Meeting Title: Omni Dashboards Review and Planning Date: 2026-04-16 Meeting participants: Scratchpad Notetaker, Mustafa Raja, Greg Stoutenburg, Nandika Jhunjhunwala, Demilade Agboola, Caitlyn Vaughn, Uttam Kumaran
WEBVTT
1 00:01:21.700 ⇒ 00:01:22.830 Greg Stoutenburg: Hey, Nautica.
2 00:01:22.830 ⇒ 00:01:24.540 Nandika Jhunjhunwala: Hi, how’s it going?
3 00:01:24.540 ⇒ 00:01:25.610 Greg Stoutenburg: Alright, are you?
4 00:01:25.930 ⇒ 00:01:31.449 Nandika Jhunjhunwala: Good I think Caitlin’s joining in a second. Okay. Right there.
5 00:01:31.910 ⇒ 00:01:32.750 Greg Stoutenburg: Sounds good.
6 00:01:49.240 ⇒ 00:01:55.959 Greg Stoutenburg: Nandika, in, I’ll show this when everybody’s on, but I’m looking right now at Omni users, and we’ve both been knocked out of the top 5.
7 00:01:56.720 ⇒ 00:02:06.410 Nandika Jhunjhunwala: Yeah, that makes sense. I haven’t opened it this week at all. Yeah. Just busy with other projects on my paid currently, but soon.
8 00:02:06.720 ⇒ 00:02:07.260 Greg Stoutenburg: Yeah.
9 00:02:07.260 ⇒ 00:02:09.080 Nandika Jhunjhunwala: Definitely be using more Morangi.
10 00:02:09.080 ⇒ 00:02:11.470 Greg Stoutenburg: Yeah, I’ll maybe just log in and just leave it there.
11 00:02:13.040 ⇒ 00:02:14.540 Greg Stoutenburg: You’re gonna win.
12 00:02:15.000 ⇒ 00:02:20.079 Greg Stoutenburg: Or connect to the MCP, and just get, like, you know, something that just generates a query every few minutes.
13 00:02:20.080 ⇒ 00:02:24.439 Nandika Jhunjhunwala: Just to get on top of the leaderboard.
14 00:02:24.670 ⇒ 00:02:28.770 Greg Stoutenburg: Yeah, this is basically the principle. Cheat to stay on top of leaderboards. That’s what I’m advocating here.
15 00:02:29.920 ⇒ 00:02:30.620 Greg Stoutenburg: Yeah.
16 00:02:32.230 ⇒ 00:02:33.730 Greg Stoutenburg: Demi,
17 00:02:34.060 ⇒ 00:02:42.369 Greg Stoutenburg: Is there any… is there any equivalent in tennis of, like, how soccer players will, like, dive to try to get a foul and give their team an advantage?
18 00:02:42.630 ⇒ 00:02:44.490 Greg Stoutenburg: Do tennis players do anything like that?
19 00:02:45.400 ⇒ 00:02:48.999 Demilade Agboola: I mean, you can… you can…
20 00:02:49.230 ⇒ 00:02:53.109 Demilade Agboola: Like, mess up the person’s flow, so you start making your time…
21 00:02:53.610 ⇒ 00:02:53.960 Greg Stoutenburg: Mmm.
22 00:02:54.370 ⇒ 00:03:02.390 Demilade Agboola: wipe your face, say you have a timeout, something’s hurting you, just to, like, don’t find a rhythm. But in terms of, like.
23 00:03:02.900 ⇒ 00:03:05.439 Demilade Agboola: Doing anything to, like, touch them, or, like, you can’t.
24 00:03:05.440 ⇒ 00:03:05.770 Greg Stoutenburg: Yeah.
25 00:03:06.340 ⇒ 00:03:08.519 Demilade Agboola: But you can just mess up their, like, mental.
26 00:03:08.960 ⇒ 00:03:17.049 Greg Stoutenburg: Okay, okay. Sort of like, sort of like in baseball, pitchers will sort of take their time, or they’ll speed up, and they’ll slow down, things like that.
27 00:03:17.410 ⇒ 00:03:19.090 Greg Stoutenburg: Throw one in real close.
28 00:03:19.510 ⇒ 00:03:22.009 Greg Stoutenburg: Make you step off the plate, that kind of thing. Okay.
29 00:03:22.560 ⇒ 00:03:23.410 Demilade Agboola: Yeah.
30 00:03:24.990 ⇒ 00:03:26.559 Greg Stoutenburg: Hey y’all, happy Thursday!
31 00:03:27.440 ⇒ 00:03:28.270 Uttam Kumaran: Hello?
32 00:03:28.270 ⇒ 00:03:29.080 Greg Stoutenburg: D.
33 00:03:29.320 ⇒ 00:03:29.760 Caitlyn Vaughn: Ghost.
34 00:03:29.920 ⇒ 00:03:33.709 Greg Stoutenburg: for, this is the crew, alright?
35 00:03:34.270 ⇒ 00:03:35.810 Greg Stoutenburg: Everybody having a good week so far?
36 00:03:37.610 ⇒ 00:03:38.330 Caitlyn Vaughn: Crazy weird.
37 00:03:38.330 ⇒ 00:03:41.990 Uttam Kumaran: And these, like, Tallboy… Tallboy Ramblers from Costco.
38 00:03:41.990 ⇒ 00:03:42.960 Caitlyn Vaughn: Cool.
39 00:03:42.960 ⇒ 00:03:49.809 Uttam Kumaran: Great. Because usually you just have the one like this, this tall, and then I’m, like, sometimes just struggling to finish, so much carbonation.
40 00:03:50.170 ⇒ 00:03:52.700 Uttam Kumaran: So spicy.
41 00:03:53.040 ⇒ 00:03:53.630 Greg Stoutenburg: So, I…
42 00:03:54.690 ⇒ 00:03:59.430 Greg Stoutenburg: I have two thoughts. The first thought is, at first I thought that said rum, and I was gonna be like, hey man.
43 00:03:59.860 ⇒ 00:04:01.929 Greg Stoutenburg: Hey man, you’re on Central Time, you just passed noon.
44 00:04:02.330 ⇒ 00:04:04.539 Uttam Kumaran: No, no, no.
45 00:04:05.200 ⇒ 00:04:11.880 Greg Stoutenburg: And then the other thought is, I believe this is, I believe this is a two-week streak of bringing Costco gear to a default.
46 00:04:12.020 ⇒ 00:04:13.650 Uttam Kumaran: True, true.
47 00:04:13.650 ⇒ 00:04:15.379 Greg Stoutenburg: Right? Last week was a great nap.
48 00:04:15.380 ⇒ 00:04:17.560 Uttam Kumaran: Maybe, maybe, yeah, yeah.
49 00:04:17.560 ⇒ 00:04:18.450 Greg Stoutenburg: So, I think that was a spot.
50 00:04:18.459 ⇒ 00:04:19.359 Uttam Kumaran: By chance.
51 00:04:19.779 ⇒ 00:04:20.359 Greg Stoutenburg: Yeah.
52 00:04:21.229 ⇒ 00:04:29.029 Greg Stoutenburg: Alright. Alright, well, let’s jump in. We’ll just go right to the… Slideshow.
53 00:04:31.669 ⇒ 00:04:38.609 Greg Stoutenburg: And I’ll kind of get through this, because, I’ve got some questions in the end, a couple things I wanted to chat through. So, alright, here we go.
54 00:04:39.069 ⇒ 00:04:58.259 Greg Stoutenburg: I always skipped that part. As you saw from Ryan, he was… it was so beautiful, he could cry. ARR dashboard reconciliation is complete, so everyone can just come to Omni now, and it’s… it’s agreed on the default side that we’re happy with the numbers there, so Omni is in place fully for ARR reporting.
55 00:04:58.769 ⇒ 00:05:10.209 Greg Stoutenburg: the customer success and revenue performance metrics, we’ve made a lot of changes from Laura’s feedback, and that’s just about finished. I’ll show what some of those changes are, it’s looking really nice.
56 00:05:10.219 ⇒ 00:05:22.399 Greg Stoutenburg: We have, product data models shipped to production, and the user engagement dashboard is complete in post-hog. Currently, finish that, we’re gonna work on user retention next.
57 00:05:22.629 ⇒ 00:05:30.379 Greg Stoutenburg: on the data platform and analytics side, finishing out those requests for customer reporting and enablement, and QA for a couple of other dashes.
58 00:05:31.459 ⇒ 00:05:37.869 Greg Stoutenburg: Sort of just mentioned that. Demi, is there anything here in particular you want to make sure that we call out?
59 00:05:37.879 ⇒ 00:06:02.089 Greg Stoutenburg: I think one thing I’ll just mention is that we’ve improved… we want to keep… this is going to be an iterative process, but we want to keep really improving documentation on all of our dashboards. One of the things that we see is that often that a question that someone has about two numbers reconciling or not often comes down to just having a definition that’s right in front of you. So, one of the things that Laura had asked about some of the charts was, like, what does LCM mean? Now, it said LCM means last complete month.
60 00:06:02.089 ⇒ 00:06:13.349 Greg Stoutenburg: But that doesn’t necessarily say, like, what are the dates of the month, right? So we clarify it’s from the very beginning day of the calendar month to the last day of that month. That’s what LCM means. It’s not like last 30 days or something like that.
61 00:06:14.209 ⇒ 00:06:17.799 Greg Stoutenburg: Yeah, anything here, Demi, that you want to call out?
