Meeting Title: PK Onboarding Date: 2025-10-02 Meeting participants: pk.arthur, Demilade Agboola, Amber Lin, Emily Giant, Uttam Kumaran
WEBVTT
1 00:00:52.540 ⇒ 00:00:53.440 Demilade Agboola: Okay.
2 00:00:56.060 ⇒ 00:00:57.200 pk.arthur: Hey, what’s up?
3 00:00:57.440 ⇒ 00:00:58.009 Amber Lin: Hi there!
4 00:00:58.010 ⇒ 00:00:58.700 Demilade Agboola: They’re good.
5 00:01:04.720 ⇒ 00:01:10.060 Demilade Agboola: Have you been able to look at the ad data and see if it… it matches.
6 00:01:10.400 ⇒ 00:01:14.240 pk.arthur: Okay, you’re talking in Looker, right?
7 00:01:15.230 ⇒ 00:01:21.160 Demilade Agboola: So I did it in dbt, so I believe these should automatically propagate to Looker.
8 00:01:21.380 ⇒ 00:01:24.300 pk.arthur: Okay, yeah, I’m trying to… so, I was thinking about it, like.
9 00:01:24.470 ⇒ 00:01:29.400 pk.arthur: not quite sure where, like, what it will be under. Let me just take a look real quick.
10 00:01:30.470 ⇒ 00:01:36.579 Demilade Agboola: Okay, alright. If there’s anything, we could always get, like, Emily into things, so we can be sure what explore is…
11 00:01:36.750 ⇒ 00:01:38.930 Demilade Agboola: The Yeah.
12 00:01:39.640 ⇒ 00:01:45.700 Amber Lin: Yeah, and I asked Emily yesterday, I think she would be coming, maybe if she’s caught up in a meeting right now.
13 00:01:48.000 ⇒ 00:01:53.349 Demilade Agboola: I would assume so. I believe it would just be any stand-up, yeah.
14 00:01:54.380 ⇒ 00:01:55.150 Amber Lin: Hi! Yay!
15 00:01:55.150 ⇒ 00:01:58.509 pk.arthur: Not sure what Explore it is, to be honest. Family?
16 00:01:58.740 ⇒ 00:01:59.910 Emily Giant: Hello!
17 00:02:03.230 ⇒ 00:02:04.509 Demilade Agboola: How’s everyone doing?
18 00:02:06.370 ⇒ 00:02:10.840 Emily Giant: My voice is a lot better today. Still a little squeaky, but…
19 00:02:10.840 ⇒ 00:02:11.349 Demilade Agboola: Here you go.
20 00:02:11.350 ⇒ 00:02:12.100 Emily Giant: Way better, though.
21 00:02:12.100 ⇒ 00:02:16.450 Demilade Agboola: There was a string there, but yeah, definitely way better, like yesterday.
22 00:02:16.450 ⇒ 00:02:23.200 Emily Giant: Yeah, every day it’s a little bit… but I went running last night, and I guess they’re, like, doing farm stuff.
23 00:02:23.300 ⇒ 00:02:30.870 Emily Giant: And I don’t know what happened to my allergies, but I’m sneezing, like, every 5 seconds.
24 00:02:30.870 ⇒ 00:02:31.250 Demilade Agboola: Oof.
25 00:02:31.250 ⇒ 00:02:39.529 Emily Giant: And the neti pot, I’ve done everything, and it’s just not budging. So, if you see me go off camera, or, like.
26 00:02:40.360 ⇒ 00:02:43.990 Emily Giant: I’m not speaking when spoken to, it’s because I’m sneezing.
27 00:02:45.620 ⇒ 00:02:46.689 pk.arthur: Dude, that’s the worst.
28 00:02:46.690 ⇒ 00:02:48.060 Emily Giant: Ugh, it’s… yeah.
29 00:02:49.010 ⇒ 00:02:50.959 Emily Giant: But other than that, I’m great!
30 00:02:54.680 ⇒ 00:02:55.950 Amber Lin: Awesome. Okay.
31 00:02:58.390 ⇒ 00:03:06.569 Demilade Agboola: I’m curious, though, like, how far along is PK into onboarding, and what do we need to get him?
32 00:03:06.960 ⇒ 00:03:08.150 Demilade Agboola: It’s a Speedway.
