Meeting Title: Honey Stinger Data Analysis Sync Date: 2025-12-01 Meeting participants: Amber Lin, Henry Zhao
WEBVTT
1 00:03:22.850 ⇒ 00:03:23.960 Henry Zhao: Hello.
2 00:03:26.140 ⇒ 00:03:27.080 Amber Lin: Hi there!
3 00:03:28.050 ⇒ 00:03:30.169 Henry Zhao: I could use your helper opinion on something.
4 00:03:30.370 ⇒ 00:03:31.360 Amber Lin: Which one?
5 00:03:32.430 ⇒ 00:03:34.449 Henry Zhao: I could use your help or opinion on something.
6 00:03:35.410 ⇒ 00:03:39.270 Henry Zhao: So I made this new chart that doesn’t have over 100% conversion rate anymore.
7 00:03:39.270 ⇒ 00:03:41.769 Amber Lin: Oh, what? How was that possible?
8 00:03:41.770 ⇒ 00:03:51.729 Henry Zhao: Well, I’m using Klaviyo for both, but the thing is… what would you do? So for Klaviyo, I just guessed which are the events, so there’s 128 events in Klaviyo.
9 00:03:52.150 ⇒ 00:03:54.070 Henry Zhao: I just guessed which ones are purchases.
10 00:03:54.260 ⇒ 00:04:00.849 Henry Zhao: So, from looking at it, I’m guessing it’s these 5 events. Would you double-check that with Honey Stinger, or would you just go with it?
11 00:04:01.970 ⇒ 00:04:06.460 Amber Lin: Was… can I say the event name?
12 00:04:07.350 ⇒ 00:04:07.680 Henry Zhao: Yeah.
13 00:04:07.680 ⇒ 00:04:09.500 Amber Lin: Is it in the spreadsheet?
14 00:04:09.770 ⇒ 00:04:11.760 Henry Zhao: U-C-Y-S, yeah.
15 00:04:20.000 ⇒ 00:04:22.160 Henry Zhao: Ordered product, so there’s ordered product.
16 00:04:23.260 ⇒ 00:04:25.280 Henry Zhao: S-Y-E-V-T-S…
17 00:04:28.100 ⇒ 00:04:29.070 Henry Zhao: Just bold them.
18 00:04:33.240 ⇒ 00:04:35.189 Henry Zhao: Something with recharge.
19 00:04:38.610 ⇒ 00:04:40.340 Henry Zhao: I should stop being silly and just…
20 00:04:40.790 ⇒ 00:04:41.760 Amber Lin: See?
21 00:04:41.760 ⇒ 00:04:42.190 Henry Zhao: Dude…
22 00:04:42.190 ⇒ 00:04:48.009 Amber Lin: Placed… what’s the difference between placed order and ordered product?
23 00:04:48.990 ⇒ 00:04:52.240 Amber Lin: Oh, because there’s multiple products in an order.
24 00:04:53.530 ⇒ 00:04:55.230 Amber Lin: Okay, that makes sense.
25 00:04:58.040 ⇒ 00:05:01.190 Henry Zhao: Order place successfully on Recharge… oh, I did it twice. Oops. Okay, I went.
26 00:05:04.570 ⇒ 00:05:06.320 Henry Zhao: Okay, so…
27 00:05:06.320 ⇒ 00:05:13.159 Amber Lin: the… the placed orders, the main ones, are X… something?
28 00:05:13.940 ⇒ 00:05:17.470 Henry Zhao: Yeah, and then I also added the recharge ones, so those are subscriptions, or…
29 00:05:17.470 ⇒ 00:05:19.380 Amber Lin: Hmm. Oh, cool.
30 00:05:19.910 ⇒ 00:05:22.550 Henry Zhao: So that’s what I did, and I got these numbers.
31 00:05:22.760 ⇒ 00:05:25.910 Henry Zhao: It seems reasonable to me, to have…
32 00:05:26.680 ⇒ 00:05:30.850 Henry Zhao: like, about 50% conversion rate. Here, it’s also hovering around.
