Meeting Title: Lifecycle Marketing Data Clarification Sync Date: 2025-09-10 Meeting participants: Henry Zhao, Judd Kuehling
WEBVTT
1 00:01:17.410 ⇒ 00:01:18.650 Henry Zhao: Hey, how’s it going?
2 00:01:18.890 ⇒ 00:01:19.450 Judd Kuehling: Good, how you doing?
3 00:01:20.870 ⇒ 00:01:21.980 Henry Zhao: Good, thanks.
4 00:01:22.680 ⇒ 00:01:23.520 Henry Zhao: Right.
5 00:01:23.520 ⇒ 00:01:25.809 Judd Kuehling: Where are you located, geographically?
6 00:01:26.540 ⇒ 00:01:27.650 Henry Zhao: Arizona.
7 00:01:28.050 ⇒ 00:01:28.810 Judd Kuehling: Oh, okay.
8 00:01:29.200 ⇒ 00:01:31.850 Judd Kuehling: I’m in, Southern California, so not…
9 00:01:32.210 ⇒ 00:01:33.520 Henry Zhao: Okay, very close.
10 00:01:33.780 ⇒ 00:01:34.360 Judd Kuehling: Yeah.
11 00:01:35.660 ⇒ 00:01:36.710 Henry Zhao: Cool.
12 00:01:37.650 ⇒ 00:01:39.960 Henry Zhao: Alright, I know it’s getting late, so I won’t keep you too long.
13 00:01:40.090 ⇒ 00:01:41.110 Judd Kuehling: Yeah, no problem.
14 00:01:41.480 ⇒ 00:01:44.259 Henry Zhao: I just wanted to get some clarification on…
15 00:01:46.300 ⇒ 00:01:50.450 Henry Zhao: Yeah, I’m a little bit stuck, so I’m gonna need my team to help me a little bit tomorrow to get this done.
16 00:01:50.980 ⇒ 00:01:56.140 Henry Zhao: I also pulled the data from 3 different sources and got 3 different numbers, so obviously…
17 00:01:56.390 ⇒ 00:01:58.180 Henry Zhao: Not that happy about that.
18 00:01:59.290 ⇒ 00:02:01.679 Henry Zhao: So we called it Lifecycle Marketing.
19 00:02:02.600 ⇒ 00:02:03.549 Henry Zhao: Oh, jeez.
20 00:02:08.759 ⇒ 00:02:09.990 Henry Zhao: Mmm, geez.
21 00:02:15.010 ⇒ 00:02:16.320 Henry Zhao: So…
22 00:02:20.250 ⇒ 00:02:24.000 Henry Zhao: Yeah, let’s start with the initial question. So the initial question was…
23 00:02:27.220 ⇒ 00:02:34.260 Henry Zhao: Can you help Judd find revenue metrics from abandoned cart flows and revenue from cross-sell flows, so he can report on these directly?
24 00:02:34.660 ⇒ 00:02:40.310 Henry Zhao: So that part, I understood, we have these three types of campaigns. We want order numbers and revenue.
25 00:02:40.570 ⇒ 00:02:41.140 Judd Kuehling: Yep.
26 00:02:43.160 ⇒ 00:02:46.189 Henry Zhao: And then this stuff might not be relevant anymore,
27 00:02:47.670 ⇒ 00:02:53.270 Henry Zhao: like, I don’t think this is relevant anymore, I think I’m just gonna look at… Whatever data we have.
28 00:02:54.010 ⇒ 00:03:00.090 Henry Zhao: for UTM Medium as email action, Twilio action, or SMS, and Source is Customer I.O.
29 00:03:03.830 ⇒ 00:03:06.830 Judd Kuehling: Yeah, I mean, attribution, I don’t know, like, how…
30 00:03:07.720 ⇒ 00:03:10.629 Judd Kuehling: Attribution, like, where the attribution is happening.
31 00:03:11.380 ⇒ 00:03:17.600 Judd Kuehling: For, for, like, the data you’re pulling, so it could be relevant for you there.
32 00:03:17.960 ⇒ 00:03:27.589 Henry Zhao: So, we had decided on LastClick, which is why I’m a little bit curious why, for the available data for Customer I.O, you only cared about the first UTMs and not the last ones.
33 00:03:32.160 ⇒ 00:03:39.280 Judd Kuehling: For our last click, I already kind of… I mean, last click, I’m trying to make them last click with the stuff I’m sending.
34 00:03:39.470 ⇒ 00:03:41.670 Judd Kuehling: first click, I want to know, kind of.
35 00:03:42.690 ⇒ 00:03:46.110 Judd Kuehling: to be able to segment them. So, like, I want to know, oh, this is a…
36 00:03:46.320 ⇒ 00:03:53.600 Judd Kuehling: Google a search customer, or a Facebook customer, I might do something different with them in email.
