Meeting Title: Urban Stems Data Integration Sync Date: 2025-10-03 Meeting participants: Demilade Agboola, Emily Giant, pk.arthur
WEBVTT
1 00:01:27.940 ⇒ 00:01:29.230 Emily Giant: Hey, hey!
2 00:01:33.630 ⇒ 00:01:34.980 Emily Giant: Hello, how are you?
3 00:01:35.510 ⇒ 00:01:37.120 Emily Giant: I’m good, how are you?
4 00:01:38.150 ⇒ 00:01:41.140 Demilade Agboola: I’m doing fine, can’t complain.
5 00:01:42.610 ⇒ 00:01:44.750 Emily Giant: I’m making my second pot of coffee.
6 00:01:45.300 ⇒ 00:01:46.420 Demilade Agboola: It’s only down there.
7 00:01:46.420 ⇒ 00:01:48.050 Emily Giant: I am here, so…
8 00:01:48.050 ⇒ 00:01:50.230 Demilade Agboola: I was about to say, isn’t it just 9am?
9 00:01:50.390 ⇒ 00:02:03.600 Emily Giant: Yeah, so ever since I got back from Europe, I’m trying to get up, because I was on a different schedule. I’ve been trying to get up much earlier, and so I’ve been getting up at, like, 6.30, 7am.
10 00:02:03.600 ⇒ 00:02:11.369 Emily Giant: So by this meeting, I definitely need my second cup of coffee. Or my second pot, excuse me, it’s not my second cup.
11 00:02:13.720 ⇒ 00:02:16.340 Demilade Agboola: Second cup is, like, 7.30.
12 00:02:16.340 ⇒ 00:02:29.789 Emily Giant: Yes, exactly. Within moments of being awake. But I ran out of my good coffee, so I don’t know what this one’s gonna taste like. I’m… I’m a little nervous. Are we waiting on PK?
13 00:02:29.790 ⇒ 00:02:33.650 Demilade Agboola: Yes, I invited him to the call.
14 00:02:35.820 ⇒ 00:02:37.680 Emily Giant: Okay. I did that, like, 10 minutes ago.
15 00:02:37.680 ⇒ 00:02:39.640 Demilade Agboola: Well, I assume he’s online?
16 00:02:39.960 ⇒ 00:02:41.600 Emily Giant: Yeah, I’ll ping him real quick.
17 00:03:16.140 ⇒ 00:03:16.890 Emily Giant: Okay.
18 00:03:18.370 ⇒ 00:03:27.609 Emily Giant: Cool, cool. While we’re waiting for him, I do have a question that I could help, your brain could probably help me.
19 00:03:27.980 ⇒ 00:03:32.329 Emily Giant: Felipe’s still having issues with, forced upgrades.
20 00:03:32.780 ⇒ 00:03:36.130 Emily Giant: Showing up as being sent.
21 00:03:36.600 ⇒ 00:03:37.860 Emily Giant: And it’s…
22 00:03:38.020 ⇒ 00:03:44.680 Emily Giant: So, essentially, the way we’ve partitioned suborders… oh, PK’s here, we can talk about this some other time.
23 00:03:44.680 ⇒ 00:03:46.019 pk.arthur: Hello. Good morning.
24 00:03:46.020 ⇒ 00:03:48.370 Emily Giant: Hi. Hi, how are you? Good morning.
25 00:03:49.010 ⇒ 00:03:50.100 pk.arthur: How’s it going?
26 00:03:51.540 ⇒ 00:03:56.109 Emily Giant: Good! I was telling them a lot I’m on my second pot of coffee.
27 00:03:56.110 ⇒ 00:03:56.919 pk.arthur: Oh my god.
28 00:03:57.140 ⇒ 00:03:58.610 Emily Giant: So I’m doing great.
29 00:04:00.210 ⇒ 00:04:02.979 pk.arthur: It’s, like, 9AM or 8 AM where you are, Emily.
30 00:04:02.980 ⇒ 00:04:08.189 Emily Giant: It’s 9 AM. Either way, it’s unacceptable, but… Yeah.
31 00:04:08.320 ⇒ 00:04:10.150 Emily Giant: But it is what it is.
32 00:04:10.730 ⇒ 00:04:12.320 Emily Giant: Are you a coffee drinker?
33 00:04:12.870 ⇒ 00:04:13.730 pk.arthur: Oh, yeah.
34 00:04:13.900 ⇒ 00:04:14.220 Emily Giant: Oh, yeah.
35 00:04:14.220 ⇒ 00:04:22.070 pk.arthur: I recently just got gifted an espresso machine for my friend, so, like, I’ve been literally going through it every day.
36 00:04:22.490 ⇒ 00:04:23.340 Demilade Agboola: Nice.
37 00:04:23.340 ⇒ 00:04:23.940 Emily Giant: brilliant.
38 00:04:24.190 ⇒ 00:04:30.070 pk.arthur: Great friends. Yeah, no, literally, like, I’m very, very, very thankful for him, because…
39 00:04:30.170 ⇒ 00:04:39.149 pk.arthur: I used to have, like, a French press, and, like, that obviously took more time, in my opinion. But the espresso is, like, right on instant, so… cannot complain.
40 00:04:39.150 ⇒ 00:04:40.080 Emily Giant: names.
41 00:04:40.080 ⇒ 00:04:43.150 pk.arthur: Literally straight to the brains and the veins.
42 00:04:43.150 ⇒ 00:04:56.500 Emily Giant: Stay to the veins and the brains. That’s hilarious. Yeah, I like a French press, but I hate cleaning it. It’s so annoying to clean that that’s, like, my one thing with it. Like, I use a pour-over, because it’s so easy to clean.
43 00:04:57.980 ⇒ 00:05:02.209 Emily Giant: But I don’t think any of us use… well, Demolata, you might. Do you use a drip coffee maker?
44 00:05:02.860 ⇒ 00:05:04.950 Emily Giant: Oh, no, I just do instant.
45 00:05:05.700 ⇒ 00:05:06.650 pk.arthur: Let’s a coffee.
46 00:05:06.650 ⇒ 00:05:07.570 Emily Giant: Brave.
47 00:05:08.070 ⇒ 00:05:09.350 pk.arthur: Yeah.
48 00:05:10.400 ⇒ 00:05:15.579 Emily Giant: You… he also, like, some AC has a 20-ounce like.
49 00:05:16.130 ⇒ 00:05:21.100 pk.arthur: 6 shots of espresso from Starbucks, so… Big shots.
50 00:05:22.230 ⇒ 00:05:29.290 Demilade Agboola: So, there are times when I get the double espresso, and I ask for more espressos. Oh my god.
51 00:05:29.290 ⇒ 00:05:29.950 Emily Giant: Howdy.
52 00:05:29.950 ⇒ 00:05:34.460 pk.arthur: Oh my god, yeah, I thought I was crazy, but that’s… that is, like, 10 times.
53 00:05:34.470 ⇒ 00:05:37.910 Demilade Agboola: Well, yeah, so that’s the… that’s the…
54 00:05:38.280 ⇒ 00:05:45.039 Demilade Agboola: I mean, I really enjoy coffee, and I started drinking coffee when I was, like, well, maybe 13, and so…
55 00:05:45.040 ⇒ 00:05:45.580 pk.arthur: But…
56 00:05:45.580 ⇒ 00:05:48.469 Demilade Agboola: It was… it’s a lifestyle for me at this point.
57 00:05:49.620 ⇒ 00:05:56.279 Emily Giant: Same. That’s awesome. Well, okay, so I think we were gonna work on the ticket…
58 00:05:56.470 ⇒ 00:05:58.740 Emily Giant: Like, trying to move…
59 00:05:58.870 ⇒ 00:06:09.180 Emily Giant: the source medium stuff into the Shopify data instead of trying to force it in Hivo data. So, Demolati, where do you want to start with this, just so we can, like, brief you?
60 00:06:09.480 ⇒ 00:06:17.939 Demilade Agboola: I think there are a couple of things I want to… before we go into that, I was curious about the whole ad thing, because I know… I want to be sure that…
61 00:06:18.130 ⇒ 00:06:22.750 Demilade Agboola: Pk’s getting the data for the ad performance, or company.
