Meeting Title: Shopify Integration and Forecast Sync Date: 2025-10-06 Meeting participants: Demilade Agboola, Emily Giant, pk.arthur
WEBVTT
1 00:03:53.440 ⇒ 00:03:54.520 Demilade Agboola: Pardon me.
2 00:03:54.520 ⇒ 00:03:55.960 Emily Giant: Hi, how’s it going?
3 00:03:56.290 ⇒ 00:03:57.529 Demilade Agboola: Pretty well, how are you?
4 00:03:57.990 ⇒ 00:04:01.980 Emily Giant: Ugh, good, good, the weekend is never long enough, but, you know.
5 00:04:03.210 ⇒ 00:04:06.879 Demilade Agboola: Yeah, yeah, especially when you come back from a vacation.
6 00:04:07.080 ⇒ 00:04:07.800 Emily Giant: Yeah.
7 00:04:08.030 ⇒ 00:04:09.509 Emily Giant: It just seems like a…
8 00:04:09.780 ⇒ 00:04:16.460 Emily Giant: like a breath, and you’re back at work. But, did you do anything this weekend interesting?
9 00:04:16.930 ⇒ 00:04:18.099 Demilade Agboola: Probably not really.
10 00:04:19.040 ⇒ 00:04:22.060 Demilade Agboola: I just played tennis, but that’s… that’s about it.
11 00:04:22.600 ⇒ 00:04:25.029 Emily Giant: Oh, that’s interesting enough. It’s fun.
12 00:04:25.230 ⇒ 00:04:35.939 Demilade Agboola: Oh yeah, yeah. I mean, at this point, it’s more of, like, a regular thing, so I guess that’s why it’s not, like… it’s fun, I do enjoy it. I probably paid about…
13 00:04:38.020 ⇒ 00:04:40.279 Demilade Agboola: 6 hours of tennis all weekend, so…
14 00:04:40.280 ⇒ 00:04:44.049 Emily Giant: Oh my god! Are you noticing, like, significant improvements?
15 00:04:44.640 ⇒ 00:04:46.529 Demilade Agboola: Yes and no.
16 00:04:47.680 ⇒ 00:04:54.729 Demilade Agboola: Yes, in the sense that, like, my coach does say that he’s noticed… because I’ve been playing for about 3 months already.
17 00:04:54.730 ⇒ 00:04:55.540 Emily Giant: Yeah.
18 00:04:55.540 ⇒ 00:04:58.769 Demilade Agboola: My coach has said I’ve come a long way in a pretty short time.
19 00:04:58.970 ⇒ 00:05:00.140 Demilade Agboola: But, like…
20 00:05:00.290 ⇒ 00:05:06.189 Demilade Agboola: No, in the sense that there’s so many things that I’m aware I want to be better at, and.
21 00:05:06.190 ⇒ 00:05:06.720 Emily Giant: Yeah, huh?
22 00:05:06.720 ⇒ 00:05:11.909 Demilade Agboola: It’s not yet there, my footwork, my forehand…
23 00:05:12.020 ⇒ 00:05:14.900 Demilade Agboola: It’s not yet a weapon, so…
24 00:05:16.010 ⇒ 00:05:19.529 Demilade Agboola: It’s… it’s still a process in that sense, so, yeah.
25 00:05:19.530 ⇒ 00:05:26.539 Emily Giant: Yeah. Well, it’s only been 3 months. In a year, you’ll probably be, like, At a totally different level.
26 00:05:27.240 ⇒ 00:05:35.060 Demilade Agboola: Yeah, yeah, I… I do look forward to that, but I didn’t know myself. In a year, I’ll probably still be looking forward to the next level, and…
27 00:05:35.060 ⇒ 00:05:49.669 Emily Giant: Yeah, yeah, that’s how you are. It’s never enough. I’m kidding. But now I need a feeling of, like, well, now that I know how to do this, why can’t I do this? Like, the more you know about the sport, the more you know what you can’t do, so…
28 00:05:50.000 ⇒ 00:05:50.620 Demilade Agboola: It’d be…
29 00:05:50.620 ⇒ 00:05:51.510 Emily Giant: gay!
30 00:05:51.510 ⇒ 00:05:52.800 pk.arthur: Good morning, guys.
31 00:05:53.440 ⇒ 00:05:54.059 Demilade Agboola: How’s it going?
32 00:05:54.890 ⇒ 00:05:56.250 pk.arthur: Good, how are you guys doing?
33 00:05:57.050 ⇒ 00:05:57.850 Emily Giant: Good.
34 00:05:58.190 ⇒ 00:06:00.499 Demilade Agboola: Do you have anything interesting this weekend?
35 00:06:00.860 ⇒ 00:06:05.080 pk.arthur: What’d I do? Nothing really, just relaxing, honestly.
36 00:06:05.300 ⇒ 00:06:05.860 Emily Giant: Yeah.
37 00:06:05.860 ⇒ 00:06:09.200 pk.arthur: Again, it’s just… there’s a late load and just…
38 00:06:09.940 ⇒ 00:06:14.210 pk.arthur: Watched some soccer, and just chilled, honestly.
39 00:06:14.570 ⇒ 00:06:17.229 Demilade Agboola: Oh, what team do you follow?
40 00:06:17.790 ⇒ 00:06:21.390 Demilade Agboola: I follow the Premier League, I’m an Arsenal fan.
41 00:06:21.390 ⇒ 00:06:25.570 pk.arthur: Woo!
42 00:06:25.570 ⇒ 00:06:26.570 Demilade Agboola: Yeah, I’m a Arsenal fan, too.
43 00:06:26.570 ⇒ 00:06:29.259 pk.arthur: I love to hear that. Okay, okay, okay, okay.
44 00:06:30.450 ⇒ 00:06:32.090 pk.arthur: Yes, yes, yes.
45 00:06:32.210 ⇒ 00:06:39.910 Demilade Agboola: Good win this weekend. Yeah, it was a great game, pretty controlled win, and then Liverpool lost too, so it’s a fantastic weekend.
46 00:06:40.140 ⇒ 00:06:43.699 Emily Giant: It was a great weekend, actually. I lied, it was a great weekend, because Liverpool…
47 00:06:43.700 ⇒ 00:06:47.770 pk.arthur: I would say that for sure.
48 00:06:48.060 ⇒ 00:06:48.740 Demilade Agboola: Yeah.
49 00:06:49.520 ⇒ 00:06:59.030 Emily Giant: Well, I’m glad that you two have this. I… do not watch any, sports. Except for, like, gymnastics.
50 00:06:59.400 ⇒ 00:07:02.900 Emily Giant: So it’s, like, every 4 years I can watch it.
51 00:07:03.710 ⇒ 00:07:06.340 Emily Giant: Did you do anything this weekend?
52 00:07:06.580 ⇒ 00:07:16.940 Emily Giant: Oh, okay. Manual labor, my typical. We’re painting our floor upstairs in this, like, hexagon pattern. It’s very cool, but, like, it’s tedious, so…
53 00:07:17.220 ⇒ 00:07:25.320 Emily Giant: This weekend, I did the second round of polyurethane on the floor. That’s what I did. But it looks really good, so…
54 00:07:25.960 ⇒ 00:07:30.950 Emily Giant: I guess it was worth it. But it didn’t work, and that seems…
55 00:07:31.110 ⇒ 00:07:35.179 Emily Giant: like, notable, that I actually did not open my computer, and that was nice.
56 00:07:36.280 ⇒ 00:07:38.899 Emily Giant: I mean, maybe I should have, but I didn’t.
57 00:07:38.900 ⇒ 00:07:44.899 Demilade Agboola: I don’t think you should have… I’m happy you were able to go the entire weekend without near a computer, that’s a…
58 00:07:45.860 ⇒ 00:07:47.670 Emily Giant: Yeah, that was a big plus.
59 00:07:47.870 ⇒ 00:07:48.680 Demilade Agboola: There.
60 00:07:48.870 ⇒ 00:08:05.039 Emily Giant: Definitely something that I’m trying to, like, incorporate into my life when possible. But anyway, well, PK, I know you had questions last, Friday, like, towards the end of the day, and I… do you want to start with those, to just make sure that, like, you have a good understanding of, like.
61 00:08:05.400 ⇒ 00:08:07.810 Demilade Agboola: Where we’re at right now and where we’re going.
62 00:08:08.020 ⇒ 00:08:17.080 pk.arthur: Yes, so my question, just to catch you up there a lot of… so, basically, I know we were planning to basically use the source from…
63 00:08:17.660 ⇒ 00:08:36.709 pk.arthur: Shopify, to incorporate that and to look at, you see, like, the… a customer’s marketing source, like, the channel where they… where they eventually saw the ads and click on it and eventually purchase, right? Yeah. I had a question. So, if we… if we go that route, does that mean that we’re not gonna use GA4 sources no more?
