2026-04-22_bdr_dashboard_review_d6924c79

Meeting Title: BDR Dashboard Review Date: 2026-04-22 Meeting participants: Greg Stoutenburg, Scratchpad Notetaker, Laura Krivec, Advait Nandakumar Menon, Caitlyn Vaughn, Nandika Jhunjhunwala, Lev Katreczko, Demilade Agboola

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WEBVTT

1 00:00:53.180 ⇒ 00:00:54.780 Greg Stoutenburg: Hey, Laura, how’s it going?

2 00:01:07.800 ⇒ 00:01:08.720 Greg Stoutenburg: Hey, there we go.

3 00:01:09.100 ⇒ 00:01:10.360 Laura Krivec: Hi!

4 00:01:10.360 ⇒ 00:01:11.370 Greg Stoutenburg: Hello!

5 00:01:12.560 ⇒ 00:01:14.140 Greg Stoutenburg: Hey y’all, how’s it going today?

6 00:01:14.570 ⇒ 00:01:15.940 Caitlyn Vaughn: Good, how are you?

7 00:01:16.110 ⇒ 00:01:18.509 Greg Stoutenburg: Good. Do I see sunshine back there?

8 00:01:19.230 ⇒ 00:01:20.170 Caitlyn Vaughn: Not mine.

9 00:01:20.170 ⇒ 00:01:23.249 Greg Stoutenburg: Yeah, did you finally get out of the, the gray? The gray zone?

10 00:01:23.250 ⇒ 00:01:24.800 Caitlyn Vaughn: Oh, it’s so gray!

11 00:01:24.800 ⇒ 00:01:27.849 Greg Stoutenburg: Oh, okay, alright, well, it’s… it’s convincing from…

12 00:01:28.450 ⇒ 00:01:30.219 Caitlyn Vaughn: We just blur it a little bit.

13 00:01:30.220 ⇒ 00:01:34.160 Greg Stoutenburg: That’s right, now you can’t… now you can’t tell.

14 00:01:34.580 ⇒ 00:01:36.460 Greg Stoutenburg: Nadica, how’s it going?

15 00:01:36.460 ⇒ 00:01:38.169 Nandika Jhunjhunwala: Hello, hi, how are you?

16 00:01:38.170 ⇒ 00:01:40.230 Greg Stoutenburg: Hello, doing great, doing great.

17 00:01:40.690 ⇒ 00:01:52.510 Greg Stoutenburg: Alright, well, we gave, half an hour, so let’s just jump right in. I think the thing to do, I think the way I’d like to approach it is just, I’ll just share my screen, and we’ll just kind of…

18 00:01:52.510 ⇒ 00:02:01.379 Greg Stoutenburg: walk through some high-level things. There are tons of charts, so we won’t, like, zoom in too much on it, as though we’re, you know, reading you the PowerPoint.

19 00:02:01.590 ⇒ 00:02:05.180 Greg Stoutenburg: Oh, sorry, was someone gonna say something, or is that just a mic blip?

20 00:02:06.990 ⇒ 00:02:14.220 Greg Stoutenburg: Okay, and then, you know, ask any questions, give any feedback, and we’ll take note and make, whatever changes we need to.

21 00:02:14.420 ⇒ 00:02:16.760 Greg Stoutenburg: Alright, here we go.

22 00:02:17.630 ⇒ 00:02:33.389 Greg Stoutenburg: Laura, I made sure that the team has access to this folder, so this is where we’ve been… this has been, like, the primary workspace for dashboards that we’re setting up, and then we sort of scaffolded it out into other folders as things are rolling out. So just did want to check to see if anyone else…

23 00:02:33.390 ⇒ 00:02:49.759 Greg Stoutenburg: should have access to this view, who hasn’t been added yet. Got the note about Sid, he got an invite for Omni, but hasn’t accepted it yet, so once he does accept it, then I can add him to this view. But I’ve got Caitlin, Deanna, you, Lev, Nandika, Ryan.

24 00:02:50.070 ⇒ 00:02:51.080 Laura Krivec: Nico.

25 00:02:51.080 ⇒ 00:02:52.130 Greg Stoutenburg: default side.

26 00:02:52.840 ⇒ 00:02:54.669 Laura Krivec: Let’s add Nico also.

27 00:02:58.300 ⇒ 00:02:59.350 Greg Stoutenburg: Alright, anyone else?

28 00:03:00.440 ⇒ 00:03:06.390 Laura Krivec: Mmm… I think… that’s it, maybe, for now?

29 00:03:06.540 ⇒ 00:03:09.289 Greg Stoutenburg: Okay, cool. If anyone else comes up, we can just add them here.

30 00:03:09.760 ⇒ 00:03:24.289 Greg Stoutenburg: Alright, so let’s jump in here. So some things that we’ve learned from other dashboards is just the utility of having a sort of overview tab at the top that says what this dashboard is for, what it’s measuring, lays out some definitions, and that kind of thing.

31 00:03:24.290 ⇒ 00:03:34.419 Caitlyn Vaughn: Also, Greg, before you scroll down on this dash, will you just filter out for the reps? Because we’ll fix that after this. Is equal to, and then go down one?

32 00:03:36.880 ⇒ 00:03:43.560 Caitlyn Vaughn: And then, let’s do Chris… Elizabeth. George. Gray.

33 00:03:44.150 ⇒ 00:03:45.160 Caitlyn Vaughn: Jack.

34 00:03:45.830 ⇒ 00:03:47.230 Caitlyn Vaughn: John Connolly.

35 00:03:48.090 ⇒ 00:03:49.220 Caitlyn Vaughn: Love.

36 00:03:50.380 ⇒ 00:03:51.789 Caitlyn Vaughn: Max Clang.

37 00:03:52.130 ⇒ 00:03:53.350 Caitlyn Vaughn: Nautica?

38 00:03:53.660 ⇒ 00:03:54.970 Caitlyn Vaughn: And then fill.

39 00:03:55.790 ⇒ 00:03:56.600 Greg Stoutenburg: Sure.

40 00:03:56.820 ⇒ 00:04:04.870 Caitlyn Vaughn: Okay, so this will be permanent. I, like, had filtered it out, right before this, but we can make this, like, a state.

41 00:04:05.540 ⇒ 00:04:07.530 Caitlyn Vaughn: Okay. Do we need to see anybody else.

42 00:04:07.750 ⇒ 00:04:12.509 Greg Stoutenburg: Okay, sure. We can make that the official list and just build that in. Yeah.

43 00:04:12.510 ⇒ 00:04:13.060 Caitlyn Vaughn: Sounds good.

44 00:04:14.080 ⇒ 00:04:16.389 Greg Stoutenburg: And then we’re looking at active…

45 00:04:16.500 ⇒ 00:04:20.059 Greg Stoutenburg: Past 30 days, and showing the last 10 complete weeks.

46 00:04:26.970 ⇒ 00:04:30.260 Greg Stoutenburg: So, tiles on the top for the highest level metrics.

47 00:04:33.800 ⇒ 00:04:42.000 Laura Krivec: I think for the weekly active… I mean, Lev, feel free to jump in, but that should be by the BDR, right? So we can compare who’s doing what.

48 00:04:43.860 ⇒ 00:04:45.320 Laura Krivec: Or is that…

49 00:04:47.840 ⇒ 00:04:49.710 Greg Stoutenburg: So this is showing total volume.

50 00:04:49.710 ⇒ 00:05:06.440 Laura Krivec: Yeah, but it should also be by VDR, right? Yeah, so there’s gonna be… we’re sharing audio in here, by the way, so that they just holler if it’s unclear, but I went through these reports, I have some notes on each one. I think generally speaking.

51 00:05:06.510 ⇒ 00:05:14.049 Laura Krivec: Especially for the global stuff, it’s gonna be really interesting to be able to easily toggle between global views.

52 00:05:14.050 ⇒ 00:05:30.479 Laura Krivec: and individual rep views. So, like, for example, that first report is in Canada for that one. On the one hand, it is nice to see total team output, but it would be equally important to be able to go in and say, okay, what is Jack’s?

53 00:05:30.760 ⇒ 00:05:38.039 Laura Krivec: output relative to Elizabeth’s, or even just in isolation. So, yeah, that would be a call-out for the first one.

54 00:05:38.250 ⇒ 00:05:43.520 Greg Stoutenburg: So maybe here, then, maybe we could do something like just have a filter for by rep.

55 00:05:43.680 ⇒ 00:05:45.929 Greg Stoutenburg: And then you choose the rep, and you get this view?

56 00:05:46.690 ⇒ 00:05:56.810 Laura Krivec: Or just duplicate this chart and have it on the, like, on the right-hand side, and just, show all the reps. I think it’s easier.

57 00:05:56.810 ⇒ 00:05:57.240 Greg Stoutenburg: Oh, okay.

58 00:05:57.240 ⇒ 00:06:03.409 Laura Krivec: See all the reps in one, chart, because you can just, quickly see who’s performing better.

59 00:06:03.410 ⇒ 00:06:06.530 Greg Stoutenburg: Yes, okay, okay, okay, understood the need there, yeah, so…

60 00:06:06.530 ⇒ 00:06:16.789 Laura Krivec: Yeah, to be able to compare performance. Dashboard is, you know, Lev should see this and be able to manage performance, so… Got it. It’s very clear and easy.

61 00:06:16.790 ⇒ 00:06:28.470 Greg Stoutenburg: Yep. Okay. Yep, that, that insight is very helpful. So, we can take this, duplicate it, so that for each of these, you see all of the tasks, and also by rep for each of them. Yep. Okay.

62 00:06:28.470 ⇒ 00:06:31.880 Laura Krivec: And sorry, what is task complete versus the other ones?

63 00:06:32.950 ⇒ 00:06:39.929 Laura Krivec: The second chart? No, no. So, what is, like, on the weekly activity volume, what’s tasks?

64 00:06:40.260 ⇒ 00:06:47.589 Laura Krivec: Yeah, so… But why don’t we… we have calls also, and then we have emails also?

65 00:06:47.850 ⇒ 00:06:49.810 Laura Krivec: Yeah, you can see it on the chart.

66 00:06:50.270 ⇒ 00:06:59.120 Laura Krivec: There’s a similar view to that one later, so… that separates, like, per each rep, volume by channel.

67 00:06:59.890 ⇒ 00:07:03.730 Laura Krivec: But I’m asking, on this chart, what is tasks?

68 00:07:03.920 ⇒ 00:07:12.649 Laura Krivec: you’re telling me it’s calls and emails, but we have separate entries for calls and emails, so tasks is calls plus emails? Yes. Yeah.

69 00:07:12.790 ⇒ 00:07:14.690 Laura Krivec: So we can remove tasks, right?

70 00:07:15.100 ⇒ 00:07:17.620 Laura Krivec: Perhaps.

71 00:07:18.070 ⇒ 00:07:21.910 Laura Krivec: Yeah, tasks completed might be slightly redundant. Okay.

72 00:07:22.340 ⇒ 00:07:27.079 Greg Stoutenburg: Yeah, so just to clarify, right, tasks is the sum of these. These are the tasks.

73 00:07:27.080 ⇒ 00:07:31.669 Laura Krivec: Okay, out of all four, I see. Okay.

74 00:07:32.570 ⇒ 00:07:33.250 Greg Stoutenburg: Right, love?

75 00:07:33.250 ⇒ 00:07:36.289 Laura Krivec: So it’s also NOA… but what is NOA account for?

76 00:07:36.290 ⇒ 00:07:38.219 Nandika Jhunjhunwala: Technically, new contact.

77 00:07:38.220 ⇒ 00:07:44.859 Laura Krivec: How did the new accounts worked, or not classified as tasks? Okay. That’s a nitpick.