33 00:05:32.870 ⇒ 00:05:37.780 Amber Lin: Oh, so what did it… What spiked?
34 00:05:38.540 ⇒ 00:05:39.210 Henry Zhao: Probably Amazon.
35 00:05:39.210 ⇒ 00:05:40.800 Amber Lin: other one, Spike.
36 00:05:41.120 ⇒ 00:05:42.070 Henry Zhao: That probably is Amazon.
37 00:05:42.070 ⇒ 00:05:44.510 Amber Lin: Oh, okay.
38 00:05:44.750 ⇒ 00:05:49.940 Amber Lin: Well, I don’t know, because the other ones, remember, these are from Shopify, so why would Amazon be in there?
39 00:05:50.330 ⇒ 00:05:54.209 Amber Lin: Yeah, I don’t know, I don’t think they’re from Shopify, it could be that…
40 00:05:55.870 ⇒ 00:06:02.689 Amber Lin: Oh, can we do one that… does your current conversion rate include, like, the recharges, so the subscriptions?
41 00:06:04.630 ⇒ 00:06:05.740 Henry Zhao: Jazz.
42 00:06:06.190 ⇒ 00:06:06.690 Henry Zhao: But I don’t know…
43 00:06:06.690 ⇒ 00:06:07.230 Amber Lin: Hmm.
44 00:06:07.230 ⇒ 00:06:12.590 Henry Zhao: It’s all of them. That’s why I was wondering, is the next step forward, should I double-check that with Honey Stinger, or…
45 00:06:15.080 ⇒ 00:06:19.820 Amber Lin: Oh… How will we do that?
46 00:06:22.740 ⇒ 00:06:25.250 Henry Zhao: We’d have to ask, we would tell my guests to check with Byron.
47 00:06:25.840 ⇒ 00:06:27.240 Henry Zhao: To see if he knows.
48 00:06:29.330 ⇒ 00:06:35.719 Amber Lin: Is the red line… so the red line is their… what is the red line again?
49 00:06:35.720 ⇒ 00:06:38.409 Henry Zhao: Purchasers, and then blue is visitors.
50 00:06:38.630 ⇒ 00:06:43.860 Amber Lin: purchasers. So, are we saying that they’re getting less purchasers?
51 00:06:45.310 ⇒ 00:06:45.830 Henry Zhao: Yeah.
52 00:06:45.830 ⇒ 00:06:46.580 Amber Lin: here.
53 00:06:47.600 ⇒ 00:06:48.889 Henry Zhao: Yeah, but we already knew that.
54 00:06:49.340 ⇒ 00:06:53.879 Amber Lin: Really? I thought they’re getting the same amount.
55 00:06:55.180 ⇒ 00:06:57.710 Henry Zhao: And it’s gonna include Walmart and Amazon.
56 00:06:59.930 ⇒ 00:07:02.550 Amber Lin: One sec…
57 00:07:10.610 ⇒ 00:07:17.530 Amber Lin: So… When you… when we did the year-over-year comparison.
58 00:07:18.180 ⇒ 00:07:22.020 Amber Lin: The number of orders are pretty much the same.
59 00:07:23.280 ⇒ 00:07:23.820 Henry Zhao: Yeah.
60 00:07:23.820 ⇒ 00:07:25.030 Amber Lin: So…
61 00:07:26.860 ⇒ 00:07:28.930 Henry Zhao: But again, also, this might not be all purchasers.
62 00:07:32.410 ⇒ 00:07:37.669 Henry Zhao: This is just out of the people that visited the site, how many made a purchase based on those 4 events that I looked at?
63 00:07:38.480 ⇒ 00:07:40.740 Amber Lin: Oh… Hmm.
64 00:07:53.440 ⇒ 00:07:54.910 Henry Zhao: And actually it’s not even.
65 00:07:57.350 ⇒ 00:08:03.160 Henry Zhao: It’s how many people… that visit each month, how many of them have ever purchased? So, actually…
66 00:08:03.530 ⇒ 00:08:05.750 Henry Zhao: For them to have purchased in that month.
