Meeting Title: Honey Stinger Amazon Followup Date: 2025-12-01 Meeting participants: Amber Lin, Robert Tseng
WEBVTT
1 00:01:21.840 ⇒ 00:01:23.030 Amber Lin: Hi, Robert.
2 00:01:27.080 ⇒ 00:01:28.150 Robert Tseng: Hi!
3 00:01:28.450 ⇒ 00:01:29.150 Robert Tseng: A…
4 00:01:30.210 ⇒ 00:01:36.119 Amber Lin: I was just working with Henry on this slide, so we’re improving some of the graphs, and then…
5 00:01:36.240 ⇒ 00:01:50.259 Amber Lin: adding explanations. I had a bit of time, so I did the repeat, so essentially what we did for Insomnia, on the Shopify data, so I have the conversion rates, the repeat rate.
6 00:01:50.390 ⇒ 00:02:04.440 Amber Lin: And the time between orders. So I think that would be… that’s a better… better, more backup claim to make. Their repeat rate is at 32%, which I think… I… I don’t know how to…
7 00:02:04.870 ⇒ 00:02:09.830 Amber Lin: like, compare that against the industry benchmark side, because I don’t know what the benchmarks.
8 00:02:09.830 ⇒ 00:02:10.799 Robert Tseng: Sharing your screen.
9 00:02:11.050 ⇒ 00:02:12.950 Amber Lin: No, sorry, I wasn’t. Okay.
10 00:02:13.080 ⇒ 00:02:15.220 Amber Lin: Here.
11 00:02:15.490 ⇒ 00:02:21.970 Amber Lin: So… I know we want to talk about Amazon stuff. This is just what I found.
12 00:02:21.970 ⇒ 00:02:22.470 Robert Tseng: Yeah, yeah.
13 00:02:22.470 ⇒ 00:02:28.750 Amber Lin: as an addition, which I think is insightful. The repeat rates at 32%.
14 00:02:29.000 ⇒ 00:02:30.260 Robert Tseng: Yeah.
15 00:02:30.330 ⇒ 00:02:38.080 Amber Lin: their conversions… Ashley, this is not 48, sorry, this is not… the right number.
16 00:02:41.260 ⇒ 00:02:51.050 Amber Lin: Yeah, so the first to second is 22, and then it’s 40, and then 53, 57. So the first to second is lower.
17 00:02:57.080 ⇒ 00:02:59.759 Amber Lin: And then the time between orders…
18 00:03:00.310 ⇒ 00:03:10.530 Amber Lin: The median average is very different, but the median, they probably usually take about 3 months to make the second order, and then eventually it goes down to about 2 months.
19 00:03:11.440 ⇒ 00:03:17.630 Amber Lin: And then, if we’re looking at the average, the average first to second is half a year.
20 00:03:18.090 ⇒ 00:03:23.029 Amber Lin: So, that could… I haven’t made sense of any of them, but that’s an insight.
21 00:03:23.600 ⇒ 00:03:34.750 Amber Lin: Anyways, I can… you want me to… I think we can add this to this week’s, maybe not if we’re gonna send the top part, like, today or tomorrow, but we can do the rest of this end of week.
22 00:03:35.320 ⇒ 00:03:35.930 Robert Tseng: Okay.
23 00:03:36.340 ⇒ 00:03:43.619 Amber Lin: Cool, okay. You said you wanted to talk about the Amazon part here.
24 00:03:43.620 ⇒ 00:03:53.219 Robert Tseng: We can send it tomorrow, by the way. I think after you finish your revision, I’m not… I might review it one more time. I mean, I don’t think I’ll review it tonight. I’ll probably review it tomorrow morning.
25 00:03:53.220 ⇒ 00:03:54.240 Amber Lin: Okay.
26 00:03:54.240 ⇒ 00:03:54.920 Robert Tseng: Yeah.
27 00:03:54.920 ⇒ 00:03:58.949 Amber Lin: I did this for Insomnia, so I think I know what to… what to say.
28 00:03:59.180 ⇒ 00:03:59.840 Robert Tseng: Yeah.
29 00:03:59.840 ⇒ 00:04:03.839 Amber Lin: Yeah, let’s see… Amazon POs.
