Meeting Title: US | Revenue Roadmap Review Date: 2025-07-24 Meeting participants: Uttam Kumaran, Zack Gibbs, Emily Giant, Demilade Agboola, Alex K, Amber Lin
WEBVTT
1 00:04:35.360 ⇒ 00:04:36.630 Zack Gibbs: Hey? How’s it going.
2 00:04:42.530 ⇒ 00:04:43.319 Uttam Kumaran: Hey! Zack!
3 00:04:44.310 ⇒ 00:04:45.210 Zack Gibbs: How you doing.
4 00:04:45.810 ⇒ 00:04:46.820 Uttam Kumaran: Good! How are you?
5 00:04:47.912 ⇒ 00:04:49.089 Zack Gibbs: Doing all right.
6 00:04:50.360 ⇒ 00:04:55.400 Zack Gibbs: Feel like like a million things going on
7 00:04:56.640 ⇒ 00:05:02.620 Zack Gibbs: with like just summer summer stuff, summer stuff alongside all the other work stuff.
8 00:05:02.940 ⇒ 00:05:07.790 Uttam Kumaran: Yes, I agree. Well, I’m finding I feel like I don’t not having a lot of like
9 00:05:07.980 ⇒ 00:05:13.839 Uttam Kumaran: summer things. I’m just in town. We’re not traveling much, which is great last year is a lot
10 00:05:14.060 ⇒ 00:05:15.120 Uttam Kumaran: for us. So.
11 00:05:15.980 ⇒ 00:05:24.250 Zack Gibbs: Yeah, my kids are just the right ages where there’s like a boatload of summer activities, and those have to be juggled alongside
12 00:05:24.620 ⇒ 00:05:26.670 Zack Gibbs: work and everything else. So.
13 00:05:26.670 ⇒ 00:05:27.050 Uttam Kumaran: Great.
14 00:05:28.400 ⇒ 00:05:29.939 Zack Gibbs: Where challenges come in.
15 00:05:35.690 ⇒ 00:05:36.740 Zack Gibbs: hey, Emily?
16 00:05:37.780 ⇒ 00:05:39.299 Emily Giant: Hi, how’s everyone doing?
17 00:05:40.020 ⇒ 00:05:40.480 Uttam Kumaran: Good.
18 00:05:40.480 ⇒ 00:05:41.320 Zack Gibbs: Good.
19 00:05:45.760 ⇒ 00:05:49.690 Uttam Kumaran: So today we’re gonna be going through. This.
20 00:05:50.200 ⇒ 00:05:53.080 Uttam Kumaran: doc, let me just make sure everyone is shared here.
21 00:06:08.400 ⇒ 00:06:11.700 Uttam Kumaran: Okay, I’ll send this in here.
22 00:06:12.670 ⇒ 00:06:20.120 Uttam Kumaran: but one thing that I started working with the team on is basically a a Tdd for
23 00:06:20.680 ⇒ 00:06:24.888 Uttam Kumaran: the revenue model. Basically,
24 00:06:25.970 ⇒ 00:06:31.489 Uttam Kumaran: Tdd stands for technical design document just given the size of
25 00:06:31.990 ⇒ 00:06:38.590 Uttam Kumaran: how much is going on. I felt like it was best to sort of get a lot of things written down first.st
26 00:06:39.023 ⇒ 00:06:58.116 Uttam Kumaran: Especially as we’re gonna make some pretty big foundational changes. I think there’s a lot of like pack boxes and stuff in this document. But the way to kind of think about this, Doc, is we have, we have, like our sort of project management plan. That’s more about the orchestration. This is really like the nitty gritty about like architecture decisions.
27 00:06:58.970 ⇒ 00:07:23.399 Uttam Kumaran: sort of logic decisions. And then starting to store all the information about like things we considered. We’ll just one is gonna be like, extremely helpful. Given like, how much stuff is going on here. Second, will be a great artifact, the future. To have these things noted here. So in this, doc, if I’ll just give you a brief overview we’re starting to just save
28 00:07:23.600 ⇒ 00:07:31.400 Uttam Kumaran: like just normal stuff about. Like other documents related purpose and goal. We jumped kind of right into the middle. Which was like.
29 00:07:31.670 ⇒ 00:07:35.922 Uttam Kumaran: what are the core problem. Statements here.
30 00:07:37.437 ⇒ 00:07:44.200 Uttam Kumaran: and like, what are the kind of foundational requirements? Of this again. Still, kind of like
31 00:07:44.730 ⇒ 00:08:01.881 Uttam Kumaran: business, Jargony, but like getting a little bit deeper into like if anyone was to open the stock and kind of get a sense of like what what the goal is. They can find it here. We’re still kind of working on this current state assessment. And this is really where Kyle has been spending time, which is on like research and discovery.
32 00:08:03.050 ⇒ 00:08:06.949 Uttam Kumaran: we’re basically starting to look at all of the core
33 00:08:07.540 ⇒ 00:08:26.450 Uttam Kumaran: upstream models. That power, the existing like revenue mart as it sounds today, basically, any any table associated with marts. So I think this is gonna be probably something that we just we’re gonna present on Monday, Emily, when we talk to Perry and Dean
34 00:08:26.942 ⇒ 00:08:48.889 Uttam Kumaran: and ideally spend most of our time answering some of these open questions. Some of these are like architecture questions like, should we do an incremental model like which fields to select? So that’s something that we will kind of make decisions on internally. Some of these are like foundational logic about like, okay, orders can have multiple boxes.
35 00:08:49.270 ⇒ 00:08:53.729 Uttam Kumaran: and then sort of like trying to visualize all the kind of the caveats here.