62 00:06:19.180 ⇒ 00:06:25.220 Demilade Agboola: I mean, I think just generally, like, we’ve been able to get to the point where,
63 00:06:26.100 ⇒ 00:06:40.299 Demilade Agboola: being able to stabilize, like, the ARR dash will therefore spill over to, like, the customer success and, like, revenue performance, dashboard, because once we all are comfortable with those numbers, it means that once we start looking at things like MRR,
64 00:06:41.470 ⇒ 00:06:53.029 Demilade Agboola: And we start understanding, like, how we’re breaking down our ARR and MRR by these different categories, we can be confident in those numbers and making business decisions off those numbers as well.
65 00:06:53.170 ⇒ 00:06:58.310 Demilade Agboola: So that is, like, the next stage, and that’ll be coming around the corner, like, basically this week.
66 00:06:59.690 ⇒ 00:07:01.320 Greg Stoutenburg: Cool, great. Thanks.
67 00:07:02.370 ⇒ 00:07:06.209 Greg Stoutenburg: These are some of the changes that we’ve seen. So, for,
68 00:07:06.230 ⇒ 00:07:18.280 Greg Stoutenburg: for the MRR charts, we had it arranged by product tier, which just sort of wasn’t the most informative or interesting way to present it. Laura had asked instead that we switch that to,
69 00:07:18.280 ⇒ 00:07:36.729 Greg Stoutenburg: to, opportunity segment, and to customer segment, so you can… I know it’s kind of hard to see on the screen share, but now we’ve created these charts, we’ve broken it up into, like, small, medium-sized business, mid-market, and enterprise, as well as for customer segment, whether they’re tier 1, 2, 3, or 4.
70 00:07:36.950 ⇒ 00:07:51.689 Greg Stoutenburg: And here’s that same graphic represented in charts. One of the things that I do want to call out here is that you’ll notice that null is, really rocking it as far as opportunity segment, and so what this is going to point to here is,
71 00:07:51.690 ⇒ 00:08:03.870 Greg Stoutenburg: for this to be most meaningful, we want to make sure that when folks are logging their entries in Salesforce, that they’re making sure to include data that’s needed for that field, so that this will populate in a way that’s useful to you all.
72 00:08:04.370 ⇒ 00:08:16.489 Greg Stoutenburg: Same for support tier, although here, you know, it’s a much smaller number. Here, only 55,000 didn’t make its way into one of the support tiers, so it’s mainly about the, opportunity segment.
73 00:08:16.880 ⇒ 00:08:21.980 Uttam Kumaran: Yeah, Caitlin, my vote in that thread was gonna be, like, start requiring some of these fields.
74 00:08:23.500 ⇒ 00:08:24.080 Greg Stoutenburg: Yep.
75 00:08:24.730 ⇒ 00:08:29.660 Uttam Kumaran: Because it’s just the easiest… that’s just the easiest hack to, like, get people to fill stuff out. This is pretty common, so…
76 00:08:29.820 ⇒ 00:08:41.729 Caitlyn Vaughn: Yeah, I think before there was zero consequence for, like, not doing anything, but now it’s, like, pretty obvious, like, when something doesn’t get done, that there is a consequence, which is great. You can definitely make that mandatory now.
77 00:08:41.950 ⇒ 00:08:42.470 Uttam Kumaran: Yep. Okay.
78 00:08:42.470 ⇒ 00:08:49.270 Greg Stoutenburg: Right? Yeah. Yeah, ideally, null would just be zero, right? If that field had been filled out consistently. Yeah.
79 00:08:49.540 ⇒ 00:09:07.779 Greg Stoutenburg: Yeah, great. Cool, wanted to call that out. User Engagement Dashboard is now live for Phoenix, and so that’s… it’s gonna look at just lots of features of product usage and count that as user engagement. We’ll narrow that down some over time as we develop hypotheses about
80 00:09:07.780 ⇒ 00:09:12.639 Greg Stoutenburg: What… what really matters for engagement, beyond just usage of any type.
81 00:09:12.640 ⇒ 00:09:15.479 Greg Stoutenburg: But wanted to call out that that’s in place now.
82 00:09:15.480 ⇒ 00:09:26.100 Greg Stoutenburg: We’ll… and I’ll show a screenshot of that in a moment. User retention dashboard is scheduled for the current cycle. That one will be relatively simple, because we’ll just look at,
83 00:09:26.100 ⇒ 00:09:48.429 Greg Stoutenburg: retention on or after some initial sign-up date, and also retention showing as activity on a particular date. And so, way that I’ve… ways that I’ve seen product analytics tools explain this is, unbounded retention just means, like, if you sign up on day zero, and then you show up on, say, day 13, then you’ll be counted as a user
84 00:09:48.430 ⇒ 00:10:04.890 Greg Stoutenburg: on day 3 who was active, or who was retained, right? So it’s gonna show, the drop-off for unbounded retention sort of always goes down, because if someone comes back at any time later in the future, they’re counted as sort of, like, not gone yet. Whereas,
85 00:10:04.890 ⇒ 00:10:17.850 Greg Stoutenburg: end day retention is, like, came back on a particular day, so that’s gonna show… that’s gonna be useful for seeing, like, how active users are on a regular basis. So we’ll show those and provide explanations of those.
86 00:10:18.240 ⇒ 00:10:41.740 Greg Stoutenburg: for the activation dashboard, I think just the last thing, Nandika, I pinged about this a bit ago, we want to look at users who are, invited, so if we need to instrument that, or we need to talk about that further, then let’s do it. Otherwise, that dashboard’s good to go, and, you know, I just give the same caveat as for user engagement, which is we’ll… we’ll winnow that out, winnow that down as we get more advanced hypotheses about what’s going on with Phoenix.
87 00:10:42.980 ⇒ 00:10:58.160 Caitlyn Vaughn: Okay, just a quick question here. So for the user engagement dashboard, as I’m looking at Omni, I see no dashboards that are labeled User Engagement Dashboard, so it’s just kind of hard to, like, go back and forth. I don’t know. Is it the customer dashboard?
88 00:10:58.160 ⇒ 00:10:58.600 Greg Stoutenburg: Sorry.
89 00:10:59.350 ⇒ 00:11:02.829 Greg Stoutenburg: Sorry, this is specifically for the product analytics, this is just in post hoc.
90 00:11:03.200 ⇒ 00:11:04.149 Caitlyn Vaughn: I see. Okay.
91 00:11:04.150 ⇒ 00:11:04.870 Greg Stoutenburg: Yep. That makes sense.
92 00:11:04.870 ⇒ 00:11:05.630 Caitlyn Vaughn: sense.
93 00:11:07.400 ⇒ 00:11:15.540 Greg Stoutenburg: So here’s a… here’s just a nice snippet from user engagement. We’re looking at daily active users is the dark blue.
94 00:11:15.540 ⇒ 00:11:37.150 Greg Stoutenburg: the purple is weekly active users, and then green is weekly active organizations. That’ll be something that’ll be interesting to keep an eye on as time goes on, because we’ll be able to see what relationship there is between user… between an organization being active, like anyone in an organization, versus individuals from that organization, right? Like,
95 00:11:37.150 ⇒ 00:11:52.019 Greg Stoutenburg: who is using Phoenix within an organization? Like, what roles tend to use, tend to use the product more than others? We’ll look at that sort of thing, which will be interesting for us, as well as being able to measure account health in terms of,
96 00:11:52.080 ⇒ 00:12:11.930 Greg Stoutenburg: In terms of activity from teams. And I know from my background, when… when running a product that is, that’s for teams, it’s tricky to figure out what a healthy cadence looks like, and so that’s something that we’ll be keeping an eye on, and that we’ll, we’ll work on in the future.
97 00:12:13.840 ⇒ 00:12:28.009 Greg Stoutenburg: Just pulled this up, connected posthog to cursor. I know, Nandika, you’d toyed around some with the post hoc MCP, but something I wanted to just sort of… I’m just sort of pushing everybody on is look at ways that you can connect
98 00:12:28.010 ⇒ 00:12:53.009 Greg Stoutenburg: the data sources that we’re setting up to the tools that are sort of immediately at your fingertips. I thought this was pretty cool. I just… I just did give me the most engaged users in Phoenix, and, just got… just got this list, right? So, thinking in terms of user engagement, obviously these are, you know, these are, like, internal people, because of where we’re at with this, but just thinking even a few months into the future, we can look at user engagement by just typing in a query like this.
99 00:12:53.010 ⇒ 00:13:09.170 Greg Stoutenburg: hitting enter, and then, you know, seeing what comes up. I can see a ton of value for this. For Laura, for example, managing customer success reps, right? Because, you know, you get a list of three, and you can look at their activities and things like that. Just, you know, pull that right up from Omni’s MCP.
100 00:13:09.170 ⇒ 00:13:13.260 Greg Stoutenburg: So, wanted to, wanted to plug that.
101 00:13:13.260 ⇒ 00:13:22.780 Greg Stoutenburg: Super easy to set it up, you just go into your AI tool, and look for connections or settings, and then you authenticate, just like you’re logging in on a browser.
102 00:13:24.920 ⇒ 00:13:28.019 Greg Stoutenburg: Things are in progress, some sort of final
103 00:13:28.130 ⇒ 00:13:43.749 Greg Stoutenburg: clean up things for customer reporting and enablement. One of them being, there have been a request to get users logged in, and, a thought here is we can… we can push on this. Demi, I know this is something that, you’d taken a look at for a source.