30 00:04:04.360 ⇒ 00:04:08.099 Amber Lin: Okay, so this is the…
31 00:04:08.350 ⇒ 00:04:12.559 Amber Lin: This is the category insight, and then the concentration.
32 00:04:14.460 ⇒ 00:04:16.970 Amber Lin: Yeah, I like the edit you did here.
33 00:04:17.430 ⇒ 00:04:22.180 Amber Lin: I think… okay, I think this… where do you want to start with…
34 00:04:22.180 ⇒ 00:04:24.450 Robert Tseng: Yeah, let’s start here. So…
35 00:04:24.650 ⇒ 00:04:29.770 Robert Tseng: Yeah, I get your point here. I think my question is basically.
36 00:04:30.000 ⇒ 00:04:38.039 Robert Tseng: Well, if you’re gonna exclude the long tail, like, you basically will not be able to detect emerging products, right?
37 00:04:38.040 ⇒ 00:04:39.660 Amber Lin: That’s true.
38 00:04:40.700 ⇒ 00:04:49.350 Robert Tseng: So… I don’t know, there… this is, like, kind of total sales concentration, but maybe there’s, like, a…
39 00:04:50.330 ⇒ 00:04:55.079 Robert Tseng: There’s gotta be, like, a time component to this as well.
40 00:04:55.080 ⇒ 00:04:55.780 Amber Lin: Yeah.
41 00:04:55.780 ⇒ 00:04:58.560 Robert Tseng: Really sure how you would view it,
42 00:05:05.830 ⇒ 00:05:11.630 Amber Lin: Maybe it’s, like, by day, or by week, or something, or… Huh.
43 00:05:13.190 ⇒ 00:05:16.740 Amber Lin: Like, the concentration by… say, by week?
44 00:05:16.740 ⇒ 00:05:21.739 Robert Tseng: Day is probably not reasonable. It’s probably by week, yeah, so…
45 00:05:22.660 ⇒ 00:05:27.590 Robert Tseng: I kind of view it as, like, a stacked bar chart up to…
46 00:05:27.590 ⇒ 00:05:30.289 Amber Lin: The one wanted for insomnia, right?
47 00:05:30.290 ⇒ 00:05:31.180 Robert Tseng: Yeah.
48 00:05:31.180 ⇒ 00:05:32.670 Amber Lin: Stacked cookies.
49 00:05:32.720 ⇒ 00:05:33.799 Robert Tseng: Yeah. Okay.
50 00:05:34.030 ⇒ 00:05:35.349 Amber Lin: That makes sense.
51 00:05:36.880 ⇒ 00:05:39.939 Robert Tseng: And then you can basically hide anything that’s not within the top 5.
52 00:05:39.940 ⇒ 00:05:40.420 Amber Lin: Yeah.
53 00:05:40.420 ⇒ 00:05:41.830 Robert Tseng: It doesn’t get, like, too crazy.
54 00:05:42.210 ⇒ 00:05:47.959 Amber Lin: The first few is, like, waffles, gel, waffle, chew. So…
55 00:05:48.430 ⇒ 00:05:54.819 Robert Tseng: Yeah, but, like, gels, I know, is their second selling… highest selling product. That was a surprise to them this year. They were not expecting gels to sell.
56 00:05:54.820 ⇒ 00:05:56.700 Amber Lin: Life.
57 00:05:56.700 ⇒ 00:06:07.710 Robert Tseng: So, I mean, I would be curious to see, like, okay, well, was there a point at which, like, they could see where gels, like, started to spike?
58 00:06:10.800 ⇒ 00:06:11.630 Robert Tseng: Yeah.
59 00:06:12.060 ⇒ 00:06:12.770 Amber Lin: Yeah.
60 00:06:16.690 ⇒ 00:06:18.450 Robert Tseng: It’s like, they need to be able to…
61 00:06:18.600 ⇒ 00:06:24.879 Robert Tseng: add new ASIN, but if it’s not, like, hitting the… the same…
62 00:06:25.700 ⇒ 00:06:35.020 Robert Tseng: like, milestones as, like, what the gel… as gels… it won’t… well, maybe gels isn’t the best benchmark, because that’s kind of an anomaly. But,
63 00:06:36.300 ⇒ 00:06:46.189 Robert Tseng: Yeah, let’s say, like, gels grew, like, 10% over the first 3… over the first month. So, like, it achieved, like, enough
64 00:06:47.440 ⇒ 00:06:48.720 Robert Tseng: penetration.
