Meeting Title: US x BF | Standup Date: 2025-10-24 Meeting participants: Demilade Agboola, Amber Lin, Uttam Kumaran, Awaish Kumar, Emily Giant
WEBVTT
1 00:00:11.770 ⇒ 00:00:13.190 Uttam Kumaran: Hello!
2 00:00:13.550 ⇒ 00:00:18.900 Amber Lin: Hi there. I checked with Emily yesterday, she said she’d be… should be free at this time.
3 00:00:19.130 ⇒ 00:00:22.439 Uttam Kumaran: Cool, and then what was our plan? We were gonna.
4 00:00:22.440 ⇒ 00:00:24.140 Amber Lin: Look at the scenarios.
5 00:00:25.340 ⇒ 00:00:27.169 Uttam Kumaran: Yes, good call. Okay.
6 00:00:31.950 ⇒ 00:00:36.830 Uttam Kumaran: The level of… the level of context switching this week and last week…
7 00:00:37.740 ⇒ 00:00:41.730 Uttam Kumaran: has been pretty… peak. I don’t think I’ve…
8 00:00:43.500 ⇒ 00:00:49.140 Uttam Kumaran: I don’t think I’ve had to hold that many, like, thoughts in my head.
9 00:00:50.600 ⇒ 00:00:51.750 Uttam Kumaran: That’s funny.
10 00:00:55.050 ⇒ 00:00:57.180 Uttam Kumaran: Well, it’s good, I thought that meeting was productive, right?
11 00:00:59.950 ⇒ 00:01:03.179 Uttam Kumaran: You guys should just let me know feedback, like, I’m… I,
12 00:01:03.390 ⇒ 00:01:09.310 Uttam Kumaran: I just want to be crystal clear about, like, what needs to be done when, And, like.
13 00:01:09.620 ⇒ 00:01:14.830 Uttam Kumaran: move past that this was wrong, this was right, like, okay, what needs to be done, when does it due, and be very clear.
14 00:01:15.730 ⇒ 00:01:19.059 Uttam Kumaran: So that’s… that’s sort of, like, what I’m finding.
15 00:01:19.600 ⇒ 00:01:20.570 Uttam Kumaran: get to.
16 00:01:27.160 ⇒ 00:01:28.190 Uttam Kumaran: Hello?
17 00:01:29.010 ⇒ 00:01:30.100 Emily Giant: Hi.
18 00:01:30.620 ⇒ 00:01:31.420 Uttam Kumaran: I…
19 00:01:32.200 ⇒ 00:01:32.810 Emily Giant: How’s it going?
20 00:01:32.810 ⇒ 00:01:34.290 Uttam Kumaran: Is everything? Good.
21 00:01:34.290 ⇒ 00:01:38.319 Emily Giant: Good! Yeah, I’m back in my office, so that’s…
22 00:01:38.320 ⇒ 00:01:44.000 Uttam Kumaran: We’re kind of matching, although I like your blue color way better. Like, I… that’s a great sweater.
23 00:01:44.560 ⇒ 00:02:03.810 Emily Giant: It’s a good one. I used to work for J.Crew back in the day, and when COVID hit, all of their merchandise kept coming in, but all the stores were closed, so for employees at the time, they marked everything to 90% off, or something like that, so I have… I don’t know if I’ve purchased clothing since COVID, because I bought, like.
24 00:02:03.810 ⇒ 00:02:04.670 Uttam Kumaran: Wow.
25 00:02:04.920 ⇒ 00:02:11.319 Emily Giant: All of it. All of my clothes and Matt’s clothes. We are just wearing our 90% off J.Crew sweaters.
26 00:02:11.320 ⇒ 00:02:14.889 Uttam Kumaran: My dad would be so jealous, he’s such a big J. Crew fan.
27 00:02:15.370 ⇒ 00:02:15.880 Uttam Kumaran: like…
28 00:02:15.880 ⇒ 00:02:18.769 Emily Giant: It’s not as good as it used to be, but it’s still solid.
29 00:02:19.260 ⇒ 00:02:24.660 Uttam Kumaran: There are some solid finds, I think it went through, like, a dip, and then they came back recently.
30 00:02:25.080 ⇒ 00:02:35.450 Uttam Kumaran: Yeah, totally. Like, it went through a pretty bad dip, I think, for a while, and then now it’s, like, kind of on the ups, like, bonobo… it’s, like, kind of like Bonobos level, but they just
31 00:02:35.830 ⇒ 00:02:38.590 Uttam Kumaran: prices are, like, ridiculous for some stuff now, like…
32 00:02:38.590 ⇒ 00:02:42.340 Emily Giant: Everywhere. You can’t even get stuff at Walmart anymore. I don’t know if they have Walmart.
33 00:02:42.340 ⇒ 00:02:42.820 Uttam Kumaran: Yeah.
34 00:02:42.820 ⇒ 00:02:45.790 Emily Giant: But in the Midwest, they do.
35 00:02:45.790 ⇒ 00:02:47.229 Uttam Kumaran: Yeah, Walmart’s here.
36 00:02:47.630 ⇒ 00:02:51.449 Uttam Kumaran: Yeah, well, even Walmart’s trying to become, like, Target, right? Like, Target…
37 00:02:51.450 ⇒ 00:02:51.880 Emily Giant: It’s the real…
38 00:02:51.880 ⇒ 00:02:55.470 Uttam Kumaran: middle class, like, if you can go to Target and be like.
39 00:02:55.560 ⇒ 00:02:59.179 Emily Giant: la-di-da, buy stuff, that’s, like, you made it, you know?
40 00:02:59.430 ⇒ 00:03:00.250 Emily Giant: Yep, you make.
41 00:03:00.250 ⇒ 00:03:10.489 Uttam Kumaran: My girlfriend loves going to Target. I’m like, we should have gone to Costco if you wanted this thing. She’s like, oh, I just wanted to, like, have a Target, hot girl Target walk, like, go to, like, buy little things, and I’m like…
42 00:03:10.990 ⇒ 00:03:13.720 Uttam Kumaran: It’s, like, 50% higher!
43 00:03:13.720 ⇒ 00:03:16.419 Emily Giant: You gotta let her go by herself. Like, I went to Tarte.
44 00:03:16.420 ⇒ 00:03:17.989 Uttam Kumaran: No, I don’t… yeah, I can’t…
45 00:03:17.990 ⇒ 00:03:18.589 Emily Giant: To just, like.
46 00:03:18.590 ⇒ 00:03:22.580 Uttam Kumaran: I’m not a fun person to shop with, because I’m a very, like…
47 00:03:23.490 ⇒ 00:03:32.319 Uttam Kumaran: I either really need, like, one thing, and I go and I don’t get distracted, and I’m like, I came here for that, let’s go get that and get out, or I’m like a…
48 00:03:32.630 ⇒ 00:03:36.660 Uttam Kumaran: huge, like, go through every aisle. Like, at the grocery store, I’m a bad… I’m like.
49 00:03:37.450 ⇒ 00:03:43.500 Uttam Kumaran: I just go every aisle, every time. I just, like, like cooking, and so I like to see new things.
