Meeting Title: Revenue Reporting Discrepancy Investigation Date: 2025-06-20 Meeting participants: Robert Tseng, Awaish Kumar, Annie Yu, Demilade Agboola
WEBVTT
1 00:01:48.410 ⇒ 00:01:50.959 Robert Tseng: Yeah, I’ll wait 1 min for for demo audit.
2 00:03:42.610 ⇒ 00:03:45.337 Robert Tseng: Okay? Then a lottery will join when he joins
3 00:03:46.330 ⇒ 00:03:49.970 Robert Tseng: I think I’m just gonna I guess this meeting is recording. So
4 00:03:51.490 ⇒ 00:03:54.460 Robert Tseng: let me see, where am I gonna start here?
5 00:03:55.085 ⇒ 00:03:57.360 Robert Tseng: Yeah, I’ll share my screen.
6 00:03:59.420 ⇒ 00:04:08.655 Robert Tseng: Okay? So investigation I’ve been doing. I guess, Annie, you’re in touch with okay, just to catch the other guys up on this.
7 00:04:10.190 ⇒ 00:04:20.359 Robert Tseng: yeah. Accounting is basically going crazy. Because, yeah, they just don’t think that what we’re reporting on in terms of revenue and orders is correct. And so.
8 00:04:20.490 ⇒ 00:04:24.320 Robert Tseng: yeah, I’ve like, tried to deflect this. And you know, we’ve
9 00:04:27.360 ⇒ 00:04:32.140 Robert Tseng: in in summary. I think. There is a
10 00:04:33.980 ⇒ 00:04:41.169 Robert Tseng: we’re not gonna match exactly what they’re expecting, because they’re looking at quickbooks. They’re just looking at the cash coming in. They don’t view the business the same way.
11 00:04:41.739 ⇒ 00:04:47.189 Robert Tseng: And so there’s I think there was under common ground, on understanding that
12 00:04:48.410 ⇒ 00:05:03.259 Robert Tseng: what accounting looks at is a monthly snapshot. We never ended up building this out, but like they, they look at a monthly snapshot. Obviously the revenue and and the and costs they they change because refunds get processed later on, and whatever so
13 00:05:03.540 ⇒ 00:05:19.539 Robert Tseng: whereas the the view that we present to the business is a like a moving window like it. It changes right like revenue that’s recognized yesterday may not be the same revenue that’s recognized today. People may cancel orders. Patients may not go through. There’s a lot of all kinds of exceptions that could happen. And so
14 00:05:19.990 ⇒ 00:05:38.230 Robert Tseng: what we claim is that our view of the business is a live view of the operational of of operations. However, there is a big gap to close. We’re saying that year to date. Revenue is 11 million dollars right? Annie kind of went in, and she started off with doing this revenue by state thing we’re quite off of there.
15 00:05:38.230 ⇒ 00:05:54.870 Robert Tseng: This is the previous report that they were using. It was a query that was asked to them last year for 2024. By by Rob, I kind of reverse engineered that we still don’t use stripe data came up with a year to date, order summary, and revenues as well as discounts. And so
16 00:05:55.000 ⇒ 00:06:01.209 Robert Tseng: from there, I’m basically saying, Okay, 11 million dollars. And they’re saying, it’s now, it’s 36 million dollars.
17 00:06:01.490 ⇒ 00:06:04.324 Robert Tseng: huge difference, 200% difference. There.
18 00:06:04.920 ⇒ 00:06:10.999 Robert Tseng: I mean, there’s yeah, that’s that’s that’s clearly something that I I don’t think my explanation is able to
19 00:06:11.486 ⇒ 00:06:22.399 Robert Tseng: address fully. It doesn’t make sense that a a snapshot versus moving window would lead to a 200 difference. So I agree with what they’re concerned there.
20 00:06:24.490 ⇒ 00:06:34.270 Robert Tseng: so that’s the context. Are we? Okay? Does that make sense any questions on kind of what I’m describing here. Anybody want to kind of like rephrase your understanding.
21 00:06:42.200 ⇒ 00:06:52.199 Demilade Agboola: I mean, I understand the the context. I I think my. My question would be, when what do they consider as revenue? Do they consider, like every single order placed
22 00:06:52.380 ⇒ 00:06:55.749 Demilade Agboola: as revenue? Or are they just like like.
23 00:06:55.900 ⇒ 00:06:59.549 Demilade Agboola: do they care about refunds like, I’m trying to just understand
24 00:06:59.700 ⇒ 00:07:07.630 Demilade Agboola: cancellations, errors like, do they? Just look at the this person made a transaction discounts as like, what’s the definition of revenue?
25 00:07:07.950 ⇒ 00:07:24.951 Robert Tseng: Yeah, okay. Great. I know. Devil. You’ve been pointing out. It’s like, Hey, this model includes this doesn’t include that. Yada. Yadda, okay. So sure. Maybe let’s just say 20 to 30% difference because of of refunds and and cancellations. I still don’t think, even before we dig into anything that that explains 200 delta.
26 00:07:25.420 ⇒ 00:07:35.250 Robert Tseng: So yeah, as far as what they actually look at. He he literally just accounting literally just went to bast exported this year to date like massive file, has a hundred 20,000 orders.
27 00:07:35.630 ⇒ 00:07:36.560 Robert Tseng: So
28 00:07:36.921 ⇒ 00:07:56.249 Robert Tseng: starting a couple of days ago, I took a cracking with like looking into this, I cut it up, looked at a few different things I noticed, hey? First, st the transaction dates and the order dates aren’t aligned. So if you look in here, even though this is supposed to be year to date data, there is a bunch, I’m gonna say, a bunch. But there are orders from 2024 here.
29 00:07:56.450 ⇒ 00:08:02.140 Robert Tseng: I also point out that there’s a particular order here that I think is interesting.
30 00:08:02.710 ⇒ 00:08:05.120 Robert Tseng: I’ll come back to one I’m on as well.
31 00:08:05.968 ⇒ 00:08:08.159 Robert Tseng: Let’s pull this one up.
32 00:08:08.770 ⇒ 00:08:09.950 Robert Tseng: It loads.
33 00:08:18.650 ⇒ 00:08:19.800 Robert Tseng: that’s it.
34 00:08:26.960 ⇒ 00:08:30.019 Robert Tseng: Maybe this is our our. I guess I was already on the same one.
35 00:08:32.130 ⇒ 00:08:35.269 Robert Tseng: I don’t know if that’s actually the one I was into.
36 00:08:39.470 ⇒ 00:08:49.290 Robert Tseng: Oh, it is okay? Well, basically, I say, well, that’s
37 00:08:52.790 ⇒ 00:08:57.349 Robert Tseng: okay. Right? So the customer created this payment in 2023
38 00:08:57.560 ⇒ 00:09:11.910 Robert Tseng: hate it in 2025. And then, if you look in our data, we’re saying that it was created in 2024. So there’s clearly like something going on here like, here’s a single order with dates that span across 3 years.
39 00:09:12.400 ⇒ 00:09:18.640 Robert Tseng: And to make that worse, like, I don’t think that this even shows up in our data.
40 00:09:19.970 ⇒ 00:09:24.800 Awaish Kumar: But it’s not that. 2023 is is when the customer was created.
41 00:09:26.970 ⇒ 00:09:29.378 Robert Tseng: Customer created. Okay? Yeah, sure. Possibly.
42 00:09:30.950 ⇒ 00:09:36.679 Robert Tseng: Okay. Well, yeah. So I mean, I don’t. I don’t think this is even the the main main issue. But like this.
