Meeting Title: Retention report discussion Date: 2026-02-03 Meeting participants: Judd Kuehling, Greg Stoutenburg, Zoran Selinger
WEBVTT
1 00:02:43.820 ⇒ 00:02:45.460 Greg Stoutenburg: Hey, Judd, how’s it going today?
2 00:02:45.750 ⇒ 00:02:47.039 Judd Kuehling: Good, how you doing?
3 00:02:47.040 ⇒ 00:02:48.090 Greg Stoutenburg: Doing alright.
4 00:02:49.270 ⇒ 00:02:53.590 Greg Stoutenburg: Let me find… your spreadsheet.
5 00:03:13.850 ⇒ 00:03:15.299 Greg Stoutenburg: Alright, cool, there we go.
6 00:03:30.200 ⇒ 00:03:37.850 Greg Stoutenburg: While we’re waiting for Zoran, maybe I can ask you about the thread with… us and Katie.
7 00:03:38.030 ⇒ 00:03:39.030 Judd Kuehling: Yeah, yeah.
8 00:03:39.410 ⇒ 00:03:40.510 Greg Stoutenburg: Yeah.
9 00:03:42.240 ⇒ 00:03:56.840 Greg Stoutenburg: Yeah, so I just wanna… let’s see, am I getting it? Okay. Yeah, I just want to make sure I was straight on the ask. So, I think you wanted groups that showed consecutive purchases by individuals in the… that were initially in the first-time purchase cohort?
10 00:03:57.690 ⇒ 00:04:00.090 Judd Kuehling: Yeah. So,
11 00:04:01.490 ⇒ 00:04:09.900 Judd Kuehling: I watched the loom, and I forgot now the details of it, and I think what you’re… what you are trying to do makes sense. I have a existing…
12 00:04:10.550 ⇒ 00:04:15.730 Judd Kuehling: campaign that has all these people in it, and when they…
13 00:04:16.880 ⇒ 00:04:19.760 Judd Kuehling: hit a second purchase, and I can…
14 00:04:20.010 ⇒ 00:04:33.100 Judd Kuehling: you know, double-check the, kind of filtering on that to use exactly what you were trying to use. I have them getting, like, a second email, and then I have basically, like, every email, so it’s, like, 3rd, 4th, 5th, 6th, 7th, all the way to, like.
15 00:04:33.470 ⇒ 00:04:38.490 Judd Kuehling: 16 months… 16th month, or something like that. And it’s a different amount, because, like, in the first…
16 00:04:38.740 ⇒ 00:04:42.439 Judd Kuehling: 5 months, we do $20, and then 6 through…
17 00:04:42.650 ⇒ 00:04:54.019 Judd Kuehling: I forget, it’s like $40, then it goes up. So it mentions the different amount in there, but there’s ways in… in Customer I.O. to send…
18 00:04:54.850 ⇒ 00:05:05.359 Judd Kuehling: data back out once people, like, kind of, qualify on that, and that’s the part that I’m, like, not really good at and clear on, is that I think there’s a way to send maybe a webhook out.
19 00:05:06.000 ⇒ 00:05:17.549 Judd Kuehling: So once someone, like, qualifies, and we’ve seen they made their second purchase, can we send a webhook back out to somewhere? I know there’s a way to send a Slack message back out.
20 00:05:17.720 ⇒ 00:05:20.679 Greg Stoutenburg: With the person’s email address.
21 00:05:21.070 ⇒ 00:05:31.619 Judd Kuehling: Because we have that set up for a lot of different things, and so, at the minimum, we could use that and kind of, like, pull that from a Slack channel into, like, an Excel file to have Katie…
22 00:05:32.190 ⇒ 00:05:39.109 Judd Kuehling: Send the second… credit. So, I’m just saying, I don’t want to, like, overly complicate, kind of, what…
23 00:05:39.390 ⇒ 00:05:51.079 Judd Kuehling: maybe we can use kind of what we already have built, is basically what I’m trying to say, instead of you building a separate thing, but if you want to do that, that works too, I don’t know, whatever you think makes sense.
24 00:05:51.080 ⇒ 00:05:59.720 Greg Stoutenburg: Yeah, yeah, no, I agree, and I didn’t want to add to the complexity. Looking at… looking at just doing it in Customer I.O. with segments, I was like.
