Meeting Title: PP2G | Quick Sync Date: 2025-04-04 Meeting participants: Kim Todaro, Luke Daque, Amber Lin
WEBVTT
1 00:06:54.780 ⇒ 00:06:58.039 Amber Lin: Hello! I didn’t see your message. I’m so sorry.
2 00:06:59.390 ⇒ 00:07:00.900 Amber Lin: Hi, Amber no worries.
3 00:07:01.920 ⇒ 00:07:02.830 Amber Lin: Yeah. I just
4 00:07:03.350 ⇒ 00:07:07.990 Luke Daque: Figured we might like discuss anything, for Tim comes in, but
5 00:07:07.990 ⇒ 00:07:13.100 Amber Lin: Sorry, your mic. It’s a little low. I can hear you, but it’s very, very low volume
6 00:07:13.100 ⇒ 00:07:16.889 Luke Daque: Alrighty about how about now?
7 00:07:16.890 ⇒ 00:07:18.020 Amber Lin: Yeah, this is good.
8 00:07:18.820 ⇒ 00:07:22.210 Luke Daque: Cool. Yeah, I think it was just far away, or something
9 00:07:22.630 ⇒ 00:07:23.260 Amber Lin: Hmm.
10 00:07:24.930 ⇒ 00:07:31.650 Luke Daque: So yeah, was planning to to maybe just
11 00:07:31.770 ⇒ 00:07:38.809 Luke Daque: talk about with him, at least, since it’s a Friday like to talk about like the documentation if he
12 00:07:39.000 ⇒ 00:07:45.010 Luke Daque: seen it already, or like, if she has any questions or something, and
13 00:07:48.440 ⇒ 00:07:57.480 Luke Daque: yeah, or like, maybe also the maybe also the the difference that.
14 00:07:57.950 ⇒ 00:08:04.389 Luke Daque: Just tell her that we reverted the real dashboard to the old one, and like but
15 00:08:04.870 ⇒ 00:08:07.649 Luke Daque: we did notice still, like some
16 00:08:07.960 ⇒ 00:08:13.560 Amber Lin: Differences and stuff like that. So yeah, okay, let me pull up.
17 00:08:14.620 ⇒ 00:08:27.229 Amber Lin: Do you know what you’re gonna present to her on the dashboard? I think I’ll go over the documentation with her, and you might need to go through the dashboard of what changed and what the difference is. Still
18 00:08:27.540 ⇒ 00:08:29.820 Luke Daque: Yeah, sure, I yeah, I’ll have real
19 00:08:29.820 ⇒ 00:08:32.870 Amber Lin: The percentages of the differences.
20 00:08:33.020 ⇒ 00:08:37.080 Amber Lin: And can you just make do a quick calculation?
21 00:08:37.240 ⇒ 00:08:38.819 Amber Lin: Yeah, let me do that real quick.
22 00:08:41.980 ⇒ 00:08:44.450 Amber Lin: Let me check that
23 00:08:46.370 ⇒ 00:08:53.380 Luke Daque: Wait. I might just need I just problem, just put this in the ticket
24 00:09:04.790 ⇒ 00:09:05.800 Luke Daque: And
25 00:10:51.210 ⇒ 00:10:53.380 Amber Lin: Initial joins
26 00:10:55.090 ⇒ 00:10:56.110 Luke Daque: Yeah.
27 00:10:59.450 ⇒ 00:11:03.749 Luke Daque: and she’s confirmed right that she’ll be joining the stand ups the daily stands
28 00:11:03.750 ⇒ 00:11:12.600 Amber Lin: Yeah, yeah, I sent her the invite. And she was like, Yeah, I’ll join. But we need to call me to remind her, because she gets busy
29 00:11:13.050 ⇒ 00:11:14.839 Amber Lin: we got a confirmation.
30 00:11:15.160 ⇒ 00:11:22.620 Amber Lin: So I think after the meeting today, I’ll be able to ask and give some updates.