33 00:03:09.270 ⇒ 00:03:10.600 Emily Giant: So…
34 00:03:10.800 ⇒ 00:03:19.979 Emily Giant: as far as… he has access to dbt, he has GitHub, so, like, we don’t need to do any of that, like, administrivia stuff. What…
35 00:03:20.100 ⇒ 00:03:23.639 Emily Giant: We do need help with is,
36 00:03:24.280 ⇒ 00:03:39.269 Emily Giant: building out Northbeam, and I know we’ve had some blockers there. Also figuring out, like, in the Shopify tables, what needs to be transitioned over from, like, HEVO OMS,
37 00:03:39.520 ⇒ 00:03:41.790 Emily Giant: Also, I haven’t touched, like…
38 00:03:41.930 ⇒ 00:03:57.239 Emily Giant: any of the Google stuff, like, I don’t know if things need to be rebuilt. I know that PK has had a lot of interference with those tables since joining the team, so I think that, like, revisiting how those are joined to Shopify…
39 00:03:57.480 ⇒ 00:04:15.710 Emily Giant: and other tables, like, what isn’t necessary anymore once we get Northbeam? Like, what I don’t have the knowledge of is what is unique to the various marketing platforms that are used, and that’s where his knowledge will come in real handy. Like, I think…
40 00:04:16.209 ⇒ 00:04:21.229 Emily Giant: even looking at Stitch… like, PK, do you know which platforms
41 00:04:21.620 ⇒ 00:04:24.600 Emily Giant: You currently use, like, off the top of your head?
42 00:04:25.200 ⇒ 00:04:29.129 pk.arthur: In terms of just, like, the marketing team and, like… Yeah. So…
43 00:04:29.370 ⇒ 00:04:34.220 pk.arthur: we have, basically, North Beam, just to, like, be able to track,
44 00:04:34.800 ⇒ 00:04:57.199 pk.arthur: it’s like a… it’s like basically a database for all the different marketing channels, so we could see, how our CPCs are doing on an hourly basis, and I see Shopify, which tells us, like, more customer data. But those are the two, I guess, besides, like, Google Ads and Bing Ads, and Google Analytics, but those are, like, the main ones we use on a daily.
45 00:04:58.810 ⇒ 00:04:59.400 Emily Giant: Okay.
46 00:05:00.070 ⇒ 00:05:01.359 Demilade Agboola: I’m going to say…
47 00:05:01.800 ⇒ 00:05:02.510 Emily Giant: Go ahead.
48 00:05:02.980 ⇒ 00:05:10.430 Demilade Agboola: I was going to say, for marketing order attribution, when you… because we’ve been looking into that for a bit,
49 00:05:10.560 ⇒ 00:05:22.170 Demilade Agboola: Is there a particular way your campaigns roll up, too? Is there, like, a TikTok, Google, Facebook sort of thing? Or, like, how do you aggregate your…
50 00:05:22.920 ⇒ 00:05:23.530 pk.arthur: So…
51 00:05:23.530 ⇒ 00:05:24.040 Demilade Agboola: Basic.
52 00:05:24.220 ⇒ 00:05:38.380 pk.arthur: So, it can go into, like, that, but also, like, we… so, yeah, so, like, the most granular form is to be, like, TikTok, TikTok, Google, or whatnot, but it could also go down to, like, the…
53 00:05:38.380 ⇒ 00:05:45.770 pk.arthur: paid media level, or referral level, or affiliate level. So, like, there’s different levels of granularity that we do use.
54 00:05:46.030 ⇒ 00:05:48.039 pk.arthur: If that answers the question.
55 00:05:48.850 ⇒ 00:05:59.719 Demilade Agboola: Okay. Alright, so, because for that, part of what we’re being considering is, like, rolling it up to see how the ad spend converts into revenue.
56 00:05:59.720 ⇒ 00:06:00.240 pk.arthur: Yeah.
57 00:06:00.240 ⇒ 00:06:04.109 Demilade Agboola: Like, orders, yeah, so… because on the orders…
58 00:06:04.230 ⇒ 00:06:06.890 Demilade Agboola: Shopify perspective, it’s more of the…
59 00:06:07.220 ⇒ 00:06:10.679 Demilade Agboola: UTM sources, so it’s, like, paid social…
60 00:06:11.560 ⇒ 00:06:14.090 Demilade Agboola: Instagram and stuff like that, yeah.
61 00:06:14.350 ⇒ 00:06:18.910 Demilade Agboola: So, being able to, like, match some level of granularity so that we can…
62 00:06:19.330 ⇒ 00:06:22.719 Demilade Agboola: What the conversion is would be very helpful.
63 00:06:22.970 ⇒ 00:06:28.359 Demilade Agboola: In terms of just onboarding, it appears we will need to…
64 00:06:29.370 ⇒ 00:06:33.579 Demilade Agboola: It seems you know what you need to see in terms of, like, output.
65 00:06:34.730 ⇒ 00:06:40.549 Demilade Agboola: The issue now is initializing it, able to build out what you want to see.
66 00:06:41.770 ⇒ 00:06:42.990 pk.arthur: I agree.
67 00:06:43.710 ⇒ 00:06:48.260 Demilade Agboola: Okay, alright, I think what might help is being able to…
68 00:06:50.440 ⇒ 00:06:53.610 Demilade Agboola: If we can have a list of, like, what you want to see.