64 00:08:37.890 ⇒ 00:08:46.680 Demilade Agboola: So, if we’re gonna incorporate… so, Shopify uses… Apprentice uses Google Analytics as well?
65 00:08:46.960 ⇒ 00:08:52.530 Demilade Agboola: That’s how it knows, like, the sessions and all that. So, what we will be doing is incorporating GA,
66 00:08:52.710 ⇒ 00:09:02.930 Demilade Agboola: But, like, session data, and, like, what’s happening in terms of, visits and all that. But in terms of, like, the customers and…
67 00:09:03.330 ⇒ 00:09:05.999 Demilade Agboola: the details, we will rely on Shopify for that.
68 00:09:06.450 ⇒ 00:09:09.810 pk.arthur: Okay, so let’s say I wanted to figure out
69 00:09:09.920 ⇒ 00:09:23.549 pk.arthur: let’s say you purchased from Urban Stems, right? Yeah. And your first purchase was in September of last year, that was pre-Shopify. If I wanted to find out your first marketing channel, would that still be possible with… if we go this route?
70 00:09:24.210 ⇒ 00:09:25.010 Emily Giant: Yes.
71 00:09:25.500 ⇒ 00:09:28.720 Demilade Agboola: Yeah, we’ll need to find OMS data for that, though.
72 00:09:29.060 ⇒ 00:09:42.939 Emily Giant: Yeah, we have it. That pipeline has been problematic, but I honestly don’t even know why. And definitely it’s not problematic prior to the migration. The migration is what broke it. So,
73 00:09:42.940 ⇒ 00:09:52.219 Emily Giant: to answer your question in a more, like, holistic way, we’re not getting rid of any historical data. What we’re gonna do is, like, do,
74 00:09:52.390 ⇒ 00:09:54.679 Emily Giant: Time boxing, so that, like.
75 00:09:54.870 ⇒ 00:10:10.340 Emily Giant: anything available through Shopify, we will use it, but if it wasn’t available in Shopify, we’ll use the data prior, and we’ll clean it up so that it, like, flows into the Shopify data, however we, adjust the tables.
76 00:10:10.540 ⇒ 00:10:12.240 pk.arthur: Okay. Yeah. Okay.
77 00:10:12.240 ⇒ 00:10:13.580 Emily Giant: Well, you won’t lose anything.
78 00:10:13.580 ⇒ 00:10:18.639 pk.arthur: Okay, that sounds good. That was just my main, question, concern about going the Shopify route.
79 00:10:19.500 ⇒ 00:10:25.810 Demilade Agboola: Yeah, yeah, we’re not… you’re not getting rid of any old data. We’re just using Shopify going forward, because…
80 00:10:26.370 ⇒ 00:10:31.420 Demilade Agboola: Since the migration, Shopify hasn’t, like, fully been integrated into the analytics.
81 00:10:31.840 ⇒ 00:10:46.980 Demilade Agboola: And we just want to be able to use that as the source of truth for things, especially going forward. Yeah. Model it, like, for the key metrics across the company. And then for things going backwards, we can always,
82 00:10:47.350 ⇒ 00:10:54.320 Demilade Agboola: I try and handle these discrepancies, but that would be second phase, or phase two of, like, once we’ve integrated Shopify properly.
83 00:10:54.520 ⇒ 00:10:56.560 pk.arthur: Okay. Okay, sounds good.
84 00:10:58.720 ⇒ 00:11:03.820 Demilade Agboola: Yeah, so also for the ad stuff, I’ve been able to make that change, so the ads data should be up to date.
85 00:11:04.360 ⇒ 00:11:06.640 pk.arthur: Okay, let me just take a look…
86 00:11:13.430 ⇒ 00:11:18.979 Emily Giant: TK, did you happen to see where he changed it? I just want you to have, like, visibility into…
87 00:11:19.690 ⇒ 00:11:26.290 Emily Giant: like, should it break again? And we need to update it. Like, you would know before having to wait for me to go do it.
88 00:11:26.290 ⇒ 00:11:27.040 pk.arthur: Yeah, this is…
89 00:11:27.040 ⇒ 00:11:27.659 Emily Giant: How to get in.
90 00:11:29.110 ⇒ 00:11:30.300 pk.arthur: Correct.
91 00:11:30.470 ⇒ 00:11:31.530 pk.arthur: Or.
92 00:11:32.070 ⇒ 00:11:33.189 Demilade Agboola: Gardening, how we said?
93 00:11:33.190 ⇒ 00:11:34.549 pk.arthur: Are this in dbt?
94 00:11:34.940 ⇒ 00:11:36.869 Demilade Agboola: Yeah, I just did a DC change.
95 00:11:39.370 ⇒ 00:11:46.950 pk.arthur: Yeah, let me open it right now to see, yeah, does the schedule 1 hour of Bing, and Google Ads run? I see that,
96 00:11:47.640 ⇒ 00:11:49.820 pk.arthur: Is it… that’s it, right?
97 00:11:50.260 ⇒ 00:11:50.900 Emily Giant: Yeah.
98 00:11:51.240 ⇒ 00:11:55.110 Demilade Agboola: Alright, so let me quickly just share my screen so I can walk you through.
99 00:11:55.330 ⇒ 00:11:55.940 pk.arthur: Okay.
100 00:11:56.120 ⇒ 00:11:59.969 Demilade Agboola: what I did.
101 00:12:07.480 ⇒ 00:12:09.750 Demilade Agboola: Alright. Can you see my screen?
102 00:12:10.730 ⇒ 00:12:11.710 pk.arthur: Yes.
103 00:12:12.090 ⇒ 00:12:13.230 Demilade Agboola: Alright, so…
104 00:12:16.630 ⇒ 00:12:22.840 Demilade Agboola: in… I’ll still… we’ll still get to the point where we’ll have to, like.
105 00:12:23.230 ⇒ 00:12:34.899 Demilade Agboola: reschedule, like, not schedule, but, like, move things around, and probably group all these plus Facebook ads into, like, one folder called, ads. So it’s Clara, Marketing, something…
106 00:12:35.030 ⇒ 00:12:37.670 Demilade Agboola: That makes it easier to find everything all at once.
107 00:12:37.880 ⇒ 00:12:40.169 Demilade Agboola: But we have AdWords data.
108 00:12:40.370 ⇒ 00:12:41.810 Demilade Agboola: We have the model.
109 00:12:42.250 ⇒ 00:12:48.850 Demilade Agboola: Which is the… AdWords campaign performance, And…
110 00:12:49.020 ⇒ 00:12:54.310 Demilade Agboola: the way dbt works is it creates… like, links to…
111 00:12:55.420 ⇒ 00:12:59.409 Demilade Agboola: This… okay, so technically, these are called, this is called ginger.
112 00:12:59.840 ⇒ 00:13:07.300 Demilade Agboola: And what this is, is it’s a way of writing in… dbt that allows us to Put things…
113 00:13:08.560 ⇒ 00:13:17.759 Demilade Agboola: Clara. So this is what’s called a source function, and it’s saying, hey, there’s a source coming in here, and it’s a table within the warehouse.
114 00:13:18.610 ⇒ 00:13:23.049 Demilade Agboola: And so the issue before was… it was Google Ads.
115 00:13:23.830 ⇒ 00:13:27.450 Demilade Agboola: But with the change, it became…
116 00:13:27.870 ⇒ 00:13:33.350 Demilade Agboola: Actually, no, it was Google Ads V1, actually, but with the change, it became Google Ads V1 Upgrade.
117 00:13:33.550 ⇒ 00:13:36.319 Demilade Agboola: That was the… let me see if I can quickly open it.
118 00:13:37.320 ⇒ 00:13:40.489 Demilade Agboola: But that was the issue that happened in Stitch.
119 00:13:42.840 ⇒ 00:13:46.239 Demilade Agboola: Right, give me a second… I mean…
120 00:14:09.660 ⇒ 00:14:10.520 Demilade Agboola: Alright.
121 00:14:11.090 ⇒ 00:14:17.950 Demilade Agboola: So the issue with… The issue before… was…
122 00:14:23.850 ⇒ 00:14:28.519 Demilade Agboola: Yes, this was the issue. So before, the data was coming from Google Ads V1,
123 00:14:29.100 ⇒ 00:14:32.600 Demilade Agboola: right, into a schema called Google Ads V1, but…
124 00:14:32.770 ⇒ 00:14:42.409 Demilade Agboola: because of the upgrade that was done. Also, by the way, when, you know, upgrades happen, it’s always very important to, like, communicate across the team that, hey.
125 00:14:42.960 ⇒ 00:14:44.570 Demilade Agboola: A change has been made.
