Meeting Title: Go over Honey Stinger data Date: 2025-11-24 Meeting participants: Amber Lin, Henry Zhao
WEBVTT
1 00:01:32.600 ⇒ 00:01:33.750 Henry Zhao: Hey, Amber.
2 00:01:35.590 ⇒ 00:01:36.410 Amber Lin: Hi there!
3 00:01:37.400 ⇒ 00:01:38.539 Henry Zhao: How are you doing?
4 00:01:39.060 ⇒ 00:01:44.079 Amber Lin: Working on insomnia, and I figured… and I… I thought it was during a…
5 00:01:44.190 ⇒ 00:01:47.829 Amber Lin: Then I discovered there’s some data issues, so…
6 00:01:47.980 ⇒ 00:01:49.080 Henry Zhao: Anything I can help with?
7 00:01:49.080 ⇒ 00:01:52.560 Amber Lin: Trying to figure out if things match up, and if that means that…
8 00:01:52.850 ⇒ 00:01:56.870 Amber Lin: Things, like, the insights are not correct, so we’ll see.
9 00:01:58.180 ⇒ 00:01:59.690 Henry Zhao: Anything I can help with, though?
10 00:02:00.190 ⇒ 00:02:08.990 Amber Lin: I don’t think so, as well as I’m gonna ask Casey or Sam when they did the export, if there were certain settings on it.
11 00:02:13.070 ⇒ 00:02:17.579 Henry Zhao: Okay, so do you want to just quickly go over,
12 00:02:18.310 ⇒ 00:02:22.019 Henry Zhao: kind of what analysis you did, and what I need to do, or…
13 00:02:22.160 ⇒ 00:02:26.350 Henry Zhao: Just kind of, like, catch me up on what happened, last week with Honey Stinger.
14 00:02:26.350 ⇒ 00:02:27.000 Amber Lin: Okay.
15 00:02:27.000 ⇒ 00:02:27.480 Henry Zhao: Got it, yeah.
16 00:02:27.480 ⇒ 00:02:33.790 Amber Lin: I’ve mostly just looked at PO data, and then the day after we talked, I
17 00:02:33.830 ⇒ 00:02:51.110 Amber Lin: I spend a lot of time, but not all of them are very fruitful. So I spent some time using statistical models, so that didn’t go very well, so that’s… I will skip half a day from what happened there. And then… mostly trying to look at…
18 00:02:51.630 ⇒ 00:03:01.039 Amber Lin: trends in PL purchases, so, let’s see, let’s go to slide 5 and go through what it is.
19 00:03:01.530 ⇒ 00:03:01.960 Henry Zhao: Yo.
20 00:03:02.810 ⇒ 00:03:05.160 Henry Zhao: So how did you see this spike?
21 00:03:05.790 ⇒ 00:03:08.830 Amber Lin: So this is the…
22 00:03:09.590 ⇒ 00:03:17.730 Amber Lin: Let’s see, so this is the orders, and then the order statuses. So I think that should be, like, the vendor…
23 00:03:17.760 ⇒ 00:03:19.390 Henry Zhao: Procurement order…
24 00:03:19.630 ⇒ 00:03:30.459 Amber Lin: items, and join with the order statuses table. So, it’s mostly, some of the orders are…
25 00:03:30.760 ⇒ 00:03:36.210 Amber Lin: received or part… not received or partially received, and I just mapped that.
26 00:03:36.370 ⇒ 00:03:40.140 Amber Lin: Map that one out based on total units.
27 00:03:41.270 ⇒ 00:03:41.810 Henry Zhao: Pam.
28 00:03:42.050 ⇒ 00:03:49.420 Amber Lin: Yeah, so that’s essentially what you and Robert said, let’s look at the statuses to see what happened around there.
29 00:03:49.810 ⇒ 00:03:55.789 Amber Lin: So mostly this is to say we don’t really fulfill, like, we miss some fulfillments when they spike.
30 00:03:56.730 ⇒ 00:04:06.490 Amber Lin: Yeah, and during the meeting, I think Byron said, overall, their fulfillment rate was pretty good. They were around, like, 90-something percent, so… Oh, that’s what I saw.
31 00:04:06.710 ⇒ 00:04:16.130 Amber Lin: And this is just… when these spikes happen, they have issues, but I think they still have a pretty good relationship with Amazon.
32 00:04:16.760 ⇒ 00:04:29.040 Amber Lin: But these spikes do happen, and when they happen, they negatively… they can’t fulfill it as well. In this one, what I did is I first defined what a PO spike is.