25 00:05:59.720 ⇒ 00:06:15.830 Greg Stoutenburg: like, basically what we would need is increasingly large segments with rules that go, you were in this segment, and then you made another purchase. And then you’re in the later segment, and so then you make another purchase, and each one is just iteratively larger. Right, right. Like, that’s not exactly elegant. It’d be clear, you can always see who’s in the group.
26 00:06:15.830 ⇒ 00:06:19.029 Judd Kuehling: Yeah, they would stay in there, I guess, or whatever.
27 00:06:19.030 ⇒ 00:06:34.629 Greg Stoutenburg: Right, yeah, so… Okay, yeah, that sounds good. So, that helps me understand that the goal is just make sure that Katie knows so that she can send the credit. Make sure that Katie knows when someone has made an additional purchase, and which purchase number that is.
28 00:06:34.630 ⇒ 00:06:43.079 Judd Kuehling: Right, and it doesn’t have to be… you know, the timing is not, like, we’re not promising that they’ll get it the second they make their second order, you know, we’re kind of…
29 00:06:43.240 ⇒ 00:06:48.060 Judd Kuehling: Saying it’s gonna be a couple days… to do it, so…
30 00:06:48.120 ⇒ 00:06:50.219 Greg Stoutenburg: That’s what I’m saying, like, if we use, kind of.
31 00:06:50.220 ⇒ 00:06:51.490 Judd Kuehling: Kind of a more, like.
32 00:06:52.720 ⇒ 00:06:59.090 Judd Kuehling: rudimentary version of, like, sending a Slack message to me, and I can put them all into an Excel file for now. I mean, we don’t want to, like…
33 00:06:59.550 ⇒ 00:07:09.199 Judd Kuehling: overcomplicated if this is a test, and it ends up not working, and then we build this huge, kind of, process that ends up not being needed, too. So,
34 00:07:09.610 ⇒ 00:07:13.039 Judd Kuehling: So yeah, let me kind of… I can think more about that, or if you want to…
35 00:07:13.290 ⇒ 00:07:22.269 Judd Kuehling: look at that campaign and customer I.O, and kind of see, kind of, how that’s built. There’s a webhook, like I said, there’s a webhook
36 00:07:23.520 ⇒ 00:07:35.139 Judd Kuehling: option in there, but I don’t know if it’s only inbound and not outbound. Like, I can create, like, an event, and I can send a Slack message, but I think the webhook, it might only be, like, for bringing data in and not…
37 00:07:35.350 ⇒ 00:07:47.750 Judd Kuehling: sending data out, but I’m kind of unclear on that, because it’s like, once you use that webhook tool, it wants you to, like, go into its, like, coding language to build it, and I’m, like, not super…
38 00:07:48.550 ⇒ 00:07:49.790 Judd Kuehling: familiar there.
39 00:07:49.790 ⇒ 00:07:52.290 Greg Stoutenburg: This is getting out of hand, you know? Yeah, I just want to know who bought.
40 00:07:52.290 ⇒ 00:07:55.530 Judd Kuehling: That’s out of my, I’m a front-facing,
41 00:07:55.780 ⇒ 00:08:00.330 Judd Kuehling: Yeah, I’m a front-end guy, I don’t… I don’t understand anything behind the scenes, so… Sure.
42 00:08:00.800 ⇒ 00:08:05.230 Greg Stoutenburg: Okay, sounds good. Alright, that’s a good follow-up. We can… I can move on that.
43 00:08:05.230 ⇒ 00:08:05.850 Judd Kuehling: Okay.
44 00:08:08.240 ⇒ 00:08:09.100 Greg Stoutenburg: Thanks, Ron.
45 00:08:13.170 ⇒ 00:08:20.850 Greg Stoutenburg: All right, Joe, do you want to take it away? I believe we’re here to follow up on the revised, retention weekly report that we discussed on the call with Mitesh.
46 00:08:22.350 ⇒ 00:08:31.600 Judd Kuehling: Yeah, so I think I… I thought I tried to send it over to you guys. I’ve kind of changed it even since then, but I can share…
47 00:08:33.070 ⇒ 00:08:36.209 Judd Kuehling: what I sent.
48 00:08:36.210 ⇒ 00:08:41.610 Greg Stoutenburg: Yeah, I put it in the, I grabbed it from our other thread, and I put it in the chat here in Zoom.