126 00:14:44.910 ⇒ 00:14:51.259 Demilade Agboola: But then, this change was made to Google Ads V1 upgrade, so that means this data stops syncing.
127 00:14:51.600 ⇒ 00:14:59.740 Demilade Agboola: So the data became still for, like, one month, basically. But this is where everything is active and actively going to.
128 00:15:00.020 ⇒ 00:15:08.090 Demilade Agboola: And so you kind of see it if you go into… the Amazon bank into Redshift.
129 00:15:11.020 ⇒ 00:15:13.769 Demilade Agboola: Have you… have you been into Redshift a bit?
130 00:15:14.300 ⇒ 00:15:18.109 pk.arthur: No, I have not. I don’t have direct access to it.
131 00:15:18.700 ⇒ 00:15:23.779 Demilade Agboola: Okay, so just for context, Redshift is just, like, the warehouse. This is where the data lives.
132 00:15:24.990 ⇒ 00:15:30.229 Demilade Agboola: And… When… this is where data is stored.
133 00:15:30.380 ⇒ 00:15:34.649 Demilade Agboola: And what we’re using dbt to do is, hey, take this raw data.
134 00:15:34.820 ⇒ 00:15:48.989 Demilade Agboola: you know, transform it, join it to this, clean this, do this, and then eventually, once it’s clean, we say, Looker, take that new clean data and present it, and that’s what you see in Looker. So that’s kind of, like, the flow of things.
135 00:15:50.710 ⇒ 00:15:59.510 Demilade Agboola: So, effectively, the data was coming in there before?
136 00:16:03.190 ⇒ 00:16:10.499 Demilade Agboola: That’s the name… Alright, yeah, so this is it. So before it was coming in here.
137 00:16:11.110 ⇒ 00:16:18.400 Demilade Agboola: Google AdWords V1, but now it’s coming in here, Google Ads V1 upgrade. So it’s an entirely new schema, it’s an entirely new…
138 00:16:19.010 ⇒ 00:16:22.610 Demilade Agboola: So schemas are… think of a schema like a separation in your database.
139 00:16:23.390 ⇒ 00:16:25.869 Demilade Agboola: Ali in your bucket of some sort, right?
140 00:16:26.070 ⇒ 00:16:29.649 Demilade Agboola: So now the tables here are exactly the same.
141 00:16:30.330 ⇒ 00:16:32.170 Demilade Agboola: But just different names.
142 00:16:32.790 ⇒ 00:16:36.809 Demilade Agboola: Sorry, not… they’re exactly the same, not different names, they’re literally the same tables.
143 00:16:37.690 ⇒ 00:16:39.540 Demilade Agboola: But just in different schema.
144 00:16:39.540 ⇒ 00:16:40.060 Emily Giant: Excuse me.
145 00:16:40.060 ⇒ 00:16:45.120 Demilade Agboola: So effectively, it was looking at the campaign performance report here.
146 00:16:45.280 ⇒ 00:16:48.219 Demilade Agboola: Which is still, because I showed, like, you know, I showed you that…
147 00:16:48.660 ⇒ 00:16:49.969 pk.arthur: Yeah, it’s toggle off.
148 00:16:49.970 ⇒ 00:16:54.300 Demilade Agboola: Exactly. And now, you should be looking at it here.
149 00:16:56.310 ⇒ 00:17:01.240 Demilade Agboola: in the upgrade schema. So what I did was I said, okay.
150 00:17:01.340 ⇒ 00:17:07.489 Demilade Agboola: So this is the source that it’s saying. When it compiles, it was compiling to View 1,
151 00:17:08.710 ⇒ 00:17:11.189 Demilade Agboola: But I needed to compile to V2.
152 00:17:11.660 ⇒ 00:17:18.460 Demilade Agboola: But the beauty with dbt is when there’s a source YAML file, which is this YAML file, before.
153 00:17:18.660 ⇒ 00:17:20.969 Demilade Agboola: it was saying, hey, the source is V1,
154 00:17:21.470 ⇒ 00:17:23.870 Demilade Agboola: But I need you to look for it in schema.
155 00:17:24.720 ⇒ 00:17:30.890 Demilade Agboola: V1, which, as we know, is the troublesome, or the stale data source.
156 00:17:32.340 ⇒ 00:17:39.650 Demilade Agboola: And it’s saying, like, so basically it’s saying, hey, in this name, when you see this name, go to this schema, and within those schemas, you would have these tables.
157 00:17:40.000 ⇒ 00:17:41.069 pk.arthur: Okay.
158 00:17:42.070 ⇒ 00:17:51.209 Demilade Agboola: But now I’m saying, hey, even though it’s still this name, I want you to not… instead of looking in the V1 schema, look in the upgrade schema of the chain.
159 00:17:52.040 ⇒ 00:17:55.960 Demilade Agboola: So now, it’s going to the upgrade schema, and it’s still going to look for the same things.
160 00:17:56.100 ⇒ 00:17:59.199 Demilade Agboola: Sudden… It doesn’t break anything else.
161 00:17:59.880 ⇒ 00:18:01.999 Demilade Agboola: But it just points you to the right direction.
162 00:18:02.820 ⇒ 00:18:03.700 pk.arthur: Got it.
163 00:18:04.040 ⇒ 00:18:12.359 Demilade Agboola: So this would still work as well. So now it’s looking for… Trina… This is…
164 00:18:12.580 ⇒ 00:18:15.180 Demilade Agboola: Might also need to change as well.
165 00:18:16.060 ⇒ 00:18:17.190 Demilade Agboola: And I look at it.
166 00:18:23.120 ⇒ 00:18:25.350 Demilade Agboola: Yeah, so this should be cool.
167 00:18:26.290 ⇒ 00:18:28.150 Demilade Agboola: Speaker Quadri one.
168 00:18:37.450 ⇒ 00:18:38.310 Demilade Agboola: Okay.
169 00:18:41.670 ⇒ 00:18:46.279 Demilade Agboola: I mean, do you know the difference between the Google Ads and Google Ads V1? Let’s look.
170 00:18:46.940 ⇒ 00:18:50.700 Emily Giant: I don’t know the difference off the top of my head, but I can…
171 00:18:52.010 ⇒ 00:18:53.920 Emily Giant: I was gonna say, I can look…
172 00:18:54.160 ⇒ 00:18:56.479 Demilade Agboola: My guess is it’s probably a still version.
173 00:18:57.200 ⇒ 00:18:58.230 Emily Giant: Yeah.
174 00:19:01.130 ⇒ 00:19:07.039 Demilade Agboola: Give me one second. Sorry, just as a side note that I fixed the issue, but I’m just… I just want to be sure.
175 00:19:07.710 ⇒ 00:19:09.830 Demilade Agboola: Well, we have, like, different…
176 00:19:12.300 ⇒ 00:19:17.179 Demilade Agboola: schemas, and if that is being maintained. If it’s not being maintained, I think there’s a part of things we should.
177 00:19:17.180 ⇒ 00:19:17.810 Emily Giant: Okay.
178 00:19:17.810 ⇒ 00:19:18.450 Demilade Agboola: Riddled.
179 00:19:18.760 ⇒ 00:19:19.580 Emily Giant: Yeah.
180 00:19:22.310 ⇒ 00:19:27.539 Emily Giant: But there’s… so, is there a difference between GA4 and, like, Google Ads?
181 00:19:27.910 ⇒ 00:19:29.550 Emily Giant: Or are those the same thing?
182 00:19:30.220 ⇒ 00:19:34.519 pk.arthur: No, it’s different. Ga4 is basically… it’s…
183 00:19:35.380 ⇒ 00:19:39.560 pk.arthur: It’s basically a source of, like, everything that happens on that website, so basically…
184 00:19:40.330 ⇒ 00:19:45.839 pk.arthur: like, any user that comes from any source. Google Ads is basically our ads, like, we’re marketing on Google.
185 00:19:45.840 ⇒ 00:19:46.770 Emily Giant: Okay.
186 00:19:46.770 ⇒ 00:19:48.420 pk.arthur: Surge, yeah.
187 00:19:48.790 ⇒ 00:19:51.050 Emily Giant: So they’re, like, fundamentally different.
188 00:19:51.240 ⇒ 00:20:10.299 pk.arthur: Yeah, so Google Ads flows into GA4, so you can see how Google Ads, in some cases, like, how the user behavior, it compares to other sources, so, like, you can compare Google Ads, users for, compared to people who actually come to the website by just, typing in our URL, just to see, like…
189 00:20:10.360 ⇒ 00:20:13.759 pk.arthur: See the stickiness of each of… each of those chords.
190 00:20:15.870 ⇒ 00:20:17.330 Emily Giant: Okay, so…
191 00:20:18.560 ⇒ 00:20:22.350 Emily Giant: Done a lot of… I don’t know if that’s helpful, but it sounds like they’re, like, different things.