36 00:08:54.900 ⇒ 00:09:00.932 Uttam Kumaran: I think the biggest thing kind of to probably talk through today is just to hear
37 00:09:01.870 ⇒ 00:09:07.649 Uttam Kumaran: I mean, I have a couple of specific things, but just to hear if there’s anything else on on the revenue side
38 00:09:07.810 ⇒ 00:09:10.859 Uttam Kumaran: that is like a gotcha that we should consider.
39 00:09:10.970 ⇒ 00:09:21.100 Uttam Kumaran: or if there’s anything Zack that you can point to for revenue related data that is like a wish list. Item that the business currently can’t report on or
40 00:09:21.490 ⇒ 00:09:22.300 Uttam Kumaran: just
41 00:09:24.070 ⇒ 00:09:33.380 Uttam Kumaran: like doesn’t have the capacity given. How complicated stuff is to report on that the business is asked for. Yeah, Alex, please go ahead, or Zack.
42 00:09:33.740 ⇒ 00:09:37.709 Alex K: Yeah, I think the question I have is, where is the
43 00:09:38.790 ⇒ 00:09:54.369 Alex K: like requirement gathering on what we want revenue to? So obviously, we need to know what it’s at in order to get to where we’re going. But, like, where’s the bits that describe where we’re going with it? I think that’s what I wanna make sure we see and like validate like I I think it’s
44 00:09:55.180 ⇒ 00:09:57.910 Alex K: slightly cart before horse to have
45 00:09:58.020 ⇒ 00:10:04.399 Alex K: existing stuff like I don’t think it’s perfectly a carpool horse, but like, I think we need to know where we are going
46 00:10:04.750 ⇒ 00:10:12.820 Alex K: before we look backwards to understand, or rather, I think the time would be better suited looking backwards if we already know where we’re going.
47 00:10:13.630 ⇒ 00:10:16.279 Alex K: I don’t know if that makes sense, or if I’m being too simplistic. But I.
48 00:10:16.280 ⇒ 00:10:17.099 Uttam Kumaran: No, it makes it makes sense.
49 00:10:17.100 ⇒ 00:10:22.690 Alex K: Doing a lot of like what exists currently and like, we don’t really care what exists currently until we know where we’re going.
50 00:10:23.260 ⇒ 00:10:31.539 Uttam Kumaran: Yeah, I think I I would probably challenge that in that, I think, knowing where we’re going, like modeling basic revenue for the business, I think.
51 00:10:31.770 ⇒ 00:10:49.500 Uttam Kumaran: is somewhat clear. I think we also are. There is existing logic that is doing parts of that. So our job is one out of without any context, there’s a lot of cleanup work and a lot of consolidation to do but in doing this we want to add new logic that needs to exist.
52 00:10:50.720 ⇒ 00:11:00.039 Uttam Kumaran: and these models we kind of have to go one by one to look at in order to piece together like what’s actually relevant and what logic is duplicated across many things.
53 00:11:00.464 ⇒ 00:11:14.610 Uttam Kumaran: I would say, we, we already have a lot of information. So the like, we’re we’re working on the stock right now, but we already have a lot of information on the dashboards that are already powering revenue reporting so ultimately the goal is to
54 00:11:14.990 ⇒ 00:11:31.950 Uttam Kumaran: power those at a minimum. And like, basically just replace what’s existing there. But I want to kind of understand if there are reports that aren’t able, we’re not able to produce today. But at a basic level, like the the goals, still continue to be
55 00:11:32.140 ⇒ 00:11:36.856 Uttam Kumaran: time to debug time to add new logic,
56 00:11:37.630 ⇒ 00:11:53.390 Uttam Kumaran: and overall simplify simplification of the the code base, like as a as just like a table stakes. But we’re also want to document right? So a lot of this stuff isn’t written down anywhere. Clearly, the interactions between these core objects and the caveats that happen
57 00:11:53.530 ⇒ 00:11:54.670 Uttam Kumaran: so
58 00:11:55.360 ⇒ 00:12:02.880 Uttam Kumaran: part of this is a little bit we have to go do that because this just isn’t doesn’t exist anywhere. Now I think it’s in in our heads as well. But
59 00:12:03.260 ⇒ 00:12:05.789 Uttam Kumaran: I’m really pushing to get this written down.
60 00:12:08.090 ⇒ 00:12:14.040 Demilade Agboola: Also, I think, in terms of access. We do have like where we’re going with it.
61 00:12:14.452 ⇒ 00:12:20.500 Demilade Agboola: Part of what we want to do and part of where we’re going with all like what we’re trying to do right now.
62 00:12:20.690 ⇒ 00:12:35.179 Demilade Agboola: if you want to have clean details. As for orders, terms of like fact orders deem orders for dimensions. Clean data sets that we’re able to put things like the refunds, the discounts clearly put together
63 00:12:35.320 ⇒ 00:12:37.310 Demilade Agboola: and in a consistent manner.
64 00:12:37.980 ⇒ 00:12:49.549 Demilade Agboola: We also want to integrate the subscriptions into this so that we can have, like what’s going on there clearly put as well. Fax subscriptions. We’ve been having conversations about looking into loop. And then we can see that data as well.
65 00:12:49.710 ⇒ 00:13:00.530 Demilade Agboola: I’m also having conversations about how for subscriptions that revenue would be realized? Is it a 1 time thing? Is it a thing of every by, every single order being fulfilled.
66 00:13:00.580 ⇒ 00:13:01.220 Zack Gibbs: Bye.