69 00:06:53.960 ⇒ 00:06:54.680 pk.arthur: Okay.
70 00:06:54.680 ⇒ 00:06:57.509 Demilade Agboola: So, like, some sort of a roadmap.
71 00:06:57.690 ⇒ 00:07:01.289 Demilade Agboola: So, for instance, if it’s like, hey, being able to attribute
72 00:07:02.430 ⇒ 00:07:09.990 Demilade Agboola: things in Shopify to this, or to the ads, or whatever. Like, just a rough idea of what we’re trying to do.
73 00:07:10.430 ⇒ 00:07:20.949 Demilade Agboola: That would allow both Emmy and I to be able to point… one point in the right direction, and two, just enable you to be able to go, hey, in dbt, this is where we have all the ad stuff.
74 00:07:21.200 ⇒ 00:07:38.950 Demilade Agboola: we can bring in more stuff. For instance, maybe we’re not ingesting some data, we can figure it out on, okay, we need to ingest more data to enable you to get more insights. Or in some cases, we need to create models that, you know, aggregate this information in a way that would be useful to you in Looker.
75 00:07:39.490 ⇒ 00:07:46.489 Demilade Agboola: But, I would just like us to have what the end goal kind of looks like, what kind of insights you want to get.
76 00:07:46.730 ⇒ 00:07:52.230 Demilade Agboola: what… the current pain points are in, for instance, if you’re using certain Explores now.
77 00:07:53.010 ⇒ 00:08:05.200 Demilade Agboola: If there are certain pain points as well, so again, we can point you in the right direction to say, hey, this is where that data is coming from, and this is kind of maybe where you might want to make those tweaks to what you’re currently using.
78 00:08:05.570 ⇒ 00:08:06.300 pk.arthur: Okay.
79 00:08:06.470 ⇒ 00:08:07.290 Demilade Agboola: Yeah.
80 00:08:07.290 ⇒ 00:08:12.750 pk.arthur: I can get you a list probably by the end of this week, or even end of this day, just have to think about it more,
81 00:08:13.350 ⇒ 00:08:14.330 pk.arthur: more comprehensive.
82 00:08:14.330 ⇒ 00:08:14.880 Demilade Agboola: Okay.
83 00:08:15.590 ⇒ 00:08:19.600 Emily Giant: We should re-share the, design doc that…
84 00:08:19.970 ⇒ 00:08:33.189 Emily Giant: we created with Chris and PK a couple… probably, like, a month and a half ago, just so, like, PK, so you don’t do extra work, because I know that we, like, at least gathered a lot of the business questions, and that might help you, like…
85 00:08:33.419 ⇒ 00:08:34.870 Emily Giant: visualize
86 00:08:35.110 ⇒ 00:08:47.189 Emily Giant: what you need as an end state. And one thing I just want to, like, bring up, because I know that it’s super important, is your FY26 forecast, and making sure that that is also
87 00:08:47.190 ⇒ 00:08:55.410 Emily Giant: joined with all of the other tables, because I think that’s a piece that’s been left out in the past, and you’ve had to do a lot of manual work there.
88 00:08:56.330 ⇒ 00:09:02.590 pk.arthur: Yes, that was the… I believe that was a task that, Chris… Chris had, right?
89 00:09:02.590 ⇒ 00:09:03.330 Emily Giant: Yep.
90 00:09:03.330 ⇒ 00:09:04.250 pk.arthur: Yeah.
91 00:09:04.580 ⇒ 00:09:11.950 Emily Giant: And it’s in Looker, and it’s in, dbt, but it’s, like, not…
92 00:09:12.420 ⇒ 00:09:19.349 Emily Giant: currently joined with anything, so it’s about as good as, like, looking at the spreadsheet itself right now.
93 00:09:21.430 ⇒ 00:09:27.050 pk.arthur: Yeah, so, actually, we’re actually in the process of, like, re-forecasting, too, so…
94 00:09:27.050 ⇒ 00:09:28.060 Emily Giant: Okay, that’s good.
95 00:09:28.360 ⇒ 00:09:35.650 pk.arthur: That might also have, like, a little, impact on what it eventually looks like, but…
96 00:09:35.650 ⇒ 00:09:36.520 Emily Giant: Totally fine.
97 00:09:36.520 ⇒ 00:09:45.259 pk.arthur: I want to connect with you, because I think Chris has some questions in regards to, like, what your ask was, because he was… because we met, like, I think earlier this week, and he said, like, you reached out to him.
98 00:09:45.260 ⇒ 00:09:53.539 Emily Giant: Yeah. I’m just trying to, like, make sure that you have a process so that you don’t have to… yeah, we’ll meet, we’ll meet.
99 00:09:53.720 ⇒ 00:09:58.900 Emily Giant: We’ll do it a different time, but definitely, maybe, like, early next week, throw some time on my calendar.
