Meeting Title: PP2G | Standup Date: 2025-04-25 Meeting participants: Kim Todaro, Luke Daque, Amber Lin
WEBVTT
1 00:00:40.310 ⇒ 00:00:41.349 Luke Daque: Hi amber.
2 00:00:42.040 ⇒ 00:00:47.859 Amber Lin: Hi, Luke, I was just checking the hours. Have you logged your hours of?
3 00:00:49.140 ⇒ 00:00:51.159 Amber Lin: Didn’t lock all of them because I don’t.
4 00:00:52.570 ⇒ 00:00:53.349 Amber Lin: It’s a week.
5 00:00:53.580 ⇒ 00:00:57.620 Luke Daque: I don’t think I’ve logged all of them. I’ll I’ll have to log them now.
6 00:00:57.910 ⇒ 00:01:07.000 Amber Lin: Okay, okay, sounds good. Looked at it last week. And this week, and both of them has like 5 h. And I really do think you did more work than that.
7 00:01:07.510 ⇒ 00:01:10.060 Luke Daque: Yeah, I’ll I’ll check. I’ll double check.
8 00:01:10.780 ⇒ 00:01:11.510 Amber Lin: Okay.
9 00:01:12.640 ⇒ 00:01:18.349 Amber Lin: do you think it’s closer to 5 h or 10 h, or like 20? Wh? What do you estimate.
10 00:01:18.350 ⇒ 00:01:21.730 Luke Daque: Parts. It’s for pool parts
11 00:01:22.150 ⇒ 00:01:31.229 Luke Daque: for this week. Maybe it’s closer to 10, or maybe even a bit more. Last week, though, I was mostly
12 00:01:31.660 ⇒ 00:01:39.089 Luke Daque: out. So maybe yeah, I’ll I’ll have to check like what else I did.
13 00:01:40.300 ⇒ 00:01:44.950 Amber Lin: So I’ll say it’s 3rd
14 00:01:45.360 ⇒ 00:01:49.329 Amber Lin: 30% of your 40 h.
15 00:01:49.880 ⇒ 00:01:50.490 Luke Daque: Perhaps.
16 00:01:50.490 ⇒ 00:01:54.029 Amber Lin: 30, 30 to 35. I’ll just put that down.
17 00:01:54.380 ⇒ 00:01:55.190 Luke Daque: Yeah.
18 00:01:55.360 ⇒ 00:01:59.790 Amber Lin: Yeah, okay, I’ll put that and.
19 00:02:01.030 ⇒ 00:02:04.250 Luke Daque: The 40 h is for the 2 weeks right or like.
20 00:02:04.680 ⇒ 00:02:05.350 Amber Lin: Huh!
21 00:02:06.440 ⇒ 00:02:08.910 Luke Daque: Oh, for the whole month! 40 h!
22 00:02:08.910 ⇒ 00:02:13.749 Amber Lin: So like a total of your time, like per week. You have 4.
23 00:02:13.750 ⇒ 00:02:14.120 Luke Daque: Yeah.
24 00:02:14.330 ⇒ 00:02:15.530 Amber Lin: Global right.
25 00:02:15.740 ⇒ 00:02:16.930 Luke Daque: Right, right.
26 00:02:17.910 ⇒ 00:02:19.410 Amber Lin: Yeah, sounds good.
27 00:02:22.670 ⇒ 00:02:29.439 Luke Daque: Cool. So yeah, let’s let’s probably wait for Cam. I did see some.
28 00:02:30.860 ⇒ 00:02:39.219 Luke Daque: I think we have pro a bit of progress since we had the call earlier. I did find like duplicates going on for Eunice
29 00:02:39.480 ⇒ 00:02:44.430 Luke Daque: orders, but and there’s still some.
30 00:02:45.730 ⇒ 00:02:47.450 Luke Daque: It’s still way off.
31 00:02:47.700 ⇒ 00:02:51.140 Luke Daque: I mean, it’s still off, not share your screen.
32 00:02:52.370 ⇒ 00:02:53.340 Luke Daque: Yeah, sure.