67 00:13:01.220 ⇒ 00:13:05.620 Demilade Agboola: Content revenue. So if I’m actually like 15 of
68 00:13:07.420 ⇒ 00:13:16.349 Demilade Agboola: where we don’t know where we’re going, we do. But obviously we need to be able to piece everything together. But we need to be able to understand what currently exists.
69 00:13:16.690 ⇒ 00:13:21.699 Demilade Agboola: what is working, what’s not working things about pre-migration, post-migration?
70 00:13:22.307 ⇒ 00:13:32.790 Demilade Agboola: What happens in, you know, different edge cases, and how that contest revenue. So just being able to put that all together is kind of how we’ll determine the final end state.
71 00:13:32.950 ⇒ 00:13:42.760 Demilade Agboola: But obviously, you know, there is business context that we will need to pick up from. You know, the residents team to be able to piece everything together until that you finalize
72 00:13:43.396 ⇒ 00:13:44.370 Demilade Agboola: and suites.
73 00:13:45.030 ⇒ 00:13:51.049 Alex K: I gotcha, I think what’s throwing me is, I’d like, where is revenue defined like? What is the definition of what counts as revenue?
74 00:13:51.250 ⇒ 00:13:56.419 Uttam Kumaran: Yeah, yeah, I I think I don’t want this to be like a
75 00:13:57.040 ⇒ 00:14:01.899 Uttam Kumaran: we’re just documenting sort of the stuff like, some of this is a little bit
76 00:14:02.210 ⇒ 00:14:11.279 Uttam Kumaran: pedantic. So really, I want what what Kyle has been doing is literally going through all the existing revenue. Doc. Revenue models. And
77 00:14:11.580 ⇒ 00:14:16.029 Uttam Kumaran: our team. We’re starting to work on like, Hey, we’re noticing
78 00:14:16.430 ⇒ 00:14:21.160 Uttam Kumaran: logic in case whens in where clauses in comments.
79 00:14:21.320 ⇒ 00:14:26.059 Uttam Kumaran: And we’re basically just like having a discussion on like whether that’s worth bringing to the next
80 00:14:26.920 ⇒ 00:14:28.109 Uttam Kumaran: version of this
81 00:14:29.370 ⇒ 00:14:38.800 Zack Gibbs: Yeah, here’s my perspective. Let’s start at the very top. Let’s talk about. I think subscriptions is a high level topic that we need to align on, and then we’ll talk about the stakeholders,
82 00:14:39.340 ⇒ 00:14:42.250 Zack Gibbs: and make sure that we’re good there.
83 00:14:43.765 ⇒ 00:14:44.860 Zack Gibbs: So
84 00:14:46.080 ⇒ 00:14:58.500 Zack Gibbs: from a stakeholder perspective, we have. We have our finance team, which is really we have. Dean Mark is the lead. Gabe. I don’t know Gabe’s last name. I’ll find it real fast.
85 00:14:58.500 ⇒ 00:14:59.480 Alex K: Rivera.
86 00:15:04.250 ⇒ 00:15:12.470 Zack Gibbs: Rivera. Yep, we have Dean and Perry.
87 00:15:16.230 ⇒ 00:15:26.190 Zack Gibbs: Who else? Our core, like revenue do is that we include? Do we include? We include Sashi on the Ecom side.
88 00:15:26.390 ⇒ 00:15:32.770 Zack Gibbs: or is she really? Is she engaging with Looker reporting versus just what’s in shopify current state.
89 00:15:34.750 ⇒ 00:15:36.609 Alex K: It’s Kristen and Sashi, for sure.
90 00:15:37.260 ⇒ 00:15:37.870 Emily Giant: Yeah.
91 00:15:47.260 ⇒ 00:15:51.569 Alex K: And in theory, Sam, but I don’t know how much we want to roll up into
92 00:15:52.220 ⇒ 00:15:53.580 Alex K: Kristen. You know what I mean.
93 00:15:56.340 ⇒ 00:15:56.720 Zack Gibbs: Do it.
94 00:15:56.720 ⇒ 00:16:03.050 Emily Giant: Definitely includes Sam. Since she’ll be like the main subscriptions Poc.
95 00:16:03.630 ⇒ 00:16:07.409 Emily Giant: and that is definitely its own logic around revenue.
96 00:16:11.540 ⇒ 00:16:13.630 Zack Gibbs: Yes. So
97 00:16:13.980 ⇒ 00:16:21.551 Zack Gibbs: let’s talk about subscriptions. You have a question here under functional requirements. Do we integrate loop into current infrastructure?
98 00:16:22.420 ⇒ 00:16:26.789 Zack Gibbs: I guess my question back to this group is like, how is that if we’re building a revenue data mark.
99 00:16:27.080 ⇒ 00:16:28.080 Zack Gibbs: and
100 00:16:28.640 ⇒ 00:16:40.450 Zack Gibbs: we’re Dtc, we also have some b 2 b stuff that is small. But we do plan to grow. That is, subscriptions captured in a revenue data mark from a best practice perspective, or is its own separate thing.
101 00:16:42.870 ⇒ 00:16:43.850 Uttam Kumaran: Good question
102 00:16:44.455 ⇒ 00:16:49.144 Uttam Kumaran: so kind of let me, if you could give you some background on how I’ve seen it.
103 00:16:50.110 ⇒ 00:16:56.260 Uttam Kumaran: typically, you know, I don’t think it’s it’s 100% common to have both
104 00:16:56.850 ⇒ 00:17:05.329 Uttam Kumaran: like host, but have multiple sort of products and and sort of the way this works. I would say, though, that it should be totally part of
105 00:17:05.770 ⇒ 00:17:19.300 Uttam Kumaran: this data, mart. It interacts with a lot of the same common objects which are customers, which is real links to inventory. The only difference in subscription is that
106 00:17:19.410 ⇒ 00:17:25.139 Uttam Kumaran: there is a the. It’s in the basically the forecasting meaning.