192 00:20:23.540 ⇒ 00:20:26.439 Demilade Agboola: Yes, no, that was helpful.
193 00:20:27.340 ⇒ 00:20:30.040 Demilade Agboola: It’s just… oh, sorry, it’s not fixed today.
194 00:20:32.750 ⇒ 00:20:40.460 Demilade Agboola: I just wanted to be sure that we are… Pointing to the right sources…
195 00:20:42.060 ⇒ 00:20:42.760 Emily Giant: Yeah.
196 00:20:45.920 ⇒ 00:20:50.159 Demilade Agboola: Yeah, so this hasn’t had anything since 2022, so it will be.
197 00:20:50.160 ⇒ 00:20:52.050 Emily Giant: Yikes.
198 00:20:53.510 ⇒ 00:20:57.959 Demilade Agboola: So… because I can’t even see the… I can’t see it in stitch.
199 00:21:01.120 ⇒ 00:21:01.830 Demilade Agboola: Alright.
200 00:21:03.060 ⇒ 00:21:04.320 Demilade Agboola: Does make sense.
201 00:21:04.640 ⇒ 00:21:10.309 Demilade Agboola: So… So, the final URL report, and then…
202 00:21:11.220 ⇒ 00:21:16.200 Demilade Agboola: the Click Performance Report, I think that’s what we’re using. Yeah.
203 00:21:16.770 ⇒ 00:21:24.800 Demilade Agboola: Alright, so I will switch this to the… Let’s see Google Ads V1…
204 00:21:31.020 ⇒ 00:21:38.169 Demilade Agboola: Or, actually, I could just make this cool ads… It’s just cleaner and neater.
205 00:21:38.640 ⇒ 00:21:42.529 Demilade Agboola: all around, and then I can do this.
206 00:21:45.140 ⇒ 00:21:46.609 Demilade Agboola: And we’re ready to go.
207 00:21:47.150 ⇒ 00:21:49.070 Demilade Agboola: Recruits, commit, and sync.
208 00:21:49.750 ⇒ 00:22:00.070 Demilade Agboola: Change to the… You know, I’m all… And meaning, and then Sean.
209 00:22:09.400 ⇒ 00:22:16.370 Demilade Agboola: Alright, so… That is… Alright, that should be fine.
210 00:22:17.150 ⇒ 00:22:19.250 Demilade Agboola: Oh, no, I haven’t saved.
211 00:22:21.810 ⇒ 00:22:23.030 Demilade Agboola: The YAML file.
212 00:22:42.950 ⇒ 00:22:47.950 Demilade Agboola: Alright, so all the Google models should now update properly.
213 00:22:50.510 ⇒ 00:22:56.170 pk.arthur: Thank you. Yeah, I’m looking at it on the Looker end, and it seems to be updated.
214 00:22:56.590 ⇒ 00:22:59.229 pk.arthur: The latest day is today, so that’s good.
215 00:22:59.660 ⇒ 00:23:05.370 Demilade Agboola: Yeah, and so now it runs on an hourly schedule, so you should be getting the data every hour.
216 00:23:05.370 ⇒ 00:23:15.389 pk.arthur: Okay. I should… the date day… I don’t know if that has, like, hourly…
217 00:23:16.510 ⇒ 00:23:18.700 pk.arthur: Times on it, though, let me see…
218 00:23:24.270 ⇒ 00:23:26.470 pk.arthur: Sorry, it’s still running right now, just give me a second, I think.
219 00:23:26.470 ⇒ 00:23:27.280 Emily Giant: Good.
220 00:23:28.890 ⇒ 00:23:30.500 Emily Giant: I’m sorry.
221 00:24:39.990 ⇒ 00:24:45.930 pk.arthur: It’s kind of slow right now, I’m not sure why, but, I’ll let you know once it’s, complete.
222 00:24:48.710 ⇒ 00:24:50.490 Demilade Agboola: I’m sure I could check for you.
223 00:24:55.090 ⇒ 00:24:57.140 pk.arthur: Yeah, if you could, that’d be great.
224 00:25:14.860 ⇒ 00:25:18.149 pk.arthur: This DC report daytime, or maybe let me see you.
225 00:25:33.800 ⇒ 00:25:35.630 pk.arthur: Date, time…
226 00:25:40.090 ⇒ 00:25:47.299 pk.arthur: Yeah, it’s a daytime column in the campaign performance table, but, it looks like it only has…
227 00:25:51.480 ⇒ 00:25:57.789 pk.arthur: just a date. If we could get the time, that would be very, very helpful, I think.
228 00:26:07.050 ⇒ 00:26:12.219 pk.arthur: So basically, just, like, every hour from, like, basically, it could be 1PM to 2PM, 3pm.
229 00:26:17.700 ⇒ 00:26:20.050 Demilade Agboola: Let me see…
230 00:27:59.710 ⇒ 00:28:01.689 Demilade Agboola: But it appears it comes in…
231 00:28:02.310 ⇒ 00:28:06.129 Demilade Agboola: That’s just the decks, without the hours.
232 00:28:10.510 ⇒ 00:28:17.020 pk.arthur: Is that from, like, the Google Ads side, or just through, redshift and the whole…
233 00:28:18.210 ⇒ 00:28:25.100 Demilade Agboola: You know, even from, like, how he just lands in the… warehouse.
234 00:28:26.290 ⇒ 00:28:30.350 Demilade Agboola: It appears to land that way. Let me see if there’s anything…
235 00:29:58.850 ⇒ 00:30:02.079 Demilade Agboola: Yeah, so let me share my screen. So I’m looking at the table.
236 00:30:03.990 ⇒ 00:30:07.170 Demilade Agboola: And… so this is what we’re ingesting.
237 00:30:08.100 ⇒ 00:30:13.560 Demilade Agboola: The ones ticked are the ones, the columns that we’re getting in, so this is the campaign performance table.
238 00:30:20.680 ⇒ 00:30:24.490 Demilade Agboola: So what we’re interested in is the… Beat.
239 00:30:27.030 ⇒ 00:30:28.300 Demilade Agboola: Which is this.
240 00:30:29.800 ⇒ 00:30:36.300 Demilade Agboola: That’s the only… That’s the most granular level that we can get. Every other thing
241 00:30:39.960 ⇒ 00:30:45.690 Demilade Agboola: Let’s see… I feel that, like…
242 00:30:47.570 ⇒ 00:30:52.790 pk.arthur: Can you search up maybe the hour of the day? I know, like, they have some weird names.
243 00:30:54.060 ⇒ 00:30:54.960 pk.arthur: Okay.
244 00:30:55.310 ⇒ 00:30:58.070 Demilade Agboola: I don’t know why it’s excluded.
245 00:31:01.260 ⇒ 00:31:05.789 Demilade Agboola: You know, so there’s a conflict, so if we want an hour of the day.
246 00:31:07.310 ⇒ 00:31:11.150 Demilade Agboola: We can’t get the bounce rates at retirement sites.
247 00:31:12.810 ⇒ 00:31:14.570 Demilade Agboola: Twin Impressions, all that.
248 00:31:15.990 ⇒ 00:31:17.799 pk.arthur: Huh, okay, okay.
249 00:31:24.490 ⇒ 00:31:29.209 Demilade Agboola: So, I guess the idea, if you want to see these numbers, you would have to calculate them yourself.
250 00:31:29.390 ⇒ 00:31:30.029 Demilade Agboola: Right beyond the odds.
251 00:31:30.030 ⇒ 00:31:34.269 pk.arthur: Honestly, the balance rates could be got through GA4, because GA4 could…
252 00:31:34.680 ⇒ 00:31:43.460 pk.arthur: could let us know that behavior from, basically, instead of getting from Google Ads. The phone impressions…
253 00:31:43.930 ⇒ 00:32:02.990 pk.arthur: I don’t… I would say it’s not really important, so is the search click share, to be honest, and average time on site, that’s also… that could also be gotten from GA4, too, so… that is not really, much needed. So the purpose of this was basically to help, like, our agency team see how our campaigns are performing by the hour, so…
254 00:32:02.990 ⇒ 00:32:07.869 pk.arthur: I would think that the hour is a much-needed metric than the balance rate, I would say.
255 00:32:09.020 ⇒ 00:32:10.699 Demilade Agboola: Alright, so let me see if…
256 00:32:11.220 ⇒ 00:32:13.259 Demilade Agboola: Now, we could just do that instead.
257 00:32:14.390 ⇒ 00:32:21.149 Demilade Agboola: And then… Another thing to do…
258 00:32:31.130 ⇒ 00:32:31.820 Demilade Agboola: Okay.