37 00:03:54.160 ⇒ 00:03:56.400 Judd Kuehling: Versus someone different.
38 00:03:56.780 ⇒ 00:04:01.089 Judd Kuehling: Last click, I’m always hoping that those people are email is their last click.
39 00:04:01.500 ⇒ 00:04:11.260 Henry Zhao: So when I add… that makes sense. So when I add the session abandoned phone numbers, for example, you want to be able to get those people to finally do an order, and that you’ll be there.
40 00:04:12.120 ⇒ 00:04:13.710 Judd Kuehling: I would be the last clip, right.
41 00:04:13.710 ⇒ 00:04:16.409 Henry Zhao: That would make… that makes sense, actually. Makes a lot of sense.
42 00:04:16.500 ⇒ 00:04:18.029 Judd Kuehling: Yeah.
43 00:04:18.810 ⇒ 00:04:19.480 Henry Zhao: So, what…
44 00:04:19.480 ⇒ 00:04:28.619 Judd Kuehling: I don’t know that I’d even use that first, that UTM source data in there, I just left it in there. I don’t know that I’ll… I don’t have any plans to use it, but I thought, like I said, potentially I could say.
45 00:04:28.730 ⇒ 00:04:34.360 Judd Kuehling: oh, like, I’ll do a different, like, email campaign to people that came in through… a Facebook.
46 00:04:34.560 ⇒ 00:04:35.029 Henry Zhao: That makes sense.
47 00:04:35.030 ⇒ 00:04:38.680 Judd Kuehling: do their Facebook campaign, because I know something about them or something, you know, like…
48 00:04:40.810 ⇒ 00:04:41.990 Henry Zhao: Okay, that makes sense.
49 00:04:42.250 ⇒ 00:04:50.950 Henry Zhao: So then, how do we want to visualize this? So, you need 3-month retention. It would be similar to the monthly reorder rate blue waterfall report.
50 00:04:51.340 ⇒ 00:04:57.979 Judd Kuehling: So the… so the, this is… this 3-month retention thing is different from those orders and revenue for those three…
51 00:04:58.240 ⇒ 00:05:03.089 Judd Kuehling: things, you saw that, right? I mean, that’s… this is kind of an add-on thing that’s separate from that.
52 00:05:03.260 ⇒ 00:05:05.439 Henry Zhao: I need that in addition to the.
53 00:05:05.850 ⇒ 00:05:08.810 Judd Kuehling: this, 3 months in. Does that make sense?
54 00:05:09.360 ⇒ 00:05:11.279 Henry Zhao: Sorry, say that again, let’s… I’m trying to follow.
55 00:05:11.280 ⇒ 00:05:13.739 Judd Kuehling: Go back to… go back to the, Slack.
56 00:05:14.430 ⇒ 00:05:17.339 Judd Kuehling: There’s a thing, something where it says, where I put in…
57 00:05:17.600 ⇒ 00:05:23.530 Judd Kuehling: Before I put… before we talked about this, in an earlier thread, when Hutter asked.
58 00:05:24.500 ⇒ 00:05:29.949 Judd Kuehling: And I said, this is specifically kind of what I need. Can you go back? I think we saw it a second ago.
59 00:05:30.850 ⇒ 00:05:32.030 Henry Zhao: here.
60 00:05:32.030 ⇒ 00:05:34.640 Judd Kuehling: So, go, like, this is like a sub… a sub…
61 00:05:34.990 ⇒ 00:05:35.780 Henry Zhao: Yeah, you’re right.
62 00:05:37.900 ⇒ 00:05:39.730 Henry Zhao: I think it’s in here, actually.
63 00:05:39.730 ⇒ 00:05:41.940 Judd Kuehling: Yeah, yeah. Yeah, go up.
64 00:05:43.400 ⇒ 00:05:48.260 Judd Kuehling: So, right here where I say, I need… I want abandoned cart orders and revenue.
65 00:05:48.440 ⇒ 00:05:52.280 Judd Kuehling: Cross-sell orders and revenue, and win-back orders and revenue.
66 00:05:52.820 ⇒ 00:05:55.159 Judd Kuehling: And I want to be able to pull that by…
67 00:05:56.320 ⇒ 00:06:04.169 Judd Kuehling: whatever timeline that I want, so I want to be able to say, like, over the last 7 days, over the last 4 weeks, over the last…
68 00:06:04.340 ⇒ 00:06:10.459 Judd Kuehling: 12 weeks, or whatever the normal kind of timeline polls are in Tableau for other reports.
