Meeting Title: Default Weekly Sync and Planning Date: 2026-01-23 Meeting participants: Mustafa Raja, Greg Stoutenburg, Demilade Agboola, Uttam Kumaran, Lev Katreczko, Nandika Jhunjhunwala, Caitlyn Vaughn
WEBVTT
1 00:00:29.790 ⇒ 00:00:30.710 Greg Stoutenburg: Hey, guys.
2 00:00:38.780 ⇒ 00:00:40.259 Demilade Agboola: Hi, how’s everyone doing?
3 00:00:40.830 ⇒ 00:00:42.690 Greg Stoutenburg: Hey, doing alright. Happy Friday.
4 00:00:43.370 ⇒ 00:00:44.860 Demilade Agboola: Yeah, yeah.
5 00:00:47.610 ⇒ 00:00:49.439 Demilade Agboola: Begins around the corner.
6 00:00:52.080 ⇒ 00:00:53.910 Greg Stoutenburg: I know you told me once, where are you located?
7 00:00:54.880 ⇒ 00:00:55.680 Demilade Agboola: Malta.
8 00:00:56.320 ⇒ 00:00:57.879 Greg Stoutenburg: Okay. That’s right, that’s right.
9 00:00:58.380 ⇒ 00:00:59.150 Demilade Agboola: Yeah, so…
10 00:00:59.150 ⇒ 00:01:01.150 Greg Stoutenburg: So, it’s really around the corner for you.
11 00:01:01.250 ⇒ 00:01:02.310 Greg Stoutenburg: It’s.
12 00:01:02.970 ⇒ 00:01:04.330 Demilade Agboola: Yeah, so for me, it’s…
13 00:01:04.330 ⇒ 00:01:06.979 Greg Stoutenburg: corner. Yeah, yeah, okay.
14 00:01:07.260 ⇒ 00:01:07.940 Demilade Agboola: So…
15 00:02:13.350 ⇒ 00:02:18.779 Demilade Agboola: I guess it’s… they probably had the meeting explained over, because, I mean, they both accept it.
16 00:02:18.930 ⇒ 00:02:19.579 Demilade Agboola: Actually, no.
17 00:02:19.580 ⇒ 00:02:20.600 Greg Stoutenburg: Yeah, yeah.
18 00:02:20.600 ⇒ 00:02:22.509 Demilade Agboola: Caitlin, Lev and Nautica.
19 00:02:25.780 ⇒ 00:02:28.480 Demilade Agboola: Let me just try and see if I can message them.
20 00:02:31.420 ⇒ 00:02:33.690 Demilade Agboola: Let’s just get some feedback.
21 00:02:36.130 ⇒ 00:02:37.040 Demilade Agboola: Steve…
22 00:02:56.550 ⇒ 00:03:00.940 Demilade Agboola: Alright, so I just tagged Caitlin and the group, just to let her know we’re in the meeting room, so…
23 00:03:01.430 ⇒ 00:03:02.020 Greg Stoutenburg: Great.
24 00:03:12.580 ⇒ 00:03:14.270 Lev Katreczko: Hey guys, sorry for the delay.
25 00:03:15.770 ⇒ 00:03:16.240 Nandika Jhunjhunwala: Hello.
26 00:03:16.240 ⇒ 00:03:16.880 Demilade Agboola: Whoa.
27 00:03:17.280 ⇒ 00:03:18.799 Greg Stoutenburg: Hey, Liv. Hey, Nadika.
28 00:03:20.880 ⇒ 00:03:22.130 Lev Katreczko: How’s it going, Greg?
29 00:03:23.160 ⇒ 00:03:24.249 Greg Stoutenburg: Doing alright, how are you?
30 00:03:25.130 ⇒ 00:03:26.659 Lev Katreczko: Good, man. Nice to meet you.
31 00:03:27.050 ⇒ 00:03:27.810 Greg Stoutenburg: Nice to meet you.
32 00:03:28.990 ⇒ 00:03:30.399 Demilade Agboola: Will Caitlin be joining?
33 00:03:31.750 ⇒ 00:03:35.969 Nandika Jhunjhunwala: Yeah, I think she… she said she’s running, like, a few minutes late, but…
34 00:03:36.190 ⇒ 00:03:39.099 Nandika Jhunjhunwala: Yeah, I think we can… we can get into it.
35 00:03:41.250 ⇒ 00:03:42.260 Demilade Agboola: Oh, okay.
36 00:03:48.290 ⇒ 00:03:50.150 Demilade Agboola: Alright, I’m sharing my screen now…
37 00:04:06.670 ⇒ 00:04:12.770 Demilade Agboola: Alright, so this is the weekly review, so basically what we’ve been up to this past week.
38 00:04:13.240 ⇒ 00:04:17.930 Demilade Agboola: So we basically…
39 00:04:18.040 ⇒ 00:04:22.989 Demilade Agboola: look at this week in, like, two major work streams. So, one is the product analytics.
40 00:04:23.180 ⇒ 00:04:27.820 Demilade Agboola: Where we’re thinking of, like, instrumenting amplitude.
41 00:04:28.190 ⇒ 00:04:34.450 Demilade Agboola: And in that… on that front, we’ve been able to, like, build a detailed GANS chat.
42 00:04:34.840 ⇒ 00:04:46.149 Demilade Agboola: So we’ve been able to align that with, concept of scope, what we need to be able to get out of it, the milestones, as well as just the sequencing. How do we want to go from one point to the next?
43 00:04:46.360 ⇒ 00:04:51.620 Demilade Agboola: And in parallel, basically, we’ve been working on some of the Salesforce data modeling.
44 00:04:51.830 ⇒ 00:04:54.009 Demilade Agboola: And that was completed this week.
45 00:04:54.280 ⇒ 00:05:03.730 Demilade Agboola: And so, if you remember, one of the things we had mentioned in the past is that we’ve been able to get a one-time export of some of the Salesforce data.
46 00:05:04.090 ⇒ 00:05:10.139 Demilade Agboola: And based off of that, we were able to use that to visualize some of the Salesforce metrics.
47 00:05:10.320 ⇒ 00:05:20.730 Demilade Agboola: And we can show some of them today, and just also further ways in which we could build off of that when we start to ingest the data on a real-time… on a daily cadence.
48 00:05:21.340 ⇒ 00:05:39.520 Demilade Agboola: Also, we then finally, like, finalized and refined the dataset for the companies that we had to focus on US coverage and just improved accuracy, so we’re able to get the companies to reflect the proper, like, URLs and proper information around all of that.
49 00:05:40.200 ⇒ 00:05:47.789 Demilade Agboola: So yes, in a… In a bullet… in a bullet point format, this is what that is.
50 00:05:50.810 ⇒ 00:06:00.240 Demilade Agboola: Again, just same, same concept, we’re just able to go about this and use this to be able to, let you, like, put things ahead.
51 00:06:00.670 ⇒ 00:06:12.799 Demilade Agboola: So what does that look like? So for the Salesforce dashboard, we’re able to be able to start showing things like, you know, the count of opportunities by stage, so we can kind of see… and I can actually just go to the dashboard, to be fair, so we don’t have to…
52 00:06:12.960 ⇒ 00:06:19.419 Demilade Agboola: look at the screenshot. So right now, we can kind of just quickly see over the past, like, one year, so 2025, for instance.
53 00:06:19.610 ⇒ 00:06:22.080 Demilade Agboola: We can see a list of, like, the opportunities
54 00:06:22.730 ⇒ 00:06:27.080 Demilade Agboola: At each stage. And bear in mind, this was done… this is the…
55 00:06:27.230 ⇒ 00:06:30.329 Demilade Agboola: This data is just a one-time export, so it’s not live data.
56 00:06:30.850 ⇒ 00:06:43.679 Demilade Agboola: But basically we can see when, like, how many opportunities were closed, how many were won, and what the different stages were. We can also see things like the amount by stage, so how much was the amount
57 00:06:43.930 ⇒ 00:06:46.530 Demilade Agboola: That was lost, versus that was one.
58 00:06:46.630 ⇒ 00:06:50.440 Demilade Agboola: How much is currently in the renewal planning pipeline?
59 00:06:50.600 ⇒ 00:06:53.709 Demilade Agboola: And how much is, like, prospecting?
60 00:06:54.630 ⇒ 00:07:07.909 Demilade Agboola: So this just gives us an idea. Bear in mind, this was done at, again, one-time export to this at that point in time. The long-term goal is to have this on a daily cadence, so you can kind of have an idea of, hey, we have a lot in the…
61 00:07:07.910 ⇒ 00:07:20.819 Demilade Agboola: Prospecting timeline… prospecting pipeline, or we have a lot in this stage, so in the discovery stage, and that allows you to be able to know where exactly you need to focus on, on a day-by-day or a weekly cadence, and you can kind of figure out
62 00:07:20.850 ⇒ 00:07:25.740 Demilade Agboola: What best to optimize. Another thing I did and just played around with was the…
63 00:07:26.140 ⇒ 00:07:29.239 Demilade Agboola: Win rate by number of days it takes from
64 00:07:29.430 ⇒ 00:07:34.239 Demilade Agboola: the opportunity created to being closed. So, how long
65 00:07:34.390 ⇒ 00:07:38.680 Demilade Agboola: Does it take for, like, good quality, like, wind?
66 00:07:38.800 ⇒ 00:07:45.050 Demilade Agboola: And we can quickly see that, like, 0 to 15 days is the best by quite some distance.
67 00:07:45.300 ⇒ 00:07:49.410 Demilade Agboola: And I think it kind of makes sense that, like, an opportunity that you…
68 00:07:49.850 ⇒ 00:07:54.929 Demilade Agboola: That goes really quickly, will close really quickly, and you can win really quickly.
69 00:07:55.060 ⇒ 00:08:05.870 Demilade Agboola: So yeah, I just played around with some of this data. We can change this around, play with different buckets, and again, the idea is we can also have this on a daily cadence showing
70 00:08:06.180 ⇒ 00:08:15.549 Demilade Agboola: Over the past, you know, month, or past two months, or past quarter, what that looks like, and maybe we might need to optimize certain parts of our,
71 00:08:15.950 ⇒ 00:08:20.019 Demilade Agboola: Funnel, and just, like, how do we get these people that are in the…
72 00:08:20.200 ⇒ 00:08:38.689 Demilade Agboola: you know, 30-day mark to, like, improve on that, or how can we try and close within 15 days? You know, just strategies around that. And also, like, win rates by deal size. So, if you have a deal, the best win rate seems to be between, 0 to 5K.
73 00:08:38.940 ⇒ 00:08:55.539 Demilade Agboola: And then we can kind of see that as well. So again, as deals are coming in, we can try and say, hey, how do we optimize certain deals better? Or, like, we can start to put an idea of which, customers are probably going to convert quite
74 00:08:55.890 ⇒ 00:09:01.819 Demilade Agboola: well, you know? An opportunity that is between 0 to 5K in…
75 00:09:02.100 ⇒ 00:09:08.379 Demilade Agboola: That just came in. We can try and see how can we get it in 15 days, to see how quickly we can convert them.
76 00:09:08.650 ⇒ 00:09:13.650 Demilade Agboola: So yeah, that’s the first part. I don’t know if anyone has any, like, feedback or questions, and I’ll share the
77 00:09:13.970 ⇒ 00:09:14.879 Demilade Agboola: Go to the team.
78 00:09:15.660 ⇒ 00:09:31.209 Lev Katreczko: Yeah, yeah, first of all, this is awesome. So, super excited to see. One quick piece of feedback is I anticipate that the data would be distributed slightly differently if we broke it down by deal source inbound versus outbound.
79 00:09:31.380 ⇒ 00:09:34.440 Lev Katreczko: So, it’d probably be cool to see this view.
80 00:09:34.580 ⇒ 00:09:41.509 Lev Katreczko: Based on the field being, I believe it’s Opportunity Source, or Lead Source, yeah.
81 00:09:41.510 ⇒ 00:09:44.800 Demilade Agboola: Alright? So yeah, that’s definitely stuff we can add.
82 00:09:44.900 ⇒ 00:09:48.879 Demilade Agboola: So I can just add a filter here, so, like, the deal source will be there, and we can kind of…
83 00:09:48.980 ⇒ 00:09:52.690 Demilade Agboola: Filter based off inbound, inbound and outbound.
84 00:09:53.290 ⇒ 00:09:53.720 Lev Katreczko: Cool.
85 00:09:53.720 ⇒ 00:09:57.090 Demilade Agboola: So that’s… that will be the idea. So I’ll share this.
86 00:09:57.700 ⇒ 00:10:01.560 Demilade Agboola: And the idea is people, as people on the team are…
87 00:10:01.870 ⇒ 00:10:10.230 Demilade Agboola: Looking at this, they’ll be able to give feedback on what they would like to see, and what other filters they would like to be able to interact with.