49 00:08:43.330 ⇒ 00:08:46.010 Judd Kuehling: Let’s see if I can share my screen,
50 00:08:47.700 ⇒ 00:08:55.749 Judd Kuehling: I haven’t shared with Zoom, so it’s asking me to… Allow privacy… sorry.
51 00:08:56.190 ⇒ 00:08:58.319 Greg Stoutenburg: That’s right. You must have a Mac.
52 00:08:58.320 ⇒ 00:09:05.789 Judd Kuehling: Yeah. And I don’t really know how to use it, to be honest. I’m kind of new to Macs, too, so, yeah.
53 00:09:05.790 ⇒ 00:09:07.289 Greg Stoutenburg: I got forced into it as well.
54 00:09:07.290 ⇒ 00:09:09.779 Judd Kuehling: Quit and reopen?
55 00:09:10.830 ⇒ 00:09:12.220 Judd Kuehling: Just to quit Zoom now.
56 00:09:12.220 ⇒ 00:09:14.810 Zoran Selinger: I’m currently debating with myself what to get.
57 00:09:14.980 ⇒ 00:09:21.370 Zoran Selinger: As well. Yeah. I re… I… Last year, I… tried Mac.
58 00:09:22.320 ⇒ 00:09:29.360 Zoran Selinger: It’s… like, the performance is so much better than… than a PC, but I…
59 00:09:29.360 ⇒ 00:09:29.720 Judd Kuehling: Yeah.
60 00:09:29.720 ⇒ 00:09:39.659 Zoran Selinger: I’m back now to a PC, like, I have my ThinkPad, and I’m enjoying Windows so much, it’s just familiar to me, feels good. Yeah.
61 00:09:39.660 ⇒ 00:09:45.770 Judd Kuehling: But you can’t beat the performance, like, Mac works so better. Like, I had a…
62 00:09:45.780 ⇒ 00:09:50.370 Zoran Selinger: I… I’m use- I used… M1 2020.
63 00:09:50.670 ⇒ 00:09:58.020 Zoran Selinger: And it outperforms this… This is almost 3,000 euro think pad from 2018.
64 00:09:58.290 ⇒ 00:09:58.940 Greg Stoutenburg: Yeah.
65 00:09:59.120 ⇒ 00:10:03.700 Zoran Selinger: the best you could get at that point, and it… mech kills it.
66 00:10:04.150 ⇒ 00:10:04.520 Greg Stoutenburg: Yeah.
67 00:10:04.520 ⇒ 00:10:07.340 Zoran Selinger: skills, it’s not even close in terms of performance.
68 00:10:07.830 ⇒ 00:10:08.180 Greg Stoutenburg: I can’t.
69 00:10:08.180 ⇒ 00:10:14.559 Zoran Selinger: And I need to get something new now, and I really… I want to use Windows, but it’s really hard to make…
70 00:10:15.030 ⇒ 00:10:21.830 Zoran Selinger: to go against, even, like, MacBook Air is…
71 00:10:22.700 ⇒ 00:10:30.529 Greg Stoutenburg: That’s what I was gonna say. Yeah, I’m a Windows guy too, but I cannot believe how powerful even this Mac… this is a MacBook Air that I’m on. I can’t believe how powerful this thing is.
72 00:10:31.140 ⇒ 00:10:36.670 Zoran Selinger: So I just slacked you guys, because I have to, like, close Zoom and do a bunch of things, and my privacy’s not hard to share.
73 00:10:36.670 ⇒ 00:10:42.220 Judd Kuehling: So I slacked it to you guys, you guys can open it, and then I can walk you through it. Yeah. That works.
74 00:10:44.360 ⇒ 00:10:45.320 Greg Stoutenburg: Yeah.
75 00:11:01.130 ⇒ 00:11:03.370 Judd Kuehling: If one of you guys want to share it.
76 00:11:03.770 ⇒ 00:11:05.170 Greg Stoutenburg: Oh, sure, yeah, I can show the screen, yeah.
77 00:11:05.170 ⇒ 00:11:05.770 Judd Kuehling: Yeah.
78 00:11:06.260 ⇒ 00:11:07.899 Greg Stoutenburg: Yeah, sounds good. Make sure we’re all in the same place.
79 00:11:07.900 ⇒ 00:11:08.520 Judd Kuehling: Yeah, yeah.
