Meeting Title: Honey Stinger Discount Strategy Review Date: 2025-12-12 Meeting participants: Robert Tseng, Demi, Dan Buri, Amber Lin, Byron Pittam
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1 00:01:21.910 ⇒ 00:01:22.870 Dan Buri: Lowe.
2 00:01:25.570 ⇒ 00:01:27.000 Robert Tseng: Hey, Dan. Hey, Demi.
3 00:01:27.900 ⇒ 00:01:28.220 Dan Buri: Hey!
4 00:01:28.220 ⇒ 00:01:30.109 Demi: Hi, Dan. Hi, Robert.
5 00:01:32.160 ⇒ 00:01:33.560 Dan Buri: Happy Friday!
6 00:01:33.560 ⇒ 00:01:34.740 Robert Tseng: Yeah, happy Friday!
7 00:01:34.740 ⇒ 00:01:36.230 Dan Buri: That’s right, happy Friday.
8 00:01:38.170 ⇒ 00:01:39.950 Robert Tseng: How’s the week for both of you?
9 00:01:40.740 ⇒ 00:01:46.199 Dan Buri: It’s great, you know? Just, getting up and running, so life is good. We’re just,
10 00:01:46.530 ⇒ 00:01:50.619 Dan Buri: getting to a good point, I think, where I can, you know, do a little bit less…
11 00:01:50.730 ⇒ 00:01:56.770 Dan Buri: You know, introductory type stuff, and do a little more actual productive things, which is great.
12 00:01:56.770 ⇒ 00:01:57.370 Robert Tseng: Yeah.
13 00:01:57.540 ⇒ 00:01:58.740 Robert Tseng: That’s good to hear.
14 00:01:58.760 ⇒ 00:02:00.769 Dan Buri: Hey, where are you based out of again, Dan?
15 00:02:01.150 ⇒ 00:02:12.810 Dan Buri: Yeah, so I’m just, just outside of Steamboat Springs. Oh, okay, great. So I’ll… I kinda, I’ll be mostly, you know, from home, like, I’m from home today, but I will be occasionally in our headquarters office.
16 00:02:12.810 ⇒ 00:02:13.370 Robert Tseng: Yeah.
17 00:02:13.540 ⇒ 00:02:14.310 Robert Tseng: Nice.
18 00:02:14.680 ⇒ 00:02:15.510 Dan Buri: Yeah, yup.
19 00:02:17.190 ⇒ 00:02:17.910 Robert Tseng: Hey, Byron.
20 00:02:18.390 ⇒ 00:02:19.169 Byron Pittam: Hi, guys.
21 00:02:20.280 ⇒ 00:02:25.100 Byron Pittam: Transitioning back to home with all the backgrounds and videos and…
22 00:02:26.040 ⇒ 00:02:26.600 Dan Buri: Yeah.
23 00:02:27.780 ⇒ 00:02:29.910 Byron Pittam: Let’s see, there we are, there we are.
24 00:02:31.730 ⇒ 00:02:33.740 Byron Pittam: How are the audio, though?
25 00:02:33.740 ⇒ 00:02:35.440 Robert Tseng: Were you at… were you at the office?
26 00:02:36.330 ⇒ 00:02:40.600 Byron Pittam: Yeah, we were at the office all, I think, yeah, Tuesday, Wednesday, Thursday, which is great.
27 00:02:40.980 ⇒ 00:02:44.369 Robert Tseng: planning for Q1, or what? You just kinda… you go there often? Yeah.
28 00:02:44.710 ⇒ 00:02:47.230 Byron Pittam: No, it was just getting… meeting Dan.
29 00:02:47.400 ⇒ 00:02:48.400 Robert Tseng: Oh, okay, great.
30 00:02:48.400 ⇒ 00:02:53.949 Byron Pittam: Yes, we had some time to kind of get up to speed with each other, and have some dinner with the CEO, and yeah.
31 00:02:54.070 ⇒ 00:02:56.650 Byron Pittam: Yeah. They.
32 00:02:56.890 ⇒ 00:02:58.849 Robert Tseng: Is the CEO the founder? I kind of forget.
33 00:02:59.360 ⇒ 00:03:00.080 Byron Pittam: No.