69 00:06:11.510 ⇒ 00:06:13.409 Judd Kuehling: And I wanted just to give me a number.
70 00:06:14.420 ⇒ 00:06:16.170 Judd Kuehling: Orders number and a revenue number.
71 00:06:16.860 ⇒ 00:06:18.129 Henry Zhao: You don’t want it by month?
72 00:06:22.690 ⇒ 00:06:26.899 Henry Zhao: So that’s the… Let me log into Tableau so we can look at it together.
73 00:06:28.750 ⇒ 00:06:29.510 Henry Zhao: Come on.
74 00:06:33.290 ⇒ 00:06:35.210 Henry Zhao: Oh my gosh, I think it’s because it timed out.
75 00:06:53.680 ⇒ 00:06:57.610 Henry Zhao: Alright, so… I got this…
76 00:06:58.040 ⇒ 00:07:00.289 Judd Kuehling: Yeah, like this. Yeah, this is fine.
77 00:07:00.600 ⇒ 00:07:10.140 Henry Zhao: So I got this far, I have last UTM source customer I.O, last UTM medium is Twilio Action, Email Action SMS, okay?
78 00:07:10.170 ⇒ 00:07:13.790 Judd Kuehling: I don’t see anything for SMS yet, okay? So, just as FYI.
79 00:07:13.980 ⇒ 00:07:17.150 Judd Kuehling: Should have something as of this week, but, yeah.
80 00:07:18.110 ⇒ 00:07:27.309 Henry Zhao: Yeah, we can look into that later. But we have abandoned car, cross-sell, win-back, other. Do you want it all the time? Do you want to look at it by campaign type, or do you want these to be filters and this to be a waterfall?
81 00:07:27.970 ⇒ 00:07:34.380 Judd Kuehling: this is… no, I like this, this is great, but I want to be able to look at this… I need to be able to pull this by, like.
82 00:07:34.750 ⇒ 00:07:39.190 Judd Kuehling: Different, time slices than just months.
83 00:07:40.500 ⇒ 00:07:42.480 Judd Kuehling: I need to be able to, like, say.
84 00:07:43.830 ⇒ 00:07:46.670 Judd Kuehling: Like, I just want to look at, like, the last 7 days.
85 00:07:48.130 ⇒ 00:07:49.290 Judd Kuehling: Or something like that.
86 00:07:49.740 ⇒ 00:07:51.199 Judd Kuehling: Is there some way to do that?
87 00:07:51.420 ⇒ 00:07:54.410 Henry Zhao: Yeah, yeah, that, that, that’s easy. Okay.
88 00:07:54.720 ⇒ 00:07:56.510 Henry Zhao: Yeah, like, this can be by month.
89 00:07:59.750 ⇒ 00:08:02.400 Henry Zhao: If you want it by day, you can duplicate this.
90 00:08:05.840 ⇒ 00:08:10.260 Henry Zhao: I just make this a date, and then… You can filter the day.
91 00:08:11.210 ⇒ 00:08:13.380 Judd Kuehling: Yeah, perfect. Yeah, this is great.
92 00:08:13.640 ⇒ 00:08:14.660 Judd Kuehling: This is perfect.
93 00:08:16.200 ⇒ 00:08:17.070 Henry Zhao: I’ve got ranges.
94 00:08:17.310 ⇒ 00:08:18.889 Henry Zhao: Whatever, relative date.
95 00:08:19.810 ⇒ 00:08:21.099 Judd Kuehling: Yeah, this is perfect.
96 00:08:21.590 ⇒ 00:08:24.859 Judd Kuehling: And then, so there’s the same version of this, but it’s,
97 00:08:25.450 ⇒ 00:08:27.930 Judd Kuehling: Order count instead of revenue, right?
98 00:08:28.120 ⇒ 00:08:30.140 Henry Zhao: Right, that’s easy, yeah.
99 00:08:30.140 ⇒ 00:08:32.730 Judd Kuehling: Okay, so that’s… that’s good, that’s done with all that.
100 00:08:33.059 ⇒ 00:08:34.020 Judd Kuehling: Now, the streamline.
101 00:08:34.020 ⇒ 00:08:35.389 Henry Zhao: Correct, yeah.
102 00:08:35.620 ⇒ 00:08:37.810 Judd Kuehling: 3-month thing is completely different from this.
103 00:08:40.039 ⇒ 00:08:40.649 Henry Zhao: Right.
104 00:08:40.939 ⇒ 00:08:44.859 Henry Zhao: That’s fine, so this will be done once I get the data verified.
105 00:08:45.240 ⇒ 00:08:46.050 Judd Kuehling: works.
106 00:08:46.050 ⇒ 00:08:46.950 Henry Zhao: Sure.