80 00:11:08.880 ⇒ 00:11:10.879 Judd Kuehling: Perfect. So, okay, so…
81 00:11:11.820 ⇒ 00:11:16.629 Judd Kuehling: So, when we think about email, we have broadcast, which means kind of, like, scheduled
82 00:11:16.720 ⇒ 00:11:37.290 Judd Kuehling: messages to wide groups, and then we have flows, which are, like, automated campaigns, which people automatically get into based on, triggers and filters and everything like that. So this first block is the broadcast data, a block below that is the flow data. Historical is basically all data in the past, and this is all from
83 00:11:37.370 ⇒ 00:11:50.209 Judd Kuehling: customer I.O, and this is all using those tags, so… using those tags on abandon. So I have abandoned cart, win-back, cross-sell, and kind of other, which I’m calling transactional.
84 00:11:50.330 ⇒ 00:11:56.450 Judd Kuehling: So anything that’s not one of those first three is kind of in that last bucket.
85 00:11:56.560 ⇒ 00:12:04.759 Judd Kuehling: So I have the historical data here. Some of this data I’ve kind of manually, like, changed, because it kind of creates the weird forecast if it’s a lot of zeros.
86 00:12:04.990 ⇒ 00:12:12.539 Judd Kuehling: But some of it still has zeros in there. The forecast, I basically have it kind of copied over, so that if you look at the next
87 00:12:13.360 ⇒ 00:12:21.499 Judd Kuehling: sheet at the bottom. It’s basically the same thing, I’m just kind of adding in a percentage. Right now, I think I have 10% bumps.
88 00:12:21.960 ⇒ 00:12:24.710 Judd Kuehling: On… on the,
89 00:12:25.040 ⇒ 00:12:37.299 Judd Kuehling: Well, some of the ones that had zeros, I, put those… yeah, so 10% bumps there on the historical, and then the actuals are what I’m reporting each week versus…
90 00:12:37.670 ⇒ 00:12:46.710 Judd Kuehling: the forecast, so… I’m showing week 4, for example, which was, like, this last week.
91 00:12:47.260 ⇒ 00:12:54.999 Judd Kuehling: for all those… same categories, and then I’m… I have the percentage of forecasted. So, to do that.
92 00:12:55.140 ⇒ 00:13:04.590 Judd Kuehling: I’m taking… this number over on that A6, which is, turning the month number into a week number.
93 00:13:05.690 ⇒ 00:13:13.620 Judd Kuehling: by basically dividing by .233, and and then I’m forecast… and then I’m comparing it to what the forecast would be.
94 00:13:14.240 ⇒ 00:13:16.900 Judd Kuehling: There, so,
95 00:13:17.210 ⇒ 00:13:26.299 Judd Kuehling: But all that to say is that the next tab is the ideal, and that’s kind of, like, what Mitesh wants to see the data kind of more formatted in, so…
96 00:13:26.910 ⇒ 00:13:38.840 Judd Kuehling: for the broadcast, he wants to see, kind of, those four categories, but also broken into, like, our customer segments. Right now, our main customer segment is called QSS.
97 00:13:38.940 ⇒ 00:13:42.089 Judd Kuehling: I forgot what that stands for, but it’s basically, like.
98 00:13:42.770 ⇒ 00:13:45.859 Judd Kuehling: It’s basically, like, kind of, busy…
99 00:13:46.500 ⇒ 00:13:52.520 Judd Kuehling: Middle-aged women that are trying to, you know…
100 00:13:52.800 ⇒ 00:13:57.830 Judd Kuehling: Control their health and their weight, but, you know, are kind of…
101 00:13:58.800 ⇒ 00:14:08.639 Judd Kuehling: I don’t know the exact definition, but basically that’s what it is. And then we have, kind of, we don’t really have the other ones defined real well yet. So, ideally, we would have…
102 00:14:10.470 ⇒ 00:14:18.969 Judd Kuehling: it broken into those different, segments that we’re looking at. Now, how we’re doing this is a little bit weird, because I don’t know how to, like, define
103 00:14:20.420 ⇒ 00:14:26.080 Judd Kuehling: like, I would have to tag, I guess, the emails by segment, and so…
104 00:14:26.570 ⇒ 00:14:29.140 Judd Kuehling: Like, I would say, this is an abandoned cart.
105 00:14:29.400 ⇒ 00:14:36.639 Judd Kuehling: tag… use the abandoned cart tag and the QSS tag on a broadcast email, and then potentially you could pull in the data.