31 00:11:23.630 ⇒ 00:11:24.810 Amber Lin: Too bad
32 00:12:06.520 ⇒ 00:12:08.860 Luke Daque: We are working straight
33 00:12:11.720 ⇒ 00:12:18.528 kim todaro: Hey, guys, sorry, my computer is really, really slow today.
34 00:12:18.880 ⇒ 00:12:26.369 Amber Lin: Oh, don’t worry, thank you for joining us. We’ll keep it as short as possible. I just want us to give and some updates before we
35 00:12:26.370 ⇒ 00:12:27.920 Amber Lin: you finish the week.
36 00:12:28.350 ⇒ 00:12:29.470 Amber Lin: So
37 00:12:29.660 ⇒ 00:12:45.910 Amber Lin: we have 2 things. So we have the documentation which I think we can go over a little bit in this meeting, and then 2. We did some changes to the dashboard based on what you showed us for the Amazon fees. So I’ll let Luke sort of run you through that, and
38 00:12:46.020 ⇒ 00:12:51.300 Amber Lin: to give you an idea of what how much we’re missing by the mark.
39 00:12:51.430 ⇒ 00:12:54.010 Amber Lin: So look if you can share a screen, that’ll be great
40 00:12:54.660 ⇒ 00:12:55.320 Luke Daque: Sure.
41 00:12:56.084 ⇒ 00:12:58.430 Luke Daque: You want to. You want us to do that first.st
42 00:12:59.500 ⇒ 00:13:02.049 Amber Lin: Yeah, sure. Let’s we’ll do. We’ll make it quick.
43 00:13:02.050 ⇒ 00:13:03.229 Luke Daque: Cool. Yeah, sure.
44 00:13:04.230 ⇒ 00:13:05.822 Luke Daque: So yeah, I just
45 00:13:06.320 ⇒ 00:13:12.350 Luke Daque: We actually reverted the real dashboard to what it was prior, like a couple of days ago.
46 00:13:13.393 ⇒ 00:13:23.980 Luke Daque: But we retained base the selling platform filter and fulfillment channel as well. We just removed the product class, because I I believe that was the cost of like
47 00:13:24.840 ⇒ 00:13:27.189 Luke Daque: the numbers, not matching up because
48 00:13:27.190 ⇒ 00:13:27.520 kim todaro: Yeah.
49 00:13:27.520 ⇒ 00:13:30.710 Luke Daque: Product classes like order item level related.
50 00:13:30.940 ⇒ 00:13:37.146 Luke Daque: And most of the other stuff are like the the order level. So anyway,
51 00:13:37.930 ⇒ 00:13:44.640 Luke Daque: yeah, let me pull up the screenshot that you had from Amazon, because it was a bit.
52 00:13:47.320 ⇒ 00:13:51.090 Luke Daque: There’s still a some discrepancy
53 00:13:54.600 ⇒ 00:13:56.830 Luke Daque: So I’m in in the real
54 00:13:57.020 ⇒ 00:14:12.460 Luke Daque: dashboard that you’re seeing right now. I’m already filtering this to just Amazon. And then it’s for the month of March, basically. So you can see here, total sales is at 175,000. And in the screenshot it’s actually
55 00:14:13.470 ⇒ 00:14:15.949 Luke Daque: showing us like 157.
56 00:14:16.110 ⇒ 00:14:21.000 Luke Daque: So we’re we’re off by like, I don’t know 13,000 or something
57 00:14:22.420 ⇒ 00:14:22.830 kim todaro: Okay.
58 00:14:22.830 ⇒ 00:14:24.330 Luke Daque: 2020,000.
59 00:14:24.440 ⇒ 00:14:30.179 Luke Daque: So this is something I’m not sure what’s causing this to be different or like. I’m also not sure
60 00:14:30.300 ⇒ 00:14:39.190 Luke Daque: which one might be correct, but based on what I initially found, I didn’t see anything that could
61 00:14:39.530 ⇒ 00:14:45.939 Luke Daque: tell us that this is incorrect, because this was like directly get. We were getting this directly from
62 00:14:46.530 ⇒ 00:14:48.590 Luke Daque: the source data.