33 00:03:02.620 ⇒ 00:03:04.230 kim todaro: Hey? Guys? Sorry I’m late.
34 00:03:04.880 ⇒ 00:03:07.030 Luke Daque: There you go! Hey, Kim! How’s it going.
35 00:03:07.490 ⇒ 00:03:08.669 kim todaro: Good! How are you?
36 00:03:08.960 ⇒ 00:03:09.910 Luke Daque: Doing well.
37 00:03:12.740 ⇒ 00:03:14.490 Amber Lin: It’s Friday. So.
38 00:03:14.910 ⇒ 00:03:16.169 kim todaro: I’m so happy.
39 00:03:17.390 ⇒ 00:03:18.770 Amber Lin: My second to last week.
40 00:03:19.625 ⇒ 00:03:28.759 Amber Lin: Yeah. So we have some good progress on this earlier. I think the few things we have is one to add the
41 00:03:29.312 ⇒ 00:03:40.949 Amber Lin: the monthly platform fees. I have a question about that. Is that fixed per month, or is it more variable on usage? So how do we get that numbers into real as well.
42 00:03:41.390 ⇒ 00:03:48.149 kim todaro: I was thinking about that. I think maybe just ignore that, for now, if Ben wants to add it in later on, I wouldn’t focus on it, though.
43 00:03:48.820 ⇒ 00:03:50.410 Amber Lin: Okay, sounds good. So.
44 00:03:50.410 ⇒ 00:03:53.179 kim todaro: And I’m gonna remove it from the sheet, too. By the way, I’m gonna remove it.
45 00:03:53.658 ⇒ 00:04:00.360 Amber Lin: Awesome. Sounds good. That’s the 1st one. The second one is, we were
46 00:04:01.192 ⇒ 00:04:05.150 Amber Lin: looking at the ship station and Unis ones, have you.
47 00:04:05.150 ⇒ 00:04:27.690 Amber Lin: We’re wondering how you got your numbers, because we got essentially all of the orders. Of the excel that gets sent to us automatically, and we did that, and we still weren’t able to get the same numbers. I remember, Luke, you say it was around 7 K. But here for ship station you have 10 K. So I was wondering where the difference is from.
48 00:04:28.350 ⇒ 00:04:37.050 kim todaro: That’s a good question. So basically, what I’ve been doing is, Chuck sends me an export when I ask for it from ship station.
49 00:04:37.340 ⇒ 00:04:38.190 Luke Daque: Hmm.
50 00:04:38.190 ⇒ 00:04:42.680 kim todaro: I really should just get access myself and do it cause I don’t like bothering him. But
51 00:04:43.210 ⇒ 00:04:53.370 kim todaro: then I get the spreadsheet, and I basically delete all the Amazon orders and all the Walmart orders. And then I just make a pivot table, and I think I grab it by.
52 00:04:53.660 ⇒ 00:04:54.580 Luke Daque: I see.
53 00:04:55.050 ⇒ 00:05:04.869 kim todaro: I think I grab it by ship date. I just I just do that. And there there’s been times in the past where there’s been like some duplicates because of what’s that issue that’s going on with split orders.
54 00:05:05.290 ⇒ 00:05:11.303 Luke Daque: Right? Yeah, I think maybe that’s causing the issue. I think that’s still an ongoing issue at the moment.
55 00:05:11.620 ⇒ 00:05:12.540 kim todaro: Yeah.
56 00:05:14.820 ⇒ 00:05:22.090 Luke Daque: Yeah, that’s that could be a good cause. That’s what I’m also doing already. Like, I I believe we are getting the same report like Chuck.
57 00:05:22.090 ⇒ 00:05:22.410 kim todaro: Yes.
58 00:05:22.660 ⇒ 00:05:28.626 Luke Daque: The the one that chopped is sending you. And basically, that’s what the data model is doing like
59 00:05:29.080 ⇒ 00:05:33.910 Luke Daque: it’s assigning all the Amazon shipstation cost to Amazon orders.