259 00:32:44.310 ⇒ 00:32:54.860 pk.arthur: Because, honestly, I don’t think we’re even ingesting the bounce rates into Looker. I’m looking at that campaign performance table, and looking at all the columns in that table, I don’t even see bounce rate, so…
260 00:32:59.230 ⇒ 00:33:00.040 Demilade Agboola: Okay…
261 00:33:00.040 ⇒ 00:33:02.690 Emily Giant: You can add that pretty easily, as long as it exists.
262 00:33:03.980 ⇒ 00:33:07.459 Demilade Agboola: Yeah, but now that we’ve answered, actually, it won’t exist anymore.
263 00:33:08.900 ⇒ 00:33:27.780 pk.arthur: Yeah, I think that’s fine, because, like, GA4 can also report it, it’s just basically how many people, like, came to our website and did not click anything and went out, and GA4, basically, that’s, like, the purpose of GA4, we can see the bounce rate across different channels, so I would say it’s not much… it’s not needed in this, connection.
264 00:33:28.510 ⇒ 00:33:32.309 Demilade Agboola: Okay, sounds good. Alright, so I will…
265 00:33:41.170 ⇒ 00:33:47.209 Demilade Agboola: Alright, make that change… So for the next thing.
266 00:33:47.770 ⇒ 00:33:48.590 pk.arthur: Okay.
267 00:33:48.750 ⇒ 00:33:49.890 pk.arthur: I will look out for it.
268 00:33:50.080 ⇒ 00:33:52.060 Demilade Agboola: 25 minutes doubled.
269 00:33:52.490 ⇒ 00:33:53.830 Demilade Agboola: will come in.
270 00:33:54.690 ⇒ 00:33:56.210 Demilade Agboola: I will need to make.
271 00:33:56.820 ⇒ 00:33:58.709 Demilade Agboola: I’m calling for it, though.
272 00:34:44.020 ⇒ 00:34:47.870 Demilade Agboola: Honestly, bounce rate wasn’t coming through, because, like, bounce sheet was not selected here.
273 00:34:48.370 ⇒ 00:34:49.080 Emily Giant: Yeah.
274 00:34:49.080 ⇒ 00:34:53.460 pk.arthur: That’s what I… Yeah, it’s not really needed, to be honest, I would say.
275 00:34:54.330 ⇒ 00:34:56.259 Emily Giant: I mean, you might want it at some point.
276 00:34:56.600 ⇒ 00:34:57.190 pk.arthur: Yeah.
277 00:34:57.190 ⇒ 00:34:58.850 Emily Giant: It won’t hurt to ingest it.
278 00:34:58.850 ⇒ 00:35:12.509 pk.arthur: Yeah, no, that’s… that’s… I agree with you, but, if we had to change between hour and balance rates, I know, like, balance rate could also be garnered from GA4, that’s why I’m not really, too concerned about this and this connection, to be honest.
279 00:35:12.510 ⇒ 00:35:13.150 Emily Giant: Okay.
280 00:35:37.900 ⇒ 00:35:39.839 Demilade Agboola: Interesting, this is an incremental.
281 00:35:40.970 ⇒ 00:35:43.320 Demilade Agboola: Yeah, it doesn’t exist yet, so…
282 00:35:43.760 ⇒ 00:35:44.410 pk.arthur: Okay.
283 00:35:44.570 ⇒ 00:35:46.570 Demilade Agboola: It’ll be hard to test, but…
284 00:35:46.890 ⇒ 00:35:53.800 Demilade Agboola: Yeah, I’ll just keep this. Once the data syncs in, like, 25 minutes, or, like, you know, 30 minutes, I’ll look at it and confirm.
285 00:35:54.090 ⇒ 00:35:55.259 pk.arthur: Okay, thank you.
286 00:35:55.500 ⇒ 00:35:56.280 Demilade Agboola: Where’s this?
287 00:36:01.130 ⇒ 00:36:02.679 Demilade Agboola: Alright, any other issues?
288 00:36:03.550 ⇒ 00:36:07.469 pk.arthur: I don’t think so, now that I can see off the top of my head.
289 00:36:08.440 ⇒ 00:36:09.550 Demilade Agboola: Okay.
290 00:36:10.290 ⇒ 00:36:12.220 Demilade Agboola: Amy, do you have any issues?
291 00:36:12.630 ⇒ 00:36:28.579 Emily Giant: One of the things I wanted to go over with PK was the FY26 forecast, the sheet we were ingesting, and, like, how we are… what our plan is to merge that with, or to join that to the other data. So, let me share my screen.
292 00:36:28.970 ⇒ 00:36:40.320 Emily Giant: So, do you want to give him some background? I don’t think it… I don’t think it’s not obvious from the title, but, like, of what the document is, the FY26 forecast?
293 00:36:43.230 ⇒ 00:36:49.090 Emily Giant: PK, are you, like, I feel like this predates you even starting, so…
294 00:36:49.760 ⇒ 00:36:52.649 Emily Giant: I don’t know how familiar you are with the document.
295 00:36:52.860 ⇒ 00:36:56.319 pk.arthur: Just, like, the forecasted numbers for this fiscal year.
296 00:36:56.320 ⇒ 00:36:57.560 Emily Giant: Yeah.
297 00:36:57.730 ⇒ 00:37:05.939 pk.arthur: Yes, so… So what we want to do, I’m guessing, Emily, is basically see how, like, just see…
298 00:37:06.050 ⇒ 00:37:13.399 pk.arthur: The weekly or daily, like, how are we comparing to the forecast? Is that what the task was?
299 00:37:14.270 ⇒ 00:37:21.649 Emily Giant: Yeah. The one thing is that I… it’s really different than the previous years. I know that Kristen, Sam, Pat started…
300 00:37:21.880 ⇒ 00:37:31.060 Emily Giant: this past fiscal year, and the one that was made for FY25 predates her, so I don’t know if that’s, like, why it’s so different.
301 00:37:31.330 ⇒ 00:37:40.430 Emily Giant: It’s fine! Excuse me, my goodness, it’s fine for it to be different, but, The outbound…
302 00:37:40.820 ⇒ 00:37:43.209 Emily Giant: I would have put this in the new…
303 00:37:46.160 ⇒ 00:37:48.469 Emily Giant: Yeah, it’s gonna be in this flow.
304 00:37:49.670 ⇒ 00:37:52.380 Emily Giant: So there’s just so many more,
305 00:37:52.670 ⇒ 00:37:56.369 Emily Giant: columns in this one than in previous years. Okay.
306 00:37:57.690 ⇒ 00:37:58.540 Emily Giant: Yes.
307 00:37:58.540 ⇒ 00:37:59.859 pk.arthur: Okay, yeah, each bit.
308 00:38:03.960 ⇒ 00:38:13.830 Emily Giant: So does this just need a date spine, Demolade? I know that that was, like, not a great explanation for you to answer that question, but essentially, these are forecasts for, like, if I do a preview.
309 00:38:14.090 ⇒ 00:38:16.880 Emily Giant: I think it’s a forecast for every day.
310 00:38:17.070 ⇒ 00:38:20.609 pk.arthur: I think we only… I can at least see your notion, I believe.
311 00:38:20.900 ⇒ 00:38:25.370 Emily Giant: Dang it! Okay, that makes sense as to why you’d be like, what?
312 00:38:26.130 ⇒ 00:38:27.010 Emily Giant: Absolutely.
313 00:38:27.010 ⇒ 00:38:30.800 Demilade Agboola: I was about to say that’s… notion is not enough for me to…
314 00:38:31.880 ⇒ 00:38:43.790 Emily Giant: Yeah, a ticket that we’re not talking about. You can’t understand what I mean from that? I don’t get it. Okay, so it’s… sorry, this is what I was meaning to show y’all.
315 00:38:44.690 ⇒ 00:38:49.060 Emily Giant: Are you still looking at my Notion? Yes. I will die. Oh my god.
316 00:38:49.820 ⇒ 00:38:54.600 Emily Giant: I can’t have two screens. It just doesn’t work for me.
317 00:38:54.930 ⇒ 00:38:57.489 Demilade Agboola: How about now? Yeah.
318 00:38:57.970 ⇒ 00:39:00.049 Emily Giant: Can you see dbt?
319 00:39:00.190 ⇒ 00:39:00.950 pk.arthur: Yes.
320 00:39:01.150 ⇒ 00:39:19.419 Emily Giant: Okay, so it’s this flow, the raw marketing purchase forecast, into the marketing purchase forecast, and so the fields look like this. Like, affiliate AOV, CTV AOV, direct mail, yadda yadda yadda. So, like, all of this different spend, and then…
321 00:39:20.300 ⇒ 00:39:22.139 Emily Giant: If I look at a preview.
322 00:39:22.470 ⇒ 00:39:27.060 Emily Giant: So, PK, this is just the forecast for every day, is that what this is?
