Meeting Title: US x BF | Sprint Retro Date: 2025-08-04 Meeting participants: Zack Gibbs, Uttam Kumaran, Alex K, Emily Giant, Amber Lin, Demilade Agboola
WEBVTT
1 00:00:35.570 ⇒ 00:00:36.290 Uttam Kumaran: Hey! Zack!
2 00:00:37.890 ⇒ 00:00:38.970 Zack Gibbs: Hey, Tom!
3 00:00:38.970 ⇒ 00:00:39.850 Uttam Kumaran: Hey!
4 00:00:41.090 ⇒ 00:00:42.260 Zack Gibbs: How’s it going.
5 00:00:43.070 ⇒ 00:00:43.900 Uttam Kumaran: Good.
6 00:01:06.910 ⇒ 00:01:08.529 Alex K: Hey? How’s it going? Utam?
7 00:01:08.800 ⇒ 00:01:09.650 Uttam Kumaran: Hey?
8 00:01:10.210 ⇒ 00:01:11.420 Uttam Kumaran: Good! How are you?
9 00:01:11.420 ⇒ 00:01:14.579 Alex K: Doing okay doing okay.
10 00:01:29.260 ⇒ 00:01:33.200 Zack Gibbs: Oh, so the Gt. The Gtm. Ticket was resolved on Friday.
11 00:01:35.620 ⇒ 00:01:36.470 Zack Gibbs: Alex.
12 00:01:37.975 ⇒ 00:01:43.469 Alex K: I think it was resolved well before then. But I think, Chris
13 00:01:43.620 ⇒ 00:01:47.480 Alex K: added, like the official findings there on Friday. Yeah.
14 00:01:49.440 ⇒ 00:01:51.490 Alex K: let me. You’re looking at the.
15 00:01:51.490 ⇒ 00:01:55.090 Zack Gibbs: So was the Tiktok Tiktok Pixel. That was causing the problem.
16 00:01:55.090 ⇒ 00:01:58.579 Alex K: Oh, the double. Yeah, the double firing, and they yep, and they pulled it out.
17 00:01:58.950 ⇒ 00:02:04.239 Alex K: That’s what they found. I didn’t talk to Chris about that. I just saw that came in on Friday.
18 00:02:14.350 ⇒ 00:02:21.430 Uttam Kumaran: Cool. Maybe I can get started. Let me just share my screen.
19 00:02:39.740 ⇒ 00:02:47.160 Uttam Kumaran: Great. So just an update for everybody. So we since we were able to kind of successfully go through
20 00:02:47.340 ⇒ 00:02:52.200 Uttam Kumaran: questions with all the core stakeholders. Last week. And I think we
21 00:02:52.570 ⇒ 00:02:58.499 Uttam Kumaran: like led to a lot of really great discussions. So we have everything here in terms of
22 00:02:58.720 ⇒ 00:03:02.100 Uttam Kumaran: like needs. Still, like some open questions about logic.
23 00:03:02.230 ⇒ 00:03:15.020 Uttam Kumaran: But I feel pretty comfortable towards the end. I think we had a lot of overlap, so I feel like we’ve captured everything. Probably the only core change since last time, and I think this is where I want to have us start executing stuff
24 00:03:15.170 ⇒ 00:03:19.520 Uttam Kumaran: today is we we started to break down like what the next
25 00:03:21.340 ⇒ 00:03:40.310 Uttam Kumaran: you know, what what the mart is actually gonna look like, basically, our goal is to start with the final mark tables. And then if there needs to be staging or intermediate models, we can build those. But we basically have now, like, what you’ll see in this document is just like how we’re gonna plan on
26 00:03:40.879 ⇒ 00:03:54.780 Uttam Kumaran: naming. I mean, I’m probably less concerned about like having all of the specific columns named. Exactly, and having this all in a plan. But really a couple of things that we heard from folks is we need a lot more
27 00:03:55.427 ⇒ 00:04:07.322 Uttam Kumaran: like clear dates for several different sort of status fields like both creation delivery. Additionally. There’s going to be notes around
28 00:04:08.670 ⇒ 00:04:26.333 Uttam Kumaran: what was actually ordered versus if it was replaced like if if I forgot what the word was, but if it was replaced, and another order was were sent in. But what those were? That was particularly for Perry. There’s a lot of things around subscriptions as well.
29 00:04:26.810 ⇒ 00:04:42.230 Uttam Kumaran: and I think something. Everybody is sort of curious about different things. It’s gonna require a little bit of a new modeling for us, just because it’s like, typically, we’ll do subscriptions on a snapshot basis. So you want to snapshot this, the status of all subscriptions.
30 00:04:42.400 ⇒ 00:04:45.349 Uttam Kumaran: typically like beginning of month end of month.
31 00:04:45.802 ⇒ 00:04:57.057 Uttam Kumaran: And then ideally over time, we could start looking at like changes daily. That way you can get a sense of how many new subscriptions are added. How many were downgraded? How many were upgraded?
32 00:04:57.720 ⇒ 00:05:02.059 Uttam Kumaran: how many were turned, and then how many were like reactivated?
33 00:05:03.770 ⇒ 00:05:18.820 Uttam Kumaran: So we’ll be working through that. We have all of these listed here. We. We’re sort of working through between demonic Kyle and myself, like we’ll be working through some of the really specific logic. Around, like.
34 00:05:19.630 ⇒ 00:05:23.967 Uttam Kumaran: basically around, like calculating different like revenue events.