60 00:05:34.020 ⇒ 00:05:38.099 Luke Daque: and then all the shopify to the shopify
61 00:05:38.310 ⇒ 00:05:43.990 Luke Daque: orders. So maybe, like the split order is probably
62 00:05:44.990 ⇒ 00:05:48.510 Luke Daque: maybe it’s not showing the split orders, because, like.
63 00:05:49.390 ⇒ 00:05:52.927 Luke Daque: it’s getting a different order number, right? It’s like
64 00:05:53.720 ⇒ 00:05:56.959 Luke Daque: it has, like the dash ships split order
65 00:05:57.070 ⇒ 00:05:59.680 Luke Daque: on it. And maybe that’s the that’s the cost.
66 00:06:01.330 ⇒ 00:06:03.199 kim todaro: Maybe I’m not sure.
67 00:06:03.200 ⇒ 00:06:07.769 Luke Daque: Ones that I have, because those might not be like counting
68 00:06:08.380 ⇒ 00:06:10.319 Luke Daque: in what I have, because, like.
69 00:06:10.450 ⇒ 00:06:12.280 Luke Daque: it’s a it’s a different name.
70 00:06:12.570 ⇒ 00:06:14.300 Luke Daque: It’s a different order name.
71 00:06:14.740 ⇒ 00:06:19.829 Luke Daque: So yeah, and that’s a good point. I’ll take a look at that. The the split orders.
72 00:06:20.100 ⇒ 00:06:24.130 Luke Daque: That’s probably I I think I’m pretty sure that’s the costing.
73 00:06:24.260 ⇒ 00:06:29.320 Luke Daque: The missing. Whatever was missing was is probably the ship split orders.
74 00:06:30.130 ⇒ 00:06:41.209 Amber Lin: Okay, awesome. So for the units, one, too, I noticed that most of it is the same number. So I was wondering if you just did an average of the days like, How does.
75 00:06:41.210 ⇒ 00:06:43.151 kim todaro: That’s exactly what I do.
76 00:06:44.550 ⇒ 00:06:49.920 kim todaro: That’s exactly what I do, which is probably very different from what you do. But what Chuck’s been doing with me is.
77 00:06:50.100 ⇒ 00:06:57.630 kim todaro: he’ll either send me like a spreadsheet over time that’s not broken out by day, but he’ll shut. Send me like a date range.
78 00:06:58.020 ⇒ 00:07:09.639 kim todaro: so I, I just kind of average. I’m like, okay, so there’s X amount of variable speed pumps times that by 20 x amount of heat pumps times that by 4, 20, or 4, 10, whatever that number is.
79 00:07:09.890 ⇒ 00:07:22.940 kim todaro: And then I average it over that base band. But yeah, shipping definitely looks a little off compared to what I’m seeing in real, because total shipment cost is like 7,800 in real. And then the reality is like.
80 00:07:23.290 ⇒ 00:07:29.729 kim todaro: it’s over. It’s 10,000 plus 8,000, or maybe maybe it’s a little less than that, but it’s still a little bit. It’s
81 00:07:29.880 ⇒ 00:07:32.120 kim todaro: still awesome. So.
82 00:07:32.740 ⇒ 00:07:40.730 Luke Daque: Yeah, that makes sense. Like, I think that’s the biggest one for units, though, we are. Actually, I think that’s where we’re we’re differing, because, like.
83 00:07:40.860 ⇒ 00:07:42.530 Luke Daque: we are getting the exact
84 00:07:43.828 ⇒ 00:07:49.929 Luke Daque: shipments by units based on the file that we’re getting. And then we’re already also like using
85 00:07:50.270 ⇒ 00:07:55.900 Luke Daque: the the tape, the mapping here that were the heat pumps.
86 00:07:57.400 ⇒ 00:08:03.240 Luke Daque: So like, if ever we’re getting variable speed pumps, I’m just using $20 for each of those.
87 00:08:03.690 ⇒ 00:08:04.060 kim todaro: Yeah.