50 00:03:43.620 ⇒ 00:03:55.109 Uttam Kumaran: at the market or whatever, and I’m like, oh, I like, this is a cool thing, and I’ll ask ChatGPT, like, what is this thing? Like, what can I do with this thing? And then I sit in the aisle for, like, 30 minutes.
51 00:03:55.520 ⇒ 00:04:06.880 Emily Giant: Yep, no, that is Matt, and I won’t… I won’t go to the grocery. I… I don’t… I don’t do that. Unless it’s Aldi or, like, a tiny one, like Trader Joe’s, I can do that, but, like, big grocery stores?
52 00:04:07.570 ⇒ 00:04:08.340 Emily Giant: -
53 00:04:08.470 ⇒ 00:04:12.389 Emily Giant: With Target, you can just set me free for hours, and I’m fine.
54 00:04:13.770 ⇒ 00:04:23.440 Uttam Kumaran: Great. Well, so I wanted to take time today for this crew to work on these scenarios for subscriptions and transactions together.
55 00:04:23.730 ⇒ 00:04:30.980 Uttam Kumaran: And then we can make a little bit of headway, and ideally, kind of, like, two things here, as I just sort of, like, pull together a little doc.
56 00:04:31.020 ⇒ 00:04:47.689 Uttam Kumaran: One is, like, I think it would be helpful for me, you, and Demolade to talk about, like, the scenarios we know. I think this, like, meeting transcript itself will be helpful, and then after that, we can make sure that in one area, I want every single, like.
57 00:04:48.140 ⇒ 00:04:55.000 Uttam Kumaran: basically, like, transaction, decision tree, to be documented, right? Nice.
58 00:04:55.330 ⇒ 00:05:03.020 Uttam Kumaran: And, like, that is… that is my goal, because ultimately, when we do QA, We should move beyond,
59 00:05:03.080 ⇒ 00:05:13.530 Uttam Kumaran: Perry looks at numbers, I guess, seems right. You know, we should go to something a little bit more sophisticated. And so ideally, we can also start to add unit tests.
60 00:05:13.560 ⇒ 00:05:26.030 Uttam Kumaran: like, in dbt, right? So given these characteristics on a row, it should end up here, and, like, we can look at input row versus output row at the end of the model. And so that’s sort of, like, what I want to get to today.
61 00:05:26.280 ⇒ 00:05:27.200 Emily Giant: Great.
62 00:05:27.590 ⇒ 00:05:36.809 Uttam Kumaran: So, the big things in my mind are transactions and subscriptions. Of course, we can do refunds, like, is there anything else that has, like, sort of similar scenario?
63 00:05:37.280 ⇒ 00:05:39.270 Uttam Kumaran: type, like, framework, Emily?
64 00:05:39.930 ⇒ 00:05:43.829 Emily Giant: Let me think… transactions, subscriptions,
65 00:05:44.960 ⇒ 00:05:51.630 Emily Giant: So, I would like to talk about how, like, fulfillments And… revenue…
66 00:05:52.190 ⇒ 00:06:06.230 Emily Giant: relate. I don’t know if that’s completely part of these scenarios, but I did a bunch of work on the, like, fulfillments tables, because I was reworking the OMS care tags. It’s the one thing that’s, like, not in Shopify at all. And…
67 00:06:06.850 ⇒ 00:06:11.919 Emily Giant: There are a lot of similarities and differences with how revenue could be…
68 00:06:12.860 ⇒ 00:06:18.399 Emily Giant: Visualized, based on the fulfillments versus orders tables, and
69 00:06:19.010 ⇒ 00:06:25.270 Emily Giant: I don’t know if this is the forum, but I feel like there are tests there that could clarify, like, what was…
70 00:06:26.400 ⇒ 00:06:30.339 Emily Giant: What needs to be filled in in some of those tables from
71 00:06:30.540 ⇒ 00:06:41.049 Emily Giant: the order tables instead of fulfillments. Anyway, let’s start, and I think I’ll know better, like, the specific things that we need to address as we go through the… what you just named.
72 00:06:42.000 ⇒ 00:06:42.630 Uttam Kumaran: Okay.
73 00:06:43.440 ⇒ 00:06:51.920 Uttam Kumaran: So let me share this stock with everybody, sec…
74 00:07:59.550 ⇒ 00:08:05.690 Uttam Kumaran: Okay, so… I’m working in here, I just sent it in… Zoom…
75 00:08:06.170 ⇒ 00:08:12.870 Uttam Kumaran: And then I’m just gonna paste in a little meeting format that I used GPT to help me… Create…
76 00:08:22.690 ⇒ 00:08:30.629 Uttam Kumaran: So, I think probably, like, kind of the way, like, this conversation can go is we’ll just start by, like, writing
77 00:08:30.840 ⇒ 00:08:35.729 Uttam Kumaran: The scenarios, and even if there’s overlap, we can maybe take 5-6 minutes to do that.
78 00:08:35.840 ⇒ 00:08:53.659 Uttam Kumaran: And then we can talk through… we can start to talk through the details of each of them, and then if we need follow-ups. The biggest thing is to make sure that at first, all of the scenarios are documented, at least in just, like, one line, and then our expectation for the scenario, we can… will take a little bit more time.
79 00:08:54.020 ⇒ 00:08:58.989 Uttam Kumaran: So, let me just get… something written here.
80 00:09:08.320 ⇒ 00:09:10.150 Uttam Kumaran: Oh, so this is it.
81 00:09:16.920 ⇒ 00:09:17.630 Uttam Kumaran: Great.
82 00:09:19.180 ⇒ 00:09:28.730 Uttam Kumaran: Cool, so I think let’s start by talking through… transactions scenarios?
83 00:09:29.420 ⇒ 00:09:34.519 Uttam Kumaran: So… Can we… maybe we can take…
84 00:09:35.660 ⇒ 00:09:42.489 Uttam Kumaran: Or how about… I don’t think maybe we should just talk through it. So, I’ll just read it out, and you can let me know,
85 00:09:42.770 ⇒ 00:09:45.930 Uttam Kumaran: if… These are fine when we want to add any, so…
86 00:09:46.150 ⇒ 00:09:51.610 Uttam Kumaran: We have, like, standard purchase, partial refund, full refund, subscription refund.
87 00:09:51.800 ⇒ 00:09:57.270 Uttam Kumaran: Payment, retry, chargeback, void… transaction.
88 00:09:57.520 ⇒ 00:10:04.770 Uttam Kumaran: I don’t think… I think probably a couple of these can get classified under, like, payment issues.
89 00:10:05.620 ⇒ 00:10:07.560 Uttam Kumaran: But what else are we missing here?
90 00:10:12.850 ⇒ 00:10:15.340 Emily Giant: Transaction. Transaction.
91 00:10:15.700 ⇒ 00:10:19.029 Emily Giant: So, can you tell me what the… like, what is the…
92 00:10:19.530 ⇒ 00:10:26.049 Emily Giant: definition in your idea of transaction? Is this, like… This can be transaction or odor.
93 00:10:26.050 ⇒ 00:10:26.649 Uttam Kumaran: What do you think?