63 00:14:48.840 ⇒ 00:14:50.790 Luke Daque: So I’m not sure maybe it’s
64 00:14:51.460 ⇒ 00:14:56.360 Luke Daque: time zone. But that, like 20 K is like too too high for like
65 00:14:56.670 ⇒ 00:14:58.680 Luke Daque: a time zone issue or something.
66 00:14:59.260 ⇒ 00:15:00.220 Luke Daque: But yeah.
67 00:15:01.180 ⇒ 00:15:01.550 kim todaro: I agree.
68 00:15:01.550 ⇒ 00:15:03.569 Luke Daque: However. Yeah.
69 00:15:03.570 ⇒ 00:15:21.110 Amber Lin: Yeah, I sent a screenshot in the in our chat. Essentially, the percentage differences. I think the main thing is, we gross sales is just 11% difference and cost of goods sold is 8%. I think that’s pretty decent. I think the sales
70 00:15:21.760 ⇒ 00:15:25.689 Amber Lin: net fees is the main one that we’re getting wrong because it’s
71 00:15:25.830 ⇒ 00:15:42.780 Amber Lin: 69% different, the real 69% higher than the Amazon report. And then the total profit is also very, very, very off. I don’t know if we have the cost Fbm fees. So maybe that’s also contributing to this
72 00:15:44.260 ⇒ 00:15:44.840 Luke Daque: And
73 00:15:45.460 ⇒ 00:15:58.949 Luke Daque: thing that we noticed like in the screenshot, the the calculation of profit is just sales minus the Amazon fees, minus cost of goods sold minus the Fbm. So it’s not a it’s not
74 00:16:00.096 ⇒ 00:16:06.499 Luke Daque: incorporating the marketing fees and the shipping fees as well as refunds which we are
75 00:16:07.090 ⇒ 00:16:07.440 kim todaro: Yep.
76 00:16:07.440 ⇒ 00:16:09.169 Luke Daque: The real dashboard. That’s why they
77 00:16:09.840 ⇒ 00:16:13.789 Luke Daque: it’s like a lot negative here in terms of like total profit.
78 00:16:14.370 ⇒ 00:16:22.239 kim todaro: Yeah, I think that. I think the marketing fees are included in column C. He didn’t break it out like I would have broken it out
79 00:16:23.550 ⇒ 00:16:25.182 kim todaro: This guy, Steven. But
80 00:16:25.940 ⇒ 00:16:39.539 kim todaro: There’s some things that are close, so I will say the sales are close to what we saw. They’re they’re off by 12,000, which isn’t the end of the world. The cogs are close, so I’m also wondering if our dashboard isn’t is maybe like not including the returns or something
81 00:16:40.610 ⇒ 00:16:44.980 Luke Daque: We should have refunds. I’m not sure if this is this, the same as returns
82 00:16:44.980 ⇒ 00:16:50.659 kim todaro: It should, it should be, but that that number is super high. So I feel like that. There, there’s an issue there. Maybe
83 00:16:51.110 ⇒ 00:16:54.729 Luke Daque: And maybe marketing as well. Right you yes, was pretty high as well
84 00:16:54.930 ⇒ 00:16:57.371 kim todaro: Yep, and marketing is really high. So
85 00:16:58.730 ⇒ 00:17:03.625 kim todaro: The good news is, I think, a lot of these things are right, but obviously a lot are wrong. So
86 00:17:04.079 ⇒ 00:17:10.410 kim todaro: If you want, I can invite Steven on to one of our calls next week, and he can kind of walk through this with us. We can use just
87 00:17:10.410 ⇒ 00:17:23.509 Amber Lin: That will be so great cause. We don’t know how he related these things. I think most of our mismatch is not that we don’t have the right data. We have the right data. But we’re calculating things differently. I think that’s the main part that’s different.
