Meeting Title: U.S. Revenue Reporting Case Study Date: 2025-10-23 Meeting participants: Hannah Wang, Demilade
WEBVTT
1 00:00:51.830 ⇒ 00:00:52.540 Hannah Wang: Hey.
2 00:00:53.260 ⇒ 00:00:54.679 Demilade: Hi, Hannah, how are you?
3 00:00:55.300 ⇒ 00:00:56.410 Hannah Wang: Good, how are you?
4 00:00:57.030 ⇒ 00:00:58.099 Demilade: I’m doing very well.
5 00:00:58.240 ⇒ 00:00:59.430 Demilade: Okay, very well.
6 00:00:59.830 ⇒ 00:01:06.199 Hannah Wang: Sorry, I’m a bit late. I… I’m at a cafe right now, and I hit last week, so… here we are.
7 00:01:06.840 ⇒ 00:01:07.550 Demilade: Okay.
8 00:01:08.360 ⇒ 00:01:25.960 Hannah Wang: Okay, so, as usual, like last time, just a case study, so I’m gonna ask you a bunch of questions, and even if I ask you a redundant one, just answer it for the transcript. So this one is gonna be the revenue reporting for Urban STEMs,
9 00:01:26.390 ⇒ 00:01:39.629 Hannah Wang: I don’t have, like, any context on this, so I think it’ll also be… just be good for me to understand, and obviously the questions I ask you will help with the case study. So we can go ahead and get started. So…
10 00:01:41.650 ⇒ 00:01:50.380 Hannah Wang: just, like, high-level overview, like, what is this project, I guess? Like, is it building a dashboard, or what is it?
11 00:01:52.140 ⇒ 00:01:54.430 Demilade: On high level is…
12 00:01:54.930 ⇒ 00:02:02.449 Demilade: Replacing the data in already existing dashboards with the new data that we would have built out for them.
13 00:02:04.110 ⇒ 00:02:09.590 Hannah Wang: Got it. Okay. And how long did this project take?
14 00:02:10.250 ⇒ 00:02:16.070 Demilade: Oof, prosture estimate, say, 3 months, 2 to 3 months?
15 00:02:16.250 ⇒ 00:02:16.950 Hannah Wang: Okay.
16 00:02:17.050 ⇒ 00:02:19.700 Hannah Wang: And then, were you, like, the…
17 00:02:19.970 ⇒ 00:02:24.669 Hannah Wang: Or who are, like, the team members involved, obviously, who worked on, like.
18 00:02:24.800 ⇒ 00:02:30.850 Hannah Wang: The data… forgive me for not knowing, but yeah, who were the team members and what did they do?
19 00:02:31.850 ⇒ 00:02:47.639 Demilade: So it was a bit of a mixed effort. So Cairo did some of the editing, auditing part. So Cairo did some of the auditing, I did the same part of the building. We also had, like.
20 00:02:47.960 ⇒ 00:02:53.030 Demilade: Emily, Emily, this is the DBT question on the client side.
21 00:02:53.300 ⇒ 00:02:55.150 Hannah Wang: Okay. Also working with us.
22 00:02:55.500 ⇒ 00:02:57.809 Demilade: And providing context when necessary.
23 00:02:58.550 ⇒ 00:03:14.119 Demilade: And then for things like verification, and just to ensure that we’re on the right track, we had some stakeholders on their team as well. Just, like, looking at the numbers and making sure that we’re directionally, you know, headed in the right… headed the right way.
24 00:03:15.140 ⇒ 00:03:17.449 Hannah Wang: And then who was the PM for this project?
25 00:03:18.040 ⇒ 00:03:18.980 Demilade: Amber.
26 00:03:19.250 ⇒ 00:03:28.599 Hannah Wang: Amber, Okay, so moving on to the context, just kind of understanding the environment, that this
27 00:03:28.940 ⇒ 00:03:38.389 Hannah Wang: this project was in before we kind of went in and replaced all the data, so I guess, like, what… why were we trying to, like.
28 00:03:38.940 ⇒ 00:03:44.200 Hannah Wang: yeah, replace the data, I guess, that they had already built out.
29 00:03:45.740 ⇒ 00:03:51.269 Demilade: Because the data was unreliable. That’s the… that would be the summary of everything, but, like.
30 00:03:51.270 ⇒ 00:03:51.879 Hannah Wang: That’s more detail.
31 00:03:51.880 ⇒ 00:03:52.630 Demilade: entails.
32 00:03:52.950 ⇒ 00:04:00.350 Demilade: We had situations where… Like, the entire revenue had been a part job.
33 00:04:00.650 ⇒ 00:04:04.610 Demilade: All along from the very beginning of when they started working with the data.
34 00:04:04.950 ⇒ 00:04:09.500 Demilade: So, right, the way things existed.
35 00:04:09.790 ⇒ 00:04:13.450 Demilade: Their revenue was a combination from two different sources.
36 00:04:13.960 ⇒ 00:04:19.830 Demilade: It required two different bits of logic, but they had never taken time out to, like, fully separate that.
37 00:04:21.060 ⇒ 00:04:25.490 Demilade: They consistently had issues where they would just call, like, revenue seems off.
38 00:04:25.880 ⇒ 00:04:27.580 Demilade: The revenue looks off.
39 00:04:27.720 ⇒ 00:04:37.450 Demilade: it was… it was more of a field thing than knowing for a fact what the revenue was. But, like, you know how, like, you have an idea that your business does say.
40 00:04:37.820 ⇒ 00:04:38.360 Hannah Wang: I don’t know.