94 00:10:27.160 ⇒ 00:10:29.760 Uttam Kumaran: Well, no, this is transaction or order, basically.
95 00:10:30.970 ⇒ 00:10:36.700 Uttam Kumaran: like, this… this can be, like… I mean, this is where we have, like, fulfillment, like, re-delivery stuff here. Yep.
96 00:10:37.360 ⇒ 00:10:43.509 Uttam Kumaran: So, if we could… we could separate those out, but I think this is just a starting point, basically, for me.
97 00:10:44.260 ⇒ 00:10:48.200 Uttam Kumaran: So, I guess if, Emily, if I was to ask you, like.
98 00:10:48.550 ⇒ 00:10:53.420 Uttam Kumaran: How are the different ways beyond Someone orders something.
99 00:10:53.750 ⇒ 00:10:59.280 Uttam Kumaran: And it gets delivered, and it’s perfect, like, what are the other situations that could possibly happen?
100 00:10:59.910 ⇒ 00:11:01.330 Emily Giant: When it’s not perfect.
101 00:11:01.570 ⇒ 00:11:02.270 Uttam Kumaran: Yes.
102 00:11:02.900 ⇒ 00:11:05.689 Emily Giant: Alright. Credits. Customer credits.
103 00:11:06.780 ⇒ 00:11:08.429 Emily Giant: Are gonna be in there, too.
104 00:11:10.440 ⇒ 00:11:12.210 Uttam Kumaran: And the issue there is…
105 00:11:12.590 ⇒ 00:11:17.689 Emily Giant: Would you classify that under… it’s a form of refund, but it’s not?
106 00:11:18.070 ⇒ 00:11:20.889 Emily Giant: Don’t worry about the… don’t worry about the classification.
107 00:11:20.890 ⇒ 00:11:25.179 Uttam Kumaran: I think just rattle them off, yeah, and I’ll ask just, like, these basic questions.
108 00:11:26.830 ⇒ 00:11:34.740 Emily Giant: So, it is expected when… when it goes awry, you mean? Like… Just like…
109 00:11:34.740 ⇒ 00:11:39.509 Uttam Kumaran: What are the scenarios related to orders and customer credits? Like.
110 00:11:39.870 ⇒ 00:11:43.949 Uttam Kumaran: Is it, like, for example, this could be they don’t have enough credits, they used credits.
111 00:11:44.840 ⇒ 00:11:50.099 Uttam Kumaran: And we didn’t bill them. Like, what are the issues? Or what are the situations that happen?
112 00:11:50.670 ⇒ 00:12:03.219 Emily Giant: It’s all of these, but a credit is an outcome. So, like, a refund and a credit would be outcomes of these things happening. So, damaged products, failed delivery,
113 00:12:03.580 ⇒ 00:12:08.890 Emily Giant: Forced upgrade is also, like, if they got a forced upgrade and didn’t like it.
114 00:12:09.420 ⇒ 00:12:14.239 Emily Giant: But you could also classify that under… damaged product. Okay.
115 00:12:15.290 ⇒ 00:12:19.759 Uttam Kumaran: And in what scenarios do the forced upgrades happen? Like, missing inventory?
116 00:12:19.930 ⇒ 00:12:25.870 Emily Giant: Yeah, inventory discrepancies. For one reason or another, it’s an inventory discrepancy. Or missing inventory, yeah.
117 00:12:27.370 ⇒ 00:12:28.050 Uttam Kumaran: Okay?
118 00:12:28.620 ⇒ 00:12:33.050 Uttam Kumaran: And then, what are other, like, scenarios for…
119 00:12:34.080 ⇒ 00:12:43.180 Uttam Kumaran: like, on the order side. So we have, like, re-deliveries, failed re-deliveries, like, what are other issues that commonly, like, come up?
120 00:12:46.650 ⇒ 00:12:49.860 Emily Giant: on… on the order side, meaning the, like, outcomes?
121 00:12:49.860 ⇒ 00:12:52.250 Uttam Kumaran: I guess a better… I guess a better question is, like.
122 00:12:53.000 ⇒ 00:12:56.630 Uttam Kumaran: We want to see, like, all the situations
123 00:12:56.960 ⇒ 00:12:58.590 Uttam Kumaran: For example, if we were to take
124 00:12:59.360 ⇒ 00:13:05.590 Uttam Kumaran: A transaction from the beginning and see its order at the end of our models.
125 00:13:05.860 ⇒ 00:13:12.809 Uttam Kumaran: there can be one or many modifications, right? So I want a list of all the potential
126 00:13:13.000 ⇒ 00:13:14.789 Uttam Kumaran: Things that can affect that.
127 00:13:14.940 ⇒ 00:13:16.380 Uttam Kumaran: initial order.
128 00:13:16.570 ⇒ 00:13:26.830 Uttam Kumaran: So that we can map it to, like, outcomes at the end, and so we know that all of these specific outcomes are changes that we expect, given a certain criteria.
129 00:13:27.160 ⇒ 00:13:36.090 Emily Giant: Yep, okay, you could have a line item removal, line item addition, fulfillment center change.
130 00:13:38.560 ⇒ 00:13:40.740 Emily Giant: A rescheduled delivery?
131 00:13:47.130 ⇒ 00:13:52.760 Emily Giant: A discount added retroactively?
132 00:14:06.860 ⇒ 00:14:10.860 Emily Giant: You already have address change, so that’s good.
133 00:14:14.920 ⇒ 00:14:19.260 Emily Giant: That’s… Truly, like, the… yeah.
134 00:14:19.560 ⇒ 00:14:27.979 Emily Giant: That’s the majority. Yeah, the holiday skip is something interesting. I guess rescheduled delivery would be, like.
135 00:14:28.180 ⇒ 00:14:35.009 Emily Giant: a failed delivery zone, and that’s a rescheduled delivery. But there’s things, like, bullet points under all of these that…
136 00:14:35.730 ⇒ 00:14:40.769 Uttam Kumaran: Yeah. Well, that’s what, just go through them, like, feel free. As they come up in your brain.
137 00:14:41.590 ⇒ 00:14:42.410 Emily Giant: Yeah.
138 00:14:45.940 ⇒ 00:14:49.739 Uttam Kumaran: It could even be examples, like, you may not have… you can just talk through, like.
139 00:14:50.290 ⇒ 00:14:52.730 Uttam Kumaran: Yeah, there was an example where this customer had this.
140 00:14:55.200 ⇒ 00:14:59.089 Emily Giant: I mean, this is the vast majority of things that could happen.
141 00:15:09.560 ⇒ 00:15:10.170 Uttam Kumaran: Okay.
142 00:15:10.270 ⇒ 00:15:13.190 Uttam Kumaran: So then, let’s say we go through, like, these…
143 00:15:13.860 ⇒ 00:15:18.450 Uttam Kumaran: questions. Let’s start with transactions. So this is going to be more on the finance side.
144 00:15:18.580 ⇒ 00:15:20.779 Uttam Kumaran: So, do these basically…
145 00:15:22.850 ⇒ 00:15:31.980 Uttam Kumaran: Are these kind of, like, everything on the transaction side, for the most part? I guess probably I would ask about, like, how partial refunds work, or partial credits work.