78 00:07:45.070 ⇒ 00:07:45.770 Greg Stoutenburg: Okay.

79 00:07:47.040 ⇒ 00:07:52.260 Laura Krivec: But yeah, I, I think we can table this one, I think it’s okay for now.

80 00:07:52.490 ⇒ 00:07:53.780 Greg Stoutenburg: Okay, okay.

81 00:07:54.390 ⇒ 00:07:58.999 Greg Stoutenburg: Alright, alright, I’ll just keep moving then.

82 00:07:59.660 ⇒ 00:08:02.719 Laura Krivec: I would say that the second one, overdue versus completed.

83 00:08:03.380 ⇒ 00:08:06.880 Laura Krivec: I’m a little bit indifferent on it.

84 00:08:06.990 ⇒ 00:08:11.319 Laura Krivec: I think it’s a little noisy as is, so we might want to circle back on, like.

85 00:08:11.420 ⇒ 00:08:13.839 Laura Krivec: The value in this, or…

86 00:08:14.770 ⇒ 00:08:30.080 Laura Krivec: I don’t know how it could be best leveraged. I think for one, this is one that’s a candidate for per rev view. It’s much more interesting to see that somebody is more organized and somebody’s less, rather than, like, the team at aggregate.

87 00:08:30.080 ⇒ 00:08:30.530 Greg Stoutenburg: So…

88 00:08:30.530 ⇒ 00:08:35.380 Laura Krivec: I’d say this one also falls into the bucket of, would prefer to be able to filter by rep.

89 00:08:37.299 ⇒ 00:08:39.269 Greg Stoutenburg: Okay, would you want,

90 00:08:39.659 ⇒ 00:08:47.049 Greg Stoutenburg: So I guess, same question as for this one. Would you want to keep this chart and then duplicate it that adds the per-rep view?

91 00:08:47.339 ⇒ 00:08:51.059 Greg Stoutenburg: Or do you just want to add a filter here, so that you can filter per… per.

92 00:08:51.060 ⇒ 00:08:52.959 Laura Krivec: Normally, the folder is fine.

93 00:08:53.260 ⇒ 00:09:01.559 Greg Stoutenburg: Okay, so overview versus completed tasks a weekend gets a filter for rep. So you can choose the rep, see their overdue tasks versus tasks.

94 00:09:01.560 ⇒ 00:09:07.330 Laura Krivec: Or, I guess, I mean, yeah, I think generally speaking, stack ranking.

95 00:09:07.450 ⇒ 00:09:11.240 Laura Krivec: is probably the way to go, if possible.

96 00:09:12.120 ⇒ 00:09:20.319 Laura Krivec: Yeah, I really think, like, this weekly activity volume dashboard is really the only one that I would probably be interested in seeing in aggregate.

97 00:09:21.420 ⇒ 00:09:27.069 Laura Krivec: I think things like this overdue task list, which falls into, like, the housekeeping category, is very much…

98 00:09:27.330 ⇒ 00:09:31.419 Laura Krivec: Something that you would want to observe on a, individual basis?

99 00:09:31.630 ⇒ 00:09:32.230 Greg Stoutenburg: Okay.

100 00:09:32.570 ⇒ 00:09:33.190 Laura Krivec: Yeah.

101 00:09:33.580 ⇒ 00:09:34.320 Greg Stoutenburg: Okay.

102 00:09:34.320 ⇒ 00:09:40.010 Nandika Jhunjhunwala: I think to add on to that, I think we don’t, assign…

103 00:09:40.740 ⇒ 00:09:56.029 Nandika Jhunjhunwala: Sorry. I don’t think we assign tasks… I don’t think they’ll assign, like, call or email tasks as, like, oh, you have to do this by the end of the week. These are just, like, sequences that go out, and then the reps, like, call and email as, like, per their discretion.

104 00:09:56.180 ⇒ 00:10:01.779 Nandika Jhunjhunwala: So I think indexing on, like, due dates or, like, completion, per se.

105 00:10:01.900 ⇒ 00:10:09.480 Nandika Jhunjhunwala: as, like, metrics for, like, their performance, and then including them in the dashboard, I’m not sure is, like…

106 00:10:10.320 ⇒ 00:10:13.689 Nandika Jhunjhunwala: the way to go, I’ll refer to that.

107 00:10:13.690 ⇒ 00:10:26.110 Laura Krivec: boost proxy for organization, so I wouldn’t index too heavily on it. I think it’s okay to have, like, one of these in there, but it should definitely not be, by any means a focus. I would say to that end, the…

108 00:10:26.410 ⇒ 00:10:32.310 Laura Krivec: The board that we’re about to roll out to, past due versus overdue, is not that helpful.

109 00:10:33.760 ⇒ 00:10:34.710 Laura Krivec: What?

110 00:10:36.110 ⇒ 00:10:42.730 Laura Krivec: Oh, I’m speeding through a lot of this, laptop. Yeah. Anyway, so yeah, I think, like, the second one…

111 00:10:42.850 ⇒ 00:10:45.450 Laura Krivec: The line graph is, like, not that helpful.

112 00:10:46.160 ⇒ 00:10:47.910 Greg Stoutenburg: Okay, do you want it revised?

113 00:10:47.910 ⇒ 00:10:48.930 Laura Krivec: Same off, man.

114 00:10:48.930 ⇒ 00:10:49.400 Greg Stoutenburg: Drop it.

115 00:10:49.400 ⇒ 00:10:50.240 Laura Krivec: And rare.

116 00:10:52.270 ⇒ 00:10:53.839 Greg Stoutenburg: Do you want us to just cut it?

117 00:10:54.070 ⇒ 00:10:55.210 Laura Krivec: Yeah, remove.

118 00:10:55.590 ⇒ 00:10:56.000 Greg Stoutenburg: Okay.

119 00:10:56.000 ⇒ 00:10:57.140 Laura Krivec: God.

120 00:10:57.140 ⇒ 00:10:59.880 Greg Stoutenburg: Remove tasks due versus overdue, okay.

121 00:11:01.300 ⇒ 00:11:10.209 Laura Krivec: Yeah, and then we’ll look into this one. I have all of this stuff noted out, so I feel like we should just run through it. Same day completion,

122 00:11:12.210 ⇒ 00:11:14.330 Laura Krivec: Yeah, this is…

123 00:11:16.880 ⇒ 00:11:25.769 Laura Krivec: This is also, like, I think over-indexing on task completion, not really interested in this one. We could probably scrap this as well.

124 00:11:25.770 ⇒ 00:11:26.360 Greg Stoutenburg: Okay.

125 00:11:26.730 ⇒ 00:11:33.650 Greg Stoutenburg: Yeah, and just to… just to be super clear then, so there’s no KPI for the BDRs that’s associated with…

126 00:11:33.860 ⇒ 00:11:38.819 Greg Stoutenburg: Timeliness, just sort of completion rate overall?

127 00:11:39.010 ⇒ 00:11:49.009 Laura Krivec: I mean, there is… well, generally speaking, like I said, I think it’s a loose proxy for organizations, so I think one report that shows their…

128 00:11:49.080 ⇒ 00:12:02.760 Laura Krivec: speed of completion relative to one another is a good thing to gloss over every once in a while. You know, aside, by the way, you happen to have the highest number of uncompleted tasks.

129 00:12:02.760 ⇒ 00:12:03.120 Greg Stoutenburg: Yeah.

130 00:12:03.120 ⇒ 00:12:09.140 Laura Krivec: But I think more than one dashboard is, like, Over-indexing on this.

131 00:12:09.140 ⇒ 00:12:09.820 Greg Stoutenburg: Okay.

132 00:12:09.820 ⇒ 00:12:10.820 Laura Krivec: Data point.

133 00:12:10.820 ⇒ 00:12:14.659 Greg Stoutenburg: Okay. Yeah, okay, yeah, not obsessing over it. So, is it, like…

134 00:12:15.240 ⇒ 00:12:23.459 Greg Stoutenburg: Is it that timeliness is sort of redundant on completion rate? Because if, you know, if you’re using, you know, a sequencing tool.

135 00:12:23.460 ⇒ 00:12:37.729 Greg Stoutenburg: an outreach tool that’s like, alright, you have, you know, whatever, 100 tasks today, and I get through 90 of them, then tomorrow, I have a 90% completion rate. I don’t also need to know that I have a 10% late rate. Is that, like, the idea?

136 00:12:38.790 ⇒ 00:12:40.490 Laura Krivec: Yeah, okay.

137 00:12:40.830 ⇒ 00:12:43.750 Greg Stoutenburg: Alright, that helps, I get, I get now why that’s…

138 00:12:43.900 ⇒ 00:12:45.630 Greg Stoutenburg: Not that useful, then. Okay, thank you.

139 00:12:45.630 ⇒ 00:12:55.010 Laura Krivec: I mean, we’re operating at such a high volume that the KPI of, like, your volume target is sort of self-fulfilling.

140 00:12:55.620 ⇒ 00:12:56.130 Greg Stoutenburg: Okay.

141 00:12:56.130 ⇒ 00:13:03.510 Laura Krivec: They’re doing a lot of outreach, and there isn’t a massive delta on an individual rep basis of, like, time to completion.

142 00:13:03.740 ⇒ 00:13:04.370 Greg Stoutenburg: Okay.

143 00:13:04.720 ⇒ 00:13:18.349 Demilade Agboola: Just to chime in here, Lev, when you’re, like, looking at a dashboard, and you’re like, oh, like, what will set your alarm bells ringing? Like, if you were to look at the dashboard, what metric will set your alarm bells ringing, in a dashboard?

144 00:13:19.540 ⇒ 00:13:26.549 Laura Krivec: Sorry, I’m trying to… Oh, yeah, we’re… oh, I think we have a hard time hearing you.

145 00:13:26.550 ⇒ 00:13:39.680 Demilade Agboola: Apologies, I’m just saying, for a dashboard, and, like, if you’re looking through a dashboard, what will set your alarm bells ringing? Like, what will trigger, like, red flags if you are going through a dashboard? What metrics will those…

146 00:13:40.410 ⇒ 00:13:56.240 Laura Krivec: Yeah, I mean, I think generally speaking, the idea is that you have, naturally, a spread of performance across the team, and it’s really helpful to see, A, how the top performer is behaving from a metrics perspective, and if there are any

147 00:13:56.260 ⇒ 00:14:15.430 Laura Krivec: anomalies in an underperformer that we could, from a data perspective, speak to. Oh, for example, top performer is really dialed in their completion rate, and, like, the bottom performer’s not. Like, that’s something that’s worth having a conversation about, in this current example. But I would say that, to that end.

148 00:14:15.720 ⇒ 00:14:20.910 Laura Krivec: understanding performance relative to each rep is really helpful.

149 00:14:21.480 ⇒ 00:14:28.549 Laura Krivec: And that’s, like, the main use case of the staff. I’m less so interested in, like, the overall pulse track of the team, because, like…

150 00:14:29.380 ⇒ 00:14:31.620 Laura Krivec: Yeah, that’s more…

151 00:14:31.770 ⇒ 00:14:33.339 Greg Stoutenburg: Yep, understood, yep.

152 00:14:33.860 ⇒ 00:14:38.460 Greg Stoutenburg: Yeah, that’s less relevant to team coaching. Okay. Alright, then I think we can move on from this one.

153 00:14:39.740 ⇒ 00:14:43.900 Greg Stoutenburg: And… Now here’s some… here’s some breakdowns by rep.