88 00:08:04.060 ⇒ 00:08:16.730 Luke Daque: Like either of these heat pumps, it would be like 400. So that’s why we’re seeing like 80. So probably for the 17th of April. There will probably 4 variable speed bumps here
89 00:08:17.230 ⇒ 00:08:21.960 Luke Daque: and stuff like that. And this one’s probably different, because it’s like an exact number.
90 00:08:23.110 ⇒ 00:08:30.910 kim todaro: Yeah, I mean, it’s worth asking. Chuck, like on April 30, th or may 1st to like.
91 00:08:31.050 ⇒ 00:08:33.949 kim todaro: see if he can reconcile the the actual sales.
92 00:08:34.179 ⇒ 00:08:35.199 Luke Daque: Okay.
93 00:08:35.360 ⇒ 00:08:38.804 kim todaro: Something’s off I don’t know, and it could be me. I don’t know
94 00:08:39.070 ⇒ 00:08:39.580 Luke Daque: Yeah.
95 00:08:40.169 ⇒ 00:08:43.475 kim todaro: But it’s hard to kind of figure out the source of truth. Sometimes.
96 00:08:44.140 ⇒ 00:08:49.840 Luke Daque: Yeah, that makes sense, yeah, for sure, though. The the ship station one.
97 00:08:50.910 ⇒ 00:08:53.403 Luke Daque: I’ll have to look into the split orders, because
98 00:08:53.790 ⇒ 00:08:57.840 Luke Daque: because I only have, like 7,000 for ship station or something.
99 00:08:58.620 ⇒ 00:08:59.490 kim todaro: Okay.
100 00:09:00.784 ⇒ 00:09:05.360 Luke Daque: So yeah. And the other thing is
101 00:09:05.780 ⇒ 00:09:12.329 Luke Daque: the returns. I think I got the gross sales correct, at least like very close already.
102 00:09:12.990 ⇒ 00:09:13.370 kim todaro: It is.
103 00:09:13.370 ⇒ 00:09:21.140 Luke Daque: Discounts, though the the thing with discounts is there are like order, level discounts and then order item level discounts.
104 00:09:21.380 ⇒ 00:09:28.449 Luke Daque: So what we’re currently doing right now. Like, if it’s an order level discount, we’re like splitting it
105 00:09:28.680 ⇒ 00:09:35.489 Luke Daque: to the items, like, if if there’s if there’s an order with a let’s say, $10 discount.
106 00:09:35.660 ⇒ 00:09:40.670 Luke Daque: And then there’s 2 items in that order like we’re assigning like
107 00:09:40.830 ⇒ 00:09:45.751 Luke Daque: $5 for each item. Discount something like that that way. We still get the
108 00:09:47.660 ⇒ 00:09:52.149 Luke Daque: If we sum it up, it’s still $10 per of per order, right? Something like that.
109 00:09:52.150 ⇒ 00:09:55.740 kim todaro: And that I’m just getting right from shopify. I’m literally.
110 00:09:55.740 ⇒ 00:10:01.119 Luke Daque: Yeah, I think I I found like where you’re getting it. I think it’s this one. This report.
111 00:10:01.510 ⇒ 00:10:07.299 kim todaro: I I actually get it from there I go to analytics. So the the link above reports.
112 00:10:07.520 ⇒ 00:10:08.270 Luke Daque: Right.
113 00:10:08.310 ⇒ 00:10:10.812 kim todaro: And I I really just look at
114 00:10:11.290 ⇒ 00:10:16.009 kim todaro: Your screen is a little bit different than mine. Actually total sales over time. Maybe click.
115 00:10:18.890 ⇒ 00:10:21.469 Luke Daque: Yeah, I think that’s the same here. This one.
116 00:10:21.930 ⇒ 00:10:25.020 kim todaro: Yeah, mine looks a little bit different. But that’s essentially what I’m
117 00:10:25.180 ⇒ 00:10:30.599 kim todaro: I’m doing is I’m getting gross sales that minus discounts, minus returns. And I’m looking at the net Sales column.
118 00:10:31.050 ⇒ 00:10:31.879 kim todaro: So yeah.