62 00:06:22.750 ⇒ 00:06:38.509 pk.arthur: Yeah, the ad models. So yeah, I looked, like, just through, like, SQL Runner to see, like, all the tables that we had, and there are a few that are basically referencing Google Ads or Bing Ads, right? There’s… I think there’s a Google Ads Performance,
63 00:06:39.170 ⇒ 00:06:48.139 pk.arthur: table in there, but, like, anytime I try to, like, just pull data from it, just to look at the most recent data, I do not see it,
64 00:06:48.260 ⇒ 00:06:55.310 pk.arthur: being up to… just up to date with the past few days, to be honest. Like, some don’t even loaded, some…
65 00:06:55.600 ⇒ 00:06:59.699 pk.arthur: Had data from, I think it was August or September, so…
66 00:06:59.930 ⇒ 00:07:03.449 pk.arthur: That was what I looked at yesterday after we met, actually.
67 00:07:03.850 ⇒ 00:07:07.709 Demilade Agboola: Okay, alright, so I’ll need to dive into that.
68 00:07:07.710 ⇒ 00:07:08.180 pk.arthur: Ayyo.
69 00:07:08.180 ⇒ 00:07:10.800 Demilade Agboola: Let me quickly share my screen.
70 00:07:13.420 ⇒ 00:07:16.599 Demilade Agboola: So, will these happen to be the models you saw?
71 00:07:17.280 ⇒ 00:07:30.909 pk.arthur: Give me a second, I’ll get my two… So… Bing ads, people… He, I did see…
72 00:07:31.210 ⇒ 00:07:38.220 pk.arthur: one called Google Ads Click Performance, but I didn’t… I didn’t see the report after the…
73 00:07:39.320 ⇒ 00:07:41.590 pk.arthur: What do you call it?
74 00:07:44.060 ⇒ 00:07:48.429 pk.arthur: Like, I don’t see the reports after, so I can show you what I’m looking at right now, if that will help you.
75 00:07:48.610 ⇒ 00:07:48.960 Demilade Agboola: Okay.
76 00:07:48.960 ⇒ 00:07:54.680 pk.arthur: Okay, let me go back to… sorry. I only have one screen today, so it’s like…
77 00:07:59.070 ⇒ 00:08:00.110 pk.arthur: more…
78 00:08:09.450 ⇒ 00:08:13.340 pk.arthur: Hey, could I share my screen? Oh yeah, it’s right there, oh my god, sorry.
79 00:08:14.180 ⇒ 00:08:18.259 pk.arthur: So share… Yeah, I see my screen?
80 00:08:18.890 ⇒ 00:08:19.680 Demilade Agboola: Yes.
81 00:08:19.680 ⇒ 00:08:37.619 pk.arthur: Yeah, so this is basically what I was looking at, just to see, like, all the different, Google Ads, just, like, tables in our, I guess, in the analytics tab, so I can see some are called AdWords Account, AdWords Performance, all these ones, so I, like, literally tried to, like, query them to see
82 00:08:37.659 ⇒ 00:08:42.170 pk.arthur: what was working and what was not working, right? So, like, let me show you, for example.
83 00:08:42.299 ⇒ 00:08:46.939 pk.arthur: If I… Try to get this one.
84 00:09:14.870 ⇒ 00:09:16.980 pk.arthur: So, let me actually order by…
85 00:09:18.980 ⇒ 00:09:20.550 Demilade Agboola: We could just do more safely.
86 00:09:20.880 ⇒ 00:09:21.580 pk.arthur: Sorry?
87 00:09:21.850 ⇒ 00:09:23.220 Demilade Agboola: We could do max d3.
88 00:09:23.860 ⇒ 00:09:25.060 pk.arthur: Magic did, okay.
89 00:09:25.330 ⇒ 00:09:27.040 Demilade Agboola: Sorry, we could just hit the latest day.
90 00:09:31.770 ⇒ 00:09:32.740 pk.arthur: So, latest.
91 00:09:33.210 ⇒ 00:09:38.349 Demilade Agboola: If you just do, like, max in bracket, date day, it will show you the latest.
92 00:09:38.350 ⇒ 00:09:39.330 pk.arthur: Oh, okay.
93 00:09:39.330 ⇒ 00:09:43.249 Demilade Agboola: Yeah, they, day. We should just skip the latest.
94 00:09:51.830 ⇒ 00:09:55.090 Demilade Agboola: Okay, so it’s… yeah, so it’s… it’s…
95 00:09:55.090 ⇒ 00:10:05.519 pk.arthur: Yeah, like, there are honestly, like, a few different ones, so I’ll just try to, like, go through each of them, and some of them do not even load, so if I just do ads, there is…
96 00:10:05.770 ⇒ 00:10:10.199 pk.arthur: So there’s a few different ones, honestly, and I try to just go through them to see what…
97 00:10:10.320 ⇒ 00:10:18.319 pk.arthur: could possibly be the one that you included, but I just could not… it was not very easy to find out, so that’s why I was asking, like, if you knew the name of it.
98 00:10:18.400 ⇒ 00:10:19.880 Demilade Agboola: So I can just double check.
99 00:10:20.730 ⇒ 00:10:29.009 Demilade Agboola: So that… that is extremely helpful info. So, I will share my screen, and I will just tell you what I’ve done, and what I mean…
100 00:10:29.320 ⇒ 00:10:30.120 Demilade Agboola: Berm.
101 00:10:30.370 ⇒ 00:10:37.950 Demilade Agboola: So what I did was we have some AdWords, models.
102 00:10:38.640 ⇒ 00:10:43.420 Demilade Agboola: And so this is the AdWords campaign performance model that you are looking at right now.
103 00:10:43.520 ⇒ 00:10:45.200 Demilade Agboola: So this is where it’s coming from.
104 00:10:45.590 ⇒ 00:10:46.340 pk.arthur: Okay.
105 00:10:46.340 ⇒ 00:10:50.520 Demilade Agboola: So everything here should be fine. It’s running on a…
106 00:10:51.330 ⇒ 00:10:57.980 Demilade Agboola: Give me one second… it’s running on an hourly schedule during the day.
107 00:10:59.240 ⇒ 00:11:02.109 Demilade Agboola: Where is it?
108 00:11:05.320 ⇒ 00:11:10.130 Demilade Agboola: I know I… Yes, not bad.
109 00:11:10.690 ⇒ 00:11:12.630 Demilade Agboola: Alright, so it’s here. So it’s running…
110 00:11:12.630 ⇒ 00:11:13.220 pk.arthur: Okay.
111 00:11:13.530 ⇒ 00:11:17.659 Demilade Agboola: Every hour during the day, like, during business hours, it just will run.
112 00:11:18.670 ⇒ 00:11:22.570 Demilade Agboola: But the issue here appears to be the source.
113 00:11:23.230 ⇒ 00:11:30.929 Demilade Agboola: So, where am I… where’s the source coming from? So I have to look into that, like, is it being ingested at the right cadence as well?
114 00:11:31.390 ⇒ 00:11:35.430 Demilade Agboola: So I would look into that, and also Bing, these are the Bing models as well.
115 00:11:35.880 ⇒ 00:11:36.620 Demilade Agboola: So I was…
116 00:11:36.620 ⇒ 00:11:43.689 pk.arthur: So, yeah, I’ll be able to go through the Google Ads, but Bing might be updated. Let me just… I can check and let you know.
117 00:11:43.690 ⇒ 00:11:48.750 Demilade Agboola: Also, you can check the Bingo campaign performance, let’s see if that is… Fine.
118 00:11:48.750 ⇒ 00:11:52.959 pk.arthur: Do you ding… Bing campaign, pretty much like that.
119 00:12:33.070 ⇒ 00:12:34.340 pk.arthur: Sorry, give me a second.
120 00:13:00.350 ⇒ 00:13:06.260 pk.arthur: Okay, so Bing looks like it’s up to date, because I see the latest day to be today, actually. So Bing is up to date.
121 00:13:06.510 ⇒ 00:13:10.120 Demilade Agboola: Alright, so for Google, in that case, I will look into Google…
122 00:13:10.370 ⇒ 00:13:15.380 Demilade Agboola: the AdWords performance, look at the source, maybe it’s pointing to the wrong source.
123 00:13:16.060 ⇒ 00:13:23.160 Demilade Agboola: And also, I would just try and see how we’re ingesting it. So, the idea is, I would look at that today.
124 00:13:23.500 ⇒ 00:13:30.169 Demilade Agboola: And hopefully, the idea is by next week, you can always start looking at the data, campaign performance hourly during business hours.
125 00:13:30.780 ⇒ 00:13:32.079 pk.arthur: Thank you, I appreciate that.