154 00:14:44.370 ⇒ 00:15:04.670 Laura Krivec: Yeah, so this is where it starts getting interesting. I think on the left-hand side, top 10 accounts, or, scale accounts should be probably metered based on, a longer window, rather than 7 days, so something like 30 to 90 days is more interesting. I would say it would be…

155 00:15:04.840 ⇒ 00:15:23.930 Laura Krivec: probably call, like, 30 or 60 to start, because, yeah, 7 days is, like, pretty even distribution. What you’re seeing here is that we have ADs and BDRs showing up in this graph, but really, if you concentrate on all of the more spiky data points, like, those are all BDRs that are essentially performing

156 00:15:23.960 ⇒ 00:15:43.089 Laura Krivec: the same over a 7-day period of time. I would imagine there’s going to be more variance over a longer period of time, so yeah, my, main thing for that would be increasing the, period of time that we’re observing in. Yep. And on the right-hand side, this is a larger call-out, scale contacts.

157 00:15:43.090 ⇒ 00:15:48.419 Laura Krivec: is not something that we should be indexing on, and I think, generally speaking.

158 00:15:48.420 ⇒ 00:16:07.640 Laura Krivec: contact ownership is something that we should largely ignore, because it’s pretty inconsistent across Salesforce. I believe that this graph on the right-hand side is the reading off of contact ownership, and that’s not something that I pay attention to or is consistent, so I would say that it’s probably not worth modeling up.

159 00:16:07.850 ⇒ 00:16:14.379 Greg Stoutenburg: Okay, we could cut this one. Cut top 10 reps by average sale contacts, and then for…

160 00:16:17.300 ⇒ 00:16:23.930 Greg Stoutenburg: for average sale accounts, make that visible over a long… longer period of time. Yeah, maybe we can add a…

161 00:16:24.080 ⇒ 00:16:29.040 Greg Stoutenburg: Maybe we could add a time filter here as well, so you can just choose how long it’s been.

162 00:16:29.190 ⇒ 00:16:30.440 Greg Stoutenburg: Still.

163 00:16:30.900 ⇒ 00:16:50.799 Laura Krivec: Yeah, and then this is a, a good fast follow-up to this that has implications over a few dashboards. I wasn’t really able to suss out how you guys are modeling account activation, but the only proxy that we should be using is the presence of sales activity.

164 00:16:50.870 ⇒ 00:17:07.300 Laura Krivec: over a period of time, there’s, like, some fields on the account of, like, status or something, and I have no observability into the accuracy of those fields then being updated. So, for example, like, there’s a greater chance that this

165 00:17:07.500 ⇒ 00:17:22.049 Laura Krivec: report on the left that’s reading off of some field that I’m not familiar with. I would more so be… I would… well, I would say we need to… we need to model this off of, like, whether or not there was a task created on an account,

166 00:17:22.119 ⇒ 00:17:27.940 Laura Krivec: You know, whatever time frame, and that’s, like, the only proxy for account activation.

167 00:17:28.750 ⇒ 00:17:33.179 Greg Stoutenburg: Demi or Advait, can you speak to how stale is defined here?

168 00:17:34.960 ⇒ 00:17:36.840 Greg Stoutenburg: If you, if you know off the top of your head.

169 00:17:39.190 ⇒ 00:17:41.510 Greg Stoutenburg: It just says untouched, touched or untouched.

170 00:17:42.440 ⇒ 00:17:46.129 Demilade Agboola: So largely, I would have… I would have to…

171 00:17:46.130 ⇒ 00:17:51.210 Greg Stoutenburg: Sorry. Good job, guys, it’s right there. Closed task activity.

172 00:17:53.810 ⇒ 00:17:54.600 Laura Krivec: 11.

173 00:17:54.840 ⇒ 00:17:56.809 Greg Stoutenburg: Lev, is that a good measure here?

174 00:17:56.810 ⇒ 00:18:10.569 Laura Krivec: Yeah, so this one might be good in that case. I did notice there are a few that read off of, like, a variable field, and I’m sure we can figure that out pretty quickly, but those will need to be reworked, likely.

175 00:18:11.090 ⇒ 00:18:26.670 Greg Stoutenburg: Okay, if you could just… I’ll share the… well, everyone has access to this folder now. If you would just share which dashboards those are, if you… if you know what they are, you could say it now, or, you know, as you review later, take a look, and we can take care of that, if we know which ones exactly you want to have clarified.

176 00:18:26.880 ⇒ 00:18:28.570 Laura Krivec: Yeah, got you.

177 00:18:28.570 ⇒ 00:18:29.150 Greg Stoutenburg: Okay.

178 00:18:29.910 ⇒ 00:18:30.650 Greg Stoutenburg: Alright.

179 00:18:34.360 ⇒ 00:18:39.419 Laura Krivec: And… What’s the timeframe on these two charts?

180 00:18:41.340 ⇒ 00:18:43.199 Laura Krivec: The ones we’re looking at? Yeah.

181 00:18:43.570 ⇒ 00:18:52.629 Laura Krivec: Like, what is Anderson, what time? Well, so, correct me if I’m wrong, but this should be variable in accordance to, like, the master date field at the top.

182 00:18:52.630 ⇒ 00:18:58.219 Greg Stoutenburg: Yep. Just as per the latest snapshot date, up at the top, latest snapshot date is today.

183 00:18:59.420 ⇒ 00:19:02.220 Laura Krivec: Right, but… Okay.

184 00:19:03.420 ⇒ 00:19:06.400 Laura Krivec: So this is everything that’s true today.

185 00:19:06.640 ⇒ 00:19:08.010 Greg Stoutenburg: Yep, this is as of right now.

186 00:19:08.310 ⇒ 00:19:09.110 Laura Krivec: Welcome.

187 00:19:09.620 ⇒ 00:19:11.570 Caitlyn Vaughn: For all eternity?

188 00:19:11.940 ⇒ 00:19:13.650 Caitlyn Vaughn: This is an all-time.

189 00:19:14.540 ⇒ 00:19:16.269 Greg Stoutenburg: Yeah, is this just a running total, then?

190 00:19:17.440 ⇒ 00:19:18.960 Greg Stoutenburg: Vader Demi, if you know.

191 00:19:22.920 ⇒ 00:19:28.079 Demilade Agboola: Yeah, I think it’s a running total of all time, and so…

192 00:19:29.250 ⇒ 00:19:38.459 Demilade Agboola: I’m not sure if the filter affects it, I might need to just confirm, because that’s what Advait was working on. But yes, it’s, like, a running total at all time.

193 00:19:38.670 ⇒ 00:19:45.800 Greg Stoutenburg: Yeah, I would think that would be what you’d want, right? Because if an account is assigned to someone, then you want them touching it. Otherwise, it would be unassigned, right?

194 00:19:46.420 ⇒ 00:19:59.240 Laura Krivec: Yeah, that’s fair. I’m not sure if this is accurate or not, but if you hover over the tooltip on accounts and outreach, there’s a note about reading off of.

195 00:19:59.240 ⇒ 00:19:59.810 Greg Stoutenburg: Oh, sorry.

196 00:19:59.810 ⇒ 00:20:07.680 Laura Krivec: owned contacts, and that was falling under the umbrella that I brought up earlier about owned contacts.

197 00:20:07.910 ⇒ 00:20:14.600 Laura Krivec: not being worth indexing on, so I didn’t figure if that was skewing the data at all.

198 00:20:16.590 ⇒ 00:20:17.480 Greg Stoutenburg: I’m.

199 00:20:17.480 ⇒ 00:20:21.170 Caitlyn Vaughn: about, Lev, is owned accounts, not owned contacts.

200 00:20:21.800 ⇒ 00:20:23.070 Laura Krivec: Say it again, Caitlin?

201 00:20:23.070 ⇒ 00:20:28.079 Caitlyn Vaughn: So what we care about, then, is owned accounts and not owned contacts.

202 00:20:28.080 ⇒ 00:20:43.839 Laura Krivec: Yeah, basically, like, as a blanket statement, we care about owned accounts with tasks completed on them, like sales completed tasks. That’s literally the only thing that designates whether or not an account is being worked.

203 00:20:44.870 ⇒ 00:20:45.430 Laura Krivec: Yes.

204 00:20:45.430 ⇒ 00:20:49.200 Greg Stoutenburg: Oh, I… okay. Okay, I was… I thought we were still over here. You’re over here now.

205 00:20:49.610 ⇒ 00:20:50.400 Laura Krivec: Yeah, yeah.

206 00:20:50.400 ⇒ 00:20:52.739 Greg Stoutenburg: I’m saying, we just… we don’t really need this, it sounds like.

207 00:20:52.740 ⇒ 00:20:54.810 Laura Krivec: Oh.

208 00:20:56.060 ⇒ 00:20:59.059 Greg Stoutenburg: Just cut contact… contact depth by rep.

209 00:21:00.130 ⇒ 00:21:02.260 Laura Krivec: Well… outside.

210 00:21:03.250 ⇒ 00:21:15.460 Greg Stoutenburg: Because this is saying accounts with contacts in outreach. So it’s still an account of accounts, yeah.

211 00:21:15.460 ⇒ 00:21:19.720 Laura Krivec: Yeah, no, I was actually talking about the one on the left-hand side.

212 00:21:19.720 ⇒ 00:21:20.200 Greg Stoutenburg: Okay.

213 00:21:20.200 ⇒ 00:21:26.330 Laura Krivec: Did you go over the tooltip, there’s a note about owed contacts being factored

214 00:21:26.500 ⇒ 00:21:31.170 Laura Krivec: into, like, accounts and outreach, the little… Yeah, yeah. Okay, this one…

215 00:21:31.170 ⇒ 00:21:32.920 Greg Stoutenburg: Okay, got it. Alright, I’m caught up.

216 00:21:32.920 ⇒ 00:21:44.799 Laura Krivec: Only counts, the last two words. Contacts, yeah. So, I’m not sure where that’s coming from, but we were not using contact ownership for anything.

217 00:21:45.540 ⇒ 00:21:46.230 Greg Stoutenburg: Okay.

218 00:21:47.110 ⇒ 00:21:52.909 Greg Stoutenburg: So, I think what this is saying that… is that we’re counting an account as being

219 00:21:53.070 ⇒ 00:22:00.100 Greg Stoutenburg: In outreach, if any contact on that account is in outreach.

220 00:22:00.950 ⇒ 00:22:01.640 Laura Krivec: We’ll…

221 00:22:01.640 ⇒ 00:22:03.539 Greg Stoutenburg: Some way of counting the account, as in…

222 00:22:03.540 ⇒ 00:22:16.510 Laura Krivec: I mean, that’s what I want to know. Two questions I have are, is the contact ownership status impacting that? And also, how are you designating that a contact is activated?

223 00:22:16.770 ⇒ 00:22:27.899 Laura Krivec: Because there’s a few ways of doing that, as I’ve noticed throughout this report, and the only way that I would want to do that is using task creation as an indicator.

224 00:22:27.900 ⇒ 00:22:28.889 Greg Stoutenburg: Right? Okay.

225 00:22:28.890 ⇒ 00:22:33.519 Laura Krivec: I’ll find you as an example, but there’s, like, a few situations where we’re reading off of the field.

226 00:22:33.680 ⇒ 00:22:42.910 Laura Krivec: On the account level, that’s, like, account status or something. And, like, that’s… that’s gonna be pulling bad data, because we don’t use that field.

227 00:22:42.910 ⇒ 00:22:50.849 Greg Stoutenburg: Okay, so is this… so you then want the same measure as staleness. So, to be stale is to be not in contact.

228 00:22:51.040 ⇒ 00:22:56.340 Greg Stoutenburg: And to be in contact is to have… Oh, either opened or closed.

229 00:22:56.880 ⇒ 00:23:01.020 Laura Krivec: Yeah, for that contact. Yeah, exactly.

230 00:23:01.020 ⇒ 00:23:01.820 Greg Stoutenburg: Okay.