43 00:09:36.820 ⇒ 00:09:59.799 Robert Tseng: okay, yeah. So spanning across 2 years. Okay, if you go going by our definitions, this order should have been a 2024 order going by accounting’s definition of when they actually paid. It was a 2025 order and so there’s probably, you know, but a good chunk of these orders that get get moved over. And so I’ve been kind of like building out this query instead, that’s looking at. Okay.
44 00:10:00.070 ⇒ 00:10:02.160 Robert Tseng: I’m looking at what
45 00:10:02.450 ⇒ 00:10:25.860 Robert Tseng: Fast says, and what accounting says is revenue. From January 2025 I get the full order list. I’m looking at our order detail. I’m looking at the order details. Table looking at the order. Summary table. I’m looking at the order total, calculating all the net revenue whatever, also pulling all the timestamps that we look at all of this is 2024. So I mean, all of these would have been excluded from our calculations.
46 00:10:26.332 ⇒ 00:10:36.769 Robert Tseng: So yeah, we we weren’t we? We definitely weren’t including any of this in our in like the data pull that I gave them when I was looking at. You know, January revenue using order Summary.
47 00:10:36.910 ⇒ 00:10:41.690 Robert Tseng: But not only that. There’s a bunch of orders that are just like, not there.
48 00:10:42.030 ⇒ 00:10:43.060 Robert Tseng: So
49 00:10:43.200 ⇒ 00:11:01.930 Robert Tseng: if I just come here and I’m filtering, let’s say, okay found in what is found in order details. If I go to blanks in January alone, there are 70 orders that don’t exist like they straight up like don’t exist. Okay, they they exist in bask.
50 00:11:03.240 ⇒ 00:11:05.210 Robert Tseng: So if I just randomly pick one.
51 00:11:12.140 ⇒ 00:11:17.470 Robert Tseng: Okay, so clearly, a real order. But if I go and I try to look for it.
52 00:11:17.730 ⇒ 00:11:19.120 Robert Tseng: An order summary
53 00:11:19.920 ⇒ 00:11:25.979 Robert Tseng: doesn’t exist. I look for it and order details also doesn’t exist, so I don’t. I don’t think I need to run that.
54 00:11:26.260 ⇒ 00:11:39.299 Robert Tseng: So that worries me. January. Even from this view alone we’re missing 70 orders. If the If the Aov of January is $400 per order. We’re off by like, close to 30,000,
55 00:11:39.460 ⇒ 00:11:46.539 Robert Tseng: 30,040 things. Yeah, 70 times. It’s 28,000 to the
56 00:11:46.750 ⇒ 00:11:49.109 Robert Tseng: yeah. We’re off. We’re off by
57 00:11:49.650 ⇒ 00:11:55.450 Robert Tseng: 700,000. Sorry. But this is like mental math that I
58 00:11:56.800 ⇒ 00:12:06.980 Robert Tseng: need to think about 70 times 400. Yeah, that was like, we’re off by. We’re off, like, you know, 30 30,000 in in January, in January alone.
59 00:12:07.230 ⇒ 00:12:11.078 Robert Tseng: And that’s not really even accounting for, like
60 00:12:12.710 ⇒ 00:12:27.700 Robert Tseng: The the differences in the in the what if it was recognized? 2024, 2025 doesn’t include. We’re not even talking about order statuses or anything. We’re just straight up. We’re missing 70 orders from our models, and this is just in January alone. So
61 00:12:27.960 ⇒ 00:12:47.893 Robert Tseng: that’s concerning to me. I don’t know what the magnitude is for the rest of the months, and we haven’t. I’m not in like, I said. I’m not even including my excluding anything else regarding payment statuses or whatever. So I think there’s just multiple things going on here. I do think we are severely under reporting revenue. So I think that’s my biggest concern.
62 00:12:48.630 ⇒ 00:12:54.990 Robert Tseng: yeah. So I think that’s there’s there’s a bit there’s there’s a big issue here. And
63 00:12:55.520 ⇒ 00:13:03.980 Robert Tseng: I mean, they’re late to the Irs filing, or whatever. And so that’s why, accounting has been like the thing. I’ve been focused on the past couple of days, and I need to get them an answer.
64 00:13:04.200 ⇒ 00:13:14.168 Robert Tseng: I think I need to close the gap between the 11 million 36 million. I just need to get much closer. I need to get within 10% somehow. And
65 00:13:14.600 ⇒ 00:13:16.949 Robert Tseng: we’re off by quite a lot right now. So
66 00:13:17.454 ⇒ 00:13:22.980 Robert Tseng: that’s that’s the situation. I think this is not. It’s not great that we
67 00:13:23.950 ⇒ 00:13:25.883 Robert Tseng: didn’t notice this before. But
68 00:13:26.900 ⇒ 00:13:31.490 Robert Tseng: okay, I mean, that’s that’s it. That’s that’s my. That’s what I’m observing.
69 00:13:31.810 ⇒ 00:13:32.889 Robert Tseng: Any thoughts.
70 00:13:35.515 ⇒ 00:13:40.189 Demilade Agboola: My immediate question is, how many orders did you say there are for this year in total
71 00:13:40.940 ⇒ 00:13:42.520 Demilade Agboola: from the Basque export.
72 00:13:42.600 ⇒ 00:13:44.840 Robert Tseng: Fast export is a hundred, 20,000.
73 00:13:46.410 ⇒ 00:14:08.171 Robert Tseng: Now, I’ve already told them. This is not possible, you know, assuming refunds, cancellations whatever. Take out 20 to 30. I mean, if we cross reference with tableau tableau shows the past. I don’t know. I think you can make some assumptions there, like, I think you know. I I don’t think tableau would show us 120,000 either. I think it’s closer to 70,000. But
74 00:14:10.700 ⇒ 00:14:11.510 Robert Tseng: But yeah.
75 00:14:12.310 ⇒ 00:14:23.760 Robert Tseng: but I mean, I would I would. I would verify. I I mean, I wouldn’t. Yeah, I don’t even know what I trust anymore at this point. Honestly, if tableau is built on all this stuff and we’re just missing, they’re just straight up orders that don’t exist in our models.
76 00:14:24.020 ⇒ 00:14:26.789 Robert Tseng: and these are all valid. I’ve like sampled like 10 of them.
77 00:14:47.620 ⇒ 00:15:16.719 Robert Tseng: So what I can do to try to help kind of put together. I’ve been just doing this in Jupiter. I just work faster, python. So I’ve kind of done some of the initial exclusions. So you take the 120,000, and then, you know, you add some filters making sure that it’s paid. It’s not a canceled order. Yada, Yada, okay, I’ve exclude 20% of them. So it’s possibly 100,000 valid orders to date, and then from there I’m like kind of doing some stuff I’m I’m just like seeing if there’s any outliers. Sure there are a few where, like
78 00:15:17.110 ⇒ 00:15:27.130 Robert Tseng: payments were made a hundred days ago, and then the orders came, and so I can exclude those 2, and you know, so maybe I I can keep cutting it down like bit by bit, and bring it down to
79 00:15:27.668 ⇒ 00:15:37.070 Robert Tseng: like, yeah, there’s clearly some drift that I need to deal with which I mean this is all data cleaning shit that I thought we should have did like I don’t know. Been processing already.
80 00:15:37.460 ⇒ 00:15:54.049 Robert Tseng: but from my like my, my super quick, like explanation. I think that we should be showing like 9 million revenue a month, not 11 million in revenue to date, which is basically what I told them. So.
81 00:15:55.620 ⇒ 00:15:56.530 Robert Tseng: I’d
82 00:15:56.700 ⇒ 00:16:18.410 Robert Tseng: like, I I don’t think that we’re actually doing like, you know, 60 million in net revenue like we haven’t had any of the deductions. But even from the top line I would expect, okay, like, we’re doing about 10, you know, 8 to 10 million a month. Like I I would expect. I mean, I I think that makes sense. That’s what that’s why everybody. That’s why everybody thinks businesses like.