67 00:08:06.410 ⇒ 00:08:07.900 Henry Zhao: You know what it might be?
68 00:08:08.780 ⇒ 00:08:13.379 Henry Zhao: It might be people that have purchased before and are coming back just to visit the site, just to look at stuff.
69 00:08:15.690 ⇒ 00:08:16.460 Henry Zhao: So…
70 00:08:16.460 ⇒ 00:08:18.930 Amber Lin: And is there traffic going down?
71 00:08:19.550 ⇒ 00:08:24.590 Henry Zhao: So we need to actually see, in that month, did they also purchase something?
72 00:08:27.260 ⇒ 00:08:28.580 Henry Zhao: Yeah, that’s true.
73 00:08:29.010 ⇒ 00:08:32.160 Henry Zhao: No, no, we don’t need to. Let’s see how this looks.
74 00:08:35.030 ⇒ 00:08:36.440 Henry Zhao: This will look even worse.
75 00:08:37.440 ⇒ 00:08:38.169 Henry Zhao: Yeah.
76 00:08:40.940 ⇒ 00:08:49.220 Amber Lin: Oh, because we’re, like, before we were just counting conversion as, like, purchase whenever. Right now, we’re.
77 00:08:49.220 ⇒ 00:08:54.039 Henry Zhao: Each purchase are… yeah, out of the people that visit here, how many people purchased ever?
78 00:08:54.200 ⇒ 00:08:57.479 Henry Zhao: This is, like, how many… other people are visiting, how many people purchased that month?
79 00:08:57.750 ⇒ 00:08:58.570 Amber Lin: Okay.
80 00:08:58.880 ⇒ 00:09:06.470 Henry Zhao: Which shouldn’t go down, because if you buy something one day, you might come back later in the month, next month, and just check out the products, but you might not buy.
81 00:09:08.110 ⇒ 00:09:09.630 Henry Zhao: I’m sickening!
82 00:09:09.910 ⇒ 00:09:10.460 Amber Lin: Oops.
83 00:09:10.460 ⇒ 00:09:13.190 Henry Zhao: It’s lower, yeah, it’s definitely lower, yeah, it’s like 20-something percent.
84 00:09:13.190 ⇒ 00:09:17.059 Amber Lin: But it looks like overall it’s getting better, like, over…
85 00:09:17.060 ⇒ 00:09:19.190 Henry Zhao: Yeah, that’s good.
86 00:09:19.880 ⇒ 00:09:21.010 Henry Zhao: That looks better.
87 00:09:23.700 ⇒ 00:09:25.819 Henry Zhao: Do this again, stupid.
88 00:09:27.920 ⇒ 00:09:29.120 Henry Zhao: So silly.
89 00:09:31.090 ⇒ 00:09:31.820 Amber Lin: Hmm.
90 00:09:39.870 ⇒ 00:09:42.010 Henry Zhao: Yeah, and conversion rate’s getting better.
91 00:09:42.860 ⇒ 00:09:43.620 Amber Lin: Okay.
92 00:09:45.050 ⇒ 00:09:54.260 Amber Lin: This is still quite a rough conversion, because I might have purchased before the visit, but it’s, like, a rough estimate, it’s okay.
93 00:09:54.400 ⇒ 00:09:58.129 Henry Zhao: This is now and not before the visit. This is just each month, how many people visited, and how many of those purchased based on
94 00:10:00.140 ⇒ 00:10:02.449 Henry Zhao: There might be mittens we’re missing, I’m not sure.
95 00:10:03.380 ⇒ 00:10:04.180 Amber Lin: Okay.
96 00:10:04.510 ⇒ 00:10:06.010 Amber Lin: Okay.
97 00:10:06.010 ⇒ 00:10:07.900 Henry Zhao: It looks better.
98 00:10:07.900 ⇒ 00:10:08.350 Amber Lin: Yeah.
99 00:10:08.900 ⇒ 00:10:11.520 Henry Zhao: Before it was very shaky, now it’s, like, better.
100 00:10:11.830 ⇒ 00:10:16.140 Amber Lin: Yeah, the number of purchasers is pretty much the same.