231 00:23:02.170 ⇒ 00:23:05.910 Greg Stoutenburg: Alright, that makes sense. We can make sure that’s applied universally,

232 00:23:06.310 ⇒ 00:23:21.310 Greg Stoutenburg: I believe that’s what this means here, but we can make sure that there’s consistency on it, and also just document that. But to clarify, though, do you want to have the single measure be opened tasks, or do you want it to be closed tasks?

233 00:23:21.310 ⇒ 00:23:22.810 Laura Krivec: Windows tasks is fine.

234 00:23:22.810 ⇒ 00:23:28.620 Greg Stoutenburg: Okay, alright, close tasks for all. That’s what it means to be doing something with an account or a contact, is to be closing tasks for them.

235 00:23:28.620 ⇒ 00:23:32.810 Laura Krivec: And that’s also much more of a surface-level read, so it should be pretty easy.

236 00:23:32.960 ⇒ 00:23:38.060 Greg Stoutenburg: Yeah, yeah, okay. Great. Alright, great. Any other feedback on…

237 00:23:38.280 ⇒ 00:23:40.630 Greg Stoutenburg: On this… on this one, or on this one?

238 00:23:41.100 ⇒ 00:23:43.850 Laura Krivec: Yeah, just on the contact death piece.

239 00:23:44.530 ⇒ 00:23:46.520 Laura Krivec: There’s,

240 00:23:46.640 ⇒ 00:24:03.099 Laura Krivec: Sorry, I’m doing a bunch of nitpicking here, but there’s a column called Average Active Contacts per account, and it’s like a decimal. I would imagine that that’s reading based on the aggregate account ownership, but

241 00:24:03.460 ⇒ 00:24:16.899 Laura Krivec: Yeah, I don’t know if that’s the case, but having, you know.7 of a percent in outreach was a little confusing to me, so I would want to, like, double-click on whether or not that’s accurate, or if it’s just, like, for an aggregated reading.

242 00:24:17.230 ⇒ 00:24:18.460 Greg Stoutenburg: Hmm, yeah.

243 00:24:23.000 ⇒ 00:24:25.609 Greg Stoutenburg: Yeah, I’m not sure how you’d get that under one.

244 00:24:26.260 ⇒ 00:24:32.540 Laura Krivec: I mean, I would assume it’s saying if you have 1,500 accounts, and you’re only working a portion of those.

245 00:24:32.660 ⇒ 00:24:41.660 Laura Krivec: Then, you know… extrapolating to the entirety of your book. On average, you have, you know.7 people.

246 00:24:41.660 ⇒ 00:24:42.550 Greg Stoutenburg: Right.

247 00:24:42.580 ⇒ 00:24:46.230 Laura Krivec: But, you know, whatever. Not a big one there.

248 00:24:46.450 ⇒ 00:24:47.030 Greg Stoutenburg: Yeah.

249 00:24:47.280 ⇒ 00:24:51.899 Greg Stoutenburg: So, I would think this might be a number you don’t care about. Am I right about that?

250 00:24:53.090 ⇒ 00:24:57.979 Caitlyn Vaughn: I think this is good to have. I also think this does make sense, because if it’s…

251 00:24:58.040 ⇒ 00:25:15.130 Caitlyn Vaughn: below one, that it means, on average… like, the blended average of all of your accounts, you’re not talking to at least one person at every account, right? And then for some of these, like, I’m looking at John Connolly, he has 1.5, so he’s talking to, like, on average, 1.5 contacts per account.

252 00:25:15.590 ⇒ 00:25:21.069 Caitlyn Vaughn: Which is good to know. And I also feel like it would be interesting to back into…

253 00:25:21.240 ⇒ 00:25:25.670 Caitlyn Vaughn: like, once we’re, you know, 6 months into Phoenix sales, like.

254 00:25:25.910 ⇒ 00:25:29.840 Caitlyn Vaughn: How many contacts per account, on average, are reps

255 00:25:30.110 ⇒ 00:25:40.290 Caitlyn Vaughn: talking to compared to, like, what percentage they’re closing, or, like, total ARR closed. Like, those are… those are interesting stats. Those are, like, small things that start

256 00:25:40.420 ⇒ 00:25:43.440 Caitlyn Vaughn: Like, once you can start seeing those things, it…

257 00:25:44.040 ⇒ 00:25:49.420 Caitlyn Vaughn: It starts getting interesting when you can compile them together and, like, start piecing together data.

258 00:25:51.030 ⇒ 00:25:54.439 Laura Krivec: Yeah, I agree. I think this one’s fine for now.

259 00:25:54.860 ⇒ 00:25:56.580 Greg Stoutenburg: Okay. Alright, sounds good.

260 00:25:56.580 ⇒ 00:26:06.760 Laura Krivec: I would say for that, for that one that we were just talking about, and for this… this one below it, like, just want to confirm how we’re classifying an activated contact.

261 00:26:07.050 ⇒ 00:26:21.779 Laura Krivec: we should pay no mind to ownership status. Ownership status should only be determined on the account level, and activated status should be determined based on closed tasks.

262 00:26:22.280 ⇒ 00:26:22.940 Greg Stoutenburg: Yes.

263 00:26:23.340 ⇒ 00:26:24.670 Laura Krivec: I’m contacting them.

264 00:26:24.670 ⇒ 00:26:27.179 Greg Stoutenburg: Yep, heard on that, yep. Okay.

265 00:26:27.730 ⇒ 00:26:30.510 Laura Krivec: In that case, this one’s good.

266 00:26:30.730 ⇒ 00:26:31.380 Greg Stoutenburg: Okay.

267 00:26:37.210 ⇒ 00:26:37.970 Laura Krivec: Awesome.

268 00:26:37.970 ⇒ 00:26:39.370 Greg Stoutenburg: Recently assigned.

269 00:26:40.210 ⇒ 00:26:44.539 Greg Stoutenburg: Yeah, so what makes it a new account is it’s a recently assigned account.

270 00:26:46.590 ⇒ 00:26:46.950 Laura Krivec: Yeah.

271 00:26:47.090 ⇒ 00:26:47.940 Greg Stoutenburg: Yeah.

272 00:26:48.180 ⇒ 00:26:48.680 Laura Krivec: Understood.

273 00:26:48.680 ⇒ 00:26:49.130 Greg Stoutenburg: touch.

274 00:26:49.130 ⇒ 00:26:54.830 Laura Krivec: I know this is accurate, but the first toolkit Says something about created accounts.

275 00:26:55.370 ⇒ 00:26:59.890 Laura Krivec: As a subset of recently assigned accounts,

276 00:27:00.480 ⇒ 00:27:07.280 Laura Krivec: I’m not sure if that’s accurate or not, but that would be skewing this, because the majority of the recently assigned accounts are not newly created.

277 00:27:07.740 ⇒ 00:27:11.840 Laura Krivec: I mean, we want to make sure that that’s taking into account every single recently assigned account.

278 00:27:12.290 ⇒ 00:27:14.190 Greg Stoutenburg: Yes, right, yep.

279 00:27:14.340 ⇒ 00:27:18.710 Greg Stoutenburg: Rather than a subset of the new accounts. Yep, got it. Okay, we’ll clarify that.

280 00:27:20.450 ⇒ 00:27:24.500 Laura Krivec: And then, yeah, I would say…

281 00:27:24.860 ⇒ 00:27:27.119 Laura Krivec: The rest of this is 5.

282 00:27:27.580 ⇒ 00:27:33.229 Laura Krivec: there’s… There’s a column called Average Days to First Touch.

283 00:27:33.450 ⇒ 00:27:38.719 Laura Krivec: That has some very large numbers on it that threw me off a little bit.

284 00:27:38.720 ⇒ 00:27:39.310 Greg Stoutenburg: Yeah.

285 00:27:40.000 ⇒ 00:27:40.960 Laura Krivec: So, I would want to…

286 00:27:40.960 ⇒ 00:27:41.540 Greg Stoutenburg: Yeah.

287 00:27:41.540 ⇒ 00:27:42.140 Laura Krivec: I’m not…

288 00:27:42.140 ⇒ 00:27:45.480 Greg Stoutenburg: Yeah, we’ll dig in on that. Yeah, that would be a very long time to wait.

289 00:27:45.590 ⇒ 00:27:51.200 Greg Stoutenburg: from the time that you have an account to contact them. So we’ll… we’ll figure out what that is.

290 00:27:51.530 ⇒ 00:27:52.230 Laura Krivec: Yeah.

291 00:27:54.040 ⇒ 00:27:57.699 Greg Stoutenburg: Come on, Phil, you waited almost a year to reach out. That’s a long time.

292 00:27:58.140 ⇒ 00:27:59.770 Laura Krivec: Yeah, it feels very offensive.

293 00:28:01.140 ⇒ 00:28:21.989 Laura Krivec: Sorry to interrupt, I have to go in 3 minutes, because I have another meeting, but, to me, this dashboard is missing the key things that we judge BDRs on performance, which is basically, qualified meetings, right? Or qualified, whatever we want to… exactly, qualified opportunities, and then also.

294 00:28:21.990 ⇒ 00:28:32.910 Laura Krivec: what was closed, right? How many leads or deals from Elizabeth resulted in money revenue to default?

295 00:28:33.030 ⇒ 00:28:48.790 Laura Krivec: What’s the average size? Like, average ACV by BDR? So none of this is here, and it was listed in the initial doc, so I don’t know what was lost in translation, but those are the key metrics that we absolutely need to see by BDR, by month.

296 00:28:48.790 ⇒ 00:29:03.649 Laura Krivec: I think you’ll have to add this on the very top. Well, so I can jump in here. I think Lava’s right, that is first and foremost of importance. There is a version of a report that has some of that further down, but…

297 00:29:03.650 ⇒ 00:29:05.729 Caitlyn Vaughn: Scroll down, it’s in there, Laura.

298 00:29:05.730 ⇒ 00:29:16.979 Laura Krivec: But it’s… it’s very immaculate. The biggest red flag is that the influence pipeline view, the pipeline numbers are…

299 00:29:17.250 ⇒ 00:29:18.690 Laura Krivec: Believable.

300 00:29:18.990 ⇒ 00:29:37.150 Laura Krivec: Yeah, I, I need to double-check, because now I realize it’s, like, a snapshot of today, but, but can we do it by, like, months, so we see it on a timeline versus this table? Yeah, this should be coming in on, like, the 30-day basis. Exactly. And to that end, these numbers are not accurate.

301 00:29:37.150 ⇒ 00:29:45.060 Greg Stoutenburg: Chris, you want to have this in a 30-day view. So, on a month-by-month basis, how much pipeline, how much close one value, how much deal size, and so on?

302 00:29:45.510 ⇒ 00:29:56.559 Laura Krivec: Yeah, by month, by BDR, and I also think we should look at the conversion rates, probably, by BDR, right? So, what’s Elizabeth’s conversion rates between the pipeline

303 00:29:56.560 ⇒ 00:30:07.060 Laura Krivec: funnel stages versus John versus Jack, so forth. So, you know, meeting to… or, like, lead to meeting book for Elizabeth, these are percentage conversion rates.

304 00:30:07.060 ⇒ 00:30:15.569 Greg Stoutenburg: Yeah, yeah, yeah, yep, yep, okay. So, I think that will be a new chart rather than another representation of this.

305 00:30:15.570 ⇒ 00:30:16.240 Laura Krivec: Yeah.

306 00:30:16.370 ⇒ 00:30:21.330 Greg Stoutenburg: Yep, we can do that. So… Yeah, we can show that.

307 00:30:21.760 ⇒ 00:30:35.700 Caitlyn Vaughn: I think the main concern, Laura, that you’re talking about is, like, it does have the right things generally, but it just needs to be prioritized in the right way, and the, like, more important charts need to be shoved towards the top, and the less important ones need to be shoved down.