33 00:04:29.190 ⇒ 00:04:34.819 Amber Lin: I use 3 times the mean absolute deviation.
34 00:04:35.020 ⇒ 00:04:39.379 Amber Lin: So, anything that’s more than that, because some of them spike…
35 00:04:39.710 ⇒ 00:04:51.920 Amber Lin: really big and absolute invariant, so in percentages, but the absolute value is pretty small, so here I just use absolute value to see which one is actually a spike that has business impact.
36 00:04:52.370 ⇒ 00:05:11.689 Amber Lin: Meaning that they need to stock a lot more inventory in that short time period. And then similarly, I just plotted for those spikes, there was, like, 30-something, I said, okay, look at traffic, look at highly available inventory, and look at sales.
37 00:05:12.720 ⇒ 00:05:13.350 Amber Lin: And…
38 00:05:13.650 ⇒ 00:05:17.639 Henry Zhao: Same query of, order status and procurement.
39 00:05:18.620 ⇒ 00:05:23.549 Amber Lin: It would say, yeah, very similar to what you had before.
40 00:05:23.820 ⇒ 00:05:24.530 Henry Zhao: Got it.
41 00:05:24.530 ⇒ 00:05:28.400 Amber Lin: just using… I just… I only looked at those spikes.
42 00:05:30.290 ⇒ 00:05:32.110 Henry Zhao: By product, right?
43 00:05:33.040 ⇒ 00:05:34.890 Amber Lin: Yeah.
44 00:05:36.370 ⇒ 00:05:36.990 Henry Zhao: Okay.
45 00:05:36.990 ⇒ 00:05:45.740 Amber Lin: So, agri… overall, so, for all of the products and all of the PO… PO orders, the spikes.
46 00:05:46.550 ⇒ 00:05:52.689 Amber Lin: And then… let’s say… And then I think, for this one.
47 00:05:52.880 ⇒ 00:06:06.239 Amber Lin: this is just a deep dive in one of their products that had a bigger spike very recently that Byron brought up during the previous meeting. So he was like, oh, the smoothie energy chews had… we didn’t expect that to
48 00:06:06.480 ⇒ 00:06:07.780 Amber Lin: To come in.
49 00:06:08.200 ⇒ 00:06:20.769 Amber Lin: And this is just plotted PO orders over time, and what we can see is that there is… there has been a big spike in September of the same magnitude.
50 00:06:20.880 ⇒ 00:06:28.480 Amber Lin: So… and I looked at a few other ones. There’s 6 other products that had
51 00:06:28.950 ⇒ 00:06:40.349 Amber Lin: spikes, multiple spikes in the last 3 months, and they only had, like, 40-something products total, so that’s a pretty big percentage of, the ones that
52 00:06:40.710 ⇒ 00:06:48.980 Amber Lin: have, say, PO spikes, so… We could look at ones that have previous spikes, too.
53 00:06:49.670 ⇒ 00:06:51.219 Amber Lin: to plan ahead.
54 00:06:51.460 ⇒ 00:06:54.329 Amber Lin: Then this relates to the next slide.
55 00:06:55.150 ⇒ 00:06:56.640 Amber Lin: That’s slide 8.
56 00:06:57.420 ⇒ 00:06:58.660 Amber Lin: Which is, essentially.
57 00:06:58.660 ⇒ 00:07:00.140 Henry Zhao: And… yeah.
58 00:07:00.370 ⇒ 00:07:07.900 Amber Lin: It occurs in a pretty cyclical pattern, is that after it spikes, it kind of resets in a sort of
59 00:07:07.930 ⇒ 00:07:21.319 Amber Lin: It goes low for a bit, and it spikes again, but as long as we know that a spike has… is… has happened, things were re-spike, reset, orders are low for a little bit until they go up again.
60 00:07:22.010 ⇒ 00:07:22.770 Henry Zhao: Gotcha.
61 00:07:22.770 ⇒ 00:07:32.280 Amber Lin: Yeah, like, this is Robert’s conclusion. I think it’s pretty helpful. And what he wanted us to do next is to look at
62 00:07:32.580 ⇒ 00:07:40.960 Amber Lin: Essentially do this by product, and by… Product category and buyer region.
63 00:07:41.620 ⇒ 00:07:42.880 Henry Zhao: What’s product category.