106 00:14:36.800 ⇒ 00:14:41.420 Judd Kuehling: Like that. And if you go down to the flows on this page…
107 00:14:42.370 ⇒ 00:14:44.500 Judd Kuehling: For the flows, we’re looking at…
108 00:14:45.210 ⇒ 00:14:51.819 Judd Kuehling: The… the type of message, abandoned cart, win-back, cross-sell, transactional, and then looking at it by treatment.
109 00:14:52.040 ⇒ 00:14:55.700 Judd Kuehling: So again, I’ve gone in… yesterday, I went in and tagged
110 00:14:55.810 ⇒ 00:14:58.169 Judd Kuehling: All the campaigns with a treatment.
111 00:14:58.810 ⇒ 00:15:02.220 Judd Kuehling: So… they should all have…
112 00:15:04.090 ⇒ 00:15:12.030 Judd Kuehling: a treatment now, and so similarly, it would be, like, pulling the data by the abandoned cart tag, but also by the…
113 00:15:12.510 ⇒ 00:15:16.089 Judd Kuehling: semaglutide tag, and I think, you know, the tags are…
114 00:15:16.890 ⇒ 00:15:23.569 Judd Kuehling: you know, some of them have different variations of the name, like, I think semaglutide says compounded semaglutide.
115 00:15:24.580 ⇒ 00:15:36.590 Judd Kuehling: for Terzepatide, we have compounded Terzepatide, but I forgot to capitalize it. Like, for Simoralin, it just says Simoralin, and everything else. NAD, it just says NAD, but basically, the idea is that
116 00:15:36.740 ⇒ 00:15:45.640 Judd Kuehling: We would have the data… Kind of in that category and then subcategory by week.
117 00:15:45.930 ⇒ 00:15:51.920 Judd Kuehling: So, this is all stuff that I could pull with… customer I.O, but it would…
118 00:15:52.720 ⇒ 00:15:55.559 Judd Kuehling: Take me a long time to do it bi-week.
119 00:15:57.240 ⇒ 00:16:03.269 Judd Kuehling: By these categories, and so it would be great to figure out a way to find an automation to do that.
120 00:16:05.300 ⇒ 00:16:13.540 Judd Kuehling: And then SMS is basically these other sheets, basically the same exact thing as email, and then we have the same ideal…
121 00:16:13.930 ⇒ 00:16:15.470 Judd Kuehling: Groups again.
122 00:16:16.590 ⇒ 00:16:22.960 Judd Kuehling: There. So, some of these things are a calculation, so you can see where it’s broken.
123 00:16:23.680 ⇒ 00:16:33.679 Judd Kuehling: Like, conversion… click-through rate and conversion rate are calculations based off of, you know, clicks over messages delivered, or orders over messages delivered.
124 00:16:34.530 ⇒ 00:16:37.889 Judd Kuehling: And so those break, obviously, but… so I haven’t kind of…
125 00:16:38.000 ⇒ 00:16:43.129 Judd Kuehling: massage some of this historical data to give us a better forecast yet for the SMS group.
126 00:16:43.620 ⇒ 00:16:50.199 Judd Kuehling: But you can see it’s basically the same format, and then back in the ideal, it’s the same thing as email, where…
127 00:16:51.990 ⇒ 00:16:59.610 Judd Kuehling: Actually, no, I haven’t updated this yet. So, the ideal would be the same thing as email, where we break it down the same way by…
128 00:16:59.950 ⇒ 00:17:04.109 Judd Kuehling: Type of message, and then segment, and then for…
129 00:17:04.280 ⇒ 00:17:08.609 Judd Kuehling: Oh, I’m sorry, I’m only looking at flows for SMS, because we don’t really do…
130 00:17:09.140 ⇒ 00:17:16.339 Judd Kuehling: broadcast RestMass, so I have those sheets… that… I have the top part of this, sheet hidden, you can see that, and then…
131 00:17:16.349 ⇒ 00:17:16.979 Greg Stoutenburg: Isaiah.
132 00:17:16.980 ⇒ 00:17:21.190 Judd Kuehling: I’m only looking at flows for SMS, because that’s really the only…
133 00:17:21.530 ⇒ 00:17:32.449 Judd Kuehling: thing we’re doing, and so this would need to be broken down. Still, I haven’t done it yet, but it would be, like, the same way that email IDEAL is, where it’s abandoned car, and then the
134 00:17:32.870 ⇒ 00:17:40.700 Judd Kuehling: 4 treatments, another, and then went back, and then the four treatments, another. And again, this would be… Tagged.