100 00:09:58.900 ⇒ 00:09:59.799 pk.arthur: Okay, sounds good.
101 00:10:00.520 ⇒ 00:10:07.310 Demilade Agboola: Also, I will say that it would be helpful to sort of prioritize, so we know, hey, these are the…
102 00:10:07.700 ⇒ 00:10:14.660 Demilade Agboola: Our priorities that we need to get out the way, or ensure that we have you in the best position to tackle them.
103 00:10:14.850 ⇒ 00:10:22.359 pk.arthur: Yeah, certainly. There’s, off the top of my mind, like, a few things that come to, my mind, just like,
104 00:10:23.140 ⇒ 00:10:41.790 pk.arthur: just about attribution in terms of, like, we had, like, some issues regarding correctly pulling in, just, ad source from Google Analytics into Looker. Emily and I tried to tackle that a couple of weeks back, but that’s one of the pain points, I would say, off the top of my head.
105 00:10:43.380 ⇒ 00:10:44.930 Demilade Agboola: Okay.
106 00:10:44.930 ⇒ 00:10:47.319 pk.arthur: I will provide the full list.
107 00:10:47.670 ⇒ 00:11:02.899 Demilade Agboola: Okay, yeah, that would be very helpful. And then, if… once we have that list, I can see what stuff I can knock out myself, and what stuff, you know, I need to just enable you to be able to take ownership of.
108 00:11:03.350 ⇒ 00:11:07.520 Demilade Agboola: say, hey, this is the model in dbt, this is kind of how it’s done.
109 00:11:07.820 ⇒ 00:11:10.780 Demilade Agboola: If you need to make tweaks, this is how you’d make your tweaks.
110 00:11:12.310 ⇒ 00:11:16.459 Demilade Agboola: Yeah, so that that way you could have ownership of certain flows.
111 00:11:20.440 ⇒ 00:11:27.189 Amber Lin: I was wondering, first, Pika, do you have access to our linear board? Have you been able to see it so far?
112 00:11:27.190 ⇒ 00:11:31.650 pk.arthur: I do think I have access to linear… let me just double-check real quick.
113 00:11:31.650 ⇒ 00:11:35.459 Amber Lin: Okay. Yes, I do. Okay. Awesome.
114 00:11:36.320 ⇒ 00:11:55.489 Amber Lin: I know you guys were talking about a few items. I want to note that down in form of tickets. I was wondering if we can, say, have one or two tickets that you can take on, and then we can book working sessions with Emily and Demoni just about those tickets, so that we know what to deliver, and then how we can help
115 00:11:55.640 ⇒ 00:11:57.239 Amber Lin: Deliver them.
116 00:11:57.570 ⇒ 00:12:04.450 Amber Lin: So, I know you guys mentioned, I think, two work streams. What would they be, so I can document them?
117 00:12:09.510 ⇒ 00:12:14.920 Demilade Agboola: I mean, Picky will need to, like, basically get everything, but I know he just mentioned.
118 00:12:14.920 ⇒ 00:12:15.600 Amber Lin: Hmm.
119 00:12:15.600 ⇒ 00:12:16.890 Demilade Agboola: Google Analytics.
120 00:12:17.620 ⇒ 00:12:35.920 pk.arthur: Yeah, so yeah, so one, like, just flow, just from the data from Google Analytics 2, I think. I’m not quite sure where it goes, whether it goes to Hivo or DBT or Redshit, I’m not quite sure, but just, we have, like, like, we have, I don’t, I don’t know what it is exactly, like, what the issue is, but, like…
121 00:12:35.920 ⇒ 00:12:39.849 pk.arthur: Just determining what traffic and, like.
122 00:12:40.370 ⇒ 00:12:52.790 pk.arthur: attribution from the different marketing channels does not seem to line up in Looker once it’s pulled in through Looker. So that’s, like, one main pain point currently that comes to the top of my head.
123 00:12:57.120 ⇒ 00:13:08.630 Amber Lin: And Demon and Emily, do you think this is a good starting point for PK to go into dbt and see how it… to see how it’s done, or is this too complicated? Okay.
124 00:13:08.630 ⇒ 00:13:09.290 Demilade Agboola: Yeah, yeah.
125 00:13:09.290 ⇒ 00:13:19.219 Emily Giant: I think that it’s a perfect starting point, especially because the table that’s being really problematic is one of the ones we’re replacing with Shopify, so it’s, like.
126 00:13:19.790 ⇒ 00:13:27.820 Emily Giant: nearly an immediate win, because we were already pulling this in. That’s what I was, sharing with, with OASH yesterday,
127 00:13:27.820 ⇒ 00:13:28.410 Amber Lin: Hmm.
128 00:13:28.870 ⇒ 00:13:33.880 Emily Giant: so that he could connect with Northbeam about what we are pulling in, but yeah,
129 00:13:34.120 ⇒ 00:13:36.490 Emily Giant: I think that would be super helpful right off the bat.