101 00:10:19.760 ⇒ 00:10:24.480 Amber Lin: How is that… So even in 2022,
102 00:10:24.950 ⇒ 00:10:30.279 Amber Lin: Their monthly purchasers is the same as right now?
103 00:10:31.760 ⇒ 00:10:35.800 Henry Zhao: Yeah, it just really hasn’t changed. But you know that from this one, from the year-to-year one.
104 00:10:36.920 ⇒ 00:10:37.440 Henry Zhao: I saw that.
105 00:10:37.440 ⇒ 00:10:41.779 Amber Lin: Yeah, but overall, their orders went up so much more.
106 00:10:42.730 ⇒ 00:10:45.679 Henry Zhao: Yeah, but probably from this. Probably from, like, Amazon and stuff.
107 00:10:46.550 ⇒ 00:10:48.700 Henry Zhao: From Shopify and Klaviyo, I’m not seeing that increase.
108 00:10:48.700 ⇒ 00:10:52.110 Amber Lin: Wait, I thought the year-over-year was just for Shopify.
109 00:10:53.240 ⇒ 00:10:54.260 Amber Lin: You know the graph?
110 00:10:54.260 ⇒ 00:10:58.140 Henry Zhao: But it was, like, it was, like, it was, like, minus… it was, like, minus 3%, etc.
111 00:10:58.820 ⇒ 00:10:59.920 Henry Zhao: The year over year.
112 00:11:02.650 ⇒ 00:11:03.130 Amber Lin: Oh…
113 00:11:03.130 ⇒ 00:11:05.069 Henry Zhao: Remember, it’s like minus 2% and 0%.
114 00:11:06.180 ⇒ 00:11:08.180 Henry Zhao: Orders as well, pretty much the same.
115 00:11:08.180 ⇒ 00:11:12.810 Amber Lin: But… see, the 2022 to 2023, the order.
116 00:11:12.810 ⇒ 00:11:15.599 Henry Zhao: No, this is… I don’t think this is complete data, I don’t think this is complete data.
117 00:11:16.900 ⇒ 00:11:19.520 Henry Zhao: I think Shopify was probably only implemented in 2022.
118 00:11:20.580 ⇒ 00:11:21.750 Amber Lin: Oh…
119 00:11:21.970 ⇒ 00:11:22.510 Henry Zhao: Yeah.
120 00:11:23.850 ⇒ 00:11:27.620 Amber Lin: So, 2022, the data starts…
121 00:11:27.620 ⇒ 00:11:28.870 Henry Zhao: For sure it does, because look at the weeks…
122 00:11:28.870 ⇒ 00:11:29.440 Amber Lin: when it’s.
123 00:11:29.440 ⇒ 00:11:30.329 Henry Zhao: That’s another thing.
124 00:11:30.330 ⇒ 00:11:35.739 Amber Lin: Okay, let me… can… can we note that down? 2022…
125 00:11:35.910 ⇒ 00:11:38.659 Henry Zhao: Yeah, so don’t even look at… don’t even look at this number, just look at these two.
126 00:11:39.090 ⇒ 00:11:52.019 Amber Lin: Only starts in… 2022… December… So, over… okay, that makes more sense. So, there…
127 00:11:52.310 ⇒ 00:11:59.910 Amber Lin: Number of orders are the same, their sales are the same, their purchasers are the same.
128 00:11:59.910 ⇒ 00:12:04.150 Henry Zhao: Yeah. Everything’s the same, and it’s very, like, wildly up and down.
129 00:12:04.530 ⇒ 00:12:06.080 Amber Lin: That’s so silly. Okay.
130 00:12:06.080 ⇒ 00:12:09.150 Henry Zhao: That’s what we’ve learned and what it’s confirmed over and over again.
131 00:12:09.150 ⇒ 00:12:09.660 Amber Lin: Okay.
132 00:12:10.590 ⇒ 00:12:12.410 Amber Lin: That’s good.
133 00:12:16.130 ⇒ 00:12:16.950 Amber Lin: Cool.