35 00:05:25.480 ⇒ 00:05:27.310 Uttam Kumaran: you know, whether we have like
36 00:05:27.480 ⇒ 00:05:49.440 Uttam Kumaran: part of the stuff that we’re trying to support here is also things for for Dean’s team. So that’s the one thing that we want to make sure that we’re able to support not only the operational metrics, but also anything they need on the financial side. While we’re going through this we have captured a lot of like the existing logic here. So I think this is gonna be like a great artifact
37 00:05:49.700 ⇒ 00:05:59.522 Uttam Kumaran: that will kind of keep long term. I would say. It’s probably like 80% there, meaning the rest of 20% will fill when once we start building
38 00:06:00.100 ⇒ 00:06:08.270 Uttam Kumaran: So I didn’t have like anything. I just wanted to sort of put this in front of anyone if there was any open questions. I have a couple of open questions, particularly
39 00:06:08.450 ⇒ 00:06:12.899 Uttam Kumaran: about loop but this is something that we want to start executing.
40 00:06:13.470 ⇒ 00:06:15.100 Uttam Kumaran: Today.
41 00:06:15.600 ⇒ 00:06:24.050 Uttam Kumaran: I know it’s like a lot to absorb. And there’s not a specific ask about like, Hey, we have questions. For the most part we’ve gotten all the business logic questions answered by the team.
42 00:06:26.100 ⇒ 00:06:30.710 Zack Gibbs: Okay. So just I’m going at the bottom of the doc. Just to make sure I
43 00:06:31.623 ⇒ 00:06:38.079 Zack Gibbs: who you chatted with. So you chat with the finance folks that included Dean Gabe and Wilder. I’m looking. I’m looking at.
44 00:06:44.300 ⇒ 00:06:45.750 Zack Gibbs: yeah. The section down.
45 00:06:46.070 ⇒ 00:06:48.979 Zack Gibbs: Think it’s slightly slightly above.
46 00:06:49.450 ⇒ 00:06:54.350 Uttam Kumaran: Oh, yeah, this one, yeah.
47 00:06:55.980 ⇒ 00:07:03.449 Zack Gibbs: Okay? Alright. So you guys met with finance those those folks
48 00:07:03.580 ⇒ 00:07:08.979 Zack Gibbs: there to document kind of the general business business questions and wish list items. Is that right?
49 00:07:08.980 ⇒ 00:07:09.720 Uttam Kumaran: Yes.
50 00:07:10.000 ⇒ 00:07:17.990 Zack Gibbs: Okay, and then snop. It was Dean and Dean, and Ian Perry
51 00:07:19.200 ⇒ 00:07:23.950 Zack Gibbs: marketing sales subscriptions, or, as Kristen Pk Sashi and Sam.
52 00:07:24.670 ⇒ 00:07:26.329 Uttam Kumaran: No, this is probably the biggest
53 00:07:26.680 ⇒ 00:07:29.680 Uttam Kumaran: meeting, because we there’s a lot around subscriptions.
54 00:07:31.190 ⇒ 00:07:32.070 Uttam Kumaran: From Sam.
55 00:07:37.860 ⇒ 00:07:38.840 Zack Gibbs: Okay.
56 00:07:43.460 ⇒ 00:07:49.469 Zack Gibbs: alright, can we talk about the. So I think I’m just going back to all the stakeholders that we identified.
57 00:07:49.970 ⇒ 00:07:56.040 Zack Gibbs: Chris Hodge was the only one that was not represented out of the list. Is that right?
58 00:07:56.780 ⇒ 00:07:57.590 Zack Gibbs: Okay,
59 00:08:03.010 ⇒ 00:08:04.440 Zack Gibbs: So it’s
60 00:08:04.710 ⇒ 00:08:11.239 Zack Gibbs: so I’m gonna jump around a little bit. That’s the 1st time that like looking at this. Can we go down to the
61 00:08:13.090 ⇒ 00:08:17.640 Zack Gibbs: the high level architecture diagram, ingestion storage modeling?
62 00:08:17.960 ⇒ 00:08:21.230 Zack Gibbs: It’s in like Number 6. Yeah.
63 00:08:21.230 ⇒ 00:08:21.890 Uttam Kumaran: Yeah.
64 00:08:25.670 ⇒ 00:08:26.440 Zack Gibbs: Okay.
65 00:08:38.179 ⇒ 00:08:45.789 Uttam Kumaran: So one open question we had, Emily that I was chatting with Kyle on was there was a note here about
66 00:08:51.149 ⇒ 00:08:53.859 Uttam Kumaran: some sort of raw shopify exports.
67 00:08:57.949 ⇒ 00:09:01.439 Uttam Kumaran: But I don’t. I don’t know if there are any of those.
68 00:09:02.240 ⇒ 00:09:07.480 Emily Giant: Raw shopify exports. I’m not. Maybe he’s talking about like the the tables in Hevo.
69 00:09:08.770 ⇒ 00:09:12.900 Uttam Kumaran: Yes, but I I was like I didn’t.
70 00:09:14.780 ⇒ 00:09:15.340 Emily Giant: Yeah.
71 00:09:17.190 ⇒ 00:09:27.179 Emily Giant: No. Csv, I’m not sure what he means, either. I’m not. I’m not familiar with any like exports or importing that we’re doing from shopify other than what’s
72 00:09:27.880 ⇒ 00:09:29.109 Emily Giant: coming to Hebo.
73 00:09:44.220 ⇒ 00:09:46.350 Alex K: Can I ask?
74 00:09:48.350 ⇒ 00:09:49.619 Alex K: Perhaps a more
75 00:09:51.200 ⇒ 00:09:56.599 Alex K: focused question than like high level at this juncture like, Is that productive? Or should I hold it till later?
76 00:10:01.130 ⇒ 00:10:03.247 Uttam Kumaran: I’m fine. I’m fine with it. Yeah,
77 00:10:04.190 ⇒ 00:10:09.710 Alex K: Yeah, I guess so with the fact line items, table.
78 00:10:10.060 ⇒ 00:10:10.690 Uttam Kumaran: Yeah.
79 00:10:10.940 ⇒ 00:10:12.960 Alex K: What is the plan?