34 00:03:00.080 ⇒ 00:03:00.660 Robert Tseng: Design, okay.
35 00:03:00.660 ⇒ 00:03:02.869 Byron Pittam: Yeah, the company started in 01.
36 00:03:02.870 ⇒ 00:03:03.370 Robert Tseng: Yeah.
37 00:03:03.800 ⇒ 00:03:12.570 Byron Pittam: with, with a couple, yeah, honey people who also started where Dan came from, Big Agnes. Did they start at the same time, Dan? Or does it…
38 00:03:12.670 ⇒ 00:03:15.830 Dan Buri: Shortly… I think Honey Stinger was shortly after Big Agnes.
39 00:03:15.830 ⇒ 00:03:16.440 Byron Pittam: some good.
40 00:03:17.440 ⇒ 00:03:18.810 Dan Buri: But in the same year.
41 00:03:20.870 ⇒ 00:03:22.019 Dan Buri: Don’t be…
42 00:03:22.020 ⇒ 00:03:30.960 Byron Pittam: Yep, Steamboat… Steamboat consolidation, and now, yeah, we’ve kind of slightly gone our own ways, you know, but still based in Steamboat.
43 00:03:30.960 ⇒ 00:03:33.630 Robert Tseng: Wait, Big Agnes, like, the sleeping bags company?
44 00:03:34.060 ⇒ 00:03:40.769 Dan Buri: Yeah, that was my previous employer, so both companies were started, by Bill Gamber. Well, Bill Gamber.
45 00:03:40.770 ⇒ 00:03:41.450 Robert Tseng: Oh, no way!
46 00:03:41.450 ⇒ 00:03:48.529 Dan Buri: Big Agnes, and then he brought on a couple other people, and together they all started, Honey Stinger. So, yeah.
47 00:03:48.530 ⇒ 00:03:51.910 Robert Tseng: I see. And eventually, in 2018, they come out.
48 00:03:51.910 ⇒ 00:03:56.699 Dan Buri: backed out of Honey Singer and sold it off, and kinda… we’re wanting to focus more on Big Agnes, so…
49 00:03:56.700 ⇒ 00:03:57.490 Robert Tseng: Oh, okay.
50 00:03:57.860 ⇒ 00:04:05.539 Robert Tseng: Very cool. Yeah. I guess, yeah, now I remember that’s where Seamboat brings… Seamboat Springs comes from, like, they have it on the… on their logo, so…
51 00:04:05.540 ⇒ 00:04:05.939 Dan Buri: And it is.
52 00:04:05.940 ⇒ 00:04:06.500 Robert Tseng: Okay.
53 00:04:06.500 ⇒ 00:04:09.329 Dan Buri: part of our branding. Yeah. Cool.
54 00:04:09.960 ⇒ 00:04:22.380 Robert Tseng: Well, yeah, I think, I think we will get started. It’s… Utom’s not gonna join today, but yeah, I guess, you guys have met Amber. I think I’ll let kind of Amber kind of share her piece first. She has some follow-up on the…
55 00:04:22.560 ⇒ 00:04:31.109 Robert Tseng: Some of the analysis questions that we had asked you last week, so it’s not really net new, it’s more of a continuation of things that we shared out last week.
56 00:04:31.110 ⇒ 00:04:43.770 Robert Tseng: And then, I guess if we have time, Byron and I sent you that proposal, so happy to kind of go and redline it with you, or kind of just chat through, like, what was actually helpful for the team, get more context there to see how we can
57 00:04:43.970 ⇒ 00:04:57.819 Robert Tseng: You know, if we want to kind of move into the tagging and tracking world, that’d be great. And then, I guess, we have some more clarity on, like, tooling decisions that we need to make coming up. So, I think that’s, those are the things that we have on our agenda.
58 00:04:58.340 ⇒ 00:05:14.000 Byron Pittam: Yeah, if there’s anything else, Robert, we’re missing from, like, our current contract, right, our current engagement, just want to make sure we’re clear there. And then Dan, yeah, you, Megan, and I can look at the UTM and kind of the tag… tag stuff as well, and see how much that is, that is worth it. I think that Looker…
59 00:05:14.000 ⇒ 00:05:18.630 Byron Pittam: Conversation that we’re trying to find a time for is… will be important to kind of figure that out.