308 00:30:35.700 ⇒ 00:30:53.830 Laura Krivec: Right, but on a timeline and by month, so it’s… so performance is very easily observed. Also, I think we want to put these charts in the office, so again, it needs to tell a story, like, for, you know, like, if… I’ve never seen this before, I want to see it in 20 seconds, and I understand what’s happening, which is currently not the case.

309 00:30:53.830 ⇒ 00:30:57.539 Laura Krivec: Yeah, I also need to clarify that, like.

310 00:30:57.610 ⇒ 00:31:16.759 Laura Krivec: the influence pipeline values are not correct. And I’m looking at, like, what’s currently open right now, which is what I would imagine is what this is supposedly reading off of. And all three… all four BDRs have different values than reality by, like, substantial margin, so…

311 00:31:16.760 ⇒ 00:31:25.969 Laura Krivec: We should probably tag out of time reading this, because I’m reading it in a different way. I’m reading it off of the ACV associated with opportunities.

312 00:31:29.660 ⇒ 00:31:32.600 Greg Stoutenburg: Yeah, this is a sum of open opportunities.

313 00:31:32.930 ⇒ 00:31:40.609 Greg Stoutenburg: Under BDR Influence C. Demi, can you speak to that, that column?

314 00:31:42.570 ⇒ 00:31:45.980 Demilade Agboola: Yeah, so, like, there’s a BDR influence data.

315 00:31:46.090 ⇒ 00:31:48.560 Demilade Agboola: And… It’s assigned.

316 00:31:48.560 ⇒ 00:31:53.039 Nandika Jhunjhunwala: I don’t know if you guys heard me, we just… can you hear me now?

317 00:31:53.040 ⇒ 00:31:53.840 Greg Stoutenburg: Yep, I hear you.

318 00:31:57.920 ⇒ 00:31:59.200 Nandika Jhunjhunwala: Do you buy it? Yeah.

319 00:31:59.560 ⇒ 00:32:00.289 Greg Stoutenburg: Oh, I hear you, yeah.

320 00:32:00.410 ⇒ 00:32:02.519 Nandika Jhunjhunwala: Alright, we’re back. Yeah.

321 00:32:05.000 ⇒ 00:32:08.709 Greg Stoutenburg: Okay, yeah, Demi, could you say that again?

322 00:32:10.160 ⇒ 00:32:21.099 Demilade Agboola: I was just saying that, like, yeah, in niche, like, opportunity for, like, for each opportunity, there’s a BDR influence,

323 00:32:21.390 ⇒ 00:32:29.529 Demilade Agboola: column, or PDL inference, status, and so this is just counting, like, the opportunities, like, the open opportunities currently assigned.

324 00:32:29.690 ⇒ 00:32:31.570 Demilade Agboola: to HBDR?

325 00:32:31.690 ⇒ 00:32:36.580 Demilade Agboola: Is there another way you would want to, like.

326 00:32:36.790 ⇒ 00:32:40.520 Demilade Agboola: look at the opportunity pipeline for BDRs.

327 00:32:40.520 ⇒ 00:32:45.559 Nandika Jhunjhunwala: I see what you’re saying. Yeah, so… open opportunities is…

328 00:32:45.840 ⇒ 00:33:02.000 Nandika Jhunjhunwala: one piece of the puzzle. That should be its own column, like, open pipeline, but it’s also very important to understand, like, qualified pipeline that was closed, because they’re still credited for that.

329 00:33:02.000 ⇒ 00:33:07.109 Nandika Jhunjhunwala: To which end, the way that we view, and like most…

330 00:33:07.270 ⇒ 00:33:16.880 Nandika Jhunjhunwala: BBR teams view performance in this respect is anytime that an opportunity reaches Stage 2, which is qualified.

331 00:33:17.390 ⇒ 00:33:33.550 Nandika Jhunjhunwala: that ACV is counted as pipeline generated, and, like, if that deal moves a few more stages and then closes, that should still be recorded. So… I can send you, like, the ways that I chop this up in Salesforce.

332 00:33:33.550 ⇒ 00:33:47.700 Nandika Jhunjhunwala: But we want to make sure that we see, like, all qualified pipeline generated on a rolling basis, in addition to active pipeline. Active pipeline, frankly, being, like, less of an interest.

333 00:33:48.710 ⇒ 00:33:50.779 Greg Stoutenburg: Okay. Yeah, actually, that would be helpful.

334 00:33:50.930 ⇒ 00:33:55.229 Greg Stoutenburg: If we can, see the way that you normally look at this, and then we’ll replicate that here.

335 00:33:55.430 ⇒ 00:33:56.660 Nandika Jhunjhunwala: Yeah, of course.

336 00:33:59.600 ⇒ 00:34:00.580 Nandika Jhunjhunwala: Cool.

337 00:34:01.160 ⇒ 00:34:01.800 Nandika Jhunjhunwala: Alright, and then…

338 00:34:01.800 ⇒ 00:34:02.869 Greg Stoutenburg: This is the last one.

339 00:34:03.700 ⇒ 00:34:09.049 Nandika Jhunjhunwala: Yeah, I think we skipped a few, because Louda brought up that one report.

340 00:34:09.050 ⇒ 00:34:15.540 Greg Stoutenburg: Yeah, yeah, no, you’re right. So we, we, we had been here, and then, yeah, now we’re here. Okay.

341 00:34:16.280 ⇒ 00:34:19.899 Nandika Jhunjhunwala: Okay, hold on, we’ll just get situated.

342 00:34:21.560 ⇒ 00:34:28.670 Nandika Jhunjhunwala: Okay, yeah, so sequence flow, I was interested in learning more about how you’re determining this.

343 00:34:32.850 ⇒ 00:34:41.630 Greg Stoutenburg: I mean, I think it’s just, yeah, it’s just enrolled contacts, Enrolled accounts, so, yep.

344 00:34:42.190 ⇒ 00:34:45.950 Greg Stoutenburg: And then… Yeah, gong identifiers.

345 00:34:46.790 ⇒ 00:34:49.310 Greg Stoutenburg: Say, if someone is active in their sequence.

346 00:34:51.389 ⇒ 00:34:58.339 Nandika Jhunjhunwala: Yeah, so, I mean, I’m not necessarily confident in our ability to pass the data on to you guys, so…

347 00:34:59.059 ⇒ 00:35:12.589 Nandika Jhunjhunwala: I don’t know if this is worth modeling. We have some fields on the contact level that pull in pretty surface-level spotty data about sequencing status, so…

348 00:35:13.079 ⇒ 00:35:22.949 Nandika Jhunjhunwala: I don’t know how confident your team was in modeling this stuff, but, like, me personally, I don’t know how confident I would be in modeling it out, given my understanding of the data, so…

349 00:35:23.079 ⇒ 00:35:31.399 Nandika Jhunjhunwala: I don’t know, I could have, like, I could try to cross-reference this with what I understand to be true from, like, our third-party tools.

350 00:35:31.509 ⇒ 00:35:37.809 Nandika Jhunjhunwala: But, yeah, I would say that we’d probably table this one and potentially get rid of it.

351 00:35:38.749 ⇒ 00:35:41.379 Nandika Jhunjhunwala: Specifically, like, contact and roll it.

352 00:35:42.920 ⇒ 00:35:43.460 Greg Stoutenburg: Okay.

353 00:35:43.460 ⇒ 00:35:48.600 Nandika Jhunjhunwala: Like, for example, I know for a fact that Phil has enough 5,000 contacts abroad, so, like, I have some.

354 00:35:50.690 ⇒ 00:35:51.810 Greg Stoutenburg: Yeah, okay.

355 00:35:52.930 ⇒ 00:35:55.290 Greg Stoutenburg: Alright, yeah, we can let that one go. I mean, would you…

356 00:35:55.290 ⇒ 00:35:55.930 Nandika Jhunjhunwala: Nope.

357 00:35:55.930 ⇒ 00:36:00.990 Greg Stoutenburg: Is there another approach you’d like to take, or are you content to say, like, actually, I don’t even need this here?

358 00:36:01.360 ⇒ 00:36:04.040 Nandika Jhunjhunwala: I’m just, like, pretty confident that…

359 00:36:04.650 ⇒ 00:36:13.060 Nandika Jhunjhunwala: it would be hard to get accurate data into Salesforce for this, and therefore, I don’t know if it’s worth modeling on top of it.

360 00:36:13.210 ⇒ 00:36:14.710 Greg Stoutenburg: Hmm. Okay.

361 00:36:14.930 ⇒ 00:36:15.860 Nandika Jhunjhunwala: Yeah.

362 00:36:16.030 ⇒ 00:36:17.980 Greg Stoutenburg: Okay. Well, we’ll push this one off then.

363 00:36:18.120 ⇒ 00:36:21.309 Greg Stoutenburg: Yeah, okay, and then…

364 00:36:21.590 ⇒ 00:36:26.520 Greg Stoutenburg: Channel mix, and then… it’s channel mix, and then meeting generation, and then we pick up where we were a moment ago.

365 00:36:26.730 ⇒ 00:36:28.989 Nandika Jhunjhunwala: Yeah, so Channelix looks good.

366 00:36:29.250 ⇒ 00:36:37.649 Nandika Jhunjhunwala: I like this one, I just wanna verify that, like, logging is correct.

367 00:36:37.850 ⇒ 00:36:46.000 Nandika Jhunjhunwala: So… Yeah, would want to revisit that with the new insight as far as how we’re looking at

368 00:36:46.530 ⇒ 00:36:55.999 Nandika Jhunjhunwala: you know, tasks and contacts and all that good stuff, and I think you guys have a good handle on that at this point. My view of the meeting generation graph…

369 00:36:56.710 ⇒ 00:36:58.479 Nandika Jhunjhunwala: It looks kind of broken.

370 00:36:59.030 ⇒ 00:37:02.649 Nandika Jhunjhunwala: As does this one, so…

371 00:37:02.650 ⇒ 00:37:04.760 Greg Stoutenburg: Yeah, this is a poor scale, that’s not…

372 00:37:05.620 ⇒ 00:37:08.060 Greg Stoutenburg: That… we need to change that x-axis.

373 00:37:09.420 ⇒ 00:37:15.420 Nandika Jhunjhunwala: Yeah, and again, like, reading off of the latest snapshot date, I think for most of this stuff.

374 00:37:15.930 ⇒ 00:37:35.139 Nandika Jhunjhunwala: I would be basically most interested in seeing, like, okay, how are we doing last 14 days? How are we doing last 30 days? Yeah. And if I really want to, what does, like, the last 90 days look like? To which then most of this stuff should really vary based on whatever master

375 00:37:35.250 ⇒ 00:37:36.729 Nandika Jhunjhunwala: Timescale we’re looking at.

376 00:37:36.730 ⇒ 00:37:37.210 Greg Stoutenburg: Yes.

377 00:37:37.210 ⇒ 00:37:40.360 Nandika Jhunjhunwala: As shared this one.

378 00:37:40.360 ⇒ 00:37:41.000 Greg Stoutenburg: Yes.

379 00:37:41.280 ⇒ 00:37:45.389 Greg Stoutenburg: Yep, and that’s, past 30.

380 00:37:47.140 ⇒ 00:37:48.150 Nandika Jhunjhunwala: Yeah.

381 00:37:48.420 ⇒ 00:38:07.589 Nandika Jhunjhunwala: And then, like, another thing, another thing that’s really important that we didn’t cover is, like, total meetings booked versus qualified opportunities, and from there, we can get good insights about conversion rate. So I think, like, that’s probably the best use case for this graph.