65 00:06:48.900 ⇒ 00:06:50.380 Amber Lin: That it was…
66 00:06:50.410 ⇒ 00:06:52.789 Robert Tseng: Able to continue to quickly spread.
67 00:06:52.900 ⇒ 00:07:01.319 Robert Tseng: Or, like, grow. If the other products don’t hit that 10%, then maybe they just never… they just never, blow up.
68 00:07:02.140 ⇒ 00:07:09.510 Amber Lin: Yeah, we… we can totally look at it, let me say… Bye…
69 00:07:09.800 ⇒ 00:07:13.709 Amber Lin: Month. I’ll do by month, so it won’t fit in the graph.
70 00:07:13.850 ⇒ 00:07:16.709 Amber Lin: Concentration by month.
71 00:07:17.960 ⇒ 00:07:22.120 Amber Lin: Another possibility is that it just randomly
72 00:07:22.330 ⇒ 00:07:35.469 Amber Lin: because of a certain, like, marketing event, or someone did a promo on TikTok, and then it started to blow up. And then, you’re right in that if we don’t list it on Amazon, then they would never have a chance to.
73 00:07:35.820 ⇒ 00:07:38.915 Amber Lin: Pop off… Boom.
74 00:07:39.680 ⇒ 00:07:40.420 Amber Lin: Excuse me.
75 00:07:43.230 ⇒ 00:07:47.449 Amber Lin: Do you think that the thing that we’re debating here is should we
76 00:07:47.610 ⇒ 00:07:51.769 Amber Lin: take off the long-tail products? Is that the…
77 00:07:51.990 ⇒ 00:07:55.669 Amber Lin: Action that we’re deciding here, or is it…
78 00:07:55.830 ⇒ 00:08:00.779 Amber Lin: Like, what… what are we working towards for Amazon’s channel strategy?
79 00:08:01.220 ⇒ 00:08:05.790 Robert Tseng: Yeah, I mean, I think your recommendation is to…
80 00:08:06.160 ⇒ 00:08:11.109 Robert Tseng: get rid of… I guess, to me, it’s less about telling them what
81 00:08:11.410 ⇒ 00:08:17.859 Robert Tseng: ASINs to take off, but, like, which ones are… have the highest potential, and, like, can we detect their growth?
82 00:08:17.860 ⇒ 00:08:18.520 Amber Lin: Mmm.
83 00:08:20.900 ⇒ 00:08:28.080 Robert Tseng: So… Yeah, like, maybe it is noisy for them because they’re having to manage so many ASINs, but…
84 00:08:28.260 ⇒ 00:08:42.100 Robert Tseng: if the volumes are low, and the POs are pretty low, to them, it’s not that big of a deal, because they already have, like, inventory in their own warehouse, and they’re just, like, putting it all on a pallet. So, I think, like.
85 00:08:43.080 ⇒ 00:08:48.860 Robert Tseng: that don’t have a problem testing new ASINs. They’re obviously hoping that it’ll become like a gel.
86 00:08:51.150 ⇒ 00:08:55.169 Amber Lin: Oh, I see. Yeah, that’s a… that’s a better angle to look at it.
87 00:08:55.490 ⇒ 00:09:02.739 Amber Lin: Do they differentiate between Amazon and Shopify’s strategy?
88 00:09:04.080 ⇒ 00:09:07.789 Robert Tseng: Yeah, well, so I, I don’t, I don’t think they were able to…
89 00:09:09.600 ⇒ 00:09:16.919 Robert Tseng: I’m sure the two platforms are different, which is why I was asking for the Shopify versus Amazon.
90 00:09:17.260 ⇒ 00:09:25.500 Robert Tseng: I guess… for Shopify, they have to determine their own strategy. For Amazon, the strategy is really just, like.