126 00:13:32.250 ⇒ 00:13:36.889 Demilade Agboola: Okay. Alright, so now we can move to the other…
127 00:13:39.560 ⇒ 00:13:45.889 Emily Giant: Yeah, the, attribution. Or we can work on,
128 00:13:46.270 ⇒ 00:13:53.659 Emily Giant: sessions, PK. I know both of those were, like, things that you needed that are in the Shopify data, so…
129 00:13:53.840 ⇒ 00:13:59.110 Emily Giant: I think… It… whichever one you think is the best to start with.
130 00:14:00.390 ⇒ 00:14:01.210 pk.arthur: Oh…
131 00:14:01.210 ⇒ 00:14:02.559 Emily Giant: Yeah, it’s fine.
132 00:14:04.300 ⇒ 00:14:08.579 pk.arthur: We could try with sessions, I think. Sessions might be…
133 00:14:08.980 ⇒ 00:14:12.690 pk.arthur: easier, quicker, I think, from just, like, looking at…
134 00:14:14.600 ⇒ 00:14:21.549 Emily Giant: Alright, so Demolade, just to get you up to speed, the sessions data, there is a…
135 00:14:21.570 ⇒ 00:14:38.149 Emily Giant: table in Hivo that we may need to ingest, but right now, it looks like, if I share my… let me share my screen and show you, in the model you built, fact, order lines, or fact orders, I forget, it already does have some of the,
136 00:14:39.370 ⇒ 00:14:48.139 Emily Giant: the fields that will be needed for PK. We’re just having trouble, like, it looks like some are missing from the,
137 00:14:48.310 ⇒ 00:14:51.910 Emily Giant: From the data table, so… Let me…
138 00:15:00.120 ⇒ 00:15:01.200 Emily Giant: Mom…
139 00:15:10.810 ⇒ 00:15:16.390 Emily Giant: I’m looking for, like, customer… visits? I’m trying to think of one of the…
140 00:15:18.350 ⇒ 00:15:22.359 Emily Giant: there was, like, PK, what are the fields called? Like, last… last.
141 00:15:22.360 ⇒ 00:15:29.730 pk.arthur: Those… those last visits, there was first visit… first visit IDs, also customer visit ID, which I thought would be…
142 00:15:29.730 ⇒ 00:15:30.380 Emily Giant: That’s right.
143 00:15:30.380 ⇒ 00:15:32.400 pk.arthur: a proxy for sessions.
144 00:15:33.190 ⇒ 00:15:39.450 Emily Giant: So, can you describe to Demolade what sessions are, just to make sure that we’re, like, pulling the right thing out.
145 00:15:39.450 ⇒ 00:15:39.950 pk.arthur: Yes.
146 00:15:39.950 ⇒ 00:15:40.590 Emily Giant: B.
147 00:15:41.170 ⇒ 00:15:57.669 pk.arthur: So, a session is basically when someone visits the website, right? So, let’s say I go on the Urban Stamps website, let’s say I just look around, I just… just go on there from, like, let’s say Google Ads, I click on the link, and just, like, browse it. So that’s a session. Let’s say I close that page out.
148 00:15:57.670 ⇒ 00:16:03.289 pk.arthur: When I come back an hour later, that’s another session, so basically it’s like every visit the customer makes to the website.
149 00:16:07.320 ⇒ 00:16:12.289 Emily Giant: Okay, so first visit ID, last visit ID, that’s what, is used
150 00:16:12.490 ⇒ 00:16:16.280 Emily Giant: to join to that customer journey table in DBT.
151 00:16:16.410 ⇒ 00:16:17.180 Demilade Agboola: Yeah.
152 00:16:17.620 ⇒ 00:16:23.820 Demilade Agboola: So, couple… I don’t think it will be accurate for anything before November.
153 00:16:24.060 ⇒ 00:16:27.039 Emily Giant: I don’t think so either, but that’s okay.
154 00:16:27.410 ⇒ 00:16:28.050 Emily Giant: Let me do…
155 00:16:28.050 ⇒ 00:16:33.479 pk.arthur: Yeah, that’s fine. I think we just needed, like, just November onwards.
156 00:16:36.790 ⇒ 00:16:37.340 Emily Giant: Nope.
157 00:16:50.650 ⇒ 00:16:57.649 Demilade Agboola: Actually, can you not… let’s see if you do it without the… and first ID is not null. I’ll see if there’s still a lot of nulls.
158 00:16:58.530 ⇒ 00:17:04.639 Demilade Agboola: It’ll be helpful to… So, just get an idea of what the data integrity looks like.
159 00:17:04.780 ⇒ 00:17:05.500 Emily Giant: Yeah.
160 00:17:16.439 ⇒ 00:17:20.899 Demilade Agboola: Alright, so basically, you’re… a session is any…
161 00:17:22.539 ⇒ 00:17:28.149 Demilade Agboola: stay on the Urban Stems website that is just a floating one, like.
162 00:17:28.959 ⇒ 00:17:32.689 Demilade Agboola: Once there’s a break, there’s a… it starts a new session, basically.
163 00:17:33.040 ⇒ 00:17:34.109 Demilade Agboola: Yes. Okay.
164 00:17:34.990 ⇒ 00:17:40.439 Emily Giant: So, PK, do you want the ones where… only where there was a purchase associated?
165 00:17:40.800 ⇒ 00:17:48.830 pk.arthur: You know, I want, like, every… every session, so that would just, let us know, like, the conversion rate, so, like, obviously, like,
166 00:17:49.350 ⇒ 00:18:01.250 pk.arthur: there will be more sessions and conversions, so, like, I want to see, like, how many people, like, came to your website, like, in a day, and how many people, like, converted out of those who came in a day, so it’ll be good to know, like, every session, essentially.
167 00:18:03.070 ⇒ 00:18:11.899 Emily Giant: Does Shopify have that information currently? I’m asking because I’m wondering if they only capture sessions related to a purchase.
168 00:18:12.450 ⇒ 00:18:17.599 pk.arthur: Now, they do have sessions on the interface. I could show you…
169 00:18:17.600 ⇒ 00:18:18.220 Emily Giant: I’ll have it.
170 00:18:18.220 ⇒ 00:18:22.640 pk.arthur: So, like, yesterday we had about 12,000 sessions for, like, 1,000 orders.
171 00:18:22.890 ⇒ 00:18:30.989 pk.arthur: So… sessions are usually, like, very high, like, in the tens to 15,000 range a day, I would say.
172 00:18:31.640 ⇒ 00:18:37.160 Demilade Agboola: I can see sessions here. My question… I think my question now will be… .
173 00:18:37.160 ⇒ 00:18:38.730 Emily Giant: Yeah.
174 00:18:40.070 ⇒ 00:18:47.239 Demilade Agboola: For conversion, if we’re… if a customer, for instance, visits now, Comes back 2 hours later.
175 00:18:47.510 ⇒ 00:18:51.729 Demilade Agboola: And then purchases, your conversion rate will be 50%.
176 00:18:52.080 ⇒ 00:18:52.880 pk.arthur: Yes.
177 00:18:54.000 ⇒ 00:18:55.509 Demilade Agboola: Even though it’s the same customer.
178 00:18:55.770 ⇒ 00:18:56.560 pk.arthur: source.
179 00:18:56.560 ⇒ 00:19:01.080 Demilade Agboola: Okay, alright, if that’s how you want to look at it. I just wanted to be sure, so all on the same page.
180 00:19:01.900 ⇒ 00:19:07.250 pk.arthur: Yeah, usually, like, we have more, like, a ton of com… ton of sessions, because that’s, like, every…
181 00:19:07.500 ⇒ 00:19:10.210 pk.arthur: Basically, like, every… every time they visit the website.
182 00:19:11.640 ⇒ 00:19:17.670 Demilade Agboola: Okay, alright, so… That means we’ll need to…
183 00:19:21.680 ⇒ 00:19:25.340 Demilade Agboola: We will need to create… Some models for that.
184 00:19:25.870 ⇒ 00:19:28.969 Demilade Agboola: Where we bring in sessions, or we look at sessions.
185 00:19:31.350 ⇒ 00:19:35.309 Demilade Agboola: I mean, do you know if we brought in sessions data, or if session’s data…
186 00:19:35.840 ⇒ 00:19:37.519 Demilade Agboola: Give me one second, I mean…
187 00:19:39.390 ⇒ 00:19:41.920 Emily Giant: Does anyone know what customer order index is?
