:10.110 ⇒ 00:31:14.970 Demilade Agboola: would be on, I mean, top of mind will just be, like, S3 access.
292 00:31:15.520 ⇒ 00:31:22.680 Demilade Agboola: So once we have the details, because we need, like, the access key, ID, and a number of other, like, details around that…
293 00:31:22.890 ⇒ 00:31:25.859 Demilade Agboola: Potomac will be ready to go.
294 00:31:27.230 ⇒ 00:31:29.869 Demilade Agboola: And we will have data, like, flowing into…
295 00:31:32.270 ⇒ 00:31:37.920 Demilade Agboola: MotherDoc instance, so that’ll… that’ll probably be that. I think access to data sources is…
296 00:31:38.230 ⇒ 00:31:42.559 Demilade Agboola: quite low, because right now, the only data sources we don’t really have access to are, like, the P2.
297 00:31:42.700 ⇒ 00:31:50.289 Demilade Agboola: data sources, so it’s not, like, hyper, but it’s just something to note, potentially, for, future.
298 00:31:50.400 ⇒ 00:31:57.679 Demilade Agboola: dashboards that we will be working on. So, those are the main, like, risks and things we would like to do to mitigate that.
299 00:31:58.230 ⇒ 00:32:01.569 Caitlyn Vaughn: Okay, one call-out here is I realized…
300 00:32:02.450 ⇒ 00:32:21.070 Caitlyn Vaughn: for Stripe, I think we’re actually gonna stay on Hyperline for now, and invoice, for, like, our sales-led deals through… through Hyperline, which is actually attached to Stripe. I would say, like, 90… 90 to 95% of our…
301 00:32:21.100 ⇒ 00:32:24.619 Caitlyn Vaughn: like, revenue is recognized in Stripe, so it’s probably…
302 00:32:24.660 ⇒ 00:32:40.480 Caitlyn Vaughn: find to hook in the current Stripe, and then we are building a new Stripe instance specifically for, like, metering credits and PLG billing, so… once that’s finished, we can hook that in as well, but maybe for now, I’ll actually get you guys access to, like, current
303 00:32:40.940 ⇒ 00:32:44.049 Caitlyn Vaughn: Stripe. Stripe. Sounds good.
304 00:32:44.050 ⇒ 00:32:44.560 Demilade Agboola: Okay.
305 00:32:46.390 ⇒ 00:32:47.030 Caitlyn Vaughn: Whoa.
306 00:32:47.800 ⇒ 00:32:54.350 Demilade Agboola: Alright, so, yeah, once we get all of that, we’ll, be able to push forward, and I think that’s the…
307 00:32:54.880 ⇒ 00:32:56.199 Demilade Agboola: That’s the final step.
308 00:32:58.020 ⇒ 00:32:59.650 Demilade Agboola: Okay, also I have one more question.
309 00:33:00.250 ⇒ 00:33:02.089 Demilade Agboola: For all of the tools…
310 00:33:02.310 ⇒ 00:33:05.760 Caitlyn Vaughn: For all the tools that we’re, like, connecting in,
311 00:33:06.760 ⇒ 00:33:12.630 Caitlyn Vaughn: I guess my understanding is, like, Polytomic’s role is to, when, like, a change is made, to, like, pull the data in.
312 00:33:12.710 ⇒ 00:33:29.040 Caitlyn Vaughn: Right? But when we initially connect it, like, for example, Stripe, we have a ton of existing data. Does it, like, pull all the existing data that we already have in Stripe, and then continue to track changes moving forward? Or is it just, like, we have to do a manual push now, and then track changes?
313 00:33:30.390 ⇒ 00:33:33.389 Demilade Agboola: You know, so we can do a historical poll, from…
314 00:33:33.390 ⇒ 00:33:33.770 Caitlyn Vaughn: Okay.
315 00:33:33.770 ⇒ 00:33:35.110 Demilade Agboola: the endpoints.
316 00:33:35.850 ⇒ 00:33:36.639 Demilade Agboola: Mother doc.
317 00:33:37.220 ⇒ 00:33:37.940 Caitlyn Vaughn: Okay, amazing.
318 00:33:37.940 ⇒ 00:33:39.430 Demilade Agboola: So… Yeah.
319 00:33:39.720 ⇒ 00:33:57.450 Uttam Kumaran: Yeah, so we’re basically, like, when we talk about ETL, it’s, like, sort of like piping in the wall, like, I really hope we don’t spend more time talking about polyatomic. It’s, like, the hope, because they’re not the star of the show. So, it’s like, we just make sure the hookup, and they manage all of the movement and refreshing.