83 00:16:18.670 ⇒ 00:16:36.799 Robert Tseng: But it will in my, when we just use order, summary or just use order details. We’re saying that their year to date total net revenue is is 11 million. And obviously, that’s that’s a huge variance like that’s, you know, 80% of a smaller business than they than they think that they’re running, which that’s just
84 00:16:38.700 ⇒ 00:16:42.680 Robert Tseng: yeah. We we can’t. We can’t be that. We can’t be that far off.
85 00:16:49.280 ⇒ 00:16:58.429 Demilade Agboola: So part of why, I’m ask, I’m asking, so revenue does revenue include abandoned? And the reason why I’m asking that is.
86 00:16:58.550 ⇒ 00:17:11.550 Demilade Agboola: if I look at the basketball hook directly, and I’m doing a count of all like order completed this year. It’s 123,000. However, when when I look at the statuses.
87 00:17:11.880 ⇒ 00:17:17.950 Demilade Agboola: abandon accounts for about 60, 68,000 orders. So that’s a huge chunk.
88 00:17:19.730 ⇒ 00:17:20.390 Robert Tseng: Okay.
89 00:17:29.050 ⇒ 00:17:34.819 Demilade Agboola: 68,004 orders. Specifically, you have a pending of 46,000.
90 00:17:38.660 ⇒ 00:17:41.300 Demilade Agboola: Yeah. So I don’t know this.
91 00:17:43.540 ⇒ 00:17:48.187 Robert Tseng: Okay. So I mean that I mean that I feel like that could bring us much closer. Sure, I think
92 00:17:49.100 ⇒ 00:18:11.500 Robert Tseng: clearly, if 60,000 of those are abandoned. That means, I mean, even just reasonably. Looking at this, a bunch of those abandoned orders have already been paid for, and that’s why accounting is including in them. And they and it’s been confirmed like they actually receive some sort of payment. Maybe there’s a refund that gets pushed to them later, or whatever like. Maybe I don’t necessarily think it’s
93 00:18:11.730 ⇒ 00:18:15.529 Robert Tseng: when the cash comes. I don’t know what that window is like.
94 00:18:15.760 ⇒ 00:18:33.450 Robert Tseng: I think that’s why, this this kind of reporting is ridiculous to do without using stripe, like we’re just using bask to figure this out, and it’s just impossible. But okay, let me just the yeah, the 60. Let’s say 60. Let’s just say 60% of this, is abandoned.
95 00:18:33.780 ⇒ 00:18:42.400 Robert Tseng: So really, we should be having 40,000 orders. Okay, 40,000 times, average average order value.
96 00:18:42.940 ⇒ 00:18:46.209 Robert Tseng: What? What did I say? Aov was.
97 00:18:47.310 ⇒ 00:18:49.940 Robert Tseng: okay, let’s just say, Aov is like, 400.
98 00:18:55.100 ⇒ 00:18:56.590 Robert Tseng: so we have.
99 00:18:58.280 ⇒ 00:19:03.840 Robert Tseng: Okay, let’s just 40,000 times 416 million
100 00:19:04.640 ⇒ 00:19:22.650 Robert Tseng: that in in total revenue. And then we discount stuff. Okay, that’s closer to the 11 million net revenue that makes sense to me. Why, we’re saying their year to date. Revenue is 11 million. So we just completely excluded everything in the abandoned, even though it’s been paid for. And even though.
101 00:19:23.270 ⇒ 00:19:29.800 Robert Tseng: yeah, I mean that I that I guess that kind of explains
102 00:19:29.950 ⇒ 00:19:33.979 Robert Tseng: that that part. I just is that right? I don’t know. I think
103 00:19:34.360 ⇒ 00:19:37.650 Robert Tseng: think that’s that’s a that’s a that’s a fair.
104 00:19:37.790 ⇒ 00:19:44.050 Robert Tseng: That’s a fair point. And that could explain like a big chunk of this difference any other thoughts.
105 00:19:52.524 ⇒ 00:19:56.259 Demilade Agboola: So if you, I’m about to share a query.
106 00:19:56.470 ⇒ 00:19:58.280 Demilade Agboola: But basically, if you’re on.
107 00:19:58.280 ⇒ 00:20:02.309 Robert Tseng: I can share my query, too. By the way, so let me just throw that in our channel. Yeah, go ahead.
108 00:20:03.100 ⇒ 00:20:05.440 Demilade Agboola: Yeah. So if you just run this query.
109 00:20:05.650 ⇒ 00:20:12.209 Demilade Agboola: you can kind of see like, yes, I just put in slack, actually, doing.
110 00:20:13.890 ⇒ 00:20:22.150 Demilade Agboola: I planted it, but effectively. You can kind of see that, like the numbers.
111 00:20:24.350 ⇒ 00:20:28.910 Robert Tseng: Oh, oops! Wrong, wrong channel. I just. DM, that to you. Okay.
112 00:20:30.330 ⇒ 00:20:32.029 Robert Tseng: Sorry. Where? Where is your query?
113 00:20:32.990 ⇒ 00:20:34.149 Robert Tseng: Go to the channels.
114 00:20:34.830 ⇒ 00:20:38.920 Demilade Agboola: Alright good! I could send it into the slack channel. It’s not. It’s not a problem.
115 00:20:39.280 ⇒ 00:20:44.230 Robert Tseng: Okay, yeah. Sorry. I have so many windows open like, I don’t know.
116 00:20:45.890 ⇒ 00:20:47.240 Demilade Agboola: Okay.
117 00:20:53.650 ⇒ 00:20:54.360 Robert Tseng: Okay.
118 00:21:01.930 ⇒ 00:21:03.719 Demilade Agboola: So we can.
119 00:21:04.560 ⇒ 00:21:06.239 Demilade Agboola: A lot of them are abandoned.
120 00:21:08.730 ⇒ 00:21:12.200 Demilade Agboola: We consider them to be in an abandoned state.
121 00:21:22.960 ⇒ 00:21:25.800 Robert Tseng: Right so.
122 00:21:31.300 ⇒ 00:21:39.889 Robert Tseng: and because they’re abandoned, they don’t. They don’t come through. They don’t come through to or like. Shouldn’t we like we? They just don’t show up in order. Details. Order, summary.
123 00:21:43.950 ⇒ 00:21:47.699 Demilade Agboola: I know a lot of our like tests is that the
124 00:21:47.820 ⇒ 00:21:51.349 Demilade Agboola: but okay, we use. Give me one second. We use the
125 00:21:51.520 ⇒ 00:21:57.039 Demilade Agboola: order updated for a lot of these things. Let me let me actually make it more precise.
126 00:21:57.490 ⇒ 00:21:58.470 Robert Tseng: A backwards.
127 00:21:58.630 ⇒ 00:22:08.200 Demilade Agboola: We use all the updated for a lot of calculations where the current status see.
128 00:22:18.550 ⇒ 00:22:20.341 Robert Tseng: Okay, while you’re looking into that.
129 00:22:20.900 ⇒ 00:22:28.110 Robert Tseng: yeah. So I think I just want to rank for this also. Like, yeah. So, Annie, when you originally set this you?
130 00:22:28.370 ⇒ 00:22:31.830 Robert Tseng: What did you use? Just like if you can, if you recall
131 00:22:31.950 ⇒ 00:22:35.540 Robert Tseng: like, how did you spit this? Or how did you send this like? What? What, what.
132 00:22:35.700 ⇒ 00:22:37.099 Robert Tseng: what were you using.