80 00:10:14.780 ⇒ 00:10:19.360 Alex K: Cause this, this is the same. Is this the same one that will be referenced
81 00:10:20.350 ⇒ 00:10:28.189 Alex K: against like inventory assignment and stuff like that, like in your inventory, mart? Or is this a separate thing.
82 00:10:32.230 ⇒ 00:10:32.850 Uttam Kumaran: Yeah.
83 00:10:32.850 ⇒ 00:10:33.720 Emily Giant: Separate.
84 00:10:34.390 ⇒ 00:10:35.280 Uttam Kumaran: It is.
85 00:10:35.630 ⇒ 00:10:41.380 Uttam Kumaran: Yeah, it’s separate. I don’t know. Demoda, do you want to talk about how we’re looking at this in inventory?
86 00:10:41.380 ⇒ 00:10:45.579 Alex K: Yeah, I think that’s my question is, how do those correlate with these fact tables.
87 00:10:46.812 ⇒ 00:10:52.460 Demilade Agboola: So there are different sources. I know the basis of a lot of inventory is netsuite.
88 00:10:52.924 ⇒ 00:10:58.430 Demilade Agboola: Hence the polyatomic. And that’s kind of like what we’re using for a lot of it.
89 00:10:59.145 ⇒ 00:11:02.950 Demilade Agboola: But for this it will be largely shopify.
90 00:11:03.650 ⇒ 00:11:12.439 Demilade Agboola: and there will be some cross-referencing, as there is currently with the inventory. Because inventory, we get some of the
91 00:11:13.240 ⇒ 00:11:19.220 Demilade Agboola: information from like Oms data which is still like shopify data enriched.
92 00:11:19.430 ⇒ 00:11:35.370 Demilade Agboola: But in this case it will kind of be. The base will be largely the shopify data. And then, when we need information from Hevo, I’m sorry from poly from netsuite, which is polytomic data, we will use it to enrich this.
93 00:11:38.470 ⇒ 00:11:44.980 Alex K: So how? How does that get correlated, then? Is there like like, what is the go ahead.
94 00:11:45.430 ⇒ 00:11:49.920 Demilade Agboola: So it depends on what the what information we’re trying to get
95 00:11:50.574 ⇒ 00:11:56.480 Demilade Agboola: for some certain things. Once we look at it in shopify
96 00:11:56.890 ⇒ 00:12:03.859 Demilade Agboola: there. There might be certain metrics we will be unable to get unless we look at it from the netsuite perspective.
97 00:12:04.850 ⇒ 00:12:10.019 Demilade Agboola: And then the other. In the other case like. That’s how we’re looking at it from
98 00:12:10.310 ⇒ 00:12:18.849 Demilade Agboola: like inventory. So for inventory, we’re looking at it from Netsuite as the base. But there were certain things we were struggling to be able to like, identify.
99 00:12:19.010 ⇒ 00:12:27.580 Demilade Agboola: or flag without looking at it from the shopify perspective as well. So it’s kind of like the opposite. In this case we’ll just be looking at it from
100 00:12:28.220 ⇒ 00:12:40.440 Demilade Agboola: the base would largely just be shopify data. But if we need to see certain flags or certain things coming in from Netsuite. Maybe netsuite transactions or Netsuite transaction lines, or, you know.
101 00:12:40.590 ⇒ 00:12:45.009 Demilade Agboola: whatever table we are looking after Netsuite, then we will use that information
102 00:12:45.280 ⇒ 00:12:49.650 Demilade Agboola: in enriching our orders, data from shopify.
103 00:12:50.030 ⇒ 00:13:00.169 Uttam Kumaran: Yeah. So shopify becomes a source of truth for anything that’s at the transaction. So again, seeing entities like orders, discounts, coupon code, redep like. That’s all stuff from shop.
104 00:13:00.970 ⇒ 00:13:05.840 Uttam Kumaran: If there, if there is a need to pull inventory related
105 00:13:06.810 ⇒ 00:13:11.150 Uttam Kumaran: like metrics or dimensions, we will then source from the inventory mark.
106 00:13:12.460 ⇒ 00:13:15.810 Alex K: And would that be at a like? At what level.
107 00:13:16.880 ⇒ 00:13:17.320 Demilade Agboola: I see.
108 00:13:17.320 ⇒ 00:13:21.969 Alex K: What I’m trying to say is like, Oh, I’m sorry.
109 00:13:22.850 ⇒ 00:13:26.870 Demilade Agboola: So we’ll tie them based on like sub orders. That’s what we did for inventory.
110 00:13:27.040 ⇒ 00:13:43.300 Demilade Agboola: So because the suborder ids were consistent across both systems. We’re able to go, hey? This is what we have in inventory, like netsuite data. And they were able to tie it back to the orders data in shopify, based off the suborder. Id.
111 00:13:44.230 ⇒ 00:13:50.650 Alex K: I guess. Let me rephrase my question. I thank you for that. That makes sense on the actual like correlation. I guess what I’m looking at is like
112 00:13:51.070 ⇒ 00:13:52.030 Alex K: isn’t.
113 00:13:52.540 ⇒ 00:13:54.620 Alex K: When I think of the fact
114 00:13:55.370 ⇒ 00:13:57.979 Alex K: of the revenue like when I when I look at an order.
115 00:13:58.220 ⇒ 00:14:19.359 Alex K: the fact table of the order. One of the questions should be, you know that I know everyone wants to ask is like, was this, what is the actual profit on this order, right and like, in order to do that, you need the fact of the inventory stuff tied with it. So I don’t know if like is when we say fact, tables. Maybe this is just me learning more about the bi structure and process.
116 00:14:19.812 ⇒ 00:14:23.680 Alex K: What is like cause like, we don’t want them necessarily just referencing
117 00:14:24.730 ⇒ 00:14:27.690 Alex K: a single table, and then having to join with other tables in look.
118 00:14:27.690 ⇒ 00:14:32.079 Uttam Kumaran: So we will, so we will. So we will bring that cogs item in
119 00:14:32.620 ⇒ 00:14:34.769 Uttam Kumaran: right. So if if the if the.