41 00:04:38.360 ⇒ 00:04:39.909 Demilade: 100K orders a day.
42 00:04:40.790 ⇒ 00:04:44.220 Demilade: If maybe on a particular day, you see that’s 20K.
43 00:04:44.220 ⇒ 00:04:44.820 Hannah Wang: Whoa.
44 00:04:45.450 ⇒ 00:05:01.840 Demilade: it feels off to you, and you’re like, okay, we need to look into that. That’s kind of how they had their data. They had an estimate or a range of how things were, but the preciseness of it wasn’t always there. If you’re expecting 100K per day, for instance, like, this is just an example, a hypothetical.
45 00:05:01.840 ⇒ 00:05:02.760 Hannah Wang: I’ll start together.
46 00:05:02.760 ⇒ 00:05:03.160 Demilade: I mean.
47 00:05:03.160 ⇒ 00:05:03.580 Hannah Wang: She’s yell.
48 00:05:03.580 ⇒ 00:05:10.050 Demilade: And it’s… you don’t know if it’s 105 or if it’s 95, it won’t ring alarm bells, but it’s around what you expect.
49 00:05:10.620 ⇒ 00:05:16.249 Demilade: But just being able to get them to the point where they can be able to say, hey, this is what.
50 00:05:16.250 ⇒ 00:05:16.980 Hannah Wang: Same.
51 00:05:17.640 ⇒ 00:05:19.150 Demilade: The amounts was…
52 00:05:19.610 ⇒ 00:05:28.419 Demilade: And this is, like, they can get very certain about it. And we’re not 100% there, but we’re definitely being very, very close to that stage.
53 00:05:28.420 ⇒ 00:05:43.240 Hannah Wang: Who had built out, like, any of the data, or, like, yeah, like, what was… were there any, like, previous efforts? Like, obviously, we’re coming in to fix it. I guess what… what was broken about the previous effort to, like.
54 00:05:43.470 ⇒ 00:05:45.610 Hannah Wang: Why was it off and unreliable?
55 00:05:46.330 ⇒ 00:05:51.410 Demilade: So, they had, sorry, I do
56 00:05:54.660 ⇒ 00:05:56.479 Demilade: Abby, I’m coming.
57 00:05:56.480 ⇒ 00:05:57.220 Hannah Wang: No worries.
58 00:05:58.370 ⇒ 00:06:01.749 Demilade: Yeah, so they had, an internal developer.
59 00:06:02.330 ⇒ 00:06:03.080 Hannah Wang: I see.
60 00:06:03.840 ⇒ 00:06:07.590 Demilade: Unfortunately, he was one person doing a lot of things.
61 00:06:08.220 ⇒ 00:06:15.480 Demilade: My guess, I never got to work with him, and, you know, when I started working on this project, I think when Brian Forest started working on this project, he had left.
62 00:06:15.920 ⇒ 00:06:26.329 Demilade: So, there wasn’t, like, an overlap to get his side of the story. But from what I can see and what I can tell from business, it feels like he was one man trying to do a lot of things.
63 00:06:26.720 ⇒ 00:06:29.939 Demilade: And I feel like that had came constantly stretched.
64 00:06:30.130 ⇒ 00:06:34.489 Demilade: And it was constantly, like, batch jobs here and there, rather than, like, a full overhaul.
65 00:06:35.450 ⇒ 00:06:36.540 Demilade: And so I guess…
66 00:06:36.660 ⇒ 00:06:41.510 Demilade: So the advantages of us coming in is we have the capability to be able to say, hey.
67 00:06:42.470 ⇒ 00:06:53.070 Demilade: let’s take a pause on this, or, like… in fact, actually, we’ve… all the things we’ve been doing, we’ve had to run two systems in parallel, so their old system still runs while we’re building out the new system.
68 00:06:53.070 ⇒ 00:06:53.720 Hannah Wang: Oh, okay.
69 00:06:53.720 ⇒ 00:06:54.990 Demilade: kind of switch.
70 00:06:55.440 ⇒ 00:06:59.339 Demilade: So we then say, okay, so now it’s ready to switch to the new one.
71 00:06:59.650 ⇒ 00:07:03.159 Demilade: So that’s what we’ll do for inventory, and that’s what we’re doing for revenue now.
72 00:07:03.240 ⇒ 00:07:05.549 Hannah Wang: So we are just kind of, like…
73 00:07:05.960 ⇒ 00:07:07.520 Demilade: Working on our own thing.
74 00:07:07.640 ⇒ 00:07:22.649 Demilade: And then when they… if there are any issues, like, anything that needs escalating with the old system, we will jump in, but, like, that’s not our priority. Our focus is to build out the new things. But I think that’s kind of what the old person, they have the opportunity to do, the opportunity to be like, hey.
75 00:07:24.070 ⇒ 00:07:31.880 Demilade: not, like, maintaining or focusing on maintaining the old system, I just need to build out and fix all the issues in the system.
76 00:07:32.920 ⇒ 00:07:36.310 Hannah Wang: I see, and I know you mentioned that we’re, like, not completely…
77 00:07:36.480 ⇒ 00:07:41.650 Hannah Wang: Like, we haven’t completed this project, so I’m assuming…
78 00:07:41.880 ⇒ 00:07:45.749 Hannah Wang: Like, it’s just an ongoing… like, this project is still ongoing, right?