323 00:39:27.670 ⇒ 00:39:32.390 pk.arthur: I think it would be on a weekly basis, but…
324 00:39:32.520 ⇒ 00:39:33.920 Emily Giant: Okay, count…
325 00:39:33.920 ⇒ 00:39:36.240 pk.arthur: How many… how many rows are there?
326 00:39:37.880 ⇒ 00:39:39.130 Emily Giant: That’s a good question.
327 00:39:45.710 ⇒ 00:39:50.930 pk.arthur: Because I just did a re-forecast, and it was on a weekly basis, but…
328 00:39:51.710 ⇒ 00:39:54.530 pk.arthur: I would assume it’s a weekly basis, I think.
329 00:40:00.380 ⇒ 00:40:01.380 Emily Giant: Yep, weekly.
330 00:40:01.380 ⇒ 00:40:03.480 pk.arthur: Yeah, it’s a weekly basis, okay, perfect.
331 00:40:06.630 ⇒ 00:40:10.290 Emily Giant: So, if I’m joining this in Looker.
332 00:40:10.560 ⇒ 00:40:15.249 Emily Giant: because we don’t necessarily need to join it to anything in, dbt.
333 00:40:15.460 ⇒ 00:40:16.980 Emily Giant: Since it’s not, like.
334 00:40:18.200 ⇒ 00:40:26.020 Emily Giant: connected to anything but the date, right? Or is there, like, something else this should be connected to?
335 00:40:26.930 ⇒ 00:40:29.610 pk.arthur: Thinking about it, I think…
336 00:40:29.740 ⇒ 00:40:32.570 pk.arthur: The best way we get connected to it
337 00:40:32.730 ⇒ 00:40:48.659 pk.arthur: table or a look will be from GA4, because either GA4 or North Beam, because that will have every single channel’s data point, so, like, talk about AOV or clicks, so that will be a way to see how, we are performing,
338 00:40:49.070 ⇒ 00:40:50.930 pk.arthur: Compared to our forecast.
339 00:40:52.960 ⇒ 00:40:53.430 Emily Giant: Okay.
340 00:40:53.430 ⇒ 00:41:00.170 pk.arthur: Some fields, I believe, may have to be calculated, like, AOV, if I’m not…
341 00:41:00.820 ⇒ 00:41:03.280 pk.arthur: If I’m not wrong, I think, but…
342 00:41:03.280 ⇒ 00:41:03.880 Emily Giant: Okay.
343 00:41:05.070 ⇒ 00:41:14.219 Emily Giant: Let me see if I did any calculations. So this is the look, and then what I need to do is figure out what this should be joined to in…
344 00:41:14.480 ⇒ 00:41:27.289 Emily Giant: the model. So, in Looker, you know, you usually use top-line sales model. There’s also this, like, user retention correlation, there’s also, like, a marketing-specific, or KPIs.
345 00:41:27.290 ⇒ 00:41:28.870 pk.arthur: Good.
346 00:41:29.000 ⇒ 00:41:32.169 Emily Giant: So I’m guessing it’s top-line sales, is what you want it…
347 00:41:32.380 ⇒ 00:41:39.170 pk.arthur: Yeah, I would say top-line sales, that’s where I can see the most, like, relevant data.
348 00:41:40.130 ⇒ 00:41:41.290 pk.arthur: I would say.
349 00:41:41.620 ⇒ 00:41:48.020 Emily Giant: Okay, so if… I’m looking in all the GA4, like, what Explorer this is tied to.
350 00:41:49.240 ⇒ 00:41:52.750 Emily Giant: Ugh, I hate all this junk in here, I need to clean that up.
351 00:41:53.070 ⇒ 00:41:56.060 Emily Giant: So it’s tied to Yachtpo, which is…
352 00:41:57.140 ⇒ 00:42:03.280 Emily Giant: That still runs, that still runs. Okay, so I would need to… Oh, wait.
353 00:42:03.280 ⇒ 00:42:07.449 pk.arthur: It would honestly have to be on a weekly level.
354 00:42:07.780 ⇒ 00:42:08.230 Emily Giant: Okay.
355 00:42:08.230 ⇒ 00:42:18.730 pk.arthur: I would say, just cause that’s where… that’s how our forecast is done. Yeah, then you can join, basically, on the channel… so… hmm…
356 00:42:20.460 ⇒ 00:42:25.649 pk.arthur: I’m not too certain if the channels will completely sync up with GA4, because…
357 00:42:25.650 ⇒ 00:42:38.580 Emily Giant: That’s okay, like, as long as I can tie the weeks, Looker will know. As long as I can say, like, this week equals this week, in this model area, it will do the calculations just fine. It’s that I don’t know where…
358 00:42:38.860 ⇒ 00:42:40.810 Emily Giant: the GA4 stuff is…
359 00:42:41.220 ⇒ 00:42:50.440 Emily Giant: In the first place. Is it called something else? Google Analytics data? Because there’s nothing tied to it at all. It’s just, like, a standalone…
360 00:42:50.940 ⇒ 00:42:53.279 pk.arthur: That’s just interesting, let me see…
361 00:43:00.120 ⇒ 00:43:05.630 Emily Giant: Like, what is the, maybe if I look at what the,
362 00:43:07.150 ⇒ 00:43:09.660 Emily Giant: The view is called that should be in it.
363 00:43:13.300 ⇒ 00:43:15.510 Emily Giant: Google Analytics Daily.
364 00:43:21.130 ⇒ 00:43:31.190 Emily Giant: Revenue, conversion rate, marketing channel, So maybe it’s this that I need to search in the model.
365 00:43:40.120 ⇒ 00:43:45.190 Emily Giant: And then there’s all this, like, KPI stuff, like, KPI TikTok, Get…
366 00:43:45.350 ⇒ 00:43:48.920 Emily Giant: So it might be in the KPI model, and not top-line sales.
367 00:43:52.430 ⇒ 00:43:55.149 pk.arthur: Let me take a look at that, too. KPI…
368 00:43:55.350 ⇒ 00:43:57.259 Emily Giant: Cause that’s… this is not even in…
369 00:43:57.370 ⇒ 00:43:59.349 Emily Giant: Topline sale. Wait, there it is.
370 00:44:00.000 ⇒ 00:44:02.750 Emily Giant: Okay, but yeah, there’s nothing tied to it.
371 00:44:04.060 ⇒ 00:44:05.200 Emily Giant: So…
372 00:44:08.600 ⇒ 00:44:13.369 pk.arthur: Do you want me to find some time on your calendar so we could go through it?
373 00:44:14.030 ⇒ 00:44:19.949 Emily Giant: Sure. Or we can do this, like, as part of these working sessions, because this is all technically part of…
374 00:44:20.170 ⇒ 00:44:23.779 Emily Giant: The work is, like, cleaning up this marketing data.
375 00:44:23.780 ⇒ 00:44:24.960 pk.arthur: Yeah, okay.
376 00:44:25.390 ⇒ 00:44:27.700 Emily Giant: But KPI Unified…
377 00:44:31.330 ⇒ 00:44:34.690 Emily Giant: Okay, so I have it tied here to…
378 00:44:35.720 ⇒ 00:44:41.699 Emily Giant: KPI unified, but I don’t know how often you use this Explorer.
379 00:44:42.170 ⇒ 00:44:44.680 pk.arthur: I… I don’t think I’ve ever used it, to be honest.
380 00:44:44.680 ⇒ 00:44:48.910 Emily Giant: Yeah, okay, so this…
381 00:44:49.090 ⇒ 00:44:49.460 pk.arthur: But…
382 00:44:49.460 ⇒ 00:44:51.270 Emily Giant: Let’s… let’s take a look at it.
383 00:44:51.560 ⇒ 00:44:59.160 Emily Giant: So, just for context, this budget purchase is the old version. This is what I was asking you and Chris about.
384 00:44:59.690 ⇒ 00:45:11.070 Emily Giant: FY25 budget purchase is the same quote-unquote document as the marketing FY26 forecast, and I’ll show you, like, what it looks like.
385 00:45:11.480 ⇒ 00:45:17.780 Emily Giant: so that… you know what I mean when I say, like, I want to historically,
386 00:45:19.090 ⇒ 00:45:22.320 Emily Giant: Like, streamline these so you can use them together.
387 00:45:22.460 ⇒ 00:45:29.039 Emily Giant: But they’re so different. So the new version has, like.
388 00:45:29.570 ⇒ 00:45:37.580 Emily Giant: all of those, like, affiliate, you can see that this one is just much smaller, so I guess I can just…
389 00:45:38.640 ⇒ 00:45:44.089 Emily Giant: combine these in dbt and leave, for previous years, just leave the measures blank?