88 00:10:10.370 ⇒ 00:10:15.450 Demilade Agboola: Also, hi Caitlin, we’re just talking about what we’ve been up to this week.
89 00:10:16.060 ⇒ 00:10:16.950 Demilade Agboola: And so you.
90 00:10:16.950 ⇒ 00:10:17.560 Caitlyn Vaughn: Hey guys!
91 00:10:17.560 ⇒ 00:10:18.170 Demilade Agboola: fun.
92 00:10:18.410 ⇒ 00:10:19.810 Demilade Agboola: On the first point for you.
93 00:10:22.810 ⇒ 00:10:29.110 Demilade Agboola: Yeah, so this is the Salesforce data dashboard, so we’ve been able to build out things from the Salesforce data.
94 00:10:29.280 ⇒ 00:10:32.450 Demilade Agboola: And I’m just kind of showing what this looks like right now.
95 00:10:32.760 ⇒ 00:10:38.890 Demilade Agboola: While also letting you understand that, like, once we have the data coming in on a daily cadence.
96 00:10:39.080 ⇒ 00:10:56.510 Demilade Agboola: we can start to have data that we can action every day and say, hey, we’re actually falling below how we normally operate, or we want to actually improve this area of operation and how we, you know, close certain, deals. So that’s kind of the idea of this. It’s more of a…
97 00:10:57.350 ⇒ 00:11:10.429 Demilade Agboola: high level, let’s be able to push this out, show what value we can get out of data we currently have, but also, like, it gets people to think about what we want to see in the ETL version, where we are having the fresh data.
98 00:11:11.780 ⇒ 00:11:16.189 Caitlyn Vaughn: So this is just the, like, the modeled Salesforce data. Like, what a…
99 00:11:16.190 ⇒ 00:11:17.260 Demilade Agboola: look like.
100 00:11:17.260 ⇒ 00:11:25.270 Caitlyn Vaughn: post. Okay. And then, did you guys get any… did you guys get access to any Salesforce, before making this?
101 00:11:25.530 ⇒ 00:11:26.100 Caitlyn Vaughn: like.
102 00:11:27.570 ⇒ 00:11:31.610 Demilade Agboola: You know, so that’s what I’m trying to say. So this is based off the one-time export.
103 00:11:31.770 ⇒ 00:11:41.480 Demilade Agboola: So I believe it was Victor, or maybe Thomas, I’m not sure who, but they exported the data and shared it with us, and were able to utilize this to be able to build this.
104 00:11:41.580 ⇒ 00:11:45.449 Demilade Agboola: So this is just a frozen snapshot of data.
105 00:11:45.620 ⇒ 00:11:51.490 Demilade Agboola: And the idea is, once we start to have the fresh data coming every day, It’ll just be…
106 00:11:51.840 ⇒ 00:12:06.439 Demilade Agboola: We’ll be able to use this to be able to meet your daily needs on, like, what’s happening on every, like, in about the past, like, 30 days with opportunities, what converts better, how can we ensure that we are hitting targets, and just, like.
107 00:12:06.560 ⇒ 00:12:09.960 Demilade Agboola: Get an idea of what the best, prospects look like.
108 00:12:11.070 ⇒ 00:12:28.670 Caitlyn Vaughn: Yeah, I think that makes sense. I’m mainly asking because, obviously, every, like, CRM instance is set up a little bit differently, and I was just wondering if this is, like, more of a general setup, or if this has, like, if you guys have accounted for our specific layout of, like, how we like to organize data inside of Salesforce.
109 00:12:29.410 ⇒ 00:12:44.519 Demilade Agboola: Okay, fair enough, that’s fair. So, yeah, the idea is, with this, I’ll share this with the team today, like, the default team today, I got some feedback from Lev about just being able to add a filter for inbound versus outbound, so you can kind of see the disparity with that.
110 00:12:44.640 ⇒ 00:12:56.439 Demilade Agboola: So yes, I’ll add that in here, and obviously the team giving feedback will be very helpful to you, to know how we can, structure it in such a way that the default team can find the most value out of it.
111 00:12:57.750 ⇒ 00:13:11.440 Nandika Jhunjhunwala: Yeah, I have some questions, too. I think in general, maybe, if possible, like, it would be interesting to filter by, like, different field types, like, if you want to, like, filter by, like, owner ID,
112 00:13:11.580 ⇒ 00:13:22.560 Nandika Jhunjhunwala: or, like, industry, like, in general. I do see, like, the industry filter, but I think having, like, different CRM fields, like, filter by, or maybe a different view might be… might be interesting.
113 00:13:23.350 ⇒ 00:13:30.769 Demilade Agboola: Yeah, I actually considered using that. I felt there were quite a lot of owner names. There are a lot of opportunities that,
114 00:13:31.180 ⇒ 00:13:45.389 Demilade Agboola: you know, the default team has interacted with. So I just felt that might be a bit overwhelming, but if it’s something that the team, like, needs or would like to have, definitely we can always put it in there, and ensure that, based off your needs, you’re able to utilize it.
115 00:13:47.570 ⇒ 00:13:52.629 Nandika Jhunjhunwala: I was just a thought, like, I don’t know, like, how Caitlin and La feel about it. Yeah.
116 00:13:53.400 ⇒ 00:13:59.890 Demilade Agboola: Yeah, that’s fine. We’ll take everything on board, and we’ll share it, and we’ll be able to, like, get a list of, like.
117 00:14:00.000 ⇒ 00:14:03.670 Demilade Agboola: Ideas and requirements and… Build off of this.
118 00:14:04.880 ⇒ 00:14:13.639 Lev Katreczko: I’m curious, as a follow-up, is it easy to add different conditions and filters on an as-needed basis in this view?
119 00:14:15.370 ⇒ 00:14:18.590 Demilade Agboola: Yeah, it’s not the hardest, it’s… you add
120 00:14:18.810 ⇒ 00:14:22.440 Demilade Agboola: Filter, and then you can kind of pick what column you want.
121 00:14:23.320 ⇒ 00:14:25.200 Demilade Agboola: Perfect. Yeah.
122 00:14:25.200 ⇒ 00:14:26.590 Lev Katreczko: Yeah, that’s awesome.
123 00:14:27.720 ⇒ 00:14:31.480 Demilade Agboola: So, yeah, it’s the… it wouldn’t be the hardest to add a new filter.
124 00:14:33.660 ⇒ 00:14:34.990 Demilade Agboola: Transit time…
125 00:14:38.240 ⇒ 00:14:39.030 Demilade Agboola: Okay.
126 00:14:39.650 ⇒ 00:14:44.079 Demilade Agboola: And then… You can get the values.
127 00:14:49.850 ⇒ 00:14:58.339 Demilade Agboola: as it goes through, so you can kind of upsell, renewals, new business. So, like, now you can just add this, and you can kind of see these…
128 00:14:58.830 ⇒ 00:15:04.330 Demilade Agboola: By, like, renewals versus upsells versus new business or existing business, so…
129 00:15:04.540 ⇒ 00:15:08.150 Demilade Agboola: It won’t be hardest to add certain filters to this at all.
130 00:15:09.220 ⇒ 00:15:10.180 Caitlyn Vaughn: Amazing.
131 00:15:10.650 ⇒ 00:15:11.340 Demilade Agboola: Yeah.
132 00:15:13.020 ⇒ 00:15:18.469 Demilade Agboola: So that’s the… that’s what we… part of what we did this week.
133 00:15:19.230 ⇒ 00:15:23.999 Demilade Agboola: Another thing we did this week was just cleaning up the company data set.
134 00:15:24.890 ⇒ 00:15:29.339 Demilade Agboola: And just ensuring that we focused and got the U.S,
135 00:15:29.490 ⇒ 00:15:38.130 Demilade Agboola: based companies done properly. So that’s what we’ve been able to do this week. So there’s the link to the data set.
136 00:15:41.380 ⇒ 00:15:43.600 Demilade Agboola: Here, and… Okay, awesome.
137 00:15:43.640 ⇒ 00:15:54.949 Caitlyn Vaughn: And then for the overhaul data, did we do primarily startups, or, was there also, like, a spread of, like, enterprise and everything that was refreshed?
138 00:15:55.110 ⇒ 00:16:15.879 Mustafa Raja: Yeah, so, we did refresh, enterprise and mid-market and SMBs also. I, overall, in the sheet, I took a look at the companies that were not from the US, and I replaced those. For the startups, I replaced every single startup,
139 00:16:16.060 ⇒ 00:16:19.020 Mustafa Raja: with a VC-backed startup.
140 00:16:19.730 ⇒ 00:16:29.950 Caitlyn Vaughn: Okay, amazing, that’s perfect. And then Lev and Nandika, I don’t know if you guys had a chance to look at this or not, or if you guys want a bit more time to look through it before we…
141 00:16:30.080 ⇒ 00:16:31.489 Caitlyn Vaughn: Thumbs up, thumbs down.
142 00:16:32.270 ⇒ 00:16:38.729 Lev Katreczko: Yeah, I had a quick pass. I think that the startup list looks significantly improved.
143 00:16:39.230 ⇒ 00:16:42.839 Lev Katreczko: I do think even just looking at this view right here, it’s like…
144 00:16:43.300 ⇒ 00:17:00.019 Lev Katreczko: a good spread of enterprise companies, and the spread is definitely pretty broad, beyond just, like, B2B SaaS and enterprise software. That’s not necessarily a problem. Maybe it’s good to pressure test these providers a little bit more, but…
145 00:17:00.250 ⇒ 00:17:07.380 Lev Katreczko: I think our… Our ICP is definitely very squarely in the enterprise software world.
146 00:17:07.550 ⇒ 00:17:12.849 Lev Katreczko: So, that’s probably gonna be, like, inevitably what gets the most heavy enrichment use.
147 00:17:13.010 ⇒ 00:17:15.910 Lev Katreczko: But that’s sort of just a… just a guess.
148 00:17:21.000 ⇒ 00:17:22.930 Caitlyn Vaughn: You’re talking about, like, B2B, right?
149 00:17:23.650 ⇒ 00:17:24.319 Lev Katreczko: Yeah.
150 00:17:25.290 ⇒ 00:17:33.260 Caitlyn Vaughn: Yeah, most of our customers, tend to sell to, like, actual companies versus individuals,
151 00:17:33.380 ⇒ 00:17:39.939 Caitlyn Vaughn: We’ve, like, marketed that way, but also, like, most of our current data sources, like, don’t really support, you know.
152 00:17:40.290 ⇒ 00:17:42.980 Caitlyn Vaughn: Individuals versus, like, company enrichment.
153 00:17:45.210 ⇒ 00:17:54.089 Caitlyn Vaughn: Yeah, but I still think that having a wider spread is good. I think there’s enough companies on here that are B2B to, like, justify… Totally. This is, like, a good enough list. It’d be good to, like.
154 00:17:54.090 ⇒ 00:17:54.919 Lev Katreczko: I think it’s a good list.
155 00:17:54.920 ⇒ 00:17:55.949 Caitlyn Vaughn: that bounce.
156 00:17:57.330 ⇒ 00:17:58.110 Caitlyn Vaughn: Cool.
157 00:17:58.650 ⇒ 00:18:00.509 Caitlyn Vaughn: Good job, Mustafa.
158 00:18:00.510 ⇒ 00:18:01.230 Mustafa Raja: Thank you.
159 00:18:05.230 ⇒ 00:18:14.640 Demilade Agboola: Okay, and then for the product analytics stream, where we’re talking about, like, or thinking about, you know, amplitude and just being able to…
160 00:18:14.760 ⇒ 00:18:20.060 Demilade Agboola: See the flow and how things are going, within like, default.
161 00:18:20.350 ⇒ 00:18:31.539 Demilade Agboola: We’ve been able to create a Gantt chart based off of that, and this was based off the SOW to just align, like, based off milestones and just, like, owners and timelines.
162 00:18:31.760 ⇒ 00:18:35.610 Demilade Agboola: And so this is what this looks like, and…
163 00:18:36.580 ⇒ 00:18:40.779 Demilade Agboola: Greg will be able to walk through this much more, like, after.
164 00:18:41.220 ⇒ 00:18:42.510 Demilade Agboola: this presentation.
165 00:18:42.880 ⇒ 00:18:54.139 Demilade Agboola: But yeah, the basic idea is where we’ll be able to, like, have an idea of, like, the planning and the implementation, and also the transition to the maintenance phase of all of it.
166 00:18:55.680 ⇒ 00:19:01.700 Demilade Agboola: All right, and in just the general project so far, I think risks and mitigations and
167 00:19:02.040 ⇒ 00:19:15.440 Demilade Agboola: there are two major risks that I think we’ve seen so far. One would just be, like, number one, just access the data sources. So, like, Salesforce and all the other data sources, so that we can start to connect this and ingest it into the warehouse.