382 00:38:07.720 ⇒ 00:38:24.080 Nandika Jhunjhunwala: is seeing over the last month, or whatever, like, for each rep, how many total meetings did they book, and how many qualified meetings came in in the same period of time. It doesn’t need to be that complicated, but that’s, like, the most interesting thing to see from a…

383 00:38:25.460 ⇒ 00:38:40.519 Greg Stoutenburg: Yeah, okay, so just to clarify, I did go and hit the filter and just limited it to the past 30, and that did change what we’re looking at here. So, the dark purple is qualified, and the light purple is unqualified.

384 00:38:41.050 ⇒ 00:38:41.970 Nandika Jhunjhunwala: Yeah.

385 00:38:42.890 ⇒ 00:38:45.630 Nandika Jhunjhunwala: In that case, like…

386 00:38:46.060 ⇒ 00:38:53.230 Nandika Jhunjhunwala: Jack, Elizabeth, and John should all have a good handful of meetings showing up here.

387 00:38:53.480 ⇒ 00:38:54.460 Greg Stoutenburg: Yeah, okay.

388 00:38:55.420 ⇒ 00:39:03.889 Greg Stoutenburg: Alright, yeah, we can explore why that’s not there. Now, as far as the representation of it, would you want to sort of take this horizontal bar and stand it up?

389 00:39:04.010 ⇒ 00:39:09.300 Greg Stoutenburg: And then go… qualified, unqualified,

390 00:39:09.420 ⇒ 00:39:18.180 Greg Stoutenburg: And… oh, that… well, that already is a sum total of meeting books, meetings booked. We can probably skip all, right, because whatever the top of the bar is, is gonna be…

391 00:39:18.300 ⇒ 00:39:19.260 Greg Stoutenburg: Total.

392 00:39:20.320 ⇒ 00:39:23.410 Nandika Jhunjhunwala: Yeah, I mean, unqualified?

393 00:39:23.740 ⇒ 00:39:25.940 Nandika Jhunjhunwala: is… interesting.

394 00:39:26.390 ⇒ 00:39:30.729 Nandika Jhunjhunwala: Perhaps we could have, like.

395 00:39:32.190 ⇒ 00:39:42.189 Nandika Jhunjhunwala: Because here’s the thing, like, the new meetings coming in are not going to be the same meetings that are qualified, necessarily, so, I mean, I would say for simplicity’s sake, yeah, probably stand them up, have each rep

396 00:39:42.400 ⇒ 00:39:48.580 Nandika Jhunjhunwala: On the x-axis, and just have, like, 3 bars for each of them of, like, net new meetings booked.

397 00:39:48.620 ⇒ 00:40:04.710 Nandika Jhunjhunwala: That’s, like, a leading indicator, qualified meetings, a lagging indicator, and then, I guess, unqualified meetings, which is just sort of a health check on the… it’s also a lagging indicator on the performance of the rep, you know, in the list.

398 00:40:05.000 ⇒ 00:40:07.439 Greg Stoutenburg: Yeah, okay, yeah, and then…

399 00:40:07.880 ⇒ 00:40:13.509 Greg Stoutenburg: Right, so then we would need a separate one for just total booked. Here, it looks like the meetings that are…

400 00:40:14.320 ⇒ 00:40:24.969 Greg Stoutenburg: Yeah, I’m noticing that the meetings that are counted as booked are classified either as qualified or unqualified. I don’t know how this is determined.

401 00:40:25.070 ⇒ 00:40:35.599 Greg Stoutenburg: But, I would think you’d want to be able to register that someone booked a meeting, even if it hasn’t been determined whether that meeting is qualified or unqualified yet, right? Because that’s going to be later.

402 00:40:35.600 ⇒ 00:40:43.310 Nandika Jhunjhunwala: Yeah, exactly. And I mean, that’s the most… that’s the most, like, instant feedback that you’ll get. Yeah. …is meeting booked, you know.

403 00:40:43.310 ⇒ 00:40:44.340 Greg Stoutenburg: meeting’s booked.

404 00:40:44.340 ⇒ 00:40:53.160 Nandika Jhunjhunwala: So, qualified meetings sometimes can take, like, 2 weeks to materialize, but that means that it is interesting to see these things. Yeah.

405 00:40:53.490 ⇒ 00:40:58.010 Nandika Jhunjhunwala: alongside one another. So, I would say it’s still worthwhile.

406 00:40:58.700 ⇒ 00:41:02.809 Greg Stoutenburg: Okay. Yeah, okay, we’ll figure out where to pull that in from, then.

407 00:41:03.080 ⇒ 00:41:15.189 Nandika Jhunjhunwala: Yeah, and really simply, it’s like, we treat Stage 2 as a qualified opportunity. Anything that gets closed lost post-Stage 2, still good for us, still a qualified meeting.

408 00:41:15.190 ⇒ 00:41:23.600 Nandika Jhunjhunwala: Right. Anything in Stage 0 or 1 is not yet qualified. Anything that closed lost before Region 2 is considered unqualified.

409 00:41:23.840 ⇒ 00:41:26.530 Nandika Jhunjhunwala: It’s pretty, pretty set in stone there.

410 00:41:26.530 ⇒ 00:41:27.770 Greg Stoutenburg: Yep, okay, that’s helpful.

411 00:41:27.770 ⇒ 00:41:31.220 Nandika Jhunjhunwala: That exists, under the opportunity object.

412 00:41:31.730 ⇒ 00:41:35.259 Nandika Jhunjhunwala: I think it’s, like, a stage field, so we should be able to…

413 00:41:35.920 ⇒ 00:41:42.340 Greg Stoutenburg: Okay, so if there’s, like, will… will that pre-populate, like, with zero…

414 00:41:42.520 ⇒ 00:41:47.840 Greg Stoutenburg: Or with 1 when a call is booked, or will it just be a null? And the question is just, what should we be looking for there?

415 00:41:47.840 ⇒ 00:41:50.700 Nandika Jhunjhunwala: Every time.

416 00:41:50.980 ⇒ 00:41:52.360 Greg Stoutenburg: It’ll default to zero.

417 00:41:52.360 ⇒ 00:41:53.110 Nandika Jhunjhunwala: Yes.

418 00:41:53.500 ⇒ 00:41:54.190 Greg Stoutenburg: Okay.

419 00:41:54.910 ⇒ 00:42:01.599 Greg Stoutenburg: Okay, so then I guess we’d be looking for… so then I guess a net new meeting is a meeting that is zero…

420 00:42:01.730 ⇒ 00:42:08.170 Greg Stoutenburg: And… has, what, either no status, or I guess status is meeting booked.

421 00:42:08.850 ⇒ 00:42:25.759 Nandika Jhunjhunwala: Yeah, you can make every decision based on opportunity stage, and basically the way that you would determine the outcome is stage prior to close lost. So, if stage prior to close lost equals 0 or 1,

422 00:42:26.100 ⇒ 00:42:34.969 Nandika Jhunjhunwala: consider unqualified. Prior to closed loss equals… You know, 2+, consider qualified.

423 00:42:34.970 ⇒ 00:42:47.359 Greg Stoutenburg: Yeah. Yeah, I’m looking for the meetings. I’m wondering what status identifies a meeting that has been booked, but doesn’t have a, there’s no determination if it’s qualified or unqualified yet.

424 00:42:47.360 ⇒ 00:42:51.350 Nandika Jhunjhunwala: Would you be… it would be… Simply put.

425 00:42:51.610 ⇒ 00:42:57.460 Nandika Jhunjhunwala: stage equals 0 or 1, those are open opportunities. Okay.

426 00:42:57.570 ⇒ 00:43:04.709 Nandika Jhunjhunwala: Meaning they’re still in flight. So, the second that an opportunity dies, it is immediately moved to close loss.

427 00:43:04.970 ⇒ 00:43:05.720 Greg Stoutenburg: Okay.

428 00:43:05.720 ⇒ 00:43:07.089 Nandika Jhunjhunwala: Which is its own stage.

429 00:43:07.260 ⇒ 00:43:08.389 Greg Stoutenburg: Okay. Okay.

430 00:43:08.390 ⇒ 00:43:13.330 Nandika Jhunjhunwala: I think to clarify, we only create… we create an opportunity when a meeting is booked.

431 00:43:13.330 ⇒ 00:43:14.490 Greg Stoutenburg: Okay, yep.

432 00:43:14.490 ⇒ 00:43:19.900 Nandika Jhunjhunwala: Existence of opportunity is equal to meeting blocked. Got it. It comes in with the default stage of zero.

433 00:43:19.900 ⇒ 00:43:21.800 Greg Stoutenburg: That helps. Okay, that helps.

434 00:43:22.720 ⇒ 00:43:24.350 Greg Stoutenburg: Okay, cool, thank you.

435 00:43:24.550 ⇒ 00:43:25.380 Greg Stoutenburg: Okay.

436 00:43:26.200 ⇒ 00:43:28.510 Greg Stoutenburg: Alright, any other comments or thoughts?

437 00:43:29.890 ⇒ 00:43:33.390 Nandika Jhunjhunwala: No, I think that’s most of it.

438 00:43:33.390 ⇒ 00:43:35.960 Greg Stoutenburg: Okay, great.

439 00:43:35.960 ⇒ 00:43:55.340 Nandika Jhunjhunwala: Sorry, I know we’re over time. I think we made some recent changes to the CRM, and we might have brought it up in passing, but just wanted to confirm that the data is modeled in reflection of those changes. So we have, like, three ownership fields now in the CRM.

440 00:43:55.380 ⇒ 00:44:00.780 Nandika Jhunjhunwala: So we have account owner, and then we have BDR owner, and then we have AE owner.

441 00:44:00.890 ⇒ 00:44:07.500 Nandika Jhunjhunwala: And I think for the purposes of the dashboard, we want to be reading from account owner left, or…

442 00:44:07.790 ⇒ 00:44:18.430 Nandika Jhunjhunwala: BDR owner. I think that’s, like, where the nuances are where you should hammer out. I think for the time being, I thought about it, we can leave it as is, reading off of…

443 00:44:18.630 ⇒ 00:44:22.149 Nandika Jhunjhunwala: the account owner. The only…

444 00:44:22.290 ⇒ 00:44:30.719 Nandika Jhunjhunwala: nuance here is that when an opportunity is created, it’s going to change the account owner from a BDR to an AE,

445 00:44:30.720 ⇒ 00:44:43.940 Nandika Jhunjhunwala: So, for any dashboards where we would want a little bit more persistence with respect to ownership, like, for example, let’s say that a BDR books meetings with 20 accounts this month.

446 00:44:43.940 ⇒ 00:44:58.359 Nandika Jhunjhunwala: and we want the activity of those accounts to show up somewhere, like, those accounts will be disappeared from these reports if there’s an active opportunity, because the owner of the account will change. So…

447 00:44:58.380 ⇒ 00:45:00.689 Nandika Jhunjhunwala: This may be something that we can…

448 00:45:01.110 ⇒ 00:45:05.529 Nandika Jhunjhunwala: sort of add, rather than change.

449 00:45:05.680 ⇒ 00:45:15.759 Nandika Jhunjhunwala: But, yeah, there are now persistent fields that designate ownership in addition to the account owner field.

450 00:45:16.050 ⇒ 00:45:18.580 Nandika Jhunjhunwala: And yeah, we should probably go…

451 00:45:18.880 ⇒ 00:45:23.300 Nandika Jhunjhunwala: That’s not going line by line and see, like, what might be changing to account for that.

452 00:45:25.060 ⇒ 00:45:28.380 Greg Stoutenburg: Okay, heard. Alright.

453 00:45:29.120 ⇒ 00:45:38.240 Nandika Jhunjhunwala: Okay. I can come up with some more specific examples of, like, which reports might need, adjustment there.

454 00:45:39.210 ⇒ 00:45:41.730 Caitlyn Vaughn: Hey Nandica, how’s your capacity right now?

455 00:45:42.750 ⇒ 00:45:44.609 Caitlyn Vaughn: Just, like, on work in general.