107 00:17:25.300 ⇒ 00:17:33.290 Uttam Kumaran: You can start to do some things like, Hey, people aren’t subscription. We can forecast future revenue versus on one time buys. There’s no
108 00:17:33.410 ⇒ 00:17:34.210 Uttam Kumaran: like
109 00:17:34.420 ⇒ 00:18:03.000 Uttam Kumaran: you don’t model the same way to give you a sense of how you know subscription business is typically modeled. You get to understand, like new revenue existing subscription revenue, turn subscription revenue. So you start to basically take all subscription revenue and bucket it right? So that way, if you look at like any sort of Sas subscription, all they’re looking at is like how much is new, how much is is existing from existing clients, how much is turned? That’s leaving?
110 00:18:04.240 ⇒ 00:18:13.589 Uttam Kumaran: But I do think that it should be part of this, because that’s still all money related, and the associated objects are still the same around customers.
111 00:18:14.030 ⇒ 00:18:15.880 Uttam Kumaran: Product things like that.
112 00:18:17.170 ⇒ 00:18:21.179 Zack Gibbs: Okay? So I think the my my gut feeling
113 00:18:21.410 ⇒ 00:18:24.219 Zack Gibbs: is that like, we need to integrate loop
114 00:18:24.630 ⇒ 00:18:35.840 Zack Gibbs: into our, you know, data set, anyway. If if that, if that’s the best practices integrated, if if it’s integrated and included in the revenue data, mark, then we absolutely should do that.
115 00:18:35.840 ⇒ 00:18:41.549 Uttam Kumaran: And then can you tell me about? Is there ex? What’s what’s the existing reporting on subscriptions today?
116 00:18:43.217 ⇒ 00:18:58.599 Emily Giant: Yeah, it deprecated when we switch systems like Jen sticky. If you look at like the former pipelines, it was very aligned to what you just said like it. It was used to forecast, and that was the main purpose was to like, break that out for inventory planning
117 00:18:59.093 ⇒ 00:19:10.049 Emily Giant: revenue planning. I think Perry would fight tooth and nail that like this exists in the revenue mark. And how she cross references data. But right now. We
118 00:19:10.180 ⇒ 00:19:19.409 Emily Giant: we do have like a subscription tag that’s ingested into like our hebo oms data. But we don’t have like forward looking information.
119 00:19:19.410 ⇒ 00:19:19.770 Uttam Kumaran: Yes.
120 00:19:19.770 ⇒ 00:19:23.220 Emily Giant: Pause, information, cancellation, and that’s all like
121 00:19:23.530 ⇒ 00:19:26.920 Emily Giant: really like the nuts and bolts of
122 00:19:27.030 ⇒ 00:19:33.160 Emily Giant: correctly forecasting for your subscriptions. They can pull that information in loop.
123 00:19:33.470 ⇒ 00:19:43.189 Emily Giant: But it’s like a not nicely cross referenced to the actual product that was sent because they’re all sent as like subscription skews.
124 00:19:44.800 ⇒ 00:19:45.910 Emily Giant: Yeah, it it.
125 00:19:45.910 ⇒ 00:19:49.319 Uttam Kumaran: And then for then, for the business as a whole, you know, you want to be able to see
126 00:19:49.520 ⇒ 00:19:56.930 Uttam Kumaran: subscription. You want to see revenue from each of the different service lines, right? Whether it’s subscription, whether it is the single buys
127 00:19:57.410 ⇒ 00:19:59.259 Uttam Kumaran: or or otherwise, you know.
128 00:19:59.770 ⇒ 00:20:03.049 Uttam Kumaran: So I would. I would. I would like to push for
129 00:20:03.930 ⇒ 00:20:07.010 Uttam Kumaran: for integrating. I have some questions on, like how we’re gonna get the data. But.
130 00:20:08.290 ⇒ 00:20:08.950 Emily Giant: Okay.
131 00:20:09.610 ⇒ 00:20:11.700 Zack Gibbs: Yeah, the business teams are using loop
132 00:20:11.880 ⇒ 00:20:19.279 Zack Gibbs: directly. Their dashboards, recordings, analytics. They’re not really utilizing what we have, which is a Miss.
133 00:20:21.470 ⇒ 00:20:24.015 Uttam Kumaran: So I guess question for loop.
134 00:20:25.570 ⇒ 00:20:28.779 Uttam Kumaran: I wanted to get approval that I can just go
135 00:20:29.490 ⇒ 00:20:34.109 Uttam Kumaran: paying polytomic and ask if we can if they can build it, and we can add to
136 00:20:34.240 ⇒ 00:20:35.840 Uttam Kumaran: our existing ingestion.
137 00:20:37.650 ⇒ 00:20:42.500 Uttam Kumaran: I’m not sure about how long that would take but that’s
138 00:20:42.630 ⇒ 00:20:45.109 Uttam Kumaran: that would be my, you know. Next steps.
139 00:20:46.087 ⇒ 00:20:53.850 Zack Gibbs: Let’s see what they have from from a.
140 00:20:57.210 ⇒ 00:21:01.300 Uttam Kumaran: I know they have Apis and I, and I have a copy of like what the Erd.
141 00:21:01.550 ⇒ 00:21:03.508 Uttam Kumaran: because 5 train has a
142 00:21:06.310 ⇒ 00:21:09.919 Uttam Kumaran: Connector. But I have like a beerd and basically
143 00:21:12.090 ⇒ 00:21:16.819 Uttam Kumaran: I would just pick a couple of the models, the core models for them to bring in.