119 00:10:32.800 ⇒ 00:10:43.690 Luke Daque: Yeah, I think the returns is also like different. Because I’m I’m not seeing we’re we don’t have like a returns table from shopify, although we have refunds. So I’m using the refunds
120 00:10:45.060 ⇒ 00:10:47.540 Luke Daque: for the returns.
121 00:10:47.890 ⇒ 00:10:51.079 Luke Daque: Is that is that any similar? Are they the same?
122 00:10:51.450 ⇒ 00:10:51.999 Luke Daque: Do you know.
123 00:10:52.000 ⇒ 00:10:53.199 kim todaro: It should be.
124 00:10:54.790 ⇒ 00:10:58.999 kim todaro: I’m looking at shopify in real for the month. I’m seeing.
125 00:10:59.320 ⇒ 00:11:02.979 Luke Daque: Total refund amount is 66, 0, 6.
126 00:11:05.220 ⇒ 00:11:07.340 Luke Daque: Let’s see, refunds.
127 00:11:12.480 ⇒ 00:11:15.870 Luke Daque: Yeah, like, 6,006, 6. 0, 6, yeah.
128 00:11:16.080 ⇒ 00:11:18.150 Luke Daque: And then here you have.
129 00:11:18.580 ⇒ 00:11:20.580 Luke Daque: Oh, wait! We don’t see it.
130 00:11:20.850 ⇒ 00:11:24.870 Luke Daque: Think I downloaded it into us. Excel.
131 00:11:26.420 ⇒ 00:11:28.119 Luke Daque: So that would be.
132 00:11:31.430 ⇒ 00:11:32.239 Luke Daque: yeah, this one.
133 00:11:38.950 ⇒ 00:11:44.170 Luke Daque: Yeah, this is 9,000. It’s a little higher than let’s
134 00:11:44.420 ⇒ 00:11:48.129 Luke Daque: and 6, 6 0, 6. It’s like 30% high.
135 00:11:48.880 ⇒ 00:11:49.640 kim todaro: Yeah.
136 00:11:50.670 ⇒ 00:11:52.700 Luke Daque: I wonder where it’s coming from?
137 00:11:54.090 ⇒ 00:11:56.549 Luke Daque: There’s there’s only like a thousand here.
138 00:11:59.620 ⇒ 00:12:07.220 Luke Daque: the the it’s the April 1st make a huge return.
139 00:12:08.480 ⇒ 00:12:11.010 Luke Daque: But yeah, that one.
140 00:12:11.250 ⇒ 00:12:17.240 Luke Daque: The other thing, though, is I, I just realized, is, we are actually also getting the
141 00:12:18.680 ⇒ 00:12:22.869 Luke Daque: this this same data. But for the shopify. Bp account.
142 00:12:25.010 ⇒ 00:12:25.790 kim todaro: Okay.
143 00:12:26.010 ⇒ 00:12:32.590 Luke Daque: So I think that’s why we are like also having more, and like in terms of, for example, the gross sales.
144 00:12:37.840 ⇒ 00:12:44.530 Luke Daque: this one. This is already both the shopify and the shopify. Bt accounts. That’s why, like, it’s 286,000.
145 00:12:44.530 ⇒ 00:12:47.229 kim todaro: Oh, I see!
146 00:12:48.350 ⇒ 00:12:50.789 Luke Daque: Yeah, I forgot about that one.
147 00:12:52.560 ⇒ 00:12:54.530 Luke Daque: Yeah, I think that’s like one
148 00:12:54.750 ⇒ 00:12:57.440 Luke Daque: other thing that’s causing the difference as well.
149 00:12:57.440 ⇒ 00:12:58.020 Amber Lin: I’m sorry.
150 00:12:58.020 ⇒ 00:12:58.889 Amber Lin: Let me use it.
151 00:12:58.890 ⇒ 00:13:01.433 Amber Lin: Hold out a second.
152 00:13:02.390 ⇒ 00:13:05.499 Luke Daque: There’s like 2 accounts for shopify.
153 00:13:06.192 ⇒ 00:13:15.940 Luke Daque: There’s the Bt swim, and then there’s the. There’s another one that’s like, shopify. Bt, I think it.