79 00:07:46.390 ⇒ 00:07:51.779 Demilade: Yeah, so we should be done very soon. We’re in the final stages of it. It’s more of a…
80 00:07:52.650 ⇒ 00:08:02.480 Demilade: ensuring that we’re testing out different edge cases, and ensuring that, like, the numbers are good, basically. I guess that’s the page, just to ensure that
81 00:08:02.810 ⇒ 00:08:09.899 Demilade: The numbers are what they should be, then it also helps with, like.
82 00:08:10.330 ⇒ 00:08:13.660 Demilade: Forecasting and everything, like, everything is in good order.
83 00:08:14.320 ⇒ 00:08:15.150 Hannah Wang: Got it.
84 00:08:15.370 ⇒ 00:08:28.999 Hannah Wang: Okay, so I guess, like, without reliable data, like, what problems does the business face? Like, what’s the consequence of not having good data, essentially, for Urban STEM?
85 00:08:29.920 ⇒ 00:08:37.620 Demilade: The consequences are they rely heavily on German… Domain expertise, which means, like.
86 00:08:38.299 ⇒ 00:08:39.939 Demilade: So you know how I said.
87 00:08:40.289 ⇒ 00:08:46.070 Demilade: So if a new person comes in, that new person can’t be independent or rely on that data.
88 00:08:46.380 ⇒ 00:08:55.230 Demilade: Because… If they come in and they say, hey, The revenue yesterday was, dave.
89 00:08:55.590 ⇒ 00:08:56.680 Demilade: balanced.
90 00:08:57.090 ⇒ 00:09:03.780 Demilade: They haven’t yet built enough context to be aware that that feels like an outlier. Something looks wrong with data.
91 00:09:04.130 ⇒ 00:09:08.140 Demilade: So that means that it’s very much held by people who
92 00:09:09.050 ⇒ 00:09:11.060 Demilade: Been in the business for a long time.
93 00:09:11.610 ⇒ 00:09:15.179 Demilade: And the thing is, you can’t always have people in the business forever.
94 00:09:15.730 ⇒ 00:09:20.170 Hannah Wang: Right. Ultimately, you need infrastructure that would outlast people.
95 00:09:21.910 ⇒ 00:09:29.549 Demilade: Like, sure, people who… yeah, so ultimately union for people are lost people, and it’s very easy to, you know.
96 00:09:29.810 ⇒ 00:09:35.459 Demilade: get by, I guess, because you’re like, oh, you know, these people can get a sense of when things are wrong.
97 00:09:35.810 ⇒ 00:09:45.389 Demilade: it’s Mother’s Day, or it’s Valentine’s Day, therefore the revenue should not be the same. There should be a spike, and there’s no spike, so we need to investigate that. That’s good, but…
98 00:09:45.780 ⇒ 00:09:53.899 Demilade: That’s not a sustainable way to run a business, especially when you want to be proactive as well with your data, especially, like, revenue is that important.
99 00:09:54.040 ⇒ 00:09:56.050 Demilade: You need to be on the front foot.
100 00:09:56.790 ⇒ 00:09:58.839 Demilade: I’m being able to see.
101 00:09:58.970 ⇒ 00:10:00.250 Hannah Wang: that as…
102 00:10:00.390 ⇒ 00:10:02.149 Demilade: Yes, possible, it’s very important.
103 00:10:02.730 ⇒ 00:10:03.230 Hannah Wang: Right.
104 00:10:03.230 ⇒ 00:10:05.859 Demilade: I guess, like, all those things,
105 00:10:06.970 ⇒ 00:10:10.520 Demilade: the things that they were lacking. They were constantly reacting to.
106 00:10:10.520 ⇒ 00:10:11.610 Hannah Wang: Right.
107 00:10:11.610 ⇒ 00:10:12.600 Demilade: issues…
108 00:10:13.170 ⇒ 00:10:14.680 Hannah Wang: And you’re supposed to have this later.
109 00:10:14.680 ⇒ 00:10:21.939 Demilade: just being dependent on human knowledge, which, again, you can’t always rely on that. People leave the company, people are out of office.
110 00:10:22.120 ⇒ 00:10:24.319 Demilade: You… you want to build better systems.
111 00:10:24.770 ⇒ 00:10:25.550 Hannah Wang: Right.
112 00:10:26.890 ⇒ 00:10:35.320 Hannah Wang: Gotcha. And just for my, like, knowledge, I know you mentioned something about forecasting, like, can you explain what that is to me?
113 00:10:36.520 ⇒ 00:10:41.259 Hannah Wang: So their system relies on subscriptions as well.
114 00:10:41.260 ⇒ 00:10:45.680 Demilade: But they have, like, You can actually buy a product.
115 00:10:45.940 ⇒ 00:10:48.299 Demilade: But you can also pay for a subscription.
116 00:10:49.440 ⇒ 00:10:53.220 Demilade: And then, that allows them to also forecast revenue.
117 00:10:53.600 ⇒ 00:10:59.960 Demilade: Based on, hey, if people have paid for, like, a monthly subs… like, a monthly delivery of this.
118 00:10:59.960 ⇒ 00:11:00.600 Hannah Wang: Safeway.
119 00:11:00.600 ⇒ 00:11:05.339 Demilade: That means for the next 6 months that they’ve paid for, we have revenue from them.
120 00:11:05.490 ⇒ 00:11:06.719 Hannah Wang: If you understand.
121 00:11:07.370 ⇒ 00:11:15.050 Demilade: So, there’s just… being able to, like, estimate what they expect from previous occurrences.
122 00:11:15.280 ⇒ 00:11:18.589 Demilade: from subscriptions as well. It’s something that they try to do.
123 00:11:20.740 ⇒ 00:11:31.009 Demilade: Obviously, If your… the base data is faulty, your extrapolation and your forecast will be faulty as well.