146 00:15:33.390 ⇒ 00:15:34.360 Emily Giant: If you have it.
147 00:15:34.360 ⇒ 00:15:35.390 Uttam Kumaran: context there.
148 00:15:35.720 ⇒ 00:15:38.539 Emily Giant: Re-delivery is technically part of
149 00:15:39.190 ⇒ 00:15:52.379 Emily Giant: well, not really a transaction, it’s a 0 re-delivery transaction. I would say authorization?
150 00:15:52.810 ⇒ 00:15:55.439 Emily Giant: And fraud should probably be in there.
151 00:15:56.330 ⇒ 00:15:56.890 Uttam Kumaran: Okay.
152 00:16:08.230 ⇒ 00:16:08.970 Uttam Kumaran: Okay?
153 00:16:10.740 ⇒ 00:16:11.720 Emily Giant: Hmm…
154 00:16:12.720 ⇒ 00:16:18.289 Uttam Kumaran: Like, this is a good question. How do we handle failed payments? Is there a flow for that? They just flow through the system?
155 00:16:19.570 ⇒ 00:16:29.330 Emily Giant: Failed, failed payment, you know, I don’t know if the customer actually is successfully getting, like.
156 00:16:29.880 ⇒ 00:16:41.110 Emily Giant: to the point of purchase with some of the, like, failed transactions, but I have seen, like, stalled authorizations that have not updated in the past, in the data.
157 00:16:44.700 ⇒ 00:16:46.050 Emily Giant: Also,
158 00:16:46.220 ⇒ 00:16:57.469 Emily Giant: This is for also demoade and his work. I talked to Dev this morning about all of those unfulfilled orders that were actually delivered. There are 48,000 of them, so I gave this to Alex.
159 00:16:57.730 ⇒ 00:16:58.849 Emily Giant: to fix.
160 00:17:01.390 ⇒ 00:17:02.330 Uttam Kumaran: Wow, okay.
161 00:17:02.330 ⇒ 00:17:04.300 Demilade Agboola: It was great helping you find all that.
162 00:17:05.240 ⇒ 00:17:14.430 Emily Giant: Yeah, it was wild. They were like, what is this, like, a handful? I was like, no, I think it’s in, like, the tens of thousands. They were like, what?
163 00:17:14.560 ⇒ 00:17:20.920 Emily Giant: Yeah, we found, like, every order that we tested was, like, marked as unfulfilled, so… yeah.
164 00:17:25.960 ⇒ 00:17:26.560 Uttam Kumaran: Cool.
165 00:17:29.990 ⇒ 00:17:38.030 Emily Giant: Do you want to add, like, a customer credit or gift card to this? Because, like, there’s a weird transactional thing where sometimes, like.
166 00:17:38.340 ⇒ 00:17:46.999 Emily Giant: If the card doesn’t have enough value, it will show a false refund, so it will charge the entire amount to a gift card.
167 00:17:47.180 ⇒ 00:17:50.990 Emily Giant: And then show a refund as… like…
168 00:17:51.480 ⇒ 00:18:01.959 Emily Giant: the balance of what the customer has to pay, and I want to make sure that we’re, like, accounting for that in the transaction table, that, like, that is not actually a refund, that is amount charged, and should, like.
169 00:18:03.370 ⇒ 00:18:06.429 Emily Giant: Should rebalance by the end of the transaction.
170 00:18:07.720 ⇒ 00:18:08.300 Uttam Kumaran: Okay.
171 00:18:10.570 ⇒ 00:18:15.649 Uttam Kumaran: So there’s, like… What… is there, like, a term for that? I guess there’s a…
172 00:18:15.940 ⇒ 00:18:20.110 Emily Giant: I would say 3D gift card refunds.
173 00:18:22.440 ⇒ 00:18:30.579 Emily Giant: Because that’s the, like, tag that you’ll see with it. I don’t think it happens in the Shopify tables, I just know it’s been a problem in the past with transactions.
174 00:18:31.260 ⇒ 00:18:31.820 Uttam Kumaran: Okay.
175 00:18:45.020 ⇒ 00:18:45.880 Uttam Kumaran: Okay.
176 00:18:46.220 ⇒ 00:18:48.009 Uttam Kumaran: Payment retried, okay.
177 00:18:48.200 ⇒ 00:18:49.399 Uttam Kumaran: This is all great.
178 00:18:50.140 ⇒ 00:18:56.960 Uttam Kumaran: And then, can we talk about subscriptions? So I know we had a bunch of… actually, these I can probably get from our last meeting.
179 00:18:57.540 ⇒ 00:19:03.569 Emily Giant: Okay, subscription transactions… Well, there’s…
180 00:19:04.280 ⇒ 00:19:07.739 Emily Giant: I don’t know. Like, there’s the prepaid versus the…
181 00:19:08.380 ⇒ 00:19:09.790 Uttam Kumaran: No, that’s all valid.
182 00:19:10.060 ⇒ 00:19:11.170 Emily Giant: Okay.
183 00:19:12.400 ⇒ 00:19:14.650 Emily Giant: Prepaid versus recurring.
184 00:19:14.650 ⇒ 00:19:15.120 Uttam Kumaran: Yeah.
185 00:19:15.120 ⇒ 00:19:18.279 Emily Giant: And how that’s charged, and that, like.
186 00:19:18.960 ⇒ 00:19:19.449 Demilade Agboola: Yeah, so.
187 00:19:19.450 ⇒ 00:19:34.070 Emily Giant: paid subs will show a high discount amount that is not real. It’s something they’ve already paid, but because of how the system transactions work, it will show, like, the amount discounted from that original purchase, because it’s getting amortized over time.
188 00:19:34.480 ⇒ 00:19:36.560 Emily Giant: But Demolade, what were you gonna say?
189 00:19:36.750 ⇒ 00:19:45.410 Demilade Agboola: I was going to say, Perry also pointed out that for prepaid subs, all revenues attached, as is amortized, it’s attached to the initial date of
190 00:19:45.830 ⇒ 00:19:46.540 Demilade Agboola: payment.
191 00:19:48.010 ⇒ 00:19:53.580 Demilade Agboola: Well, for recurring, it’s attached to just the date of that recurring subscription.
192 00:19:59.390 ⇒ 00:20:00.000 Uttam Kumaran: Okay.
193 00:20:06.820 ⇒ 00:20:13.589 Uttam Kumaran: Is there any other, like, order-related scenario, Demolade, like, for us to consider?
194 00:20:17.050 ⇒ 00:20:20.139 Demilade Agboola: order-related? No, not really.
195 00:20:20.140 ⇒ 00:20:22.239 Uttam Kumaran: And we could also talk about inventory.
196 00:20:26.910 ⇒ 00:20:27.570 Demilade Agboola: Mmm…
197 00:20:30.610 ⇒ 00:20:33.239 Demilade Agboola: Like, inventory as relates to orders.
198 00:20:33.460 ⇒ 00:20:35.069 Demilade Agboola: Or just inventory by itself.