60 00:05:18.630 ⇒ 00:05:19.610 Robert Tseng: I say thank you. Okay.
61 00:05:19.610 ⇒ 00:05:34.540 Byron Pittam: off, see what can go into Looker, and how that can be a single source of truth, as opposed to, you know, GA4 and all the other places. But, you know, I think there’s… once again, there’s always the, like, who’s gonna look at it question, as well.
62 00:05:34.990 ⇒ 00:05:41.589 Robert Tseng: Okay, great. Yeah, I do think the green group, call got rescheduled again, so it’s probably not today, right? So…
63 00:05:42.310 ⇒ 00:05:50.039 Amber Lin: Not today. They’re asking for, Monday or Tuesday. I think we can schedule that at the end of this call once we finish everything.
64 00:05:50.040 ⇒ 00:05:53.790 Robert Tseng: Yeah, well, since we have everyone here, we’ll just pick a time. Yeah, okay, that sounds good.
65 00:05:53.790 ⇒ 00:05:54.320 Amber Lin: Awesome.
66 00:05:54.420 ⇒ 00:05:55.820 Robert Tseng: Alright, I’ll get started.
67 00:05:56.180 ⇒ 00:05:56.740 Robert Tseng: Yeah.
68 00:06:01.060 ⇒ 00:06:01.610 Amber Lin: Alright.
69 00:06:02.910 ⇒ 00:06:10.210 Amber Lin: So… Last week, we talked about… Shopify sales.
70 00:06:10.390 ⇒ 00:06:15.449 Amber Lin: And then we saw that traffic was declining, conversion.
71 00:06:15.450 ⇒ 00:06:19.000 Robert Tseng: Yeah, I can do that. Okay, thanks.
72 00:06:19.000 ⇒ 00:06:37.739 Amber Lin: conversion rates were about the same, and then we had a question about discounts. So we wanted to know, how does discounts influence the sales? How much of it is discounts? How does it influence the type of customer we get in, and how does it influence our bottom line?
73 00:06:39.070 ⇒ 00:06:54.310 Amber Lin: So, let’s start off here. So, just a very high-level overview, and we can see here on the left that 65% of orders use a discount code. This is just order volume, and we can see that
74 00:06:54.800 ⇒ 00:07:05.810 Amber Lin: it’s two times more than people who don’t really use discounts, which is our baseline demand. And you can see here, this, light
75 00:07:06.100 ⇒ 00:07:21.560 Amber Lin: green-blue section, that the baseline demand is about stable, year over year, with slight fluctuations in seasonalities. And then the main spikes come from orders that do use discount codes.
76 00:07:21.700 ⇒ 00:07:27.369 Byron Pittam: Amber, just so I’m clear, it looks like the subscriptions are all without discount?
77 00:07:27.650 ⇒ 00:07:28.960 Byron Pittam: Is that correct?
78 00:07:30.030 ⇒ 00:07:31.919 Amber Lin: Subscriptions? Let’s see.
79 00:07:31.920 ⇒ 00:07:34.729 Byron Pittam: Subscription services also use…
80 00:07:35.000 ⇒ 00:07:44.550 Amber Lin: If… This code, sub-ship, It is a discount code, then it includes, then discount also includes.
81 00:07:44.710 ⇒ 00:07:47.869 Byron Pittam: Most people don’t end up using the sub-ship.
82 00:07:48.160 ⇒ 00:07:50.450 Byron Pittam: So, yeah, I’m kind of curious… okay.
83 00:07:51.800 ⇒ 00:07:53.570 Amber Lin: Gotcha, I can note that down.
84 00:07:53.660 ⇒ 00:08:06.850 Byron Pittam: But I’m wondering if we… I mean, my end goal is to understand if we should have a discount, whether it automatically applies or not, and that’d be how we handle the 15% off for the subscriptions, just to kind of make it clear that, like.
85 00:08:06.850 ⇒ 00:08:17.079 Byron Pittam: These are truly, like, full, full-priced orders. Because I would assume, you know, of those 64,000 orders, like, 40 of them are subscriptions.
86 00:08:25.420 ⇒ 00:08:26.650 Amber Lin: Gotcha. Okay.