154 00:13:16.530 ⇒ 00:13:20.560 Luke Daque: Yeah, this one shopify bt, move parts.
155 00:13:20.770 ⇒ 00:13:26.730 Luke Daque: So so basically, we’re like merging the 2 accounts together.
156 00:13:28.050 ⇒ 00:13:37.620 kim todaro: Yeah. So the the Bt shopify account is for parts. So let me ask. Let me ask Ben if he wants that to be part of this report.
157 00:13:38.120 ⇒ 00:13:44.159 Luke Daque: Okay. One thing that I can do, though, probably is like, split it here, maybe split the cell.
158 00:13:44.160 ⇒ 00:13:45.500 Luke Daque: Oh, yeah, you could split it.
159 00:13:45.600 ⇒ 00:13:47.300 kim todaro: That would be better. Yeah.
160 00:13:47.300 ⇒ 00:13:49.680 Luke Daque: Yeah, okay, let’s do that. Then.
161 00:13:50.190 ⇒ 00:13:57.750 kim todaro: Yeah, that would be that would be good to isolate it. Another thing is, and I am still trying to get to the bottom of this, because I haven’t yet
162 00:13:58.090 ⇒ 00:14:05.449 kim todaro: is, and your numbers can be completely right. But I’m grabbing cogs in my report from real.
163 00:14:05.940 ⇒ 00:14:06.800 Luke Daque: Yeah.
164 00:14:07.991 ⇒ 00:14:10.300 kim todaro: And they just super high.
165 00:14:11.570 ⇒ 00:14:13.889 Luke Daque: Which report I like here in in here.
166 00:14:13.890 ⇒ 00:14:18.609 kim todaro: Yeah. But now that I’m thinking about it, maybe some of them are coming from Bt parts.
167 00:14:19.240 ⇒ 00:14:22.040 Luke Daque: That could be true as well, just like we’re.
168 00:14:23.620 ⇒ 00:14:28.189 kim todaro: So your total cogs for April. You’re seeing that.
169 00:14:29.110 ⇒ 00:14:31.430 kim todaro: How come I see something more.
170 00:14:31.890 ⇒ 00:14:33.099 kim todaro: Let me look at mine.
171 00:14:34.010 ⇒ 00:14:35.640 Luke Daque: Yours is a hundred, 20.
172 00:14:35.820 ⇒ 00:14:39.529 kim todaro: I’m seeing a hundred 23. And then what do my spreadsheets say?
173 00:14:40.190 ⇒ 00:14:41.349 Luke Daque: 120.
174 00:14:41.600 ⇒ 00:14:47.209 kim todaro: A 120. Okay, so never mind, I’m not seeing something different. But yeah, it just like to me. I look at this, and I’m like.
175 00:14:47.600 ⇒ 00:14:52.209 kim todaro: are cogs really 50% of all net sales, you know.
176 00:14:52.560 ⇒ 00:14:53.420 Luke Daque: Yeah.
177 00:14:54.610 ⇒ 00:15:00.609 kim todaro: That seems high to me, and I asked Ben and Chuck this morning to check it in their accounting software.
178 00:15:02.670 ⇒ 00:15:09.360 kim todaro: I’m not saying what we have is wrong. I’m just saying from like someone who’s never really like looked at the cogs report that just seems high to me.
179 00:15:10.400 ⇒ 00:15:14.529 Luke Daque: Yeah, we can check with big chuck with Ben as well.
180 00:15:15.000 ⇒ 00:15:15.440 kim todaro: Yeah.
181 00:15:15.440 ⇒ 00:15:21.010 Luke Daque: These are the numbers. But yeah, like you mentioned, this could be like, shopify bt, as well. So maybe that.
182 00:15:21.010 ⇒ 00:15:21.620 kim todaro: That’s right.
183 00:15:21.620 ⇒ 00:15:23.489 Luke Daque: I’ll be doing. I’ll split to the both.
184 00:15:23.490 ⇒ 00:15:23.830 kim todaro: Yeah.