456 00:45:46.020 ⇒ 00:45:48.880 Nandika Jhunjhunwala: it’s okay, I think.

457 00:45:48.880 ⇒ 00:45:49.570 Caitlyn Vaughn: Okay.

458 00:45:50.450 ⇒ 00:45:51.110 Nandika Jhunjhunwala: Yep.

459 00:45:51.110 ⇒ 00:45:57.889 Caitlyn Vaughn: I think, maybe this is, like, a good place for us to start. I feel like a lot of the changes that we want to make are, like.

460 00:45:58.160 ⇒ 00:46:10.330 Caitlyn Vaughn: context-heavy, I almost feel like it would make sense for us to go through this dashboard and, like, make a bulk of these changes, because I was just going through and I made half the changes already.

461 00:46:10.330 ⇒ 00:46:10.769 Greg Stoutenburg: There you go.

462 00:46:10.770 ⇒ 00:46:18.209 Caitlyn Vaughn: I could knock this out in, like, an hour, and then anything that we can’t figure out or we still need help with, maybe we can pass on.

463 00:46:18.640 ⇒ 00:46:20.799 Nandika Jhunjhunwala: Yeah, totally. Yeah, that’s true. That sounds great.

464 00:46:21.050 ⇒ 00:46:23.620 Caitlyn Vaughn: Because I would love to get this done by Friday.

465 00:46:23.870 ⇒ 00:46:24.550 Greg Stoutenburg: There you go.

466 00:46:24.730 ⇒ 00:46:29.480 Demilade Agboola: Also, any, like, obvious, like, mis…

467 00:46:29.520 ⇒ 00:46:46.579 Demilade Agboola: calculations or modeling changes that we need to make, please feel free to flag. Or anything that you feel like we’re not defining properly, or in the way you see it internally, please do, again, feel free to flag so we can make those modifications to this dashboard.

468 00:46:47.420 ⇒ 00:47:06.929 Greg Stoutenburg: Yep, yeah, definitely. And we’ll… we’ll, you know, we’ll lean on our notes and the transcript to summarize what all of the changes were that you flagged, and, I can share that with you as well. But yeah, I think it makes perfect sense. Like, if you already know… if you already know what you want to change, and… and you’re capable… you’re capable of changing it, want to go for it.

469 00:47:06.930 ⇒ 00:47:07.640 Caitlyn Vaughn: Yeah.

470 00:47:07.640 ⇒ 00:47:08.750 Greg Stoutenburg: Yeah, by all means.

471 00:47:08.750 ⇒ 00:47:12.210 Caitlyn Vaughn: I think, yeah, there’s probably at least half of these we could just knock out.

472 00:47:12.210 ⇒ 00:47:13.340 Greg Stoutenburg: There you go. Okay, perfect.

473 00:47:13.340 ⇒ 00:47:16.469 Nandika Jhunjhunwala: The only question I have here is, like, I was looking at

474 00:47:16.620 ⇒ 00:47:22.300 Nandika Jhunjhunwala: what data these charts were pulling from. Like, if you click on the workbook logo, Greg?

475 00:47:22.440 ⇒ 00:47:24.740 Nandika Jhunjhunwala: At the top, yeah.

476 00:47:24.870 ⇒ 00:47:31.040 Nandika Jhunjhunwala: So I just wasn’t sure how to, like, look at the queries you were running, like.

477 00:47:31.040 ⇒ 00:47:31.620 Greg Stoutenburg: Yeah.

478 00:47:32.150 ⇒ 00:47:36.670 Nandika Jhunjhunwala: to, like, sort of go and, like, edit them. Yeah.

479 00:47:36.810 ⇒ 00:47:40.699 Nandika Jhunjhunwala: And… because I think you’re pulling from, like, model of arts.

480 00:47:40.700 ⇒ 00:47:57.460 Nandika Jhunjhunwala: And there’s, like, fields like average 7 days and stuff. So, would love to understand, like, to what extent do the… does the model data underneath need to be… needs to be changed, or is it just, like, we can sort of do the aggregations and pull in different fields to, like, modularly change these insights?

481 00:47:57.550 ⇒ 00:48:02.429 Nandika Jhunjhunwala: Because then that would depend on how long this dashboard takes for us to, like, change.

482 00:48:03.020 ⇒ 00:48:08.090 Greg Stoutenburg: Yeah, can you speak to that part, Demi? About what’s… sort of what’s fixed here and what’s not?

483 00:48:08.390 ⇒ 00:48:25.419 Demilade Agboola: Yeah, so, like, a lot of the core logic does live within, like, dbt, and there are a number of reasons why, so, like, the… which is why we integrated dbt. So, dbt holds the logic, every change is easily traceable, so if anyone makes a change, it’s easy to figure out who made the change, and you can figure out

484 00:48:25.420 ⇒ 00:48:35.709 Demilade Agboola: Based off of, like, the commit history, why they made those changes. So it’s much easier to track and manage, like, going forward as the team scales, and as more people

485 00:48:35.710 ⇒ 00:48:43.729 Demilade Agboola: utilize, the data infrastructure and make those changes. In terms of, like, this, and yes, there are, there are, like.

486 00:48:44.030 ⇒ 00:48:56.489 Demilade Agboola: minor, calculations going on here. Usually more of, like, aggregation or rolling things up to the right level that we need to show it as. But in terms of, like, defining, like.

487 00:48:56.490 ⇒ 00:49:05.689 Demilade Agboola: hardcore business logic, in terms of, like, what counts as a qualified account, that would probably leave, within DVT, exactly.

488 00:49:06.390 ⇒ 00:49:17.670 Nandika Jhunjhunwala: And… that’s fair. So, is there, like, a running workbook of all the insights in this dashboard? Or, like, how do I look at where the core business logic needs to be changed?

489 00:49:17.780 ⇒ 00:49:20.100 Nandika Jhunjhunwala: For us to then make these changes ourselves.

490 00:49:20.580 ⇒ 00:49:23.380 Demilade Agboola: You’ll… like, in terms of…

491 00:49:23.380 ⇒ 00:49:40.979 Demilade Agboola: It depends on what the logic is. If the logic is more of, like, a, hey, I’m looking at these numbers, or this definition, and it doesn’t seem to be defined properly, you can always click in, kind of see what’s going on there. Most likely, if it’s a definition thing, you won’t be able to change it within Omni, so you can flag it.

492 00:49:40.980 ⇒ 00:49:41.330 Nandika Jhunjhunwala: Express.

493 00:49:41.870 ⇒ 00:49:45.110 Demilade Agboola: But if it’s more of, like, an aggregation thing.

494 00:49:45.330 ⇒ 00:49:49.140 Demilade Agboola: Yes, you probably will be able to do it, in… in Omni.

495 00:49:50.020 ⇒ 00:50:09.969 Nandika Jhunjhunwala: Can you walk through, like, a live example of, like, an aggregation change? Like, for example, Lev mentioned, like, steel accounts, you know, it’s reading from the past 7 days. Can I make that aggregation change in Omni for it to be 30 days, like right now? Like, because I see, like, Omni date, like, metric date, like, that first line that you’re…

496 00:50:09.970 ⇒ 00:50:11.940 Nandika Jhunjhunwala: I’m assuming, like, sort of…

497 00:50:12.710 ⇒ 00:50:16.429 Greg Stoutenburg: Sorry, I was just trying to find the one that was, you know, calculated by 7-day, so…

498 00:50:17.140 ⇒ 00:50:18.750 Greg Stoutenburg: Here’s still contacts.

499 00:50:18.750 ⇒ 00:50:19.430 Nandika Jhunjhunwala: Yes.

500 00:50:19.430 ⇒ 00:50:21.040 Greg Stoutenburg: Here’s what the sequel looks like.

501 00:50:21.040 ⇒ 00:50:31.960 Nandika Jhunjhunwala: So it says… the second line, too, it says steel contacts 7D. Now, is that logic that needs to be changed in dbt, or can I change that on my end?

502 00:50:32.450 ⇒ 00:50:37.719 Demilade Agboola: So if it still contacts 7 days, that will… that appears to be coming from dbt, so you’ll need to.

503 00:50:37.720 ⇒ 00:50:38.230 Nandika Jhunjhunwala: Factual.

504 00:50:38.300 ⇒ 00:50:39.220 Demilade Agboola: activity, yeah.

505 00:50:39.220 ⇒ 00:50:40.220 Caitlyn Vaughn: Hmm…

506 00:50:40.410 ⇒ 00:50:48.200 Nandika Jhunjhunwala: So then there’s not much modularity or flexibility here for me to do anything, besides, like, having to ask you guys to make those changes.

507 00:50:48.870 ⇒ 00:50:59.050 Demilade Agboola: Yeah, in certain models, like, so, like, these are, like, some of these tables are reporting logic, so the context is already embedded within the model itself.

508 00:50:59.170 ⇒ 00:51:14.000 Demilade Agboola: But yeah, in terms of this, yeah, we can always make those changes if you need it to… if you need those specific changes made. In other cases, especially for, like, these sort of charts, we can also make them more…

509 00:51:15.690 ⇒ 00:51:17.130 Demilade Agboola: more granular.

510 00:51:17.130 ⇒ 00:51:40.889 Demilade Agboola: So you can then have the flexibility to make those changes, because if we want to say, like, if we’re going to make this chart into more, into one where the filter makes its change based off of, like, whoever is looking at it, so maybe, for instance, Lev wants to look at it from a 30-day perspective, and then potentially flip it to a 60-day perspective, in that case, yeah, we’ll have to make this chart a more modular, like, more granular chart.

511 00:51:40.890 ⇒ 00:51:45.350 Demilade Agboola: And so that way, the filter will be able to work and make it, and make it.

512 00:51:46.260 ⇒ 00:52:02.500 Demilade Agboola: like, move around based off of the selected time filter. So again, that would be the feedback, like, hey, we need to… we need a more flexible, filter on this, and that automatically will make it, like, we can make the model, more granular in that case.

513 00:52:04.240 ⇒ 00:52:09.049 Nandika Jhunjhunwala: Yeah, so that’s, like, more to my question. How do I know what’s…

514 00:52:09.290 ⇒ 00:52:19.739 Nandika Jhunjhunwala: fixable for me within Omni versus what needs to be a request to you. It sort of takes a lot of calories to figure out, because I don’t know how the data is modeled as of now.

515 00:52:19.910 ⇒ 00:52:29.520 Nandika Jhunjhunwala: And to what end could you enable me or educate me for me to, like, know that maybe it’s the schema, if I need access to dbt, like, what would that look like? Because…

516 00:52:30.040 ⇒ 00:52:38.520 Nandika Jhunjhunwala: we would need to have, like, an onboarding or, like, ramping up process for me that I need to own, so I am… I’m a little confused here.

517 00:52:39.310 ⇒ 00:52:43.570 Demilade Agboola: I don’t know, how comfortable are you in Omni? I think that would be the first question.

518 00:52:44.050 ⇒ 00:52:45.900 Nandika Jhunjhunwala: Not, not super comfortable.

519 00:52:46.610 ⇒ 00:53:01.489 Demilade Agboola: Okay, alright, so that makes, it trickier, because, like, if you’re comfortable within Omni, at least you can, you know, play around with the data to get into the chart, like, kind of like how Greg is looking at it now.

520 00:53:01.790 ⇒ 00:53:11.470 Demilade Agboola: And once you’re there, you can kind of see, like, the SQL logic exposed to you as to how the chart is being produced.

521 00:53:11.630 ⇒ 00:53:16.339 Nandika Jhunjhunwala: No, I can do that, that’s not the issue. The issue is, like, figuring out

522 00:53:16.410 ⇒ 00:53:23.250 Nandika Jhunjhunwala: like, just looking at SQL logic is giving me, like, pieces of how the schema is organized, like.