107 00:08:47.330 ⇒ 00:08:49.300 Henry Zhao: I also have order number…
108 00:08:53.250 ⇒ 00:08:57.210 Henry Zhao: Just do… I just keep it here just to remind myself.
109 00:08:59.650 ⇒ 00:09:01.770 Henry Zhao: Measure account distinct…
110 00:09:03.720 ⇒ 00:09:08.630 Henry Zhao: This will be your… I don’t know how you want to visualize it, but yeah, eventually it’ll be revenue and order count.
111 00:09:08.630 ⇒ 00:09:09.990 Judd Kuehling: Okay, yeah.
112 00:09:09.990 ⇒ 00:09:14.520 Henry Zhao: I don’t know if you want it in two separate charts, or you want it, like, stacked like this for ease of use.
113 00:09:15.150 ⇒ 00:09:19.299 Judd Kuehling: Does it have, like, a total somewhere?
114 00:09:20.390 ⇒ 00:09:22.370 Judd Kuehling: Alright, alright.
115 00:09:36.860 ⇒ 00:09:37.650 Henry Zhao: The total.
116 00:09:39.160 ⇒ 00:09:43.959 Henry Zhao: I’ll figure out where it is, I don’t need to bore you with this. Let me just…
117 00:09:44.910 ⇒ 00:09:48.070 Henry Zhao: Actually, let me… Let me just do this real quick.
118 00:09:55.050 ⇒ 00:09:57.599 Judd Kuehling: I know I can output the data and get the total, but…
119 00:09:58.000 ⇒ 00:10:02.089 Judd Kuehling: like, Excel or whatever, but it’d be cool to have it as a row.
120 00:10:03.430 ⇒ 00:10:04.020 Henry Zhao: Yeah.
121 00:10:06.180 ⇒ 00:10:09.870 Henry Zhao: Trying to figure out what’s the best way to look at this, to not be kind of crowded.
122 00:10:10.590 ⇒ 00:10:15.990 Henry Zhao: So I’ll put it like this, maybe? Measure names as columns.
123 00:10:17.040 ⇒ 00:10:18.540 Henry Zhao: I don’t know if this is maybe better.
124 00:10:19.370 ⇒ 00:10:21.580 Henry Zhao: Do you want row totals or column totals?
125 00:10:22.560 ⇒ 00:10:25.450 Judd Kuehling: All of the book.
126 00:10:26.240 ⇒ 00:10:26.930 Henry Zhao: Or both.
127 00:10:27.390 ⇒ 00:10:29.930 Judd Kuehling: Yeah, for, like, the date… whatever date range, that’s…
128 00:10:30.970 ⇒ 00:10:33.839 Judd Kuehling: I want it all… I want the abandoned cart.
129 00:10:34.270 ⇒ 00:10:39.160 Judd Kuehling: total for… random selection, like, for whatever date range I choose.
130 00:10:40.820 ⇒ 00:10:42.979 Henry Zhao: Okay, order numbers…
131 00:11:08.840 ⇒ 00:11:14.370 Henry Zhao: Yeah, so date range, I’ll show you this filter. You can just show filter, and you can pick your dates here, okay?
132 00:11:14.630 ⇒ 00:11:15.230 Judd Kuehling: Okay.
133 00:11:16.050 ⇒ 00:11:17.789 Henry Zhao: So, say you just want September.
134 00:11:23.490 ⇒ 00:11:24.280 Henry Zhao: No.
135 00:11:26.270 ⇒ 00:11:27.480 Henry Zhao: I’ll use the calendar.
136 00:11:30.380 ⇒ 00:11:31.829 Henry Zhao: And there you go.
137 00:11:32.100 ⇒ 00:11:32.890 Judd Kuehling: Yep.
138 00:11:35.110 ⇒ 00:11:37.229 Henry Zhao: And then, order number…
139 00:11:47.550 ⇒ 00:11:51.160 Judd Kuehling: Never seen a… Bit low, but I guess it’s…
140 00:11:51.870 ⇒ 00:11:54.490 Henry Zhao: Yeah, this is why I need to check, like, if the data’s correct.
141 00:11:54.490 ⇒ 00:11:55.030 Judd Kuehling: Okay.
142 00:11:59.150 ⇒ 00:12:01.360 Henry Zhao: On that, don’t want any…
143 00:12:03.610 ⇒ 00:12:08.000 Henry Zhao: I also need to check why there are some orders that are $0. It’s probably with a discount.
144 00:12:08.690 ⇒ 00:12:10.389 Judd Kuehling: Brother. Yeah.
145 00:12:10.390 ⇒ 00:12:14.600 Henry Zhao: This one has a one order with zeros. I just want to check if that’s the, because of,
146 00:12:14.860 ⇒ 00:12:16.430 Henry Zhao: Order discount.