320 00:33:59.020 ⇒ 00:33:59.590 Uttam Kumaran: Yeah.
321 00:33:59.880 ⇒ 00:34:00.949 Caitlyn Vaughn: Okay, amazing.
322 00:34:02.480 ⇒ 00:34:14.340 Demilade Agboola: Okay, so yeah, the idea was, like, so once we get S3 access, I think we can, like, be done with the polyatomic conversations, and we can start, like, having more,
323 00:34:15.130 ⇒ 00:34:29.870 Demilade Agboola: data-focused conversations about, like, oh, what do we want to see, what do we want to do, how do we want to present this data, what data should… how do you want this data in Omni, those sort of conversations, and yeah, I’m looking forward to that, like, that phase of it all.
324 00:34:31.000 ⇒ 00:34:33.419 Caitlyn Vaughn: I am missing as a spreadsheet.
325 00:34:33.429 ⇒ 00:34:39.819 Demilade Agboola: Any other questions, just feedback, or things that you would like us to take a crack at?
326 00:34:41.460 ⇒ 00:34:52.489 Caitlyn Vaughn: I pinged Victor about getting an S3, instance set up, so hopefully that will get done this week. Cross our fingers and toes.
327 00:34:53.199 ⇒ 00:34:59.699 Caitlyn Vaughn: Other than that… That’s about it. Okay, yeah. Yeah.
328 00:34:59.700 ⇒ 00:35:13.469 Uttam Kumaran: Yeah, one thing I just wanted to highlight is, like, there is data in Omni already, and so I feel like, you know, that we are gonna build a lot of new models, but I kind of want to just press again on, like.
329 00:35:13.600 ⇒ 00:35:33.239 Uttam Kumaran: see if y’all can already start using some of the AI features in Omni to query. Like, you’re… you should already be able to ask questions like, who are our highest revenue customers? Like, who has the most meetings last week? Like, you should be able to ask a lot of those, straight to the AI bot in Omni, and I think both of y’all, sort of.
330 00:35:33.520 ⇒ 00:35:53.089 Uttam Kumaran: taking the venture and trying it is gonna allow us to just, like, fix any issues, and then… I really feel like that’s gonna be the primary way that a lot of people start to get access to this data. Yes, I think people have opinions on dashboards, but ultimately, I think if we set up the sort of context and the semantic layer here in the right way.
331 00:35:53.300 ⇒ 00:36:00.030 Uttam Kumaran: you should just be able to ask… I mean, the Omni, they call their thing Blobby, so you should be able to ask Blobby basically anything you want.
332 00:36:00.030 ⇒ 00:36:02.450 Caitlyn Vaughn: That’s insane.
333 00:36:02.730 ⇒ 00:36:08.959 Uttam Kumaran: I really, I really want to just press that, like, maybe give it a shot, and, like.
334 00:36:08.960 ⇒ 00:36:11.320 Nandika Jhunjhunwala: Ask it a bunch of questions and let us know what you.
335 00:36:11.320 ⇒ 00:36:11.760 Uttam Kumaran: designed.
336 00:36:13.620 ⇒ 00:36:20.780 Caitlyn Vaughn: Okay, yeah, yeah, yeah, we definitely will do. I know I keep, like, bugging you guys that I need quick data, but I will try to be more self-sufficient.
337 00:36:20.780 ⇒ 00:36:30.039 Uttam Kumaran: No, no, it’s not even for that. The thing that you asked us for, I think that was net new, but, like, I just think in case you’re… even in your… if you’re in a meeting, and you’re like, I wonder this question, just try it.
338 00:36:30.050 ⇒ 00:36:31.300 Caitlyn Vaughn: Because…
339 00:36:31.300 ⇒ 00:36:49.909 Uttam Kumaran: then we’ll get feedback and be able to just, like, iterate, because you’re now so close to the… to us on the data side that I think you have a lot of faith in it, but for, like, the random default employee, like, they may get nervous to ask stuff, or they… if it doesn’t work on their first try, they may sort of be like, it doesn’t work, and so I just want to see if we can…
340 00:36:50.530 ⇒ 00:36:55.050 Uttam Kumaran: just make sure we get some real questions going to the AI system, and
341 00:36:55.470 ⇒ 00:36:57.090 Uttam Kumaran: Make sure it’s, like, set up well.
342 00:36:57.720 ⇒ 00:36:58.520 Caitlyn Vaughn: Yeah.
343 00:36:59.080 ⇒ 00:37:01.520 Caitlyn Vaughn: And that’s static data for now, right?