88 00:17:23.859 ⇒ 00:17:24.269 kim todaro: Yeah, and
89 00:17:24.270 ⇒ 00:17:25.450 Luke Daque: 100%. Yeah.
90 00:17:25.780 ⇒ 00:17:34.719 kim todaro: I can probably share the live spreadsheet with you, because I actually had him go over over it with me this morning because it was very. It’s it like I said, not how I I set things up
91 00:17:35.960 ⇒ 00:17:37.100 Luke Daque: Yeah, that’s great.
92 00:17:37.320 ⇒ 00:17:40.149 kim todaro: So I will send that to you, and if you want to like.
93 00:17:40.650 ⇒ 00:17:45.362 kim todaro: look at it, and then, if you have questions, we can meet with him next week. He’s very open.
94 00:17:45.610 ⇒ 00:17:46.780 Amber Lin: Totally would you
95 00:17:46.780 ⇒ 00:17:48.010 kim todaro: I’ll send that to you
96 00:17:48.290 ⇒ 00:18:01.869 Amber Lin: Maybe make a group chat like, Introduce us. I don’t know if he’s in our channel, but I’ll get his email, and I’ll I’ll add him to the stand up. I think you can, too, I think, on the permissions. You can just add people
97 00:18:02.150 ⇒ 00:18:07.150 kim todaro: Yep, I’ll text him and find out. And I’ll just forward this whole sheet over to you guys
98 00:18:07.150 ⇒ 00:18:08.320 Amber Lin: Hey? Okay.
99 00:18:09.792 ⇒ 00:18:15.540 kim todaro: It’s it’s definitely not like super intuitive, like. There’s a lot of things that only he probably knows. But
100 00:18:16.740 ⇒ 00:18:23.679 kim todaro: I I think you can figure out a lot of it by me just giving it to you, since you guys are probably pretty proficient at excel
101 00:18:25.630 ⇒ 00:18:26.400 Luke Daque: Yeah, sounds good.
102 00:18:26.400 ⇒ 00:18:27.871 Amber Lin: This is our job.
103 00:18:30.023 ⇒ 00:18:41.096 kim todaro: Okay, great. So that’s for Amazon. That’s that’s good. I think we’re on on a good track there. I think there’s definitely some things that need to be adjusted. But then I’m like the total total sales the cogs. Those look decent.
104 00:18:41.700 ⇒ 00:18:42.250 kim todaro: so
105 00:18:42.250 ⇒ 00:18:49.749 Luke Daque: I guess. Yeah, I can. I can focus on like marketing and refund refunds just to try to see like what what’s probably causing this
106 00:18:49.950 ⇒ 00:18:50.520 Amber Lin: Yeah.
107 00:18:50.520 ⇒ 00:18:53.195 kim todaro: And the shipment obviously to the shipments off.
108 00:18:54.290 ⇒ 00:18:57.679 kim todaro: so yeah, like the cost. The cost Fbm
109 00:18:58.530 ⇒ 00:19:01.659 kim todaro: is, gonna be the shipping costs like that. G,
110 00:19:01.660 ⇒ 00:19:02.340 Luke Daque: And
111 00:19:03.140 ⇒ 00:19:11.870 kim todaro: Yep, that’s that means fulfillment by merchant, and then I think there might also be some shipping costs that are piled up between B and C.
112 00:19:13.190 ⇒ 00:19:17.139 Luke Daque: So we can ask him that next week sounds good
113 00:19:17.540 ⇒ 00:19:17.900 Amber Lin: I think
114 00:19:19.237 ⇒ 00:19:23.499 kim todaro: column G is what Chuck sends out from our team’s fulfillment by us.
115 00:19:23.500 ⇒ 00:19:24.580 Amber Lin: I know.
116 00:19:24.580 ⇒ 00:19:31.149 kim todaro: Yep, I believe there’s some fees that are included in C.