133 00:22:37.885 ⇒ 00:22:43.524 Annie Yu: I think I still have my query somewhere, but from what I can recall now.
134 00:22:44.150 ⇒ 00:22:55.340 Annie Yu: I got the I use rote number to get only one revenue from each order, and then.
135 00:22:55.340 ⇒ 00:22:58.629 Robert Tseng: Order. Summary. Yep, and then the delivery to state, yeah.
136 00:23:01.610 ⇒ 00:23:08.099 Robert Tseng: that’s from, and the delivery to state, I guess whoever knows this is from Basque. It’s not from ship. Oh, right.
137 00:23:09.626 ⇒ 00:23:15.970 Annie Yu: I don’t have the full knowledge of that. I just use that delivered to state column.
138 00:23:18.420 ⇒ 00:23:19.070 Robert Tseng: Okay.
139 00:23:19.580 ⇒ 00:23:23.990 Annie Yu: Because I think that’s the only column that indicates state.
140 00:23:24.440 ⇒ 00:23:24.960 Robert Tseng: Okay.
141 00:23:24.960 ⇒ 00:23:25.470 Annie Yu: I know.
142 00:23:25.470 ⇒ 00:23:26.090 Robert Tseng: My.
143 00:23:26.420 ⇒ 00:23:43.559 Robert Tseng: my assumption is that it does not use ship out, because when I did reverse engineer this query that Rob had previously built, he did not use Chippo at all. He just purely used fast, and I think I had. I dropped a bunch of messages in our channel. But this is one of the queries I had shared. So
144 00:23:57.160 ⇒ 00:24:00.670 Robert Tseng: okay, a wish, any thoughts.
145 00:24:02.250 ⇒ 00:24:05.090 Awaish Kumar: So only one thing in the sheet.
146 00:24:05.480 ⇒ 00:24:11.349 Awaish Kumar: I don’t know if if it also has the transaction. Id, because in our tables.
147 00:24:11.460 ⇒ 00:24:16.980 Awaish Kumar: for example, if a bundle like. If a bundle is ordered.
148 00:24:17.180 ⇒ 00:24:25.070 Awaish Kumar: then if like a a order with 3 different products, right then we have 3 rows
149 00:24:25.760 ⇒ 00:24:30.080 Awaish Kumar: with each with different order, number, unique order, number.
150 00:24:30.290 ⇒ 00:24:33.910 Awaish Kumar: but it they have the same transaction id.
151 00:24:34.430 ⇒ 00:24:42.710 Awaish Kumar: and based on that we and but they all have the same revenue, like the same portal.
152 00:24:42.710 ⇒ 00:24:44.359 Robert Tseng: They’re over reporting revenue.
153 00:24:45.180 ⇒ 00:24:46.829 Awaish Kumar: So I don’t like.
154 00:24:47.290 ⇒ 00:24:50.540 Awaish Kumar: I don’t know if it has transaction Id. If it, then we can verify.
155 00:24:50.540 ⇒ 00:24:51.670 Robert Tseng: I don’t think it does.
156 00:24:51.670 ⇒ 00:25:01.270 Awaish Kumar: Otherwise, like in order details. Basically like we got this logic from order details right? And in order details. What they were doing is they make the 0.
157 00:25:01.390 ⇒ 00:25:09.359 Awaish Kumar: So if a bundle is ordered and has 3 products, it will have 3 rows. One will have the actual revenue, and the other will become 0.
158 00:25:09.730 ⇒ 00:25:27.249 Robert Tseng: Do you have an example of this bundle? Just so we could just pull it up on the best platform, because this comes straight from the best platform, you know. I I’ve seen like my hypothesis is like it’s not so. I’ll just take this one, for example. I I think I mean, I understand what you’re what you’re saying.
159 00:25:29.630 ⇒ 00:25:37.040 Robert Tseng: so this is like a $1,400 order. There’s no way. This was all in one transaction. This is probably 3 transactions is my guess
160 00:25:37.480 ⇒ 00:25:46.059 Robert Tseng: order. Total, 1,400 I mean, I guess I could check this one fact transactions. But
161 00:25:48.350 ⇒ 00:25:50.360 Robert Tseng: don’t honestly off the top of my head.
162 00:25:51.640 ⇒ 00:25:58.579 Robert Tseng: Alright, I’m just gonna use check back transactions for order.
163 00:25:58.690 ⇒ 00:25:59.530 Robert Tseng: Where?
164 00:26:20.120 ⇒ 00:26:22.300 Robert Tseng: Okay? Okay? Let’s see.
165 00:26:27.530 ⇒ 00:26:30.349 Robert Tseng: no, this is just a single transaction.
166 00:26:30.740 ⇒ 00:26:36.690 Robert Tseng: Yeah, we need to get one with bundles. Okay? So maybe we’re bundle.
167 00:26:38.600 ⇒ 00:26:42.100 Robert Tseng: Id is not null.
168 00:26:46.520 ⇒ 00:26:49.260 Robert Tseng: Alright, let’s just one
169 00:26:54.150 ⇒ 00:26:55.240 Robert Tseng: what? Oh.
170 00:27:01.600 ⇒ 00:27:06.240 Robert Tseng: cannot, though it’s not, you know.
171 00:27:19.240 ⇒ 00:27:23.399 Robert Tseng: Okay, think this one might work.
172 00:27:39.330 ⇒ 00:27:41.540 Robert Tseng: Oh, this one’s fully refunded, though.
173 00:27:42.180 ⇒ 00:27:43.680 Robert Tseng: Okay, well,
174 00:27:46.320 ⇒ 00:27:53.269 Robert Tseng: I’m assuming away. You’re also looking for one. But yeah, I’m just trying to find, like the actual bundled product where I could see the different transactions
175 00:27:53.650 ⇒ 00:27:56.060 Robert Tseng: from something that was not refunded.
176 00:28:30.710 ⇒ 00:28:32.340 Robert Tseng: Okay, so looks promising.
177 00:28:41.140 ⇒ 00:28:43.840 Robert Tseng: transaction. Id.
178 00:28:45.130 ⇒ 00:28:49.790 Robert Tseng: Where is Canada? But there’s no transaction id here unless that’s what this is
179 00:28:49.890 ⇒ 00:28:54.169 Robert Tseng: isn’t the same thing. Id and transaction id. Is that what I would consider transaction? Id.
180 00:28:57.900 ⇒ 00:29:00.920 Awaish Kumar: Transaction id is separate, id.
181 00:29:02.430 ⇒ 00:29:04.730 Robert Tseng: Okay, does it mean that.
182 00:29:05.080 ⇒ 00:29:10.179 Awaish Kumar: For the for the orders without bundle, like the single orders. Transaction. Id is none.
183 00:29:11.550 ⇒ 00:29:12.869 Robert Tseng: But this one is bundle.
184 00:29:14.190 ⇒ 00:29:14.840 Awaish Kumar: Okay.
185 00:29:15.570 ⇒ 00:29:17.180 Awaish Kumar: So I don’t know why.
186 00:29:21.880 ⇒ 00:29:28.590 Robert Tseng: Okay? So I mean, I I don’t. I? I heard what you were saying. You’re basically trying to say, Okay, it’s possible
187 00:29:29.210 ⇒ 00:29:35.100 Robert Tseng: that in this spreadsheet. There are orders that they’re they’re
188 00:29:35.580 ⇒ 00:29:41.610 Robert Tseng: they look like different orders, but they’re part of the same order.
189 00:29:41.730 ⇒ 00:29:43.869 Robert Tseng: or like they’re part of the same transaction.