188 00:19:43.160 ⇒ 00:19:49.210 pk.arthur: Yeah, I saw that also in the Looker yesterday, but I wasn’t quite sure what that even meant.
189 00:19:49.490 ⇒ 00:19:50.010 Demilade Agboola: Mmm.
190 00:19:50.010 ⇒ 00:19:54.200 Emily Giant: I’m gonna have to look that one up, because that seems like part of the customer journey stuff, but…
191 00:19:54.880 ⇒ 00:19:59.400 Emily Giant: Alright, so as far as sessions data.
192 00:20:00.410 ⇒ 00:20:03.769 Emily Giant: I can log into HEVO, it’s probably gonna make me do two-factor.
193 00:20:04.520 ⇒ 00:20:07.320 Demilade Agboola: And so I’m actually just going to, like,
194 00:20:08.780 ⇒ 00:20:10.570 Demilade Agboola: Redshift, I’m trying to see what we have.
195 00:20:13.520 ⇒ 00:20:14.450 Demilade Agboola: So…
196 00:20:14.680 ⇒ 00:20:17.959 Emily Giant: Customer’s customer journey summary is probably…
197 00:20:26.170 ⇒ 00:20:26.850 Emily Giant: No.
198 00:20:34.730 ⇒ 00:20:39.120 Emily Giant: Okay, so we have customer visit, Customer Journey Summary.
199 00:20:39.360 ⇒ 00:20:41.370 Emily Giant: Customer address, let’s see.
200 00:20:41.990 ⇒ 00:20:45.659 Emily Giant: Customer visit, journey summary… why is it doing this?
201 00:20:49.190 ⇒ 00:20:50.180 Emily Giant: Okay.
202 00:20:50.460 ⇒ 00:21:00.010 Emily Giant: So this looks like… already… completely joined to fact orders. Like, all of these are already there.
203 00:21:00.580 ⇒ 00:21:04.679 Demilade Agboola: Yeah, we’re definitely getting information from here. Can we see customer visit, though?
204 00:21:06.700 ⇒ 00:21:07.830 Emily Giant: Oh, yeah.
205 00:21:08.850 ⇒ 00:21:10.700 Demilade Agboola: And yeah, so this is kind of…
206 00:21:12.660 ⇒ 00:21:16.139 Demilade Agboola: But it’s also title order ID…
207 00:21:16.720 ⇒ 00:21:22.490 pk.arthur: So is it only, like, when a customer places an order, then it’s tied to it? Like, is that how it joined, or…
208 00:21:22.880 ⇒ 00:21:25.630 Emily Giant: I don’t think so, I think it’s on customer ID.
209 00:21:27.060 ⇒ 00:21:31.910 Demilade Agboola: No, no, no, it’s not, like, there’s an order ID column.
210 00:21:32.610 ⇒ 00:21:33.890 Emily Giant: For sure.
211 00:21:33.890 ⇒ 00:21:34.630 Demilade Agboola: I don’t know.
212 00:21:35.710 ⇒ 00:21:40.189 Demilade Agboola: Let me… let me quickly look at that, okay, I’m in Redshift.
213 00:21:40.700 ⇒ 00:21:41.420 Emily Giant: Okay.
214 00:21:49.150 ⇒ 00:21:54.419 Demilade Agboola: So… but there might be sessions… Inventory locations now.
215 00:21:54.880 ⇒ 00:22:07.450 Demilade Agboola: Collections and events, draft orders… Balance transactions, balance… Banned on checkouts, shop battlefields.
216 00:22:07.790 ⇒ 00:22:10.359 Emily Giant: All the ones above we’re tracking.
217 00:22:10.570 ⇒ 00:22:13.019 Emily Giant: But there’s another page, I think.
218 00:22:13.190 ⇒ 00:22:14.040 Emily Giant: Yep.
219 00:22:17.830 ⇒ 00:22:20.360 Demilade Agboola: Can you search? Because there’s a search on the top right.
220 00:22:21.090 ⇒ 00:22:25.890 Demilade Agboola: Let me search for sessions and see if there’s any… Anything to that?
221 00:22:26.200 ⇒ 00:22:28.339 Emily Giant: I think they call it visits.
222 00:22:28.930 ⇒ 00:22:32.180 Emily Giant: I’m pretty sure PK and I,
223 00:22:33.690 ⇒ 00:22:41.000 Emily Giant: looked through all the tables to find, like, a proxy for what he sees in Shopify, but would that be helpful to, like.
224 00:22:42.020 ⇒ 00:22:50.669 Emily Giant: show you what he sees in Shopify, because all of the analytics there are, like, using these exact same tables.
225 00:22:51.340 ⇒ 00:22:52.180 Demilade Agboola: Oh, okay.
226 00:22:53.060 ⇒ 00:22:58.389 Emily Giant: Would it be helpful? Or, I mean, if this is the same thing, I don’t know if that actually matters.
227 00:22:58.390 ⇒ 00:23:00.910 Demilade Agboola: Yeah, it would be helpful, it’s just so we can get an idea.
228 00:23:00.910 ⇒ 00:23:03.240 Emily Giant: Yeah, okay. PK, can you share a screen?
229 00:23:03.500 ⇒ 00:23:05.930 pk.arthur: Yeah, I just opened up really quickly.
230 00:23:06.310 ⇒ 00:23:07.560 pk.arthur: One second…
231 00:23:25.130 ⇒ 00:23:26.099 pk.arthur: Can you see it?
232 00:23:27.760 ⇒ 00:23:28.400 Emily Giant: Yep.
233 00:23:28.780 ⇒ 00:23:31.350 pk.arthur: That’s what the Shopify interface looks like.
234 00:23:31.570 ⇒ 00:23:38.260 pk.arthur: And… basically, we can get all the different types of data, like sessions, total sales orders, conversion rate.
235 00:23:38.390 ⇒ 00:23:39.410 pk.arthur: It costs…
236 00:23:39.410 ⇒ 00:23:41.290 Emily Giant: Sessions in the platform.
237 00:23:41.290 ⇒ 00:23:42.900 pk.arthur: Yeah. Yeah.
238 00:23:42.900 ⇒ 00:23:43.540 Emily Giant: Huh.
239 00:23:46.390 ⇒ 00:23:49.179 pk.arthur: Oh, of course, Ms. Math. Sorry.
240 00:23:56.500 ⇒ 00:24:10.509 pk.arthur: Oh, perfect. Okay. Yeah, so, it’s called Sessions, that’s why we’re trying to find one called Sessions, Emily, and there was just, like, no, like… it was… from what I remember, like, it wasn’t even in the… on the back end, right? It was only, like, visits, so…
241 00:24:10.820 ⇒ 00:24:11.500 Emily Giant: Yeah, that’s true.
242 00:24:12.170 ⇒ 00:24:13.230 Emily Giant: That’s odd.
243 00:24:20.080 ⇒ 00:24:21.859 pk.arthur: Yeah,
244 00:24:22.480 ⇒ 00:24:27.960 pk.arthur: Is there anything else, like, you want to see? Like, we can see different reports where we have the different data, essentially, but…
245 00:24:28.120 ⇒ 00:24:35.570 pk.arthur: They usually name it as sessions, unless it’s something else in the metadata, but I don’t think so, to be honest.
246 00:24:38.280 ⇒ 00:24:39.070 Demilade Agboola: Okay.
247 00:24:39.310 ⇒ 00:24:49.839 Demilade Agboola: Let’s see… I’m trying to look at the…
248 00:25:01.080 ⇒ 00:25:02.350 Emily Giant: I’m gonna Google it.
249 00:25:03.400 ⇒ 00:25:08.189 Emily Giant: I know we’ve already been here, PK, but, like, maybe this is… Maybe this is it.
250 00:25:16.370 ⇒ 00:25:21.690 Demilade Agboola: So I’m trying to… I’m querying the visits… table in BigQuery.
251 00:25:22.030 ⇒ 00:25:24.210 Demilade Agboola: Redshirts, why do I keep seeing BigQuery?
252 00:25:24.530 ⇒ 00:25:25.190 pk.arthur: Thank you.
253 00:25:26.960 ⇒ 00:25:31.989 pk.arthur: I could confirm, like, the number of visits, like, for yesterday, or sessions, to see if…
254 00:25:32.410 ⇒ 00:25:33.380 Demilade Agboola: Yeah.