117 00:19:31.280 ⇒ 00:19:32.300 kim todaro: Maybe
118 00:19:33.140 ⇒ 00:19:36.279 Amber Lin: I see. Okay, very confusing.
119 00:19:36.890 ⇒ 00:19:38.180 kim todaro: Yes, yes.
120 00:19:39.610 ⇒ 00:19:40.510 Luke Daque: Gotcha.
121 00:19:40.950 ⇒ 00:19:45.599 Luke Daque: Yeah, I think we have it here, like Seller fulfilled is like the the ones that you fulfilled
122 00:19:46.156 ⇒ 00:19:49.719 kim todaro: So it should. It should essentially match supposedly like
123 00:19:50.050 ⇒ 00:19:53.410 Luke Daque: This one. It’s pretty high. So it’s I think this is wrong as well
124 00:19:54.290 ⇒ 00:19:54.970 kim todaro: Yeah.
125 00:19:55.550 ⇒ 00:20:01.120 Luke Daque: Compared to like platform fulfilled, which is like Amazon fulfilled, whether it’s yeah or something
126 00:20:01.790 ⇒ 00:20:03.950 kim todaro: Yeah, those are really high, for sure.
127 00:20:04.230 ⇒ 00:20:05.760 Luke Daque: Sure. Yeah, I’ll take a look at that
128 00:20:06.130 ⇒ 00:20:06.540 kim todaro: Awesome.
129 00:20:06.540 ⇒ 00:20:22.259 Amber Lin: Yeah, and also, and on now that we have the shopify data, we can also look into that as well. So I know you sent us a spreadsheet. Is there anything there you wanted to go over, or do you think it’s pretty intuitive
130 00:20:23.349 ⇒ 00:20:31.979 kim todaro: I think it’s pretty intuitive. The only thing I mean. So column J. Marketing costs
131 00:20:32.270 ⇒ 00:20:39.880 kim todaro: that is just going to equal C through I the sum of
132 00:20:39.880 ⇒ 00:20:44.320 Luke Daque: I can see the formulas, I think if we see the formulas they’ll be they’ll be great
133 00:20:44.320 ⇒ 00:20:48.599 kim todaro: Yeah, yeah, you guys should be good. It’s mine. I broke out by like
134 00:20:48.980 ⇒ 00:20:51.720 kim todaro: a little bit more simplified than the other worksheet.
135 00:20:52.310 ⇒ 00:20:59.920 kim todaro: But yeah, the gross. The sales is shopify. Sales is just gross sales minus discounts and returns. Those are the
136 00:21:00.280 ⇒ 00:21:12.250 kim todaro: the fee. All the fees from the platform there. There’s some that also, and maybe we can. This doesn’t have to be figured out right now, but some marketing partners we use. They charge us a flat fee plus
137 00:21:13.590 ⇒ 00:21:14.600 kim todaro: more.
138 00:21:15.300 ⇒ 00:21:21.169 kim todaro: So we might have to like get a little customized with with getting those costs. But those are all the
139 00:21:21.330 ⇒ 00:21:31.869 kim todaro: most. Those are mostly partners that don’t cost a lot of money. Anyway. Google and Meta are really the ones that have to be correct, and those are probably the easiest to ship in Api. Wise
140 00:21:34.920 ⇒ 00:21:35.300 Amber Lin: Sounds good
141 00:21:35.300 ⇒ 00:21:35.910 Luke Daque: Okay.
142 00:21:38.230 ⇒ 00:21:41.870 kim todaro: But I think I think the stuff you guys can figure out. And then I
143 00:21:42.030 ⇒ 00:21:48.170 kim todaro: I think I need access to notion. So I haven’t been able to go through that documentation yet, but
144 00:21:48.170 ⇒ 00:21:48.650 Amber Lin: Oh, my!