120 00:14:34.910 ⇒ 00:14:46.129 Uttam Kumaran: if if we want to see line item profitability. We will make sure that we source that column from the inventory. But this is the thing we’re not. Instead of typically what’s happening now is
121 00:14:46.740 ⇒ 00:14:49.040 Uttam Kumaran: revenue is not like
122 00:14:49.240 ⇒ 00:15:16.710 Uttam Kumaran: tableau items, except they’re all going directly to the source meaning there’s no reuse of like logic, right? So the reason why we have the inventory model now is that’s the trusted source for inventory. Related metrics. So it went. So now, revenue mark. If they there’s a question. And we need to source something regarding inventory. We can just pull from the revenue from the inventory mark table versus going all the way back and taking something direct from netsuite. So it’s it’s basically
123 00:15:17.204 ⇒ 00:15:34.410 Uttam Kumaran: happens as needed. So I additionally, you’re right like anything where there’s like a join happens very often we’ll make that available in the fact table. But also typically, we can create summary views pretty easily, and all the analysts will be trained on like, okay, here are the common
124 00:15:34.710 ⇒ 00:15:41.109 Uttam Kumaran: fields between them, if you need to join them more ad hoc. So the nice thing about what? Why we went first.st
125 00:15:41.350 ⇒ 00:15:46.540 Uttam Kumaran: Commonly, when you do data modeling like this, many teams skip
126 00:15:46.890 ⇒ 00:16:06.320 Uttam Kumaran: this step. You know, which, like they, they get a sense for, okay, we generally want to answer subscription questions. But typically the A team will just build the models. And then you kind of have to go back. One of the reasons why I wanted to prioritize. This is because we will actually be able to share the query
127 00:16:06.520 ⇒ 00:16:16.799 Uttam Kumaran: that can go answer these questions at the end of the mark development. Basically. So we go back and say, cool, you can now get this run this query on the smart table.
128 00:16:18.010 ⇒ 00:16:26.610 Uttam Kumaran: So if it falls under this question list, we will either solve it by all being made available in one table, or a simple join between
129 00:16:26.730 ⇒ 00:16:29.650 Uttam Kumaran: a few like. That’s sort of the expectation.
130 00:16:30.040 ⇒ 00:16:32.386 Alex K: Cool that I’m I’m aligned. Yeah, I think.
131 00:16:33.600 ⇒ 00:16:39.629 Alex K: when I when I saw like the proposed thing, and I don’t see some of the key questions answered in that fact table. I just wanna understand.
132 00:16:39.630 ⇒ 00:16:44.429 Uttam Kumaran: That’s why I’m like the the I’m I would say the column level
133 00:16:44.740 ⇒ 00:16:48.040 Uttam Kumaran: like, it’s just so granular at this point that yeah yeah
134 00:16:48.090 ⇒ 00:16:51.950 Uttam Kumaran: month planning. So I’m I sort of just wanted to get like
135 00:16:52.150 ⇒ 00:17:06.129 Uttam Kumaran: 60 to 80% there, which, like cool, we have these core entities when we develop the fact orders model, the goal will be, look back and make sure. We can answer these questions with that model. So whatever the the mix of fields are to do that
136 00:17:06.369 ⇒ 00:17:08.650 Uttam Kumaran: cool, you know. We’ll we’ll make there. Yeah.
137 00:17:08.819 ⇒ 00:17:10.239 Alex K: Thank you for breaking that down. I think that.
138 00:17:10.240 ⇒ 00:17:10.810 Uttam Kumaran: Yeah.
139 00:17:10.819 ⇒ 00:17:12.929 Alex K: 60% learning for me. So thank you.
140 00:17:12.930 ⇒ 00:17:13.770 Uttam Kumaran: Yeah.
141 00:17:14.849 ⇒ 00:17:30.119 Uttam Kumaran: I I just want to really balance between, like, I know, it can seem like a lot of planning and like over planning. So I want us to move like, now that we have all the questions we have the general sense of the fact tables that we’re going to drive towards
142 00:17:30.310 ⇒ 00:17:34.100 Uttam Kumaran: like we would. We’ll begin to ticket that out and drive towards that.
143 00:17:37.720 ⇒ 00:17:46.850 Demilade Agboola: And the just to kind of add to what I’m just said. The advantage of having like very clear fact tables is, it’s much easier to create some reviews
144 00:17:47.000 ⇒ 00:17:53.599 Demilade Agboola: where we’re able to aggregate these numbers in easy to
145 00:17:53.660 ⇒ 00:18:21.850 Demilade Agboola: easy to use views for like dashboards, because once we already know what the orders are. It’s easy to roll it up to like revenue by date, for instance. So if we’re able to aggregate like, we have our subscriptions clearly put together, orders clearly put together. It’s easy to just roll up to revenue by month, or whatever like aggregates we would like to see for the dashboard, so that allows us to be to build it out much faster.
146 00:18:25.450 ⇒ 00:18:26.290 Alex K: Thumbs up.
147 00:18:29.540 ⇒ 00:18:32.139 Zack Gibbs: Do we wanna talk about loop? And next steps there.
148 00:18:32.820 ⇒ 00:18:37.401 Uttam Kumaran: Yes, how to go with
149 00:18:38.810 ⇒ 00:18:40.529 Uttam Kumaran: Do they get back to us on anything.
150 00:18:42.507 ⇒ 00:18:46.680 Zack Gibbs: I just pinged them this morning. They they haven’t gotten back with.
151 00:18:50.200 ⇒ 00:19:08.060 Zack Gibbs: I think you had written up like the endpoints consume web hooks. The general use cases. They haven’t gotten back with us. I asked them. Could we set up a call? We like talk, talk through things in terms of. I think there’s a couple of questions on our end. One is, do we have a sense for like
152 00:19:09.140 ⇒ 00:19:14.379 Zack Gibbs: row, count slash increased cost from polytomic here.