344 00:37:02.110 ⇒ 00:37:12.660 Uttam Kumaran: It’s all static data for them, but we have all the historicals loaded in, so… yes, like, you can’t… you can’t ask what happened yesterday, but you can’t ask what has happened in the history of the company.
345 00:37:12.830 ⇒ 00:37:17.310 Uttam Kumaran: So, I will say equally valuable.
346 00:37:17.610 ⇒ 00:37:19.130 Caitlyn Vaughn: It’s so funny when, like.
347 00:37:19.990 ⇒ 00:37:30.930 Caitlyn Vaughn: people, like, our sales reps are, like, they’ll ask a question, and I’m like, oh no, that data set is, like, not in Optney yet, and they’re just like, oh, never mind. And I’m like, no, no, like, there’s still a lot of stuff in there, though. They’re like, no.
348 00:37:30.930 ⇒ 00:37:31.730 Uttam Kumaran: Yeah.
349 00:37:31.730 ⇒ 00:37:34.140 Caitlyn Vaughn: Like, no, no, it can, but, you know.
350 00:37:34.140 ⇒ 00:37:44.919 Uttam Kumaran: No, it’s pretty common to be, like, oh, it’s, like, not up to date, but, like, you know, there’s… you saw the, kind of, the stuff Amber was able to produce, and so there’s actually a lot of things in there around
351 00:37:44.920 ⇒ 00:37:55.300 Uttam Kumaran: what, how fast people took to, like, onboard, how their ramp-up of usage looked like, what types of companies are there. So there is a lot of information in there.
352 00:37:55.670 ⇒ 00:38:03.729 Caitlyn Vaughn: Yeah, totally. I see the value, obviously, I’m sure Nandika does too, as we do, but yeah, we’ll definitely get there.
353 00:38:04.160 ⇒ 00:38:04.710 Uttam Kumaran: Cool.
354 00:38:06.740 ⇒ 00:38:07.450 Caitlyn Vaughn: Cool.
355 00:38:10.080 ⇒ 00:38:11.660 Demilade Agboola: Okay, sounds good.
356 00:38:12.700 ⇒ 00:38:13.390 Demilade Agboola: Hmm…
357 00:38:13.710 ⇒ 00:38:18.959 Demilade Agboola: If there aren’t any more, like, questions or feedbacks, I guess we could call it a day, or a week at this point.
358 00:38:19.570 ⇒ 00:38:23.009 Caitlyn Vaughn: Yeah, I think we’re good on our end. Yeah, no, thank you so much. If…
359 00:38:23.210 ⇒ 00:38:32.750 Caitlyn Vaughn: Greg, if you… if you want to meet in, like, if I can be of any help, or assistance, with the tracking plan, like…
360 00:38:32.920 ⇒ 00:38:37.910 Caitlyn Vaughn: I’m definitely available, like, throughout the week, tomorrow.
361 00:38:37.910 ⇒ 00:38:52.210 Greg Stoutenburg: Yeah, that sounds good. So, my plan was to reach out when I have, at least a flow mapped out and review it with you, but I’ll reach out and just put time on the calendar so we can have that and be proactive about it.
362 00:38:52.880 ⇒ 00:38:57.750 Caitlyn Vaughn: Sounds good, thank you. Oh, last thing, next week I’m gonna be in London.
363 00:38:57.940 ⇒ 00:39:09.149 Caitlyn Vaughn: So, I’ll be, like, around, but probably slow to respond, and, like, probably not available for, meetings, so I’m gonna lean on Monica next week to take a lot of the comms for this.
364 00:39:10.300 ⇒ 00:39:10.900 Greg Stoutenburg: Go on.
365 00:39:11.040 ⇒ 00:39:11.510 Demilade Agboola: Okay, so…
366 00:39:11.510 ⇒ 00:39:12.170 Greg Stoutenburg: Go ahead.
367 00:39:13.430 ⇒ 00:39:16.010 Caitlyn Vaughn: Cool. All from our end.
368 00:39:16.030 ⇒ 00:39:16.520 Greg Stoutenburg: Thank you.
369 00:39:16.520 ⇒ 00:39:17.020 Uttam Kumaran: Thank you.
370 00:39:17.370 ⇒ 00:39:18.410 Caitlyn Vaughn: Thanks, guys!
371 00:39:18.410 ⇒ 00:39:19.519 Demilade Agboola: See you next week. Bye soon.
372 00:39:19.520 ⇒ 00:39:20.010 Caitlyn Vaughn: Right.