64 00:07:42.880 ⇒ 00:07:52.130 Amber Lin: So, essentially, like, all their waffle product is waffles, or their chews are chews, so chews, waffles, gels,
65 00:07:52.340 ⇒ 00:07:53.419 Amber Lin: I think the astronomy.
66 00:07:55.760 ⇒ 00:08:01.040 Henry Zhao: So I’d probably do it here, do, like… Energy gel, right?
67 00:08:01.630 ⇒ 00:08:06.199 Henry Zhao: Chew, gel, gel, gel, gel, etc, right? Just like that.
68 00:08:06.520 ⇒ 00:08:07.230 Amber Lin: Yeah.
69 00:08:07.600 ⇒ 00:08:13.520 Amber Lin: I did classify using AI, so you can also try that out.
70 00:08:13.520 ⇒ 00:08:15.150 Henry Zhao: Not that many, so…
71 00:08:15.150 ⇒ 00:08:17.360 Amber Lin: Yeah.
72 00:08:17.360 ⇒ 00:08:18.220 Henry Zhao: Take 2 seconds.
73 00:08:20.460 ⇒ 00:08:32.269 Amber Lin: Also, when they say by a region, we can find that by using the fulfillment center’s region code, so we’ll need to look… I haven’t looked at it yet.
74 00:08:32.270 ⇒ 00:08:33.420 Henry Zhao: I couldn’t find that data.
75 00:08:34.039 ⇒ 00:08:35.239 Amber Lin: Oh, really?
76 00:08:35.240 ⇒ 00:08:39.720 Henry Zhao: Yeah, I don’t… I didn’t find any… I didn’t find any data on FCs.
77 00:08:40.590 ⇒ 00:08:41.480 Amber Lin: Hmm.
78 00:08:42.000 ⇒ 00:08:43.400 Henry Zhao: Did you?
79 00:08:43.409 ⇒ 00:08:50.259 Amber Lin: send it to. It’s the sender, and so, you know, in the order, the order, I think…
80 00:08:51.140 ⇒ 00:08:51.980 Henry Zhao: I have it here.
81 00:08:52.460 ⇒ 00:08:53.310 Henry Zhao: Which one is it?
82 00:08:53.890 ⇒ 00:08:55.370 Amber Lin: Oi, oy.
83 00:08:56.160 ⇒ 00:09:01.339 Amber Lin: like… Vendor sales permit orders?
84 00:09:02.580 ⇒ 00:09:10.479 Amber Lin: Procurement order, yeah, should be that one. Retail. Sorry, vendor re… Till… to order.
85 00:09:11.780 ⇒ 00:09:20.639 Amber Lin: So in that, that should be the sender… can you scroll down on the left so I can see the fields?
86 00:09:21.790 ⇒ 00:09:24.299 Amber Lin: On the left, right here.
87 00:09:24.710 ⇒ 00:09:29.919 Amber Lin: I should be, like, buying party?
88 00:09:30.730 ⇒ 00:09:32.159 Amber Lin: ad for this, or, like…
89 00:09:32.160 ⇒ 00:09:34.430 Henry Zhao: They’re all null, I feel like, unless it’s…
90 00:09:34.430 ⇒ 00:09:44.010 Amber Lin: Spelling Party ID? What’s not normal? It’s somewhere there. It’s like the buying party… spelling parties at, like, their code.
91 00:09:44.430 ⇒ 00:09:45.320 Amber Lin: Oh…
92 00:09:45.320 ⇒ 00:09:47.539 Henry Zhao: Do they have these codes mapped somewhere?
93 00:09:47.840 ⇒ 00:09:51.459 Amber Lin: Yeah, that should be… that should be related to there.
94 00:09:51.780 ⇒ 00:09:54.340 Amber Lin: Let me also look at that, too.
95 00:09:54.340 ⇒ 00:09:55.769 Henry Zhao: Yeah, if you can look at that.
96 00:09:55.770 ⇒ 00:10:02.050 Amber Lin: So that is related to their, like, warehouse, fulfillment center.
97 00:10:02.220 ⇒ 00:10:05.060 Amber Lin: Region, and then we can use that to…
98 00:10:20.640 ⇒ 00:10:23.019 Henry Zhao: Yeah, if we can figure out where the mapping is, that would help.
99 00:10:23.840 ⇒ 00:10:27.870 Amber Lin: Do you think it’s the ship to party print ID?
100 00:10:28.200 ⇒ 00:10:29.849 Henry Zhao: I don’t know.