255 00:25:43.720 ⇒ 00:25:45.959 pk.arthur: Let’s do something really quickly, if I…
256 00:25:52.700 ⇒ 00:25:57.529 Emily Giant: Okay, so this is what I found, via Google.
257 00:25:57.760 ⇒ 00:26:15.160 Emily Giant: Shopify does not have a native user-accessible schema table for sessions that can be directly queried. Sessions data is primarily an analytics metric, not part of a standard transactional customer, product, or order data available. So it says, it’s a calculation.
258 00:26:15.250 ⇒ 00:26:21.739 Emily Giant: You can either extract it, Or calculate it yourself. So,
259 00:26:22.640 ⇒ 00:26:27.850 Emily Giant: So I think we just need to find the, calculation sessions.
260 00:26:27.950 ⇒ 00:26:29.050 pk.arthur: And then…
261 00:26:29.160 ⇒ 00:26:31.619 Emily Giant: build it into the dbt table.
262 00:26:35.600 ⇒ 00:26:36.830 Demilade Agboola: Let’s see…
263 00:26:46.760 ⇒ 00:26:51.879 Emily Giant: Okay, so if you’re using a custom ETL pipeline, you can create your own schema table.
264 00:26:52.220 ⇒ 00:26:59.059 Emily Giant: A typical schema for customer sessions might include the following. I’ll just save this webpage.
265 00:26:59.410 ⇒ 00:27:02.679 Emily Giant: Because it essentially tells you how to build it in dbt.
266 00:27:03.640 ⇒ 00:27:04.640 Demilade Agboola: Gotcha.
267 00:27:13.060 ⇒ 00:27:21.050 Demilade Agboola: I think the import… the… My question will be where… let me see, so… I think…
268 00:27:21.390 ⇒ 00:27:22.930 Demilade Agboola: Can you share the link, please?
269 00:27:23.210 ⇒ 00:27:23.870 Emily Giant: Yeah.
270 00:27:29.010 ⇒ 00:27:29.850 Emily Giant: -Oh.
271 00:27:32.750 ⇒ 00:27:36.510 Emily Giant: Sorry, it’s like a… summary.
272 00:27:36.820 ⇒ 00:27:41.500 Emily Giant: Of, like, several I have access to using REST API Shopify.
273 00:27:46.010 ⇒ 00:27:48.040 Emily Giant: I’m gonna send you a doc.
274 00:27:48.210 ⇒ 00:27:49.270 Emily Giant: Instead.
275 00:28:17.240 ⇒ 00:28:18.160 Emily Giant: Alright.
276 00:28:19.690 ⇒ 00:28:20.570 Emily Giant: There you go.
277 00:29:50.920 ⇒ 00:29:54.480 Demilade Agboola: Okay, so it does appear that they have an API.
278 00:29:55.110 ⇒ 00:29:55.820 Emily Giant: Yeah.
279 00:30:25.030 ⇒ 00:30:31.829 Emily Giant: Okay, the most common and robust method is to use a separate analytics platform. Thanks, thanks a bunch. Wait, but we have GA, don’t we?
280 00:30:32.640 ⇒ 00:30:38.879 pk.arthur: Yes, we do have GA, but also, the issue with GA was that the source, remember, we were trying to figure out?
281 00:30:39.260 ⇒ 00:30:46.870 Emily Giant: So, as long as we can connect the customer ID, we can join those tables, because,
282 00:30:47.130 ⇒ 00:30:56.820 Emily Giant: Shopify has the source, it’s the sessions that we are not pulling in from Shopify. So if they have sessions.
283 00:30:57.640 ⇒ 00:30:59.619 Emily Giant: We can pull the other stuff.
284 00:31:00.190 ⇒ 00:31:02.270 Emily Giant: from GA, like, the.
285 00:31:02.270 ⇒ 00:31:02.990 pk.arthur: Therefore.
286 00:31:02.990 ⇒ 00:31:07.200 Emily Giant: We can pull sessions from GA, and then all the other stuff from Shopify.
287 00:31:08.650 ⇒ 00:31:09.290 Demilade Agboola: Yeah.
288 00:31:10.720 ⇒ 00:31:14.019 Emily Giant: And just join on, like, customer email or something like that.
289 00:31:18.040 ⇒ 00:31:20.899 Emily Giant: Now, I don’t know about the integrity there, if people are, like.
290 00:31:21.050 ⇒ 00:31:23.659 Emily Giant: not logged in, I don’t know…
291 00:31:24.590 ⇒ 00:31:27.470 Emily Giant: If you need, like, IP address or something?
292 00:31:30.190 ⇒ 00:31:32.009 Demilade Agboola: What was the issue with GA?
293 00:31:33.000 ⇒ 00:31:35.640 Emily Giant: So…
294 00:31:36.390 ⇒ 00:31:47.019 Emily Giant: I actually can’t believe that it’s still not working, because it works in my staging, and I’m very annoyed that it’s, like… So, basically, when we switched to Shopify,
295 00:31:47.730 ⇒ 00:31:57.769 Emily Giant: the order column in, one of the, like, midstream, intermediate HEVO models, the format of order number changed.
296 00:31:58.060 ⇒ 00:32:12.800 Emily Giant: And it was referring to, like, the Shopify-specific ID instead of the OMS ID. And, we went in and changed it, like, a month ago, so that it should be corrected. I’ll show you where it is,
297 00:32:18.860 ⇒ 00:32:24.610 Emily Giant: trying to remember what that intermediate model is called, PK, the one that I kept having to… Fix.
298 00:32:24.610 ⇒ 00:32:25.540 pk.arthur: Oh…
299 00:32:37.440 ⇒ 00:32:39.490 Emily Giant: I know where it’s… what it’s near.
300 00:32:43.950 ⇒ 00:32:46.990 Emily Giant: It’s around this one, so I can find it in the lineage.
301 00:32:47.280 ⇒ 00:32:52.810 Demilade Agboola: Also, can anyone… could you have access to the GA? Like, the actual GA? No.
302 00:32:52.810 ⇒ 00:32:54.389 pk.arthur: The website itself?
303 00:32:54.390 ⇒ 00:32:55.020 Demilade Agboola: Yeah.
304 00:32:55.190 ⇒ 00:32:55.980 pk.arthur: Yeah, yeah.
305 00:32:56.270 ⇒ 00:32:58.299 Demilade Agboola: Alright, would you want to see it?
306 00:32:58.460 ⇒ 00:33:03.590 Demilade Agboola: Yes, please. I want to see if the numbers, like, tally or, like, where we… Similar.
307 00:33:04.060 ⇒ 00:33:05.849 pk.arthur: Becca. Give me a second.
308 00:33:08.290 ⇒ 00:33:09.980 Emily Giant: Oh, I’m not sharing my screen.
309 00:33:10.250 ⇒ 00:33:11.290 Demilade Agboola: Oh, no.
310 00:33:17.470 ⇒ 00:33:21.469 pk.arthur: So, it looks like it’s very similar based off… where’s the…
311 00:33:27.460 ⇒ 00:33:33.080 Demilade Agboola: It appears that Shopify uses GA, like, To get the sessions.
312 00:33:33.470 ⇒ 00:33:37.909 pk.arthur: Yeah, it looks… looking at yesterday’s data from… let me see, double checking…
313 00:33:38.070 ⇒ 00:33:45.329 pk.arthur: Yesterday was $12.76 on Shopify versus 12597 on GA4.
314 00:33:46.590 ⇒ 00:33:48.630 pk.arthur: So, it’s very close, it’s like…
315 00:33:48.840 ⇒ 00:33:51.280 pk.arthur: Plus or minus, like, 200, but that’s, like…
316 00:33:51.720 ⇒ 00:33:55.039 Emily Giant: Yeah, that could be… Time zone issue.
317 00:33:55.040 ⇒ 00:33:55.610 pk.arthur: Yeah.
318 00:33:57.700 ⇒ 00:33:59.539 Demilade Agboola: And are we ingesting GA data?
319 00:34:01.080 ⇒ 00:34:04.630 Emily Giant: Yep, but I’ll show you something funky.
320 00:34:04.780 ⇒ 00:34:08.639 Emily Giant: It had me do an upgrade?
321 00:34:10.420 ⇒ 00:34:13.649 Emily Giant: And I need to check… this was, like, right before my trip.
322 00:34:15.190 ⇒ 00:34:20.700 Emily Giant: It’s said that, like, They were no longer… you’ve gotta be kidding me. Hold on.