124 00:11:31.230 ⇒ 00:11:36.280 Demilade: Right. So, you know, giving them that good platform for them to start to look forward and say, hey.
125 00:11:36.280 ⇒ 00:11:36.669 Hannah Wang: A pleasure.
126 00:11:36.670 ⇒ 00:11:38.680 Demilade: We should be expecting, you know.
127 00:11:38.880 ⇒ 00:11:43.189 Demilade: XYZ amount next month, or in the next two months, or something.
128 00:11:43.690 ⇒ 00:11:46.539 Demilade: They have been a good foundation to feel confident about.
129 00:11:48.030 ⇒ 00:11:53.739 Hannah Wang: Gotcha. That makes sense. Okay, so, just moving into the solution part of everything.
130 00:11:53.750 ⇒ 00:12:12.129 Hannah Wang: This is where it can get super technical, even if I won’t understand it, okay? So, just… yeah, explain to me, like, what the solution involved, and you can also share your screen if that helps. That would help me, if there is anything to show, but yeah, just feel free to dig into it.
131 00:12:16.150 ⇒ 00:12:24.359 Demilade: So, they basically… But basically, they had two different systems.
132 00:12:25.040 ⇒ 00:12:26.650 Demilade: Like I said earlier.
133 00:12:31.190 ⇒ 00:12:31.930 Hannah Wang: export.
134 00:12:33.980 ⇒ 00:12:45.129 Demilade: And they had… they had, new data coming in from Shopify, so Shopify is a… I’ll do that.
135 00:12:45.620 ⇒ 00:12:47.580 Demilade: Like a payment processing platform.
136 00:12:47.820 ⇒ 00:12:50.769 Demilade: And he helps, like, it helps you run your shops, basically.
137 00:12:51.410 ⇒ 00:12:54.960 Demilade: And so what we helped them do was ingest their data.
138 00:12:56.110 ⇒ 00:13:00.230 Demilade: into, like, Redshift, so Redshift is the data warehouse.
139 00:13:00.670 ⇒ 00:13:03.730 Demilade: And then we started building out data models.
140 00:13:04.020 ⇒ 00:13:06.749 Demilade: would answer the questions that they needed.
141 00:13:07.430 ⇒ 00:13:08.600 Demilade: Yeah, data.
142 00:13:08.870 ⇒ 00:13:09.960 Demilade: Loving you.
143 00:13:10.140 ⇒ 00:13:11.480 Demilade: things like…
144 00:13:12.460 ⇒ 00:13:13.150 Hannah Wang: Oh, yeah.
145 00:13:14.600 ⇒ 00:13:20.170 Demilade: of all the revenue that they got, how much was actually fulfilled. So, fulfillment means that they actually sent it out.
146 00:13:22.700 ⇒ 00:13:33.839 Demilade: Because since they work with a perishable business, like a perishable goods business, things can spoil. It’s possible that people order for something, they run out of stock.
147 00:13:34.190 ⇒ 00:13:38.719 Demilade: Like, the patch that they have goes bad.
148 00:13:38.970 ⇒ 00:13:40.870 Demilade: And then they have to, you know.
149 00:13:40.870 ⇒ 00:13:41.430 Hannah Wang: Oh.
150 00:13:41.430 ⇒ 00:13:42.699 Demilade: If they mean new.
151 00:13:43.630 ⇒ 00:13:44.390 Hannah Wang: Love that, Chris.
152 00:13:44.390 ⇒ 00:13:46.360 Demilade: A new version at their cost.
153 00:13:46.360 ⇒ 00:13:47.120 Hannah Wang: We lost kind of…
154 00:13:47.120 ⇒ 00:13:48.670 Demilade: And I’ve just been able to classify a lot.
155 00:13:48.670 ⇒ 00:13:49.590 Hannah Wang: as I was looking.
156 00:13:50.650 ⇒ 00:13:59.860 Demilade: And so we started that process of being able to understand the business context of the view of their data, what counted as revenue, what did not count as revenue.
157 00:14:01.250 ⇒ 00:14:08.489 Demilade: And then we start applying that logic into the Shopify data. So that required us building up a ton of models.
158 00:14:12.550 ⇒ 00:14:13.330 Demilade: There’s my screen now.
159 00:14:13.330 ⇒ 00:14:14.770 Hannah Wang: Yep.
160 00:14:16.120 ⇒ 00:14:19.910 Demilade: So that allowed us to build on us a ton of models, where we started from…
161 00:14:20.790 ⇒ 00:14:32.129 Demilade: the staging models to intermediate models to the MART models. So, the staging models just basically means that the initial models that you’re building
162 00:14:32.670 ⇒ 00:14:37.629 Demilade: right there, you’re not doing a lot of transformation. The goal there is just to…
163 00:14:39.300 ⇒ 00:14:41.950 Demilade: The goal there is just to have…
164 00:14:42.650 ⇒ 00:14:49.690 Demilade: a clear idea, so you can see we have the Hivo Shopify, and this is the Hivo OMS. OMS is what they used to rely on before.
165 00:14:50.480 ⇒ 00:14:52.240 Demilade: For every single theme.
166 00:14:52.400 ⇒ 00:15:00.689 Demilade: But now we have, like, the Shopify data coming in. We have, like, the refunds, the fulfillments, the orders themselves.
167 00:15:01.130 ⇒ 00:15:06.500 Demilade: the lines, the other lines are, like, the individual line items. So, for instance, if I buy
168 00:15:09.640 ⇒ 00:15:10.910 Demilade: mid phase.