190 00:29:44.190 ⇒ 00:29:52.841 Robert Tseng: and maybe they’re duplicating or tripling, or whatever like the the payment. I don’t think we’ve proven that yet. So that’s what I’m trying to prove here. But
191 00:29:54.580 ⇒ 00:29:59.150 Robert Tseng: yeah, I was like, that’s that’s your hypothe. That’s 1 of your hypotheses right to try to like.
192 00:29:59.150 ⇒ 00:29:59.630 Awaish Kumar: It flows.
193 00:29:59.630 ⇒ 00:30:00.900 Robert Tseng: The gap. Okay.
194 00:30:01.320 ⇒ 00:30:07.290 Robert Tseng: so yeah, like, how do we prove that out? Because I haven’t found an order that really helps me to say that.
195 00:30:11.420 ⇒ 00:30:14.999 Robert Tseng: I mean, I guess I could. Just, I’m gonna just keep looking like there’s some.
196 00:30:15.120 ⇒ 00:30:18.819 Robert Tseng: There’s some orders here like, maybe I’ll find one. Okay. So
197 00:30:19.350 ⇒ 00:30:30.460 Robert Tseng: well, I mean, now, we’re like seeing all these weird things. It’s like, okay. You think that there needs to be a transaction Id when there’s a bundle. But there are clearly orders that I’m showing here that do not have transaction ids.
198 00:30:32.130 ⇒ 00:30:40.020 Robert Tseng: Okay, perfect and transaction. Id is not at all.
199 00:30:40.920 ⇒ 00:30:45.170 Robert Tseng: Let’s I cannot type today.
200 00:30:52.390 ⇒ 00:30:55.070 Robert Tseng: Okay, let’s let’s give this one a shot.
201 00:30:56.910 ⇒ 00:31:05.529 Robert Tseng: And then, Demoto, whenever you’re like, done looking to it, you can feel free to jump back in. But I’m just, we’re exploring 2 different hypotheses right now, just trying to
202 00:31:06.510 ⇒ 00:31:07.940 Robert Tseng: gather this context.
203 00:31:18.380 ⇒ 00:31:24.070 Robert Tseng: Yeah. And then, Annie, I just have you on this call, because I mean, 1st I wanted to get what you were doing before.
204 00:31:24.180 ⇒ 00:31:29.689 Robert Tseng: and I also want you to understand the extent of what this investigation has become. So
205 00:31:30.470 ⇒ 00:31:33.259 Robert Tseng: I don’t expect this to come up often, but
206 00:31:33.570 ⇒ 00:31:43.059 Robert Tseng: I would like you to observe, and also be able to give feedback on what I’m doing. So you can investigate this.
207 00:31:43.960 ⇒ 00:31:45.910 Robert Tseng: or some, you know, do something
208 00:31:46.100 ⇒ 00:31:49.419 Robert Tseng: like this more in like with if it comes up again.
209 00:31:55.920 ⇒ 00:31:56.550 Annie Yu: Yeah.
210 00:31:57.140 ⇒ 00:31:57.970 Robert Tseng: Yeah, okay.
211 00:31:57.970 ⇒ 00:32:00.449 Annie Yu: I do have. One quick question is.
212 00:32:00.450 ⇒ 00:32:01.180 Robert Tseng: Yeah.
213 00:32:01.180 ⇒ 00:32:06.630 Annie Yu: The order detail is that a table I should be looking at constantly, or.
214 00:32:06.630 ⇒ 00:32:07.710 Robert Tseng: No.
215 00:32:07.710 ⇒ 00:32:08.239 Annie Yu: You have a boy.
216 00:32:08.240 ⇒ 00:32:09.070 Robert Tseng: Well.
217 00:32:09.830 ⇒ 00:32:19.230 Robert Tseng: yeah, so sorry to cut you off, but like order details was the previous model. I know that I’ve kind of been kind of snappy about this, because
218 00:32:20.440 ⇒ 00:32:21.440 Robert Tseng: a
219 00:32:21.740 ⇒ 00:32:31.479 Robert Tseng: yeah, frankly, I’m not clearing this every day like you guys are maintaining building these models and stuff. And so order summary. Like. Maybe I didn’t fully understand how it should be used. But
220 00:32:31.690 ⇒ 00:32:41.379 Robert Tseng: order details is what the what was being used before we were here, and so everybody anchors, their understanding of the business to that model, and
221 00:32:41.570 ⇒ 00:32:46.220 Robert Tseng: and as well like. There were certain nuances about Order Summary that I didn’t fully understand.
222 00:32:46.410 ⇒ 00:32:48.420 Robert Tseng: I do believe it gives me
223 00:32:48.640 ⇒ 00:33:03.869 Robert Tseng: roughly the same answer, like, I think they’re not that different. There’s like some exclusions here and there. Order summary includes all the order updates. And that’s why there are duplicates and stuff in there. So I understand that it was built for a different purpose.
224 00:33:04.060 ⇒ 00:33:11.500 Robert Tseng: But that said, Yeah, I guess we just have not really modeled the data in a way that’s
225 00:33:11.630 ⇒ 00:33:19.320 Robert Tseng: useful for the accounting finance side of the house, which is not no one’s to blame for that. I don’t like that.
226 00:33:19.440 ⇒ 00:33:22.450 Robert Tseng: They sprung it up on us literally
227 00:33:22.960 ⇒ 00:33:26.439 Robert Tseng: last week, like I mean on a Friday of last week.
228 00:33:27.340 ⇒ 00:33:37.740 Robert Tseng: when the Irs deadline was Monday of this week, so I literally had one day to do this. We’re we’re late now, and we have to pay penalty, or whatever which is.
229 00:33:38.250 ⇒ 00:33:39.040 Robert Tseng: Yes.
230 00:33:39.560 ⇒ 00:33:44.899 Robert Tseng: Well, anyway, like to, this has become a much bigger effort than they expected. But
231 00:33:45.230 ⇒ 00:33:50.800 Robert Tseng: from the stakeholder perspective you can understand if they’re just asking a simple like.
232 00:33:51.120 ⇒ 00:33:57.509 Robert Tseng: why can’t we talk about revenue by why, why do we not know revenue by state during this past year like.
233 00:33:57.640 ⇒ 00:34:00.420 Robert Tseng: understand that? That’s a very frustrating question.
234 00:34:00.560 ⇒ 00:34:02.870 Robert Tseng: The executives are
235 00:34:02.970 ⇒ 00:34:10.000 Robert Tseng: freaking out about that, because, like, have we done all of this work, and then not? We’re not able to answer that question confidently.
236 00:34:10.239 ⇒ 00:34:14.719 Robert Tseng: Understand that? That’s why they’re they’ve been really frustrated with with this part.
237 00:34:15.576 ⇒ 00:34:28.040 Robert Tseng: So I think, coming, we’re gonna just get to the bottom of this explain the difference? And then we need to get this on the roadmap to be able to build some modeling for the accounting team.
238 00:34:28.449 ⇒ 00:34:31.740 Robert Tseng: I know more or less what they need. Now, I just
239 00:34:32.260 ⇒ 00:34:35.559 Robert Tseng: it’s just it’s just taken like this past week of
240 00:34:35.949 ⇒ 00:34:38.929 Robert Tseng: in order in order to figure figure that out so.
241 00:34:40.250 ⇒ 00:34:46.799 Robert Tseng: I don’t know if that that probably answered more than just your single question, but it’s trying to just
242 00:34:46.949 ⇒ 00:34:54.339 Robert Tseng: scare the situation with with with you guys. So I know that we’ve fought a lot of different fires this week.
243 00:34:54.630 ⇒ 00:35:01.415 Robert Tseng: you guys that definitely definitely helps in other areas. But this is where a lot of my time has been going into. So
244 00:35:02.190 ⇒ 00:35:03.020 Robert Tseng: yeah.
