Meeting Title: Amazon Shopify Analysis Sync Date: 2025-12-08 Meeting participants: Amber Lin, Henry Zhao
WEBVTT
1 00:01:59.560 ⇒ 00:02:00.590 Amber Lin: Hello.
2 00:02:26.020 ⇒ 00:02:30.089 Henry Zhao: awareness. So, what were your initial thoughts on the…
3 00:02:30.930 ⇒ 00:02:33.060 Henry Zhao: the stuff I mentioned in the looms.
4 00:02:34.980 ⇒ 00:02:40.690 Amber Lin: Seems like part of it, it’s mostly for demand forecasting.
5 00:02:43.250 ⇒ 00:02:56.860 Amber Lin: I don’t know how much we can use this for our… for the analysis I’ve been doing. What are your thoughts? Do you think this is a new analysis that will come in? Or, like, a new…
6 00:02:56.860 ⇒ 00:03:01.379 Henry Zhao: I don’t know, I haven’t finished watching this video yet, so I was gonna finish this video.
7 00:03:03.430 ⇒ 00:03:08.680 Henry Zhao: And just kind of see what are the missing pieces that we can… We can give to them.
8 00:03:10.760 ⇒ 00:03:13.520 Henry Zhao: Do you want to give me a quick, like, just recap of this meeting?
9 00:03:14.130 ⇒ 00:03:21.929 Amber Lin: Sure, yeah. So we did it in two parts. We did Amazon, and then we did Shopify. On Amazon’s side.
10 00:03:22.060 ⇒ 00:03:25.990 Amber Lin: I showed them the graphs of…
11 00:03:26.450 ⇒ 00:03:30.560 Amber Lin: Let’s see… the graphs of…
12 00:03:30.990 ⇒ 00:03:39.819 Amber Lin: their PO data by category. I’m telling them, hey, this makes more sense by category, something’s always in the lead, and then showed them that
13 00:03:39.950 ⇒ 00:03:45.380 Amber Lin: the PO spikes are pretty consistent before the big Amazon events.
14 00:03:45.540 ⇒ 00:03:50.619 Amber Lin: And then lastly, we talked about geography.
15 00:03:50.890 ⇒ 00:03:56.400 Amber Lin: And then also showed them my… assume, like…
16 00:03:56.740 ⇒ 00:04:05.979 Amber Lin: what is it? Also showed them what I projected would be the actual sales of Amazon based on their repeat customers table.
17 00:04:06.180 ⇒ 00:04:15.630 Amber Lin: And I’m doing some further analysis on that to show them how it changed year over year. I think most of the insights, they…
18 00:04:15.840 ⇒ 00:04:24.330 Amber Lin: would already know… know, or is not too new, but they said they would use the graphs to present to their
19 00:04:25.260 ⇒ 00:04:31.959 Amber Lin: board or stakeholders, so that’s a good thing. And then on the Shopify side… let’s see…
20 00:04:32.400 ⇒ 00:04:42.529 Amber Lin: mostly showed them our analysis, and then they had a question on discounts, which is what I… what I am doing.
21 00:04:43.260 ⇒ 00:04:52.079 Amber Lin: So, how much of the sales is from promo-driven customers or from discount-seeking customers?
22 00:04:52.950 ⇒ 00:04:53.600 Henry Zhao: Okay.
23 00:04:53.890 ⇒ 00:04:54.480 Amber Lin: Yeah.
24 00:04:55.870 ⇒ 00:05:01.870 Henry Zhao: Okay, so… I guess this inventory tracker, is this, I think, just Amazon, or…
25 00:05:02.890 ⇒ 00:05:03.899 Henry Zhao: Do you know?
26 00:05:04.610 ⇒ 00:05:10.309 Amber Lin: This inventory tracker should be everything, right?
27 00:05:10.570 ⇒ 00:05:13.950 Amber Lin: Or is it just sending to Amazon?