153 00:19:15.740 ⇒ 00:19:22.320 Uttam Kumaran: I don’t know until I see the data. But I guess my message in the Channel was like.
154 00:19:22.740 ⇒ 00:19:29.400 Uttam Kumaran: polytomic will build it. But they will basically build it and tell us what it’s gonna cost. And then we can mix it at that point.
155 00:19:30.032 ⇒ 00:19:33.010 Uttam Kumaran: So all of like for something like loop.
156 00:19:33.690 ⇒ 00:19:47.780 Uttam Kumaran: They build most of their connectors as soon as a customer requested, so I think it’ll probably take them like a week or so to build it give us like maybe a week, to play around and get a sense for the volume, and we would get it. So I don’t think we’re gonna be.
157 00:19:48.370 ⇒ 00:19:53.770 Uttam Kumaran: We’re not gonna be like turning something on without knowing. So I do think we have a lot of safety there.
158 00:19:54.930 ⇒ 00:20:00.029 Zack Gibbs: And then, did you guys talk about in the revenue in the meeting with with Sam?
159 00:20:00.150 ⇒ 00:20:03.539 Zack Gibbs: For like, how frequent that data needs to be refreshed.
160 00:20:04.630 ⇒ 00:20:07.280 Uttam Kumaran: Yeah, I basically told them,
161 00:20:08.030 ⇒ 00:20:30.780 Uttam Kumaran: like we were, we’re gonna aim for monthly like before they they had some needs for weekly. I don’t think there was anything on the Daily side, but they they want to start to see like weekly subscription changes for me like monthly, is very typical. To look at like active sub active subs basically. And and so that’s what our initial aim will be for
162 00:20:30.920 ⇒ 00:20:40.509 Uttam Kumaran: the way we we do. That also is through snapshots, so we’ll we’ll have snapshots of subscription statuses at those 2 points in the month.
163 00:20:41.281 ⇒ 00:20:44.970 Uttam Kumaran: But their their ask was for weekly. Eventually.
164 00:20:45.740 ⇒ 00:20:46.370 Zack Gibbs: Okay.
165 00:20:48.830 ⇒ 00:20:54.519 Zack Gibbs: Okay, yeah. I don’t think that there’s is there any concern from the from our group about
166 00:20:54.770 ⇒ 00:20:58.859 Zack Gibbs: just having getting the request in for polytomic to build a connection to loop.
167 00:21:02.140 ⇒ 00:21:03.360 Zack Gibbs: No, okay.
168 00:21:04.100 ⇒ 00:21:07.955 Zack Gibbs: I think we should do that, especially if it’s gonna if they’re gonna have a lead time there.
169 00:21:08.670 ⇒ 00:21:18.799 Uttam Kumaran: Another point on north north for North Beam. We’re actually having them build us the North Beam Connector for another one of our clients. So as soon as that’s
170 00:21:19.120 ⇒ 00:21:24.520 Uttam Kumaran: made available, happy to get a measurement as well.
171 00:21:25.650 ⇒ 00:21:26.025 Zack Gibbs: Okay.
172 00:21:29.080 ⇒ 00:21:33.950 Zack Gibbs: yeah, that one. We still there’s, I think, an internal meeting to chat through
173 00:21:35.040 ⇒ 00:21:44.020 Zack Gibbs: current current team usage of north beam. And like what data is specifically there versus in other places. That we can only get from north beam. So.
174 00:21:44.350 ⇒ 00:21:47.879 Uttam Kumaran: So mainly we’re looking at, spend, spend at spend and attribution.
175 00:21:48.970 ⇒ 00:21:52.010 Uttam Kumaran: Yeah.
176 00:21:53.090 ⇒ 00:22:01.249 Uttam Kumaran: shopify, shopify also will do attribution. But North Beam will is most likely what the team’s using as source of truth back to us.
177 00:22:02.070 ⇒ 00:22:10.089 Zack Gibbs: Yeah, I, yeah, I think what I want to know is like, what attribution is only available in north beam versus, like, what? What’s the addition.
178 00:22:10.470 ⇒ 00:22:11.683 Zack Gibbs: additional unlock
179 00:22:12.660 ⇒ 00:22:12.965 Zack Gibbs: But
180 00:22:13.650 ⇒ 00:22:19.179 Zack Gibbs: okay, that’s good to know that they’re they’re building, that. They’re building it actively, or they they already have it built.
181 00:22:19.450 ⇒ 00:22:24.520 Uttam Kumaran: We we are. They’re building it right now, like I think we we sent it to them on Friday
182 00:22:24.780 ⇒ 00:22:30.059 Uttam Kumaran: for another client of ours, so I’ll be able to show you. Get a sense of like what the scheme is.
183 00:22:30.770 ⇒ 00:22:31.350 Zack Gibbs: Okay.
184 00:22:31.740 ⇒ 00:22:33.010 Uttam Kumaran: Probably later this week.
185 00:22:35.730 ⇒ 00:22:46.549 Uttam Kumaran: The probably only other point is like we’re gonna do our, I mean, i. 1 of the questions that came up was moving as much of our reporting to shop from pulling from shopify. I think everything I heard from.
186 00:22:46.830 ⇒ 00:22:50.390 Uttam Kumaran: It’s all possible. I we’ve modeled shopify
187 00:22:50.820 ⇒ 00:23:02.074 Uttam Kumaran: like a dozen times. I haven’t seen anything in particular questions, especially like the more complicated questions about discounts, coupons.
188 00:23:02.860 ⇒ 00:23:10.544 Uttam Kumaran: the shipment updates. Like all those dates. I feel like we’re should be pretty good for, especially things like looking at.