169 00:15:11.310 ⇒ 00:15:12.849 Demilade: Those are two things.
170 00:15:12.950 ⇒ 00:15:18.210 Demilade: But then, the order is just the summation, like, how much each of those things… each of those individual things cost.
171 00:15:18.520 ⇒ 00:15:19.000 Hannah Wang: Okay.
172 00:15:19.000 ⇒ 00:15:22.159 Demilade: Yeah, the order lines, the orders themselves.
173 00:15:22.160 ⇒ 00:15:22.960 Hannah Wang: Secular Army.
174 00:15:22.960 ⇒ 00:15:25.930 Demilade: The product, the taxis on it.
175 00:15:26.060 ⇒ 00:15:30.729 Demilade: And so, once we tied in all of that, we then started,
176 00:15:31.710 ⇒ 00:15:34.529 Demilade: Dining these things together in our intermediate model.
177 00:15:35.180 ⇒ 00:15:39.379 Demilade: Now we have… things arise.
178 00:15:41.420 ⇒ 00:15:46.679 Demilade: We have things also around subscriptions, so subscriptions are coming from an entirely different system called Loop.
179 00:15:47.220 ⇒ 00:15:49.949 Demilade: But we had to integrate Loop into this.
180 00:15:52.640 ⇒ 00:15:55.470 Demilade: And so, we used Polytomic for that.
181 00:15:55.890 ⇒ 00:16:00.750 Demilade: As well as, we had to create a cluster connection to loop.
182 00:16:00.950 ⇒ 00:16:02.530 Demilade: It wasn’t just indirect.
183 00:16:03.130 ⇒ 00:16:08.210 Demilade: Once we got the shortcut in, the Shopify data in.
184 00:16:08.820 ⇒ 00:16:13.679 Demilade: We then started to use that, put that together to build out
185 00:16:14.190 ⇒ 00:16:17.170 Demilade: The representation of what each order
186 00:16:18.080 ⇒ 00:16:24.620 Demilade: what happened on each order. If you see come here, you’ll see that we have…
187 00:16:25.050 ⇒ 00:16:34.210 Demilade: At the end of the day, what we ended up creating is every order line, we try to get the unique value. Then we have the Shopify order ID,
188 00:16:34.430 ⇒ 00:16:36.190 Demilade: the number ID.
189 00:16:36.770 ⇒ 00:16:45.499 Demilade: other line ID, so that means, like, in an order, each of the lines, like I said, you know, so if you buy, like, flowers, chocolate, and oughies, those are 3 different lines.
190 00:16:47.260 ⇒ 00:16:48.640 Demilade: for that order.
191 00:16:48.830 ⇒ 00:16:56.839 Demilade: product ID on that line, the variant ID, which is the… what variant of the product was that, the name, the SKU, the sales quantity.
192 00:16:56.990 ⇒ 00:16:58.799 Demilade: Was that order fulfilled or not?
193 00:16:59.190 ⇒ 00:17:05.439 Demilade: If you’re also fueled the quantity, What was the SP pre-tax?
194 00:17:05.720 ⇒ 00:17:07.359 Demilade: It’s off the talks.
195 00:17:07.690 ⇒ 00:17:11.169 Demilade: A total… the total discount on that.
196 00:17:12.609 ⇒ 00:17:17.440 Demilade: ETC, but you get the idea. So for each of those lines, we’re able to see what’s happening
197 00:17:17.710 ⇒ 00:17:22.339 Demilade: every order. We’re able to see what’s the status on the financial side.
198 00:17:23.079 ⇒ 00:17:33.770 Demilade: And then if it was refunded, we can also see what was the quantity refunded, what was the refunded amount, taxed on that, what was the idea of the refund, and was it refunded at?
199 00:17:34.940 ⇒ 00:17:44.219 Demilade: From that, we can see what’s the net quantity. So if, for instance, someone ordered 5 quantities or something, and then they got refunded 3 quantities, the net quantity would be 2, you know.
200 00:17:45.340 ⇒ 00:17:47.150 Demilade: And then we’ll start in an amount.
201 00:17:47.490 ⇒ 00:17:52.140 Demilade: Unfulfilled revenue, how much has really been fulfilled on the others?
202 00:17:52.290 ⇒ 00:17:55.779 Demilade: Good revenue, how much revenue has been brought in.
203 00:17:57.060 ⇒ 00:17:59.049 Demilade: Paid for by those customers.
204 00:17:59.190 ⇒ 00:18:01.019 Demilade: Oh, gospel feeling.
205 00:18:02.640 ⇒ 00:18:07.329 Demilade: We can kind of see how much, quote-unquote, is in… is… well, waiting to ship out.
206 00:18:07.510 ⇒ 00:18:19.780 Demilade: If you understand what I’m trying to say. Like, if I… if someone pays for an order today, we’ve not yet sent them… we’ve not sent it to them, so it’s not fulfilled. However, we… we have gotten the revenue, so we accrued.
207 00:18:19.900 ⇒ 00:18:25.160 Demilade: just how much I can fulfill, so we can kind of still lies in the balance, and… fulfill.
208 00:18:26.980 ⇒ 00:18:29.989 Demilade: Yeah, so that’s kind of, like, what we’ve been building out.
209 00:18:32.450 ⇒ 00:18:37.939 Demilade: Being able to put it together and build, like, Level models of this.
210 00:18:38.600 ⇒ 00:18:43.670 Demilade: Like, when we started replacing some of the old models that existed before.