523 00:53:23.250 ⇒ 00:53:36.799 Nandika Jhunjhunwala: I don’t know how the schema’s organized. Like, I don’t know what mods are modeled on what data, what the freshness is, and, like, where it lives. Like, I can’t look at the, sort of, like, database visualization… visualization and be like, okay, this is what…

524 00:53:36.890 ⇒ 00:53:41.289 Nandika Jhunjhunwala: is pulling from, so… I don’t know if, like, this makes sense. I, like…

525 00:53:41.410 ⇒ 00:53:47.870 Nandika Jhunjhunwala: Like, in terms of, like, handing that ownership back to us internally, like, how do we then…

526 00:53:48.740 ⇒ 00:53:54.310 Nandika Jhunjhunwala: Like, you know, in the future, future-proof this, in terms of changing how the data is modeled, if…

527 00:53:54.490 ⇒ 00:53:57.900 Nandika Jhunjhunwala: Everything is, like, living in dbt.

528 00:53:57.900 ⇒ 00:54:14.790 Demilade Agboola: Yeah, so, like, within dbt, there’s documentation on how things are, but if you need me to, like, expose some of that, like, to you, that’s not a problem. I can always, like, send you a document containing, like, the different tables and, like, the definition of things, and so you can kind of see that.

529 00:54:14.790 ⇒ 00:54:16.210 Nandika Jhunjhunwala: Yeah, sounds great.

530 00:54:16.210 ⇒ 00:54:27.710 Demilade Agboola: But part of dbt and how we, like, model is to be able to also add that context in there, and also add, like, tests for business logic as well. So, like, if we, for instance, realize that, like,

531 00:54:27.750 ⇒ 00:54:47.410 Demilade Agboola: there should be no day in which, like, a BDR has less than zero accounts, or on their, like, plate. We can also model that in, and so if there’s that sort of data… if that data comes in, it will flag it, and potentially that could just be an issue of, like, hey, the data is still, and that’s why there’s that lag in…

532 00:54:47.410 ⇒ 00:54:49.899 Demilade Agboola: the value for the, BDRs.

533 00:54:49.900 ⇒ 00:55:02.469 Demilade Agboola: So that’s kind of, like, why we use dbt for, like, a lot of things, because we can add documentation, testing, and with the version control, we can also see the changes that are being made when they’re made, and why they were made as well.

534 00:55:02.470 ⇒ 00:55:17.859 Greg Stoutenburg: So, Nandika, for the immediate reason of wanting to get as many of these changes done by the end of the week as you can, would it be helpful if we were able to provide some pointers on when you do open a workbook, and you do click on a tab for a chart.

535 00:55:17.870 ⇒ 00:55:27.620 Greg Stoutenburg: like, pointers for where you can look at the SQL and find something like this, and go, oh, okay, that’s not gonna be changeable, that’s modeled. If we’re able to just give you, like.

536 00:55:28.690 ⇒ 00:55:33.630 Greg Stoutenburg: guidelines on what that looks like. Would that be helpful for what you’re trying to get done by Friday?

537 00:55:33.630 ⇒ 00:55:48.729 Nandika Jhunjhunwala: Yeah, totally. I looked at most of the charts and the SQL queries for most of them, and, like, as far as I could tell, all of it was mostly modeled in dbt, which didn’t give us much flexibility in Omni to then go and change.

538 00:55:48.990 ⇒ 00:55:52.390 Nandika Jhunjhunwala: But if you have examples that are…

539 00:55:52.650 ⇒ 00:56:01.350 Nandika Jhunjhunwala: you know, we can talk through about, like, we can change this currently in… in Omni with a SQL query, like, I can take that on, like, right now. So, yeah.

540 00:56:01.350 ⇒ 00:56:01.890 Greg Stoutenburg: Yeah. Okay.

541 00:56:02.010 ⇒ 00:56:02.820 Nandika Jhunjhunwala: Great.

542 00:56:03.330 ⇒ 00:56:13.070 Greg Stoutenburg: Okay, well, let us huddle and, come up with, you know, even a small partial handoff plan so that you can, get working on this with Caitlin this week.

543 00:56:13.550 ⇒ 00:56:14.220 Nandika Jhunjhunwala: Sounds good.

544 00:56:14.220 ⇒ 00:56:25.170 Caitlyn Vaughn: The other thing, as I’m, like, as I’m listening to this, I think this is probably a learning that’s happened now that I wish I had context on, you know, 6 months ago, but something like…

545 00:56:25.410 ⇒ 00:56:29.820 Caitlyn Vaughn: Like, average sale con- contacts in a 7-day period?

546 00:56:30.210 ⇒ 00:56:44.410 Caitlyn Vaughn: that’s such a, like, granular thing to be modeled. Like, if we could have modeled it in a way where it was much more flexible, I think that would make a lot more sense for us as an org, especially since we have no data engineer and nobody that’s, like.

547 00:56:44.410 ⇒ 00:56:44.970 Greg Stoutenburg: Like.

548 00:56:44.970 ⇒ 00:57:00.179 Caitlyn Vaughn: specifically going in and remodeling over specific, you know, periods of time. Like, we would want the flexibility to be able to adjust that through Omni, or, like, with our non-technical people, and I think that’s a trend I’ve seen across…

549 00:57:00.570 ⇒ 00:57:05.990 Caitlyn Vaughn: the, like, Omni instance and the different SQL queries behind it,

550 00:57:06.110 ⇒ 00:57:11.389 Caitlyn Vaughn: I imagine that would be, like, a pretty big overhaul to, like, roll that back, but…

551 00:57:11.810 ⇒ 00:57:17.479 Caitlyn Vaughn: If we could at least see what has been modeled specifically now, that’s probably a good place for us to start.

552 00:57:18.540 ⇒ 00:57:34.309 Demilade Agboola: Yeah, I mean, we can definitely, like, roll some of, like, the changes, like, the modularity back, or, like, rinarity back. It’s basically, again, it exists in dbt, it’s aggregated, and then handed over to Omni, where it then exists.

553 00:57:34.330 ⇒ 00:57:40.959 Demilade Agboola: I mean, there are a couple of reasons why, like, it’s rolled up this way, so that, like, in terms of

554 00:57:41.810 ⇒ 00:57:54.239 Demilade Agboola: the volume of the table, it’s much smaller now, so it’s much easier for you to utilize. Therefore, like, even build times are shorter, because you’re not necessarily building out every single combination that you might need for your filters.

555 00:57:54.480 ⇒ 00:58:05.569 Demilade Agboola: But again, like I said, if you do need it, available in, like, larger quantities in that sense, so you can have better filtering based off of,

556 00:58:05.810 ⇒ 00:58:09.789 Demilade Agboola: Like, more flexibility based off the filters you would use, that wouldn’t be a problem.

557 00:58:10.090 ⇒ 00:58:15.369 Demilade Agboola: Once we can define the logic that exists, and then it will just be…

558 00:58:15.710 ⇒ 00:58:21.690 Demilade Agboola: a function of just not rolling it up as much, and then that would be available within Army.

559 00:58:22.160 ⇒ 00:58:35.359 Caitlyn Vaughn: Okay, yeah. I mean, this seems like a good shortcut, but from the perspective of, like, having changing needs as an early-stage startup that’s, like, constantly changing, it would probably be better for us to have flexibility and to, like.

560 00:58:36.330 ⇒ 00:58:41.149 Caitlyn Vaughn: Accept those primitives as what is best practice for default.

561 00:58:41.960 ⇒ 00:58:43.369 Demilade Agboola: Okay, fair enough.

562 00:58:43.370 ⇒ 00:58:44.340 Greg Stoutenburg: Heard, yeah.

563 00:58:44.600 ⇒ 00:58:53.439 Demilade Agboola: something to look into. So, like, yeah, first… first things first, we’ll be obviously getting these changes out, and trying to get them…

564 00:58:54.010 ⇒ 00:59:03.349 Demilade Agboola: visible, so that, like, you know, Lev, Laura, everyone can have access to this and utilize it. And then, yeah, we’ll… we can then look at, like, the granularity levels as to…

565 00:59:03.640 ⇒ 00:59:13.230 Demilade Agboola: How we want to, like, modify the data so that the granularity levels look, in the format that can allow for, like, flexibility for the default team.

566 00:59:15.120 ⇒ 00:59:19.519 Nandika Jhunjhunwala: That would be great, and if possible, could you invite me to your DVD?

567 00:59:19.690 ⇒ 00:59:25.209 Nandika Jhunjhunwala: instance that you’re using for default, so I can start looking around,

568 00:59:25.440 ⇒ 00:59:37.759 Nandika Jhunjhunwala: I’ve been looking around on MotherDoc as well, and I, like, spend a ton of time looking around on this dashboard. I, again, like, don’t know what tables live where, so any overview you have of, like.

569 00:59:37.890 ⇒ 00:59:45.269 Nandika Jhunjhunwala: what the data architecture is, like, from MotherDoc to Omni, like, to via DBT, or whatever that looks like, that would be super helpful.

570 00:59:45.420 ⇒ 00:59:47.839 Demilade Agboola: Yeah, sure. I mean, dbt is basically, like.

571 00:59:48.130 ⇒ 01:00:03.100 Demilade Agboola: a local repository, like, you have access to the GitHub, so that’s all you need, basically. Once you have access to GitHub, you can clone it, you have all the latest changes, every single time you pull the master branch or the main branch, you’ll be able to see any change that has been made.

572 01:00:04.290 ⇒ 01:00:07.109 Nandika Jhunjhunwala: No, that makes sense, yeah. If you could invite me, that would be great.

573 01:00:07.110 ⇒ 01:00:09.739 Demilade Agboola: No, you are. I believe you are already on the…

574 01:00:09.740 ⇒ 01:00:10.830 Nandika Jhunjhunwala: Oh, okay.

575 01:00:12.590 ⇒ 01:00:16.529 Demilade Agboola: Because I know I looked at the users, like, sometime last week,

576 01:00:17.140 ⇒ 01:00:20.830 Demilade Agboola: And I believe you have access to the… Brunch.

577 01:00:21.160 ⇒ 01:00:24.229 Caitlyn Vaughn: Is this the Brainforge branch in GitHub?

578 01:00:24.430 ⇒ 01:00:26.579 Demilade Agboola: Yes, the Brainforge BI branch.

579 01:00:26.580 ⇒ 01:00:28.110 Caitlyn Vaughn: Yeah. Okay.

580 01:00:30.360 ⇒ 01:00:33.869 Nandika Jhunjhunwala: Do I need the software separately, or… No, just…

581 01:00:33.870 ⇒ 01:00:51.429 Demilade Agboola: Yeah, if you need me to, like, we can book time on if you need, like, help to set up with dbt, but it’s basically like a local, like, you can have it on your computer, just pull the branch, and then you can get the dbt CLI or dbt Core version on your computer, and you’re good to go.

582 01:00:51.850 ⇒ 01:00:53.100 Demilade Agboola: That’s simple.

583 01:00:53.460 ⇒ 01:00:54.250 Demilade Agboola: Okay.

584 01:00:55.670 ⇒ 01:00:56.310 Greg Stoutenburg: Cool.

585 01:00:56.860 ⇒ 01:01:11.430 Greg Stoutenburg: Okay, we’ll get back to you very quickly, Nandika, with identifying what’s modeled, what’s not, and, so you can make those changes right away. And, other than that, yeah, feel free, make any of the changes that you want to make, and, we’ll get to work on the ones that you don’t.

586 01:01:11.700 ⇒ 01:01:13.700 Caitlyn Vaughn: Well, thank you guys so much.

587 01:01:13.700 ⇒ 01:01:16.520 Greg Stoutenburg: Alright, thanks all. Have a good one. See ya.