189 00:23:11.400 ⇒ 00:23:18.180 Uttam Kumaran: the shipment provider stuff like that. Like, I, I feel like we’re gonna be okay on a lot of those. I didn’t hear anything particularly. That
190 00:23:18.890 ⇒ 00:23:21.749 Uttam Kumaran: was not something we’ve seen before.
191 00:23:22.360 ⇒ 00:23:26.870 Uttam Kumaran: So we’re gonna do our best to kind of move all as much of that source of truth to
192 00:23:27.300 ⇒ 00:23:30.090 Uttam Kumaran: to shopify for for revenue.
193 00:23:30.210 ⇒ 00:23:35.499 Uttam Kumaran: Probably the only thing that came up that was a point of discussion was markdowns versus
194 00:23:36.580 ⇒ 00:23:46.479 Uttam Kumaran: markdowns versus discounts, markdowns or price adjustments to the price in shopify discounts of a Co. Are, of course, like
195 00:23:46.950 ⇒ 00:23:58.669 Uttam Kumaran: percentage off, or or things like that. The question from the team was like whether markdowns are considered discounts. And Perry basically said, No, that’s something that I’ll I’ll compare. I’ll I will probably
196 00:23:58.930 ⇒ 00:24:10.750 Uttam Kumaran: shoot an email, or or we can. We can just confirm that with everybody but that markdowns are just literally moving the price of the product, I think what the team is interested in frankly is like
197 00:24:11.070 ⇒ 00:24:17.140 Uttam Kumaran: do, which should they be using? Because discounts goes to affect gross margin.
198 00:24:17.930 ⇒ 00:24:23.090 Uttam Kumaran: But markdowns, you’re literally just lowering the revenue, right? So
199 00:24:23.270 ⇒ 00:24:30.630 Uttam Kumaran: probably my question is like I. The mechanism is like, whatever more about what should get used as a lever when
200 00:24:31.539 ⇒ 00:24:34.679 Uttam Kumaran: right? Because I think this they’re probably adjusting both.
201 00:24:36.370 ⇒ 00:24:41.380 Uttam Kumaran: You know, without clarity on when to effect either. So.
202 00:24:44.320 ⇒ 00:24:49.940 Zack Gibbs: One they come on, they they’re so in shopify.
203 00:24:51.100 ⇒ 00:24:56.089 Zack Gibbs: The E-com slash sales team are going in and actually changing the actual price.
204 00:24:56.240 ⇒ 00:24:56.910 Uttam Kumaran: Yes.
205 00:24:57.180 ⇒ 00:24:58.480 Zack Gibbs: The list, price.
206 00:24:58.920 ⇒ 00:25:02.850 Uttam Kumaran: But it it. Yeah, so come so. But it’ll look like a slash through
207 00:25:03.280 ⇒ 00:25:07.390 Uttam Kumaran: right, or it’ll say, like, this is no longer a hundred bucks. It’s 80 bucks.
208 00:25:07.750 ⇒ 00:25:13.349 Uttam Kumaran: but that’s not a discount, and it that your revenue is still the 80 bucks.
209 00:25:13.460 ⇒ 00:25:14.199 Uttam Kumaran: It’s not.
210 00:25:14.530 ⇒ 00:25:19.949 Uttam Kumaran: Your revenue isn’t a hundred bucks, and then minus 20 discount right? Which, which
211 00:25:20.930 ⇒ 00:25:27.100 Uttam Kumaran: again, you could play both sides, is like, well, should that be a discount, because we should be tracking
212 00:25:27.220 ⇒ 00:25:32.216 Uttam Kumaran: that. We did that. And now we’re our sales went up. And so we lost that money.
213 00:25:32.790 ⇒ 00:25:43.799 Uttam Kumaran: but similarly like, yeah, like, discounts can be time box. They can be. There’s different mechanisms to that. Basically. What I want to look at is in shopify. You could see all the price rule price adjustments.
214 00:25:43.940 ⇒ 00:25:50.669 Uttam Kumaran: So I so you’ll be able to see the price changes to products and like the amount that was impacted.
215 00:25:51.203 ⇒ 00:25:54.129 Uttam Kumaran: So I’ll be looking to bring to bring a view of of
216 00:25:54.310 ⇒ 00:26:01.839 Uttam Kumaran: price changes in. So you can start to see markdowns. I think, for the business. It’s basically like.
217 00:26:02.040 ⇒ 00:26:05.459 Uttam Kumaran: whether you how, in what situations you want to use either.
218 00:26:06.930 ⇒ 00:26:11.839 Uttam Kumaran: Right? But I for for me, for being honest, the price adjustments
219 00:26:12.180 ⇒ 00:26:16.619 Uttam Kumaran: like are probably a sneaky way to not get hit by discounts.
220 00:26:16.900 ⇒ 00:26:22.979 Uttam Kumaran: The discounts will be really obvious, like, price adjustments.
221 00:26:25.380 ⇒ 00:26:27.919 Uttam Kumaran: Yeah, I don’t know. I feel that feels like a bigger change.
222 00:26:28.680 ⇒ 00:26:31.489 Uttam Kumaran: but I feel like now they’re both are being used.
223 00:26:31.950 ⇒ 00:26:44.580 Emily Giant: Yeah, I think the concern there is that historically, it would change everything, since they’ve always done it that way. But I agree that it’s probably something that
224 00:26:44.800 ⇒ 00:26:51.259 Emily Giant: would be interesting to be able to query to consider whether we should
225 00:26:51.790 ⇒ 00:26:56.030 Emily Giant: be counting markdowns towards discounts. But I know, like from Perry.
226 00:26:56.340 ⇒ 00:27:04.669 Emily Giant: I know that it was less about the discount. Hit more about how it would skew all of the comps to year every year.
227 00:27:13.100 ⇒ 00:27:18.529 Zack Gibbs: yeah, I mean, I think if there, if there’s something that’s being done out of best practice here, we would you know.
228 00:27:18.890 ⇒ 00:27:21.429 Uttam Kumaran: Okay, that’d be good. That’d be good to know. And.