101 00:10:29.850 ⇒ 00:10:31.879 Amber Lin: Like, they are different.
102 00:10:35.470 ⇒ 00:10:37.439 Henry Zhao: I mean, we at least have the values, which is a…
103 00:10:37.630 ⇒ 00:10:38.920 Amber Lin: more than I had.
104 00:10:39.940 ⇒ 00:10:42.240 Amber Lin: Build party.
105 00:10:47.390 ⇒ 00:10:52.950 Amber Lin: Mmm… That seems like the only one.
106 00:10:54.680 ⇒ 00:10:59.509 Amber Lin: It probably is the buying party party ID.
107 00:10:59.960 ⇒ 00:11:05.260 Amber Lin: But we need to check… Most of them is no data.
108 00:11:08.700 ⇒ 00:11:15.770 Amber Lin: Amazon, we fulfill… And center code.
109 00:11:21.680 ⇒ 00:11:30.120 Amber Lin: Oh, yeah. I searched… the code… let me see… if you search one of those codes.
110 00:11:30.590 ⇒ 00:11:36.339 Amber Lin: on Google, and just say Amazon Fulfillment Center, it gives an address?
111 00:11:37.390 ⇒ 00:11:40.889 Amber Lin: So I think it is… a place.
112 00:11:41.380 ⇒ 00:11:46.369 Henry Zhao: Oh… Oh, that… cool!
113 00:11:46.370 ⇒ 00:11:49.400 Amber Lin: Like, it is, it is the location.
114 00:11:49.530 ⇒ 00:11:55.229 Henry Zhao: This is the only thing that’s not null, there’s only, like, 5 fields here and there. Two of them are the same.
115 00:11:55.690 ⇒ 00:12:05.479 Amber Lin: So, let me see if we can get a list of… Amazon Fulfillment center codes.
116 00:12:10.790 ⇒ 00:12:14.120 Amber Lin: Are there, like, FBA warehouse locations?
117 00:12:14.910 ⇒ 00:12:17.279 Amber Lin: I think you can get that there.
118 00:12:18.350 ⇒ 00:12:20.509 Henry Zhao: Amazon Fulfillment Center codes.
119 00:12:21.050 ⇒ 00:12:22.140 Amber Lin: Yeah.
120 00:12:23.690 ⇒ 00:12:24.490 Henry Zhao: Look!
121 00:12:24.760 ⇒ 00:12:26.789 Amber Lin: Yeah, you can find it.
122 00:12:27.120 ⇒ 00:12:27.830 Henry Zhao: Is that?
123 00:12:29.330 ⇒ 00:12:33.020 Amber Lin: This should… yeah, that should… that makes sense. Should be that.
124 00:12:33.490 ⇒ 00:12:37.230 Amber Lin: And just match… and we realistically only need this.
125 00:12:38.210 ⇒ 00:12:39.690 Henry Zhao: We’re really just the airport codes.
126 00:12:40.150 ⇒ 00:12:41.310 Amber Lin: Yeah.
127 00:12:42.230 ⇒ 00:12:45.100 Amber Lin: I mean, that’s enough for location.
128 00:12:47.390 ⇒ 00:12:50.349 Henry Zhao: Why are these all HOQ?
129 00:12:51.500 ⇒ 00:12:53.389 Henry Zhao: Selling party, party ID.
130 00:13:02.040 ⇒ 00:13:06.560 Henry Zhao: No, I don’t feel good about this. There’s only two distinct values.
131 00:13:09.040 ⇒ 00:13:12.340 Amber Lin: It should be the one that has 47.
132 00:13:13.790 ⇒ 00:13:16.480 Amber Lin: Shift to party, party ID.
133 00:13:16.480 ⇒ 00:13:19.059 Henry Zhao: That seems very low to me.
134 00:13:20.570 ⇒ 00:13:23.280 Amber Lin: I mean, there’s only that many states.
135 00:13:24.480 ⇒ 00:13:29.620 Amber Lin: They probably don’t sell to all states?
136 00:13:30.260 ⇒ 00:13:32.720 Henry Zhao: But it’s… I don’t think it’s just one per state.
137 00:13:38.680 ⇒ 00:13:46.860 Amber Lin: Because they also can have… like, if it’s a close state, they might just truck it over… So…
138 00:13:47.580 ⇒ 00:13:52.990 Henry Zhao: Okay, GYR3, Indianapolis, Houston, Las Vegas… okay, this kind of makes… this makes sense.