104 00:13:43.830 ⇒ 00:14:02.280 Greg Stoutenburg: we can look at that. Something that we might consider is treating PostHog as the initial source of truth for that, and trying to send that into Omni, or even just saying, like, hey, if you want to see logged-in users, you know, log into PostHog, because that’s where we have that, without having to do anything special.
105 00:14:03.370 ⇒ 00:14:10.020 Greg Stoutenburg: Qa checklist for GTM financial summary, sign-off, and then,
106 00:14:10.150 ⇒ 00:14:20.669 Greg Stoutenburg: Making sure we’ve got, you know, agreed upon requirements for the BDR dashboard. We’ve already got that scoped out, like, what’s supposed to be in that dashboard, but those are some things that are in progress and that we’re getting to the finish line on.
107 00:14:22.150 ⇒ 00:14:30.259 Nandika Jhunjhunwala: So, sorry, on that, yeah, on this slide it says I have to add metrics,
108 00:14:30.630 ⇒ 00:14:39.670 Nandika Jhunjhunwala: Is that something we should align on, like, with Lev? Because I think he has the most context on what he’s looking for.
109 00:14:39.670 ⇒ 00:14:40.280 Demilade Agboola: Yep.
110 00:14:40.280 ⇒ 00:14:45.389 Nandika Jhunjhunwala: So, I wouldn’t want to lean on him for metrics or definitions that he has in mind.
111 00:14:46.610 ⇒ 00:14:51.120 Demilade Agboola: Yeah, I mean, he’s definitely put, like, the metrics that he wants to see, in the.
112 00:14:51.120 ⇒ 00:14:51.470 Nandika Jhunjhunwala: Yep.
113 00:14:51.470 ⇒ 00:14:56.559 Demilade Agboola: I know Caitlin has also shared the documents with him, so he’s also gone in personally to
114 00:14:56.660 ⇒ 00:15:00.810 Demilade Agboola: Make the modifications that he, you know, wanted to see.
115 00:15:00.950 ⇒ 00:15:13.830 Demilade Agboola: I know you have also mentioned that there are some metrics, because you work close to the team, so I guess this is just, like, an opportunity where if you have, because of your proximity to the team, if there are any things that you feel like, they’ve asked.
116 00:15:13.830 ⇒ 00:15:14.250 Nandika Jhunjhunwala: Got it.
117 00:15:14.530 ⇒ 00:15:21.299 Demilade Agboola: Like, any other context that you might be able to add to those metrics that, already exist. It’s within the document, and you.
118 00:15:21.300 ⇒ 00:15:21.900 Nandika Jhunjhunwala: Totally.
119 00:15:22.010 ⇒ 00:15:22.590 Demilade Agboola: patient.
120 00:15:22.590 ⇒ 00:15:23.829 Nandika Jhunjhunwala: Okay, sounds good.
121 00:15:24.420 ⇒ 00:15:28.620 Caitlyn Vaughn: Do you feel like you have enough to make a first pass at this BDR dashboard, Demi?
122 00:15:30.280 ⇒ 00:15:31.780 Demilade Agboola: Sorry, I didn’t get the question.
123 00:15:32.400 ⇒ 00:15:40.499 Caitlyn Vaughn: Do you think that you have enough from Lev and us on the BDR requirements to build out the first version of the dashboard?
124 00:15:40.500 ⇒ 00:15:49.350 Demilade Agboola: Oh, yes, yes, definitely. We have a lot of contacts within, like, Salesforce to be able to start to utilize them, especially along,
125 00:15:49.700 ⇒ 00:15:55.169 Demilade Agboola: Those, details that… Being put in the document, so we’ll just make a first pass.
126 00:15:55.470 ⇒ 00:16:05.850 Demilade Agboola: And then we’ll try and show that to Lev, get some context as well, and as well as, feedback on what he would like to see, or things that we might need to change to fit,
127 00:16:06.380 ⇒ 00:16:09.260 Demilade Agboola: To maximize its utility on, like, a day-to-day.
128 00:16:09.960 ⇒ 00:16:13.219 Demilade Agboola: And yeah, we’ll definitely just be making a first pass on that.
129 00:16:13.340 ⇒ 00:16:19.929 Demilade Agboola: Because currently, the model is currently under works, and we’re just QAing it and ensuring that the numbers match what it should.
130 00:16:20.090 ⇒ 00:16:20.680 Demilade Agboola: Before we…
131 00:16:21.500 ⇒ 00:16:23.090 Demilade Agboola: Modeling out of it.
132 00:16:23.820 ⇒ 00:16:26.209 Caitlyn Vaughn: Okay, amazing. I think…
133 00:16:26.730 ⇒ 00:16:37.650 Caitlyn Vaughn: Okay, I think that the… the CS dashboard does everything… everything, like, added into there that was supposed to be the same with the go-to-market. I think the go-to-market dashboard, the ARR one, looks good.
134 00:16:37.810 ⇒ 00:16:41.110 Caitlyn Vaughn: And same with the financial.
135 00:16:41.880 ⇒ 00:16:42.210 Greg Stoutenburg: Yep.
136 00:16:42.210 ⇒ 00:16:51.920 Demilade Agboola: Yeah, so for the CS dashboard, yeah, I think most things are done. It’s tied, again, built upon the ARR dashboard, where, like.
137 00:16:52.390 ⇒ 00:16:54.950 Demilade Agboola: We’re all, like, aligned on.
138 00:16:55.330 ⇒ 00:17:02.439 Demilade Agboola: So yes, effectively, it’s more of a structural perspective, like, do we want this here, do we need to remove this, like…
139 00:17:02.660 ⇒ 00:17:04.290 Demilade Agboola: Format kind of theme.
140 00:17:04.700 ⇒ 00:17:14.729 Demilade Agboola: And once we can all align on that, again, that’s also ready to go. But the numbers are fine, and they tie into the ARR dashboard, which is why we spent so much time trying to align on that.
141 00:17:15.490 ⇒ 00:17:22.810 Caitlyn Vaughn: Okay, amazing. So, if there are things that need to be moved on the CS dashboard, do you have
142 00:17:22.970 ⇒ 00:17:27.969 Caitlyn Vaughn: the context of what does need to be changed, or do you still need to align with the end on that?
143 00:17:28.690 ⇒ 00:17:35.620 Demilade Agboola: Yeah, I mean, we’ve got some context from Laura about what she would like to see in the dashboard. I think the final step will just be,
144 00:17:35.940 ⇒ 00:17:43.480 Demilade Agboola: showing some of the results, because I know she said things like, oh, like to say mid-market, enterprise, and all of that.
145 00:17:43.650 ⇒ 00:17:46.889 Demilade Agboola: But, you know, we’re looking at the numbers, and not a lot of…
146 00:17:47.040 ⇒ 00:17:52.159 Demilade Agboola: Those numbers, like, there are a lot of nulls there, so it might not be the most informative, like…
147 00:17:52.410 ⇒ 00:18:03.769 Demilade Agboola: measure to add into all of this. So it would just be, like, showing her, letting her understand what’s going on with that. She might say, hey, maybe we need to, like, ensure we, like, label it so we get more context to that data.
148 00:18:03.990 ⇒ 00:18:05.120 Demilade Agboola: Or, like.
149 00:18:05.370 ⇒ 00:18:09.969 Demilade Agboola: we might just say, she might say, let’s get rid of it. So it’s just that kind of feedback that we’ll need from her.
150 00:18:10.210 ⇒ 00:18:14.920 Demilade Agboola: But generally speaking, yeah, like, We’re… the numbers are there.
151 00:18:15.320 ⇒ 00:18:18.950 Demilade Agboola: Where we should be, and it will just be how do we want to move forward.
152 00:18:19.060 ⇒ 00:18:21.719 Demilade Agboola: Around certain things, around that, you know, data.
153 00:18:22.460 ⇒ 00:18:26.299 Caitlyn Vaughn: Okay, great. So this is for… you’re talking about the customer reporting dashboard?
154 00:18:27.660 ⇒ 00:18:33.919 Demilade Agboola: So no, this is about the CS dashboard, the Customer Success and Revenue Performance Metrics, the one…
155 00:18:34.100 ⇒ 00:18:34.860 Greg Stoutenburg: This one.
156 00:18:35.490 ⇒ 00:18:38.240 Greg Stoutenburg: This is one that I flashed up a little bit ago with the nulls on it.
157 00:18:38.240 ⇒ 00:18:40.920 Uttam Kumaran: Yeah, so, like, it’s… Yeah, go ahead.
158 00:18:41.300 ⇒ 00:18:42.849 Caitlyn Vaughn: Can you go back to the other slide?
159 00:18:44.630 ⇒ 00:18:49.470 Caitlyn Vaughn: So this customer reporting and enablement dashboard is different.
160 00:18:49.470 ⇒ 00:18:55.900 Demilade Agboola: Yes, so that relates more to, like, the usage, of the customers themselves, like, you know.
161 00:18:56.320 ⇒ 00:18:58.419 Demilade Agboola: And this is more for, like, Deanna.
162 00:18:58.640 ⇒ 00:18:59.530 Demilade Agboola: And…
163 00:18:59.800 ⇒ 00:19:06.459 Demilade Agboola: How we will… the idea is this will be a dashboard that they can show to see, you know, number of meetings booked, and all of that, like, tickets.
164 00:19:06.590 ⇒ 00:19:07.780 Caitlyn Vaughn: Oh, yeah, I’m following.