130 00:13:36.490 ⇒ 00:13:41.219 Amber Lin: Okay, what’s… Deliverable, or what’s the table called?
131 00:13:42.190 ⇒ 00:13:45.349 Emily Giant: I believe it’s called Fact Orders.
132 00:13:45.640 ⇒ 00:13:46.340 Amber Lin: Okay.
133 00:13:46.770 ⇒ 00:13:49.820 Amber Lin: Fact orders table, and what are we trying to do?
134 00:13:50.290 ⇒ 00:13:58.970 Emily Giant: We are trying to… Connect a… attribution channel.
135 00:13:59.620 ⇒ 00:14:00.930 Emily Giant: to revenue.
136 00:14:03.580 ⇒ 00:14:05.279 Emily Giant: Is that right, PK?
137 00:14:05.280 ⇒ 00:14:10.860 pk.arthur: Yeah, to revenue, and basically match it to a customer ID, if that’s possible.
138 00:14:11.370 ⇒ 00:14:12.000 Emily Giant: It is.
139 00:14:12.000 ⇒ 00:14:12.909 pk.arthur: I think they have.
140 00:14:13.530 ⇒ 00:14:14.200 Amber Lin: Hmm.
141 00:14:14.640 ⇒ 00:14:23.159 Demilade Agboola: Yeah, so we have UTM sources there, but again, part of what we will need to get in place is just figure out
142 00:14:24.710 ⇒ 00:14:36.580 Demilade Agboola: what exists currently in terms of how much data we’ve been able to get in, and then just match it together before we then expose it to Looker, and then we can start building off that.
143 00:14:38.550 ⇒ 00:14:47.130 Emily Giant: And the good news is, this is all already built out in Looker. So, once Demulade and I are able to, like.
144 00:14:47.460 ⇒ 00:14:53.279 Emily Giant: refine what revenue is based on, like, subscription, etc. This’ll…
145 00:14:54.190 ⇒ 00:14:58.460 Emily Giant: I mean, PK, you have the access, you and Perry are the only ones, I think, and…
146 00:14:58.570 ⇒ 00:15:07.219 Emily Giant: this group here, but you can even play with it now in that Shopify Orders Explorer, just to see, like, what is…
147 00:15:07.870 ⇒ 00:15:09.549 Emily Giant: Accessible to you.
148 00:15:09.980 ⇒ 00:15:14.460 pk.arthur: Yeah, I looked at it this morning, I think orders, line up,
149 00:15:15.040 ⇒ 00:15:19.700 pk.arthur: with what I’m seeing in Shopify. My only question for you was regarding…
150 00:15:19.860 ⇒ 00:15:36.169 pk.arthur: still the visits slash session. So, I remember in the past, like, we concluded that, it would be a good idea to basically use visits as a proxy for sessions, because we do not see, like, any data on the back end, right?
151 00:15:36.170 ⇒ 00:15:43.260 pk.arthur: So I was just, like, just quality checking it with what I see, what I saw on Shopify, and just looking at…
152 00:15:43.520 ⇒ 00:15:58.889 pk.arthur: I know we have, like, a few, dimensions, so, like, there’s a… there’s a customer visit ID, as well as lab visit ID, friend visit ID, but, like, just comparing those, the counts of those visits on a daily basis to what I’m seeing Shopify.
153 00:15:59.400 ⇒ 00:16:03.080 pk.arthur: Matchup, for yesterday, specifically, for example.
154 00:16:04.380 ⇒ 00:16:11.670 Emily Giant: So, that’s probably… it’s either a refresh issue, or we, I need to go back and look. I do think UTAM…
155 00:16:12.410 ⇒ 00:16:26.170 Emily Giant: joined the customer journey table to the fact orders table, so that should be in there. I’m just wondering if I’m, like, not exposing it correctly. But I’m wondering if, like, how far off was it?
156 00:16:26.510 ⇒ 00:16:34.579 pk.arthur: It was a good amount. I think, looking at Shopify, I see about 13,000 sessions for yesterday.
157 00:16:34.860 ⇒ 00:16:41.040 pk.arthur: And… I maybe also be doing it wrong, just because I’m using the customer,
158 00:16:41.180 ⇒ 00:16:43.969 pk.arthur: the customer visit ID, sir, right? So I just kind of, like.
159 00:16:43.970 ⇒ 00:16:44.330 Emily Giant: every customer.
160 00:16:44.330 ⇒ 00:16:53.440 pk.arthur: ID4 yesterday, just to see if that makes sense, but it was still, about… 10,000 off.
161 00:16:53.850 ⇒ 00:16:56.850 Emily Giant: Yeah, I wonder if it was, like, a… maybe using account unique?