185 00:15:23.830 ⇒ 00:15:24.920 Luke Daque: Shopify accounts.
186 00:15:25.270 ⇒ 00:15:28.110 kim todaro: So then, yeah, why don’t we split them up 1st and see how that looks?
187 00:15:29.640 ⇒ 00:15:36.060 Luke Daque: Let’s see, I think there’s some modeling that I need to do.
188 00:15:36.260 ⇒ 00:15:37.380 Luke Daque: We split that.
189 00:15:37.820 ⇒ 00:15:41.120 Luke Daque: I can try to do it after the call. I can send you the screenshot.
190 00:15:41.270 ⇒ 00:15:44.270 Luke Daque: Okay, the split numbers.
191 00:15:45.920 ⇒ 00:15:47.900 Luke Daque: So yeah, I think the the
192 00:15:48.210 ⇒ 00:15:55.040 Luke Daque: based on that. I think it’s just a ship station. And uni that’s like causing, like, most of the big.
193 00:15:55.040 ⇒ 00:15:56.550 Luke Daque: yeah, much difference. Yeah.
194 00:15:57.180 ⇒ 00:16:01.999 kim todaro: Yeah. And another thing is, with the cogs issue like you could see some days
195 00:16:02.330 ⇒ 00:16:05.639 kim todaro: like, especially if you look at the 23, rd for example.
196 00:16:08.380 ⇒ 00:16:10.849 kim todaro: No, I’m sorry. That’s a bad example. The 20 second.
197 00:16:11.200 ⇒ 00:16:14.620 kim todaro: you see how the cogs exceed the shopify sales.
198 00:16:18.000 ⇒ 00:16:21.270 Luke Daque: Yeah, it’s yeah. It doesn’t make sense. Right?
199 00:16:21.540 ⇒ 00:16:22.450 kim todaro: Right.
200 00:16:22.580 ⇒ 00:16:23.350 Luke Daque: Yeah.
201 00:16:23.630 ⇒ 00:16:28.579 kim todaro: So maybe if you could just check that modeling, and then it’s not like over counting some things, because.
202 00:16:29.590 ⇒ 00:16:31.700 Luke Daque: Maybe it’s like duplicating or something.
203 00:16:32.000 ⇒ 00:16:47.039 kim todaro: Yeah. And I like, I, I don’t think the marketing costs are like, Yeah, that’s what I’m afraid of. The mar. The marketing costs are that big of a deal. Right now, I think you guys under report by a little bit. But I’m not really concerned about that. A lot of that is just little stuff added up. So
204 00:16:48.020 ⇒ 00:16:50.160 kim todaro: again, like I, that’s definitely like.
205 00:16:50.310 ⇒ 00:16:58.999 kim todaro: not a priority. Right now. I would just focus still on the double, checking the cogs and splitting out the shopify from.
206 00:17:00.610 ⇒ 00:17:01.360 Luke Daque: Okay.
207 00:17:02.150 ⇒ 00:17:02.920 Luke Daque: Sounds good.
208 00:17:02.920 ⇒ 00:17:08.939 kim todaro: That’s a good point. I didn’t. I didn’t realize that, so let me I’ll I’ll let Chuck and Ben know that, too.
209 00:17:09.210 ⇒ 00:17:15.579 Luke Daque: Yeah, and the cogs as well. I already also already removed the warranty excuse for this one. So this is even.
210 00:17:15.589 ⇒ 00:17:16.159 kim todaro: Thank you.
211 00:17:16.160 ⇒ 00:17:19.109 Luke Daque: Lower than what it would have been.
212 00:17:19.540 ⇒ 00:17:20.589 kim todaro: Thank you. Yeah.
213 00:17:21.380 ⇒ 00:17:21.800 Luke Daque: Okay.
214 00:17:21.800 ⇒ 00:17:22.450 kim todaro: That’s great!
215 00:17:24.430 ⇒ 00:17:30.749 Luke Daque: Sounds good. I’ll prioritize looking into the cogs then, and then work on the marketing stuff.
216 00:17:31.860 ⇒ 00:17:34.439 Luke Daque: Okay, English. The shopify accounts.