91 00:09:26.730 ⇒ 00:09:33.510 Robert Tseng: especially since it’s all Amazon-fulfilled, they’re letting Amazon figure out what the strategy is, so…
92 00:09:33.510 ⇒ 00:09:34.250 Amber Lin: Yes.
93 00:09:34.550 ⇒ 00:09:46.589 Robert Tseng: Yeah, like, ideally, they would learn something from the Amazon strategy, and they’d be able to go and implement it themselves on Shopify. That’s kind of the… that’s what we’re hoping to get out of it.
94 00:09:49.100 ⇒ 00:09:55.230 Robert Tseng: But it’s also different, because it’s their product in a marketplace versus their direct site.
95 00:09:55.230 ⇒ 00:09:55.680 Amber Lin: Yeah. You’re directed.
96 00:09:55.680 ⇒ 00:10:02.190 Robert Tseng: site traffic, they can influence what traffic gets there, be it based on how they do the targeted messaging and the paid ads.
97 00:10:02.580 ⇒ 00:10:14.770 Robert Tseng: Whereas, like, on Amazon, it’s really just off of keywords. Some… they do some sponsored listings, and… I mean, I don’t exactly know what else they do. I still haven’t read Acosta’s, like.
98 00:10:15.160 ⇒ 00:10:17.240 Robert Tseng: Doc, but… Yeah.
99 00:10:18.560 ⇒ 00:10:25.869 Amber Lin: Okay, yeah, I think for now, I’ll do the… I’ll look at the concentration by month, and then…
100 00:10:26.220 ⇒ 00:10:32.359 Amber Lin: Probably, I’ll pull out, like, the top ones and see when they started popping off.
101 00:10:32.660 ⇒ 00:10:38.970 Amber Lin: And try to answer, like, try to start answering the question of how do we predict
102 00:10:39.390 ⇒ 00:10:41.950 Amber Lin: popular ASINs.
103 00:10:42.500 ⇒ 00:10:43.270 Robert Tseng: Yeah, I mean, I looked at…
104 00:10:43.270 ⇒ 00:10:43.790 Amber Lin: Apparently.
105 00:10:43.790 ⇒ 00:10:53.339 Robert Tseng: We looked at search terms and trends, at keyword trends, so we see that that’s, you know, search traffic is a leading indicator by, of, of,
106 00:10:53.440 ⇒ 00:10:55.390 Robert Tseng: Of sales, to some extent.
107 00:10:56.090 ⇒ 00:11:08.169 Amber Lin: I’d have to choose… it’s not… I feel like… okay, I don’t have long enough search terms data. I think our search terms is pretty short. We only have.
108 00:11:08.170 ⇒ 00:11:08.770 Robert Tseng: Oh.
109 00:11:08.770 ⇒ 00:11:09.420 Amber Lin: beta.
110 00:11:09.850 ⇒ 00:11:12.289 Amber Lin: The search term started from September.
111 00:11:14.010 ⇒ 00:11:15.050 Robert Tseng: I see.
112 00:11:21.330 ⇒ 00:11:22.020 Robert Tseng: Shoot.
113 00:11:22.900 ⇒ 00:11:23.890 Robert Tseng: Mr. Pizza.
114 00:11:25.030 ⇒ 00:11:26.320 Robert Tseng: statutes.
115 00:11:33.250 ⇒ 00:11:35.019 Robert Tseng: So, what’s next to the foundation.
116 00:11:38.800 ⇒ 00:11:42.110 Amber Lin: I think those are the two things I can start looking at.
117 00:11:42.110 ⇒ 00:11:42.730 Robert Tseng: Search.
118 00:11:42.920 ⇒ 00:11:43.460 Amber Lin: Okay.
119 00:11:44.520 ⇒ 00:11:53.759 Amber Lin: Yeah, and then we have something here. Yes, these are the fulfillment centers that they’re shipped to, so based on the.
120 00:11:54.450 ⇒ 00:11:55.120 Robert Tseng: Okay.
121 00:11:55.800 ⇒ 00:11:58.390 Amber Lin: Ship to order code.
122 00:11:59.130 ⇒ 00:12:00.979 Amber Lin: For the procurement orders.