139 00:13:53.460 ⇒ 00:13:54.749 Henry Zhao: Okay, pretty cool.
140 00:13:55.150 ⇒ 00:14:00.679 Amber Lin: Yeah. I think that will help us do the region… regional… There’s you?
141 00:14:01.280 ⇒ 00:14:02.060 Amber Lin: Yeah.
142 00:14:02.060 ⇒ 00:14:03.570 Henry Zhao: We’re in LA, right?
143 00:14:04.020 ⇒ 00:14:05.159 Amber Lin: Yeah, I’m in LA.
144 00:14:05.400 ⇒ 00:14:09.440 Henry Zhao: But why is there only one value… why is there only one value for, like, Philadelphia? Like…
145 00:14:10.710 ⇒ 00:14:12.389 Henry Zhao: This is all time…
146 00:14:12.790 ⇒ 00:14:21.710 Henry Zhao: Let’s see, just see what it is for… where’s Philadelphia? Unless they just don’t ship to Philadelphia… from Philadelphia that much. But this is Ship 2.
147 00:14:23.310 ⇒ 00:14:27.960 Henry Zhao: I’m not really understanding, unless this table’s not fully backfilled, which shouldn’t be.
148 00:14:30.100 ⇒ 00:14:34.899 Henry Zhao: Purchase order date 5-28. Do you think, really, they only made one purchase order from Philadelphia?
149 00:14:36.530 ⇒ 00:14:38.320 Henry Zhao: Like, of all, all items?
150 00:14:38.920 ⇒ 00:14:39.720 Amber Lin: Hmm.
151 00:14:41.860 ⇒ 00:14:43.380 Henry Zhao: Maybe we want to ask Byron this.
152 00:14:43.620 ⇒ 00:14:49.169 Amber Lin: Can we… can we first check, like, the date of this specific table, the date range?
153 00:14:49.690 ⇒ 00:14:50.230 Henry Zhao: Yeah.
154 00:15:05.930 ⇒ 00:15:08.530 Henry Zhao: March 2024 all the way to today.
155 00:15:08.650 ⇒ 00:15:09.730 Henry Zhao: Well, last…
156 00:15:10.820 ⇒ 00:15:11.700 Amber Lin: Hmm.
157 00:15:15.820 ⇒ 00:15:25.539 Amber Lin: Let’s see… They also don’t buy to each of their warehouses every time. Let’s say, okay, So that’s about…
158 00:15:36.770 ⇒ 00:15:39.849 Amber Lin: That’s about, like, 86 weeks.
159 00:15:40.190 ⇒ 00:15:43.589 Amber Lin: They make an order each week.
160 00:15:44.200 ⇒ 00:15:47.530 Amber Lin: I don’t know if they order to all states.
161 00:15:48.000 ⇒ 00:15:49.340 Amber Lin: Each week.
162 00:15:58.460 ⇒ 00:16:01.170 Amber Lin: So they may order… can you just look at…
163 00:16:01.320 ⇒ 00:16:10.380 Amber Lin: Philadelph… Philadelphia or Pennsylvania only has, like, not… not the fifth one, but just all of it that contains that code.
164 00:16:11.460 ⇒ 00:16:14.000 Amber Lin: Just PHL, maybe?
165 00:16:15.210 ⇒ 00:16:20.560 Amber Lin: Maybe they order… maybe they order to different… warehouses?
166 00:16:23.630 ⇒ 00:16:24.650 Henry Zhao: Nope, just that one.
167 00:16:26.430 ⇒ 00:16:27.959 Henry Zhao: Maybe all-time career.
168 00:16:30.740 ⇒ 00:16:31.650 Amber Lin: Hmm.
169 00:16:34.970 ⇒ 00:16:39.760 Henry Zhao: We can go check with Byron, like, does that sound right? Like, do they only have that many purchase orders?
170 00:16:39.760 ⇒ 00:16:45.519 Amber Lin: Yeah, of all… can we count? What’s the total number of purchase orders?
171 00:16:46.420 ⇒ 00:16:48.460 Henry Zhao: Total number of purchase orders is…
172 00:16:48.460 ⇒ 00:16:49.460 Amber Lin: Yeah…
173 00:16:53.020 ⇒ 00:16:54.220 Henry Zhao: 951.
174 00:16:54.920 ⇒ 00:16:55.830 Amber Lin: Oh.