211 00:18:44.110 ⇒ 00:18:48.159 Demilade: But, you know, like I said, this is a perishable goods business, so they love, like.
212 00:18:48.550 ⇒ 00:18:56.039 Demilade: Weird edge cases where, oh, if this happens, if there’s a re-delivery of this, if this was delivered, but this happened.
213 00:18:56.170 ⇒ 00:19:02.900 Demilade: The revenue comes at zero, revenue is this, revenue… that’s kind of what we’re trying to ensure that we handle for.
214 00:19:07.780 ⇒ 00:19:11.249 Hannah Wang: So you, like, gather… aggregate all of that data and…
215 00:19:11.470 ⇒ 00:19:15.829 Hannah Wang: Is there, like, a dashboard? Like, what is delivered to…
216 00:19:15.980 ⇒ 00:19:23.470 Hannah Wang: the client? Like, do they see, like, some dashboard, or, like, yeah, what’s, like, the end…
217 00:19:23.600 ⇒ 00:19:25.620 Hannah Wang: I guess, prior to that?
218 00:19:26.150 ⇒ 00:19:26.839 Hannah Wang: Yes.
219 00:19:26.840 ⇒ 00:19:27.330 Demilade: all day.
220 00:19:27.330 ⇒ 00:19:28.230 Hannah Wang: Yeah.
221 00:19:29.680 ⇒ 00:19:33.339 Demilade: There is a revenue… Give me one sec.
222 00:19:34.310 ⇒ 00:19:36.030 Demilade: But that revenue dashboard.
223 00:19:40.430 ⇒ 00:19:41.630 Demilade: So…
224 00:20:16.050 ⇒ 00:20:17.890 Demilade: I’m trying to, like, signing on stuff.
225 00:20:18.690 ⇒ 00:20:19.440 Hannah Wang: No worries.
226 00:21:00.670 ⇒ 00:21:01.809 Hannah Wang: Google doesn’t pay anything.
227 00:21:02.470 ⇒ 00:21:03.340 Hannah Wang: Good evening.
228 00:21:03.910 ⇒ 00:21:04.590 Hannah Wang: you got it.
229 00:21:15.330 ⇒ 00:21:17.649 Demilade: So I’ve seen one, I’m trying to find the second.
230 00:21:46.070 ⇒ 00:21:49.109 Demilade: I have a number of revenue dashboards, as you would imagine.
231 00:21:49.310 ⇒ 00:21:49.990 Hannah Wang: Yeah.
232 00:21:51.590 ⇒ 00:21:54.699 Demilade: But this is basically the product name, the product SKU.
233 00:21:55.030 ⇒ 00:21:58.249 Demilade: The units sold in the delivery week, the revenue on that.
234 00:21:59.720 ⇒ 00:22:01.850 Demilade: To the hotel unit sold peak.
235 00:22:02.670 ⇒ 00:22:08.760 Demilade: So, you can kind of see that, so you can easily get an idea of, you know, what’s selling well that week.
236 00:22:09.350 ⇒ 00:22:10.700 Demilade: what isn’t?
237 00:22:12.310 ⇒ 00:22:14.129 Demilade: Across the different places.
238 00:22:14.940 ⇒ 00:22:16.070 Demilade: That’s one.
239 00:22:17.440 ⇒ 00:22:19.219 Demilade: It’s another…
240 00:22:23.280 ⇒ 00:22:25.959 Demilade: This is kind of the same theme.
241 00:22:27.340 ⇒ 00:22:36.509 Demilade: So the revenue part… So they just want to understand, like, people that are purchasing, time period.
242 00:22:37.720 ⇒ 00:22:44.080 Demilade: much purchased ETC, ETC, ETC, you can get an idea of what’s going on with that.
243 00:22:44.480 ⇒ 00:22:45.030 Hannah Wang: Yep.
244 00:22:45.320 ⇒ 00:22:51.910 Demilade: There’s another one I’m trying to see… Good.
245 00:22:56.950 ⇒ 00:22:57.710 Demilade: Okay.
246 00:23:19.040 ⇒ 00:23:20.310 Demilade: to see…
247 00:23:22.480 ⇒ 00:23:23.529 Hannah Wang: That water,
248 00:23:30.120 ⇒ 00:23:35.339 Demilade: But I know that there’s one I saw that was a bit more comprehensive, or…
249 00:23:35.750 ⇒ 00:23:37.380 Demilade: Things going on in there.
250 00:23:38.750 ⇒ 00:23:42.180 Demilade: Mmm… it ultimately…
251 00:23:42.580 ⇒ 00:23:43.270 Hannah Wang: Very loud.
252 00:23:54.510 ⇒ 00:23:56.529 Demilade: Okay, I think it’s this fast.
253 00:24:05.400 ⇒ 00:24:08.540 Demilade: You know, this is just, and explore.
254 00:24:35.060 ⇒ 00:24:36.980 Demilade: I guess so.
255 00:24:37.160 ⇒ 00:24:37.890 Demilade: Thanks.
256 00:24:38.650 ⇒ 00:24:43.939 Demilade: Yeah, you can kind of see the different, like, delivery date weeks, when should it be delivered.
257 00:24:45.650 ⇒ 00:24:47.300 Hannah Wang: Yeah. Be purchased.
258 00:24:52.140 ⇒ 00:24:54.410 Demilade: Today’s the 23rd.
259 00:24:56.070 ⇒ 00:25:02.490 Demilade: You can kind of see, so for instance, people are purchasing things this week or today against the future.
260 00:25:04.310 ⇒ 00:25:10.869 Demilade: People are purchasing… people purchased things yesterday for this week, or against next week, or against the week after that.