245 00:35:03.020 ⇒ 00:35:10.000 Demilade Agboola: Yeah. Sorry. Quick question to Annie, please. Can you share the the model? Like the query used to give the 11 million answer.
246 00:35:10.830 ⇒ 00:35:16.280 Demilade Agboola: and also to our dashboards, also align with that 11 million. Answer is also what I want to be sure
247 00:35:16.770 ⇒ 00:35:21.249 Demilade Agboola: like, if we look at our regular dashboards for the year, and we’re like, Oh, you have to date, is it?
248 00:35:21.250 ⇒ 00:35:29.839 Robert Tseng: I think I gave the 11 million answer. I don’t think any feedback, I think 1st is less actually so I can go and dig that up, but I will.
249 00:35:30.080 ⇒ 00:35:37.159 Demilade Agboola: The reason. The reason why I’m asking is because I’m looking at fact transactions. And I just did a sum of order like order totals.
250 00:35:37.650 ⇒ 00:35:42.180 Demilade Agboola: which is the like, the individual ordered value
251 00:35:42.930 ⇒ 00:35:47.590 Demilade Agboola: this year. And that’s like 37 million. So I’m actually not trying to figure out, okay, so
252 00:35:47.720 ⇒ 00:35:50.209 Demilade Agboola: where? Where? Where do we get a drop off.
253 00:35:50.920 ⇒ 00:36:01.949 Robert Tseng: Yeah, well, it’s just that we can’t map it back to orders. So I understood the 37 million as well. I think we’re just having a hard time being able to roll, get orders and transactions to like.
254 00:36:02.730 ⇒ 00:36:09.080 Robert Tseng: yeah, I can tell them what it what it is, but then I don’t don’t know how to tell them what what orders are actually there. So.
255 00:36:15.180 ⇒ 00:36:22.469 Demilade Agboola: Okay. So I just wanted to understand how we got the 11 million in the 1st place. So we can just like, okay.
256 00:36:23.590 ⇒ 00:36:24.139 Demilade Agboola: figure out.
257 00:36:27.240 ⇒ 00:36:29.700 Robert Tseng: Where did I even put that query?
258 00:36:38.000 ⇒ 00:36:40.440 Robert Tseng: Maybe it was possibly this one.
259 00:36:42.820 ⇒ 00:36:44.900 Robert Tseng: Maybe I put in somewhere else.
260 00:36:49.810 ⇒ 00:36:50.700 Robert Tseng: I don’t know.
261 00:36:51.660 ⇒ 00:36:57.760 Robert Tseng: Hope this is it? Let’s take a look, see?
262 00:36:58.750 ⇒ 00:37:01.480 Robert Tseng: And and
263 00:37:05.370 ⇒ 00:37:14.290 Robert Tseng: 39 million 32 million stay for
264 00:37:22.490 ⇒ 00:37:25.600 Robert Tseng: no, that does. That was a 37.
265 00:37:38.350 ⇒ 00:37:39.590 Robert Tseng: I just like.
266 00:37:42.010 ⇒ 00:37:47.050 Robert Tseng: Oh, he! He said, it was 11 million. I don’t think I ever said it was 11 million.
267 00:37:50.310 ⇒ 00:37:58.900 Demilade Agboola: Even our dashboard, the exact dashboard, just eyeballing it like even just general Loan. It’s 7 million. So there’s no way we are saying it’s 11 million.
268 00:37:59.250 ⇒ 00:38:00.060 Robert Tseng: What?
269 00:38:00.540 ⇒ 00:38:01.099 Demilade Agboola: Like you said.
270 00:38:01.100 ⇒ 00:38:02.630 Demilade Agboola: We look at the account.
271 00:38:03.410 ⇒ 00:38:09.789 Robert Tseng: Did I literally just not assume, wait. What? Okay? No, no, no, no, yeah, yeah.
272 00:38:13.320 ⇒ 00:38:17.030 Demilade Agboola: Sorry. I just sent the exact dash exact dashboard.
273 00:38:17.180 ⇒ 00:38:18.160 Robert Tseng: Yeah. Thanks.
274 00:38:19.990 ⇒ 00:38:21.060 Demilade Agboola: So
275 00:38:21.400 ⇒ 00:38:26.999 Demilade Agboola: also any. It appears like our top line. Numbers are not showing on the exact in the exact dashboard.
276 00:38:29.750 ⇒ 00:38:33.870 Demilade Agboola: so, like the revenue orders. Naov, I can tell by numbers.
277 00:38:33.990 ⇒ 00:38:37.359 Demilade Agboola: It just shows like the last last month, and this month.
278 00:38:39.530 ⇒ 00:38:42.239 Annie Yu: Yeah, wait. So what’s the question here?
279 00:38:42.520 ⇒ 00:38:44.589 Demilade Agboola: It’s not a question. It’s just an observation.
280 00:38:44.920 ⇒ 00:38:53.490 Annie Yu: Yeah, yeah, I didn’t change anything. But if we want to have the total, we definitely can do that.
281 00:38:56.470 ⇒ 00:39:00.230 Demilade Agboola: Oh, oh, it’s it’s international. We’re not supposed to have a total.
282 00:39:01.290 ⇒ 00:39:07.840 Annie Yu: Oh, no, no! Wait! What do you mean? You’re you’re saying the top line showing the current month and the past month right.
283 00:39:09.460 ⇒ 00:39:12.769 Demilade Agboola: Yeah, yeah, I’ve seen like we don’t have like a total for the last 6 months.
284 00:39:13.050 ⇒ 00:39:19.120 Demilade Agboola: cause this is the field is last 6 months. But we don’t like it’s not going. This is the revenue for the last 6 months. Number.
285 00:39:21.270 ⇒ 00:39:23.999 Demilade Agboola: It’s just okay. All right. Yeah.
286 00:39:26.910 ⇒ 00:39:37.339 Demilade Agboola: But yeah, like the I don’t know if you can see, but the the dashboard literally is like 7 million for for January, 6 million for 6.5 million for February
287 00:39:37.600 ⇒ 00:39:45.660 Demilade Agboola: 6.4 from March 5.9 for April, 5.3 for me.
288 00:39:46.390 ⇒ 00:39:47.480 Demilade Agboola: So.
289 00:39:51.590 ⇒ 00:40:04.549 Robert Tseng: Okay, I mean the the yeah. I don’t really know where he got his 11 date. But the the point is like, How do you then break this out into orders right? And do those orders really match like what we have here in bask? I think
290 00:40:04.810 ⇒ 00:40:08.685 Robert Tseng: that is still, that’s still the question. And
291 00:40:13.450 ⇒ 00:40:17.840 Robert Tseng: yeah, cause like Annie’s query, I guess.
292 00:40:18.510 ⇒ 00:40:22.510 Robert Tseng: Yeah, I’ve been. I’ve been way too deep in this, like, I don’t even remember.
293 00:40:26.800 ⇒ 00:40:28.640 Robert Tseng: This is Annie’s query.
294 00:40:29.160 ⇒ 00:40:35.420 Robert Tseng: Revenue by States supposedly year to date, is it?
295 00:40:36.140 ⇒ 00:40:39.529 Robert Tseng: It’s not even year to date. It’s just like partial.
296 00:40:39.760 ⇒ 00:40:40.300 Annie Yu: They prefer.
297 00:40:40.300 ⇒ 00:40:42.599 Annie Yu: And yeah, April to.
298 00:40:43.660 ⇒ 00:40:47.820 Robert Tseng: Yeah. And and she’s saying, it’s like 1.7, right? So like
299 00:40:48.060 ⇒ 00:40:53.410 Robert Tseng: when you’re looking at just I mean, I’m sure I’m assuming she went off of Order summary. And then that’s what she got
300 00:40:53.670 ⇒ 00:40:58.110 Robert Tseng: if we use transactions. And this is what we get, and it’s closer to their understanding.