390 00:45:44.380 ⇒ 00:45:49.730 Emily Giant: That don’t have… Any numbers for the previous years, that’s…
391 00:45:50.330 ⇒ 00:46:02.099 Emily Giant: easy. I just wanted to make sure that works for y’all. Because if you try to, like, for example, go back and do direct AOV or direct mail orders, it’s not going to exist in previous years.
392 00:46:03.320 ⇒ 00:46:06.389 pk.arthur: Previously, that’s meaning, like, from FYI, like, what, 24?
393 00:46:06.390 ⇒ 00:46:12.179 Emily Giant: Yeah, and 25, but for FY26, which started in July, you’ll have all of that.
394 00:46:14.350 ⇒ 00:46:14.700 pk.arthur: Okay.
395 00:46:14.700 ⇒ 00:46:20.089 Emily Giant: So as long as you’re not trying to, like, balance a budget from 2 years ago, you should be okay.
396 00:46:21.590 ⇒ 00:46:27.509 pk.arthur: Yeah, I think that should be fine. We don’t really balance much budgets, I would say.
397 00:46:27.510 ⇒ 00:46:28.290 Emily Giant: Okay.
398 00:46:28.700 ⇒ 00:46:33.630 Emily Giant: Or, like, you know, do comps, year-over-year comps. Like, if you’re saying, like, did we…
399 00:46:34.520 ⇒ 00:46:41.599 Emily Giant: with direct mail, were we more aligned this year as opposed to last year? You’re not gonna have… you’re not gonna be able to pull that data.
400 00:46:41.600 ⇒ 00:46:45.470 pk.arthur: Okay, and that… Hmm.
401 00:46:48.140 ⇒ 00:46:53.579 Emily Giant: And I can write this all out to you and Chris, so it’s easier. Just say, like, hey, okay, these are…
402 00:46:54.240 ⇒ 00:46:56.690 Emily Giant: There are, like, what, 80…
403 00:46:57.220 ⇒ 00:47:07.069 Emily Giant: fields in the new model, and there are, like, 16 in FY25. So, just FYI, if you try to do unattributed sessions.
404 00:47:07.660 ⇒ 00:47:10.770 Emily Giant: For last year, you’re not gonna be able to.
405 00:47:10.770 ⇒ 00:47:15.690 pk.arthur: Yeah, no, I think that makes sense, like, if there was… if literally it was not available last.
406 00:47:15.690 ⇒ 00:47:16.390 Emily Giant: Yeah.
407 00:47:17.590 ⇒ 00:47:30.339 pk.arthur: last forecast and document that you got… that you received, then yeah, like, we’ll have to probably do it a different way, but I know, like, Chris… Chris has, other sources, like, he keeps on Google, on Google Drive.
408 00:47:30.340 ⇒ 00:47:34.220 Emily Giant: Yeah, that’s what I was wondering, like, if it is available.
409 00:47:34.340 ⇒ 00:47:40.279 Emily Giant: I’m… Demolade and the team are trying to move away from Google Docs as much as we can.
410 00:47:40.280 ⇒ 00:47:49.929 pk.arthur: Then, honestly, that might be available, I think, because I’ve seen, like, let me just see, I know he has a working, like, week-over-week metric sheet that he keeps,
411 00:47:50.080 ⇒ 00:47:53.409 pk.arthur: just as a backup, but it has data from, I think.
412 00:47:53.840 ⇒ 00:48:00.449 pk.arthur: 2022? Yeah, 2020. 20… from 2020, that’s here. Okay.
413 00:48:00.450 ⇒ 00:48:00.900 Emily Giant: So…
414 00:48:00.900 ⇒ 00:48:05.009 pk.arthur: So, we have that data,
415 00:48:05.480 ⇒ 00:48:17.710 pk.arthur: And it’s broken down by, like, what you said, like, search, affiliates, meta, and you can see the cost per… cost per click, and all that different metrics… metrics, so… maybe, I think…
416 00:48:18.580 ⇒ 00:48:27.579 pk.arthur: if we want to go, like… I don’t know how far back he would want to go regarding this, but I think definitely at least, like…
417 00:48:28.900 ⇒ 00:48:33.069 Emily Giant: Okay, yeah, as long as I have that information, we can,
418 00:48:33.460 ⇒ 00:48:43.489 Emily Giant: we can do the logic in dbt so that it’s not, like, a separate view in Looker, because that’s when it gets really messy and hard to control.
419 00:48:43.800 ⇒ 00:48:48.749 Emily Giant: For… for historical purposes, because then, like, it’s gonna look like a different…
420 00:48:48.790 ⇒ 00:48:55.710 Emily Giant: set every time you go to Looker, and I would like it to just be one set that has these.
421 00:48:55.710 ⇒ 00:49:12.250 Emily Giant: forever, and no matter what year you query, it’s there for you. But as of now, it’s just that spreadsheet. So, if you could get that from Chris, so in the marketing purchase forecast, I’m just leaving notes. Where are these fields prior to FY26?
422 00:49:12.250 ⇒ 00:49:15.739 Emily Giant: We need to add the logic to DPD prior to it hitting Looker.
423 00:49:17.080 ⇒ 00:49:21.930 Emily Giant: So that… Year-over-year comps, are possible.
424 00:49:22.540 ⇒ 00:49:24.550 Emily Giant: Alright, and this is just in my…
425 00:49:24.590 ⇒ 00:49:43.169 Emily Giant: inventory discrepancies branch, but we’re probably going to deploy something from this branch, today. So, this will be in the DBT when you look on your, local as well, but it won’t be there right now. So that was… that was my one question, was like, okay, so right now, this FY26 forecast.
426 00:49:43.240 ⇒ 00:49:53.949 Emily Giant: it’s in KPIs, and it’s in, like, nowhere in top-line sales, but what it looks like in the KPIs, it’s in the unified
427 00:49:54.100 ⇒ 00:49:55.380 Emily Giant: explore.
428 00:49:58.920 ⇒ 00:50:06.260 Emily Giant: And I think we’d like to, like, kill this in the next couple months, and make it all joined.
429 00:50:06.450 ⇒ 00:50:11.119 Emily Giant: To, like, your marketing model, so that it’s, yeah, it’s right here.
430 00:50:12.620 ⇒ 00:50:14.290 pk.arthur: So, KPI 85, okay.
431 00:50:14.520 ⇒ 00:50:15.140 pk.arthur: Bye.
432 00:50:15.140 ⇒ 00:50:19.149 Emily Giant: It is available, it’s just not good. It’s not joined with any…
433 00:50:19.470 ⇒ 00:50:25.460 Emily Giant: previous years, it’s just joined on week, so you can technically use it with…
434 00:50:26.000 ⇒ 00:50:31.620 Emily Giant: Google, you should be able to use it with Google, but all I see is fiscal calendar stuff in this, so…
435 00:50:32.430 ⇒ 00:50:34.470 Emily Giant: I don’t know how useful this really is.
436 00:50:34.750 ⇒ 00:50:51.909 pk.arthur: Yeah, honestly, I have to go through, I haven’t really seen… honestly, I haven’t been watching this, Explorer that much. But, also, I have a one-on-one with Chris later today, so I could bring this to him and let him know what the exact, like, issue is, and we can see from there.
437 00:50:52.350 ⇒ 00:51:02.899 Emily Giant: Okay, yeah, KPI Unified is the name of the Explorer to go over with him, and just… I would do some, like, QA on your end of, like, what is this stuff?
438 00:51:02.900 ⇒ 00:51:05.760 pk.arthur: Yeah. Do you use this stuff? Does anyone?
439 00:51:05.790 ⇒ 00:51:09.669 Emily Giant: Cause we’d like to set you up in a better place with your…
440 00:51:10.030 ⇒ 00:51:16.169 Emily Giant: Your forecast, so you can actually use it with The ingested data tables.
441 00:51:16.170 ⇒ 00:51:17.969 pk.arthur: Yeah, that makes sense.
442 00:51:18.470 ⇒ 00:51:22.859 Emily Giant: Yeah, and I’ve talked about this with Chris a couple times, but I don’t think I did a good job of explaining, like.
443 00:51:23.220 ⇒ 00:51:29.439 Emily Giant: it’s not joined to any previous years, so I don’t know how useful it is.
444 00:51:30.740 ⇒ 00:51:34.420 pk.arthur: Okay, yeah, that makes sense. I’ll relay that information to him.
445 00:51:34.420 ⇒ 00:51:40.490 Emily Giant: Yeah, and now that you know the tables that we have a little better, you’ll be able to, like.
446 00:51:41.070 ⇒ 00:51:51.459 Emily Giant: Explain to him what the possibilities are, and, like, what we’re working on, so that we just have a better notion of, like, where this dock is supposed to live.
447 00:51:51.460 ⇒ 00:51:52.230 pk.arthur: Yeah.
448 00:51:52.230 ⇒ 00:51:54.410 Emily Giant: Yeah, cool. Thank you.