87 00:08:26.860 ⇒ 00:08:34.030 Byron Pittam: And we have a decent amount of orders. I assume this noise is also pulled out, too, like, just the freebie orders, the comp orders that we have? Any zeroed-out orders?
88 00:08:36.309 ⇒ 00:08:38.299 Amber Lin: What do you mean, zeroed-out orders?
89 00:08:38.299 ⇒ 00:08:39.719 Byron Pittam: Free orders.
90 00:08:44.100 ⇒ 00:09:03.199 Byron Pittam: We use, I mean, we use our Shopify site, right, as many myriad tools, right? Our ambassador team, they get 40% off, expert voice, pass-through, they get 35% to 25% off, like, comp orders for pro… like, you know, for everybody from NIL athletes to our staff to everybody in between. So we kind of, we leverage it for a lot of different ways, and…
91 00:09:03.200 ⇒ 00:09:06.319 Amber Lin: It’s, you know, the more we can just look at customer.
92 00:09:06.530 ⇒ 00:09:10.470 Byron Pittam: data, as opposed to this… that other… those other pieces, the better.
93 00:09:10.470 ⇒ 00:09:11.530 Amber Lin: Yeah, sounds good.
94 00:09:11.530 ⇒ 00:09:16.609 Byron Pittam: I don’t think it’s going to be a massive amount of things, but, I mean, I think there’s, you know, probably
95 00:09:17.020 ⇒ 00:09:21.860 Byron Pittam: 30 to 40 free orders a month that go through the system, at the very least.
96 00:09:21.860 ⇒ 00:09:22.350 Amber Lin: Yeah.
97 00:09:22.350 ⇒ 00:09:24.230 Byron Pittam: We’re our… through our sports marketing team.
98 00:09:24.510 ⇒ 00:09:33.890 Robert Tseng: So yeah, so we just didn’t exclude all the… I mean, basically, certain discount codes are just kind of signals of, like, these are not, like, customer orders, so we need to exclude more of those.
99 00:09:33.890 ⇒ 00:09:39.720 Byron Pittam: May, yeah, maybe, but I honestly, I’m not sure if it’ll change the core, you know, the end data.
100 00:09:39.720 ⇒ 00:09:43.010 Robert Tseng: Yeah, but it might not change the main takeaway, but yeah, okay.
101 00:09:43.270 ⇒ 00:09:43.820 Amber Lin: Yeah.
102 00:09:44.580 ⇒ 00:09:57.179 Amber Lin: So, I think the next slide here sort of tailors to your question of, if it… it’s the amount of discounts, of how much, are we discounting off?
103 00:09:57.350 ⇒ 00:10:09.210 Amber Lin: the total sale. So here, you can see this is by customer, so this is not by orders. On the left, you see there’s about 60% of people who
104 00:10:09.240 ⇒ 00:10:24.760 Amber Lin: only purchase using discounts. Of course, it could include people who only purchased one time so far, but since this data does go back to 2021, a lot of these people only purchase using discounts.
105 00:10:24.770 ⇒ 00:10:30.039 Amber Lin: Which, on the right, you can see that these people only take up about 29%
106 00:10:30.150 ⇒ 00:10:41.770 Amber Lin: of the net revenue, which is sales minus discounts. So we’re able to see how much they contribute to our bottom line. So to… and, I think that’s…
107 00:10:42.840 ⇒ 00:10:44.940 Amber Lin: Let’s see… Yeah.
108 00:10:45.110 ⇒ 00:10:54.320 Amber Lin: And I think that’s the main takeaway here, that discounts do take up a lot of our, GMV, and
109 00:10:54.690 ⇒ 00:11:05.080 Amber Lin: we do want to look at strategically how we use discounts. Is there ways to optimize it? Is there a true lift
110 00:11:05.630 ⇒ 00:11:17.050 Amber Lin: of using discounts to acquire customers, is there a true, revenue lift, retention lift, down the line? And that’s what we’ll be looking at next.
111 00:11:17.300 ⇒ 00:11:17.880 Dan Buri: Great.