162 00:16:57.020 ⇒ 00:17:01.510 Emily Giant: Instead of… We should be using, like, count…
163 00:17:03.120 ⇒ 00:17:07.390 Emily Giant: what that field actually is counting. And maybe it’s just not…
164 00:17:08.119 ⇒ 00:17:15.460 Emily Giant: correctly named, but I’m not super familiar with that table. I think it’s a good thing for us three to go over in a working session.
165 00:17:15.460 ⇒ 00:17:16.060 Demilade Agboola: Yeah.
166 00:17:16.060 ⇒ 00:17:16.420 Amber Lin: Yes.
167 00:17:16.420 ⇒ 00:17:17.390 Emily Giant: separate one.
168 00:17:17.980 ⇒ 00:17:33.630 Amber Lin: I was wondering, especially, I think it’s related to this ticket, I… I know that PK would need working sessions to get started, because it’s just a new tool and a new place. I was thinking if we can book the working session now, and then
169 00:17:33.630 ⇒ 00:17:42.289 Amber Lin: So you guys can have a discussion about it, because we have… we don’t have that much time left. I know there’s some QA stuff, that we want to confirm.
170 00:17:44.330 ⇒ 00:17:45.730 Demilade Agboola: Okay, yeah, we can do that.
171 00:17:46.750 ⇒ 00:17:47.230 Amber Lin: Yeah.
172 00:17:47.230 ⇒ 00:18:05.499 Demilade Agboola: Yeah, we do… we have… we have working sessions. Amy and I have working sessions, we can always invite PK, so that. That is not a big, a big deal. I did want to also say that, potentially, in the working sessions, we might also have to think about, like, Shopify and potential, like, calibrations that might need to go in.
173 00:18:05.770 ⇒ 00:18:12.169 Demilade Agboola: Especially when it comes to getting to, like, UTM sources and stuff, just to be sure that everything is firing how it should fire.
174 00:18:13.510 ⇒ 00:18:14.260 Demilade Agboola: Yeah.
175 00:18:14.260 ⇒ 00:18:16.669 Amber Lin: Would that be something that you would do?
176 00:18:18.020 ⇒ 00:18:18.850 Demilade Agboola: I mean, we’ll just have to…
177 00:18:18.850 ⇒ 00:18:19.210 Amber Lin: Dude.
178 00:18:19.940 ⇒ 00:18:27.790 Demilade Agboola: Yeah, sure. Well, we’ll just look into it and just be sure that everything in Shopify is set up the way it should be set up, so that we can
179 00:18:28.170 ⇒ 00:18:32.639 Demilade Agboola: get all the attribution necessary. Attribution details necessary.
180 00:18:32.850 ⇒ 00:18:33.540 Amber Lin: Okay.
181 00:18:36.110 ⇒ 00:18:49.909 pk.arthur: And I guess back to your question earlier, Demolade, about the ad models, and which Explorer should be. Yeah, I’m still not quite sure. Emily, do you, happen to know, like, what Explorer the ad models will be under in Looker?
182 00:18:50.590 ⇒ 00:18:51.940 Emily Giant: Which ones?
183 00:18:52.680 ⇒ 00:18:56.059 pk.arthur: I’m not quite sure, I think it was the ad models right, Demolade?
184 00:18:56.360 ⇒ 00:19:09.019 Demilade Agboola: Yeah, so we had some ad models for AdWords and Bing ads, so I set them up on an early cadence, but I don’t actually know what they feed in Looker, so that’s the… that’s what he’s asking about.
185 00:19:09.970 ⇒ 00:19:11.730 Emily Giant: Honestly, I don’t either.
186 00:19:11.730 ⇒ 00:19:20.400 pk.arthur: Okay, I can do some more as I’m digging. I think I might see something about under Google Ads Accounts, under the table.
187 00:19:20.600 ⇒ 00:19:24.769 pk.arthur: I’m looking at SQL runners, so I can look at that really quickly.
188 00:19:24.930 ⇒ 00:19:25.620 Amber Lin: Okay.
189 00:19:25.620 ⇒ 00:19:27.359 pk.arthur: Confirm, soon.
190 00:19:28.180 ⇒ 00:19:36.060 Amber Lin: Yeah, Emily, I know we also created this ticket, originally, to say for, to do for PK. Do you think this is something that…
191 00:19:36.390 ⇒ 00:19:44.540 Amber Lin: Maybe in next week, or in the following working session, we can address, or is this too complicated for now?
192 00:19:47.190 ⇒ 00:19:57.490 Emily Giant: I think that it’s… It’s gonna require some, investigation of, like.