144 00:21:19.860 ⇒ 00:21:26.410 Emily Giant: Yeah, like, I, I’m pretty clear on what information is missing that’s making it difficult to forecast.
145 00:21:27.920 ⇒ 00:21:32.413 Emily Giant: So we probably won’t need to bring in like everything.
146 00:21:33.550 ⇒ 00:21:38.800 Emily Giant: I can also reach out to loop and see like what kind of connectors.
147 00:21:38.800 ⇒ 00:21:52.640 Uttam Kumaran: Yeah, I mean, the only other thing that’s in there that’s like a little bit sort of like left field is like they have returns and return reviews like, I don’t think that’s probably immediately necessary. The main things I want to look at are
148 00:21:56.730 ⇒ 00:21:59.170 Uttam Kumaran: are the actual like subscriptions themselves.
149 00:22:03.310 ⇒ 00:22:04.479 Uttam Kumaran: So that’s what I would.
150 00:22:06.340 ⇒ 00:22:06.850 Uttam Kumaran: Yeah.
151 00:22:06.850 ⇒ 00:22:07.560 Emily Giant: Yeah.
152 00:22:09.150 ⇒ 00:22:18.499 Emily Giant: I know one of the the things is like, we’ve done so many changes to subscriptions like people can self select what’s sent, and we don’t have visibility into
153 00:22:19.000 ⇒ 00:22:23.730 Emily Giant: if somebody chose, like a mystery skew versus like
154 00:22:24.110 ⇒ 00:22:26.770 Emily Giant: choosing what they were going to get.
155 00:22:31.220 ⇒ 00:22:34.450 Emily Giant: But there’s yeah. There’s a lot of misses in subscriptions right now.
156 00:22:39.930 ⇒ 00:22:51.329 Demilade Agboola: And also beyond the technical ingestion part of it. We’ll also need to have, like a working well, a conversation with the stakeholders on how they
157 00:22:51.970 ⇒ 00:22:55.320 Demilade Agboola: recognize revenue from subscriptions in particular?
158 00:22:55.979 ⇒ 00:23:00.170 Demilade Agboola: What counts as realized? Revenue? How is it partitioned over?
159 00:23:00.650 ⇒ 00:23:06.639 Demilade Agboola: Are they forecast with it, and how do they partition it over like the future as well.
160 00:23:07.030 ⇒ 00:23:11.500 Alex K: Yeah. Loop loop. This is a different product for 5. Tran. Sorry? Just a
161 00:23:11.760 ⇒ 00:23:16.040 Alex K: oh, yeah, no. I’m I’m literally just seeing that product. Yes.
162 00:23:16.040 ⇒ 00:23:21.470 Uttam Kumaran: So this loop is actually in, built into shopify. Then maybe this is just comes in through the existing shopify.
163 00:23:23.582 ⇒ 00:23:34.099 Alex K: No, it’s an app like loop subscriptions like loopwork.co, is there thing their website?
164 00:23:35.775 ⇒ 00:23:37.350 Alex K: So like that.
165 00:23:37.940 ⇒ 00:23:42.669 Alex K: That is where I would go for any of their information, but I did not.
166 00:23:42.866 ⇒ 00:23:43.260 Uttam Kumaran: I see!
167 00:23:43.260 ⇒ 00:23:47.949 Alex K: When we looked at this like when we were, I don’t know. November last year, there was not any
168 00:23:48.270 ⇒ 00:23:51.214 Alex K: pre-existing integrations for Etls
169 00:23:52.710 ⇒ 00:23:57.279 Alex K: which is why we don’t have one right? So I think that’s that’s the the question is like, it
170 00:23:57.800 ⇒ 00:23:59.710 Alex K: probably is like
171 00:24:01.110 ⇒ 00:24:14.690 Alex K: grabbing something off the Api, and if it’s something small, then I would probably say we do it in Hevo, because we’re already paying for that like, we already have the credits for that if it’s not small, then maybe. And it’s more complicated. Maybe we.
172 00:24:15.260 ⇒ 00:24:17.110 Alex K: you know, figure out another path.
173 00:24:17.500 ⇒ 00:24:20.480 Alex K: But that’s that’s kind of my 2 cents. There.
174 00:24:21.480 ⇒ 00:24:29.379 Uttam Kumaran: Yeah, I’m I’m finding the docs here actually, I just put them underneath where the common is, and it looks pretty expansive.
175 00:24:29.860 ⇒ 00:24:46.000 Zack Gibbs: Yeah, yeah, we just I just ping the loop, the loop folks. We’ll get a see if they have any like office shelf, etl connector integrations and kind of go from there. Either way, if that’s the best practice we want. That should be a requirement that we are ingesting that data. In some way right.
176 00:24:46.000 ⇒ 00:24:49.830 Uttam Kumaran: Okay, okay, great.
177 00:24:50.280 ⇒ 00:24:56.970 Uttam Kumaran: So that was probably the only thing we’re meeting with. We have 2 working sessions on this document on Monday and Tuesday.
178 00:24:58.940 ⇒ 00:25:02.470 Uttam Kumaran: and I’m taking some time today to sort of review some of Kyle’s comments.
179 00:25:03.149 ⇒ 00:25:07.060 Uttam Kumaran: But this is really we want to have locked down next week before we.
180 00:25:08.110 ⇒ 00:25:13.000 Uttam Kumaran: and basically get signed off on everybody that the not only the output tables
181 00:25:13.190 ⇒ 00:25:16.249 Uttam Kumaran: have the right dimensionality and and metrics.