112 00:11:18.340 ⇒ 00:11:19.659 Amber Lin: So, right here…
113 00:11:20.620 ⇒ 00:11:39.389 Dan Buri: So, you mentioned, yeah, like, to that previous piece that Byron mentioned, you know, we could get into, you know, excluding certain discount codes and things like that, but does that breakdown also include, like, draft orders that are placed at a discount, or is that just full, you know, orders through the site?
114 00:11:40.230 ⇒ 00:11:49.739 Amber Lin: I used the order table, so everything in there should have already gone through, so they should have their shipping address,
115 00:11:50.010 ⇒ 00:11:50.410 Dan Buri: Sure.
116 00:11:50.410 ⇒ 00:11:52.630 Amber Lin: all the items applied.
117 00:11:52.630 ⇒ 00:11:54.410 Dan Buri: Maybe Byron, I think to…
118 00:11:54.410 ⇒ 00:12:06.239 Byron Pittam: Dan, I think the answer is yes to what you’re asking. Yeah, okay. It eventually went through the system with the discount. The one thing, Amber, and maybe we’re getting to this in a second, too, also, is, like, discount, you know, conversion rate based on percentage.
119 00:12:06.410 ⇒ 00:12:18.080 Amber Lin: Oh yeah, that’s… that’s what you’re… Based on… I haven’t looked into it based on the percentage of the discount codes, so we haven’t dove… dived in as granularly.
120 00:12:18.080 ⇒ 00:12:21.899 Byron Pittam: Yeah, I’m curious how sensitive our customer is to that level of price, right?
121 00:12:22.180 ⇒ 00:12:35.150 Byron Pittam: we tested a quick pop-up, you know, two months ago, of, like, free shipping versus, you know, 20% off, and free shipping got smacked. Like, it didn’t even, like, register to people, which I thought was really interesting, right? Like,
122 00:12:35.150 ⇒ 00:12:48.679 Byron Pittam: But, you know, Dan and I are looking at the back end and trying to figure out also, like, what our fulfillment costs actually are on a 55 order. Like, I think, based on picks and packs and all that other stuff, like, a $55 order with free shipping
123 00:12:48.680 ⇒ 00:12:55.580 Byron Pittam: costs us more than a $35 order with free shipping, because it would be one or two products in a smaller box.
124 00:12:55.820 ⇒ 00:13:03.700 Byron Pittam: frequency of, you know, that kind of level. I think FedEx has a pretty big price change when you get above 8 pounds or something like that, so…
125 00:13:03.700 ⇒ 00:13:04.030 Robert Tseng: Yeah.
126 00:13:04.030 ⇒ 00:13:11.589 Byron Pittam: like, if we get to 50, 55 bucks, like, or, you know, especially 80 bucks, like, those boxes are 12, 14 pounds.
127 00:13:11.890 ⇒ 00:13:16.560 Byron Pittam: So anyway, yeah, a lot… a couple of questions, but we’ll get through that, I’m sure.
128 00:13:16.970 ⇒ 00:13:32.829 Amber Lin: Yeah, that’s a great question, just know that down. So here, we look at the overall retention and repeat rate based on their first order. Did they use a discount on their first order with us? And…
129 00:13:33.390 ⇒ 00:13:47.599 Amber Lin: we can… we know that the repeat rate, of people who are acquired based on discount or acquired based on full cost doesn’t really differ that much. There’s only a 1.5% difference, but as…
130 00:13:47.900 ⇒ 00:13:54.469 Amber Lin: People make more orders, so the customers who end up being highly loyal
131 00:13:54.980 ⇒ 00:14:08.459 Amber Lin: the people who purchased using a discount on their first order, they have a higher conversion rate later on. So just… this just means that, this cohort of people.
132 00:14:08.680 ⇒ 00:14:12.890 Amber Lin: Might be, more loyal in the future.
133 00:14:13.120 ⇒ 00:14:13.900 Amber Lin: But…
134 00:14:14.080 ⇒ 00:14:17.820 Byron Pittam: And once again, I wonder if that’s because of the actual no-discount
135 00:14:18.060 ⇒ 00:14:25.799 Byron Pittam: quote-unquote, is that subscribe and save people, and they’re just… they’re getting to 8, 9, 10 orders, which is awesome.