301 00:40:58.290 ⇒ 00:41:05.589 Robert Tseng: Yeah. So the gap is still like the orders to transactions like, How how do these transactions break out into the orders?
302 00:41:06.210 ⇒ 00:41:07.070 Robert Tseng: That’s
303 00:41:10.540 ⇒ 00:41:16.420 Robert Tseng: that’s that’s the list that we need to be able to send to the Irs to be able to say, like.
304 00:41:16.730 ⇒ 00:41:23.179 Robert Tseng: Yeah, the 7 million in January is from this list of orders like, I think that’s
305 00:41:23.310 ⇒ 00:41:25.900 Robert Tseng: that’s that seems to be the main objective.
306 00:41:36.050 ⇒ 00:41:43.370 Demilade Agboola: So I think what what might help with nomenclature is like fact. Transactions is technically facts or fact. Orders
307 00:41:43.750 ⇒ 00:41:50.039 Demilade Agboola: like it’s each line is an individual order. Id. That also has a transaction. Id.
308 00:41:50.350 ⇒ 00:41:56.050 Demilade Agboola: It might help to like rename that. But like we could just do a list of things from January.
309 00:41:57.450 ⇒ 00:42:01.460 Demilade Agboola: I’m pretty sure it let me try the query and see if it matches.
310 00:42:05.570 ⇒ 00:42:08.959 Demilade Agboola: but that will be a good list of things in January.
311 00:42:11.380 ⇒ 00:42:16.359 Robert Tseng: Okay, so let’s assume that that works. I’m fine with kind of doing that. I
312 00:42:17.310 ⇒ 00:42:22.920 Robert Tseng: don’t have the query for the 11 million. Right now I will have to
313 00:42:23.770 ⇒ 00:42:31.649 Robert Tseng: get back to you on that like I don’t know off top my head like I I thought I had scored that query somewhere, but I can’t pull it up.
314 00:42:34.340 ⇒ 00:42:35.520 Demilade Agboola: Yeah, that.
315 00:42:35.520 ⇒ 00:42:36.220 Robert Tseng: And then
316 00:42:41.000 ⇒ 00:42:48.169 Robert Tseng: perhaps it was just cause it was a partial cut off. I literally think that’s why. Because when I sent this, this is only like
317 00:42:48.330 ⇒ 00:42:57.619 Robert Tseng: there’s a 10 MB like limit here. I think that’s literally what I told him, so that he just took that, and he just called it, and he just said that we were saying 11 million, which
318 00:42:59.120 ⇒ 00:43:04.380 Robert Tseng: I don’t really think that’s what I was trying to say. So I I don’t. I don’t believe I can replicate that.
319 00:43:08.450 ⇒ 00:43:20.479 Robert Tseng: Okay, so revenue wise where we match. Let’s just kind of get get like the number of top line orders. And we need to be able to compare that to to bask. So I want to be able to say.
320 00:43:20.800 ⇒ 00:43:34.569 Robert Tseng: Okay, revenue wise. We’re actually very close to bass. We’re different because of XYZ. Orders were off by X percentage because of Xyz is the same kind of loop like we’re just. I’m always having to defend, defend this, defend this thing?
321 00:43:35.700 ⇒ 00:43:44.079 Robert Tseng: but I do think that I pointed out a few valid things in this investigation. One is that we are not like. We just have missing orders.
322 00:43:45.590 ⇒ 00:43:53.330 Robert Tseng: like there’s a there’s a whole list of stuff coming from bask. If I expanded this list to beyond sorry which list this one
323 00:43:54.220 ⇒ 00:43:58.850 Robert Tseng: like this is just January, but I could go and pull this full list of
324 00:43:59.290 ⇒ 00:44:19.300 Robert Tseng: you know from, and I I don’t know like how we would backfill. This is something that we would go and ask fast, be like, Where is this data? Why do we not get the web hooks like, do we need to go in and backfill this data is that like part of the answer, because this is 70 missing orders in the month of January alone. Awesome, you know
325 00:44:20.040 ⇒ 00:44:25.109 Robert Tseng: that. That’s that could be hundreds of orders. And that’s, you know, hundreds of thousands of dollars right there.
326 00:44:26.280 ⇒ 00:44:31.920 Robert Tseng: Like. So I think that’s that’s part of it. Maybe the bundle like
327 00:44:32.040 ⇒ 00:44:40.910 Robert Tseng: it waste like I, the the bundle transaction thing that you pointed out that could still be like one of the causes. I don’t know. I don’t think we really
328 00:44:41.480 ⇒ 00:44:51.149 Robert Tseng: figured that out on this call, so I think I would need you to kind of confirm that that hypothesis is true or like, you can rule it out.
329 00:44:51.674 ⇒ 00:45:08.229 Robert Tseng: And then, yeah, I think between the 3 of those things, we, if we can actually get them in order. List that match that rolled up to that 36 37 million for this for this year, I think we can close this investigation, and that will be fine for for now.
330 00:45:08.660 ⇒ 00:45:12.939 Robert Tseng: and we can go work on the accounting models for a different sprint of work.
331 00:45:13.210 ⇒ 00:45:17.479 Robert Tseng: Does that sound like a like. Does that sound fair? For, like what next steps will look like.
332 00:45:21.700 ⇒ 00:45:25.929 Demilade Agboola: Sure a quick question, though, before we hop off
333 00:45:27.270 ⇒ 00:45:30.150 Demilade Agboola: cause I’m looking at. Can I quickly share my screen.
334 00:45:30.320 ⇒ 00:45:31.270 Robert Tseng: Yes, please.
335 00:45:33.360 ⇒ 00:45:35.859 Demilade Agboola: Okay? So I’m looking at like
336 00:45:37.680 ⇒ 00:45:42.229 Demilade Agboola: of actions, actions like the high level view of things.
337 00:45:43.110 ⇒ 00:45:47.150 Demilade Agboola: So when I look at like payments, status.
338 00:45:48.010 ⇒ 00:45:52.250 Robert Tseng: What those are. Order totals, I see. Okay.
339 00:45:53.170 ⇒ 00:45:59.330 Demilade Agboola: Okay, so we have, like for paid, we have, like 7.3 million. We have 4, like 4 million
340 00:45:59.540 ⇒ 00:46:05.880 Demilade Agboola: or so, 400,000 turn back. Not sure. We have no full payment status, but that’s like that’s what we’re getting from bask
341 00:46:08.073 ⇒ 00:46:11.790 Demilade Agboola: but in terms of what I said about the like.
342 00:46:12.518 ⇒ 00:46:17.120 Demilade Agboola: Total number of orders for this year. I think we’re fine.
343 00:46:17.575 ⇒ 00:46:23.630 Demilade Agboola: I’m just not sure when we want to look at the because we’re trying to say want to send like 7 million
344 00:46:24.880 ⇒ 00:46:26.790 Demilade Agboola: for the month of January.
345 00:46:30.810 ⇒ 00:46:32.960 Demilade Agboola: Are we looking at center pharmacy
346 00:46:34.520 ⇒ 00:46:39.290 Demilade Agboola: and shipped because that will be like 7.8 alien.
347 00:46:42.090 ⇒ 00:46:46.940 Demilade Agboola: So I think that’s that’s what.