182 00:25:16.705 ⇒ 00:25:22.500 Uttam Kumaran: But also, I kind of want to hear what the wish list of things that aren’t possible today are
183 00:25:24.430 ⇒ 00:25:29.239 Uttam Kumaran: so that we we leave this exercise above like, it’s not just a
184 00:25:29.530 ⇒ 00:25:35.389 Uttam Kumaran: it’s not just a pure, cleanup documentation thing. I want to also see what other reporting we can enable.
185 00:25:36.640 ⇒ 00:25:58.429 Zack Gibbs: Yeah, I mean, I think that that information. Emily probably has a good context there. But not all the details like, that’s why I wanted to talk about the stakeholders, and I think the stakeholders need to tell us that. Give us that data. We may have a view on it, but we should capture it directly from them. So that way we’re not misconstruing things and going down the wrong path.
186 00:25:58.940 ⇒ 00:25:59.530 Uttam Kumaran: Okay.
187 00:26:00.730 ⇒ 00:26:05.225 Zack Gibbs: And I know that some of that had already been done. A long time ago we should read we should redo it
188 00:26:06.106 ⇒ 00:26:10.019 Uttam Kumaran: And just use that as supplementary information as part of those interviews.
189 00:26:10.840 ⇒ 00:26:25.240 Amber Lin: Yeah. So right now, our meetings next week is booked with Dean Dean and Perry. If we want to also capture requirements from the other stakeholders. I’ll coordinate with Emily and get their time as well.
190 00:26:25.970 ⇒ 00:26:41.039 Zack Gibbs: Yeah, yeah, I would. I would break this into. I would break this into distinct sessions. I would break it into a I have a finance session with Dean Mark and Gabe Rivera. I’d have an snop session with Dean Capel and Perry.
191 00:26:41.838 ⇒ 00:26:47.100 Zack Gibbs: You could bundle the marketing and the sales and the subscriptions altogether.
192 00:26:47.100 ⇒ 00:26:48.770 Amber Lin: Awesome. Okay.
193 00:26:48.770 ⇒ 00:26:50.910 Zack Gibbs: 3 distinct sessions with those
194 00:26:51.320 ⇒ 00:26:58.869 Zack Gibbs: folks. Are we missing other? Do we need to have like Pk in here? From an analyst perspective?
195 00:27:01.010 ⇒ 00:27:02.882 Emily Giant: I don’t think so.
196 00:27:03.810 ⇒ 00:27:06.245 Emily Giant: I don’t think he deals a lot with
197 00:27:06.710 ⇒ 00:27:11.270 Emily Giant: revenue. His is going to be more of like the marketing sprint.
198 00:27:13.010 ⇒ 00:27:13.620 Zack Gibbs: Okay.
199 00:27:18.110 ⇒ 00:27:25.790 Amber Lin: Awesome. We’re currently broken out like that. We’ll. I’ll just invite the marketing sales and subscriptions to a different meeting.
200 00:27:27.320 ⇒ 00:27:37.179 Uttam Kumaran: Yeah, I and I can. I’ll be there, too. I can just. I’ll prepare some stuff on how we’ve modeled descriptions in the past. So we’ll skip a couple of steps there.
201 00:27:38.750 ⇒ 00:27:43.440 Uttam Kumaran: And then I would ideally want to see kind of what reporting they’re getting at the loop. Basically.
202 00:27:45.065 ⇒ 00:28:06.780 Zack Gibbs: Yeah, and Sam Shield is about to go out on maternity leave Kristen will be kind of a somewhat of a backup for her. But she’s gonna I think the question for her is, who is her in this like build out who is representing subscription data modeling business requirements? It’s an open question. Open question to her.
203 00:28:11.810 ⇒ 00:28:14.959 Zack Gibbs: That’s why I think Pk may be helpful here.
204 00:28:16.450 ⇒ 00:28:20.959 Uttam Kumaran: I mean I I once we write the thing about subscriptions. We can just send it to him over slack and.
205 00:28:23.470 ⇒ 00:28:28.859 Emily Giant: It wouldn’t hurt to have him. He’s extremely data literate.
206 00:28:29.230 ⇒ 00:28:30.060 Emily Giant: So.
207 00:28:30.670 ⇒ 00:28:31.280 Zack Gibbs: Yeah.
208 00:28:32.340 ⇒ 00:28:32.665 Uttam Kumaran: Okay.
209 00:28:39.550 ⇒ 00:28:42.700 Amber Lin: And just to confirm, do we want?
210 00:28:43.100 ⇒ 00:28:48.439 Amber Lin: Oh, okay. So we want Pk, in the last session with Christine, Sach and Sam.
211 00:28:49.000 ⇒ 00:28:50.059 Amber Lin: Okay. Now that.
212 00:28:50.060 ⇒ 00:28:51.139 Emily Giant: I think so. Yeah.
213 00:28:51.140 ⇒ 00:28:56.920 Amber Lin: Okay, Emily, I’ll get in contact with you. I’ll find a we’ll find a slot on their calendar.
214 00:29:04.660 ⇒ 00:29:07.369 Zack Gibbs: Felipe Felipe is not needed here.
215 00:29:09.230 ⇒ 00:29:10.000 Zack Gibbs: No.
216 00:29:10.000 ⇒ 00:29:11.309 Emily Giant: I don’t think so.
217 00:29:18.960 ⇒ 00:29:29.989 Zack Gibbs: Yeah, I think ahead of those calls, my recommendation would be to give them a template of like, what are the questions you’re gonna be asking them ask them to do some pre work and documentation. So that way, it’s more of like
218 00:29:30.600 ⇒ 00:29:37.860 Zack Gibbs: going through those areas. And getting clarity versus just documenting it on a call.