136 00:14:25.930 ⇒ 00:14:34.699 Byron Pittam: And then, Dan, we could dive more deeply into, is the 15% discount the right… right amount? Because it looks like once we get them past 3, like.
137 00:14:34.860 ⇒ 00:14:42.869 Byron Pittam: like, maybe the first two orders are 25%, and then after that, it’s, like, 10. Pull the rug, because
138 00:14:43.270 ⇒ 00:14:45.969 Byron Pittam: They’re pretty consistent after, especially after 5.
139 00:14:46.140 ⇒ 00:14:58.440 Amber Lin: I also wanted to, this is something I actually wanted to dive deeper in, as a follow-up. I went in and looked at what type of codes do people use
140 00:14:58.440 ⇒ 00:15:16.569 Amber Lin: in later orders. And I think that sort of ties into what you want to look at. Do people respond to 15%? Yeah. Do they respond to 30%? And most people here, these big parts are 30% off, 40% off,
141 00:15:16.570 ⇒ 00:15:25.099 Amber Lin: like, the summer sales, autumn sales, ambassador sales, they’re big discounts. So, that’s something we can definitely dive into next time.
142 00:15:25.310 ⇒ 00:15:25.830 Dan Buri: Yeah.
143 00:15:25.990 ⇒ 00:15:32.299 Byron Pittam: A lot of those ambassadors, right, are being incentivized to purchase our product with those 40% off codes.
144 00:15:32.690 ⇒ 00:15:33.070 Byron Pittam: stop.
145 00:15:33.070 ⇒ 00:15:33.620 Dan Buri: Right.
146 00:15:33.970 ⇒ 00:15:47.030 Dan Buri: Yeah, and I think a big part of that kind of change, you know, obviously the welcome code is only usable once, right? So, you know, there’s, like, whereas the sub-ship code is not restricted that way, so I think that’s another kind of lens to look at that through.
147 00:15:48.110 ⇒ 00:15:57.510 Byron Pittam: Yeah, I do wonder, Dan, if it’s worth looking at that subship code, just to make sure, like, how Shopify’s able to auto-apply that, because people automatically get it, and we don’t have to worry about it.
148 00:15:58.550 ⇒ 00:16:01.439 Dan Buri: It can do that, so yeah, take a look at that.
149 00:16:01.440 ⇒ 00:16:03.460 Byron Pittam: They’ve gotten way better with their auto-applied.
150 00:16:03.790 ⇒ 00:16:07.910 Dan Buri: Yeah, with all the discount compatibility stuff, it’s gotten way better recently, so…
151 00:16:07.910 ⇒ 00:16:08.490 Byron Pittam: Yup.
152 00:16:10.830 ⇒ 00:16:11.480 Amber Lin: Awesome.
153 00:16:11.770 ⇒ 00:16:15.929 Amber Lin: Alright, so the next question, naturally, we’re curious about,
154 00:16:15.990 ⇒ 00:16:22.249 Amber Lin: How do the benefits… if there is a benefit, how do the benefits of discounts
155 00:16:22.250 ⇒ 00:16:39.799 Amber Lin: carry throughout people’s life cycle. So, in order to look at that, we divided people into cohorts each month, and we defined months where 75% or more of orders used a discount, and we said, okay, this is a discount month. A lot of people
156 00:16:39.800 ⇒ 00:16:58.830 Amber Lin: This is a promo month where a lot of people use the code to purchase. And we took that, and we compared two months that have lower percentage of people using discounts. And what we found is that the net revenue retention, so the people who
157 00:16:59.180 ⇒ 00:17:14.029 Amber Lin: So overall, the fluctuations of people who order, versus don’t order, and we saw that the promo months, the people who are acquired during those promos, have a pretty consistent higher
158 00:17:14.349 ⇒ 00:17:27.289 Amber Lin: net revenue retention over time, which is this area over here. It’s generally positive, which tells us that the promos does do something, for the overall
159 00:17:27.290 ⇒ 00:17:34.920 Amber Lin: And it does… they’re… they have a positive effect on revenue, on sales, and that it’s sustained over…
160 00:17:35.740 ⇒ 00:17:49.559 Amber Lin: over a long period of time. Like, this is 48 months shown here, so it’s over the 3 years that they’re with us. We do see the benefit of using promos to acquire customers over time.