134 00:12:18.040 ⇒ 00:12:21.760 Amber Lin: Alright.
135 00:12:24.350 ⇒ 00:12:32.860 Amber Lin: No customers… For the new customers each week graph, where’s the data?
136 00:12:36.140 ⇒ 00:12:37.460 Henry Zhao: Or the… which one?
137 00:12:37.870 ⇒ 00:12:41.360 Amber Lin: I’ll… it’s okay, I’ll go find it. Can we look at slide…
138 00:12:42.830 ⇒ 00:12:48.300 Amber Lin: Oh, let’s update slide 8 with our new graph. I think that’s a nice graph that we can add in.
139 00:12:54.920 ⇒ 00:12:56.070 Henry Zhao: Where’s your food being.
140 00:12:58.010 ⇒ 00:13:04.550 Amber Lin: And I’ll say conversion events counted… R…
141 00:13:10.400 ⇒ 00:13:11.769 Amber Lin: Do you have a.
142 00:13:12.450 ⇒ 00:13:13.990 Henry Zhao: No, I…
143 00:13:14.210 ⇒ 00:13:18.960 Amber Lin: I heard a meow, I think I’m hallucinating. Maybe…
144 00:13:18.960 ⇒ 00:13:19.420 Henry Zhao: Clark.
145 00:13:19.420 ⇒ 00:13:26.660 Amber Lin: Purchases… Visited… I’m on the maid.
146 00:13:41.110 ⇒ 00:13:42.470 Henry Zhao: Did we put that chart in?
147 00:13:42.860 ⇒ 00:13:43.450 Amber Lin: Yeah.
148 00:13:44.140 ⇒ 00:13:44.730 Henry Zhao: Okay.
149 00:13:54.070 ⇒ 00:13:57.879 Amber Lin: What’s… I’m gonna add the other events as well.
150 00:13:58.940 ⇒ 00:14:03.980 Amber Lin: So… We added…
151 00:14:12.290 ⇒ 00:14:13.620 Amber Lin: Recharge.
152 00:14:16.570 ⇒ 00:14:19.509 Amber Lin: Is… did you put 4 events, or 5?
153 00:14:20.560 ⇒ 00:14:21.230 Henry Zhao: Or…
154 00:14:21.420 ⇒ 00:14:22.000 Amber Lin: Okay.
155 00:14:22.370 ⇒ 00:14:27.999 Amber Lin: 1, 2, 3. Placed order, order product, order placed on recharge. What else?
156 00:14:28.860 ⇒ 00:14:30.220 Henry Zhao: What do you want me to title the chart?
157 00:14:30.730 ⇒ 00:14:42.640 Amber Lin: Graphic… Customers and conversion rate over time.
158 00:14:43.380 ⇒ 00:14:44.280 Henry Zhao: static.
159 00:14:45.130 ⇒ 00:14:50.859 Henry Zhao: Static purchaser… Count and conversion rate over time.
160 00:14:51.470 ⇒ 00:14:56.020 Amber Lin: What’s static purchaser count? Oh, I meant traffic. Sorry.
161 00:14:57.190 ⇒ 00:14:59.900 Amber Lin: Traffic, purchase traffic…
162 00:15:03.220 ⇒ 00:15:06.150 Amber Lin: Should we use customers instead of purchasers?
163 00:15:11.410 ⇒ 00:15:12.340 Henry Zhao: Like that.
164 00:15:12.340 ⇒ 00:15:15.359 Amber Lin: Or should we say, like, yeah, customers that month?
165 00:15:15.510 ⇒ 00:15:20.179 Amber Lin: Do we use monthly, or was this weekly? This is monthly, right?
166 00:15:21.790 ⇒ 00:15:22.470 Amber Lin: Go ahead.
167 00:15:24.480 ⇒ 00:15:28.870 Amber Lin: Seems like we have 2021… Data.
168 00:15:30.450 ⇒ 00:15:31.550 Amber Lin: Crazy.
169 00:15:31.890 ⇒ 00:15:33.620 Henry Zhao: Yeah, we do. For Klaviyo, at least.