175 00:16:56.030 ⇒ 00:16:57.069 Henry Zhao: Sounds reasonable.
176 00:16:57.070 ⇒ 00:16:58.140 Amber Lin: Oh, wow.
177 00:17:00.750 ⇒ 00:17:03.599 Henry Zhao: I think it sounds reasonable, actually. Yeah.
178 00:17:03.600 ⇒ 00:17:07.130 Amber Lin: They have, like, 44 products, probably, like.
179 00:17:08.550 ⇒ 00:17:12.369 Amber Lin: Probably ranging from, like, 10 to 50.
180 00:17:12.640 ⇒ 00:17:17.319 Amber Lin: Purchase orders… purchase orders per product, depending on how new it is.
181 00:17:17.890 ⇒ 00:17:19.170 Amber Lin: Yeah. Cool.
182 00:17:20.369 ⇒ 00:17:22.670 Amber Lin: That doesn’t make sense.
183 00:17:23.130 ⇒ 00:17:27.120 Henry Zhao: Okay, well, I feel good about this. Do you want to switch to talking about Walmart?
184 00:17:27.619 ⇒ 00:17:32.289 Amber Lin: Sure, yeah, I looked at it, it looked at all… all looks like…
185 00:17:32.389 ⇒ 00:17:35.269 Amber Lin: online data, I don’t see anything that’s…
186 00:17:35.500 ⇒ 00:17:36.220 Henry Zhao: Which is fine.
187 00:17:36.220 ⇒ 00:17:42.350 Amber Lin: had this much. None of them… all of them had this… these column names.
188 00:17:42.350 ⇒ 00:17:42.900 Henry Zhao: Yeah.
189 00:17:42.900 ⇒ 00:17:44.080 Amber Lin: They’re essentially the same.
190 00:17:44.080 ⇒ 00:17:47.860 Henry Zhao: So, what can we really get out of this data? Like, I don’t think there’s anything useful out of this data.
191 00:17:47.860 ⇒ 00:18:01.980 Amber Lin: The only thing we can do, I think what I asked Robert this morning, is that we can compare the traffic trends to Amazon traffic trends, see… essentially see if…
192 00:18:02.590 ⇒ 00:18:14.100 Amber Lin: Maybe Amazon could be manipulating the traffic, or they could be restricting certain sales because they don’t Inventory.
193 00:18:14.100 ⇒ 00:18:18.970 Henry Zhao: Okay, so could… what’d you say? So could Amazon be manipulating…
194 00:18:18.990 ⇒ 00:18:24.870 Amber Lin: I guess a better question is, how does the traffic on Amazon and Walmart compare?
195 00:18:25.050 ⇒ 00:18:26.749 Henry Zhao: Right. That makes sense?
196 00:18:28.060 ⇒ 00:18:30.000 Amber Lin: And similarly, sales trends.
197 00:18:30.770 ⇒ 00:18:32.550 Henry Zhao: What are the sales trends?
198 00:18:34.930 ⇒ 00:18:40.329 Amber Lin: I think before we can compare that, we’ll need to get the Amazon purchase…
199 00:18:40.640 ⇒ 00:18:46.580 Amber Lin: like, by product category, because Walmart ones are all… like, category.
200 00:18:46.580 ⇒ 00:18:47.230 Henry Zhao: I already categorized.
201 00:18:47.230 ⇒ 00:18:48.199 Amber Lin: Peanut butter waffle.
202 00:18:48.200 ⇒ 00:18:51.159 Henry Zhao: I guess I need to categorize mini energy and peanut butter.
203 00:18:51.460 ⇒ 00:18:52.080 Amber Lin: Yeah.
204 00:18:52.740 ⇒ 00:18:57.399 Henry Zhao: So… I’ll do that.
205 00:18:59.130 ⇒ 00:19:01.800 Henry Zhao: prerequisite… Wow.
206 00:19:05.140 ⇒ 00:19:11.370 Henry Zhao: categorize… Amazon product names by same categories.
207 00:19:12.010 ⇒ 00:19:13.669 Henry Zhao: Walmart, that makes sense.
208 00:19:16.710 ⇒ 00:19:17.970 Henry Zhao: Anything else you can think of?
209 00:19:18.510 ⇒ 00:19:22.300 Henry Zhao: What were you saying about manipulating? Could, Amazon be manipulating something?