168 00:19:15.730 ⇒ 00:19:20.769 Demilade Agboola: And also, like, there’s the ETL tool sign-off, and I know we have a call with, Polytomic.
169 00:19:20.950 ⇒ 00:19:23.269 Demilade Agboola: But ultimately, you know, until…
170 00:19:23.560 ⇒ 00:19:31.040 Demilade Agboola: there’s a contract with Default and Polyatomic, and we can’t get the go-ahead to start ingesting this data, so…
171 00:19:31.180 ⇒ 00:19:40.430 Demilade Agboola: Yeah, the idea is once we can get those two out of the way, we can just start pushing it, and we can start to make a lot of insights based off data that comes in.
172 00:19:40.740 ⇒ 00:19:41.809 Demilade Agboola: To the warehouse.
173 00:19:42.500 ⇒ 00:19:57.019 Caitlyn Vaughn: Okay, perfect. Yeah, as soon as we have Polytomic stood up, assuming that’s the way that we’re gonna go, why don’t we, every time we’re, like, ready to connect in a new source, I can push that forward internally and make sure you guys get access. We can just keep moving along.
174 00:19:57.040 ⇒ 00:20:03.000 Caitlyn Vaughn: I would obviously rather that not be a blocker, and I’m sure you guys would too. So, let’s work together on that.
175 00:20:03.890 ⇒ 00:20:05.150 Demilade Agboola: Alright, sounds good.
176 00:20:06.500 ⇒ 00:20:11.090 Demilade Agboola: And yeah, I think that’s it from us for this week. I know… we…
177 00:20:11.090 ⇒ 00:20:11.385 Caitlyn Vaughn: Well.
178 00:20:11.680 ⇒ 00:20:15.270 Demilade Agboola: We do have, Greg here, who’s gonna be able to walk through.
179 00:20:15.560 ⇒ 00:20:24.889 Demilade Agboola: on the product analytics part of this. But before we start that, I just wanted to ask if you had any questions, any feedback about this week in general.
180 00:20:25.060 ⇒ 00:20:29.190 Demilade Agboola: Or if there are any things we would, like, just like to talk.
181 00:20:29.300 ⇒ 00:20:32.140 Demilade Agboola: About or, touch on before we move on.
182 00:20:33.160 ⇒ 00:20:36.320 Caitlyn Vaughn: Just a quick question, can you pull up the Gantt chart again?
183 00:20:37.480 ⇒ 00:20:39.119 Demilade Agboola: the product analytics scant?
184 00:20:39.430 ⇒ 00:20:40.350 Caitlyn Vaughn: Yes.
185 00:20:40.710 ⇒ 00:20:41.220 Demilade Agboola: Okay.
186 00:20:41.220 ⇒ 00:20:51.979 Caitlyn Vaughn: Because I think we did the Gantt chart last week, right? And I was asking about, if we could factor in, like, hours needed per section or whatever, per week.
187 00:20:52.520 ⇒ 00:20:53.090 Demilade Agboola: Okay.
188 00:20:53.090 ⇒ 00:20:54.150 Caitlyn Vaughn: Are you able to do that.
189 00:20:55.300 ⇒ 00:20:58.100 Demilade Agboola: So, there are currently two gunshots, Annie?
190 00:20:58.400 ⇒ 00:20:59.170 Demilade Agboola: Blop.
191 00:21:01.470 ⇒ 00:21:02.060 Demilade Agboola: Yeah.
192 00:21:02.060 ⇒ 00:21:07.560 Mustafa Raja: The hours aren’t accounted for per task right now, but we will definitely work on that.
193 00:21:08.780 ⇒ 00:21:09.260 Caitlyn Vaughn: Okay, awesome.
194 00:21:10.400 ⇒ 00:21:11.070 Demilade Agboola: Darren.
195 00:21:11.070 ⇒ 00:21:15.380 Greg Stoutenburg: Yeah, the one that Mustafa and I put together is specifically for the product analytics implementation.
196 00:21:15.910 ⇒ 00:21:16.240 Caitlyn Vaughn: Okay.
197 00:21:16.240 ⇒ 00:21:17.309 Greg Stoutenburg: And that’s the one that…
198 00:21:17.310 ⇒ 00:21:18.040 Caitlyn Vaughn: the other one?
199 00:21:18.270 ⇒ 00:21:20.380 Greg Stoutenburg: That’s one that Demi showed very quickly.
200 00:21:20.670 ⇒ 00:21:22.920 Demilade Agboola: So there are… so there are two?
201 00:21:23.420 ⇒ 00:21:32.399 Demilade Agboola: So there are two flows, that’s why we have them as two work streams. So one is this, where we have the, just regular
202 00:21:33.440 ⇒ 00:21:40.399 Demilade Agboola: product and, sorry, data analytics, where we’re going to be thinking about, like, GTMs and Salesforce exploration and all of that.
203 00:21:40.880 ⇒ 00:21:47.540 Demilade Agboola: And then we have… The product analytics stream.
204 00:21:47.990 ⇒ 00:21:52.910 Demilade Agboola: Where this is what, Greg will be presenting on and talking about today.
205 00:21:52.910 ⇒ 00:21:53.450 Caitlyn Vaughn: Hmm…
206 00:21:53.450 ⇒ 00:21:58.030 Demilade Agboola: Well, this is the one Greg will largely be driving, where he’ll be thinking about, like.
207 00:21:58.460 ⇒ 00:22:03.399 Demilade Agboola: The, amplitude, and just how to set that up, and how we’re going to transition that as well.
208 00:22:04.070 ⇒ 00:22:10.960 Caitlyn Vaughn: Okay, awesome, I understand now. So this is more, like, amplitude-specific versus the other one is, like, the BI project in general.
209 00:22:11.320 ⇒ 00:22:12.270 Demilade Agboola: Exactly.
210 00:22:12.980 ⇒ 00:22:13.990 Caitlyn Vaughn: Okay, awesome.
211 00:22:14.170 ⇒ 00:22:16.120 Caitlyn Vaughn: Sounds good, no more questions for me.
212 00:22:18.760 ⇒ 00:22:25.009 Demilade Agboola: Okay, so if anyone doesn’t have any other questions or feedback, I think I can hand it over to Greg.
213 00:22:25.850 ⇒ 00:22:27.399 Caitlyn Vaughn: Nanda Collab, anything?
214 00:22:28.950 ⇒ 00:22:30.390 Nandika Jhunjhunwala: Good, yeah, thank you.
215 00:22:30.930 ⇒ 00:22:31.440 Caitlyn Vaughn: Cool.
216 00:22:32.280 ⇒ 00:22:37.869 Greg Stoutenburg: Great, alright. Mustafa, do you want to share your screen real quick and talk through the Gantt?
217 00:22:39.600 ⇒ 00:22:40.310 Mustafa Raja: Yeah.
218 00:22:43.050 ⇒ 00:23:06.570 Greg Stoutenburg: Alright, so what we’ll do, so from here, this is sort of… we’re gonna pivot in the direction of the product analytics project, and, so, you know, we said that we would have this workshop where we would review your user flows, and figure out what it is we want to map. I’ve got a board put together, we’ll collaborate on that shortly. The first step here is just taking a look at what we’ve done to turn the statement of work and all those deliverables that we talked about before.
219 00:23:06.570 ⇒ 00:23:15.950 Greg Stoutenburg: into a plan to deliver it by the, within 3 months. So, once… it looks like once Mustafa’s connection is…
220 00:23:15.950 ⇒ 00:23:20.010 Mustafa Raja: This doesn’t matter. Oh, okay.
221 00:23:20.360 ⇒ 00:23:43.840 Mustafa Raja: Yeah. So, a high-level, view of this Gantt chart would be, for the phase one, we will be, having this, stakeholder kickoff, today, and then, we’ll also be talking about, the events we want to track, and then, funnel definitions like activation, retention, and engagement, and sort of have,
222 00:23:43.840 ⇒ 00:23:48.820 Mustafa Raja: Our master documentation, for, for events, we want to track.
223 00:23:48.820 ⇒ 00:24:11.040 Mustafa Raja: So, once all of that is planned, we will move to the Phase 2, that’s more towards, implementation. We’ll, implement a new, amplitude organization, and then, we will implement, the user properties and, track events that we will have planned in the Phase 1.
224 00:24:11.040 ⇒ 00:24:28.269 Mustafa Raja: And, add any more necessary, data integrations. Once we have all of the data available to ourselves, we will then move towards reporting and dashboarding. And then, this phase will end with a documentation and a handoff.
225 00:24:28.270 ⇒ 00:24:39.270 Mustafa Raja: And then, for the last phase, we will be training internal, team of default, and then, plan next steps.
226 00:24:39.840 ⇒ 00:24:41.639 Mustafa Raja: And that’s pretty much it.
227 00:24:48.390 ⇒ 00:24:49.949 Greg Stoutenburg: Cool, so sorry to say.
228 00:24:50.100 ⇒ 00:24:51.480 Greg Stoutenburg: comments or questions.
229 00:24:52.460 ⇒ 00:24:54.209 Nandika Jhunjhunwala: Quick question on my end?
230 00:24:54.210 ⇒ 00:24:54.640 Caitlyn Vaughn: Yeah, go ahead.
231 00:24:55.110 ⇒ 00:24:55.860 Caitlyn Vaughn: Thanks, Andy.
232 00:24:56.660 ⇒ 00:25:02.979 Nandika Jhunjhunwala: I was… so I think… I’m not sure if I got this right, but I think last week we talked about,
233 00:25:03.320 ⇒ 00:25:06.059 Nandika Jhunjhunwala: keeping, like, Omni and amplitude separate and not.
234 00:25:06.060 ⇒ 00:25:07.180 Caitlyn Vaughn: Plugging.
235 00:25:07.180 ⇒ 00:25:23.519 Nandika Jhunjhunwala: in more data sources into Amplitude, and having, like, Amplitude as, like, the source of, like, product data, and then Omni as, like, the data warehouse and, like, the other sources of data. Is that still the plan, or are we plugging in, like, different data sources into Amplitude as well?
236 00:25:25.640 ⇒ 00:25:26.120 Caitlyn Vaughn: We…
237 00:25:26.120 ⇒ 00:25:33.800 Greg Stoutenburg: We can keep them separate, and then as we go, if there’s some particular piece of data we think would be valuable to add to amplitude, we can do that.
238 00:25:34.130 ⇒ 00:25:41.230 Greg Stoutenburg: As, you know, on a case-by-case basis. But yeah, we certainly… we don’t need to have all of our data going to both Omni and to Amplitude.
239 00:25:42.940 ⇒ 00:25:46.259 Caitlyn Vaughn: Yeah, I think that was my understanding as well, Nantika.
240 00:25:46.450 ⇒ 00:25:51.180 Caitlyn Vaughn: I think we’re… I guess… I thought the plan was to set up all of the events inside of…
241 00:25:51.380 ⇒ 00:25:59.060 Caitlyn Vaughn: Amplitude, and then feed that into Omni, but I don’t see Amplitude as being, like, a destination for any other data sources.
242 00:26:04.010 ⇒ 00:26:07.100 Greg Stoutenburg: All right. Okay. Yeah, go ahead.
243 00:26:07.400 ⇒ 00:26:19.349 Demilade Agboola: I was just gonna say, I think that would be dependent… if there are any use cases where we might need to, like, merge Omni’s data with other data from other sources, then potentially we could have it, like, some of that data in Omni.
244 00:26:19.490 ⇒ 00:26:23.049 Demilade Agboola: So that we can, enrich the data we surmount PTU data.
245 00:26:23.320 ⇒ 00:26:27.240 Demilade Agboola: But otherwise, not… I think most of it would just be an amplitude, for the most part.
246 00:26:28.850 ⇒ 00:26:34.049 Caitlyn Vaughn: Or are we not porting over, Amplitude into Omni? We’re not planning on, like, merging it.
247 00:26:34.770 ⇒ 00:26:36.869 Demilade Agboola: I think, in terms of visualization.
248 00:26:36.980 ⇒ 00:26:50.100 Demilade Agboola: Potentially, I think a lot of the dashboards that will be the most effective will probably be in amplitude, but again, this is Greg’s call. If he feels like we can, you know, visualize it in such a way that,
249 00:26:50.630 ⇒ 00:26:53.069 Demilade Agboola: we can do that in Omni in such a way that it would…
250 00:26:53.460 ⇒ 00:26:56.399 Demilade Agboola: be as effective? Yeah, sure, then we’ll do an Omni then.
251 00:26:57.020 ⇒ 00:26:59.759 Caitlyn Vaughn: Okay, nice! This might actually simplify things, then.