147 00:12:20.740 ⇒ 00:12:22.970 Henry Zhao: No, $0 discount.
148 00:12:25.750 ⇒ 00:12:27.250 Henry Zhao: What is calculation 2?
149 00:12:33.870 ⇒ 00:12:35.410 Henry Zhao: There we go, I think that looks better.
150 00:12:37.610 ⇒ 00:12:42.539 Henry Zhao: Let me get the total. I was gonna ask you, did you want row totals, or column totals, or both?
151 00:12:42.540 ⇒ 00:12:43.770 Judd Kuehling: column.
152 00:12:44.190 ⇒ 00:12:45.080 Judd Kuehling: Yeah.
153 00:12:45.080 ⇒ 00:12:46.350 Henry Zhao: They’re here at the bottom right.
154 00:12:46.640 ⇒ 00:12:47.240 Judd Kuehling: Yeah.
155 00:13:01.780 ⇒ 00:13:02.440 Henry Zhao: Where is it?
156 00:13:10.610 ⇒ 00:13:12.040 Henry Zhao: No totals.
157 00:13:27.560 ⇒ 00:13:28.990 Henry Zhao: Analysis totals.
158 00:13:30.600 ⇒ 00:13:31.610 Henry Zhao: That was right here.
159 00:13:40.550 ⇒ 00:13:42.019 Henry Zhao: Alright, so something like this.
160 00:13:42.980 ⇒ 00:13:45.369 Judd Kuehling: Yeah. Right. Perfect. Yep.
161 00:13:45.390 ⇒ 00:13:47.600 Henry Zhao: Okay, that’s one view by day, and then…
162 00:13:47.750 ⇒ 00:13:52.849 Henry Zhao: How do you want to filter or look at it? Let me duplicate this again by for the 3-month retention thing.
163 00:13:53.490 ⇒ 00:13:58.840 Judd Kuehling: Yeah, so… 3-month retention thing, can you go back to that, like, blue chart.
164 00:14:00.180 ⇒ 00:14:00.550 Henry Zhao: Yeah.
165 00:14:00.550 ⇒ 00:14:01.140 Judd Kuehling: Where?
166 00:14:01.590 ⇒ 00:14:09.060 Judd Kuehling: Blue, yeah. So basically, this kind of already exists, in a way, so… I basically want this…
167 00:14:09.990 ⇒ 00:14:16.540 Judd Kuehling: exact thing, except for I would only have that That, third column.
168 00:14:16.740 ⇒ 00:14:19.510 Judd Kuehling: Or the column has a 3 open. Yeah.
169 00:14:19.720 ⇒ 00:14:21.110 Henry Zhao: So you don’t even want 1 or 2.
170 00:14:21.110 ⇒ 00:14:21.760 Judd Kuehling: No.
171 00:14:23.100 ⇒ 00:14:28.530 Henry Zhao: But I’m still having a little bit of trouble understanding who are the people that are in this third column. What am I dividing?
172 00:14:29.440 ⇒ 00:14:36.640 Judd Kuehling: Everybody. Everybody that… now, look over here, everybody that month of first order was April 2024.
173 00:14:38.840 ⇒ 00:14:45.020 Judd Kuehling: you basically took it, everybody that was their month of first order was April 2024, and you… as the…
174 00:14:45.380 ⇒ 00:14:49.230 Judd Kuehling: Denominator, and then the numerator, the top number is
175 00:14:49.820 ⇒ 00:14:52.399 Judd Kuehling: Everybody that’s still around at month 3.
176 00:14:53.120 ⇒ 00:14:53.750 Henry Zhao: Oh, okay.
177 00:14:53.750 ⇒ 00:14:59.720 Judd Kuehling: It makes a third… I shouldn’t say I’m around at month three, I should say, Makes a third…
178 00:15:00.770 ⇒ 00:15:02.810 Judd Kuehling: Pays for a third order.
179 00:15:04.510 ⇒ 00:15:05.550 Judd Kuehling: It is for sure.
180 00:15:05.550 ⇒ 00:15:07.350 Henry Zhao: Only for monthly orders, or…
181 00:15:08.210 ⇒ 00:15:09.710 Judd Kuehling: No, well, so…
182 00:15:09.710 ⇒ 00:15:10.290 Henry Zhao: order.
183 00:15:11.720 ⇒ 00:15:15.800 Judd Kuehling: This is kind of confusing to me, because I don’t really understand the status of people well.