219 00:29:39.370 ⇒ 00:29:39.940 Uttam Kumaran: Okay.
220 00:29:45.590 ⇒ 00:29:53.160 Uttam Kumaran: is there anything else we missed here, Emily, I’ll probably tag you. And like, really, my work is going to be in this. In this like meet section.
221 00:29:55.630 ⇒ 00:30:05.820 Uttam Kumaran: so that we can. I I just want to answer some of Kyle’s open questions about some of the existing logic. And then we’re gonna start today on the output. We actually have some of this
222 00:30:06.960 ⇒ 00:30:09.330 Uttam Kumaran: ready to go. But I just need to move it into here.
223 00:30:09.855 ⇒ 00:30:15.390 Uttam Kumaran: Overall, though the one feedback I gave to the team is that I want to list like
224 00:30:15.880 ⇒ 00:30:19.730 Uttam Kumaran: the bit, literally the business questions that we’ll be answering as part of this
225 00:30:20.270 ⇒ 00:30:24.890 Uttam Kumaran: that that doesn’t need to be like, how much money do we make today? But the more nuanced questions.
226 00:30:25.396 ⇒ 00:30:27.960 Uttam Kumaran: that way I could. We can really
227 00:30:28.300 ⇒ 00:30:33.639 Uttam Kumaran: sign off on the fact that the metrics and dimensions needed to answer. Those will be available.
228 00:30:33.860 ⇒ 00:30:44.149 Uttam Kumaran: and that will sort of be the running log that, as we meet with folks that will start to get populated more. But ideally, by the time we get to the last person they’re like this. Any all these questions. As long as they’re supported
229 00:30:44.700 ⇒ 00:30:45.739 Uttam Kumaran: we’ll be ready.
230 00:30:48.630 ⇒ 00:30:54.320 Demilade Agboola: When it’s also be great to be able to figure out the different ways in which different things occur
231 00:30:54.640 ⇒ 00:31:01.380 Demilade Agboola: in terms of like. So what does trend revenue mean? And like just being able to figure out the different nuances of that?
232 00:31:01.530 ⇒ 00:31:09.590 Demilade Agboola: But we can also accommodate that, and allow us again to visualize revenue in terms of revenue lost, and all that.
233 00:31:18.256 ⇒ 00:31:25.989 Zack Gibbs: In terms of general tooling. You mentioned metaplane, which is a which was is a data dog product for.
234 00:31:25.990 ⇒ 00:31:26.510 Uttam Kumaran: Yeah.
235 00:31:26.510 ⇒ 00:31:29.610 Zack Gibbs: Just monitoring pipeline health. Is that what that does.
236 00:31:29.610 ⇒ 00:31:38.634 Uttam Kumaran: Yeah, we were. I’ve been sort of dragging my feet on demoing it to this team. We just we’re working on other stuff. But we’re we sort of set up a proof of concept.
237 00:31:39.070 ⇒ 00:31:49.090 Uttam Kumaran: Basically. It’s just the kind of like best tool in class for monitoring Cardinality Row counts
238 00:31:50.890 ⇒ 00:32:07.569 Uttam Kumaran: like freshness things like that. We’re using it for some other clients, and it just alerts directly in slack. So ideally, Emily, like the problem that Perry is having right now. Some of the other issues. We we’re just gonna build monitors directly on these marts and ideally, just the marts.
239 00:32:08.113 ⇒ 00:32:12.489 Uttam Kumaran: So that we can start to track like when errors happen, and we get automated alerts.
240 00:32:13.274 ⇒ 00:32:18.799 Uttam Kumaran: I don’t know necessarily whether I want to like loop that decision into this.
241 00:32:19.940 ⇒ 00:32:20.640 Uttam Kumaran: But
242 00:32:20.900 ⇒ 00:32:26.580 Uttam Kumaran: we are. We want it. We we do have notes on all of our Tds on observability. So
243 00:32:28.370 ⇒ 00:32:29.829 Uttam Kumaran: that’s what we’re thinking about.
244 00:32:32.830 ⇒ 00:32:42.880 Uttam Kumaran: But I kinda wanna I wanna do a demo where I share like how that, how that works and then and get you guys pricing if the pricing is on a per table basis, and I think it starts at
245 00:32:43.000 ⇒ 00:32:50.999 Uttam Kumaran: 500 bucks a month, and then, if you sign a longer deal, it goes down ideally. My goal is like, I just want to have alerting and
246 00:32:51.170 ⇒ 00:32:54.520 Uttam Kumaran: observability on the top. Query tables.
247 00:32:55.085 ⇒ 00:33:00.479 Uttam Kumaran: and probably the layer below them, like whatever the core. Intermediary models are.
248 00:33:01.067 ⇒ 00:33:06.629 Uttam Kumaran: Because I feel like every week or 2 weeks. That’s where we’re we’re spending time doing that.
249 00:33:07.970 ⇒ 00:33:08.330 Emily Giant: Yeah.
250 00:33:08.330 ⇒ 00:33:09.960 Zack Gibbs: Okay, yeah, I think it’s a longer discussion.
251 00:33:10.310 ⇒ 00:33:10.650 Uttam Kumaran: Yeah.
252 00:33:10.650 ⇒ 00:33:14.849 Zack Gibbs: Versus what we currently have today and gaps that we currently have. So.
253 00:33:15.820 ⇒ 00:33:28.090 Emily Giant: I know we only have 1 min, but I’m and and we’ll take time to review this document and give feedback. But just in terms of like source of truth, listed. I know I’ve mentioned a couple of times that we should
254 00:33:28.460 ⇒ 00:33:36.650 Emily Giant: probably migrate our source of truth to shopify’s native tables for several of these, because, while they are
255 00:33:36.940 ⇒ 00:33:44.164 Emily Giant: technically the source of truth, there were a couple of months where the source was not ingesting.