161 00:17:50.390 ⇒ 00:17:51.310 Amber Lin: Of course, we…
162 00:17:51.310 ⇒ 00:17:57.249 Byron Pittam: And hopefully those spikes are… those spikes are product launches and our, and our, you know.
163 00:17:57.250 ⇒ 00:18:14.770 Byron Pittam: specific sales. Dan, one thing that we’ve done in the past is kind of, like, take this form to your dealer, your favorite place to buy Honey Stinger, right? Like, think independent, you know, specialty run or something. And just say, like, here are the products that Honey Stinger carries, like, and here’s who to contact to, like, get them set up.
164 00:18:14.820 ⇒ 00:18:30.050 Byron Pittam: Could be worth, like, an ambassador activity, or, like, our super engaged, like, 10, 12, 14, you know, order members, something along those lines, too, to kind of be like, hey, like, we see you shop on HoneySinger.com all the time, where do you not see Honey Stinger, that you would like to locally?
165 00:18:30.320 ⇒ 00:18:34.340 Dan Buri: Right, so basically using the Ambassador Program to solicit new wholesale accounts.
166 00:18:34.590 ⇒ 00:18:35.340 Byron Pittam: Exactly.
167 00:18:36.070 ⇒ 00:18:36.920 Dan Buri: Interesting, okay.
168 00:18:36.920 ⇒ 00:18:40.409 Robert Tseng: What is your wholesale, kind of, like, strategy right now?
169 00:18:43.690 ⇒ 00:18:48.470 Byron Pittam: Sorry, I have to laugh a little. Sorry, I’m done laughing. It’s not…
170 00:18:48.470 ⇒ 00:19:08.939 Byron Pittam: the e-com side of things is not well fleshed out. We’ve set up, kind of, the specialty grocery and specialty run bike, ski, etc, as, kind of, they have a portal to go through. The onboarding process is relatively arduous with regard to, kind of, needing tax forms and all sorts of other compliance issues and reseller certificates, etc.
171 00:19:09.110 ⇒ 00:19:24.010 Byron Pittam: you know, for whatever reason, our team gets really hung up on tax, which is probably good. But, I would assume that any, you know, retailer worth their salt has a reseller certificate, so we don’t need to charge them tax. They charge the end user tax. Anyway.
172 00:19:24.010 ⇒ 00:19:24.370 Robert Tseng: Yeah.
173 00:19:24.370 ⇒ 00:19:33.639 Byron Pittam: All that to be said, we… we have, I think, 1,200 or so people set up in our B2B portal. We end up doing, like, a million to a million and a half in sales on that a year.
174 00:19:33.640 ⇒ 00:19:33.970 Robert Tseng: Okay.
175 00:19:33.970 ⇒ 00:19:44.120 Byron Pittam: customers that are purchasing 8, 9, 10 times, right? Like, I keep using Big Peach Running Company as an example often, and they, you know, they purchased since we set it up 4 years ago. Like, I mean, Shopify’s
176 00:19:44.230 ⇒ 00:19:50.760 Byron Pittam: products have gotten better. We went to another platform for a second, and then quickly came back to Shopify.
177 00:19:51.190 ⇒ 00:20:00.580 Byron Pittam: But, like, Big Peach Running Company, for example, has placed, like, 60-some-odd orders in the 3-year time period, so they’re placing a substantial amount of orders, 3 to 600 bucks.
178 00:20:00.580 ⇒ 00:20:11.360 Byron Pittam: At a time, and then, you know, get it shipped in a, you know, single FedEx box, and it’s… it’s great. Nobody has to place that order for them on Sunday night, etc. It’s, you know, evening revenue, if you will, right?
179 00:20:11.360 ⇒ 00:20:21.740 Byron Pittam: But there’s, you know, a lot of customers and a lot of dealers still prefer to kind of be handheld, in that way, or buy through a distributor, like BTI, have you ever heard of that? Yeah.
180 00:20:22.010 ⇒ 00:20:25.599 Byron Pittam: So… I’d like to flesh that out more,
181 00:20:25.720 ⇒ 00:20:37.449 Byron Pittam: you know, in the future, and, you know, Dan has a little more background with that, with regard to his dealer services background at Big Agnes, but I think there’s some education that we can still do for our sales team to get that portal.