184 00:15:16.020 ⇒ 00:15:18.980 Henry Zhao: I’m kind of new, I don’t really understand how this works, but…
185 00:15:19.580 ⇒ 00:15:26.769 Judd Kuehling: So if someone, like, is a… is a… gets on a 6-month plan and doesn’t, like, cancel and ask for a refund, then I want to count those people, too.
186 00:15:26.770 ⇒ 00:15:28.710 Henry Zhao: Okay? Because they’ve been able to…
187 00:15:29.040 ⇒ 00:15:31.450 Judd Kuehling: They paid up front the 6 month, I think, or the.
188 00:15:31.450 ⇒ 00:15:32.890 Henry Zhao: Okay, that counts, makes sense.
189 00:15:33.570 ⇒ 00:15:37.839 Judd Kuehling: or the 3 months up front. So if they are still, like, an active…
190 00:15:38.030 ⇒ 00:15:41.469 Judd Kuehling: Customer, I just don’t know how that’s defined by us, you know what I’m saying?
191 00:15:42.540 ⇒ 00:15:43.799 Judd Kuehling: I don’t know how that’s…
192 00:15:44.390 ⇒ 00:15:54.720 Henry Zhao: So, first order month is still people that, when they made their first order, came from a customer.io SMS action, or Twilio action, or email action campaign, or all people.
193 00:15:54.930 ⇒ 00:15:55.750 Judd Kuehling: All people.
194 00:15:56.030 ⇒ 00:16:03.490 Henry Zhao: Okay, so just all people, like, here, so if it was injectable SEMA, it’d be the same number, 2,641, 2,480,
195 00:16:03.730 ⇒ 00:16:05.110 Henry Zhao: And then…
196 00:16:05.370 ⇒ 00:16:05.940 Judd Kuehling: Yep.
197 00:16:07.020 ⇒ 00:16:12.729 Henry Zhao: But then what are we changing? So, are we saying, for row column 3, you want column 3,
198 00:16:12.860 ⇒ 00:16:16.720 Henry Zhao: Yeah. Like, it is here, but only people that have touched one of your campaigns.
199 00:16:17.010 ⇒ 00:16:18.129 Judd Kuehling: Nope, I don’t care.
200 00:16:19.600 ⇒ 00:16:20.159 Henry Zhao: So then, what.
201 00:16:20.160 ⇒ 00:16:21.180 Judd Kuehling: Everybody.
202 00:16:21.650 ⇒ 00:16:24.840 Henry Zhao: So then why… what is different from this chart, then, if you’re just looking at.
203 00:16:24.840 ⇒ 00:16:26.980 Judd Kuehling: Nothing. Nothing.
204 00:16:28.180 ⇒ 00:16:41.650 Judd Kuehling: Nothing’s different from this chart, that’s why I don’t understand why they want me to do this. Nothing is different from this chart. It’s just, like, it’s just gonna look different, because they’re gonna… they wanna look at it in a different way, or they want to, like, you know, see it.
205 00:16:41.890 ⇒ 00:16:45.409 Judd Kuehling: But I just want to basically have that chart and not have to take out the other columns.
206 00:16:45.800 ⇒ 00:16:52.029 Henry Zhao: Are you just doing campaigns for the third month? Like, why would they want you to do that? Maybe I should… let me just confirm with Cutter tomorrow.
207 00:16:52.590 ⇒ 00:16:54.689 Judd Kuehling: Yeah, they just want to look at
208 00:16:55.870 ⇒ 00:17:00.270 Judd Kuehling: basically want to look at, like, how I’m doing over time, so…
209 00:17:00.700 ⇒ 00:17:06.970 Judd Kuehling: Like, for the people that came in in, june of 2025.
210 00:17:08.210 ⇒ 00:17:19.179 Judd Kuehling: Let’s say June 1st, 2025. July, August, I guess you have to go through a whole calendar month, so all the people that came in in June of 2025,
211 00:17:19.900 ⇒ 00:17:26.790 Judd Kuehling: At the end… at the beginning of September, how many people are still active?
212 00:17:26.790 ⇒ 00:17:27.500 Henry Zhao: Mmm.
213 00:17:28.119 ⇒ 00:17:41.989 Judd Kuehling: And then I’ll look at… and that number was gonna be, like, 55%, let’s say. And hopefully, the next month, all the people that came in in July, that number is 56. The goal is to try and grow that number. That’s kind of my goal.
214 00:17:42.079 ⇒ 00:17:48.849 Judd Kuehling: try and grow that number. Now, email and SMS has a huge effect on that number, but it’s not, like I said, it’s not the only effect.
215 00:17:49.009 ⇒ 00:17:57.179 Judd Kuehling: Right. I’m not looking only at people that opened an email, I’m not looking at people that only came from an email, I’m looking at people that maybe never opened an email.