229 00:27:22.455 ⇒ 00:27:23.080 Zack Gibbs: The.
230 00:27:23.080 ⇒ 00:27:25.050 Uttam Kumaran: See, because that team isn’t like
231 00:27:25.260 ⇒ 00:27:35.570 Uttam Kumaran: that team may not. I don’t think they realize that, like the math on the gross margin calculation. So they’re probably just tweak. They’re just tweaking it, based on
232 00:27:36.030 ⇒ 00:27:38.140 Uttam Kumaran: their marketing sense versus like.
233 00:27:38.780 ⇒ 00:27:39.150 Zack Gibbs: Yeah.
234 00:27:39.520 ⇒ 00:27:40.210 Uttam Kumaran: Yeah, you know.
235 00:27:40.210 ⇒ 00:27:44.420 Zack Gibbs: They want. They want the site to. They want the site to behave a certain way. Therefore they’re they’re
236 00:27:44.420 ⇒ 00:27:44.900 Zack Gibbs: yes.
237 00:27:44.900 ⇒ 00:27:56.210 Zack Gibbs: manual changes to make that happen, but it’s they don’t know the downstream impact of that. So if if that’s the case, and there’s a best practice to show this in the data in a different way. I’m sure we can enable that front end behavior
238 00:27:56.330 ⇒ 00:27:57.290 Zack Gibbs: separately.
239 00:27:57.950 ⇒ 00:27:58.510 Uttam Kumaran: Yeah.
240 00:28:04.500 ⇒ 00:28:12.129 Zack Gibbs: Are we track? Are we? Currently track? Is Perry or anybody else currently tracking the difference between markdowns and discounts.
241 00:28:12.790 ⇒ 00:28:14.300 Uttam Kumaran: No, no.
242 00:28:14.590 ⇒ 00:28:17.250 Zack Gibbs: Well, that’s problematic. We should try to fix that. I think.
243 00:28:17.250 ⇒ 00:28:17.900 Uttam Kumaran: Yeah.
244 00:28:22.100 ⇒ 00:28:26.570 Uttam Kumaran: I mean, so basically, there’s a, there’s a price rules and price rule changes table that
245 00:28:26.730 ⇒ 00:28:28.999 Uttam Kumaran: we’ll have everything for us.
246 00:28:33.190 ⇒ 00:28:36.170 Zack Gibbs: Yeah, enabling the front end to show show that.
247 00:28:38.630 ⇒ 00:28:40.589 Zack Gibbs: I think it’s a different ask and need.
248 00:28:40.590 ⇒ 00:28:41.330 Uttam Kumaran: Yes.
249 00:28:41.740 ⇒ 00:28:49.680 Zack Gibbs: So, okay, yeah, we should. We should try to model it where we are able to to
250 00:28:50.210 ⇒ 00:28:54.630 Zack Gibbs: see those 2 buckets distinctly. So.
251 00:29:01.756 ⇒ 00:29:09.163 Zack Gibbs: what needs to happen on the loop side? Do we? Do we need a like an intro email, or call with them?
252 00:29:09.500 ⇒ 00:29:14.830 Uttam Kumaran: Yeah, I’ll I’ll I’ll need a I’ll need an Api key.
253 00:29:18.110 ⇒ 00:29:23.520 Uttam Kumaran: but I’m I would also happy to if you want to throw me into that. I can then push that forward.
254 00:29:24.970 ⇒ 00:29:25.330 Zack Gibbs: Okay.
255 00:29:39.460 ⇒ 00:29:43.990 Uttam Kumaran: Modeling subscriptions is like there’s not. There shouldn’t be anything unique about loop versus
256 00:29:44.710 ⇒ 00:29:48.340 Uttam Kumaran: all of your other, you know, typical subscription platforms. So
257 00:29:48.520 ⇒ 00:29:54.649 Uttam Kumaran: it’s mainly I wanna I just wanna see. But the I would say the only distinction is, there’s like several
258 00:29:54.970 ⇒ 00:30:02.164 Uttam Kumaran: nontraditional like status fields, right? Emily, like we were talking to Sam about
259 00:30:03.420 ⇒ 00:30:12.209 Uttam Kumaran: a couple of them like I noted them down. Yeah, we have a pause.
260 00:30:13.629 ⇒ 00:30:18.490 Uttam Kumaran: We have paused, reactivated, and resumed.
261 00:30:18.680 ⇒ 00:30:22.009 Uttam Kumaran: Right like. So you could pause, you could cancel.
262 00:30:22.230 ⇒ 00:30:27.459 Uttam Kumaran: So there’s a couple of more mechanisms that I want to make sure that we can categorize
263 00:30:29.625 ⇒ 00:30:34.960 Uttam Kumaran: we also have, like prepaid versus non prepaid.
264 00:30:35.500 ⇒ 00:30:39.840 Uttam Kumaran: So there’s just a little bit more interesting things there.
265 00:30:48.090 ⇒ 00:30:52.130 Zack Gibbs: Okay, I can generate you an Api token.
266 00:31:03.270 ⇒ 00:31:07.600 Zack Gibbs: Okay, I’ll do that as a next step for for loop.
267 00:31:09.320 ⇒ 00:31:11.119 Uttam Kumaran: So on our end, I think we’re gonna
268 00:31:11.610 ⇒ 00:31:15.089 Uttam Kumaran: ticket these out. Get started with the
269 00:31:16.850 ⇒ 00:31:24.700 Uttam Kumaran: with the core orders, sub or sub like core. I line items, orders, models,
270 00:31:26.300 ⇒ 00:31:32.521 Uttam Kumaran: and then I think, for demalade and amber. We can make sure that
271 00:31:34.030 ⇒ 00:31:37.370 Uttam Kumaran: One thing that I want to make really clear whether that’s here.