199 00:20:35.360 ⇒ 00:20:36.790 Uttam Kumaran: Inventory by itself.
200 00:20:44.870 ⇒ 00:20:50.050 Demilade Agboola: I mean, that’s its own topic, but usually it’s about hub and spoke.
201 00:20:50.630 ⇒ 00:20:54.209 Demilade Agboola: That was really the tricky part of the inventory.
202 00:20:54.980 ⇒ 00:20:58.760 Demilade Agboola: So basically, there’s a hub that then
203 00:20:59.250 ⇒ 00:21:05.730 Demilade Agboola: Where they get shipped things to, and then it goes to, like, different fulfillment centers from the hub, so hub-and-spoke model.
204 00:21:09.740 ⇒ 00:21:15.530 Demilade Agboola: And that… so that was one of the major logic pieces in the inventory.
205 00:21:16.060 ⇒ 00:21:20.949 Demilade Agboola: What else?
206 00:21:20.950 ⇒ 00:21:24.480 Emily Giant: Yeah, there’s still… there’s still some issues…
207 00:21:24.640 ⇒ 00:21:29.100 Emily Giant: Well, actually, those were more, like, source-driven, so that doesn’t really matter. Hey, Kitty.
208 00:21:30.230 ⇒ 00:21:30.870 Uttam Kumaran: Okay.
209 00:21:31.520 ⇒ 00:21:35.240 Demilade Agboola: what was happening with the source, like, NetSuite? Or…
210 00:21:35.240 ⇒ 00:21:38.780 Emily Giant: The forced upgrade tags are not successfully
211 00:21:39.270 ⇒ 00:21:44.050 Emily Giant: populating in NetSuite, so I assigned that ticket to Alex. I thought it was something we did, but it’s not.
212 00:21:44.150 ⇒ 00:21:48.130 Emily Giant: It’s a NetSuite problem.
213 00:21:48.910 ⇒ 00:21:49.760 Demilade Agboola: Okay.
214 00:21:51.780 ⇒ 00:21:52.480 Emily Giant: Excuse.
215 00:21:52.810 ⇒ 00:22:03.539 Demilade Agboola: Yeah, so how much work was the major thing, because that was the thing that affected most of the calculation, so there will be a calculation for every other thing, and then home and spoke kind of have its own logic.
216 00:22:09.540 ⇒ 00:22:10.130 Uttam Kumaran: Okay.
217 00:22:11.520 ⇒ 00:22:15.229 Uttam Kumaran: For this one, I can go look into the inventory models and piece together anything.
218 00:22:15.230 ⇒ 00:22:16.130 Demilade Agboola: Oh, yeah.
219 00:22:16.130 ⇒ 00:22:16.690 Uttam Kumaran: refund…
220 00:22:16.990 ⇒ 00:22:21.869 Demilade Agboola: There’s also stuff about buffers, by the way. Okay, if you think about that way.
221 00:22:22.000 ⇒ 00:22:23.659 Demilade Agboola: How buffers work.
222 00:22:24.060 ⇒ 00:22:26.740 Demilade Agboola: There’s another piece of logic,
223 00:22:29.900 ⇒ 00:22:33.169 Demilade Agboola: So, there was the care buffer, the hold buffer.
224 00:22:37.260 ⇒ 00:22:47.789 Emily Giant: the… Like, damaged inventory getting removed from available counts, or on-hand counts, not available on hand.
225 00:22:54.000 ⇒ 00:22:55.110 Emily Giant: Uncommitted.
226 00:22:55.270 ⇒ 00:22:58.449 Emily Giant: Versus kind of… yeah. We’re on the same page.
227 00:22:58.450 ⇒ 00:23:01.259 Demilade Agboola: I was gonna say committed, it was also another part of it.
228 00:23:01.540 ⇒ 00:23:02.330 Emily Giant: Yep.
229 00:23:07.110 ⇒ 00:23:12.859 Demilade Agboola: And then another part, and so this is different from buffers, would be on hand versus on order.
230 00:23:16.450 ⇒ 00:23:18.670 Demilade Agboola: Which is also part of that hub-and-spoke.
231 00:23:19.390 ⇒ 00:23:20.460 Demilade Agboola: Situation.
232 00:23:45.920 ⇒ 00:23:48.190 Uttam Kumaran: Trying to think if there’s anything else that I remember.
233 00:23:54.640 ⇒ 00:23:56.120 Emily Giant: That was a crazy time.
234 00:23:56.820 ⇒ 00:24:00.180 Demilade Agboola: Yeah. Also, for inventory, there was just the…
235 00:24:01.440 ⇒ 00:24:09.750 Demilade Agboola: the concept of, like, lots, I mean, it’s the high-level concept, but the concept of, like, lots, because that’s kind of what was messing up PK’s view of…
236 00:24:10.310 ⇒ 00:24:11.790 Demilade Agboola: our…
237 00:24:12.910 ⇒ 00:24:21.830 Demilade Agboola: tracking. Yeah, snapshot model, because he forgot that lots keep getting added, so he was wondering why the inventory kept increasing.
238 00:24:23.390 ⇒ 00:24:27.160 Demilade Agboola: No, cloths, basically, so, like, it’s… Oh, yeah.
239 00:24:27.710 ⇒ 00:24:29.020 Demilade Agboola: itch,
240 00:24:30.840 ⇒ 00:24:41.770 Demilade Agboola: Each product gets sent to different lots, and so the product… so if you’re viewing it by the product ID, it can increase, because now there are new lots that it’s been sent to.
241 00:24:42.270 ⇒ 00:24:51.290 Demilade Agboola: Yeah, so… But, yeah, a lot of the unique value within that table. They’re, like, the primary key.
242 00:24:53.990 ⇒ 00:24:57.080 Emily Giant: And the concept of lauded versus unlotted goods.
243 00:24:57.350 ⇒ 00:24:58.700 Emily Giant: That’s another incentive.
244 00:24:59.020 ⇒ 00:25:01.950 Emily Giant: For… ever to build.
245 00:25:04.860 ⇒ 00:25:08.110 Demilade Agboola: Yeah, because, like, hard goods are unlotted, basically.
246 00:25:09.400 ⇒ 00:25:12.049 Demilade Agboola: So, it’s the floral goods that are allotted.
247 00:25:14.960 ⇒ 00:25:20.860 Uttam Kumaran: Okay, so there also is something around, like, good type.
248 00:25:22.800 ⇒ 00:25:24.660 Uttam Kumaran: Okay.
249 00:25:25.560 ⇒ 00:25:28.389 Uttam Kumaran: So, kind of, like, where this is going to go…
250 00:25:28.990 ⇒ 00:25:30.800 Uttam Kumaran: In the last couple minutes here.
251 00:25:31.130 ⇒ 00:25:41.070 Uttam Kumaran: For each of these, I’m going to find… we’re gonna… we’re gonna look to find a transaction, an order, an inventory ID, and show…
252 00:25:41.490 ⇒ 00:25:43.120 Uttam Kumaran: Like, an example.
253 00:25:43.570 ⇒ 00:25:51.030 Uttam Kumaran: That is basically queryable to go find, like, here’s a scenario of an order which had this characteristic, and here’s how it ends up.