165 00:19:07.780 ⇒ 00:19:12.739 Uttam Kumaran: Yeah, this is all custom… this is all CS-related, yeah, so, like, single client view, and then basically, like.
166 00:19:12.990 ⇒ 00:19:14.110 Uttam Kumaran: Customer health.
167 00:19:14.970 ⇒ 00:19:17.150 Caitlyn Vaughn: I see. Okay, yeah, yeah, yeah. Awesome.
168 00:19:17.150 ⇒ 00:19:19.280 Nandika Jhunjhunwala: to… sorry.
169 00:19:20.190 ⇒ 00:19:20.810 Caitlyn Vaughn: Go ahead.
170 00:19:21.380 ⇒ 00:19:27.940 Nandika Jhunjhunwala: I was just gonna say, is it possible to align on, like, dashboard names? I think… It’s slightly confusing.
171 00:19:27.940 ⇒ 00:19:28.520 Greg Stoutenburg: Yes, it is.
172 00:19:28.970 ⇒ 00:19:29.630 Demilade Agboola: Yeah.
173 00:19:29.630 ⇒ 00:19:43.699 Nandika Jhunjhunwala: Yeah, I saw your message about that, too. I think anything customer success related should be for dashboards that Deanna owns, and then Laura should be, like, business overview, or AR overview, and, like, those kind of terminologies.
174 00:19:45.580 ⇒ 00:19:46.050 Greg Stoutenburg: Yes.
175 00:19:46.050 ⇒ 00:19:46.390 Caitlyn Vaughn: Yeah.
176 00:19:46.390 ⇒ 00:19:47.080 Nandika Jhunjhunwala: Yes.
177 00:19:47.080 ⇒ 00:19:55.940 Caitlyn Vaughn: There is, there is a little bit of context on this, which is, at the moment, I think Laura is overseeing CS.
178 00:19:56.210 ⇒ 00:20:02.060 Caitlyn Vaughn: So I think Laura is, wanting to see the, like, individual rep address.
179 00:20:02.170 ⇒ 00:20:04.939 Caitlyn Vaughn: Which is what they’re… I think, what you guys are referring to.
180 00:20:04.940 ⇒ 00:20:05.790 Uttam Kumaran: Yes, exactly.
181 00:20:05.790 ⇒ 00:20:13.300 Caitlyn Vaughn: than the customer reporting one, but I guess it’s a little confusing, because that’s also not in that slide. Like, there’s two, but then there’s only one.
182 00:20:13.470 ⇒ 00:20:23.960 Uttam Kumaran: Yeah, like, well, like, rep performance is kind of more on the sales. The CS stuff was more like, okay, are people adding seats more often? Like, what is the story around renewals and expansion?
183 00:20:25.430 ⇒ 00:20:33.459 Uttam Kumaran: So I think that’s why maybe, Greg, it’s worth just sort of confirming, like, does Laura want a separate dash? Is it all going to go in the primary ARR dash?
184 00:20:33.880 ⇒ 00:20:35.529 Caitlyn Vaughn: Dash was a separate dash.
185 00:20:35.530 ⇒ 00:20:42.770 Uttam Kumaran: Okay, okay. And then Demi was mainly highlighting, like, hey, if Laura’s like, I want to see Enterprise, but, like, the Salesforce fields aren’t filled out, then…
186 00:20:43.270 ⇒ 00:20:45.790 Uttam Kumaran: It’s like, what can we do at that point? So, I think that’s…
187 00:20:46.170 ⇒ 00:20:50.329 Uttam Kumaran: what he’s just gonna… I think, Demi, when you meet with her, or Greg, when you meet with her, like, just…
188 00:20:50.600 ⇒ 00:20:56.600 Uttam Kumaran: you could just share, like, kind of what the progress is there. When we… it’s totally possible, like, I think we have all the dimensions, so…
189 00:20:56.780 ⇒ 00:21:15.050 Greg Stoutenburg: Yeah, as far as… I mean, I think a note… I think two things just to comment on from my end here. One is, you know, like I said this before, Caitlin, right? Like, when I saw you were asking questions about, like, wait, which one is this? It’s like, okay, that’s not a position we want to be in. We need to… we need to, like, nail that down. I think we’ve all sort of found ourselves in the habit of saying things like Lev’s dashboard.
190 00:21:15.050 ⇒ 00:21:18.910 Greg Stoutenburg: Right? That’s not… we’re not gonna put something in Omni and call it Lev’s dashboard, just as an example.
191 00:21:19.330 ⇒ 00:21:37.590 Greg Stoutenburg: So, with, with Garrett, who is on our team now, and everyone on the default team, put together this, this new tab in your data platform documentation, spreadsheet. So we’ve just got this tab now that’s just called Dashboards, where we’re trying to make crystal clear what we’re referring to
192 00:21:37.590 ⇒ 00:21:46.249 Greg Stoutenburg: what the… what the source of the spec was in the doc, and then what Omni name has come out of this, so that when we say things like.
193 00:21:46.250 ⇒ 00:22:08.929 Greg Stoutenburg: customer success dashboard or customer reporting dashboard. It’s just crystal clear which one we’re talking about. This still needs a little bit of polish, as you can see, I’ve not finished putting in the links or the names yet, but I just want you to know that this is here. This is… and we always treat… for any clients with data platform documentation spreadsheet, we treat that as, like, the buck stops here, you know, this is the source of truth for your data stuff.
194 00:22:08.930 ⇒ 00:22:15.410 Greg Stoutenburg: So, that is in here now. Totally heard the confusion around naming and metrics and things like that.
195 00:22:16.330 ⇒ 00:22:21.169 Caitlyn Vaughn: Awesome. I think the other thing that is maybe slightly opaque here is…
196 00:22:21.570 ⇒ 00:22:34.040 Caitlyn Vaughn: where the finish line is for a lot of these, because it seems to be, like, you know, many weeks were of, like, oh, we have, like, a little more tweaking to do, and I think if we can get in writing, like, what
197 00:22:34.440 ⇒ 00:22:40.670 Caitlyn Vaughn: You know, the original scope is, and, like, what the additional asks were, or, like, what has changed, or maybe what was…
198 00:22:40.840 ⇒ 00:22:47.509 Caitlyn Vaughn: done wrong versus is changing, you know, like, from a data perspective, that would be helpful, because it seems like…
199 00:22:47.510 ⇒ 00:23:02.740 Caitlyn Vaughn: things are just kind of bleeding, and it’s really easy to be like, oh, just another week when some of these, like, dashboards are starting to compound. Like, we now have, like, 5 dashboards, and we’re gonna do another, like, 3 or 4, so if there’s, like, a clear, this is what we need to do.
200 00:23:02.780 ⇒ 00:23:05.030 Caitlyn Vaughn: I’d love that outlined, and then done.
201 00:23:05.520 ⇒ 00:23:26.660 Greg Stoutenburg: Yeah, that sounds good. And I think, I think, yeah, and that’s… that brings me to sort of my second comment here. I think we should treat a dashboard as signed off when it has met the initial spec requirements, and the stakeholder who is asking about it says, yes, this is the day that I’m looking for. If there are then additional requests, like, okay, now that I see it, actually, I want to make some tweaks.
202 00:23:26.970 ⇒ 00:23:29.040 Greg Stoutenburg: The dashboard is still completed, but.
203 00:23:29.470 ⇒ 00:23:45.209 Greg Stoutenburg: We’ve got some, sort of, like, just requests for new work, and that’s fine. So, for example, I think, you know, Laura was happy with what we put together for the, customer reporting and enablement dashboard, but just decided upon seeing it, like, actually, product tier is maybe not the most valuable.
204 00:23:45.210 ⇒ 00:23:45.830 Caitlyn Vaughn: Totally.
205 00:23:45.830 ⇒ 00:23:51.460 Greg Stoutenburg: thing that I want to see here, so let’s break it down by market instead. I think what we can say to that is, okay, great, so the dashboard’s done.
206 00:23:52.010 ⇒ 00:23:53.970 Greg Stoutenburg: And we’ll make that fix for you as a…
207 00:23:53.970 ⇒ 00:23:57.239 Caitlyn Vaughn: Second iteration of this is to change X. Yep.
208 00:23:57.240 ⇒ 00:24:05.130 Greg Stoutenburg: Yes, that’s exactly what I think, and so, so, actually, for that reason, I’m just going to click,
209 00:24:05.350 ⇒ 00:24:23.499 Greg Stoutenburg: wait, that’s this one. I’m just going to click done. And just say, with the understanding, there’s still a little bit of follow-on work to do. But that way we stay on our path, on our timeline, and if we decide that we need to pivot or reprioritize something because, you know, getting it by market is so important, then we say, okay, we’re gonna do that.
210 00:24:23.670 ⇒ 00:24:34.200 Greg Stoutenburg: But that’s gonna cost us a day on progress on this other one, so just making sure we’re super explicit about those things. Bring it around, we’ll put in writing, here’s what we call a done dashboard.
211 00:24:34.810 ⇒ 00:24:46.219 Caitlyn Vaughn: Cool, that would be awesome. And then if you just want to add, like, a column to this, or throw it in notes of, like, what is being added, maybe, like, maybe, like, a phase two or something, where it’s like, okay, now that we’re done.
212 00:24:46.870 ⇒ 00:24:48.749 Caitlyn Vaughn: Things that are going to be added or changed.
213 00:24:48.750 ⇒ 00:24:52.650 Greg Stoutenburg: Yep, yep, that sounds good. We can do that, I will do it right now.