135 00:17:41.170 ⇒ 00:17:43.450 Judd Kuehling: Using the tags, so it’s not…
136 00:17:44.230 ⇒ 00:17:52.950 Judd Kuehling: super complicated to define, and it’s just about trying to figure out how to build an automated report that can pull this data in in a way other than me
137 00:17:53.760 ⇒ 00:17:59.600 Judd Kuehling: Going through line by line and manually Putting it in.
138 00:18:00.190 ⇒ 00:18:02.009 Greg Stoutenburg: Yeah, this’ll be a whole day every week.
139 00:18:02.010 ⇒ 00:18:02.690 Judd Kuehling: Yeah.
140 00:18:02.690 ⇒ 00:18:03.700 Greg Stoutenburg: It’s entering all the numbers.
141 00:18:03.700 ⇒ 00:18:08.709 Judd Kuehling: It already is, like, a half day every week, and then the way Mitesh wants it, it makes it a whole day every week if I do.
142 00:18:08.710 ⇒ 00:18:10.709 Greg Stoutenburg: Yeah, yeah, it looks like it.
143 00:18:10.880 ⇒ 00:18:11.670 Judd Kuehling: Yeah.
144 00:18:12.160 ⇒ 00:18:13.930 Judd Kuehling: So.
145 00:18:14.710 ⇒ 00:18:21.540 Zoran Selinger: I think unavoidable thing that we must do is the cleanup of the tags. You can change historically.
146 00:18:21.640 ⇒ 00:18:24.969 Zoran Selinger: You can clean up the tags from before that you added?
147 00:18:24.970 ⇒ 00:18:25.620 Judd Kuehling: Yeah.
148 00:18:25.890 ⇒ 00:18:30.160 Zoran Selinger: I think that just has to be done, if you want to do anything programmatically, yeah.
149 00:18:30.160 ⇒ 00:18:42.340 Judd Kuehling: For sure, for sure. We can clean up the tags, however you guys see fit. You know, I tried to tag things as best as possible, but we can go through that and check on that, too.
150 00:18:42.550 ⇒ 00:18:45.950 Judd Kuehling: As well, if we want to change the tag names, I think it should…
151 00:18:46.370 ⇒ 00:18:52.580 Judd Kuehling: if you change… I don’t know how to change the tag names, I think there’s a way to do it, but it should be retroactive and…
152 00:18:53.100 ⇒ 00:18:54.829 Judd Kuehling: And take,
153 00:18:55.380 ⇒ 00:19:06.269 Judd Kuehling: like, if you change it from a one word to, like, an abbreviation of that word, it should still pull in retroactively everything that you were… that were written in the original word, I think.
154 00:19:06.820 ⇒ 00:19:07.620 Judd Kuehling: Yeah.
155 00:19:08.020 ⇒ 00:19:10.080 Judd Kuehling: So, yeah, I think.
156 00:19:10.350 ⇒ 00:19:15.179 Zoran Selinger: It’s just about consistency, right? I don’t know, Greg, if you have…
157 00:19:15.320 ⇒ 00:19:19.229 Zoran Selinger: a different opinion. It’s just about consistency, as long as we know
158 00:19:19.430 ⇒ 00:19:22.520 Zoran Selinger: Eat by product and by… by flow.
159 00:19:23.600 ⇒ 00:19:28.500 Zoran Selinger: we should be good. And yeah, end the segment, if we start going into that, yeah.
160 00:19:29.310 ⇒ 00:19:46.139 Greg Stoutenburg: Completely agree. It’s just about consistency. And something that we do when we’re, when we’re doing other analytics implementations, and this just falls in the same category, is just have a master document where, you know, we just say, like, officially, anytime you’re gonna, you know, mention SEMA, you know, it’s…
161 00:19:46.140 ⇒ 00:19:46.690 Judd Kuehling: Yeah.
162 00:19:46.690 ⇒ 00:19:55.440 Greg Stoutenburg: you pick, you know, is it capital S, the rest is lowercase, semiglutide? Is it just SEMA? Is it whatever? And you just pick, and then we just have to be consistent.