216 00:17:57.589 ⇒ 00:18:04.429 Judd Kuehling: But ideally, I’m getting… I’m using email and SMS to… to affect those people in order to get them to stay.
217 00:18:05.290 ⇒ 00:18:07.840 Henry Zhao: Definitely we needed this call, because I would have completely not…
218 00:18:07.840 ⇒ 00:18:10.659 Judd Kuehling: Yeah, no, I understand. I know, I know.
219 00:18:10.660 ⇒ 00:18:18.579 Henry Zhao: Okay, so I just confirmed with Cutter tomorrow, and if that’s what it is, it should be very easy. I literally would just duplicate this one, move the other columns.
220 00:18:18.880 ⇒ 00:18:19.860 Judd Kuehling: Yeah, yeah, exactly.
221 00:18:19.860 ⇒ 00:18:24.340 Henry Zhao: Like, you don’t… like, do you even need revenue per customer, orders per customer, and MER, or just the…
222 00:18:24.570 ⇒ 00:18:27.760 Henry Zhao: cohort size and… Column 3.
223 00:18:28.170 ⇒ 00:18:31.179 Judd Kuehling: Yeah, the cohort size would be good, to know that, like.
224 00:18:31.180 ⇒ 00:18:31.580 Henry Zhao: Right, yeah.
225 00:18:31.580 ⇒ 00:18:38.249 Judd Kuehling: accidentally had, like, two people in there, then it’s, like, the number is obviously, like, not really a good mathematical number to use.
226 00:18:38.360 ⇒ 00:18:45.200 Judd Kuehling: But I would like to see the cohort size, so I can kind of understand how that’s changing over time, too, but, I don’t need anything else.
227 00:18:45.650 ⇒ 00:18:46.300 Henry Zhao: Okay.
228 00:18:46.300 ⇒ 00:18:50.079 Judd Kuehling: I don’t care what the revenue is, I just want to know that they paid something.
229 00:18:50.790 ⇒ 00:18:53.240 Henry Zhao: So, I can remove first-order product also, right?
230 00:18:54.720 ⇒ 00:18:56.040 Judd Kuehling: Yes.
231 00:18:56.170 ⇒ 00:18:57.640 Henry Zhao: I’ll give you a filter, so you can filter.
232 00:18:57.640 ⇒ 00:19:08.919 Judd Kuehling: It’d be nice to filter off of it, theoretically. Potentially, I might use it to filter off of that. Like, I’m only… I’m doing, like, all this work on trying to grow, like.
233 00:19:08.920 ⇒ 00:19:09.530 Henry Zhao: Goodbye.
234 00:19:09.880 ⇒ 00:19:11.810 Judd Kuehling: You know, one particular product
235 00:19:11.910 ⇒ 00:19:17.540 Judd Kuehling: retention or something like that. It would be nice to be able to filter off that to see, like, oh, see, like.
236 00:19:17.670 ⇒ 00:19:19.109 Judd Kuehling: The moral then, like.
237 00:19:19.310 ⇒ 00:19:24.560 Judd Kuehling: grew a lot, but the other ones didn’t, or something like that. But that’s kind of, like, a later use case.
238 00:19:25.600 ⇒ 00:19:27.330 Henry Zhao: Okay, I’ll keep it there, yeah.
239 00:19:27.640 ⇒ 00:19:28.270 Judd Kuehling: Yeah.
240 00:19:31.970 ⇒ 00:19:32.700 Henry Zhao: Okay.
241 00:19:32.810 ⇒ 00:19:37.540 Henry Zhao: Alright, I have enough to go off of. Hopefully tomorrow I can resolve the data issues with my team.
242 00:19:37.940 ⇒ 00:19:40.979 Henry Zhao: So I can get it done tomorrow instead of pushing it to Friday.
243 00:19:41.330 ⇒ 00:19:46.190 Judd Kuehling: Cool, awesome. Yeah, and like I said, those numbers look a little bit low, so… .
244 00:19:46.560 ⇒ 00:19:48.879 Henry Zhao: I have some other numbers that look even lower, so…
245 00:19:48.880 ⇒ 00:19:49.860 Judd Kuehling: Not really? No.
246 00:19:49.990 ⇒ 00:19:51.420 Henry Zhao: Yeah, I’m looking at this one, which…
247 00:19:51.610 ⇒ 00:19:53.569 Henry Zhao: I said I’m gonna make one in Mixpanel.
248 00:19:54.020 ⇒ 00:19:58.529 Henry Zhao: The numbers just don’t agree with each other, so I looked at 2504ACGLP1,
249 00:19:58.660 ⇒ 00:20:01.770 Henry Zhao: And, like, here, it’s $12,000 for July.