214 00:24:53.370 ⇒ 00:24:57.750 Greg Stoutenburg: That… I didn’t do it. I messed that up. This over? Okay.
215 00:24:58.980 ⇒ 00:25:12.280 Demilade Agboola: Yeah, I also think, like, we’ll need to… like, I understand, like, why we’ll need to, like, maybe put things in, like, a phase approach, like, this is the first phase where things are done, and where things might, you know, might need to,
216 00:25:12.780 ⇒ 00:25:19.509 Demilade Agboola: rework certain things. I think also, ultimately, the dashboards are to drive, like, utility and, like, business usage.
217 00:25:20.110 ⇒ 00:25:20.440 Caitlyn Vaughn: Oh, sure.
218 00:25:20.440 ⇒ 00:25:32.750 Demilade Agboola: Sometimes in that process, part of the delay and part of what we’re experiencing is, people will need to see what they need on a day-to-day, and what they need to, you know, make whatever decisions they’re making on a day-to-day.
219 00:25:33.050 ⇒ 00:25:34.639 Demilade Agboola: And so,
220 00:25:35.030 ⇒ 00:25:40.399 Demilade Agboola: That’s kind of, like, what’s happening with the bleeding, where it’s like, oh, we’re doing this.
221 00:25:40.550 ⇒ 00:25:49.549 Demilade Agboola: But due to the fact that, like, maybe Laura needs to see something, Deanna needs to see something, that also, like, affects the, overall delivery time.
222 00:25:49.810 ⇒ 00:26:01.900 Demilade Agboola: But ultimately, we do want to make these dashboards as useful for them as possible, and that is the ultimate goal in building out these dashboards, because if we do one, or we build out five dashboards.
223 00:26:02.210 ⇒ 00:26:16.159 Demilade Agboola: And no one ever utilizes it. It’s kind of pointless, you know? So we want to be able to put out as many dashboards as possible, but also the most impactful dashboards that, you know, everyone can utilize on a day-to-day.
224 00:26:17.500 ⇒ 00:26:19.360 Caitlyn Vaughn: Cool, that sounds great. Yep.
225 00:26:21.200 ⇒ 00:26:22.800 Greg Stoutenburg: Alright, back to it.
226 00:26:25.650 ⇒ 00:26:28.800 Greg Stoutenburg: We put together a new Gantt.
227 00:26:29.650 ⇒ 00:26:38.280 Greg Stoutenburg: That’s just a little more visually appealing and shows at a, probably a better level of detail what’s been done when.
228 00:26:38.830 ⇒ 00:26:51.429 Greg Stoutenburg: since this is shared with you, I don’t really have much by way of commentary for this, but just wanted to show, where we are as of right now. We’re over here in April, where, you know, you can see that we’re,
229 00:26:51.580 ⇒ 00:27:00.349 Greg Stoutenburg: doing the dashboarding work for GTM and revenue, customer success, and customer reporting, the product analytics work, and the financial summary,
230 00:27:00.460 ⇒ 00:27:04.540 Greg Stoutenburg: sort of track down here at Workstream 4.
231 00:27:05.270 ⇒ 00:27:20.180 Greg Stoutenburg: This is something that Garrett, who I think needs to be added to this call, can present on as well in more detail, but this is something we’ll want to just make sure to put in front of you so you’re able to see regularly, you know, here’s where we are on progress on all of these things.
232 00:27:20.830 ⇒ 00:27:25.199 Caitlyn Vaughn: Okay, great. I’m glad that you guys created this. Can you go back.
233 00:27:25.480 ⇒ 00:27:26.450 Greg Stoutenburg: Oh, yep.
234 00:27:26.560 ⇒ 00:27:39.169 Caitlyn Vaughn: Because there are a lot of things in here that I’m looking at that are… have been sorted already and should not be on here, and also maybe some misalignment here. So the first is, we talked about not doing any more reverse ETL.
235 00:27:39.180 ⇒ 00:27:53.860 Caitlyn Vaughn: We’ve removed Catalysts as a vendor about 2 months ago, maybe a month ago, so that can be removed. And then, I think the goal for us is to wrap up this project by the end of May, meaning in 6 weeks.
236 00:27:54.130 ⇒ 00:27:54.730 Greg Stoutenburg: Yep.
237 00:27:55.410 ⇒ 00:27:58.460 Caitlyn Vaughn: So, this needs to get… Either scope.
238 00:27:58.460 ⇒ 00:27:59.950 Greg Stoutenburg: It’s down? Yeah.
239 00:27:59.950 ⇒ 00:28:05.240 Caitlyn Vaughn: scoped down and, like, paired out to end in May, or,
240 00:28:05.690 ⇒ 00:28:08.380 Caitlyn Vaughn: We need to figure out how to condense this.
241 00:28:08.570 ⇒ 00:28:18.670 Greg Stoutenburg: Yeah, yeah. Okay, heard, yeah. Let, let me… let me get back on this and do tweaks and run a second version by you, and then have that conversation about scope if we need to.
242 00:28:19.030 ⇒ 00:28:23.929 Caitlyn Vaughn: Yep. Also, the amplitude is in here. We were… we switched to post-hog.
243 00:28:24.410 ⇒ 00:28:29.260 Caitlyn Vaughn: Yes. Yeah.
244 00:28:29.710 ⇒ 00:28:34.010 Caitlyn Vaughn: So, source audit architecture, did I… Polyatomic workspace setup.
245 00:28:34.860 ⇒ 00:28:38.559 Caitlyn Vaughn: What, like, polyatomic workspace setup, we did that.
246 00:28:38.830 ⇒ 00:28:40.829 Caitlyn Vaughn: Like, a few months ago, right?
247 00:28:41.400 ⇒ 00:28:42.469 Caitlyn Vaughn: The second thing on here.
248 00:28:42.470 ⇒ 00:28:45.360 Greg Stoutenburg: Yeah. Yeah, let me,
249 00:28:45.710 ⇒ 00:28:50.189 Greg Stoutenburg: Let me get back to the Garrett and see… see… see what we… what we missed here.
250 00:28:50.190 ⇒ 00:28:50.650 Caitlyn Vaughn: Yeah.
251 00:28:50.650 ⇒ 00:28:51.289 Greg Stoutenburg: This was an old.
252 00:28:51.290 ⇒ 00:28:55.910 Caitlyn Vaughn: Salesforce Postgres on April and QuickBooks. Yeah.
253 00:28:55.910 ⇒ 00:28:57.380 Greg Stoutenburg: I’m gonna hide this now.
254 00:28:57.380 ⇒ 00:28:58.920 Caitlyn Vaughn: Yeah, maybe we’re gonna move on from that.
255 00:28:58.920 ⇒ 00:28:59.779 Greg Stoutenburg: We’re gonna hide this.
256 00:28:59.780 ⇒ 00:29:00.620 Caitlyn Vaughn: Try again.
257 00:29:00.620 ⇒ 00:29:06.749 Greg Stoutenburg: And, we’re just gonna rewind about 48 seconds or so, and move forward. So, alright, cool.
258 00:29:06.750 ⇒ 00:29:07.390 Caitlyn Vaughn: Okay, great.
259 00:29:07.390 ⇒ 00:29:11.309 Greg Stoutenburg: Yeah, Ctrl-Z. Alright. Now,
260 00:29:11.810 ⇒ 00:29:21.160 Greg Stoutenburg: Some… some questions. As we think about ways to better provide enablement of your data sources, this was… I was hoping that Lev would be on so I could ask him this question.
261 00:29:22.210 ⇒ 00:29:22.920 Greg Stoutenburg: But…
262 00:29:22.920 ⇒ 00:29:47.880 Greg Stoutenburg: just, you know, for you and Annika, then. What would you like to be able to ask Blobby about BDR or CSM performance? And the reason I’m asking this question is because, of course, you can just type in anything and see what Blobby says. But, we can also do work to make sure that certain types of questions that are likely to be especially important are ones that we’ve built in our semantic layer that, you know, will point to particular sources, or will answer in certain characteristic ways, or will
263 00:29:47.880 ⇒ 00:29:55.209 Greg Stoutenburg: ask certain type of follow-ups. Like, there are ways that we can sort of, you know, shape the behavior of Blobby that would be extra useful here.
264 00:29:55.400 ⇒ 00:30:13.549 Nandika Jhunjhunwala: For sure, I can give you some context here. It’s, like, a conversation I’ve had a ton with Lev. So I think, in general, we want to see metrics broken down by BDR, and so, like, when we come to, like, VDR activity, we’re asking questions like, how many, like.
265 00:30:13.770 ⇒ 00:30:25.220 Nandika Jhunjhunwala: average sales touchpoints they had on average per account that they own. That’s one for how many dials did they make per opportunity created?
266 00:30:25.540 ⇒ 00:30:38.349 Nandika Jhunjhunwala: how many dials did they make per qualified opportunity, which is any opportunity that moved past Stage 2 or got to stage 2, so moved beyond the first call. We want to look at,
267 00:30:38.690 ⇒ 00:30:45.969 Nandika Jhunjhunwala: sort of, like, total activity by BDR, broken down by, like, email and, phone calls.
268 00:30:46.550 ⇒ 00:30:58.960 Nandika Jhunjhunwala: And this terminology that I think a stakeholder would use is different from the data. So, like, in the data, for example, Salesforce calls and emails live under this table called tasks.
269 00:30:59.220 ⇒ 00:31:07.930 Nandika Jhunjhunwala: And the task table is where, like, you know, these task calls or emails get assigned to BDRs. Yeah.