123 00:12:01.740 ⇒ 00:12:07.779 Robert Tseng: Yeah, so if we don’t know anything else about these customers other than That they were…
124 00:12:07.990 ⇒ 00:12:16.500 Robert Tseng: I mean, this is not even customer data, this is, like, FC data. So, you’re saying that most of… 18% of POs get shipped to Indiana FC.
125 00:12:19.350 ⇒ 00:12:30.130 Robert Tseng: Well, if they get sent to FC, then, like, you know, it’s probably that they’re being sold around that area. I mean, for Indiana, it’s probably being sold into Chicago, or, like, kind of other places in the Midwest.
126 00:12:30.330 ⇒ 00:12:42.690 Robert Tseng: So yeah, to me, this is like, okay, well, look at Shopify Midwest, kind of, customers, try to understand their LTV CAC, their gross profit CAC, you know, just use some of their profitability metrics.
127 00:12:42.690 ⇒ 00:12:47.949 Amber Lin: See how that compares to West Coast, which is maybe, like, California, Arizona, Nevada.
128 00:12:47.950 ⇒ 00:12:49.710 Robert Tseng: Versus, versus Texas.
129 00:12:49.970 ⇒ 00:12:55.910 Robert Tseng: And versus, like, their average. So, like, those are 4 cuts that you could make,
130 00:12:56.570 ⇒ 00:13:02.659 Robert Tseng: you know, maybe you’ll find that, like, their Midwest customers have the highest,
131 00:13:02.910 ⇒ 00:13:06.300 Robert Tseng: have the highest… have the highest LTB CAC.
132 00:13:08.690 ⇒ 00:13:15.029 Amber Lin: Very cool. Anything… LTV CAC needs to be above 3. Anything under 3 is probably just, like, a bad…
133 00:13:15.030 ⇒ 00:13:21.850 Robert Tseng: is not… is not very efficient. That means it takes, up to…
134 00:13:22.230 ⇒ 00:13:24.440 Amber Lin: How would I find the tack?
135 00:13:24.970 ⇒ 00:13:27.040 Amber Lin: Maybe it’s in a table somewhere.
136 00:13:28.710 ⇒ 00:13:31.189 Robert Tseng: Yeah, if they don’t have CAC…
137 00:13:32.490 ⇒ 00:13:35.579 Robert Tseng: on Amazon… they may not have CAC on Amazon, but…
138 00:13:36.650 ⇒ 00:13:38.450 Robert Tseng: They may have it on Shopify for it.
139 00:13:38.780 ⇒ 00:13:42.639 Robert Tseng: Maybe that’s a re… that’s, like, a way for us to go and get that ad spend from that.
140 00:13:43.320 ⇒ 00:13:44.520 Robert Tseng: Anyway.
141 00:13:44.520 ⇒ 00:13:45.220 Amber Lin: Okay.
142 00:13:45.510 ⇒ 00:13:46.030 Robert Tseng: Yeah.
143 00:13:46.740 ⇒ 00:13:50.050 Amber Lin: Cool. I’ll go look at that.
144 00:13:50.410 ⇒ 00:13:59.270 Amber Lin: Sounds good. That’s… that’s quite a bit. I’ll come back.
145 00:13:59.830 ⇒ 00:14:04.620 Amber Lin: with some findings, and then tomorrow morning, I think, I’ll have the…
146 00:14:04.620 ⇒ 00:14:12.659 Robert Tseng: Yeah, let’s just, like, kind of just tie up the stuff for tomorrow, like, for today, so we could send something out from last week, and then some of the stuff is more kind of continuing on for.
147 00:14:12.660 ⇒ 00:14:15.930 Amber Lin: And then after tomorrow, I’ll probably do the, like…
148 00:14:16.260 ⇒ 00:14:24.239 Amber Lin: Shopify comparison, the customer CAC for the software stays. I’ll save that for after tomorrow when we send it.
149 00:14:25.300 ⇒ 00:14:26.800 Robert Tseng: Okay, sounds good.
150 00:14:26.800 ⇒ 00:14:28.049 Amber Lin: Okay, thank you.
151 00:14:28.050 ⇒ 00:14:29.330 Robert Tseng: Alright, thanks, Amber. Bye.
152 00:14:29.330 ⇒ 00:14:30.030 Amber Lin: Bye.