252 00:27:00.250 ⇒ 00:27:01.759 Caitlyn Vaughn: To, like, keep it separately.
253 00:27:03.910 ⇒ 00:27:08.770 Greg Stoutenburg: Hope so, yeah. Yeah, and I guess the way that I think about this is,
254 00:27:08.770 ⇒ 00:27:29.959 Greg Stoutenburg: we go, what problem are we trying to solve, or what question are we trying to answer? And then have our data decisions just be informed by that. So, if we just want to see what user journeys look like, then we can just keep that data in Amplitude. We don’t necessarily need to know, you know, if those are only questions, we don’t need to be sending data from Omni into Amplitude. And then if there are certain
255 00:27:29.960 ⇒ 00:27:35.419 Greg Stoutenburg: product usage-related things that we think would be valuable to have in Omni, we can send that data over to Omni from Amplitude.
256 00:27:36.040 ⇒ 00:27:38.080 Caitlyn Vaughn: Okay, awesome. Yeah, that makes a lot of sense.
257 00:27:38.770 ⇒ 00:27:53.680 Greg Stoutenburg: Alright, so… let’s get started. I just put this in the Slack, but I’ll put it here as well. This is the board that we’ll use. Everyone should be able to access it. The password is… you’re going to love this password, it is default.
258 00:27:53.680 ⇒ 00:28:01.779 Greg Stoutenburg: So, go ahead and type that in, take a look, and I’ll just sort of walk through what we’re looking at here.
259 00:28:02.390 ⇒ 00:28:15.290 Greg Stoutenburg: you don’t necessarily have to, you know, make comments or create stickies, but I think that it’s helpful if we all have an eye on it, and anywhere that you want to make a note, go ahead. You can grab a sticky.
260 00:28:15.520 ⇒ 00:28:24.980 Greg Stoutenburg: off the bottom of the board. I mean, probably everyone here is used to FigJam, but, you know, if not, you can grab a sticky off this toolbar down here. It’ll have your name on it, so we’ll know who’s contributed an idea.
261 00:28:25.350 ⇒ 00:28:32.869 Greg Stoutenburg: And, so here’s what we’re gonna do. We’re going to take some time going through the product and just mapping out the user journeys that you’re interested in.
262 00:28:33.020 ⇒ 00:28:48.360 Greg Stoutenburg: Once we’ve done that, we’ll get… drill down a little bit and identify particular events that we’ll use to, to track those, track those journeys. Any event property or user property we think is relevant, we’ll make a note of it.
263 00:28:48.950 ⇒ 00:29:06.700 Greg Stoutenburg: I will then turn that into a tracking plan, and here’s an example of what that will look like, if you haven’t seen this sort of thing before. We’ll… I… Will create something like this for default using the events and properties that we will identify together.
264 00:29:06.780 ⇒ 00:29:13.319 Greg Stoutenburg: We’ll do, sort of, one thorough pass through some user journeys, get that set up in a tracking plan.
265 00:29:13.500 ⇒ 00:29:32.950 Greg Stoutenburg: get it implemented, and then create some charts that we think are gonna measure the sorts of things that we’re interested in measuring. We’ll look at those charts together, we’ll see if anything seems to be missing, anything seems to be redundant, we’ll add, we’ll subtract, and then, you know, through a couple of iterations like that, we’ll end up with a tracking plan that we like, and an implementation of amplitude that will, that will do
266 00:29:33.030 ⇒ 00:29:35.729 Greg Stoutenburg: You know, that’ll help you answer the questions that you want to answer.
267 00:29:36.070 ⇒ 00:29:37.470 Greg Stoutenburg: So…
268 00:29:37.470 ⇒ 00:29:45.150 Caitlyn Vaughn: Hey, Greg, before we start here, it looks like we only have viewing and commenting access. Can you give us editing access?
269 00:29:45.580 ⇒ 00:29:46.510 Greg Stoutenburg: Yes.
270 00:29:47.060 ⇒ 00:29:47.770 Caitlyn Vaughn: Thank you.
271 00:29:48.050 ⇒ 00:29:52.719 Greg Stoutenburg: Anyone with password… Says, edit.
272 00:29:54.080 ⇒ 00:29:57.600 Greg Stoutenburg: Let me reload.
273 00:30:00.030 ⇒ 00:30:01.300 Greg Stoutenburg: And see what happens.
274 00:30:01.690 ⇒ 00:30:03.099 Greg Stoutenburg: Maybe a refresh will help.
275 00:30:06.340 ⇒ 00:30:07.939 Greg Stoutenburg: Password can edit.
276 00:30:12.020 ⇒ 00:30:18.480 Nandika Jhunjhunwala: I see a start editing button, so… like, next to… Oh, okay.
277 00:30:18.480 ⇒ 00:30:21.779 Greg Stoutenburg: Do you maybe have to, yeah, do you maybe have to hit a button to get access? Send a.
278 00:30:22.590 ⇒ 00:30:33.379 Caitlyn Vaughn: Yeah, I sent a request to you, it now says I can do FigJam for 3 days while you’re deciding on approving or denying, Greg, so you can think about it for up to 3 days, you just let us.
279 00:30:33.380 ⇒ 00:30:38.420 Greg Stoutenburg: Here we go. Alright, well, that was,
280 00:30:39.180 ⇒ 00:30:42.149 Greg Stoutenburg: This is the, this is the live demo curse, right?
281 00:30:43.230 ⇒ 00:30:44.879 Caitlyn Vaughn: Never do a live demo.
282 00:30:44.880 ⇒ 00:30:46.949 Greg Stoutenburg: We’ll do this live, it’ll be great.
283 00:30:47.690 ⇒ 00:30:50.999 Greg Stoutenburg: Come on. Come on, junk mail folder.
284 00:30:51.210 ⇒ 00:30:52.899 Caitlyn Vaughn: Alright, one second.
285 00:30:54.200 ⇒ 00:30:56.719 Caitlyn Vaughn: That’s good. I can edit it now.
286 00:30:56.890 ⇒ 00:30:59.460 Caitlyn Vaughn: You can… you can approve it later if you want.
287 00:31:00.240 ⇒ 00:31:01.230 Greg Stoutenburg: Oh, we did let you add it.
288 00:31:02.930 ⇒ 00:31:05.830 Nandika Jhunjhunwala: I cannot yet… oh, I can edit it now.
289 00:31:06.370 ⇒ 00:31:09.180 Caitlyn Vaughn: Yeah, I have, like, temporary access to edit it.
290 00:31:09.890 ⇒ 00:31:11.749 Greg Stoutenburg: That’s funny, I still haven’t gotten…
291 00:31:12.400 ⇒ 00:31:17.280 Greg Stoutenburg: I still haven’t gotten a request coming through, but when I do, I’ll, I’ll handle that. Okay.
292 00:31:18.180 ⇒ 00:31:19.980 Greg Stoutenburg: Where were we? Alright.
293 00:31:21.230 ⇒ 00:31:37.669 Greg Stoutenburg: Okay, so participants, that’s us, I’ll add that later. The resources for us, keep in mind, the SOW that we talked about last week, that’s been what I use as the resource for Mustafa and I to create that Gantt chart.
294 00:31:38.030 ⇒ 00:32:02.849 Greg Stoutenburg: the GTM funnel map, which you have already shared. This shows some things that we want to make sure are going to be instrumented. We can just focus on the user journeys for today, but being aware that one of the goals is to capture these users who are signing up, get source attribution for them, understand something about their journey before sign-up, so things like referrer and source will be properties that we’ll want to have of our
295 00:32:02.850 ⇒ 00:32:07.240 Greg Stoutenburg: sign-up events and of our users. And then… will…
296 00:32:07.240 ⇒ 00:32:15.719 Greg Stoutenburg: At a later stage, we’ll understand what these qualification steps are, so that we can route users and put them into cohorts within amplitude.
297 00:32:17.340 ⇒ 00:32:20.520 Caitlyn Vaughn: Going back. So… Just to, like…
298 00:32:20.750 ⇒ 00:32:26.890 Caitlyn Vaughn: reiterate, and sorry, I’m gonna need you to repeat yourself, because I was marinating on not being able to edit the doc while you were talking.
299 00:32:27.400 ⇒ 00:32:29.510 Caitlyn Vaughn: Where are we starting?
300 00:32:30.280 ⇒ 00:32:49.250 Greg Stoutenburg: So where we’ll start is we’ll go to default.com, and we’ll… we’ll take a look at a user who has showed up at your website, and look at their journey from sign-up into onboarding. So, that’ll be the first user journey. And the reason why we’ll start there is because
301 00:32:49.430 ⇒ 00:32:53.599 Greg Stoutenburg: According to the… well, let’s go here to look at this.
302 00:32:54.460 ⇒ 00:33:11.369 Greg Stoutenburg: according to the statement of work, as we’ve discussed it, and of course, you know, at any time, say, I actually want to go in this other direction, that’s fine. Some of the things that we’re looking at are, we want to measure activation, we want to measure retention, we want to measure engagement, we want to understand the product journey. So, since these are some of the goals that we have in mind.
303 00:33:11.430 ⇒ 00:33:24.589 Greg Stoutenburg: in order to try to make improvements on customer lifetime value and acquisition costs, we’ll make sure that we’re going to be able to measure those things by tracking the events that have to do with activation and retention and so on.
304 00:33:25.040 ⇒ 00:33:26.680 Caitlyn Vaughn: Okay, I’m following.
305 00:33:26.960 ⇒ 00:33:28.440 Greg Stoutenburg: Yep, sound good? Okay, great.
306 00:33:28.670 ⇒ 00:33:29.850 Greg Stoutenburg: Okay.
307 00:33:30.040 ⇒ 00:33:39.499 Greg Stoutenburg: And then some other resources, Amplitude has a guide here if anyone feels like looking at it, that’s mostly for me. And then the Master Tracking Plan is this, is this that I showed you a moment ago.
308 00:33:41.410 ⇒ 00:33:42.450 Caitlyn Vaughn: Okay, awesome.
309 00:33:42.940 ⇒ 00:33:44.079 Greg Stoutenburg: So many tabs.
310 00:33:44.360 ⇒ 00:33:57.580 Greg Stoutenburg: Again, live demo. Okay, let’s jump in. So these are just some… some things that we can look at and keep in mind. Let’s go to… I think the most useful place to go is just going to be to…
311 00:33:57.840 ⇒ 00:34:06.329 Greg Stoutenburg: default.com. I cheated a little bit, and I teed up a new user. So one thing that we’ll want will be the page landing.
312 00:34:08.290 ⇒ 00:34:14.530 Greg Stoutenburg: So, we can go… First… well, not first page landing.
313 00:34:15.400 ⇒ 00:34:18.690 Greg Stoutenburg: Page landing at default.com.
314 00:34:21.320 ⇒ 00:34:23.750 Greg Stoutenburg: We’ll call this… Onboarding.
315 00:34:27.239 ⇒ 00:34:33.400 Greg Stoutenburg: that new user to be able to get in. They can go… Login.
316 00:34:35.580 ⇒ 00:34:42.760 Greg Stoutenburg: We want to measure this as well. That’s one of our important funnel steps, so it’ll be, clicks login, and this is where you can get your free trial.
317 00:34:44.190 ⇒ 00:34:49.969 Greg Stoutenburg: The free trial, if I’m not mistaken, I didn’t find a place where it says on the website you can get a free trial. I think you just have to click login.
318 00:34:54.719 ⇒ 00:34:56.619 Caitlyn Vaughn: There’s no free trial yet.
319 00:34:56.739 ⇒ 00:34:57.979 Caitlyn Vaughn: It’s all sales-led.
320 00:34:57.980 ⇒ 00:35:05.070 Greg Stoutenburg: Oh, sorry, yeah. Sorry, yes, that’s where you can… that’s where you can create a user for the first time, by clicking login from the main page.
321 00:35:12.730 ⇒ 00:35:13.870 Greg Stoutenburg: That sound right?
322 00:35:14.270 ⇒ 00:35:26.890 Caitlyn Vaughn: So, I guess one of the issues with the current system that we are changing in the new system is we’re, like, fully sales-led right now, and you actually can’t create an account unless your domain has been approved. And it’s only approved.
323 00:35:26.890 ⇒ 00:35:27.260 Greg Stoutenburg: Okay.
324 00:35:27.260 ⇒ 00:35:30.759 Caitlyn Vaughn: I’ve spoken to the team, gone through, like, a full sales lead.
325 00:35:31.380 ⇒ 00:35:32.549 Greg Stoutenburg: I see. Okay.
326 00:35:32.550 ⇒ 00:35:51.669 Caitlyn Vaughn: So, by the time somebody is able to create a free account, they’re probably going with our implementation team, like, hands-on, white glove, and, like, walking through the product together. Now, where we’re going is, like, a freemium PLG kind of model, so… Yeah. The next version… I’m actually working on onboarding right now, and, like.