270 00:31:07.990 ⇒ 00:31:25.900 Nandika Jhunjhunwala: So, I’m not sure how you would marry that in the semantic layer for lobby, so that when a stakeholder is querying it in their terminology, that it pops up the right information that they need. But that’s, like, what’s going on in my head. Yeah, yeah.
271 00:31:26.120 ⇒ 00:31:33.530 Greg Stoutenburg: Yeah, that’s helpful. So, like, just… just, like, riffing on an example, right? So, if, if Lev wants to go in and type in, you know,
272 00:31:33.970 ⇒ 00:31:41.279 Greg Stoutenburg: tell me how the team is performing, or tell me how the BDR team is performing this week. That would then surface things like.
273 00:31:41.490 ⇒ 00:31:47.379 Greg Stoutenburg: You know, activity… so, some relationships between activity and output, right?
274 00:31:47.570 ⇒ 00:32:00.819 Greg Stoutenburg: things like that, right? You know, some ratio of calls to meetings booked, for example. Like, that sort of thing, where there’s a connection between the tasks and, you know, ARR, opportunities, stuff like that.
275 00:32:01.750 ⇒ 00:32:03.059 Greg Stoutenburg: That’s kind of what I’m hearing.
276 00:32:03.670 ⇒ 00:32:06.000 Greg Stoutenburg: Yeah. Yeah, yeah, good.
277 00:32:06.000 ⇒ 00:32:16.510 Caitlyn Vaughn: The other thing that we would love to be able to see at a lobby that I’ve started realizing is going to be important is seeing raw data sources plus transformed data.
278 00:32:16.800 ⇒ 00:32:29.169 Caitlyn Vaughn: So, for example, the question that I had asked you guys to pull yesterday, or Nico had asked, which was, like, seeing ARR between Hyperline, Salesforce, and Omni.
279 00:32:29.170 ⇒ 00:32:29.990 Greg Stoutenburg: Yeah.
280 00:32:29.990 ⇒ 00:32:38.469 Caitlyn Vaughn: I tried to do it in Lobby, and it didn’t… I don’t think it quite gave me what I needed. Obviously, it didn’t otherwise wouldn’t have asked, but
281 00:32:38.510 ⇒ 00:32:48.559 Caitlyn Vaughn: being able to see both raw data plus transformed data or modeled data is super helpful, because then we can start, you know, seeing the diffs and being able to, like.
282 00:32:48.580 ⇒ 00:33:01.069 Caitlyn Vaughn: There’s so much context, obviously, that you guys have had to sort through, and it’s pretty granular, so being able to, like, print a view of, like, this is all the dips between these three sources would be really, really helpful and very interesting.
283 00:33:01.470 ⇒ 00:33:08.300 Greg Stoutenburg: Got it. Okay, so this is… okay, you were looking at this the other day, so this must be… this is you.
284 00:33:09.520 ⇒ 00:33:10.960 Greg Stoutenburg: Number of prompts, 1!
285 00:33:11.450 ⇒ 00:33:16.110 Caitlyn Vaughn: Yeah. I did it once, and then I, and then I just messaged you guys.
286 00:33:16.330 ⇒ 00:33:21.660 Greg Stoutenburg: Yeah, well, I mean, yeah, but, well, this is good for your quality score, though. Remember we were talking last week about.
287 00:33:21.660 ⇒ 00:33:22.180 Caitlyn Vaughn: That’s right.
288 00:33:22.180 ⇒ 00:33:24.060 Greg Stoutenburg: Results per.
289 00:33:24.550 ⇒ 00:33:38.989 Greg Stoutenburg: Yeah, sessions to prompts, yeah, very good. That’s right. Okay, yep, yep, bring that up. Let me see if I can just pull this one right here. Yeah, so we can go right here to the session URL, and I just want to flag this, because I think that this answers part of the question, but just to…
290 00:33:39.410 ⇒ 00:33:54.960 Greg Stoutenburg: just to show where this comes from. So, anytime that Blobby’s gonna run a query, it’ll… you’ll sort of, like, get this expand if you want to bar, and it’ll… it’ll show all the reasoning, it’ll show what it went through, but if you click on Explore.
291 00:33:55.910 ⇒ 00:33:58.460 Greg Stoutenburg: You can see the sequel.
292 00:33:59.010 ⇒ 00:34:00.200 Caitlyn Vaughn: Is that…
293 00:34:00.430 ⇒ 00:34:04.779 Greg Stoutenburg: Is that the sort of thing that, you’d want to look at?
294 00:34:05.910 ⇒ 00:34:07.340 Caitlyn Vaughn: Yes, it’s definitely helpful.
295 00:34:07.340 ⇒ 00:34:12.330 Greg Stoutenburg: Because it’s pointing to, you know, like, it’s pointing to this.
296 00:34:13.310 ⇒ 00:34:16.619 Greg Stoutenburg: Like, the account name’s coming from, it’s straight out of that table.
297 00:34:18.250 ⇒ 00:34:24.490 Demilade Agboola: So another thing that we could also do in terms of modeling, because, like, modeling is where…
298 00:34:24.650 ⇒ 00:34:37.999 Demilade Agboola: we start to add context to some of the raw things, because, like, the raw data is literally just random tables of, like, users, and customers, and transactions, and tasks, and, like, it doesn’t always, like, have all the information on
299 00:34:38.170 ⇒ 00:34:42.610 Demilade Agboola: Like, what counts as churn, what counts as, a renewal?
300 00:34:42.810 ⇒ 00:34:50.410 Demilade Agboola: Fully. So, what we can do is, we can start to create, like, tables where some of these things can live.
301 00:34:50.530 ⇒ 00:34:57.590 Demilade Agboola: So that it’s much easier for you to, you know, prompt it and get that, joined on properly.
302 00:34:58.200 ⇒ 00:35:01.399 Demilade Agboola: Because, obviously, you know, you run the risk of…
303 00:35:01.550 ⇒ 00:35:11.059 Demilade Agboola: like, bad joints occurring, and just, like, without the right context. So we can start to, build that out, and ensure that,
304 00:35:12.220 ⇒ 00:35:18.889 Demilade Agboola: those things exist. I will say, though, that, like, some of the things in Hyperline, like, the values are way high.
305 00:35:19.000 ⇒ 00:35:27.489 Demilade Agboola: like, if you check, like, Cherry, for instance, Cherry has, like, 209,000 as their ARR in Hyperline, which, you know, is not true, it’s, like, 59,000.
306 00:35:27.680 ⇒ 00:35:30.329 Demilade Agboola: So just being able to…
307 00:35:31.200 ⇒ 00:35:37.459 Demilade Agboola: Put that in there, but also emphasize the context in which, like, the numbers
308 00:35:37.740 ⇒ 00:35:43.290 Demilade Agboola: coming from Omni are the real source of truth, because again, we don’t want Blobby showing those kind of numbers.
309 00:35:43.400 ⇒ 00:35:51.329 Demilade Agboola: To people when they’re asking what’s the error of Cherry, for instance, and it goes 59,000.
310 00:35:52.280 ⇒ 00:35:55.450 Demilade Agboola: We’re just able to put that context in there.
311 00:35:58.180 ⇒ 00:36:05.190 Greg Stoutenburg: Which is something we can do. We can tell Blobby, like, hey, whenever reporting on Hyperline, include a caveat about
312 00:36:05.480 ⇒ 00:36:12.629 Greg Stoutenburg: how Hyperline measures this, or, you know, we defer to… we treat Omni as the source of truth on this type of measure.
313 00:36:14.310 ⇒ 00:36:19.960 Caitlyn Vaughn: Yeah, so, I guess specifically for this request…
314 00:36:19.960 ⇒ 00:36:21.260 Greg Stoutenburg: Whoa!
315 00:36:21.260 ⇒ 00:36:21.900 Caitlyn Vaughn: Sorry.
316 00:36:22.260 ⇒ 00:36:23.620 Caitlyn Vaughn: I’m.
317 00:36:23.620 ⇒ 00:36:25.090 Demilade Agboola: I was a bit confused for a minute.
318 00:36:26.040 ⇒ 00:36:41.320 Caitlyn Vaughn: I just came into the room because I need to say this in here and not, like, out… outside. I mean, the reason for this specific request, like, I agree that we don’t want it typically showing, like, hyperline numbers, but we have been reporting on
319 00:36:42.510 ⇒ 00:36:44.219 Caitlyn Vaughn: Hyperline numbers.
320 00:36:45.300 ⇒ 00:36:50.460 Caitlyn Vaughn: And… These are…
321 00:36:51.850 ⇒ 00:37:08.030 Caitlyn Vaughn: the numbers that we’ve been reporting to the board. So, we’re about to have a board meeting here shortly, and we have to give, like, a pretty solid explanation why our numbers were off by 400,000. Okay. Which, the numbers in Omni are correct. Like, I feel very confident about that, and about the work that we’ve done.
322 00:37:08.030 ⇒ 00:37:17.239 Caitlyn Vaughn: And I think that’s great and fine. However, we haven’t had access to that data till now. So, as we’re going to the board and saying, oh, by the way, we actually
323 00:37:17.290 ⇒ 00:37:19.690 Caitlyn Vaughn: didn’t hit this. Some remove hit this.
324 00:37:19.820 ⇒ 00:37:36.899 Caitlyn Vaughn: I think it’s fine as long as there’s, like, a very clear explanation as to why that information was given before and after. So, this is a good example of, like, you’re right, we don’t want, you know, Hyperlane data pulled in and, like, used, but we do need to be able to see it, and maybe that is, like, a permissions thing of, like.