261 00:25:11.740 ⇒ 00:25:13.650 Demilade: You can kind of see, like.
262 00:25:13.840 ⇒ 00:25:18.110 Demilade: what is happening. That’s part of what I mean by the forecast part of.
263 00:25:18.110 ⇒ 00:25:18.750 Hannah Wang: Right.
264 00:25:19.540 ⇒ 00:25:27.399 Demilade: Because if you see when they’re purchasing, like I said, because, you know, it’s a delivery system, they don’t just do right here, right then, people are ordering for the future.
265 00:25:27.520 ⇒ 00:25:30.429 Demilade: They can kind of see what’s happening also next.
266 00:25:31.120 ⇒ 00:25:35.689 Demilade: week we’re supposed to fulfill right now. We have $97K worth of orders.
267 00:25:35.950 ⇒ 00:25:41.290 Demilade: And that’s just, like, people, I think, purchasing. You also have, like, subscription orders as well.
268 00:25:42.280 ⇒ 00:25:48.010 Demilade: So yeah, like, that helps you with planning and kind of seeing what’s going on in that regard.
269 00:25:48.960 ⇒ 00:25:49.640 Hannah Wang: break.
270 00:25:52.220 ⇒ 00:25:53.720 Hannah Wang: Wow, that’s like…
271 00:25:53.890 ⇒ 00:25:58.499 Hannah Wang: I mean, I don’t look at data that often, so it’s, like, cool seeing all this stuff kind of…
272 00:25:59.050 ⇒ 00:26:01.930 Hannah Wang: ingested, and you can’t even forecast, like, that’s…
273 00:26:02.440 ⇒ 00:26:05.750 Hannah Wang: That’s the whole point of data, I guess, to use it to…
274 00:26:05.910 ⇒ 00:26:07.860 Hannah Wang: To get insights so that you can…
275 00:26:08.130 ⇒ 00:26:11.489 Hannah Wang: Help the business, but it’s, like, cool seeing that in practice.
276 00:26:12.040 ⇒ 00:26:13.050 Hannah Wang: Tell me too long ago.
277 00:26:13.050 ⇒ 00:26:18.350 Demilade: Definitely. There’s a lot of cool things people are doing with data, and it’s really nice to be able to see it.
278 00:26:19.280 ⇒ 00:26:24.299 Demilade: And see how people are, you know, trying to get ahead, and it’s great to be a part of it.
279 00:26:24.700 ⇒ 00:26:28.579 Demilade: They’ll see how you can help, you know, after that.
280 00:26:30.170 ⇒ 00:26:32.900 Demilade: The data looks good, everything is fine.
281 00:26:34.780 ⇒ 00:26:39.119 Demilade: It can be… Stressful on some days, but ultimately, it’s very rewarding.
282 00:26:39.880 ⇒ 00:26:40.690 Hannah Wang: Totally.
283 00:26:41.280 ⇒ 00:26:42.190 Hannah Wang: So…
284 00:26:42.500 ⇒ 00:27:01.050 Hannah Wang: I guess, what’s, like, is there any numbers that you can give me in terms of, like… Yeah, I mean, obviously, with any case study, you want results and you want metrics, right? So, I don’t know if you have any numbers for me, like, what improved, what percentage, like, yada yada. Is there anything like that that you can share with me?
285 00:27:02.850 ⇒ 00:27:04.630 Demilade: Not yet.
286 00:27:04.990 ⇒ 00:27:05.330 Hannah Wang: Excuse me.
287 00:27:05.330 ⇒ 00:27:07.010 Demilade: Can you give me till end of week?
288 00:27:07.510 ⇒ 00:27:08.810 Demilade: I guess tomorrow.
289 00:27:08.990 ⇒ 00:27:11.879 Demilade: You can just scroll through and get some, you know, look at some.
290 00:27:11.880 ⇒ 00:27:12.240 Hannah Wang: Totally.
291 00:27:12.240 ⇒ 00:27:14.060 Demilade: Yeah, sure.
292 00:27:15.040 ⇒ 00:27:15.830 Hannah Wang: Totally.
293 00:27:15.950 ⇒ 00:27:20.180 Hannah Wang: Yeah, I’m gonna ask Anne, our other designer, to, like.
294 00:27:20.400 ⇒ 00:27:26.390 Hannah Wang: mock up something, but then I’ll put, like, placeholders for the numbers and stuff like that, so… .
295 00:27:26.390 ⇒ 00:27:27.850 Demilade: Okay, sounds good.
296 00:27:28.930 ⇒ 00:27:33.430 Hannah Wang: Okay, cool. I don’t know if we, like, Was there any…
297 00:27:33.540 ⇒ 00:27:45.220 Hannah Wang: I know it’s not complete yet, but was… is there any feedback so far from the client about, I guess, how we built this out for them? Was it positive, negative, like, hopefully not negative, but…
298 00:27:46.770 ⇒ 00:27:54.890 Demilade: I mean, so far, so far it’s been pretty good. Like I said, the feedback has tended to be about, like, edge cases, where, like, oh.
299 00:27:55.040 ⇒ 00:28:13.329 Demilade: you didn’t factor this in, and, you know, that’s part of why you work… you always have to work with stakeholders, because, you know, you understand the data and how to manipulate it, they understand the business, and we’ll be there. So, you know, they get… they give you feedback of, hey, you’re not handling the re-deliveries as well as you should, this is what happens here.
300 00:28:13.460 ⇒ 00:28:18.910 Demilade: In the case where, you know, we… this customer bought something.