193 00:19:58.050 ⇒ 00:20:00.020 Emily Giant: Where these separate…
194 00:20:00.280 ⇒ 00:20:08.529 Emily Giant: promo devices are coming from, so I think it depends a lot on PK’s, like, pre-existing knowledge of
195 00:20:09.090 ⇒ 00:20:13.819 Emily Giant: promos, which I think he has a lot of, so it might be a safer call
196 00:20:13.970 ⇒ 00:20:25.390 Emily Giant: to have him answer that, because I know that, like, Shopify isn’t necessarily our only device for Promos.
197 00:20:25.550 ⇒ 00:20:27.980 Emily Giant: And I don’t know whether it captures
198 00:20:28.430 ⇒ 00:20:40.150 Emily Giant: accurately promos from wherever they initialize. Does that make sense? PK, do you know how to answer this question better than me? I just think it’s very complicated, but maybe it’s because I never work with it.
199 00:20:40.150 ⇒ 00:20:46.389 pk.arthur: Yeah, what is the question? So, categorization for promo codes to provide financial view. So…
200 00:20:46.760 ⇒ 00:20:51.469 pk.arthur: basically wants you… basically wants, promo categories? Is that what the.
201 00:20:51.470 ⇒ 00:20:57.659 Emily Giant: Yeah, I want to pull out… so, a portion of this is pulling out that huge logic.
202 00:20:57.660 ⇒ 00:20:58.050 pk.arthur: Yeah.
203 00:20:58.050 ⇒ 00:21:06.179 Emily Giant: manually put in Looker and make, like, a sustainable format in dbt for, like, if there’s a new code.
204 00:21:06.180 ⇒ 00:21:09.829 pk.arthur: Using logic instead of having to manually.
205 00:21:09.830 ⇒ 00:21:10.270 Emily Giant: Like…
206 00:21:10.270 ⇒ 00:21:10.940 pk.arthur: Yeah, so…
207 00:21:10.940 ⇒ 00:21:12.169 Emily Giant: Are you adding that?
208 00:21:12.870 ⇒ 00:21:13.830 pk.arthur: the…
209 00:21:13.880 ⇒ 00:21:27.229 pk.arthur: we would always have to manually add some promo codes, so, like, when the e-comm team has, like, a promotion, like, let’s say next week, right, they, like, decide to create a new promo code, let’s say it is October 3rd promo.
210 00:21:27.230 ⇒ 00:21:38.300 pk.arthur: then, in that instance, we have to obviously go in and manually input it. But, like, looking at the ones in the past, I don’t think, like… so, the biggest issue with the loyalty code is that one… that’s the one that has
211 00:21:38.390 ⇒ 00:21:41.250 pk.arthur: a huge block in there. So in the past, I think…
212 00:21:41.250 ⇒ 00:21:51.179 Emily Giant: loyalty codes were random letters and numbers, as I told you, Emily, like, 7 different… I think 7 digits of… made up of numbers and letters, so…
213 00:21:51.360 ⇒ 00:22:01.670 pk.arthur: That’s what I had to manually put in, but, like, moving forward, every loyalty code starts with, like, L dash something, so that’s an easy way to, capture those, now and in the future.
214 00:22:02.310 ⇒ 00:22:10.099 Emily Giant: I wonder if the e-com team can, like, if there’s some kind of… Function in Shopify, where…
215 00:22:10.270 ⇒ 00:22:11.879 Emily Giant: they can tag
216 00:22:12.930 ⇒ 00:22:27.500 Emily Giant: things that they create so that, like, we’re using logic to… by tag instead of, like, the random string of numbers, because, like, that could accidentally wind up being your whole job, is, like, manually figuring out these promo codes.
217 00:22:27.500 ⇒ 00:22:28.180 pk.arthur: Yeah, I like that.
218 00:22:28.180 ⇒ 00:22:29.430 Emily Giant: You know what I mean?
219 00:22:29.430 ⇒ 00:22:30.590 pk.arthur: It took me a long time to get.
220 00:22:30.590 ⇒ 00:22:39.670 Emily Giant: It just seems like there’s gotta be something in Shopify, and maybe we touch base with them and see, like, if there’s a 5-second additional…
221 00:22:40.030 ⇒ 00:22:46.420 Emily Giant: meta field that they can put that categorizes these promo codes? Because that will come out in the data.
222 00:22:46.860 ⇒ 00:22:47.330 Amber Lin: Okay.
223 00:22:47.330 ⇒ 00:23:03.129 Demilade Agboola: Yeah, so that’s part of what I said about the whole Shopify categorization. So, us just being able to be on top of things in terms of how Shopify is, you know, categorizing attribution and just whatever is happening with the orders.
224 00:23:03.540 ⇒ 00:23:05.399 Demilade Agboola: So now we’re on top of everything.
225 00:23:05.920 ⇒ 00:23:11.430 Amber Lin: Awesome. So I know you guys have a working session tomorrow,
226 00:23:12.570 ⇒ 00:23:17.409 Amber Lin: Can you confirm that you’re available at that time? What time is it?