254 00:25:52.870 ⇒ 00:25:58.289 Uttam Kumaran: And that is, like, this is, like, the ultimate piece of documentation on, like, each of these scenarios, basically.
255 00:25:58.430 ⇒ 00:26:04.530 Uttam Kumaran: That way, if we have any question on, like, how a certain thing works, you have a referenceable
256 00:26:04.780 ⇒ 00:26:09.719 Uttam Kumaran: ID of an object that went through that scenario.
257 00:26:10.840 ⇒ 00:26:15.349 Demilade Agboola: And that way, also, if… yeah, go ahead, go ahead. Take it out to this,
258 00:26:15.520 ⇒ 00:26:34.359 Demilade Agboola: So, Urban Sems doesn’t necessarily use the things as they occur. They also have their financial year, like, sheets that they now map it to. So that’s what helps them with, like, forecasting and all that. So, it’s just something to note on the side that, like.
259 00:26:35.840 ⇒ 00:26:43.759 Demilade Agboola: Things don’t… you don’t just use dates as they are, you kind of map those dates to other, like, their financial calendars, or financial year calendars.
260 00:26:44.280 ⇒ 00:26:45.840 Uttam Kumaran: Wait, can you say that again?
261 00:26:46.240 ⇒ 00:26:50.530 Demilade Agboola: So, Urban Stems has, like, financial calendars that they have, right?
262 00:26:50.680 ⇒ 00:27:05.499 Demilade Agboola: And why that’s important is, if you look at the final tables that we use in Looker, they don’t just have one date, even though the dbt models have one date. We eventually map those dates to the financial year calendars.
263 00:27:05.560 ⇒ 00:27:12.130 Demilade Agboola: And so you have, like, the fiscal year, the fiscal month, the fiscal this, but then they also have ways in which they
264 00:27:12.680 ⇒ 00:27:20.490 Demilade Agboola: I’m trying to see how I can explain it. But basically, it starts to map out, and then that’s kind of what they use for forecasting.
265 00:27:21.320 ⇒ 00:27:22.340 Uttam Kumaran: Okay, okay.
266 00:27:28.890 ⇒ 00:27:34.960 Demilade Agboola: And it’s done across all, like, stuff, so inventory, orders, Sobs.
267 00:27:35.070 ⇒ 00:27:38.079 Demilade Agboola: I believe. I’m not sure of ourselves, but eventually others.
268 00:27:38.630 ⇒ 00:27:39.250 Uttam Kumaran: Okay.
269 00:27:46.850 ⇒ 00:27:47.560 Uttam Kumaran: Okay.
270 00:27:49.550 ⇒ 00:27:51.660 Uttam Kumaran: Okay, great, so I feel…
271 00:27:53.090 ⇒ 00:27:59.269 Uttam Kumaran: Pretty good, as this is, like, a good starting point, so I’m gonna take this conversation and this and, like, rework a little bit of this document.
272 00:27:59.600 ⇒ 00:28:04.270 Uttam Kumaran: And this is… this meeting was probably as painful as I thought it was gonna be, so that’s fine.
273 00:28:04.460 ⇒ 00:28:09.019 Uttam Kumaran: I think this is naturally, like, these are, like, things that, as we…
274 00:28:09.450 ⇒ 00:28:17.349 Uttam Kumaran: As we go through models and we have new scenarios, it’s at least one place where, hey, that’s a new scenario, let’s go update the scenario sheet, basically.
275 00:28:17.570 ⇒ 00:28:21.710 Uttam Kumaran: But I can put together sort of a first draft of this.
276 00:28:21.820 ⇒ 00:28:24.220 Uttam Kumaran: And then I’ll probably… can get help.
277 00:28:24.550 ⇒ 00:28:27.670 Uttam Kumaran: From folks on digging up specific examples.
278 00:28:27.950 ⇒ 00:28:32.309 Uttam Kumaran: But this is, again, something helpful for us, and then something just helpful for the company to have.
279 00:28:32.380 ⇒ 00:28:33.550 Emily Giant: Yeah.
280 00:28:33.680 ⇒ 00:28:35.080 Uttam Kumaran: I guess in the last…
281 00:28:35.650 ⇒ 00:28:44.520 Uttam Kumaran: few minutes. Like, anything else for this week that we want to, like, close out? I know there’s a couple, like, job issues right now.
282 00:28:44.660 ⇒ 00:28:53.280 Uttam Kumaran: But maybe let’s first start with, like, stuff in this… In this cycle…
283 00:28:53.450 ⇒ 00:29:00.860 Uttam Kumaran: Like, anything we can, anything we can move, or… move back.
284 00:29:01.020 ⇒ 00:29:02.249 Uttam Kumaran: Let me know.
285 00:29:03.240 ⇒ 00:29:07.739 Emily Giant: I wanted to look at the… so, the transaction table. I know we got a little stalled on that.
286 00:29:08.020 ⇒ 00:29:13.190 Uttam Kumaran: Yeah, so I just pinged… I just pinged the way she’s doing the QA right now, I think.
287 00:29:13.190 ⇒ 00:29:13.830 Emily Giant: Okay.
288 00:29:14.070 ⇒ 00:29:14.710 Emily Giant: Great.
289 00:29:14.710 ⇒ 00:29:15.300 Uttam Kumaran: Yes.
290 00:29:16.010 ⇒ 00:29:20.090 Uttam Kumaran: So Way, should we just… you can just send in Slack, like, when that’s ready.
291 00:29:20.430 ⇒ 00:29:21.570 Awaish Kumar: Yep.
292 00:29:21.960 ⇒ 00:29:23.140 Uttam Kumaran: Okay, I know where she.
293 00:29:23.140 ⇒ 00:29:27.670 Emily Giant: as long as the table looks, like, once the QA is done.
294 00:29:27.940 ⇒ 00:29:28.540 Uttam Kumaran: Okay.
295 00:29:28.750 ⇒ 00:29:34.899 Uttam Kumaran: I know this North Beam thing is continuing Saga, so I’m gonna move to next cycle.
296 00:29:35.810 ⇒ 00:29:39.320 Uttam Kumaran: And then, Jess, tell me about…
297 00:29:39.730 ⇒ 00:29:40.739 Awaish Kumar: Sorry, go ahead.
298 00:29:41.550 ⇒ 00:29:42.040 Awaish Kumar: I haven’t.
299 00:29:42.040 ⇒ 00:29:42.699 Uttam Kumaran: Oh, you’re meeting today?
300 00:29:42.700 ⇒ 00:29:43.429 Awaish Kumar: I don’t remember that.
301 00:29:43.430 ⇒ 00:29:47.280 Uttam Kumaran: Yeah, okay, okay. So, update the ticket and let me know, like, what we end up with.
302 00:29:47.720 ⇒ 00:29:49.050 Awaish Kumar: Oh, sure, sure.
303 00:29:50.740 ⇒ 00:29:53.849 Uttam Kumaran: Okay, great.
304 00:29:54.870 ⇒ 00:30:00.049 Uttam Kumaran: So, how about stuff that’s in progress or in to-do? Can I move any of these back?