217 00:17:35.060 ⇒ 00:17:35.800 kim todaro: Perfect.
218 00:17:38.750 ⇒ 00:17:45.419 Amber Lin: Hey? Everything was good for the Amazon parts right? And Amazon shipping is is good.
219 00:17:46.464 ⇒ 00:17:51.329 kim todaro: I think we last. I heard Luke was still working on some of the fees.
220 00:17:52.060 ⇒ 00:17:56.929 Luke Daque: Yeah, it’s it’s yeah. That’s still the same thing. It’s like in progress, the the fees one
221 00:17:57.622 ⇒ 00:18:02.150 Luke Daque: because it’s it’s quite challenging to get the fees without
222 00:18:03.050 ⇒ 00:18:06.639 Luke Daque: any related orders, because the fees that we are getting from
223 00:18:06.830 ⇒ 00:18:09.950 Luke Daque: Amazon are like always related to orders.
224 00:18:10.200 ⇒ 00:18:13.950 Luke Daque: So yeah, I’ll have to like dig further into like
225 00:18:14.240 ⇒ 00:18:15.999 Luke Daque: finding out where to get those.
226 00:18:16.460 ⇒ 00:18:21.569 Luke Daque: And if you need it, like, yeah, probably probably like the
227 00:18:22.090 ⇒ 00:18:28.646 Luke Daque: if I can’t really find an any way to automate it. I’ll have to like export. A report similar to like with what is
228 00:18:29.530 ⇒ 00:18:31.900 Luke Daque: Stephen is up is doing for the fleece.
229 00:18:32.010 ⇒ 00:18:35.230 Luke Daque: exporting it, and like loading it to our, to our data here.
230 00:18:36.540 ⇒ 00:18:44.070 kim todaro: Yeah, Steven. Can hop on the call whenever we need him to. If you know, next week we want to talk to him, and or if we have any questions.
231 00:18:45.180 ⇒ 00:18:48.130 Luke Daque: Yep, equipment.
232 00:18:49.020 ⇒ 00:18:55.719 Amber Lin: Sounds good, and I remember there was an issue, I think, earlier this week or last week, about the shipping stuff.
233 00:18:55.960 ⇒ 00:18:58.320 Amber Lin: for Chug is now resolved.
234 00:18:58.930 ⇒ 00:19:03.460 Luke Daque: Not yet. That’s the split orders that was mentioned earlier.
235 00:19:03.460 ⇒ 00:19:04.600 Amber Lin: Oh, okay.
236 00:19:04.600 ⇒ 00:19:15.639 Luke Daque: That was, I think, that was related to ship station automation rules and stuff like that. We’ll have to get back to ship station. I already sent an email to their support
237 00:19:16.198 ⇒ 00:19:21.719 Luke Daque: team. Because, like, it looks like the the automation rules aren’t working for some orders.
238 00:19:22.180 ⇒ 00:19:23.270 Luke Daque: So yeah.
239 00:19:24.970 ⇒ 00:19:25.860 Amber Lin: Sounds good.
240 00:19:29.700 ⇒ 00:19:30.280 Luke Daque: Cool.
241 00:19:31.570 ⇒ 00:19:37.519 kim todaro: Okay, great. Thanks, guys. I appreciate it. Have a good weekend and let me know if you need anything.
242 00:19:38.180 ⇒ 00:19:46.289 Amber Lin: Of course. Yeah. Well, I’ll I think we’ll we can all update Ben on some progress, and I’ll update Tom, on our progress, too.
243 00:19:46.680 ⇒ 00:19:48.010 kim todaro: Okay. Sounds good.
244 00:19:48.010 ⇒ 00:19:49.499 Luke Daque: Sounds good. Thanks. Kim.
245 00:19:49.780 ⇒ 00:19:50.520 Amber Lin: Thanks guys.
246 00:19:50.910 ⇒ 00:19:52.190 Luke Daque: Bye, nice rest of the day.
247 00:19:52.190 ⇒ 00:19:52.790 kim todaro: Bye.