210 00:19:22.830 ⇒ 00:19:27.470 Amber Lin: I think that’s what the first question will tell us. I was like…
211 00:19:27.850 ⇒ 00:19:41.519 Amber Lin: it either tells us people have different preferences when they go to Amazon or Walmart, or that, if Walmart… like, we probably can look at Shopify traffic, and then at that point, we can say, like.
212 00:19:41.910 ⇒ 00:19:46.290 Amber Lin: Shopify is still… .
213 00:19:46.730 ⇒ 00:19:48.010 Henry Zhao: Just compare all three, right?
214 00:19:48.010 ⇒ 00:19:48.670 Amber Lin: Yeah.
215 00:19:48.960 ⇒ 00:19:53.069 Henry Zhao: Lots of Walmart to Shopify trends. I think this is what we’re saying is the next step, right?
216 00:19:53.070 ⇒ 00:19:59.740 Amber Lin: Yeah, Shopify probably will take longer, because now we… we… like, the Walmart data is so simple.
217 00:20:00.490 ⇒ 00:20:03.869 Amber Lin: Don’t need to classify and explore all the Shopify tables.
218 00:20:04.310 ⇒ 00:20:06.209 Henry Zhao: Actually, I think Shopify’s pretty easy, too.
219 00:20:06.910 ⇒ 00:20:08.999 Henry Zhao: There’s just this many tables.
220 00:20:09.220 ⇒ 00:20:13.720 Henry Zhao: And orders are orders, right? So, I think this is pretty easy. I think Amazon was a tricky one, because there’s all these tables.
221 00:20:14.050 ⇒ 00:20:16.330 Henry Zhao: And you kind of have to understand Amazon’s business model.
222 00:20:16.820 ⇒ 00:20:18.930 Henry Zhao: So I think Shopify shouldn’t be too bad.
223 00:20:19.110 ⇒ 00:20:28.600 Henry Zhao: And Shopify might already be in Klaviyo, so I also need to answer, like, Ehhhhhh Is Shopify…
224 00:20:28.880 ⇒ 00:20:31.529 Henry Zhao: Data already in the Klaviyo event table.
225 00:20:37.520 ⇒ 00:20:38.570 Henry Zhao: Alright.
226 00:20:39.400 ⇒ 00:20:44.930 Henry Zhao: what priority would you say this is? Like, is Robert presenting this week, do you think? Or is he gonna present next week since it’s Thanksgiving?
227 00:20:45.170 ⇒ 00:20:49.170 Amber Lin: I think we can… since this is…
228 00:20:49.630 ⇒ 00:20:53.740 Amber Lin: This is such a short week, probably…
229 00:20:53.920 ⇒ 00:20:59.500 Amber Lin: Like, the first two we can easily give out this week, like, the third one might not get done.
230 00:20:59.680 ⇒ 00:21:06.690 Amber Lin: But, like, they… I think they asked for the regions, they asked for the…
231 00:21:06.930 ⇒ 00:21:19.600 Amber Lin: product category, like, those two things they specifically asked for, and Walmart data is more of an exploration that we know we want to do. But during that meeting, like, the category and region is what they asked for.
232 00:21:20.410 ⇒ 00:21:21.090 Henry Zhao: Okay.
233 00:21:21.620 ⇒ 00:21:25.190 Henry Zhao: Alright, anything else that we needed to do as a result of last week’s meeting?
234 00:21:25.190 ⇒ 00:21:30.860 Amber Lin: There’s, like, an inventory tracker and stuff, but I don’t think they gave us access, so no
235 00:21:32.310 ⇒ 00:21:39.909 Amber Lin: I guess my question is, do you want to split the work with me, or how do you… how do we plan to approach this? Are you going to do it?
236 00:21:39.910 ⇒ 00:21:44.709 Henry Zhao: Well, how’s your work… how’s your workload this week, and are you working Thursday and Friday?
237 00:21:44.710 ⇒ 00:21:50.839 Amber Lin: not working Thursday and Friday, but I also don’t think you should. But, like, I have…
238 00:21:50.940 ⇒ 00:21:58.210 Amber Lin: time. I’m mostly finishing up a song here today, and I’ll have some time tomorrow and Wednesday, so, like, we can…
239 00:21:58.320 ⇒ 00:22:00.850 Amber Lin: Switch between, like, we can…
240 00:22:01.360 ⇒ 00:22:05.259 Amber Lin: Hand off certain, like, split the tasks, depending on how you want to do it.