163 00:19:55.780 ⇒ 00:20:04.129 Judd Kuehling: Yeah, I can kind of start that, and then you guys can, let me know if I need to change the format or anything like that, but I can start that with…
164 00:20:05.040 ⇒ 00:20:09.989 Judd Kuehling: The treatment tags, and the, message type tags.
165 00:20:10.600 ⇒ 00:20:13.149 Judd Kuehling: And then, you guys can…
166 00:20:13.350 ⇒ 00:20:19.669 Judd Kuehling: edit it, or you can tell me to do it differently, or whatever, but let me do that, and I’ll get that over to you.
167 00:20:21.500 ⇒ 00:20:22.170 Greg Stoutenburg: Okay.
168 00:20:22.420 ⇒ 00:20:24.620 Greg Stoutenburg: That sounds like a good start to me. What do you think, Soren?
169 00:20:25.610 ⇒ 00:20:32.060 Zoran Selinger: Yeah, I think, yeah, that’s absolutely what… what the first, the first step…
170 00:20:32.560 ⇒ 00:20:37.440 Zoran Selinger: Yeah, I’m thinking about anything programmatic, what we can do, it’s…
171 00:20:37.610 ⇒ 00:20:44.299 Zoran Selinger: There’s a lot of complexity in this, because you don’t have, like, okay, we can…
172 00:20:45.300 ⇒ 00:20:47.609 Zoran Selinger: This must be done before we can pull
173 00:20:48.100 ⇒ 00:20:54.410 Zoran Selinger: the data daily by tags, so this just simply has to be done. Once we do have
174 00:20:54.550 ⇒ 00:21:02.490 Zoran Selinger: daily campaign report with updated tags in it, then we can start looking at breaking it down.
175 00:21:04.690 ⇒ 00:21:05.390 Zoran Selinger: And…
176 00:21:06.160 ⇒ 00:21:20.650 Zoran Selinger: We’ll have to talk to our data engineers and see what the best approach there is, but this is definitely the first step, is the cleanup of the tank. I think at that point, we have everything that we need inside the platform.
177 00:21:22.760 ⇒ 00:21:27.130 Zoran Selinger: And we’ll be able to… to… To create a report.
178 00:21:28.040 ⇒ 00:21:31.210 Judd Kuehling: Okay. This is… by the way, Greg, this is…
179 00:21:32.940 ⇒ 00:21:38.470 Zoran Selinger: more or less a duplicate of what I need to do for the KPI dashboard.
180 00:21:39.010 ⇒ 00:21:39.790 Greg Stoutenburg: Oh, okay.
181 00:21:39.790 ⇒ 00:21:41.319 Zoran Selinger: For this particular channel.
182 00:21:41.870 ⇒ 00:21:49.429 Zoran Selinger: Okay. So, what Judd did here is exactly what he wants on every channel.
183 00:21:50.100 ⇒ 00:22:03.119 Zoran Selinger: That they have. Obviously, there’s gonna be different metrics on other channels, but exactly that is needed on every channel. So, essentially, we are solving one part of that.
184 00:22:03.640 ⇒ 00:22:08.069 Zoran Selinger: KPI dash that, that Mitesh wants, with this.
185 00:22:08.570 ⇒ 00:22:09.480 Greg Stoutenburg: Yeah, and then we can…
186 00:22:09.480 ⇒ 00:22:14.010 Zoran Selinger: I’ll… I’ll separately do that for other channels as well. Yeah. Yeah.
187 00:22:14.010 ⇒ 00:22:17.880 Greg Stoutenburg: Yeah, and then we can apply what we learned for all of them, and then just have a way that we do this.
188 00:22:18.630 ⇒ 00:22:30.020 Zoran Selinger: Yeah, I guess, I guess, yeah. It’s, we should be done by the mid-month, mid-Feb, but it’s, I think that’s unrealistic now.
189 00:22:31.100 ⇒ 00:22:34.859 Zoran Selinger: hopefully Q… end of Q1, we can have that stash.
190 00:22:34.920 ⇒ 00:22:35.800 Greg Stoutenburg: Yeah.
191 00:22:38.200 ⇒ 00:22:38.710 Greg Stoutenburg: Nope.
192 00:22:38.830 ⇒ 00:22:39.700 Greg Stoutenburg: Okay.
193 00:22:40.950 ⇒ 00:22:41.540 Greg Stoutenburg: All right.