325 00:37:36.900 ⇒ 00:37:37.610 Uttam Kumaran: Yes.
326 00:37:37.780 ⇒ 00:37:43.019 Caitlyn Vaughn: seeing raw data sources only for admins, I think, is fine. Or for, like.
327 00:37:43.330 ⇒ 00:37:48.070 Caitlyn Vaughn: me, Nico, maybe Nandika, Laura, and then outside of.
328 00:37:48.070 ⇒ 00:37:48.650 Uttam Kumaran: Yeah.
329 00:37:48.910 ⇒ 00:37:52.819 Caitlyn Vaughn: Just using the model data that we’ve decided is… probably makes sense.
330 00:37:53.660 ⇒ 00:37:54.100 Greg Stoutenburg: Okay.
331 00:37:54.100 ⇒ 00:37:55.929 Uttam Kumaran: A bit of permissions thing, Greg, yeah.
332 00:37:56.840 ⇒ 00:38:00.109 Greg Stoutenburg: Oh, Tom, can you help me guide that? I mean, I can set up… I know how to set up the.
333 00:38:00.110 ⇒ 00:38:07.700 Uttam Kumaran: Yeah, I mean, basically, we’ll just do a user group for… we’ll do, like, an exec user group, and then it’ll just filter certain topics or models, so we should do that, yeah.
334 00:38:07.700 ⇒ 00:38:08.650 Greg Stoutenburg: Yeah, we can do that.
335 00:38:08.650 ⇒ 00:38:10.619 Uttam Kumaran: Start Hyperline for everybody else.
336 00:38:11.650 ⇒ 00:38:12.459 Greg Stoutenburg: We can do that.
337 00:38:12.970 ⇒ 00:38:14.119 Caitlyn Vaughn: I just don’t.
338 00:38:14.120 ⇒ 00:38:14.450 Greg Stoutenburg: Okay.
339 00:38:14.450 ⇒ 00:38:16.810 Caitlyn Vaughn: really loud in the office, so sorry. Okay.
340 00:38:16.930 ⇒ 00:38:17.930 Greg Stoutenburg: No, no, well, yeah.
341 00:38:18.450 ⇒ 00:38:21.809 Greg Stoutenburg: That makes perfect sense. And you have to meet with the board in a little bit, and I understand that we’re sort of.
342 00:38:22.140 ⇒ 00:38:34.149 Greg Stoutenburg: on the end of scheduled time, but if the higher priority is getting that narrative right, and you want to jam on that now, that’s something that we could do, or we could just sort of wrap it up so you can prepare your presentation.
343 00:38:34.650 ⇒ 00:38:46.070 Caitlyn Vaughn: Yeah, our board meeting isn’t until May 13th, so we do have a bit of time until then. This is just, like, I know Nico’s starting to work through a lot of the stuff, and he’s gonna have to, you know, have some…
344 00:38:46.290 ⇒ 00:38:49.749 Caitlyn Vaughn: some conversations with the board members.
345 00:38:49.750 ⇒ 00:38:50.220 Greg Stoutenburg: I thought you meant that.
346 00:38:50.220 ⇒ 00:38:50.750 Caitlyn Vaughn: So…
347 00:38:50.970 ⇒ 00:38:52.460 Greg Stoutenburg: Okay.
348 00:38:52.460 ⇒ 00:38:53.199 Caitlyn Vaughn: What’d you say?
349 00:38:53.200 ⇒ 00:38:55.429 Greg Stoutenburg: I thought you meant in, like, a half an hour.
350 00:38:55.430 ⇒ 00:39:11.089 Caitlyn Vaughn: No, no, no, no, no. No, it’s in a few weeks, which we’re trying to, like, be more prepared for, so I’d love to not do it, like, last minute, but in next week is fine. Also, I did see Ryan respond… he, like, started a separate thread with Nico and I, and, like, started putting in
351 00:39:11.130 ⇒ 00:39:20.460 Caitlyn Vaughn: some of the specifics of what we did in Omni, like, to fix the data, so I think that was probably helpful, and I just need to talk to Nico and see
352 00:39:20.520 ⇒ 00:39:23.629 Caitlyn Vaughn: if that was satisfactory, or if we still need, like, if it was…
353 00:39:23.630 ⇒ 00:39:24.540 Uttam Kumaran: I think, Kalen, the…
354 00:39:24.890 ⇒ 00:39:30.979 Uttam Kumaran: acquiring the fields in Salesforce is gonna change a lot, and it’s also gonna be basically forced
355 00:39:31.210 ⇒ 00:39:39.919 Uttam Kumaran: all past opportunities to have this. So there just has to be, like, a really rich, like, hygiene session on, like, any past opportunities.
356 00:39:40.150 ⇒ 00:39:43.400 Uttam Kumaran: As many fields as you can force to be required is, like.
357 00:39:43.400 ⇒ 00:39:43.920 Greg Stoutenburg: Yep.
358 00:39:43.920 ⇒ 00:39:46.180 Uttam Kumaran: Gonna solve a lot of… of things, you know?
359 00:39:46.180 ⇒ 00:39:46.730 Greg Stoutenburg: Yeah.
360 00:39:47.480 ⇒ 00:39:55.279 Greg Stoutenburg: Yeah, there might be something, you know, the team needs to do next week, like, hey, everyone needs to take 3 hours and go through every single opportunity that they’ve touched, and .
361 00:39:55.640 ⇒ 00:40:00.600 Greg Stoutenburg: And update this, and that might… that might make the work for the presentation a lot easier.
362 00:40:00.600 ⇒ 00:40:15.369 Uttam Kumaran: Yeah, and for the board meeting as well, like, if you want us to help prepare, I mean, we have a lot of data now, so even though, you know, there is this, like, miscalculation, I think we can just tell a much different story, I feel like, than is probably told with data before.
363 00:40:15.370 ⇒ 00:40:16.480 Caitlyn Vaughn: So…
364 00:40:16.480 ⇒ 00:40:20.199 Uttam Kumaran: like, would be more than happy to support. I mean, whether that’s just, like.
365 00:40:20.330 ⇒ 00:40:29.710 Uttam Kumaran: Pulling up the dashboard, or if you want to export specific views, like, some of the, like, enterprise, like, the customer segmentation, a lot of the revenue metrics are really good.
366 00:40:30.200 ⇒ 00:40:32.780 Caitlyn Vaughn: Yeah. Okay, awesome, yeah.
367 00:40:33.260 ⇒ 00:40:37.499 Demilade Agboola: I think, ultimately, yeah, the numbers are going up, like, it’s still, like, growth, so it’s not like.
368 00:40:38.310 ⇒ 00:40:55.099 Demilade Agboola: numbers were, like, there has been, you know, a huge drop in sense of, like, we’ve been losing customers, we’re losing revenue. The error is still on the upward trajectory, it’s just, obviously, the numbers were a bit more inflated than before, and so just being able to identify where that came from has been helpful.
369 00:40:55.740 ⇒ 00:40:57.099 Demilade Agboola: I’m gonna be able to utilize.
370 00:40:57.270 ⇒ 00:40:59.769 Demilade Agboola: To, you know, preparing for your board.
371 00:41:01.680 ⇒ 00:41:05.810 Caitlyn Vaughn: Yep, it’s good, executive information.
372 00:41:06.180 ⇒ 00:41:06.760 Uttam Kumaran: Meh.
373 00:41:06.930 ⇒ 00:41:07.510 Uttam Kumaran: Cool.
374 00:41:08.860 ⇒ 00:41:09.490 Greg Stoutenburg: Okay.
375 00:41:10.220 ⇒ 00:41:11.530 Greg Stoutenburg: Okay.
376 00:41:12.300 ⇒ 00:41:37.300 Greg Stoutenburg: Just a couple of other things. This is mostly a note. Anything that’s useful data that you want to have a dashboard for in Omni, we can also set up, Slack notifications, or… sorry, Slack deliveries, or email deliveries. So, if there’s something that, you know, like, someone wants to see every day, and it’s easier to just put it in their inbox than to have them log in and stuff, that’s something that we can do. Really just mentioning it, because, again, you know, we’re speccing out all these things
377 00:41:37.300 ⇒ 00:41:38.989 Greg Stoutenburg: Building all these things. We can also just…
378 00:41:38.990 ⇒ 00:41:46.230 Greg Stoutenburg: say, as an add-on, like, hey, we can deliver it to you. So, just keep that in mind as we go. Just a quick note. And then.
379 00:41:46.230 ⇒ 00:41:51.529 Nandika Jhunjhunwala: I actually wanted to connect Omni, with Slack. I did add it, but…
380 00:41:51.530 ⇒ 00:41:51.940 Greg Stoutenburg: Okay.
381 00:41:51.940 ⇒ 00:42:04.800 Nandika Jhunjhunwala: I was trying to figure out how to, like, use it, so what I, like, found, and correct me if I’m wrong, is that you can’t query on me in Slack, but you can send reports to Slack. That’s right.
382 00:42:04.800 ⇒ 00:42:05.120 Greg Stoutenburg: Yep.
383 00:42:05.120 ⇒ 00:42:07.990 Nandika Jhunjhunwala: I would love to set up a session with somebody to, like.
384 00:42:08.240 ⇒ 00:42:12.960 Nandika Jhunjhunwala: configure that, or, like, be able to configure that on my own, so I can, like.
385 00:42:12.960 ⇒ 00:42:13.380 Greg Stoutenburg: Yep.