305 00:30:06.410 ⇒ 00:30:08.740 Demilade Agboola: What do you mean by back?
306 00:30:09.540 ⇒ 00:30:11.730 Uttam Kumaran: Well, move it to next cycle, sorry, yeah.
307 00:30:12.870 ⇒ 00:30:20.160 Emily Giant: I would say the, prepaid versus recurring logic needs to go to next cycle. I probably won’t finish that today.
308 00:30:20.750 ⇒ 00:30:21.460 Emily Giant: I might.
309 00:30:21.460 ⇒ 00:30:22.030 Uttam Kumaran: Okay.
310 00:30:22.410 ⇒ 00:30:23.210 Emily Giant: Probably not.
311 00:30:23.590 ⇒ 00:30:29.420 Emily Giant: Fact suborders kicked to the next one.
312 00:30:30.760 ⇒ 00:30:31.580 Emily Giant: 297.
313 00:30:31.580 ⇒ 00:30:38.389 Uttam Kumaran: Create a label called, moves… back for now.
314 00:30:41.500 ⇒ 00:30:44.239 Uttam Kumaran: Okay, so this one moved back, and then…
315 00:30:45.550 ⇒ 00:30:47.829 Emily Giant: 296, the fax suborders.
316 00:30:48.390 ⇒ 00:30:49.290 Uttam Kumaran: Oh, yeah, okay.
317 00:30:56.270 ⇒ 00:30:56.980 Uttam Kumaran: Okay?
318 00:30:59.690 ⇒ 00:31:02.690 Emily Giant: This one, Demolade, I can move back, too.
319 00:31:03.670 ⇒ 00:31:08.309 Uttam Kumaran: Or… Do you want me to send a note for this?
320 00:31:13.660 ⇒ 00:31:20.010 Demilade Agboola: I’m not sure I remember the context of this, but I will… it’s basically reaching out to PK, and just need to be sure…
321 00:31:20.010 ⇒ 00:31:20.610 Uttam Kumaran: Okay.
322 00:31:21.690 ⇒ 00:31:23.429 Demilade Agboola: Girl, what we’re trying to reach out to him for.
323 00:31:25.820 ⇒ 00:31:28.460 Uttam Kumaran: Okay. Yeah, I forgot what this was.
324 00:31:30.880 ⇒ 00:31:34.660 Awaish Kumar: Yeah, like, this was, like, we ingested data from.
325 00:31:36.310 ⇒ 00:31:42.550 Awaish Kumar: G, and then… The data wasn’t matching with the…
326 00:31:42.980 ⇒ 00:31:49.119 Awaish Kumar: Like, on the GA, we see different number of sessions than in… In the looker?
327 00:31:49.320 ⇒ 00:31:56.530 Awaish Kumar: And the reason was that in the looker, we were using a table called audience behavior to count the sessions.
328 00:31:57.600 ⇒ 00:32:02.449 Awaish Kumar: But… And another one was, like, using default channels.
329 00:32:03.240 ⇒ 00:32:14.459 Awaish Kumar: table to basically count the sessions, and it matches with GA4, but the PK says we don’t use that default. Like, upper stem have
330 00:32:15.010 ⇒ 00:32:21.699 Awaish Kumar: There are custom features for that, so… Yeah, we might have…
331 00:32:21.700 ⇒ 00:32:24.610 Uttam Kumaran: Outcome, like, the Yeah, go ahead.
332 00:32:25.420 ⇒ 00:32:27.329 Awaish Kumar: So, like…
333 00:32:27.720 ⇒ 00:32:35.739 Awaish Kumar: There was a report mismatch, like, the ingestion of data, and then we found a report mismatch, and now is that we might have to…
334 00:32:36.290 ⇒ 00:32:39.689 Awaish Kumar: In just a few more tables in there, basically.
335 00:32:42.930 ⇒ 00:32:47.579 Uttam Kumaran: Like, do we need a meeting on GA, or, like, what do you think is… like, do you have a clear sense of what’s next?
336 00:32:47.580 ⇒ 00:32:56.239 Awaish Kumar: We… we just need to, like, meet… maybe, like, talk to PK on what exact Like, custom configuration is…
337 00:32:57.120 ⇒ 00:33:00.799 Awaish Kumar: Is being used, and then only in just that table.
338 00:33:01.230 ⇒ 00:33:02.610 Awaish Kumar: In the red.
339 00:33:04.420 ⇒ 00:33:12.299 Demilade Agboola: Also, I think… I think, Christine mentioned that we want to move off GA, so I don’t know if this is…
340 00:33:12.830 ⇒ 00:33:16.040 Awaish Kumar: Yeah. For in a couple of months.
341 00:33:25.950 ⇒ 00:33:28.689 Uttam Kumaran: Okay. Alright, cool, so let’s do a meeting on this next week.
342 00:33:33.980 ⇒ 00:33:35.839 Uttam Kumaran: I’ll get to make sure that’s booked.
343 00:33:36.140 ⇒ 00:33:39.020 Uttam Kumaran: Alright, great.
344 00:33:40.150 ⇒ 00:33:43.219 Uttam Kumaran: So this one… done, we worked on this.
345 00:33:45.340 ⇒ 00:33:50.299 Uttam Kumaran: Okay. And then… OMS refunds away…
346 00:33:50.550 ⇒ 00:33:52.740 Uttam Kumaran: Do you think you’ll end up getting to this today?
347 00:33:55.460 ⇒ 00:33:57.359 Awaish Kumar: Yeah, I’ll, I will take care.
348 00:33:58.250 ⇒ 00:34:03.640 Uttam Kumaran: Okay, cool. And then… This… PK has this ticket.
349 00:34:03.930 ⇒ 00:34:05.200 Uttam Kumaran: I can…
350 00:34:08.540 ⇒ 00:34:11.810 Uttam Kumaran: I guess we mentioned we’ll do this together.
351 00:34:12.650 ⇒ 00:34:14.409 Uttam Kumaran: What do you think, Demolade?
352 00:34:20.150 ⇒ 00:34:25.779 Demilade Agboola: I will, you know, I’ll just text him, find out how far it’s gone. If not, I will do this myself.
353 00:34:30.440 ⇒ 00:34:32.899 Uttam Kumaran: I mean, we can also do this in our meeting with him next week.
354 00:34:33.800 ⇒ 00:34:35.540 Uttam Kumaran: But can you message him anyways?
355 00:34:35.850 ⇒ 00:34:41.080 Demilade Agboola: Yeah, we have, china. I’m just texting.
356 00:34:44.960 ⇒ 00:34:46.669 Uttam Kumaran: So I’m gonna move this back for now.
357 00:34:46.870 ⇒ 00:34:48.059 Uttam Kumaran: Yeah, how far I got.
358 00:34:48.730 ⇒ 00:34:52.060 Uttam Kumaran: Okay, and then…
359 00:34:52.750 ⇒ 00:34:59.950 Uttam Kumaran: This is the last one, Emily, for… this is, like, some old tickets, so I don’t know what this is really related to.