145 00:21:48.650 ⇒ 00:21:49.709 kim todaro: We have this step
146 00:21:51.350 ⇒ 00:21:55.650 Amber Lin: Yeah, let me share my screen. I think I shared.
147 00:21:56.900 ⇒ 00:22:05.549 Amber Lin: Do you think I shared access to it now? But maybe you have to check. If you have
148 00:22:08.828 ⇒ 00:22:11.339 Amber Lin: accepted the invite, maybe
149 00:22:13.290 ⇒ 00:22:18.549 kim todaro: It says, no access to this page. You can access this page if someone approves your request. So I requested access
150 00:22:19.230 ⇒ 00:22:21.879 Amber Lin: Oh, I see. Okay, okay, sounds good.
151 00:22:22.568 ⇒ 00:22:29.249 Amber Lin: Look, do you want to share your screen for that? I can go look at the access and go approve it.
152 00:22:29.970 ⇒ 00:22:30.700 Luke Daque: Sure.
153 00:22:34.950 ⇒ 00:22:42.510 Luke Daque: So basically, we have 2 types of documentation here. One is like a a little bit technical in terms of like it has, like the calculation
154 00:22:42.780 ⇒ 00:22:45.249 Luke Daque: or the formula for total profit.
155 00:22:45.939 ⇒ 00:22:49.980 Luke Daque: Like components of the of the revenues and stuff.
156 00:22:50.270 ⇒ 00:22:51.729 Luke Daque: And the other one is
157 00:22:52.000 ⇒ 00:23:00.830 Luke Daque: yeah. The other ones are less technical. Just so in case, like whoever is reading is it? It’s not. It’s not very technical. It’s just showing
158 00:23:01.560 ⇒ 00:23:06.090 Luke Daque: things in word, form format, like, what Amazon sales are
159 00:23:06.350 ⇒ 00:23:11.549 Luke Daque: coming from where they’re coming from. It’s just the item price times the quantity of the order
160 00:23:11.890 ⇒ 00:23:12.240 kim todaro: Okay.
161 00:23:12.795 ⇒ 00:23:13.350 Luke Daque: Yeah.
162 00:23:13.580 ⇒ 00:23:21.980 Luke Daque: And then it includes both Fba and seller fulfilled orders. So stuff like that. So yeah, it’d be great if you can like.
163 00:23:22.300 ⇒ 00:23:26.319 Luke Daque: Read through the documentation here, and see if there’s anything that
164 00:23:26.520 ⇒ 00:23:31.510 Luke Daque: we might be missing, or might be wrong in terms of how the logic is
165 00:23:32.220 ⇒ 00:23:35.319 Luke Daque: of like calculating the stuff.
166 00:23:36.230 ⇒ 00:23:37.610 Luke Daque: Yeah.
167 00:23:38.090 ⇒ 00:23:42.570 kim todaro: I will look, look through that and I’m just looking at
168 00:23:42.860 ⇒ 00:23:53.259 kim todaro: some reports right now on shopify and try to compare it to what’s in real now, because it’s a little bit after you reverted it. It looks a lot better. The data
169 00:23:54.160 ⇒ 00:23:54.850 Luke Daque: Nice
170 00:23:55.050 ⇒ 00:23:59.720 kim todaro: So Steven
171 00:24:02.610 ⇒ 00:24:03.550 Luke Daque: Gonna check.
172 00:24:04.310 ⇒ 00:24:09.509 Luke Daque: So for March, I think it’s the other one right?
173 00:24:20.360 ⇒ 00:24:21.680 kim todaro: Shopify.
174 00:24:22.420 ⇒ 00:24:24.069 Luke Daque: Alright let me see
175 00:24:26.710 ⇒ 00:24:28.919 kim todaro: Yeah. Total sales looks a little high.
176 00:24:29.790 ⇒ 00:24:32.439 Luke Daque: Yeah, it’s it’s way higher, I think. Yeah.
177 00:24:32.700 ⇒ 00:24:34.790 kim todaro: Total discounts is low.