323 00:34:26.909 ⇒ 00:34:31.250 Emily Giant: It said that they were going to, like, deprecate the old table.
324 00:34:31.600 ⇒ 00:34:35.960 Emily Giant: And, no, no, that was Google Ads. Is that different?
325 00:34:36.110 ⇒ 00:34:40.439 Emily Giant: Yeah, it is. GA4 is fine. It’s these two that are, like.
326 00:34:41.530 ⇒ 00:34:48.789 Demilade Agboola: Also, the GA… the Google Ads, that’s actually the reason why the other tier we’re looking at is out of… is out of…
327 00:34:48.790 ⇒ 00:34:49.760 pk.arthur: Oh…
328 00:34:49.760 ⇒ 00:34:53.130 Demilade Agboola: It’s off. Google Ads V1.
329 00:34:53.469 ⇒ 00:35:00.049 Demilade Agboola: Being off is why… the table PK is using for campaign performance is…
330 00:35:01.460 ⇒ 00:35:02.240 Emily Giant: Oh.
331 00:35:02.240 ⇒ 00:35:06.439 Demilade Agboola: Yeah, I’ll look into that. Like, I forgot… I forgot it was… it was something here.
332 00:35:06.880 ⇒ 00:35:07.700 Emily Giant: Okay.
333 00:35:07.820 ⇒ 00:35:13.590 Emily Giant: So this is fine, but this is… yeah. So I had to upgrade this, because…
334 00:35:14.200 ⇒ 00:35:20.920 Emily Giant: it gave me a warning that they were no longer, like, supporting this table. So this is the new one.
335 00:35:21.050 ⇒ 00:35:24.600 Emily Giant: That… Should just replace that.
336 00:35:24.890 ⇒ 00:35:29.849 Demilade Agboola: Yeah, but it appears that I will need to connect the model that is using it.
337 00:35:29.850 ⇒ 00:35:31.380 Emily Giant: Yeah, yeah.
338 00:35:31.380 ⇒ 00:35:36.610 Demilade Agboola: All those endpoints into this… So the, new… table.
339 00:35:36.800 ⇒ 00:35:42.000 Demilade Agboola: So, that’s what I’ll do. Yeah, so once I do that, you should be able to get the latest data.
340 00:35:42.130 ⇒ 00:35:43.230 Demilade Agboola: Pk.
341 00:35:43.870 ⇒ 00:35:44.859 pk.arthur: Alright, thank you.
342 00:35:44.860 ⇒ 00:35:47.460 Demilade Agboola: Yeah, so…
343 00:35:48.060 ⇒ 00:35:55.280 Demilade Agboola: And this also runs hourly, too, so that’s good. I was gonna say, so the GA4 data, can you click into GA4?
344 00:35:55.450 ⇒ 00:35:56.050 Emily Giant: Yeah.
345 00:35:58.460 ⇒ 00:36:04.390 Emily Giant: She mini Christmas. Sorry, we’re… Way over that plan limit.
346 00:36:07.660 ⇒ 00:36:13.969 Demilade Agboola: I’m just trying to see what audience behavior, content conversion, daily, demographic…
347 00:36:17.200 ⇒ 00:36:21.620 Demilade Agboola: E-commerce event report, in-app orders.
348 00:36:22.250 ⇒ 00:36:29.029 Demilade Agboola: Order ads, order geo, orders, testing… That is wrong.
349 00:36:35.740 ⇒ 00:36:38.260 pk.arthur: Here’s a bunch of session stuff. Yeah.
350 00:36:38.260 ⇒ 00:36:38.830 Demilade Agboola: I pissed.
351 00:36:38.830 ⇒ 00:36:43.329 pk.arthur: The medium… the source of the medium might also be possibly why, maybe?
352 00:36:45.610 ⇒ 00:36:49.039 Emily Giant: That is loading incorrectly, it’s that join.
353 00:36:49.470 ⇒ 00:36:50.979 pk.arthur: Yeah, you’re right, you’re right, yeah.
354 00:36:50.980 ⇒ 00:36:57.900 Emily Giant: the reason that’s failing, but, that should be fixed. So, I am still, like, baffled.
355 00:36:58.080 ⇒ 00:37:04.320 Emily Giant: Either way, I hate that whole lineage needs to be replaced with Shopify stuff, so this is worthwhile to…
356 00:37:05.140 ⇒ 00:37:07.090 Emily Giant: Replace where we can.
357 00:37:07.670 ⇒ 00:37:12.240 Emily Giant: But yeah, all of this is potentially useful.
358 00:37:13.490 ⇒ 00:37:19.629 Demilade Agboola: Alright, so I think what we just need to do… I will look into this, but this part of what to do…
359 00:37:20.270 ⇒ 00:37:27.800 Demilade Agboola: on… I will create… I’ve created a ticket for the Google Ads stuff.
360 00:37:29.430 ⇒ 00:37:32.110 Demilade Agboola: Oh, let me just grab another ticket for this.
361 00:38:25.450 ⇒ 00:38:30.380 Demilade Agboola: Okay, so once we’re able to ingest the data, the next step will be modeling.
362 00:38:30.510 ⇒ 00:38:31.169 Demilade Agboola: The dates off.
363 00:38:32.610 ⇒ 00:38:35.429 Demilade Agboola: So that we can start to have an idea of…
364 00:38:37.140 ⇒ 00:38:41.840 Demilade Agboola: the conversion. In terms of conversion, what would you like to see?
365 00:38:42.680 ⇒ 00:38:46.060 Demilade Agboola: So, is it just a…
366 00:38:46.340 ⇒ 00:38:52.809 Demilade Agboola: high-level conversion rates, is there any… like, what… how would you like to drill into the data, is sort of my question.
367 00:38:53.220 ⇒ 00:39:04.240 pk.arthur: It would be great if we could be able to see that, like, the, channel, channel, channel groups are, like, whether it be paid search, paid social, direct, SMS,
368 00:39:04.350 ⇒ 00:39:08.119 pk.arthur: even further, I’m looking… I’m thinking about one of the… one of the…
369 00:39:08.280 ⇒ 00:39:13.130 pk.arthur: requests I had from my boss, Chris, was to be able to
370 00:39:13.320 ⇒ 00:39:23.049 pk.arthur: just help our, third-party agency to be able to see, like, conversion rate at an hourly level across Bing and Google, so it might have to be even more granular, so…
371 00:39:23.350 ⇒ 00:39:26.350 pk.arthur: If possible, if we could do, like,
372 00:39:26.460 ⇒ 00:39:36.949 pk.arthur: TikTok, Facebook, Instagram, Google, that would be great, too, but I think, like, a first step would definitely be, at least at the channel, the channel group, for sure.
373 00:39:38.750 ⇒ 00:39:41.450 Demilade Agboola: Okay, alright,
374 00:39:42.640 ⇒ 00:39:51.430 Demilade Agboola: So, I’ll look around to what we currently have, because I can see that we have some Google Analytics orders, and just get an idea of what’s going on.
375 00:39:51.720 ⇒ 00:39:56.970 Demilade Agboola: But I think the idea would be to see where the sessions are coming from.
376 00:39:58.090 ⇒ 00:40:03.940 Demilade Agboola: And create a model that has sessions as well as sources.
377 00:40:04.330 ⇒ 00:40:08.310 Demilade Agboola: And tie that to Shopify data, so we have a…
378 00:40:08.760 ⇒ 00:40:13.070 Demilade Agboola: A pipeline into what’s going to drill down deeper into.
379 00:40:14.240 ⇒ 00:40:15.520 Demilade Agboola: what converts.
380 00:40:23.570 ⇒ 00:40:27.380 Demilade Agboola: Okay. I think we have, like, a framework of what we want to do.
381 00:40:27.870 ⇒ 00:40:28.550 Emily Giant: Yeah.
382 00:40:31.090 ⇒ 00:40:34.849 Demilade Agboola: Is there any other…
383 00:40:37.280 ⇒ 00:40:38.090 Emily Giant: So…
384 00:40:38.210 ⇒ 00:40:48.070 Emily Giant: Let me think, those were the two major ones. The other thing that I don’t know if we want to go over today, or if this is enough, is the,
385 00:40:48.300 ⇒ 00:40:50.660 Emily Giant: Promo, codes, and discounts.
386 00:40:50.660 ⇒ 00:40:51.500 pk.arthur: Oh, yeah.