449 00:51:54.780 ⇒ 00:52:01.899 Emily Giant: Yeah, thank you. And then that sh… it shouldn’t be hard to set up, since it’s, like, just a week… a weekly document.
450 00:52:01.900 ⇒ 00:52:03.760 pk.arthur: All I have to do is say, like.
451 00:52:03.980 ⇒ 00:52:12.480 Emily Giant: join this date on this date, and this date on this date, in Looker, once we have the logic set up, and it will work, but we just need to know where to stick it.
452 00:52:12.670 ⇒ 00:52:13.280 pk.arthur: Yeah.
453 00:52:13.620 ⇒ 00:52:14.500 pk.arthur: Okay.
454 00:52:15.230 ⇒ 00:52:18.920 Emily Giant: Demolade, do you have any questions about the doc? Does this make sense? Like, what I’m…
455 00:52:19.140 ⇒ 00:52:21.469 Emily Giant: Trying to figure out…
456 00:52:22.400 ⇒ 00:52:30.879 Demilade Agboola: Yeah, it does make sense. I guess once, like, PK runs with Chris about it, or talk to Chris about it, we can just know if, like, that works for them.
457 00:52:31.360 ⇒ 00:52:38.160 Emily Giant: Okay. And then, as far as the plan for KPI Unified, like, We’re planning to, like…
458 00:52:39.070 ⇒ 00:52:49.790 Emily Giant: kill these, right? Like, some of these ad hoc models that… seem really… like… Google Sheet.
459 00:52:50.030 ⇒ 00:52:51.730 Emily Giant: Upload-specific?
460 00:52:52.180 ⇒ 00:52:59.020 Demilade Agboola: Yeah, as best as… as much as possible, we want to have more standardized, like, explores.
461 00:52:59.020 ⇒ 00:52:59.770 Emily Giant: Yeah.
462 00:53:00.140 ⇒ 00:53:00.880 Demilade Agboola: Cool, too.
463 00:53:01.310 ⇒ 00:53:09.519 Emily Giant: Okay, because this, like, even looking at the names of these things, I know that there’s no way CDL cost is being upkept by anyone.
464 00:53:09.690 ⇒ 00:53:11.399 Emily Giant: And if it is, I…
465 00:53:12.100 ⇒ 00:53:14.939 Emily Giant: Pfft. I don’t know about that.
466 00:53:15.500 ⇒ 00:53:18.839 Emily Giant: So, yeah, I think most of these are deprecated anyway.
467 00:53:19.220 ⇒ 00:53:21.250 pk.arthur: I don’t even know what CDL class even means.
468 00:53:21.660 ⇒ 00:53:27.940 Emily Giant: It’s one of our really teeny tiny shipping providers in, like, New Jersey.
469 00:53:27.940 ⇒ 00:53:29.010 pk.arthur: Oh, okay.
470 00:53:29.010 ⇒ 00:53:35.659 Emily Giant: But I know that, like, there’s a lot of manual work around shipping costs.
471 00:53:36.160 ⇒ 00:53:39.749 Emily Giant: And, so I’ve been, like, touching a lot of the…
472 00:53:39.970 ⇒ 00:53:49.550 Emily Giant: carrier-specific fields recently with, like, fact suborders, and there’s… there’s just no way someone’s upkeeping these costs, you know, all the time.
473 00:53:50.440 ⇒ 00:54:03.439 Emily Giant: So, I’m pretty sure a lot of these are deprecated, although FedEx IPD is there, and that’s brand new. So, anyway, these are just existing in a vacuum, and not terribly useful, because they’re not joined with anything. It’s like looking at your spreadsheet.
474 00:54:03.720 ⇒ 00:54:07.000 Emily Giant: That’s all it is, is looking at the spreadsheet in Looker.
475 00:54:07.000 ⇒ 00:54:09.409 pk.arthur: Yeah, that’s not really a good use of Looker.
476 00:54:09.410 ⇒ 00:54:18.609 Emily Giant: No, it’s not. But that’s all I had. Demolade, I do have, like, a quick question. PK, feel free to hang… actually, you know what, I think I figured it out.
477 00:54:19.260 ⇒ 00:54:20.740 Emily Giant: It’s with the,
478 00:54:21.680 ⇒ 00:54:40.079 Emily Giant: It’s with the forced upgrade thing. I was adding in a table because there were inventory discrepancies, all of them were related to forced upgrades, and, I would be able to return the suborder that had a forced upgrade in mode, and then when I would go one model downstream.
479 00:54:40.500 ⇒ 00:54:49.399 Emily Giant: to suborders with lots, it wouldn’t return it, but I think it’s because, forced upgrades don’t pull an inventory number ID
480 00:54:50.220 ⇒ 00:54:56.210 Emily Giant: in… that initial transaction line model. So, for example.
481 00:54:57.000 ⇒ 00:55:04.630 Emily Giant: I couldn’t figure out… and then the join downstream is on inventory number ID, so I narrowed it down to, like, okay, it’s because that is blank.
482 00:55:04.970 ⇒ 00:55:11.969 Emily Giant: So… In that model, if I… Do a left join.
483 00:55:13.070 ⇒ 00:55:18.889 Emily Giant: to… Here, so this is what suborder inventory…
484 00:55:19.030 ⇒ 00:55:36.259 Emily Giant: like, adding the lot, adding the, like, inventory unit detail used to be this, but I’m just, like, doing some logic for forced upgrades before it goes on to that model. If I do a coalesce with the inventory number ID, which I know it’s joined on the transaction line.
485 00:55:36.910 ⇒ 00:55:44.439 Emily Giant: and the transaction line ID. This is always present for the forced upgrade, so I’m thinking that, like, if I can fill in the inventory number ID from that.
486 00:55:44.560 ⇒ 00:55:46.139 Emily Giant: It will solve the problem.
487 00:55:46.490 ⇒ 00:55:51.530 Emily Giant: But, just wanted to brief you on that. That’s why I said, like, don’t deploy this when I accidentally…
488 00:55:52.040 ⇒ 00:55:55.150 Emily Giant: submitted that PR on Friday, that’s what it was.
489 00:55:55.710 ⇒ 00:56:00.620 Demilade Agboola: Okay, but in that case, would the inventory not be… So the transaction…
490 00:56:00.880 ⇒ 00:56:05.159 Demilade Agboola: ID will be seen in the lot, though, so transaction and the…
491 00:56:06.280 ⇒ 00:56:10.469 Demilade Agboola: Can you go back? I can’t remember the other. Transaction line.
492 00:56:11.890 ⇒ 00:56:15.419 Demilade Agboola: This is the join to inventory assignment, which will give it.
493 00:56:15.810 ⇒ 00:56:24.480 Emily Giant: an inventory number ID in the event that it doesn’t exist in the transaction line model already. And prior to
494 00:56:25.630 ⇒ 00:56:33.359 Emily Giant: this morning, I hadn’t added this, because I didn’t realize the reason it wasn’t populating downstream was because this was missing.
495 00:56:33.800 ⇒ 00:56:42.089 Demilade Agboola: Okay, alright. Just a heads up, just… it would be helpful to do, like, a count of rows prior and a count of rows after.
496 00:56:42.440 ⇒ 00:56:43.020 Demilade Agboola: the joint.
497 00:56:43.020 ⇒ 00:56:43.979 Emily Giant: Yeah, that makes sense.
498 00:56:44.300 ⇒ 00:56:49.640 Demilade Agboola: Like, you don’t… the numbers don’t change in a drastic manner, in any manner, to be honest.
499 00:56:50.170 ⇒ 00:56:50.820 Emily Giant: Yeah.
500 00:56:51.010 ⇒ 00:56:52.360 Emily Giant: Let me add that.
501 00:56:52.790 ⇒ 00:56:53.570 Emily Giant: Huh.
502 00:56:54.670 ⇒ 00:56:57.420 Emily Giant: count of… Rows.
503 00:56:57.530 ⇒ 00:56:59.740 Emily Giant: Prior to this model existing.
504 00:57:02.880 ⇒ 00:57:04.029 Emily Giant: Okay, cool.
505 00:57:04.730 ⇒ 00:57:10.010 Emily Giant: I will add that… I’ll do some QA on that before resubmitting.
506 00:57:10.010 ⇒ 00:57:11.330 Demilade Agboola: Alright, sounds good.
507 00:57:11.330 ⇒ 00:57:13.430 Emily Giant: Alright, cool. Thanks, everybody!
508 00:57:13.890 ⇒ 00:57:14.680 pk.arthur: cute.
509 00:57:14.680 ⇒ 00:57:16.070 Emily Giant: Alright, talk to you later.
510 00:57:16.240 ⇒ 00:57:17.059 pk.arthur: Bye, guys.