241 00:22:05.960 ⇒ 00:22:17.679 Henry Zhao: Up to you, you can let me know, and then I’ll plan out my week accordingly. But I have some bandwidth this week because I’m, pretty good on Eden, and… we need to just get access to this, right? This is the inventory tracker you were just saying?
242 00:22:18.360 ⇒ 00:22:19.179 Amber Lin: I think so.
243 00:22:20.460 ⇒ 00:22:22.029 Amber Lin: Mmm, yeah.
244 00:22:22.290 ⇒ 00:22:23.010 Henry Zhao: That’s crucial.
245 00:22:23.010 ⇒ 00:22:24.600 Amber Lin: They’re not in the thread.
246 00:22:25.910 ⇒ 00:22:32.420 Henry Zhao: Oh, okay. Well, just let me know how your bandwidth is, if you’re short on work, we can split it up. If not, I don’t mind just taking it.
247 00:22:32.820 ⇒ 00:22:38.459 Amber Lin: Okay. Are you planning on starting on it today? Because I will not have time to do it today.
248 00:22:38.740 ⇒ 00:22:41.730 Henry Zhao: No, I’ll probably finish eating today, and then tomorrow I’ll do Honey Stinger.
249 00:22:41.730 ⇒ 00:22:43.860 Amber Lin: Okay, when are you starting work tomorrow?
250 00:22:45.080 ⇒ 00:22:47.460 Henry Zhao: Probably 9 Eastern.
251 00:22:47.940 ⇒ 00:22:51.359 Amber Lin: 9 Eastern, that’s, at 4.
252 00:22:51.360 ⇒ 00:22:54.039 Henry Zhao: That’s like 5 o’clock LA time, right?
253 00:22:54.590 ⇒ 00:23:13.239 Amber Lin: Probably, like, 6. So, I start, like, 7.30, so I guess if you get started early, like, I’ll ping you, and then we can think about, like, how we can split the work, because if we get it done early, you don’t have to work on Thursday and Friday. Like, that sounds miserable.
254 00:23:13.710 ⇒ 00:23:23.129 Henry Zhao: Oh, no worries, thank you. But, I think my one thing I want to get done tomorrow morning is figure out if Shopify data is in Klaviyo, so if we can work on that together, that’d be great, and just share that knowledge.
255 00:23:24.630 ⇒ 00:23:34.750 Henry Zhao: Cool, okay, yeah, probably we can do, like, a working session and pair it tomorrow. I just want to know, like, if Shopify, Klaviyo are MCI, or are they the same thing, or are they subsets, so… I see.
256 00:23:34.960 ⇒ 00:23:42.059 Amber Lin: You’ll have to introduce me to that, because I have not even seen Klaviyo, I have no clue what that is, so we’ll… we’ll take a look.
257 00:23:42.060 ⇒ 00:23:43.879 Henry Zhao: This is good as teamwork, I think.
258 00:23:44.090 ⇒ 00:23:49.120 Amber Lin: Cool. Okay, I’ll… I’ll book a Zoom session with you tomorrow, and then we can look at it together.
259 00:23:49.550 ⇒ 00:23:50.230 Henry Zhao: Okay.
260 00:23:50.230 ⇒ 00:23:50.690 Amber Lin: Okay.
261 00:23:50.700 ⇒ 00:23:54.390 Henry Zhao: Also, I will be in LA in January.
262 00:23:54.720 ⇒ 00:23:57.400 Amber Lin: Oh, awesome! Like, when in January?
263 00:23:57.400 ⇒ 00:24:00.039 Henry Zhao: January 16th, so if you want to go to, like, a WeWork, that would be fun.
264 00:24:00.040 ⇒ 00:24:09.779 Amber Lin: Yeah, Hannah’s also here, I think the new person at GoToMarket, like, Joseph Good, is also here. So, yeah, we’ll hang out.
265 00:24:10.040 ⇒ 00:24:12.910 Henry Zhao: Sounds good. And then the 17th, I’m going to Asia, so I will…
266 00:24:12.910 ⇒ 00:24:13.590 Amber Lin: Wow.
267 00:24:14.540 ⇒ 00:24:15.470 Amber Lin: Okay.
268 00:24:15.640 ⇒ 00:24:17.709 Amber Lin: Alright, thanks talking to you. See you tomorrow.
269 00:24:17.710 ⇒ 00:24:19.560 Henry Zhao: Alright, same. Thank you, see you. Bye-bye.
270 00:24:19.560 ⇒ 00:24:20.230 Amber Lin: Right.