360 00:35:01.850 ⇒ 00:35:07.309 Uttam Kumaran: Like, did we do… do we have any more inventory-related Looker changes to make?
361 00:35:07.910 ⇒ 00:35:15.210 Emily Giant: So many, because we don’t… Okay. So, Malade, what’s the status of, historical revenue?
362 00:35:16.010 ⇒ 00:35:23.470 Demilade Agboola: Testing, it’s not… the test keeps filling certain parts of the test, but I’m hoping to finish that today, and then push it off.
363 00:35:24.890 ⇒ 00:35:29.480 Emily Giant: So, will that be part of the fact Order lines table.
364 00:35:30.200 ⇒ 00:35:34.279 Demilade Agboola: It will be Fact or the Lion’s table, yes.
365 00:35:34.280 ⇒ 00:35:35.360 Emily Giant: Okay.
366 00:35:35.360 ⇒ 00:35:39.490 Demilade Agboola: But just so things don’t break, I am trying to…
367 00:35:41.410 ⇒ 00:35:50.840 Demilade Agboola: I am trying to version it, basically, and so it will be its own version, and once I… once I merge it.
368 00:35:51.230 ⇒ 00:35:58.570 Demilade Agboola: And we’ve tested, and everything’s fine. You can switch versions within Looker and to look at the new table with still the same color.
369 00:35:59.180 ⇒ 00:36:01.319 Demilade Agboola: So that, that’s defined that.
370 00:36:02.370 ⇒ 00:36:18.480 Uttam Kumaran: So this one, too, I would like to maybe… yeah, I’d like to probably do a meeting just to understand where we’re at on, like, entirely with, like, getting our models into production in Looker, and maybe we can create a little action plan, and then we can start to break up tickets, because that’s certainly stuff that we can help work on, too, so…
371 00:36:19.490 ⇒ 00:36:21.839 Uttam Kumaran: Okay, so let’s do that next week as well.
372 00:36:22.420 ⇒ 00:36:26.870 Uttam Kumaran: Great. Okay, I feel pretty good about where stuff is now.
373 00:36:27.110 ⇒ 00:36:37.130 Uttam Kumaran: I guess for the… for the existing job stuff, like, do we need any help on… on those?
374 00:36:37.130 ⇒ 00:36:40.169 Emily Giant: I… I just deployed a fix that…
375 00:36:40.690 ⇒ 00:36:48.279 Emily Giant: well, that should fix it. I don’t know why that started happening, because that has been…
376 00:36:48.460 ⇒ 00:36:57.579 Emily Giant: those tables haven’t changed in days, and it was running fine yesterday, but whatever. Yeah, so that should be fixed. I’ll keep my eye on it.
377 00:36:57.970 ⇒ 00:37:00.450 Emily Giant: But I don’t think we need to go over anything.
378 00:37:01.000 ⇒ 00:37:06.429 Demilade Agboola: Okay, if they… if it gets to the point where you need my help, just text me.
379 00:37:06.830 ⇒ 00:37:10.400 Emily Giant: Okay. Yeah, I think it was just an issue with the union.
380 00:37:10.560 ⇒ 00:37:13.080 Emily Giant: It was… it needed, like, a full refresh.
381 00:37:13.770 ⇒ 00:37:18.679 Emily Giant: And… That seems to have fixed.
382 00:37:18.840 ⇒ 00:37:24.269 Emily Giant: it in my staging. So, yeah, it was that OMS comp
383 00:37:24.380 ⇒ 00:37:29.660 Emily Giant: Base XF was saying, like, it had different union, counts.
384 00:37:29.850 ⇒ 00:37:35.829 Emily Giant: I don’t think I changed anything, I just refreshed it, and then it was okay. Like, full refreshed it.
385 00:37:37.420 ⇒ 00:37:39.020 Demilade Agboola: Oh, okay, sounds good.
386 00:37:39.230 ⇒ 00:37:39.570 Emily Giant: Yeah.
387 00:37:39.570 ⇒ 00:37:47.340 Uttam Kumaran: Okay, so I’ll just monitor, let me know, Emily, if I can… because, yeah, I’d rather you get the… I can come in and patch that if you want to get the transaction stuff out, so just let me know.
388 00:37:47.340 ⇒ 00:37:48.680 Emily Giant: Okay, yeah.
389 00:37:51.580 ⇒ 00:37:52.620 Uttam Kumaran: Okay, cool.
390 00:37:52.970 ⇒ 00:38:01.440 Uttam Kumaran: Alright, so I’ll probably send a note, on just, like, updates, and then I guess my only ask, Emma, if you have a chance today to look at that deck, and just give your feedback.
391 00:38:01.440 ⇒ 00:38:02.690 Emily Giant: Yeah. That would be…
392 00:38:02.690 ⇒ 00:38:07.139 Uttam Kumaran: Helpful. I was gonna… we’re gonna, we have a meeting with Zach next week.
393 00:38:07.620 ⇒ 00:38:25.220 Uttam Kumaran: Just gonna kind of present, like, what we’ve done this month, and then talk through a few opportunities. I think the biggest thing we… Demolade and I kind of talked about is just… I think the current, like, analyst team is just, like, slammed with day-to-day stuff, but now in our new models, we’re sitting on a lot of, like, great data to answer some, like, really
394 00:38:25.360 ⇒ 00:38:28.869 Uttam Kumaran: like, I think some high ROI business questions.
395 00:38:29.090 ⇒ 00:38:31.569 Uttam Kumaran: So my push to him is, like.
396 00:38:32.750 ⇒ 00:38:37.610 Uttam Kumaran: We need, like, some, like, really hardcore analyst time to, like, go after some of that.
397 00:38:37.740 ⇒ 00:38:42.900 Uttam Kumaran: And I’m like, where is that gonna come from, basically? It’s, like, one of the asks that I have.
398 00:38:43.180 ⇒ 00:38:49.030 Uttam Kumaran: Because not only have we made a lot of the models better, but we could do a lot more interesting analysis, and…
399 00:38:49.320 ⇒ 00:38:54.730 Uttam Kumaran: Just don’t know whether we have the time from folks internally right now, versus they’re just… Dealing with, like.
400 00:38:55.740 ⇒ 00:38:56.500 Uttam Kumaran: the usual.
401 00:38:56.500 ⇒ 00:38:57.070 Emily Giant: Yeah.
402 00:38:57.690 ⇒ 00:39:01.600 Emily Giant: the non-optimized versions of everything, yeah.
403 00:39:01.600 ⇒ 00:39:02.300 Uttam Kumaran: Yeah.
404 00:39:03.750 ⇒ 00:39:05.410 Uttam Kumaran: But yeah, let me know what you think.
405 00:39:06.000 ⇒ 00:39:07.100 Emily Giant: Okay, I will.
406 00:39:07.540 ⇒ 00:39:11.589 Uttam Kumaran: Okay, alright. Well, thank you, everyone. Talk to you soon, have a good weekend.
407 00:39:12.010 ⇒ 00:39:13.690 Emily Giant: too. Bye. Bye.
408 00:39:13.690 ⇒ 00:39:14.290 Demilade Agboola: Bye.