178 00:24:37.540 ⇒ 00:24:42.630 kim todaro: Marketing cost is high, shipment cost is high and refund is high.
179 00:24:43.930 ⇒ 00:24:46.239 kim todaro: the closest one there is. The total sales
180 00:24:47.020 ⇒ 00:24:47.750 Luke Daque: Hmm.
181 00:24:47.980 ⇒ 00:24:52.070 kim todaro: So I’ll look over the documentation and see if I can.
182 00:24:52.964 ⇒ 00:24:55.935 kim todaro: Understand things a little bit better. But
183 00:24:56.530 ⇒ 00:25:00.040 kim todaro: yeah, where do you guys want to go from here for shopify
184 00:25:03.900 ⇒ 00:25:11.940 Luke Daque: For shopify. Yeah, I’ll take a look because you just sent us the the raw numbers as well. So I can like compare it with this, and like.
185 00:25:12.300 ⇒ 00:25:15.160 Luke Daque: start with my investigation
186 00:25:15.160 ⇒ 00:25:17.843 kim todaro: So those are only from
187 00:25:18.920 ⇒ 00:25:25.999 kim todaro: the last 4 days. Do you want me to do march as like a as a whole, like line item on another tab. Would that be helpful?
188 00:25:28.635 ⇒ 00:25:30.349 Luke Daque: This is just daily, right? So I
189 00:25:30.350 ⇒ 00:25:30.730 kim todaro: Yeah.
190 00:25:30.730 ⇒ 00:25:33.829 Luke Daque: Basically, I need to filter just one day, for example.
191 00:25:34.460 ⇒ 00:25:34.840 kim todaro: Yeah.
192 00:25:35.280 ⇒ 00:25:39.999 Luke Daque: Yeah, yeah, they should be doing great. They should be good.
193 00:25:40.160 ⇒ 00:25:45.850 Luke Daque: like, even just these 4. Yeah, I’ll let you know. I’ll just slack you, if like. I need the whole month, or something
194 00:25:46.220 ⇒ 00:25:54.370 kim todaro: Perfect, and then I’ll like I want to see like how you’re deriving the marketing costs exactly, and all that. So if that’s in the documentation. I can go through that
195 00:25:54.370 ⇒ 00:25:56.209 Luke Daque: Yep, yeah, that should be there.
196 00:25:56.210 ⇒ 00:26:13.440 Amber Lin: Yeah, I realized that it was because it was in a different hub. So I moved it to our client Hub. So you should have access right now. I sent it in our meeting chat. I’ll send it to you via slack as well later. But essentially we have all the how we calculate
197 00:26:13.920 ⇒ 00:26:22.010 Amber Lin: these different metrics and say, the different shipping costs, how it’s calculated.
198 00:26:22.210 ⇒ 00:26:29.570 Amber Lin: Yeah, these. And just let Luke know or just comment on the document. What’s not really clear. I kinda I didn’t
199 00:26:29.780 ⇒ 00:26:38.963 Amber Lin: cause I wanted to give you some more actionable steps. So okay, what do I need to verify? This is with AI. So let me know if it’s not accurate
200 00:26:39.260 ⇒ 00:26:40.070 kim todaro: Bye.
201 00:26:40.445 ⇒ 00:26:53.599 Amber Lin: Just maybe we could verify like, oh, this one, or maybe these 3 different selling fees. But just just a good launch pad for you to know. Okay, what do I need to actually check
202 00:26:54.170 ⇒ 00:27:00.445 kim todaro: Okay, I should mention this, because, like I said, Ben is very
203 00:27:01.560 ⇒ 00:27:06.885 kim todaro: the Amazon stuff’s good, I would say in order of priority. It’s shopify Amazon Walmart.
204 00:27:07.240 ⇒ 00:27:07.590 Amber Lin: Oh!
205 00:27:07.590 ⇒ 00:27:10.448 kim todaro: So for the shopify stuff.