348 00:46:46.940 ⇒ 00:47:06.470 Robert Tseng: Well, yeah, we just have to know what’s like, what’s valid. Right? So like the the thing that they need to send is just like monthly revenue broken out by state, and the orders for each one like that’s that’s still like what we’re trying to get to right cause like when they when we pay taxes we have to like pay on different have to pay different taxes in different months. That’s a different states. So
349 00:47:07.049 ⇒ 00:47:10.009 Robert Tseng: I think everything here is valid. I think
350 00:47:10.190 ⇒ 00:47:18.130 Robert Tseng: it’s what’s valid is the payment status. It’s not really the order. I don’t think the order status matters so much.
351 00:47:18.640 ⇒ 00:47:26.319 Robert Tseng: Okay, so if we’re looking at the payment status I’m just trying to use, just know. So we can. I know what to filter and send to you as an order list.
352 00:47:27.030 ⇒ 00:47:31.400 Demilade Agboola: So if we say the payment status is valid, do we only look at paid statuses?
353 00:47:31.860 ⇒ 00:47:32.340 Demilade Agboola: Hey?
354 00:47:32.340 ⇒ 00:47:34.432 Robert Tseng: Prepaid, I guess.
355 00:47:36.620 ⇒ 00:47:44.100 Robert Tseng: I I yeah, I mean, we have more filtering here than what best platform does. I think bass platform, or actually, what is best. What is this?
356 00:47:44.340 ⇒ 00:47:46.110 Robert Tseng: They have a whatever.
357 00:47:46.360 ⇒ 00:47:48.569 Robert Tseng: No, they have all the same ones. So
358 00:47:50.640 ⇒ 00:47:57.400 Robert Tseng: yeah, yeah, we’re only looking at paid, I I guess. Yeah. Paid prepaid and prepaid.
359 00:47:58.230 ⇒ 00:48:05.380 Demilade Agboola: Okay, well, okay, prepaid is 0. But like, what about refunded? Wouldn’t I have to count against prepaid.
360 00:48:05.540 ⇒ 00:48:11.920 Robert Tseng: Yes, it would like that. That would end up going. And that’s how we’re. That’s how they’re gonna get to net revenue. I guess.
361 00:48:12.600 ⇒ 00:48:15.940 Demilade Agboola: But I’m just trying to align us on on top line, even so.
362 00:48:17.190 ⇒ 00:48:22.189 Demilade Agboola: alright. So do you want me to send every single thing? Or should I just do? Paid and refunded.
363 00:48:27.350 ⇒ 00:48:28.720 Demilade Agboola: because.
364 00:48:29.550 ⇒ 00:48:40.090 Robert Tseng: Yeah, well, so this is the thing where I’m saying, this is just a straight guess for me like it. That’s it sounds like it makes sense. But I don’t really know like what this is from bask, so
365 00:48:40.220 ⇒ 00:48:44.069 Robert Tseng: from from stripe, which is their main payment processor, like what?
366 00:48:44.920 ⇒ 00:48:51.530 Robert Tseng: What statuses are actually valid from for for stripe. Like I I don’t know like we’re we’re kind of guessing.
367 00:48:52.800 ⇒ 00:48:57.589 Demilade Agboola: Oh, okay, cause, this is like this, like, without filtering. It’s just.
368 00:48:57.590 ⇒ 00:49:05.700 Robert Tseng: I mean 22,000 is not not not is not much so. I don’t really think it matters as well like, I said. If we get within 10%, I think that’s fine.
369 00:49:06.320 ⇒ 00:49:09.420 Demilade Agboola: Alright, sure!
370 00:49:09.420 ⇒ 00:49:10.070 Robert Tseng: Yeah.
371 00:49:10.650 ⇒ 00:49:11.250 Demilade Agboola: Right.
372 00:49:14.680 ⇒ 00:49:16.060 Demilade Agboola: Got this.
373 00:49:19.130 ⇒ 00:49:22.500 Demilade Agboola: So this is for January. I’ll export the Csv.
374 00:49:26.710 ⇒ 00:49:30.960 Robert Tseng: You have to make sure, like the export. I think the whole point was that like.
375 00:49:31.110 ⇒ 00:49:34.640 Robert Tseng: yeah, he’ll it only saved 17,000 rows, right? So.
376 00:49:36.100 ⇒ 00:49:41.549 Demilade Agboola: You want it by states, too, though that’s that’s the important thing. So I haven’t done that. Let me quickly add that, like.
377 00:49:46.220 ⇒ 00:49:54.429 Robert Tseng: When I looked into it, something like 5 to 10% of orders did not have state. I think I used the same way that Annie was looking into it.
378 00:49:55.070 ⇒ 00:49:55.676 Demilade Agboola: Oh, okay.
379 00:49:57.150 ⇒ 00:49:58.650 Demilade Agboola: Drawing.
380 00:49:59.210 ⇒ 00:50:09.629 Robert Tseng: Which once again is fine, if we’re off by 5% fine, and then if we get 5% of the payment status is wrong. Also fine. That’s combined, you know. That’s that’ll probably still be less than 10%.
381 00:50:09.830 ⇒ 00:50:35.459 Robert Tseng: And yeah, I just if we can get closer to that. But the Csv. That you just that you just exported. Gotta be careful because of the the cutoff limits. I think that’s why accounting thinks that we were stuck on 11 million because I’d send them a Csv. That was just like too small like, I don’t actually think I saw it was that that small it just when I sent them the the raw file it was wasn’t the full list.
382 00:50:35.850 ⇒ 00:50:41.167 Robert Tseng: Yeah, I just saw an error message that’s only able to save 17,000 rows out of.
383 00:50:41.730 ⇒ 00:50:42.420 Demilade Agboola: So.
384 00:50:42.720 ⇒ 00:50:47.020 Robert Tseng: Yeah. So I don’t know. We have. We have to figure out to get around that. Yeah.
385 00:50:47.580 ⇒ 00:50:52.489 Robert Tseng: okay, I don’t wanna kind of drag this out too long. I think it’s clear kind of next steps.
386 00:50:53.910 ⇒ 00:51:05.210 Robert Tseng: I will. I mean this. I’ll try to turn this stuff in the ticket. We don’t need to answer all those questions now. Sounds like some of this is an investigation for later that order list. I do need Dame a lot like I I did.
387 00:51:06.083 ⇒ 00:51:13.846 Robert Tseng: We we yeah, we do need to send something over to them. I think I know enough now to kind of explain the difference.
388 00:51:15.430 ⇒ 00:51:21.799 Robert Tseng: But yeah, I think this was kind of a messy investigation. So hopefully, we can, we can debrief this.
389 00:51:23.840 ⇒ 00:51:31.009 Robert Tseng: I don’t. Wanna I don’t wanna talk about this anymore. So we’ll we’ll come back to this next week and and discuss like how we can
390 00:51:31.200 ⇒ 00:51:34.040 Robert Tseng: can handle these types of requests moving forward.
391 00:51:34.160 ⇒ 00:51:39.840 Robert Tseng: We’ll we’re gonna get something like this every quarter. And so I mean, this basically took
392 00:51:40.640 ⇒ 00:51:48.680 Robert Tseng: a week for us to resolve. It’s not even fully there yet. We we need to figure out how to speed speed this up. So,
393 00:51:49.500 ⇒ 00:51:50.290 Robert Tseng: yeah.
394 00:51:51.100 ⇒ 00:51:58.899 Robert Tseng: okay, any other questions. Let me know. Thank you for jumping on last minute. I really don’t like pulling you guys into meetings like this. I just
395 00:51:59.440 ⇒ 00:52:06.110 Robert Tseng: everything has been a fire this week, so kind of just needed needed to make sure we were. We’re on the same page here.
396 00:52:08.870 ⇒ 00:52:10.450 Demilade Agboola: Okay. Sounds good.
397 00:52:10.630 ⇒ 00:52:12.640 Robert Tseng: Okay, thanks. Everyone. Bye.