28 00:05:15.080 ⇒ 00:05:20.379 Henry Zhao: Like, when somebody orders on Amazon, does it get filled from their inventory, or from the POs that Amazon purchases?
29 00:05:20.750 ⇒ 00:05:27.839 Amber Lin: When they order from… Amazon fulfills them, so Amazon… I think…
30 00:05:27.840 ⇒ 00:05:28.799 Henry Zhao: Buys from them.
31 00:05:28.970 ⇒ 00:05:29.540 Amber Lin: Yes.
32 00:05:29.540 ⇒ 00:05:35.410 Henry Zhao: They obviously need to have the inventory. So, but, like, when Amazon fulfills it, does it go through the street? You know what I mean? I’m trying to ask?
33 00:05:35.450 ⇒ 00:05:46.900 Amber Lin: That’s not their, concern, so… Okay. I think that that sheet is only…
34 00:05:50.790 ⇒ 00:05:51.690 Amber Lin: Only when.
35 00:05:51.690 ⇒ 00:05:58.440 Henry Zhao: What do you say their concern is? Yeah, just so I have a direction of, like, what to analyze.
36 00:05:58.440 ⇒ 00:06:00.790 Amber Lin: Let’s see…
37 00:06:02.460 ⇒ 00:06:04.530 Henry Zhao: That’s where I’m a little bit stuck, you know?
38 00:06:05.400 ⇒ 00:06:07.490 Amber Lin: Can you give me some options?
39 00:06:13.480 ⇒ 00:06:14.569 Henry Zhao: I don’t know,
40 00:06:16.540 ⇒ 00:06:26.310 Henry Zhao: Like, I was gonna look at this, like, PO dollars, and how much was accepted, and figure out why… why in, like, December 23rd, so few were accepted.
41 00:06:28.400 ⇒ 00:06:29.579 Henry Zhao: That’s really low.
42 00:06:31.820 ⇒ 00:06:34.499 Henry Zhao: Seems like every once in a while, they have these, like, really low weeks.
43 00:06:34.990 ⇒ 00:06:36.629 Henry Zhao: And I don’t know why that is.
44 00:06:38.420 ⇒ 00:06:43.200 Amber Lin: Excuse me.
45 00:06:44.200 ⇒ 00:06:49.280 Henry Zhao: I guess what I should probably do is just finish watching this video, let me… I’m free.
46 00:06:49.280 ⇒ 00:06:55.800 Amber Lin: later today, if you want to talk, I’m also doing something for insomnia, so I can grab this for later.
47 00:06:56.160 ⇒ 00:07:03.449 Henry Zhao: You don’t have to move it, I would just say I’ll work on this until Wednesday, and then, since I’m off Friday, Thursday, and Friday, if there’s anything else that needs to be done by then, I will pass it off to you.
48 00:07:03.610 ⇒ 00:07:08.029 Henry Zhao: Cool. There might not even be, like, there might not be enough useful stuff in those docs that…
49 00:07:08.030 ⇒ 00:07:08.560 Amber Lin: Yeah.
50 00:07:08.560 ⇒ 00:07:10.389 Henry Zhao: that are worth presenting, you know what I mean?
51 00:07:10.390 ⇒ 00:07:12.109 Amber Lin: Sounds good. Let me know.
52 00:07:12.110 ⇒ 00:07:12.720 Henry Zhao: We’ll do that then.
53 00:07:12.720 ⇒ 00:07:14.699 Amber Lin: Let me know once you watch it, what you find.
54 00:07:14.700 ⇒ 00:07:17.229 Henry Zhao: And I’ll ask you any questions on the Friday meeting.
55 00:07:17.230 ⇒ 00:07:17.580 Amber Lin: Cool.
56 00:07:17.580 ⇒ 00:07:18.389 Henry Zhao: Okay, will do.
57 00:07:18.390 ⇒ 00:07:20.119 Amber Lin: Thanks. Great. Bye.
58 00:07:20.120 ⇒ 00:07:21.699 Henry Zhao: Thanks, Amber. Bye.