194 00:22:41.540 ⇒ 00:22:48.880 Judd Kuehling: That file that I shared, it’s open, you guys can…
195 00:22:50.600 ⇒ 00:23:02.079 Judd Kuehling: you know, do whatever you edit if you want, or do whatever you need to to that file. I have it open, and I’m doing stuff to it fairly often, but feel free to do whatever, and then I will create…
196 00:23:02.540 ⇒ 00:23:07.349 Judd Kuehling: Kind of a master tagged definition sheet.
197 00:23:07.350 ⇒ 00:23:07.740 Greg Stoutenburg: the way.
198 00:23:07.740 ⇒ 00:23:09.120 Judd Kuehling: Let’s send that over.
199 00:23:09.500 ⇒ 00:23:11.380 Greg Stoutenburg: Yep, perfect. Sounds good.
200 00:23:11.600 ⇒ 00:23:17.319 Zoran Selinger: Good, good, yeah. And, John, I have to say, I’ve seen something similar before.
201 00:23:17.570 ⇒ 00:23:33.170 Zoran Selinger: for whatever reason, this is clearer to me than it was before. I like… I feel like I understand exactly what you guys are doing now. For whatever reason, I get this. Okay, cool. It really helps. It really helps. Okay, awesome.
202 00:23:33.170 ⇒ 00:23:35.069 Judd Kuehling: The other thing is, like, I have…
203 00:23:35.770 ⇒ 00:23:41.839 Judd Kuehling: the date, like, the other thing is the week versus month thing. So, like, for me, like, I prefer months.
204 00:23:41.940 ⇒ 00:23:48.799 Judd Kuehling: Mitesh wants to see weeks, data and weeks, so… when I pulled the data for the historical, I pulled it by month.
205 00:23:49.220 ⇒ 00:23:52.690 Judd Kuehling: Because if I had pulled it by week, you know, it would have taken me…
206 00:23:52.800 ⇒ 00:24:00.410 Judd Kuehling: It took me, you know, two days to do it by pulling it by a month. Like, it would have taken me forever to pull it by week, so,
207 00:24:00.790 ⇒ 00:24:06.059 Judd Kuehling: So, if you… if we want to pull it by week instead, it’s probably more…
208 00:24:06.310 ⇒ 00:24:13.310 Judd Kuehling: accurate. For me, in my opinion, email, like, it’s not like, you know, Google search, it’s not like…
209 00:24:13.570 ⇒ 00:24:20.280 Judd Kuehling: meta spend, it’s not like it changes day to day to day, like, and every single day you can look at the numbers, and it’s like…
210 00:24:20.420 ⇒ 00:24:38.190 Judd Kuehling: the way email works, there’s a tail on it, right? Because I send an email, someone doesn’t do anything for a couple days sometimes. So, looking at weeks sometimes is, like, a hard… like, not my preferred way to do it. That’s the way Mitesh wants to do it. So, you know, let me know if there’s any kind of issues there that come up, or kind of…
211 00:24:39.040 ⇒ 00:24:40.949 Judd Kuehling: You know, how we can figure that out, too.
212 00:24:42.080 ⇒ 00:24:42.870 Zoran Selinger: Okay.
213 00:24:43.880 ⇒ 00:24:44.440 Judd Kuehling: Yeah.
214 00:24:46.040 ⇒ 00:24:56.339 Judd Kuehling: But yeah, nothing… no, there aren’t too many, complicated calculations in that… in that, in that sheet. Yeah, for sure. Yeah, you’ll figure it… you’ll figure it out just looking at it, I think.
215 00:24:57.150 ⇒ 00:25:10.080 Greg Stoutenburg: Yeah, cool. Sounds good. Alright, so I think, I think we align that the next step is going to be that you’re going to organize those tags, and then we’ll pick up and figure out what we need to do to programmatically pull this data and get it into the format that you need.
216 00:25:10.480 ⇒ 00:25:11.960 Judd Kuehling: Cool. Sounds good.
217 00:25:12.460 ⇒ 00:25:13.920 Greg Stoutenburg: Okay. Awesome. Sounds good.
218 00:25:13.920 ⇒ 00:25:14.510 Zoran Selinger: Come on.
219 00:25:14.510 ⇒ 00:25:17.579 Greg Stoutenburg: Cool. Alright. Thanks, guys. Thank you, guys.
220 00:25:17.580 ⇒ 00:25:18.460 Zoran Selinger: Thanks, bye.