387 00:40:54.510 ⇒ 00:40:56.530 Emily Giant: I mean, we have 20 minutes, we might as well.
388 00:40:56.900 ⇒ 00:41:00.359 Emily Giant: so, can you still see my screen?
389 00:41:00.550 ⇒ 00:41:01.120 Demilade Agboola: Yes.
390 00:41:01.120 ⇒ 00:41:01.690 pk.arthur: Yes.
391 00:41:01.690 ⇒ 00:41:10.899 Emily Giant: Alright, so just to give you a little background, Demolade, if I go to our favorite Explorer, Tableau Items XF,
392 00:41:11.220 ⇒ 00:41:21.770 Emily Giant: Pk has built, like, super extensive, manual… Discount categorization?
393 00:41:22.150 ⇒ 00:41:26.750 Emily Giant: And… I do think that there’s some, like.
394 00:41:27.410 ⇒ 00:41:36.810 Emily Giant: Opportunity to chat with the people creating the discounts, but, like, this is currently what we’re using to categorize discounts, and it’s gonna slow down…
395 00:41:37.250 ⇒ 00:41:43.019 Emily Giant: Like, the query time quite a bit. If he’s…
396 00:41:43.200 ⇒ 00:41:45.370 pk.arthur: Having to use this frequently.
397 00:41:45.370 ⇒ 00:41:50.439 Emily Giant: But it’s like… A thousand rows.
398 00:41:50.440 ⇒ 00:41:51.370 pk.arthur: Yeah.
399 00:41:51.380 ⇒ 00:41:54.110 Emily Giant: So I’m…
400 00:41:54.110 ⇒ 00:41:56.719 Demilade Agboola: So, the idea of this is that…
401 00:41:56.870 ⇒ 00:42:01.469 Demilade Agboola: Are you trying to… you’re trying to join this into the Tableau’s iTunes XF?
402 00:42:02.440 ⇒ 00:42:10.530 Emily Giant: So it’s currently… yeah. Well, my goal is to… build this into dbt.
403 00:42:10.870 ⇒ 00:42:16.640 Emily Giant: In a, like, sustainable way, so that he doesn’t have to upkeep this list.
404 00:42:17.020 ⇒ 00:42:18.869 Demilade Agboola: How often do you make this list?
405 00:42:19.700 ⇒ 00:42:21.510 pk.arthur: Honestly, I think…
406 00:42:21.820 ⇒ 00:42:31.629 pk.arthur: It depends on the season and the amount of promos that we have. So, like, I know for the rest of the… of this fiscal year, we might have, like, probably 5…
407 00:42:32.030 ⇒ 00:42:50.250 pk.arthur: to 8 more promos that I’m aware of right now, so probably would just be doing it 8 more times. So that is not much, but, like, just all you see that, like, all the codes that you see right there, that’s from, like, pre-Shopify, so… I think pre-Shopify, like, everyone got, like, a…
408 00:42:50.620 ⇒ 00:43:09.490 pk.arthur: different, loyalty promo codes, so, like, that had to be manually entered, but, like, since Shopify, it all starts with L dash, so you see, you see, like, inline 8813 when it says, like, L dash, that’s the… the new way we categorize loyalty codes, but, like, all that, under that.
409 00:43:09.580 ⇒ 00:43:16.150 pk.arthur: It’s just how we could keep track of, like, pre-Shopify loyalty codes.
410 00:43:16.360 ⇒ 00:43:21.640 Demilade Agboola: Okay, so my question is, the old ones, that long… that long-ass list, is that going to…
411 00:43:23.170 ⇒ 00:43:25.899 pk.arthur: That would not change. That should not change.
412 00:43:26.220 ⇒ 00:43:31.730 Demilade Agboola: So what we’re gonna do is… can you send me… can you, like, do a spreadsheet of it and send it to me?
413 00:43:32.420 ⇒ 00:43:33.430 pk.arthur: Yes.
414 00:43:33.430 ⇒ 00:43:34.989 Emily Giant: You probably already have that.
415 00:43:35.580 ⇒ 00:43:36.300 pk.arthur: Me?
416 00:43:36.470 ⇒ 00:43:41.390 Emily Giant: Yeah, because if you want that in, I’m like, I bet you already have that in a spreadsheet somewhere.
417 00:43:41.390 ⇒ 00:43:43.170 pk.arthur: Yeah, it’s…
418 00:43:44.090 ⇒ 00:43:51.190 Demilade Agboola: Because, so, and then I will look at the Explore as well. So, what we’ll just do is we’ll create a seed for it in dbt.
419 00:43:51.310 ⇒ 00:44:00.260 Demilade Agboola: So these seeds are, like, basically CSV files that are static, or ideally they don’t change often, they’re slow changing dimensions.
420 00:44:00.420 ⇒ 00:44:07.400 Demilade Agboola: And then what we will do is we will feed that to Tableau’s items XF directly.
421 00:44:07.670 ⇒ 00:44:11.890 Demilade Agboola: So when it comes into, looker.
422 00:44:12.010 ⇒ 00:44:17.550 Demilade Agboola: it already has that logic applied to it, so you don’t have to… we don’t have to do that within Looker as well.
423 00:44:20.810 ⇒ 00:44:21.690 pk.arthur: That’s good.
424 00:44:22.740 ⇒ 00:44:25.860 Demilade Agboola: Yeah, so once you can just share that spreadsheet, okay.
425 00:44:25.970 ⇒ 00:44:27.229 Demilade Agboola: Yeah. Is it to do?
426 00:44:27.230 ⇒ 00:44:31.509 pk.arthur: Okay, yeah, I have to compile it, I think, but I can do that really quickly.
427 00:44:32.290 ⇒ 00:44:34.060 Demilade Agboola: Okay, sounds good.
428 00:44:35.240 ⇒ 00:44:38.170 Emily Giant: I wonder if this discount allocations is anything…
429 00:44:38.680 ⇒ 00:44:44.119 Emily Giant: That could help us, like, in the future. I’ll look into this and just see if it’s anything useful.
430 00:44:45.170 ⇒ 00:44:47.990 Emily Giant: I know it’s not gonna be useful for those old ones, but…
431 00:44:49.280 ⇒ 00:44:49.890 Emily Giant: Yeah.
432 00:44:53.250 ⇒ 00:44:57.640 Emily Giant: Is this my… is this what they… Okay.
433 00:44:58.090 ⇒ 00:44:58.889 Emily Giant: I don’t hate it.
434 00:44:59.350 ⇒ 00:45:01.030 pk.arthur: You ever seen Bob’s Burgers?
435 00:45:01.270 ⇒ 00:45:04.709 Emily Giant: Yes. You look like… Yeah, totally.
436 00:45:05.320 ⇒ 00:45:06.000 pk.arthur: Thanks.
437 00:45:07.210 ⇒ 00:45:08.290 Emily Giant: Oh, that’s great.
438 00:45:09.210 ⇒ 00:45:10.180 Emily Giant: Okay.
439 00:45:10.480 ⇒ 00:45:12.400 Emily Giant: And then there’s no issues, like.
440 00:45:13.350 ⇒ 00:45:23.799 Emily Giant: parsing out loyalty versus gift card versus, like, credits given to customers. Is that all pretty clear in your data currently?
441 00:45:24.350 ⇒ 00:45:26.109 pk.arthur: I did not really…
442 00:45:27.080 ⇒ 00:45:33.660 Emily Giant: Currently, or I haven’t, in the past 5 months, haven’t had to deal with much gift card and, like, credits.
443 00:45:33.890 ⇒ 00:45:40.430 pk.arthur: But I’ve done a recent project where I’ve had to collaborate with Laura from the care team.
444 00:45:40.840 ⇒ 00:45:45.519 pk.arthur: Yeah, they have, like, good data, because I was using that data, so that… that looks fine, at least.
445 00:45:45.630 ⇒ 00:45:47.840 pk.arthur: Jason, what I have to do on my end.
446 00:45:48.290 ⇒ 00:45:49.090 pk.arthur: Yeah.
447 00:45:50.000 ⇒ 00:45:51.260 Emily Giant: Yeah, I,
448 00:45:52.090 ⇒ 00:45:56.759 Emily Giant: That’s… that’s my data. That’s what I created before I was on the deaf team, so it’s decent.
449 00:45:57.620 ⇒ 00:45:59.920 Emily Giant: I’m like, yeah, I like hearing that.