301 00:28:19.330 ⇒ 00:28:26.870 Demilade: And they couldn’t get it, and then the upgrade was forced up on us to give them a new
302 00:28:28.520 ⇒ 00:28:30.860 Demilade: Well, the revenue should not count as the…
303 00:28:31.300 ⇒ 00:28:35.859 Demilade: new products that we gave them, which is more expensive, it should count as the old products.
304 00:28:36.040 ⇒ 00:28:37.520 Demilade: they initially bought.
305 00:28:38.290 ⇒ 00:28:42.870 Demilade: Even though we might be making a loss on it. Do you understand? Do you understand that thing?
306 00:28:43.060 ⇒ 00:28:45.240 Demilade: Like, if I said, hey, come by.
307 00:28:47.420 ⇒ 00:28:57.760 Demilade: an iPhone 13, you paid for it, but then I ran out of stock of iPhone 13, I still have to give you something. Right. I might have an iPhone 14, so I may give you the 14.
308 00:28:58.410 ⇒ 00:29:01.759 Demilade: And even though the 14 is more expensive than the 13,
309 00:29:02.400 ⇒ 00:29:05.919 Demilade: The revenue for that transaction is still deferred in.
310 00:29:06.170 ⇒ 00:29:14.079 Demilade: But I need to use the revenue as what you paid for the 13 to represent the revenue on the 14.
311 00:29:14.880 ⇒ 00:29:20.029 Demilade: If you get what I’m trying to say, instead of the dollar amount of the 14, so things like that.
312 00:29:20.240 ⇒ 00:29:23.040 Hannah Wang: Right. Which is where we might have to get feedback on.
313 00:29:24.840 ⇒ 00:29:25.510 Demilade: There’s other…
314 00:29:25.510 ⇒ 00:29:26.150 Hannah Wang: Yeah, that’s cheaper.
315 00:29:26.150 ⇒ 00:29:29.239 Demilade: like that, too. Yeah. But all the edge cases like that, too.
316 00:29:29.370 ⇒ 00:29:33.699 Demilade: Where we’ve had to be able to, like, you know, figure things out, and just have to…
317 00:29:33.860 ⇒ 00:29:42.350 Demilade: interesting things. But, yeah, ultimately, stakeholder is fine, she likes the direction everything’s exploring, she feels like we’re directionally accurate.
318 00:29:42.890 ⇒ 00:29:45.150 Demilade: I just need to be able to…
319 00:29:46.340 ⇒ 00:29:52.810 Demilade: ensure that the things remain the same, I guess, like, you know, the system is much cleaner.
320 00:29:53.510 ⇒ 00:29:54.340 Demilade: We’ll do it.
321 00:29:55.150 ⇒ 00:30:02.889 Demilade: And whatever disparities, so that’s part of what we need to investigate. Like, we do have some differences, but like I said, from our system wasn’t the most reliable.
322 00:30:03.000 ⇒ 00:30:07.670 Demilade: So the idea is, do the directions match? Not necessarily do the numbers match.
323 00:30:09.590 ⇒ 00:30:10.100 Hannah Wang: Yeah.
324 00:30:10.100 ⇒ 00:30:14.600 Demilade: For instance, if the old system says they did 2.5 million.
325 00:30:14.910 ⇒ 00:30:18.279 Demilade: Month. They did $3.2 million this month.
326 00:30:18.940 ⇒ 00:30:25.059 Demilade: If we get 2.4 million last month, And 3.1 million this month.
327 00:30:25.280 ⇒ 00:30:30.919 Demilade: might not be the same, but the direction is close enough to know that, like, we’re in a similar range.
328 00:30:31.610 ⇒ 00:30:36.639 Demilade: But let’s investigate what those disparities seem to be. But, like, we should not be overly worried.
329 00:30:36.800 ⇒ 00:30:38.510 Demilade: like, DayZ.
330 00:30:39.300 ⇒ 00:30:41.679 Demilade: Like, it’s if, for instance, we got…
331 00:30:42.860 ⇒ 00:30:53.449 Demilade: If the numbers increase from month to month, and we… our numbers are reducing from month to month, then obviously something is wrong. Or if any similar ballpark increases, then yeah, we know something is wrong.
332 00:30:53.700 ⇒ 00:30:55.220 Demilade: But so far, it’s pretty good.
333 00:30:55.930 ⇒ 00:30:56.640 Hannah Wang: Okay.
334 00:30:56.800 ⇒ 00:31:00.730 Hannah Wang: That’s good, at least so far, it’s good.
335 00:31:01.220 ⇒ 00:31:08.469 Hannah Wang: Alright, well, yeah, I’ll look forward to those results by end of week, or even early next week. I’ll… I’ll bump you if…
336 00:31:08.580 ⇒ 00:31:12.159 Hannah Wang: I don’t hear from you by tomorrow, and…
337 00:31:12.530 ⇒ 00:31:28.469 Hannah Wang: Yeah, I’ll probably ask you to look over the case study once it’s fleshed out, just to make sure that, everything is accurate, and we should be good to go from there. So, appreciate you, as always. Thank you for walking through this with me and being very thorough. I appreciate it.
338 00:31:30.430 ⇒ 00:31:31.270 Hannah Wang: Alright, well…
339 00:31:31.270 ⇒ 00:31:32.279 Demilade: Right? Sounds good.
340 00:31:32.520 ⇒ 00:31:34.490 Hannah Wang: Yep, have a good one. Bye.
341 00:31:34.490 ⇒ 00:31:35.980 Demilade: You too, bye.