227 00:23:18.670 ⇒ 00:23:19.739 Emily Giant: 9 AM.
228 00:23:19.740 ⇒ 00:23:21.110 pk.arthur: Not Amy, that’s fine.
229 00:23:21.110 ⇒ 00:23:25.430 Amber Lin: Okay, so I think tomorrow, if we can do initial look at
230 00:23:25.510 ⇒ 00:23:41.779 Amber Lin: these two, and if you can give me a time estimate of how long these would take, and I… I expect that they will take a bit longer, because we do need to have working sessions, so if I can get that after tomorrow, after you guys meet, that would be awesome.
231 00:23:41.940 ⇒ 00:23:51.509 Amber Lin: And I think I have a question of, do you want to join our… are you able to join our stand-ups? We have, I think.
232 00:23:51.880 ⇒ 00:24:04.330 Amber Lin: Monday, Wednesday, Friday. I can invite you to them, and you can tell me that you can just accept or decline if you… if it works for you, and I’ll… we can work to find a time that works for all of us.
233 00:24:05.190 ⇒ 00:24:05.530 pk.arthur: Oh, okay.
234 00:24:05.530 ⇒ 00:24:06.900 Amber Lin: minutes-ish.
235 00:24:07.190 ⇒ 00:24:09.519 pk.arthur: Oh, yeah, that should be fine, I can pass it.
236 00:24:10.070 ⇒ 00:24:10.700 Amber Lin: Yeah.
237 00:24:10.930 ⇒ 00:24:20.849 Amber Lin: Mostly just so that you know, oh, they’re working on this on revenue, and they’re doing this, and then if there’s any questions, we’re all there, so it’s a good time to ask.
238 00:24:21.030 ⇒ 00:24:25.639 pk.arthur: Okay, yeah, quite some time, like, whether it be in the morning or later in the day, whichever works.
239 00:24:25.830 ⇒ 00:24:35.239 Amber Lin: Okay, it’s probably later in the day. We have them closer to my noon time, which is probably around, your… your guys’, like, 2 to 3 PM-ish.
240 00:24:35.880 ⇒ 00:24:43.189 pk.arthur: okay, Wednesdays might be, like, the hardest thing, because I got, like, another, conflicting meeting, but…
241 00:24:43.190 ⇒ 00:24:43.950 Amber Lin: Okay.
242 00:24:44.140 ⇒ 00:24:46.409 pk.arthur: Mondays and Fridays look perfect.
243 00:24:46.410 ⇒ 00:24:51.859 Amber Lin: Okay, awesome. I’ll send it your way, and then just let me know if you can join.
244 00:24:51.860 ⇒ 00:24:53.440 pk.arthur: Okay, sounds good, thank you.
245 00:24:53.440 ⇒ 00:24:55.450 Amber Lin: Yeah.
246 00:24:55.590 ⇒ 00:25:09.449 Amber Lin: I think that’s what I wanted to do on my end, just confirm the tickets, confirm the schedules. I know, Emily, you’re going to reach out to the care team, and after you do that, we’ll be able to do the QAs based on the scenario, so…
247 00:25:09.450 ⇒ 00:25:20.309 Amber Lin: I’ll just leave that until we have, we can start it, and then I’ll… we’ll loop… keep PK in for the… I think for the revenue or subscriptions, QA.
248 00:25:20.310 ⇒ 00:25:20.900 Emily Giant: Yes.
249 00:25:20.900 ⇒ 00:25:21.360 Amber Lin: Okay.
250 00:25:21.360 ⇒ 00:25:28.620 Emily Giant: Yeah, I think it’ll be a huge asset for everything we’re working on, but especially the marketing. But yeah, that sounds good.
251 00:25:28.620 ⇒ 00:25:29.330 Amber Lin: Okay.
252 00:25:29.330 ⇒ 00:25:32.099 Emily Giant: I’m excited! Awesome. Yay! Welcome, PK!
253 00:25:32.100 ⇒ 00:25:34.290 Amber Lin: Yay! We have a bigger team now!
254 00:25:34.540 ⇒ 00:25:34.950 Emily Giant: I love it.
255 00:25:34.950 ⇒ 00:25:41.479 pk.arthur: Thank you. And I’m gonna send a list of just, like, pain points to you guys, I guess sometime today.
256 00:25:41.760 ⇒ 00:25:46.860 Amber Lin: Yeah, just feel free to drop in the channel, brain dump, and then we’ll clean it up, don’t worry.
257 00:25:46.860 ⇒ 00:25:47.880 pk.arthur: Perfect, thank you.
258 00:25:47.880 ⇒ 00:25:49.029 Amber Lin: Yeah, thanks.
259 00:25:49.240 ⇒ 00:25:50.370 Amber Lin: Alright, see you later.
260 00:25:50.640 ⇒ 00:25:51.480 Demilade Agboola: Bye, guys. Bye.