170 00:15:34.040 ⇒ 00:15:35.299 Amber Lin: I guess so.
171 00:15:35.400 ⇒ 00:15:44.329 Amber Lin: We don’t have the year-over-year… Okay, sounds good. Will we be able to add a trend line?
172 00:15:49.250 ⇒ 00:15:49.920 Henry Zhao: Sure.
173 00:16:00.490 ⇒ 00:16:01.370 Henry Zhao: Pretty good.
174 00:16:04.720 ⇒ 00:16:05.890 Amber Lin: Okay.
175 00:16:07.670 ⇒ 00:16:16.199 Amber Lin: What’s the slope on… Traffic… And is, like…
176 00:16:16.460 ⇒ 00:16:17.939 Henry Zhao: I have to change all these titles.
177 00:16:18.480 ⇒ 00:16:19.300 Amber Lin: Okay.
178 00:16:19.610 ⇒ 00:16:21.000 Henry Zhao: Don’t really make sense anymore, yeah.
179 00:16:21.200 ⇒ 00:16:21.770 Amber Lin: Yeah.
180 00:16:23.030 ⇒ 00:16:25.879 Amber Lin: Do we still need to smooth it? Like, do we’re moving…
181 00:16:25.880 ⇒ 00:16:26.650 Henry Zhao: Where is he?
182 00:16:27.710 ⇒ 00:16:28.050 Amber Lin: Oh.
183 00:16:28.050 ⇒ 00:16:29.300 Henry Zhao: No, the average didn’t help.
184 00:16:30.190 ⇒ 00:16:33.319 Henry Zhao: Because it’s so bouncy that, like, it’s still bouncy.
185 00:16:33.610 ⇒ 00:16:34.340 Amber Lin: Okay.
186 00:16:34.870 ⇒ 00:16:37.550 Amber Lin: Let’s see…
187 00:16:42.380 ⇒ 00:16:47.639 Amber Lin: Dropping… Traffic dropping year over year.
188 00:16:56.290 ⇒ 00:17:02.899 Henry Zhao: Not really dropping, I just feel like in the beginning, they did these big blasts, and then they just kind of remained stable afterwards, you know what I mean?
189 00:17:03.530 ⇒ 00:17:04.260 Amber Lin: Hmm.
190 00:17:04.460 ⇒ 00:17:05.880 Henry Zhao: That’s kinda what it looks like to me.
191 00:17:07.140 ⇒ 00:17:08.289 Amber Lin: Are they stable?
192 00:17:08.750 ⇒ 00:17:10.790 Amber Lin: Let me see… It looks…
193 00:17:10.790 ⇒ 00:17:11.480 Henry Zhao: disabled now.
194 00:17:12.160 ⇒ 00:17:13.369 Amber Lin: Okay, traffic…
195 00:17:21.240 ⇒ 00:17:25.010 Amber Lin: And… how do I say it? Draft from…
196 00:17:32.070 ⇒ 00:17:33.140 Amber Lin: Okay.
197 00:17:34.680 ⇒ 00:17:39.599 Amber Lin: Okay, I’ll polish it later. Conversion is the same.
198 00:17:41.360 ⇒ 00:17:48.589 Amber Lin: Is this the conversion we… We concluded, so their conversion is about 50% stable.
199 00:17:48.590 ⇒ 00:17:52.850 Henry Zhao: Yeah, I just don’t know if I have all the events in Klaviyo that relate to purchase.
200 00:17:52.850 ⇒ 00:17:53.550 Amber Lin: Okay.
201 00:17:53.560 ⇒ 00:17:55.470 Henry Zhao: I have…
202 00:17:55.700 ⇒ 00:17:59.399 Amber Lin: 3 events here. What was the… what was the…
203 00:17:59.770 ⇒ 00:18:03.210 Henry Zhao: The ones that are bolded in here, so you can just look for the ones that are bolded. I bolded the ones that I…
204 00:18:03.210 ⇒ 00:18:03.790 Amber Lin: Correct.