450 00:45:59.920 ⇒ 00:46:09.939 pk.arthur: Yeah, it was… it was good. It was pretty straightforward. One thing that… which kind of helped me, because I didn’t know if, because I was working on, like, side… I was working on,
451 00:46:10.200 ⇒ 00:46:14.269 pk.arthur: Just a project to receive how much,
452 00:46:14.950 ⇒ 00:46:30.280 pk.arthur: orders, people… people place after they have, like, a… like, an issue with the… with an order, right? So, like, I need to see if it was broken down by… if, if the person who reached out to the care team was a sender recipient, but actually, we already have that data in there, so that was pretty cool to see.
453 00:46:30.280 ⇒ 00:46:30.980 Emily Giant: Yeah.
454 00:46:32.860 ⇒ 00:46:33.519 pk.arthur: So yeah, so…
455 00:46:33.840 ⇒ 00:46:47.250 Emily Giant: HEVO data that we’re gonna have to keep, Demolade. Like, there’s certain things that only happen in OMS currently that will have to use the HEVO tables, and that’s, like, customer issues and, like, problem tags, but that should…
456 00:46:47.360 ⇒ 00:46:48.510 Emily Giant: be fine.
457 00:46:49.240 ⇒ 00:47:02.960 Demilade Agboola: Yeah, I think… I mean, obviously, we’re not going to get rid of OMS completely, it’s just… we just want to be able to create the framework with Shopify, and then we can reach it with OMS data, rather than OMS data being the source of truth.
458 00:47:03.830 ⇒ 00:47:05.829 Emily Giant: Yeah, I dig that. I like that.
459 00:47:06.060 ⇒ 00:47:07.470 Emily Giant: Okay.
460 00:47:08.000 ⇒ 00:47:15.349 Demilade Agboola: Alright, so I’ve been able to create, like, 3 different tickets based off this call, so let me just go through so we’re all on the same page.
461 00:47:15.690 ⇒ 00:47:21.130 Demilade Agboola: So one is to get Google AdWords source up to date.
462 00:47:22.890 ⇒ 00:47:27.919 Demilade Agboola: The order… the other one is to ingest GA Sessions data.
463 00:47:29.270 ⇒ 00:47:30.060 Emily Giant: In wheat.
464 00:47:30.320 ⇒ 00:47:35.540 Demilade Agboola: And then the final one is to create an ad promo series to table those items accept.
465 00:47:36.340 ⇒ 00:47:37.120 Emily Giant: Yep.
466 00:47:38.040 ⇒ 00:47:44.029 pk.arthur: Okay, so, sorry, just the GA sessions data, would that also include, I guess, the marketing channels, right?
467 00:47:45.930 ⇒ 00:47:50.469 Emily Giant: in Shopify. It’s being able to, like, connect that to the sessions.
468 00:47:51.510 ⇒ 00:47:52.770 pk.arthur: Okay, okay, okay, okay.
469 00:47:52.970 ⇒ 00:47:59.819 Demilade Agboola: Alright, so the first part is just to get sessions in. So once we have the sessions in, the next part, the next step will be to…
470 00:48:00.100 ⇒ 00:48:03.899 Demilade Agboola: connect it, join it to Shopify data.
471 00:48:04.510 ⇒ 00:48:05.000 pk.arthur: I bet.
472 00:48:05.000 ⇒ 00:48:10.550 Demilade Agboola: We’ll be able to start doing aggregations on, conversion and all of that.
473 00:48:10.930 ⇒ 00:48:19.899 pk.arthur: Okay, that sounds good. I have a question. I just wanted to follow up, because I saw, like, in the Brain Forward channel about North Beam and everything,
474 00:48:20.300 ⇒ 00:48:22.890 pk.arthur: What’s the update regarding that?
475 00:48:23.420 ⇒ 00:48:26.869 Demilade Agboola: Yeah, so we did send an email, I believe, 2 days ago.
476 00:48:28.570 ⇒ 00:48:32.730 Demilade Agboola: But effectively, what we were able to see is that
477 00:48:33.100 ⇒ 00:48:38.350 Demilade Agboola: We’re able to get a high-level categorization by UTM source in Shopify.
478 00:48:38.480 ⇒ 00:48:57.659 Demilade Agboola: So we’re able to see where the orders are coming from based off those UTM sources, and that would allow us to be able to, at the high level, connect it back to Northbeam and say, hey, this is how much spend was done in Northbeam, and these are the orders and the UTM sources.
479 00:48:57.990 ⇒ 00:49:03.189 Demilade Agboola: that, like, and the revenue associated with those UTM sources, so we can tie that together to see…
480 00:49:03.690 ⇒ 00:49:06.690 Demilade Agboola: What the spend is, as well as what the revenue is.
481 00:49:07.250 ⇒ 00:49:08.229 pk.arthur: Okay, okay.
482 00:49:08.640 ⇒ 00:49:09.729 pk.arthur: Yeah, thank you.
483 00:49:09.970 ⇒ 00:49:10.560 Demilade Agboola: Yeah.
484 00:49:13.180 ⇒ 00:49:20.269 Emily Giant: Alright, cool. Yeah, I think that that’s a good list to go off of as a first step.
485 00:49:20.870 ⇒ 00:49:23.840 Emily Giant: Oh, wow, sorry,
486 00:49:24.110 ⇒ 00:49:29.969 Emily Giant: I don’t know if you can still see my screen, but I was looking at the reason that gift cards were…
487 00:49:30.450 ⇒ 00:49:31.359 Emily Giant: You should.
488 00:49:32.160 ⇒ 00:49:39.769 Emily Giant: I guess this is the table that’s connected to a Shopify ID. So, anyway, it’s one of those crappy HEVO tables. Anyway, just…
489 00:49:39.910 ⇒ 00:49:41.099 Emily Giant: Don’t mind me.
490 00:49:41.520 ⇒ 00:49:42.210 pk.arthur: Excuse me.
491 00:49:43.780 ⇒ 00:49:48.020 Emily Giant: Alright, so you’re gonna create the tickets, and we’ll go from there.
492 00:49:48.640 ⇒ 00:49:49.560 Demilade Agboola: Okay, sounds good.
493 00:49:49.560 ⇒ 00:50:01.009 pk.arthur: Yes, and actually, I’ll send you the, that long loyalty list, as well as just… I think… still thinking of all the pain points that we currently have, but I think this is a good starting point, to be honest.
494 00:50:01.920 ⇒ 00:50:03.600 Demilade Agboola: Okay, alright, sounds good.
495 00:50:03.600 ⇒ 00:50:08.920 Emily Giant: Alright, well, oh, it’s Friday! I guess I’ll chat with everyone Monday.
496 00:50:10.740 ⇒ 00:50:12.030 pk.arthur: Happy Friday guy, yeah?
497 00:50:12.030 ⇒ 00:50:20.510 Demilade Agboola: So, Vicki, how, close to the solutions do you want to be? Like, how clo… how much in the loop? Like, how… yeah, how into the loop.
498 00:50:20.510 ⇒ 00:50:31.750 pk.arthur: I would prefer, because, like, honestly, like, I would prefer to know where to look when, like, things go wrong, because, like, I think that’s been, like, my biggest challenge, like…
499 00:50:31.980 ⇒ 00:50:39.080 pk.arthur: Emily can probably attest to this, I always, like, reach out to her when I have no idea, like, where to look for things, so if I could…
500 00:50:39.290 ⇒ 00:50:44.539 pk.arthur: Just know where… just to be self-sufficient, like, in the future, that would be great.
501 00:50:44.780 ⇒ 00:50:49.449 Demilade Agboola: Okay, sounds good. So as I’m solving things, I will queue… I would, like…
502 00:50:49.980 ⇒ 00:50:54.590 Demilade Agboola: Let you have an idea of what’s happening, where things are, and how everything comes together.
503 00:50:55.190 ⇒ 00:50:56.540 pk.arthur: Appreciate that, thank you.
504 00:50:56.850 ⇒ 00:50:57.900 Demilade Agboola: Sounds good.
505 00:50:59.210 ⇒ 00:51:00.499 pk.arthur: Have a good Friday, guys.
506 00:51:00.710 ⇒ 00:51:02.359 Emily Giant: You too, talk soon!
507 00:51:02.360 ⇒ 00:51:02.990 Demilade Agboola: Bye, bye.
508 00:51:02.990 ⇒ 00:51:03.590 pk.arthur: buddy.
509 00:51:03.780 ⇒ 00:51:04.260 Emily Giant: Bye.