206 00:27:11.520 ⇒ 00:27:25.439 kim todaro: that’s definitely most important. And when it comes to shipping costs I know. So there’s 2 sources of that. There’s 1 is ship station, where where I’ve been getting data. And then there’s another source that I haven’t plugged in yet. And it’s
207 00:27:25.770 ⇒ 00:27:44.329 kim todaro: it’s basically so Chuck, who works in our warehouse. He has ship station, but some things gets sent out of our warehouses. I believe they’re in like Florida and Texas, and those costs aren’t automated. So I have to see your documentation and see how those are being added into the the shipment. The total shipment costs
208 00:27:45.350 ⇒ 00:27:46.020 Luke Daque: Okay.
209 00:27:46.750 ⇒ 00:27:52.399 kim todaro: But I will say, like that number that you have, that you’re looking at for. Oh, you’re looking at April one to 2
210 00:27:52.630 ⇒ 00:27:56.960 Luke Daque: Yeah, it’s pretty. It’s it’s very high. Looks like for April one.
211 00:28:01.000 ⇒ 00:28:03.029 Luke Daque: Cogs. It’s like double
212 00:28:03.460 ⇒ 00:28:05.250 kim todaro: Yeah, okay?
213 00:28:06.900 ⇒ 00:28:08.989 kim todaro: And sales is lower. Right?
214 00:28:10.440 ⇒ 00:28:12.539 kim todaro: Sales should be like over 30
215 00:28:14.180 ⇒ 00:28:21.179 Luke Daque: Sales is like supposed to be 13,000 for April one in it, and here, in real, we have, like 27,000
216 00:28:21.570 ⇒ 00:28:22.490 kim todaro: Okay.
217 00:28:24.410 ⇒ 00:28:29.159 kim todaro: But are you looking at March 31st to the first, st or are you looking from April 1st to second
218 00:28:29.790 ⇒ 00:28:32.359 Luke Daque: This is April 1st to second, I guess
219 00:28:32.360 ⇒ 00:28:32.880 kim todaro: Yep.
220 00:28:36.950 ⇒ 00:28:37.580 Luke Daque: Yeah.
221 00:28:40.860 ⇒ 00:28:42.300 kim todaro: So that should be like 38,
222 00:28:42.680 ⇒ 00:28:43.260 Luke Daque: Yeah.
223 00:28:43.510 ⇒ 00:28:43.865 kim todaro: Okay.
224 00:28:47.600 ⇒ 00:28:49.430 Luke Daque: Yeah, yeah, we’ll we’ll. I’ll
225 00:28:49.850 ⇒ 00:28:54.149 Luke Daque: focus on shopify 1st thing, if that’s that’s like higher priority for you.
226 00:28:54.580 ⇒ 00:28:57.770 kim todaro: Great, that’s perfect.
227 00:29:01.370 ⇒ 00:29:18.209 Amber Lin: Yeah, I think that’s all from our side. We’ll try to get something pushed to Ben as soon as possible. I know he’s frustrated so we’ll have some progress on this, and and the fact that we’re meeting daily will give Ben some confidence that will push it forward
228 00:29:18.720 ⇒ 00:29:33.590 kim todaro: Yeah, for sure. Right now, I’m just making him this report manually every day. It really doesn’t take me that long. So the the sooner the better. But yeah, let me know if you guys need anything over the weekend, I’m usually online. So if any questions come up, let me know
229 00:29:34.090 ⇒ 00:29:35.749 Amber Lin: Sounds good. Thank you so much.
230 00:29:36.659 ⇒ 00:29:37.569 kim todaro: Okay.
231 00:29:37.570 ⇒ 00:29:39.871 Amber Lin: I’ll talk to you. Okay, bye, bye.
232 00:29:40.200 ⇒ 00:29:40.630 kim todaro: Bye, guys.
233 00:29:40.630 ⇒ 00:29:42.310 Luke Daque: Thanks, guys. Bye-bye