327 00:35:51.670 ⇒ 00:35:51.990 Greg Stoutenburg: Okay.
328 00:35:51.990 ⇒ 00:36:03.410 Caitlyn Vaughn: on how people will, you know, understand the platform as they’re self-serving. So this is, like, interesting, and we can approach it from, like, the lens of if we did have self-serve in some ways.
329 00:36:03.730 ⇒ 00:36:11.170 Caitlyn Vaughn: But it’s not… yeah, it’s not, like, quite fully accurate, because we actually can’t create an account.
330 00:36:11.170 ⇒ 00:36:14.980 Greg Stoutenburg: Okay. Okay, for now, would it be…
331 00:36:15.990 ⇒ 00:36:19.900 Greg Stoutenburg: Do we want to just get to the point where the user is in the application already?
332 00:36:20.600 ⇒ 00:36:22.899 Greg Stoutenburg: And start onboarding there, if that’s…
333 00:36:22.900 ⇒ 00:36:29.719 Caitlyn Vaughn: Let’s… honestly, we can keep going, because once somebody creates an account, like, there are a handful of people that…
334 00:36:30.120 ⇒ 00:36:43.920 Caitlyn Vaughn: are, like, helping themselves onto the platform. And the other thing is, I think even though we’re building a new platform, it’s still, you know, in theory, the same… the same idea, just, like, overhauled. So I imagine the way that people will use the platform
335 00:36:44.160 ⇒ 00:36:47.299 Caitlyn Vaughn: In the future is, like, similar to how they will use it today.
336 00:36:47.650 ⇒ 00:37:02.280 Greg Stoutenburg: Okay, okay. And I think that might also be useful information as you go in more of a PLG direction, you can see what differences there are in activation success. So, if we still start from the landing, and then go to sign up, user does the…
337 00:37:02.280 ⇒ 00:37:12.670 Greg Stoutenburg: you know, the tutorials and things, I think that’ll give you useful information. So, once someone types in their email and they sign up, this is the next page that they get. So…
338 00:37:13.550 ⇒ 00:37:15.099 Greg Stoutenburg: Here,
339 00:37:15.250 ⇒ 00:37:29.970 Greg Stoutenburg: everyone’s going to have to do these steps, I don’t… I don’t… I never saw a way that you can get out of this, so I think, someone just has to go through these steps, and there are 6 pages worth. You don’t have to upload an image, but we might be interested in users who do.
340 00:37:32.600 ⇒ 00:37:33.220 Caitlyn Vaughn: Why?
341 00:37:34.790 ⇒ 00:37:47.999 Greg Stoutenburg: especially as you look in more of a PLG direction, we might find that that shows something for user engagement. You know, it might be that users who upload their picture will then, you know, retain better or something because they feel more engaged.
342 00:37:48.000 ⇒ 00:37:48.610 Caitlyn Vaughn: Hmm.
343 00:37:48.770 ⇒ 00:37:50.130 Caitlyn Vaughn: Yeah, that’s interesting.
344 00:37:50.130 ⇒ 00:37:55.230 Greg Stoutenburg: We might also find that they don’t. But that’s why, that’s how we’re setting it up.
345 00:37:55.770 ⇒ 00:37:57.010 Greg Stoutenburg: Someone have a…
346 00:37:57.300 ⇒ 00:37:59.060 Caitlyn Vaughn: Extra intent, perhaps.
347 00:37:59.340 ⇒ 00:37:59.990 Greg Stoutenburg: Yeah.
348 00:38:01.740 ⇒ 00:38:03.220 Greg Stoutenburg: Extra intent, yep.
349 00:38:04.980 ⇒ 00:38:07.200 Greg Stoutenburg: Alright, so we will do…
350 00:38:10.460 ⇒ 00:38:11.250 Greg Stoutenburg: Nope.
351 00:38:12.890 ⇒ 00:38:14.640 Greg Stoutenburg: Great profile…
352 00:38:18.930 ⇒ 00:38:23.170 Greg Stoutenburg: I put a picture. Alright, everyone will have to do that,
353 00:38:23.620 ⇒ 00:38:26.149 Greg Stoutenburg: I don’t… no, I don’t need to do that.
354 00:38:26.900 ⇒ 00:38:28.550 Greg Stoutenburg: Got excited and hit the button.
355 00:38:30.770 ⇒ 00:38:35.979 Greg Stoutenburg: Oh, I love this, but I just… I like that a lot. I like that as soon as I started doing it, it shows up.
356 00:38:36.270 ⇒ 00:38:37.300 Caitlyn Vaughn: Satisfying.
357 00:38:37.650 ⇒ 00:38:38.400 Greg Stoutenburg: Yep.
358 00:38:39.560 ⇒ 00:38:41.209 Greg Stoutenburg: Gonna join the workspace.
359 00:38:45.010 ⇒ 00:38:47.110 Greg Stoutenburg: Join workspace, connect to calendar.
360 00:38:55.310 ⇒ 00:39:06.380 Greg Stoutenburg: Now, when you go in the PLG direction, this will… this will be useful as well, because, all these setup steps are important to a user achieving value, but if… some users will just try to rush right through it, and…
361 00:39:06.490 ⇒ 00:39:08.060 Greg Stoutenburg: Not do any of these things.
362 00:39:14.630 ⇒ 00:39:16.440 Greg Stoutenburg: Yes, Google, just have everything.
363 00:39:16.780 ⇒ 00:39:17.909 Greg Stoutenburg: Just take it all.
364 00:39:18.090 ⇒ 00:39:20.770 Greg Stoutenburg: Alright, and now we are in.
365 00:39:21.690 ⇒ 00:39:25.350 Greg Stoutenburg: I believe everyone will land here.
366 00:39:26.010 ⇒ 00:39:28.230 Greg Stoutenburg: But we’ll just say, you know, successful sign-in.
367 00:39:38.940 ⇒ 00:39:39.730 Greg Stoutenburg: Okay.
368 00:39:42.600 ⇒ 00:39:50.280 Greg Stoutenburg: Good. The user is now in. Now… From here…
369 00:39:51.320 ⇒ 00:39:55.339 Greg Stoutenburg: My goal for creating a new user was to see… I know that there are some…
370 00:39:55.590 ⇒ 00:39:58.939 Greg Stoutenburg: Initial onboarding things that should pop up.
371 00:40:00.130 ⇒ 00:40:00.730 Caitlyn Vaughn: Okay.
372 00:40:01.350 ⇒ 00:40:10.069 Caitlyn Vaughn: There’s also, there’s different flows for, like, creating a new workspace and for joining an existing one, so this is, like, you would typically be dropped into here, actually.
373 00:40:10.480 ⇒ 00:40:15.079 Greg Stoutenburg: Okay, okay. So you’d be dropped into Guide. New user gets dropped into Guide.
374 00:40:20.620 ⇒ 00:40:21.410 Greg Stoutenburg: Okay.
375 00:40:22.120 ⇒ 00:40:26.600 Greg Stoutenburg: We’re going to track onboarding as a funnel to drop off, or conversion through…
376 00:40:27.210 ⇒ 00:40:31.130 Greg Stoutenburg: the flow. Both. So, by understanding what our…
377 00:40:31.160 ⇒ 00:40:49.809 Greg Stoutenburg: onboarding looks like, we’ll be able to correlate that with anything that we want to. So one of the things that we see often in PLG is that users who undertake certain actions that are related to setup will have different outcomes as far as retention and engagement and conversion down the line. So we want to understand activation partly so that we can understand those other things as well.
378 00:40:51.000 ⇒ 00:40:51.650 Nandika Jhunjhunwala: Sure.
379 00:40:53.050 ⇒ 00:40:59.430 Greg Stoutenburg: Okay. Now, here, okay, here I think… It would make sense…
380 00:40:59.550 ⇒ 00:41:06.969 Greg Stoutenburg: to look at users who are doing these things for the purpose of understanding activation, so we might just have events for…
381 00:41:07.570 ⇒ 00:41:09.540 Greg Stoutenburg: Completing any one of these things.
382 00:41:10.420 ⇒ 00:41:11.550 Greg Stoutenburg: Yeah, unfortunately.
383 00:41:13.710 ⇒ 00:41:18.209 Greg Stoutenburg: Yeah. Let’s see, so this one, Connect Calendar, did that already, invite teammates.
384 00:41:18.750 ⇒ 00:41:24.059 Greg Stoutenburg: Setup schedule… Connecting CRM, okay.
385 00:41:24.610 ⇒ 00:41:28.090 Greg Stoutenburg: I think we’re gonna want separate events for all of these.
386 00:41:34.660 ⇒ 00:41:35.630 Greg Stoutenburg: Cap.
387 00:41:39.720 ⇒ 00:41:45.640 Greg Stoutenburg: And again, at any point, if any of this doesn’t seem like valuable information, we can, you know, back away from it.
388 00:41:45.640 ⇒ 00:41:55.799 Caitlyn Vaughn: No, it definitely is, but I guess as I’m thinking through this, we plan on no longer selling the current platform in, like, a week.
389 00:41:56.240 ⇒ 00:41:56.920 Greg Stoutenburg: Okay.
390 00:41:57.470 ⇒ 00:41:59.670 Caitlyn Vaughn: So, I don’t know if it’s worth us, like.
391 00:42:00.080 ⇒ 00:42:11.800 Caitlyn Vaughn: setting all of this up to track it now, when we’re gonna, like, overhaul all of this really shortly, but I will say, I think this is interesting to do, to see, like, the workflow in general, and how we should think about this, and then also.
392 00:42:11.800 ⇒ 00:42:12.120 Greg Stoutenburg: So…
393 00:42:12.120 ⇒ 00:42:16.380 Caitlyn Vaughn: We’re, like, training with Nautica on, like, how to set up events inside of.
394 00:42:16.380 ⇒ 00:42:17.800 Greg Stoutenburg: Yeah. Instead of amplitude.
395 00:42:18.410 ⇒ 00:42:25.690 Greg Stoutenburg: Okay, so… am I able to access the new space, then? So that we can be, mapping the right thing?
396 00:42:26.170 ⇒ 00:42:28.090 Caitlyn Vaughn: I’m, like, building it right now.
397 00:42:28.090 ⇒ 00:42:32.510 Greg Stoutenburg: Oh, okay. Oh, I see. Alright. Not like, oh yeah, here’s a login.
398 00:42:32.550 ⇒ 00:42:33.839 Caitlyn Vaughn: Yeah, yeah. Okay.
399 00:42:34.050 ⇒ 00:42:41.770 Greg Stoutenburg: Okay, so… So just this part won’t exist?
400 00:42:42.680 ⇒ 00:42:45.590 Caitlyn Vaughn: I mean, the entire platform is changing.
401 00:42:46.100 ⇒ 00:42:46.600 Greg Stoutenburg: Okay.
402 00:42:46.600 ⇒ 00:43:03.839 Caitlyn Vaughn: including, I’m, like, doing the onboarding flow with the… with the mindset of, like, people will be onboarding via self-serve, right? So, like, a lot of things are changing, but relatively, like, we’re still gonna have tables, we’re still gonna have workflows, we’re still gonna have, like, the core parts of our platform. Okay.
403 00:43:04.460 ⇒ 00:43:17.199 Caitlyn Vaughn: But I’m just saying, like, I don’t… I don’t know if it makes sense for you to spend all of this time setting up, like, the specific onboarding details for, like, everything at a micro level, but it is interesting to do it at, like, a macro level, just for, like.
404 00:43:17.200 ⇒ 00:43:18.069 Greg Stoutenburg: Okay, got it.
405 00:43:18.070 ⇒ 00:43:19.230 Caitlyn Vaughn: conception of doing it.
406 00:43:19.640 ⇒ 00:43:29.610 Greg Stoutenburg: Okay, okay, great. We can leave this here, then, and say that that’s… this… this is our… this is our onboarding from,
407 00:43:30.600 ⇒ 00:43:39.620 Greg Stoutenburg: landing to… sign in, and we can just leave it there. We can have that flow. And…
408 00:43:39.790 ⇒ 00:43:53.069 Greg Stoutenburg: Anything that we’re not actually able to inst… if we can’t instrument yet, then we can just pause on that part, and right now we can just be focused on this stage of looking at, which parts of the journey we want to capture.
409 00:43:53.430 ⇒ 00:43:55.139 Caitlyn Vaughn: Yeah, what is instrumenting?
410 00:43:55.510 ⇒ 00:44:10.379 Greg Stoutenburg: So, right now, we’re just mapping out which things we want to capture. The instrumentation part will be when, with some engineering help, we get Amplitude events that are connected to your platform that are sending data into amplitude.
411 00:44:10.680 ⇒ 00:44:15.959 Greg Stoutenburg: So right now, we’re just at a purely conceptual stage, right? I mean, we can do this for any website we want.