386 00:42:13.380 ⇒ 00:42:14.070 Nandika Jhunjhunwala: Comment.
387 00:42:14.070 ⇒ 00:42:15.230 Caitlyn Vaughn: 2 o’clock? Yeah.
388 00:42:15.230 ⇒ 00:42:15.680 Nandika Jhunjhunwala: It’s pretty tough.
389 00:42:15.680 ⇒ 00:42:16.780 Caitlyn Vaughn: Pretty easy to do.
390 00:42:19.960 ⇒ 00:42:22.059 Greg Stoutenburg: She’s right there.
391 00:42:22.060 ⇒ 00:42:22.390 Nandika Jhunjhunwala: Okay.
392 00:42:22.390 ⇒ 00:42:25.689 Greg Stoutenburg: You’re muted, Caitlin’s not, so whatever you say, we’re still gonna hear it.
393 00:42:25.690 ⇒ 00:42:27.050 Nandika Jhunjhunwala: Oh, sorry.
394 00:42:27.410 ⇒ 00:42:29.790 Nandika Jhunjhunwala: Caitlin said that it’s pretty to do the refrigeration.
395 00:42:29.790 ⇒ 00:42:31.199 Caitlyn Vaughn: I’m not muted.
396 00:42:31.560 ⇒ 00:42:34.730 Greg Stoutenburg: Yeah, yeah, that’s right, yeah. It’s not privacy, it’s only one way.
397 00:42:34.730 ⇒ 00:42:36.490 Nandika Jhunjhunwala: Oh, okay.
398 00:42:36.490 ⇒ 00:42:37.200 Greg Stoutenburg: Yeah.
399 00:42:37.200 ⇒ 00:42:37.590 Nandika Jhunjhunwala: Cool.
400 00:42:37.590 ⇒ 00:42:41.120 Greg Stoutenburg: Well, I’ll… I mean, I’ll actually… I’ll show you. So, so, like…
401 00:42:41.500 ⇒ 00:42:51.229 Greg Stoutenburg: here, for any dashboard, and, like, these stats are a dashboard. You can go to, sort of select the dashboard.
402 00:42:51.570 ⇒ 00:42:54.579 Greg Stoutenburg: No, give me, like, just this dashboard. Okay.
403 00:42:54.980 ⇒ 00:42:56.620 Greg Stoutenburg: Let’s click to…
404 00:42:56.750 ⇒ 00:43:04.750 Greg Stoutenburg: Let’s click to, like, here, and if we can get to… yeah, File, there we go. In a dashboard, you go File, Deliveries and Alerts.
405 00:43:06.830 ⇒ 00:43:10.180 Greg Stoutenburg: And then right here is the scheduling pane. So…
406 00:43:10.620 ⇒ 00:43:21.509 Nandika Jhunjhunwala: Can we… can we, like, if we, like, chat with Bobby, and it generates, sort of, like, some, like, insights, and we want to send that to Slack, like, on a…
407 00:43:21.740 ⇒ 00:43:29.309 Nandika Jhunjhunwala: on a periodic basis, is that gonna be something we’d have to make a dashboard out of first to then send to Slack, or can we just…
408 00:43:29.440 ⇒ 00:43:31.620 Nandika Jhunjhunwala: directly sent us back.
409 00:43:32.080 ⇒ 00:43:39.880 Greg Stoutenburg: You can send… You can send individual AI logs, for example.
410 00:43:39.880 ⇒ 00:43:51.379 Demilade Agboola: I would assume it needs to be a dashboard first, because the dashboard will have a refresh cadence, because if you do a one-time, like, view, or, like, a one-time query, it just gets the results then, but, like.
411 00:43:51.780 ⇒ 00:43:56.929 Demilade Agboola: In two weeks’ time, you will need the newest results, and then the dashboard can then be sent.
412 00:43:56.930 ⇒ 00:43:57.290 Nandika Jhunjhunwala: Yeah.
413 00:43:57.290 ⇒ 00:44:00.150 Demilade Agboola: That cadence, to… whoever needs it.
414 00:44:00.150 ⇒ 00:44:01.709 Nandika Jhunjhunwala: Got it. That makes sense.
415 00:44:02.380 ⇒ 00:44:03.010 Greg Stoutenburg: Yeah.
416 00:44:05.090 ⇒ 00:44:10.799 Greg Stoutenburg: Yeah, let me… let me verify that, though, because it might be that it’ll just give you the most recent day’s information.
417 00:44:11.790 ⇒ 00:44:14.669 Greg Stoutenburg: So you’d at least see what’s new, and have that delivered to you.
418 00:44:15.670 ⇒ 00:44:21.899 Nandika Jhunjhunwala: So, do you see… Delta numbers for updates, or do you just see the total number again?
419 00:44:22.160 ⇒ 00:44:29.790 Greg Stoutenburg: Yeah, that’s what I want to ask about, because I think what you’d see is that… I think you’d see a new total, but if that’s AI sessions with full conversations.
420 00:44:30.190 ⇒ 00:44:36.200 Greg Stoutenburg: then that’s going to… I believe then you should always see the newest stuff on top.
421 00:44:36.750 ⇒ 00:44:37.919 Greg Stoutenburg: But let me, let me just verify that.
422 00:44:37.920 ⇒ 00:44:44.779 Demilade Agboola: I think it just depends on how you set up the dashboard. So, because we have, like, delivery set up for some… another client, where
423 00:44:44.780 ⇒ 00:45:00.159 Demilade Agboola: Some dashboards are, like, the last 24 hours, so they see what happened over the last 24 hours, sent to them every morning. And ultimately, you can also have, like, a total of the entire month, or the total over a time frame, like, the last 3 months, sent to every single day as well.
424 00:45:00.160 ⇒ 00:45:02.930 Demilade Agboola: So you can kind of see the growth every single day.
425 00:45:03.070 ⇒ 00:45:11.599 Demilade Agboola: So it’s just basically how the dashboard is set up that will be sent on whatever cadence you want, and then you can see that delta, based off of…
426 00:45:11.720 ⇒ 00:45:21.639 Demilade Agboola: Either a representation of just the past 24 hours, which you can show, or, like, a running total based off every single day or every single, you know, week.
427 00:45:25.010 ⇒ 00:45:28.920 Nandika Jhunjhunwala: But… so when you were configuring the deliveries.
428 00:45:29.400 ⇒ 00:45:42.580 Nandika Jhunjhunwala: it had that option below the dashboard, where it says, yeah, AI sessions. So is that where you’re querying the dashboard? Then, that you’re sending those queries to Slack? A little confused on… on that.
429 00:45:42.850 ⇒ 00:45:43.730 Demilade Agboola: So, not…
430 00:45:43.730 ⇒ 00:45:51.549 Greg Stoutenburg: So this, it’s very hard to read for you. This says dashboard charts, so this is… the AI usage is just… these are just individual charts that are on that.
431 00:45:51.550 ⇒ 00:45:52.910 Nandika Jhunjhunwala: Oh, okay, got it.
432 00:45:52.910 ⇒ 00:46:00.789 Greg Stoutenburg: So, the one that I selected here that says AI Sessions Full Conversations is just this view here, and then it would drop it to you in Slack.
433 00:46:01.270 ⇒ 00:46:03.740 Nandika Jhunjhunwala: That makes sense. Yeah. I was confused, yes.
434 00:46:03.740 ⇒ 00:46:18.080 Greg Stoutenburg: Yeah, so you can see who was, like, banging their head against the wall, like, over here, Advait looks like… he asked the same thing a few times, like, why a line chart? Bar chart is not better? Like, come on, come on, Bobby, give me what I want! So, you can… you can look through for gems like that as well.
435 00:46:19.880 ⇒ 00:46:23.830 Greg Stoutenburg: Well, let’s find out, let’s, I know we need to wrap, so,
436 00:46:24.330 ⇒ 00:46:38.260 Greg Stoutenburg: I’ll take a second and make sure that you’re set up as a delivery recipient, Nandica, and maybe let me just… let’s just schedule this to you for the next few days in a row, and see if what you see is useful, and then maybe we, you know, spread that out as we need to.
437 00:46:40.220 ⇒ 00:46:40.890 Greg Stoutenburg: Yeah.
438 00:46:41.340 ⇒ 00:46:51.930 Greg Stoutenburg: Last thing that I had in mind was just asking who should set up OmniMCP. It’s pretty straightforward, but if anyone, like, needs help doing it, I can reach out to them and make sure that they’re enabled.
439 00:46:52.410 ⇒ 00:46:53.319 Caitlyn Vaughn: We got it.
440 00:46:54.610 ⇒ 00:46:56.520 Greg Stoutenburg: We’re like that. Okay.
441 00:46:56.880 ⇒ 00:46:57.390 Greg Stoutenburg: Cool.
442 00:46:57.390 ⇒ 00:46:58.200 Caitlyn Vaughn: Awesome.
443 00:46:58.550 ⇒ 00:46:59.929 Greg Stoutenburg: That’s it. That’s a wrap.
444 00:46:59.930 ⇒ 00:47:01.989 Caitlyn Vaughn: Alright, thank you guys!
445 00:47:01.990 ⇒ 00:47:03.580 Uttam Kumaran: Thanks, everybody. Thank you. I appreciate it.
446 00:47:03.580 ⇒ 00:47:04.060 Greg Stoutenburg: Bye.
447 00:47:04.340 ⇒ 00:47:04.880 Greg Stoutenburg: Bye.