205 00:18:04.360 ⇒ 00:18:05.100 Henry Zhao: Cool.
206 00:18:05.600 ⇒ 00:18:07.550 Henry Zhao: You can put that, if you want, you can put that in the…
207 00:18:08.050 ⇒ 00:18:15.729 Amber Lin: Yeah, I’ll have to put it in there, because then when they look at it, they’ll be like, oh, it’s also this other thing that we wanted to use.
208 00:18:15.730 ⇒ 00:18:16.759 Henry Zhao: Yeah, exactly.
209 00:18:17.060 ⇒ 00:18:17.690 Amber Lin: Mmm…
210 00:18:17.690 ⇒ 00:18:19.310 Henry Zhao: Should be right, though. Nothing else seems like it’s…
211 00:18:19.310 ⇒ 00:18:22.010 Amber Lin: Subscription charge started.
212 00:18:22.590 ⇒ 00:18:24.210 Amber Lin: on recharge.
213 00:18:25.100 ⇒ 00:18:25.930 Amber Lin: Boom.
214 00:18:27.310 ⇒ 00:18:28.240 Amber Lin: Okay.
215 00:18:30.010 ⇒ 00:18:36.380 Amber Lin: Subscription stable… conversion rate… Let’s watch.
216 00:18:36.850 ⇒ 00:18:39.579 Amber Lin: Perfect, stable, everything’s kind of flat.
217 00:18:39.950 ⇒ 00:18:42.320 Amber Lin: Okay.
218 00:18:51.250 ⇒ 00:18:52.140 Amber Lin: Okay.
219 00:18:52.630 ⇒ 00:18:54.599 Henry Zhao: Okay, that’s good. Commissioner already so happy to present.
220 00:18:54.600 ⇒ 00:18:58.639 Amber Lin: Let’s do slide 9. How do we do that?
221 00:19:02.090 ⇒ 00:19:07.099 Amber Lin: Like, what was the table we used? And, like, Robert had a question.
222 00:19:07.660 ⇒ 00:19:13.340 Henry Zhao: I don’t trust this anymore if we’re gonna go with this line, because we were using this revenue from Shopify.
223 00:19:14.080 ⇒ 00:19:15.990 Henry Zhao: Well, actually, it should still be right.
224 00:19:16.240 ⇒ 00:19:19.350 Amber Lin: Should be right, I just don’t think we, like…
225 00:19:20.110 ⇒ 00:19:21.220 Henry Zhao: Yeah, she’ll be right.
226 00:19:21.220 ⇒ 00:19:25.449 Amber Lin: Yeah, I don’t think we said this is the best way to…
227 00:19:26.140 ⇒ 00:19:29.989 Amber Lin: He asked, why is this the best way to approximate ROAS?
228 00:19:30.260 ⇒ 00:19:32.700 Amber Lin: I don’t think that’s what we’re saying here.
229 00:19:35.090 ⇒ 00:19:36.979 Amber Lin: What table did you use?
230 00:19:38.110 ⇒ 00:19:39.050 Henry Zhao: So we…
231 00:19:39.050 ⇒ 00:19:40.839 Amber Lin: Traffic and borders?
232 00:19:40.840 ⇒ 00:19:43.999 Henry Zhao: The visitors, I use the Klaviyov table, the same as the before.
233 00:19:45.540 ⇒ 00:19:49.960 Henry Zhao: And… revenue, I use the Shopify orders table.
234 00:19:50.200 ⇒ 00:19:50.890 Amber Lin: Okay.
235 00:19:52.780 ⇒ 00:19:53.460 Amber Lin: Cool.
236 00:19:53.700 ⇒ 00:20:00.239 Amber Lin: Does this say anything about ROAS?
237 00:20:05.740 ⇒ 00:20:08.830 Henry Zhao: I guess it does, right? Because… Yeah, it does.
238 00:20:09.370 ⇒ 00:20:19.250 Amber Lin: Anything… okay, return on ad spend, because we don’t have ad spend. Do you… have you seen their ad spend data? Is it in Kaviyo, or is it in Shopify?