412 00:44:15.960 ⇒ 00:44:16.900 Caitlyn Vaughn: Sure, yeah, yeah.
413 00:44:16.900 ⇒ 00:44:18.189 Greg Stoutenburg: Right? You know, we did.
414 00:44:18.190 ⇒ 00:44:18.590 Caitlyn Vaughn: Yeah.
415 00:44:18.840 ⇒ 00:44:24.960 Greg Stoutenburg: So we can… okay, we’ll continue on that, but we’ll just… we’ll just not get too granular. Yeah.
416 00:44:24.960 ⇒ 00:44:28.600 Caitlyn Vaughn: Should we still set up some of these, like, together with Amplitude, though, today?
417 00:44:29.570 ⇒ 00:44:34.119 Greg Stoutenburg: We can get something set up quickly, and let’s just, you know…
418 00:44:34.360 ⇒ 00:44:36.979 Greg Stoutenburg: Let’s just pick what it is.
419 00:44:37.500 ⇒ 00:44:48.520 Greg Stoutenburg: Let’s see, Lev said percent to completion across integrations is excellent data to have. Yes, I think so. So when I was suggesting, let’s map all of these, for someone who’s
420 00:44:49.310 ⇒ 00:45:02.069 Greg Stoutenburg: you know, whatever the new platform shows, a user who’s going through your onboarding in a really thorough way, we want to be able to measure that, because something that I’ve found through multiple different products is that,
421 00:45:02.070 ⇒ 00:45:09.400 Greg Stoutenburg: there are… there will be just, like, little hidden gems. Like, there will be some secret sauce in here where you’ll find out a user who, you know, connects their calendar.
422 00:45:09.400 ⇒ 00:45:13.710 Greg Stoutenburg: connects Slack and invites a teammate, you know, retention is…
423 00:45:14.190 ⇒ 00:45:21.800 Greg Stoutenburg: 60% compared to anyone else, you know. On average, it’s, you know, 20, or something like that. And then you go, okay.
424 00:45:21.970 ⇒ 00:45:30.120 Greg Stoutenburg: revision 2 of my onboarding flow is gonna make everybody connect Slack, make everybody connect their calendar, you know, you start experimenting with things like that.
425 00:45:31.450 ⇒ 00:45:31.860 Caitlyn Vaughn: interesting.
426 00:45:31.860 ⇒ 00:45:44.560 Lev Katreczko: I also think there’s an interesting argument to be had to not make them do everything up front. Let’s say that somebody wants to use a very particular feature, and they’re overwhelmed by these 6 options to integrate their entire life.
427 00:45:44.560 ⇒ 00:45:54.900 Lev Katreczko: We should… there’s an argument to be made to let them use what they want to use, and then maybe programmatically email them, specifically inviting them to add a different integration for a specific purpose.
428 00:45:55.390 ⇒ 00:45:57.729 Greg Stoutenburg: Yeah, yep, yep, good call out.
429 00:45:57.860 ⇒ 00:46:08.370 Greg Stoutenburg: Right. Or you see them engaging with something, and it would be… you can tell them at that time, hey, this would be even better and faster and more amazing if you connected this integration.
430 00:46:10.280 ⇒ 00:46:18.229 Greg Stoutenburg: Okay, good. Alright, so we’ll back away from that, and we’ll say that our… the onboarding events that we’re interested in are at a high level.
431 00:46:18.820 ⇒ 00:46:22.720 Greg Stoutenburg: Landing, clicking login, Creating a profile.
432 00:46:22.820 ⇒ 00:46:25.930 Greg Stoutenburg: And I wonder what else… can be done.
433 00:46:26.520 ⇒ 00:46:30.790 Greg Stoutenburg: Well, I mean, we can instrument these now, and then, you know, as we make the change, we’ll make sure that the code
434 00:46:31.020 ⇒ 00:46:34.339 Greg Stoutenburg: Is updated along with it on your new platform.
435 00:46:34.520 ⇒ 00:46:42.310 Greg Stoutenburg: Yeah. For onboarding stage 2, we’re just gonna hold on this for now, and go into a different part of the user journey.
436 00:46:42.560 ⇒ 00:46:51.850 Greg Stoutenburg: So, once someone has… Come in, and they’re going to use… They’re going to use default.
437 00:46:54.160 ⇒ 00:46:55.740 Greg Stoutenburg: More mature one, rare.
438 00:46:55.950 ⇒ 00:47:00.800 Greg Stoutenburg: I’ve got one of these tabs is my completely logged in, real one.
439 00:47:01.730 ⇒ 00:47:04.099 Caitlyn Vaughn: Yeah, y’all’s tabs are really stressing me out.
440 00:47:04.460 ⇒ 00:47:06.260 Caitlyn Vaughn: You have so many open.
441 00:47:06.680 ⇒ 00:47:08.670 Greg Stoutenburg: Yeah, I do.
442 00:47:11.160 ⇒ 00:47:17.470 Greg Stoutenburg: I know there are these ways you can, like, collapse them and group them and things like that, but it just never… it just never makes sense to me.
443 00:47:21.830 ⇒ 00:47:38.050 Greg Stoutenburg: Don’t let me down, 1Password. Here we go. Alright, good, now I have all my stuff. Okay. From here, thinking about a user, who’s going to retain and be engaged, which actions are we most interested in them performing?
444 00:47:38.540 ⇒ 00:47:41.650 Greg Stoutenburg: Like, what do we want to… in other words, what do we want to map out next?
445 00:47:42.380 ⇒ 00:47:50.099 Caitlyn Vaughn: Yeah, in the current platform, the best things to do are to get people to connect in their Google Calendar.
446 00:47:50.320 ⇒ 00:47:59.829 Caitlyn Vaughn: And then, probably their CRM. Those are, like, the two big ones. And then after that, probably setting up, setting up an event.
447 00:48:00.150 ⇒ 00:48:02.100 Caitlyn Vaughn: Inside of our scheduler.
448 00:48:02.430 ⇒ 00:48:05.830 Caitlyn Vaughn: Setting up a routing queue, and then building their first workflow.
449 00:48:06.390 ⇒ 00:48:07.090 Greg Stoutenburg: Okay.
450 00:48:10.140 ⇒ 00:48:15.510 Greg Stoutenburg: Alright, we’re interested in… Connecting their CRM.
451 00:48:15.670 ⇒ 00:48:20.990 Greg Stoutenburg: Which was not… in the current onboarding flow, that’s not one of the things that they need to do, or that is,
452 00:48:21.120 ⇒ 00:48:22.260 Greg Stoutenburg: Suggested.
453 00:48:22.400 ⇒ 00:48:23.709 Greg Stoutenburg: And then.
454 00:48:23.710 ⇒ 00:48:28.370 Caitlyn Vaughn: Is, in the current onboarding flow, it does ask you to connect in your calendar, right?
455 00:48:28.570 ⇒ 00:48:29.580 Greg Stoutenburg: Yes, yup.
456 00:48:29.580 ⇒ 00:48:30.090 Caitlyn Vaughn: Okay, cool.
457 00:48:30.090 ⇒ 00:48:33.869 Greg Stoutenburg: Yep, that’s the one that we just saw. Okay, so connect the CRM, and then…
458 00:48:34.170 ⇒ 00:48:36.530 Greg Stoutenburg: The next one was…
459 00:48:37.880 ⇒ 00:48:40.660 Caitlyn Vaughn: Create… create a queue.
460 00:48:41.040 ⇒ 00:48:44.409 Caitlyn Vaughn: Well, let’s do, create an event, create an event, and then create a queue.
461 00:48:46.730 ⇒ 00:48:51.119 Greg Stoutenburg: Are those sequential steps? Someone needs to create an event, and then they create a queue?
462 00:48:53.620 ⇒ 00:48:55.749 Greg Stoutenburg: So, an event would be…
463 00:48:55.750 ⇒ 00:48:59.300 Caitlyn Vaughn: Yep, an event is under scheduler, an event type, yep.
464 00:49:00.010 ⇒ 00:49:08.359 Caitlyn Vaughn: And you can click, And then you can click New Event on the right.
465 00:49:08.750 ⇒ 00:49:09.770 Greg Stoutenburg: Okay.
466 00:49:10.290 ⇒ 00:49:17.880 Caitlyn Vaughn: Right? And then you would create, like, a personal event, or, like, a team. This is, like, the equivalent of, like, Calendly, if you’re familiar.
467 00:49:17.880 ⇒ 00:49:19.159 Greg Stoutenburg: She’ll put it here, okay.
468 00:49:20.470 ⇒ 00:49:22.609 Greg Stoutenburg: Great event, and then…
469 00:49:26.490 ⇒ 00:49:29.350 Greg Stoutenburg: New event type, alright, name the event.
470 00:49:32.560 ⇒ 00:49:34.550 Caitlyn Vaughn: And then you’re gonna create a routing queue.
471 00:49:34.960 ⇒ 00:49:35.939 Greg Stoutenburg: Routing queue.
472 00:49:38.620 ⇒ 00:49:40.040 Caitlyn Vaughn: Okay.
473 00:49:47.730 ⇒ 00:49:48.490 Caitlyn Vaughn: Ruby.
474 00:49:49.690 ⇒ 00:49:54.310 Caitlyn Vaughn: Yeah, you can imagine, like, the main use case for default right now is, like.
475 00:49:54.500 ⇒ 00:50:10.960 Caitlyn Vaughn: managing inbound lead routing, right? So you have a team of AEs, so you create an event that says 30-minute, you know, demo session, then you create a queue with all of your AEs, and then you hold, like, a simple workflow to, like, help make sure that no meeting is missed, right?
476 00:50:11.320 ⇒ 00:50:12.590 Greg Stoutenburg: Okay, great.
477 00:50:13.010 ⇒ 00:50:14.910 Greg Stoutenburg: We’ll say we’ve created this event type.
478 00:50:16.700 ⇒ 00:50:26.179 Greg Stoutenburg: Okay, yeah, and there’s the calendar view… It’s test event… Ta-da-da, none.
479 00:50:26.570 ⇒ 00:50:32.020 Greg Stoutenburg: Post… Guests can invite, sure. Okay, and then I can publish this.
480 00:50:37.760 ⇒ 00:50:40.370 Greg Stoutenburg: but I’ve not now created a queue, right?
481 00:50:40.840 ⇒ 00:50:44.120 Caitlyn Vaughn: Correct. So then you’re gonna go back over in default.
482 00:50:44.120 ⇒ 00:50:46.529 Greg Stoutenburg: Actually, I can do this here. Publish event.
483 00:50:48.900 ⇒ 00:50:50.540 Greg Stoutenburg: Okay, now I’m gonna go back.
484 00:50:50.940 ⇒ 00:50:52.770 Caitlyn Vaughn: And go to Routing.
485 00:50:53.600 ⇒ 00:50:55.100 Caitlyn Vaughn: Below scheduler, yep.
486 00:50:55.250 ⇒ 00:50:56.550 Caitlyn Vaughn: And then queues.
487 00:50:57.280 ⇒ 00:50:57.850 Greg Stoutenburg: Okay.
488 00:50:57.850 ⇒ 00:51:04.739 Caitlyn Vaughn: And then, yeah, you have a couple of here set up already, if you want to use, like, an existing template, or create your own.
489 00:51:13.170 ⇒ 00:51:14.930 Greg Stoutenburg: webinar with you, Tom.
490 00:51:16.620 ⇒ 00:51:17.450 Greg Stoutenburg: Okay.
491 00:51:18.920 ⇒ 00:51:21.299 Greg Stoutenburg: And this is where I would create a queue. Am I able to…
492 00:51:21.780 ⇒ 00:51:28.910 Greg Stoutenburg: Oh, those are the existing ones, or I can do a new queue. So I can either select a queue, or choose a new queue. Those are my two options in this area.
493 00:51:28.910 ⇒ 00:51:29.560 Caitlyn Vaughn: Yep.
494 00:51:29.560 ⇒ 00:51:30.230 Greg Stoutenburg: Okay.
495 00:51:41.730 ⇒ 00:51:42.440 Greg Stoutenburg: Okay.
496 00:51:47.240 ⇒ 00:51:49.720 Greg Stoutenburg: Test queue… .
497 00:51:51.380 ⇒ 00:51:51.890 Caitlyn Vaughn: That’s so cute.
498 00:51:51.890 ⇒ 00:51:55.720 Greg Stoutenburg: Maximize fairness. That’s a… that’s great. Maximize fairness.
499 00:51:56.190 ⇒ 00:51:57.030 Greg Stoutenburg: Okay.
500 00:51:57.650 ⇒ 00:51:58.530 Greg Stoutenburg: Great.