250 00:20:02.450 ⇒ 00:20:04.110 Henry Zhao: Actually, I think that’s higher, right?
251 00:20:04.900 ⇒ 00:20:06.290 Henry Zhao: 12,000 for July.
252 00:20:06.620 ⇒ 00:20:07.680 Henry Zhao: Here, what was it?
253 00:20:10.360 ⇒ 00:20:14.029 Henry Zhao: Yeah, not even altogether is $12,000, so this might be better.
254 00:20:14.940 ⇒ 00:20:17.219 Judd Kuehling: You’re looking at just that campaign specifically?
255 00:20:17.500 ⇒ 00:20:18.110 Henry Zhao: Yeah.
256 00:20:18.670 ⇒ 00:20:19.989 Henry Zhao: I was gonna say, I can look…
257 00:20:20.170 ⇒ 00:20:28.159 Judd Kuehling: I can see… I can see, conversions, I can see orders by campaign, but I can’t see…
258 00:20:28.860 ⇒ 00:20:31.810 Judd Kuehling: revenue…
259 00:20:32.370 ⇒ 00:20:37.049 Judd Kuehling: Well, I should be able to… I mean, I can see revenue, I guess, in GA, but I can’t see revenue in customer I.O.
260 00:20:37.310 ⇒ 00:20:40.540 Judd Kuehling: We only have, that’s a good idea.
261 00:20:40.560 ⇒ 00:20:41.650 Henry Zhao: Yeah, okay.
262 00:20:42.190 ⇒ 00:20:45.820 Henry Zhao: So you’re saying we couldn’t check this off of Google Analytics, right?
263 00:20:47.610 ⇒ 00:20:55.860 Judd Kuehling: It should, it should be the same. I don’t know, I don’t know… some people have told me that Google Analytics is, like, not super trustworthy, I don’t know.
264 00:20:55.860 ⇒ 00:20:56.910 Henry Zhao: Scott, yeah.
265 00:20:57.090 ⇒ 00:20:59.380 Henry Zhao: Because they want to get…
266 00:21:00.440 ⇒ 00:21:01.180 Judd Kuehling: Yeah.
267 00:21:01.470 ⇒ 00:21:03.290 Judd Kuehling: I mean, you should be able to…
268 00:21:03.610 ⇒ 00:21:10.670 Judd Kuehling: I mean, it’s getting the UTMs in Google Analytics, and I can see them in there, generally, so it should be.
269 00:21:11.670 ⇒ 00:21:15.860 Henry Zhao: If you can, can you pull, like, this one campaign’s, like, monthly revenue?
270 00:21:16.510 ⇒ 00:21:17.529 Judd Kuehling: Yeah, that’s me.
271 00:21:17.530 ⇒ 00:21:18.830 Henry Zhao: Just press the double check.
272 00:21:22.560 ⇒ 00:21:25.259 Henry Zhao: Can you pull the last 3 months’ revenue by month?
273 00:21:27.200 ⇒ 00:21:29.960 Henry Zhao: Or… from GA4.
274 00:21:31.920 ⇒ 00:21:34.099 Henry Zhao: Yeah, if you could help me with that, that would be really helpful.
275 00:21:34.660 ⇒ 00:21:37.549 Judd Kuehling: Yeah, I’ll, I can pull that, and I’ll send it to you.
276 00:21:37.760 ⇒ 00:21:52.769 Henry Zhao: what’s gonna be worse is if we have four data sources, they’re all different, and that’s probably what’s gonna happen. So, I just… I want to go to my team more with these four data sources and say, like, what’s… what’s wrong… what’s… like, what’s one’s more trustworthy? How are they different? Some are, like, different attribution windows, some are first touch, maybe not…
277 00:21:53.030 ⇒ 00:22:00.299 Henry Zhao: maybe not last touch, some are, like, stitched together with segments, so… we’ll figure out the happy medium, and then I’ll just use that source.
278 00:22:01.600 ⇒ 00:22:04.400 Judd Kuehling: Okay, and yeah.
279 00:22:04.520 ⇒ 00:22:05.220 Judd Kuehling: Okay.
280 00:22:05.530 ⇒ 00:22:06.470 Judd Kuehling: Whoops.
281 00:22:06.470 ⇒ 00:22:07.190 Henry Zhao: Thank you.
282 00:22:07.300 ⇒ 00:22:08.629 Henry Zhao: Those aren’t really good.
283 00:22:09.150 ⇒ 00:22:09.880 Judd Kuehling: For sure.
284 00:22:10.350 ⇒ 00:22:11.500 Henry Zhao: Alright, see you sometime tomorrow.
285 00:22:11.920 ⇒ 00:22:12.979 Judd Kuehling: Sounds good, take care.