239 00:20:19.250 ⇒ 00:20:25.650 Henry Zhao: No, but it helps us infer the ad spend, right? So, like, I would be willing to get paid this much to bring in a new visitor, that’s what we were saying.
240 00:20:26.850 ⇒ 00:20:35.590 Amber Lin: Do you have time to look at Shopify or Klaviyo to find the spend data? Because that’s what… I think that’s part of the…
241 00:20:35.590 ⇒ 00:20:38.510 Henry Zhao: They wouldn’t have it, because the admin would be in the platforms.
242 00:20:38.730 ⇒ 00:20:40.990 Henry Zhao: It would be in, like, Facebook, Google…
243 00:20:42.260 ⇒ 00:20:44.540 Henry Zhao: Wherever they’re actually spending money on the campaigns.
244 00:20:44.960 ⇒ 00:20:48.960 Amber Lin: Oh, would it be in, like, customer data?
245 00:20:49.250 ⇒ 00:20:50.310 Henry Zhao: Maybe campaigns.
246 00:20:52.200 ⇒ 00:20:54.880 Amber Lin: Like, ad spend per customer?
247 00:21:06.680 ⇒ 00:21:10.270 Amber Lin: Oh, predicted… let’s predict a sprint tier.
248 00:21:17.470 ⇒ 00:21:19.109 Henry Zhao: I guess we just need our monthly spend.
249 00:21:19.400 ⇒ 00:21:22.359 Henry Zhao: We just need their monthly ad spend here, and I could calculate it.
250 00:21:22.360 ⇒ 00:21:24.600 Amber Lin: Okay.
251 00:21:24.700 ⇒ 00:21:26.649 Amber Lin: We probably have to.
252 00:21:27.390 ⇒ 00:21:29.020 Amber Lin: I asked them for that.
253 00:21:29.190 ⇒ 00:21:30.820 Henry Zhao: Do you want to ask for it, or do you want me to do it?
254 00:21:31.850 ⇒ 00:21:39.120 Amber Lin: Sure, I probably will send it with the message, but we can say if we can’t, like, if we can’t find it.
255 00:21:39.300 ⇒ 00:21:43.680 Amber Lin: Then we’ll tell Rob, really, hey, we can’t find the admin data.
256 00:21:43.680 ⇒ 00:21:44.300 Henry Zhao: Okay.
257 00:22:15.870 ⇒ 00:22:19.139 Henry Zhao: Okay, are we good on these slides, or is there more that we need to do, you think?
258 00:22:19.140 ⇒ 00:22:22.850 Amber Lin: Let me do a last check.
259 00:22:23.840 ⇒ 00:22:27.840 Amber Lin: I think this is good. I’ll do the… I’ll do the rest.
260 00:22:31.220 ⇒ 00:22:34.349 Amber Lin: Yeah, it is good. I’ll polish it up and send it to
261 00:22:35.910 ⇒ 00:22:49.539 Amber Lin: Okay. Yeah. What about the other side note? So here he said some stuff, too. He said the follow-up analysis is good. Yeah, I just talked to him, so I have some stuff to do, but mostly, like, probably not gonna be tomorrow to send.
262 00:22:49.680 ⇒ 00:22:50.370 Amber Lin: Yeah.
263 00:22:50.370 ⇒ 00:22:52.400 Henry Zhao: Okay, so nothing needed from me for now.
264 00:22:52.660 ⇒ 00:22:58.189 Amber Lin: No. I’ll let you know if there’s something that comes up.
265 00:22:58.550 ⇒ 00:22:59.420 Henry Zhao: Okay.
266 00:22:59.420 ⇒ 00:22:59.960 Amber Lin: Yeah.
267 00:23:00.190 ⇒ 00:23:00.730 Amber Lin: Okay.
268 00:23:00.730 ⇒ 00:23:03.730 Henry Zhao: All right. Thanks, Amber. Thanks. Yeah. Take care.
269 00:23:03.730 ⇒ 00:23:04.410 Amber Lin: Bye!
270 00:23:04.690 ⇒ 00:23:05.290 Henry Zhao: Right.