501 00:52:01.410 ⇒ 00:52:03.710 Greg Stoutenburg: Okay, and then I can add members to a queue?
502 00:52:06.270 ⇒ 00:52:07.550 Caitlyn Vaughn: Add members.
503 00:52:11.250 ⇒ 00:52:14.169 Greg Stoutenburg: Okay. And this will route calls to me.
504 00:52:15.810 ⇒ 00:52:18.530 Caitlyn Vaughn: This will route 100% of calls to you.
505 00:52:18.800 ⇒ 00:52:19.630 Greg Stoutenburg: Okay, great.
506 00:52:19.630 ⇒ 00:52:29.979 Caitlyn Vaughn: So you technically wouldn’t need a queue if you just wanted it to be you on the event, but if you wanted it to be several people, then you would create a queue versus setting, like, an existing host.
507 00:52:30.530 ⇒ 00:52:31.799 Greg Stoutenburg: Got it. Okay.
508 00:52:32.590 ⇒ 00:52:33.450 Greg Stoutenburg: Okay.
509 00:52:33.830 ⇒ 00:52:37.660 Greg Stoutenburg: Great queue, and now, at this point,
510 00:52:38.100 ⇒ 00:52:44.759 Greg Stoutenburg: Yeah, the user can accept calls. Here, we’ll just do… call this scheduling workflow instead.
511 00:52:47.340 ⇒ 00:52:48.030 Greg Stoutenburg: Alright.
512 00:52:48.030 ⇒ 00:52:49.509 Caitlyn Vaughn: That seems like a good place to stop.
513 00:52:49.900 ⇒ 00:52:52.310 Greg Stoutenburg: Yeah, yeah, I think so.
514 00:52:53.880 ⇒ 00:52:58.650 Greg Stoutenburg: Alright, yeah, because the user will, by this… Time have made it possible.
515 00:52:58.780 ⇒ 00:53:01.899 Greg Stoutenburg: to accept calls. Now…
516 00:53:03.370 ⇒ 00:53:09.489 Greg Stoutenburg: What might the next workflow be? Or… or did you mean stop this call that we’re on right now, and pick it up later?
517 00:53:09.810 ⇒ 00:53:18.730 Caitlyn Vaughn: No, no, no, I guess, I mean, this is a good place to stop in the sense of, like, we’re kind of practicing what we would be tracking, right?
518 00:53:19.150 ⇒ 00:53:22.600 Caitlyn Vaughn: maybe a good next step would be for us to go into Amplitude and, like.
519 00:53:22.850 ⇒ 00:53:25.690 Caitlyn Vaughn: actually map out how we would do this.
520 00:53:25.690 ⇒ 00:53:26.020 Greg Stoutenburg: Yeah, yeah.
521 00:53:26.020 ⇒ 00:53:26.810 Caitlyn Vaughn: I’ve left.
522 00:53:27.480 ⇒ 00:53:31.049 Greg Stoutenburg: Yeah, we’ve scheduled until 1245.
523 00:53:31.050 ⇒ 00:53:41.500 Caitlyn Vaughn: Okay, I probably need to jump in, like, 7 minutes, only because we have all hands after this, and I need to update a slide, but I think that Nautica should be fine to, like, actually go through the amplitude work for the next…
524 00:53:41.500 ⇒ 00:53:41.950 Greg Stoutenburg: Yeah.
525 00:53:41.950 ⇒ 00:53:43.370 Caitlyn Vaughn: 20 or so minutes.
526 00:53:43.540 ⇒ 00:53:49.729 Greg Stoutenburg: Yeah, that’s fine. So what I’ll have to do here is, is create the beginning of that tracking plan spreadsheet.
527 00:53:50.100 ⇒ 00:53:57.249 Greg Stoutenburg: And then we can start doing some instrumenting, but, she and I can create the new Amplitude org so that we can get fresh data going in.
528 00:53:57.850 ⇒ 00:53:59.749 Caitlyn Vaughn: Okay. Yeah, that would be cool.
529 00:53:59.930 ⇒ 00:54:00.540 Greg Stoutenburg: Yep.
530 00:54:00.540 ⇒ 00:54:10.230 Caitlyn Vaughn: Do we need for amplitude, we don’t need, like, polyatomic setup, because we wouldn’t be moving it from amplitude yet, we would just be tracking it in amplitude.
531 00:54:10.410 ⇒ 00:54:27.030 Greg Stoutenburg: Yep, yeah, that’s right. So, all that will be needed then is to get, you know, just get a little bit of engineering help, in the platform, where someone will designate the event in relevant page or action, and then that will be in the streaming data into Amplitude.
532 00:54:27.690 ⇒ 00:54:36.399 Caitlyn Vaughn: Okay, so my question now is, can Nandica set that up, or is that something we need, like, an actual full-stack engineer for?
533 00:54:36.630 ⇒ 00:54:52.840 Greg Stoutenburg: If someone’s got some front-end chops, they’ll probably be okay doing it. I can share the documentation for it and try to work with her on it. I’m not an engineer either, but I can point to the right resources, and if she’s got access to what she needs to have access to, then she’ll probably be in good shape.
534 00:54:53.070 ⇒ 00:54:53.689 Greg Stoutenburg: If you can work.
535 00:54:53.690 ⇒ 00:54:57.839 Caitlyn Vaughn: I think that she’s… yeah, I think she’s the right person to do this, then.
536 00:54:57.840 ⇒ 00:54:58.520 Greg Stoutenburg: Okay.
537 00:54:59.290 ⇒ 00:55:06.899 Caitlyn Vaughn: Yes, I will just, like, mirror something I said earlier, which is, like, this should be for practice purposes, because in a week, we’ll have completely different… Yeah.
538 00:55:07.080 ⇒ 00:55:08.200 Caitlyn Vaughn: everything.
539 00:55:08.560 ⇒ 00:55:28.040 Greg Stoutenburg: Yeah, yep, no, totally understood. And for, so we can do that, we’ll make sure that we’ve got… we’ll use this as, well, the conceptual exercise is important, it’ll remain important when the new platform goes up. Totally. So the work that we’ve done in the FigJam is good, we get to keep it. And then as far as for events coming in,
540 00:55:28.320 ⇒ 00:55:43.820 Greg Stoutenburg: what we can do when the new platform is stood up is we can make it so that the onboarding events that we set up now will, will continue using the same events on the new platform once you’ve changed it, so that we can… we can look at that onboarding before and after. Would you like to do that?
541 00:55:44.870 ⇒ 00:55:54.779 Caitlyn Vaughn: So… Let me show you, like, for one minute here. I’m, like, starting to work on onboarding, right? And…
542 00:55:55.170 ⇒ 00:55:57.699 Caitlyn Vaughn: This is a great example of, like.
543 00:55:58.110 ⇒ 00:56:06.399 Caitlyn Vaughn: Yes, our platform is going to stay the same in a lot of ways, but it’s also gonna change, so I’m not even sure if it’s gonna map one-to-one. Alright, can you see my screen here?
544 00:56:06.960 ⇒ 00:56:07.620 Greg Stoutenburg: Yep.
545 00:56:07.620 ⇒ 00:56:25.359 Caitlyn Vaughn: Here’s the… here’s the onboarding that I’ve, like, started thinking about. This is basically, like, the form, right? Like, you sign up, now we’re actually gonna have an onboarding flow for self-serve. You create an account, you put in your information, you create a workspace, or join an existing one, you install a browser extension.
546 00:56:25.470 ⇒ 00:56:27.390 Caitlyn Vaughn: For, like, agent access.
547 00:56:27.500 ⇒ 00:56:43.759 Caitlyn Vaughn: Then it asks some more questions, onboarding, onboarding, onboarding, and then it drops you into the product for a product tour, and this is, like, what the new interface is gonna look like on our product. It’s basically, like, an OS format with, like, widgets that are apps.
548 00:56:43.850 ⇒ 00:56:47.940 Caitlyn Vaughn: And we’re actually gonna have people interact with our app via this agent.
549 00:56:48.100 ⇒ 00:56:54.050 Caitlyn Vaughn: So it’s like, here is our new app, like, this is what you can do with it. We have this new data model.
550 00:56:54.440 ⇒ 00:57:03.969 Caitlyn Vaughn: And the flow we’re gonna send people through is to basically, like, let’s try some questions, like, which closed-loss account should I reach out to, or like…
551 00:57:04.080 ⇒ 00:57:07.239 Caitlyn Vaughn: Are there any leads at risk of churning? Or, like…
552 00:57:07.440 ⇒ 00:57:14.580 Caitlyn Vaughn: show me CRM records with missing data, and then it will pull a query from our data model and, like, spit out, you know.
553 00:57:14.830 ⇒ 00:57:23.620 Caitlyn Vaughn: it’ll basically walk people through how to do much bigger workflows, and the reason why I think this is important for you to understand is, like.
554 00:57:23.780 ⇒ 00:57:31.010 Caitlyn Vaughn: If I go over to our pricing and packaging, Eventually…
555 00:57:31.370 ⇒ 00:57:35.379 Caitlyn Vaughn: You can see people will be onboarding onto our free tier.
556 00:57:35.740 ⇒ 00:57:36.119 Greg Stoutenburg: But they will…
557 00:57:36.120 ⇒ 00:57:39.380 Caitlyn Vaughn: not have access to is routing or scheduling.
558 00:57:39.540 ⇒ 00:57:51.340 Caitlyn Vaughn: Right? Which is basically the thing that we’re pushing them to do now. We’re, like, shifting our nucleus from, like, being a lead routing platform to, like, being a data platform.
559 00:57:51.660 ⇒ 00:57:53.499 Greg Stoutenburg: Yeah, yeah, okay.
560 00:57:54.450 ⇒ 00:57:55.909 Greg Stoutenburg: Okay, cool. So.
561 00:57:55.910 ⇒ 00:58:01.879 Caitlyn Vaughn: As we’re talking about, like, you know, setting up onboarding tracking now, and then porting it over, like.
562 00:58:02.100 ⇒ 00:58:06.190 Caitlyn Vaughn: You tell me if it makes sense, just off the information that you saw, like…
563 00:58:06.610 ⇒ 00:58:13.180 Caitlyn Vaughn: Would it make sense for us to set up now, or should we, like, kind of practice now, and then really set it up once we’re able to launch the new product?
564 00:58:13.180 ⇒ 00:58:15.159 Greg Stoutenburg: Yeah, I think,
565 00:58:16.370 ⇒ 00:58:26.190 Greg Stoutenburg: if you would like to have that before-after data, to understand what onboarding looks like, once you’ve made the changes, then we can start, we can instrument it now. Okay.
566 00:58:26.200 ⇒ 00:58:40.600 Greg Stoutenburg: I’m not sure, though. I mean, because the changes are so significant, I’m not sure that really seeing that before and after data is going to be that useful. And you might instead find yourself in a position where two months down the road, when you’re trying to look at how effective activation is.
567 00:58:41.010 ⇒ 00:58:53.160 Greg Stoutenburg: you feel like you’ve got basically this data from January, when you had an old system, and it’s just polluting the numbers down the road. So, that is… if I have to pick one, it’s that second one.
568 00:58:53.500 ⇒ 00:58:54.610 Caitlyn Vaughn: Okay, actually, I think.
569 00:58:54.610 ⇒ 00:58:55.150 Greg Stoutenburg: I agree with that.
570 00:58:55.150 ⇒ 00:59:01.919 Caitlyn Vaughn: I mean, it would be hard to go from, like, 100% conversion, since, like, people have already purchased the platform.
571 00:59:01.920 ⇒ 00:59:16.000 Greg Stoutenburg: Right. To, like, 3% conversion or whatever. Right, yeah. What happened? Caitlin ruined the platform. That’s what we found. Everything was going fine, when we started with, you’re a customer, by definition, you have converted.
572 00:59:16.000 ⇒ 00:59:33.290 Caitlyn Vaughn: Yeah, we don’t need that data. Yeah, yeah. Okay, so what’s probably going to be the most helpful, then, is can you do a session probably with Nautica and walk through, like, how to set up, events and amplitude and, like, how we would actually go about this? And then maybe we’ll just put a pin in…
573 00:59:33.730 ⇒ 00:59:39.630 Caitlyn Vaughn: setting up amplitude. We are, like, we are launching SKUs one at a time, so perhaps
574 00:59:39.740 ⇒ 00:59:42.889 Caitlyn Vaughn: some of our SKUs that are already done, we could start tracking.
575 00:59:43.070 ⇒ 00:59:47.190 Caitlyn Vaughn: And then when the total platform is out, like, we can do… just do things as they come.
576 00:59:47.750 ⇒ 00:59:49.270 Greg Stoutenburg: Yeah, yep, sounds good.
577 00:59:49.270 ⇒ 00:59:51.820 Demilade Agboola: So, what’s the timeline for Phoenix?