256 00:33:44.880 ⇒ 00:33:52.259 Emily Giant: all of the data that was needed. And there are still issues with like deleted orders. So I think that there needs to be some investigation on, like
257 00:33:53.340 ⇒ 00:33:59.542 Emily Giant: what is in the shopify native tables? What do we need to potentially add to them?
258 00:34:00.760 ⇒ 00:34:04.420 Emily Giant: from our side, to get what we would need
259 00:34:05.020 ⇒ 00:34:12.370 Emily Giant: to remove as much of what we can from like the hevo oms hybrid.
260 00:34:12.889 ⇒ 00:34:17.089 Uttam Kumaran: Yeah, okay.
261 00:34:29.959 ⇒ 00:34:33.629 Uttam Kumaran: yeah, I want to see what the current splits are right now. But okay.
262 00:34:35.799 ⇒ 00:34:38.569 Uttam Kumaran: so I, our overall. Our goal is to get this
263 00:34:39.399 ⇒ 00:34:42.459 Uttam Kumaran: in a sign off ready state by next week.
264 00:34:43.529 ⇒ 00:34:50.769 Uttam Kumaran: so I I don’t want like there’s so much going on that, like a document like this can easily start to just like.
265 00:34:50.929 ⇒ 00:34:55.509 Uttam Kumaran: roll over, roll over. But I want to put a cap that we can at least get started on work
266 00:34:55.809 ⇒ 00:34:57.629 Uttam Kumaran: if even if we just have, like
267 00:34:58.249 ⇒ 00:35:01.519 Uttam Kumaran: 40% locked in. And there’s still discussion.
268 00:35:01.629 ⇒ 00:35:04.049 Uttam Kumaran: we can continue to host discussion.
269 00:35:04.239 ⇒ 00:35:09.249 Uttam Kumaran: But there is some. There is already immediate work that I see that I want to get started on so.
270 00:35:10.066 ⇒ 00:35:16.889 Zack Gibbs: Yeah, I mean, the architecture diagram of current current versus interstate proposal. I feel like is a missing piece that we need to.
271 00:35:17.360 ⇒ 00:35:18.540 Uttam Kumaran: Okay, talk through.
272 00:35:19.298 ⇒ 00:35:21.850 Zack Gibbs: And that you got like placeholders in there.
273 00:35:24.930 ⇒ 00:35:27.149 Zack Gibbs: The the meeting with the teams
274 00:35:27.280 ⇒ 00:35:33.460 Zack Gibbs: and getting, you know, gap areas documented can be done in parallel.
275 00:35:37.650 ⇒ 00:35:38.410 Zack Gibbs: So.
276 00:35:40.540 ⇒ 00:35:41.200 Uttam Kumaran: Okay.
277 00:35:42.290 ⇒ 00:35:45.259 Zack Gibbs: I would just ask for. I would just ask
278 00:35:46.620 ⇒ 00:35:48.950 Zack Gibbs: for like a a rough like.
279 00:35:50.290 ⇒ 00:35:53.639 Zack Gibbs: what’s the start and end? Proposed timing here
280 00:35:54.126 ⇒ 00:35:59.540 Zack Gibbs: now, now that we’ve kicked this off from high level overview perspective.
281 00:35:59.720 ⇒ 00:36:06.850 Zack Gibbs: how do? How long do we expect this to be in kind of discovery versus core development versus
282 00:36:07.220 ⇒ 00:36:08.513 Zack Gibbs: rollout, release
283 00:36:09.360 ⇒ 00:36:09.830 Uttam Kumaran: Okay.
284 00:36:09.830 ⇒ 00:36:13.469 Zack Gibbs: Nice nice to see that in a consolidated view, so we can start to share that.
285 00:36:13.750 ⇒ 00:36:24.339 Uttam Kumaran: Yeah, amber. If we can produce that, we’ve Kyle’s been. We’ve already been working on the discovery piece of this. So we’re we’re not like kicking off today. We we’ve already been working on this for about a week. So.
286 00:36:24.810 ⇒ 00:36:32.740 Zack Gibbs: Okay, sounds good. I have to run to a next week meeting which is the last thing that I wanna do. Alex, you don’t have to. You don’t have to join if you don’t want to.
287 00:36:33.120 ⇒ 00:36:33.710 Uttam Kumaran: Let me know!
288 00:36:33.710 ⇒ 00:36:34.429 Alex K: Be around.
289 00:36:34.430 ⇒ 00:36:35.970 Uttam Kumaran: Anything. Yeah, sorry. Yeah.
290 00:36:35.970 ⇒ 00:36:36.820 Alex K: Go on, does it.
291 00:36:38.610 ⇒ 00:36:41.669 Uttam Kumaran: Yeah, I just said, Let me know if Loop says anything that I can help with.
292 00:36:46.390 ⇒ 00:36:46.870 Emily Giant: Interesting.
293 00:36:47.213 ⇒ 00:36:47.899 Uttam Kumaran: Think, from.
294 00:36:47.900 ⇒ 00:36:48.230 Demilade Agboola: Yeah.
295 00:36:48.230 ⇒ 00:36:51.235 Uttam Kumaran: Alright! That’s that’s all right. No worries, thanks everyone.
296 00:36:51.710 ⇒ 00:36:53.160 Emily Giant: Alright! Thanks everyone. Bye.
297 00:36:53.160 ⇒ 00:36:55.840 Amber Lin: Thanks, bye.