272 00:31:37.560 ⇒ 00:31:47.630 Uttam Kumaran: I mean, I maybe it’s here we do that here in notion is that we go back to these questions, and then we start to map out the queries that can answer these as soon as they’re made available.
273 00:31:48.033 ⇒ 00:31:59.199 Uttam Kumaran: So that’s something that we can keep all here, because as soon as we have the table we can. We can just ping this crew and say, these these are now available. Whether we do that here
274 00:31:59.560 ⇒ 00:32:02.749 Uttam Kumaran: or we do this in the spreadsheet. I don’t.
275 00:32:03.710 ⇒ 00:32:18.590 Uttam Kumaran: whatever, but I do kind of systematically want to go through one by one by one as soon as the unlock is made to then send. This is now available. Here’s a query you can run. Here’s a couple of other interesting queries that we ran in case you’re interested.
276 00:32:18.700 ⇒ 00:32:21.849 Uttam Kumaran: and then we push it on the analyst and make it available in Looker.
277 00:32:23.280 ⇒ 00:32:38.208 Amber Lin: Totally. And also there’s something I would love to formalize for the inventory mark as well. I we’ve been working very closely with Lipa and Perry. But we don’t have a document like this in place. It might be easier for them if we have everything here.
278 00:32:38.520 ⇒ 00:32:54.600 Uttam Kumaran: Yeah, one thing that we can do, maybe we create a ticket for is just to get them to create these recurring questions. This is also something that like is gonna is gonna just last quite a while. So the next time someone has these questions, it’ll be very clear where to go do that, we will have some schema, shift and stuff, but
279 00:32:54.780 ⇒ 00:32:56.059 Uttam Kumaran: for the most part
280 00:32:56.240 ⇒ 00:33:00.819 Uttam Kumaran: this is a great, this this will be great to have, and this is what we’ll do for the next mark as well.
281 00:33:05.090 ⇒ 00:33:11.210 Zack Gibbs: Alright. I generated the loop. Api token. I sent it to you directly, Tom. It’s all read only scopes so.
282 00:33:16.180 ⇒ 00:33:20.050 Uttam Kumaran: Okay, great. So I think, probably update
283 00:33:20.180 ⇒ 00:33:32.399 Uttam Kumaran: I want to have is, tomorrow we we have spring kickoff. So I want to get a couple. I want to get as many of these things ticketed out amber. Today. I’ll have some time later today to meet that.
284 00:33:32.760 ⇒ 00:33:35.719 Uttam Kumaran: and then we can get some of these. If if we want to do like.
285 00:33:36.130 ⇒ 00:33:39.769 Uttam Kumaran: maybe we could do, we could do a locker grouping tomorrow, too.
286 00:33:40.215 ⇒ 00:33:45.240 Uttam Kumaran: Just given that we have. We’re gonna have several of these come through, but
287 00:33:45.680 ⇒ 00:33:48.196 Uttam Kumaran: I’ll I’ll leave it to you on how you want to plan it.
288 00:33:48.390 ⇒ 00:33:48.923 Amber Lin: Go ahead!
289 00:33:49.190 ⇒ 00:33:52.559 Uttam Kumaran: But this is basically ready to start breaking up.
290 00:33:53.350 ⇒ 00:33:54.460 Amber Lin: Sounds good.
291 00:33:55.693 ⇒ 00:34:01.666 Amber Lin: I’ll see when I have, when we have time to meet, maybe today or early tomorrow.
292 00:34:02.320 ⇒ 00:34:02.750 Uttam Kumaran: Okay.
293 00:34:03.106 ⇒ 00:34:08.459 Amber Lin: We’ll have to do the kickoff after week room, but we’ll we’ll figure that out.
294 00:34:08.699 ⇒ 00:34:16.509 Uttam Kumaran: Okay, yeah. Then, one thing I think we can. Maybe I guess I was. Gonna ask Emily, like, I’m not sure how the working sessions are are
295 00:34:17.159 ⇒ 00:34:27.649 Uttam Kumaran: so far. But I would like to start to do some while we’re in this revenue development phase would like to do some type of working session or have some block on the calendar
296 00:34:27.829 ⇒ 00:34:31.019 Uttam Kumaran: to work on these like together?
297 00:34:32.369 ⇒ 00:34:40.299 Uttam Kumaran: so maybe amber. We can also talk about that. This is gonna be a lot of modeling. So we’re it’s gonna be tough, everything Async.
298 00:34:40.943 ⇒ 00:34:48.039 Uttam Kumaran: So at least like once once a week would be great for all the folks like, just just for us to meet and talk about
299 00:34:48.749 ⇒ 00:34:50.579 Uttam Kumaran: things work we’re each working on.
300 00:34:50.819 ⇒ 00:34:57.099 Uttam Kumaran: And and I can take a lot of the subscriptions work, so I’ll cruise through that. Feel like that’d be a big win if we can get that soon.
301 00:35:00.060 ⇒ 00:35:16.700 Emily Giant: Yeah, we still have that block on the calendar every morning. So if we just want to pick a day like maybe Wednesday or Thursday. So that like, if there’s worked on over the weekend which you know, it happens we can all get like synced up, and then midweek. Make sure we’re touching base.
302 00:35:17.680 ⇒ 00:35:18.340 Uttam Kumaran: Okay.
303 00:35:21.670 ⇒ 00:35:22.490 Uttam Kumaran: perfect.
304 00:35:25.260 ⇒ 00:35:27.230 Uttam Kumaran: Great. Okay. That’s all. I have.
305 00:35:28.820 ⇒ 00:35:29.410 Zack Gibbs: Name.
306 00:35:29.630 ⇒ 00:35:30.670 Zack Gibbs: Okay.
307 00:35:31.500 ⇒ 00:35:33.700 Uttam Kumaran: Awesome. Okay. Talk to everyone.
308 00:35:34.476 ⇒ 00:35:36.029 Emily Giant: Thank you.