578 00:59:52.650 ⇒ 00:59:54.879 Caitlyn Vaughn: For Phoenix, we’re hoping…
579 00:59:54.890 ⇒ 01:00:13.510 Caitlyn Vaughn: Yeah, we’re hoping to, like, launch our initial internal version, mid-February, but realistically, like, I’m working on PLG billing onboarding, and that’s gonna be done for probably an additional 4 weeks after. So let’s say, like, mid-March is our realistic date for launching PLG.
580 01:00:14.820 ⇒ 01:00:22.950 Demilade Agboola: Okay, alright, sounds good. Because I would like us to be able to also, like, integrate that, so as it’s going live, we’re not behind the…
581 01:00:23.210 ⇒ 01:00:27.439 Demilade Agboola: So we can really see what’s going on as people are onboarding.
582 01:00:27.860 ⇒ 01:00:29.249 Demilade Agboola: That’ll be very important.
583 01:00:29.760 ⇒ 01:00:32.050 Demilade Agboola: Yeah, I think so too. Yeah.
584 01:00:32.500 ⇒ 01:00:38.810 Caitlyn Vaughn: Yeah, that’s true, like, our… Date to launch is also our deadline to have analytics stood up in Amplitude.
585 01:00:40.320 ⇒ 01:00:43.019 Demilade Agboola: So, it’s very important to keep track of both of them, yeah.
586 01:00:44.030 ⇒ 01:00:44.890 Caitlyn Vaughn: amazing.
587 01:00:45.110 ⇒ 01:00:57.859 Greg Stoutenburg: Yeah, sounds good. Okay, great. Well, I know you’ve got to hop. Nanaka, if you can stand for a few more minutes, I’ll show you some of the resources that I put on the board already, and then we can just figure out where… what the next step looks like.
588 01:00:58.940 ⇒ 01:01:00.619 Caitlyn Vaughn: Amazing. Thanks, guys. See you later.
589 01:01:00.620 ⇒ 01:01:06.490 Greg Stoutenburg: Yep, thanks, talk to you soon. Alright, bye. Anyone who needs to hop, feel free, I’ll just, show Nonica some stuff here.
590 01:01:06.490 ⇒ 01:01:07.559 Lev Katreczko: Yeah, I’m gonna hang.
591 01:01:08.280 ⇒ 01:01:09.140 Greg Stoutenburg: Yep, cool.
592 01:01:13.300 ⇒ 01:01:16.929 Greg Stoutenburg: Alright, we’re here now. Okay, so…
593 01:01:17.280 ⇒ 01:01:22.010 Greg Stoutenburg: This all looks good, but we’ll… I’ll just put a…
594 01:01:22.710 ⇒ 01:01:26.599 Greg Stoutenburg: Need some kind of symbol to put here to mean, don’t do this now.
595 01:01:27.200 ⇒ 01:01:31.400 Greg Stoutenburg: No, I want no… I want the no symbol. No.
596 01:01:31.400 ⇒ 01:01:37.209 Nandika Jhunjhunwala: There’s a… there’s, like, an ono, if that… that works. Or soft, yeah.
597 01:01:43.670 ⇒ 01:01:45.520 Greg Stoutenburg: Give me, give me better stuff.
598 01:01:46.620 ⇒ 01:01:48.040 Greg Stoutenburg: Okay, how about this?
599 01:01:48.660 ⇒ 01:01:51.829 Greg Stoutenburg: Don’t do that. Alright, okay.
600 01:01:52.000 ⇒ 01:01:58.860 Greg Stoutenburg: So… The next step would be, again, creating a tracking plan where this’ll be on me.
601 01:01:58.970 ⇒ 01:02:14.850 Greg Stoutenburg: I’ll give events names, what it’ll look like, what it… what KPI it’s related to, what exactly triggers it, and then if there’s a… and yeah, this is for user properties, any… what the user property type is going to be,
602 01:02:15.340 ⇒ 01:02:27.649 Greg Stoutenburg: And all this kind of stuff that’ll need to be used to be implemented. And then when it is implemented, you just, you know, you say yes, and then we’ll… we’ll test it. So, this will be on me to set up.
603 01:02:28.140 ⇒ 01:02:31.119 Greg Stoutenburg: As far as how to do this, is this the…
604 01:02:31.860 ⇒ 01:02:33.620 Greg Stoutenburg: I think this is the one.
605 01:02:34.090 ⇒ 01:02:41.300 Greg Stoutenburg: These are the docs that Amplitude provides on creating a data taxonomy, and I think it’s at the bottom…
606 01:02:42.560 ⇒ 01:02:48.260 Greg Stoutenburg: Where it’s gonna link to the developer resources that tell you how to actually create an event.
607 01:02:49.550 ⇒ 01:02:51.179 Nandika Jhunjhunwala: in your application.
608 01:03:01.150 ⇒ 01:03:06.350 Greg Stoutenburg: Planning and instrumentation workflow. Okay, yeah, okay, it was the next tab. Instrument events.
609 01:03:07.000 ⇒ 01:03:10.829 Greg Stoutenburg: This is the part that I think you’re going to need.
610 01:03:19.670 ⇒ 01:03:23.290 Greg Stoutenburg: Yeah, you start implementing with the updated tracking plan in your branch.
611 01:03:23.920 ⇒ 01:03:27.380 Greg Stoutenburg: use the Ampli CLI to generate a new tracking library that matches the…
612 01:03:27.540 ⇒ 01:03:29.470 Greg Stoutenburg: Changes in the amplitude data branch.
613 01:03:32.550 ⇒ 01:03:34.429 Greg Stoutenburg: Does this… does this track…
614 01:03:36.230 ⇒ 01:03:41.270 Nandika Jhunjhunwala: So… you… just so I understand correctly, like.
615 01:03:41.780 ⇒ 01:03:48.239 Nandika Jhunjhunwala: like, Amti needs to be set up, like, on the backend, where I need to be working off of, like, a GitHub branch to…
616 01:03:48.300 ⇒ 01:04:03.379 Nandika Jhunjhunwala: set up instrumentation, like, on the backend, or… I know there’s, like, some, like, more user-friendly ways of doing it, like, via, like, the browser extension, and then you, like, click on some features or buttons, and then it gets tracked, but I don’t know if, like, there’s a script that you need to embed.
617 01:04:03.700 ⇒ 01:04:06.819 Nandika Jhunjhunwala: For how that’s done, exactly.
618 01:04:08.760 ⇒ 01:04:13.270 Greg Stoutenburg: Yeah, I’ve never been the person doing the instrumentation.
619 01:04:13.270 ⇒ 01:04:13.780 Nandika Jhunjhunwala: Yeah.
620 01:04:13.980 ⇒ 01:04:18.580 Greg Stoutenburg: this does sound to me like…
621 01:04:18.780 ⇒ 01:04:21.010 Greg Stoutenburg: More of a heavy lift than
622 01:04:21.240 ⇒ 01:04:24.869 Greg Stoutenburg: I believe an amplitude instrumentation is supposed to be.
623 01:04:25.730 ⇒ 01:04:27.899 Greg Stoutenburg: So I’m not actually sure that that’s correct.
624 01:04:32.580 ⇒ 01:04:40.060 Nandika Jhunjhunwala: I… I also thought maybe, like, Brainforge would set up, like, Amplitude in our stack, or is that something we need to…
625 01:04:40.060 ⇒ 01:04:41.519 Greg Stoutenburg: Yeah, we can help with it, just, just…
626 01:04:41.640 ⇒ 01:04:48.099 Greg Stoutenburg: No, that’s fine, yeah. I mean, that is something that we can do. As long as we get the right permissions, then we can do the engineering part of it. Yeah.
627 01:04:48.220 ⇒ 01:04:52.219 Greg Stoutenburg: So, if that’s… if that’s what we decide is a better approach, then that’s the approach that we can take.
628 01:04:52.820 ⇒ 01:04:58.069 Nandika Jhunjhunwala: I can look through this documentation and then discuss internally with my kit and
629 01:04:58.180 ⇒ 01:05:04.089 Nandika Jhunjhunwala: And maybe get back to you on, like, how we want to, like, implement the instrumentation with our stack, and…
630 01:05:04.240 ⇒ 01:05:13.700 Nandika Jhunjhunwala: we can go from there, maybe? I don’t have a GitHub account, but I’m happy to get on one and bid a branch if needed.
631 01:05:15.250 ⇒ 01:05:21.309 Greg Stoutenburg: Trying to tag you. Oh, are you in the, are you in the Brainforge channel with us?
632 01:05:22.080 ⇒ 01:05:24.090 Nandika Jhunjhunwala: Yes, the partnership, Spring Forge?
633 01:05:24.300 ⇒ 01:05:29.650 Greg Stoutenburg: the, no, the External Client Default Data Team.
634 01:05:31.420 ⇒ 01:05:33.009 Nandika Jhunjhunwala: I’m not on that one, but…
635 01:05:33.130 ⇒ 01:05:40.130 Demilade Agboola: I think you are, because I know you’ve reacted to my, like, daily update, one of my daily updates.
636 01:05:40.360 ⇒ 01:05:40.980 Nandika Jhunjhunwala: Yeah, yeah.
637 01:05:40.980 ⇒ 01:05:44.290 Greg Stoutenburg: Maybe I’m not in the channel. Okay.
638 01:05:44.290 ⇒ 01:05:46.920 Demilade Agboola: I would just confirm that everyone is in the appropriate channel.
639 01:05:46.920 ⇒ 01:05:47.390 Greg Stoutenburg: Put it there.
640 01:05:47.460 ⇒ 01:05:48.370 Demilade Agboola: Oh, yeah.
641 01:05:48.370 ⇒ 01:05:50.249 Nandika Jhunjhunwala: Okay, Greg’s not in that channel, yeah.
642 01:05:50.250 ⇒ 01:05:53.840 Greg Stoutenburg: Yeah, okay. So…
643 01:05:54.270 ⇒ 01:06:00.099 Greg Stoutenburg: Thank you. There’s, there’s the, the link to the docs there. So, oh, okay, cool.
644 01:06:01.570 ⇒ 01:06:02.510 Greg Stoutenburg: I’m in.
645 01:06:03.900 ⇒ 01:06:15.580 Greg Stoutenburg: Oh, great. Yeah, look at all this. It’s good stuff. Okay, so I think… I think for now, that’s probably where we pause. And so, if you can take a look at that, the next steps will be you review
646 01:06:15.730 ⇒ 01:06:31.079 Greg Stoutenburg: those docs, see what you think, we can make choices about who’s gonna do some engineering work. In the meantime, I will begin working on the tracking plan just for the onboarding bit, and and we’ll work toward getting some data coming into Amplitude.
647 01:06:32.600 ⇒ 01:06:33.820 Nandika Jhunjhunwala: Sounds good, yeah.
648 01:06:34.180 ⇒ 01:06:40.250 Greg Stoutenburg: In the meantime, something else that I put on the…
649 01:06:40.540 ⇒ 01:06:43.340 Greg Stoutenburg: On the board here, is…
650 01:06:43.710 ⇒ 01:06:58.850 Greg Stoutenburg: we took a look, and we’ll… we want to make sure, you know, that you and Caitlin are both, you know, ready to go on this, but there’s not really… the existing default amplitude org can just go. I mean, there’s so little data in it.
651 01:06:58.850 ⇒ 01:07:06.729 Greg Stoutenburg: We think we can just start fresh. Brainforge can get you a discount if you sign up for annual billing. You can use this link that’s here.
652 01:07:06.900 ⇒ 01:07:14.710 Greg Stoutenburg: And, and get the discount from us. It’s 12%… it’s a 12% discount, that’s in addition to the discount that they give you for annual billing.
653 01:07:15.920 ⇒ 01:07:16.639 Nandika Jhunjhunwala: Sounds good.
654 01:07:17.860 ⇒ 01:07:24.019 Greg Stoutenburg: Cool. Okay, great. Anyone have any questions or anything else that we should touch on before we…
655 01:07:24.180 ⇒ 01:07:27.439 Greg Stoutenburg: Say goodbye for the day, and… Enjoy our weekend.
656 01:07:30.100 ⇒ 01:07:36.230 Greg Stoutenburg: Okay, sounds good. Cool, great. Thanks, everybody. Take a look at those stocks, and we’ll talk soon.
657 01:07:36.750 ⇒ 01:07:38.900 Nandika Jhunjhunwala: Yeah, for sure. I’ll get back to you end of day today.
658 01:07:39.190 ⇒ 01:07:40.030 Greg Stoutenburg: Sounds good.
659 01:07:40.720 ⇒ 01:07:41.600 Greg Stoutenburg: See y’all, thanks.
660 01:07:41.600 ⇒ 01:07:43.369 Demilade Agboola: Bye. Bye. Thank you. Bye.