182 00:20:37.730 ⇒ 00:20:39.100 Byron Pittam: Up and running better.
183 00:20:39.200 ⇒ 00:20:54.609 Dan Buri: Yeah, I think that that’s kind of priority number one for that program, is to kind of figure out ways to grease those wheels and make everything a little bit easier and preferable to use our site versus, you know, a distributor versus, or, you know, having a direct relationship with us. So…
184 00:20:54.610 ⇒ 00:20:59.900 Dan Buri: So yeah, that I think, is problem number one, and then we can kind of examine where we want to go from there.
185 00:21:00.510 ⇒ 00:21:05.389 Robert Tseng: Okay, yeah, I mean, I just asked, because we have a client that, wholesale’s a big part of the strategy.
186 00:21:05.400 ⇒ 00:21:21.049 Robert Tseng: And, you know, they’ve, they’ve scaled up from, like, 500 to 1,300, kind of, kind of partners on, on their, on their, on their wholesale strategy. We kind of help them, like, kind of break into, like, specialty retail. So, like, I mean, I’m very familiar with the wholesale, kind of.
187 00:21:21.070 ⇒ 00:21:24.349 Robert Tseng: kind of expansion playbooks and stuff, so, you know, if that’s.
188 00:21:24.350 ⇒ 00:21:32.980 Byron Pittam: If you have any… if you have any quick advice, like, happy to… I’d love to chat through that, too. You know, I mean, I’m pulling the numbers up right now, like, I mean…
189 00:21:32.980 ⇒ 00:21:33.650 Robert Tseng: Okay.
190 00:21:33.650 ⇒ 00:21:41.409 Byron Pittam: last month, we pulled in team sales and stuff as well, so we didn’t have that in their last year at this time. Okay.
191 00:21:41.480 ⇒ 00:21:58.289 Byron Pittam: But, like, I mean, this last November, we were up 80% almost, and then kind of year-to-date, once again, with that extra platform, that’s why we were testing another platform, and that team sales was the test group. Yeah. But overall, year-to-date, you know, we’re up 80% in net sales.
192 00:21:58.290 ⇒ 00:22:00.779 Robert Tseng: Year-to-date on the wholesale portal.
193 00:22:00.780 ⇒ 00:22:14.940 Byron Pittam: That being said, right, like, I’ll scroll down to, returning customers, you know, and like, we did see a good spike, a good push in November, which is great. What’s… Yeah. Where’d it go? No,
194 00:22:16.150 ⇒ 00:22:16.890 Byron Pittam: Where is it?
195 00:22:17.790 ⇒ 00:22:26.020 Byron Pittam: Classic. They have… Yeah, they’ve moved the returning customer dashboard, but I will find that.
196 00:22:26.250 ⇒ 00:22:26.680 Robert Tseng: Okay.
197 00:22:26.680 ⇒ 00:22:34.040 Byron Pittam: But it’s just, like, I mean, it was literally, like, there was a one point two years ago, when I was looking at it, and be like, one new customer this month.
198 00:22:34.100 ⇒ 00:22:47.570 Byron Pittam: You’re like, cool, like, 90 returning, you know, like, we’re at, let’s see how many orders total this year. Like, 5,500 orders on this platform, right? And, like, right now, I would say, I would assume
199 00:22:47.570 ⇒ 00:22:55.880 Byron Pittam: 4,500 of those are repeat, repeat customers, versus, you know, like, getting new people into the mix.
200 00:22:57.040 ⇒ 00:23:09.900 Byron Pittam: Yes, but, like, there’s only so many fleet feats, right? Like, there’s not a lot of change in that overall. But, like, if we can add more people to it, then obviously a big win. I mean, the average order on it is 410. Like.
201 00:23:10.050 ⇒ 00:23:10.650 Robert Tseng: Yeah.
202 00:23:10.650 ⇒ 00:23:20.029 Byron Pittam: That’s great. So yeah, I mean, I love seeing conver… yeah, it’s showing me everything but what I want to see right now, but, you know, our conversion rate’s over 40%, like, so…
203 00:23:20.280 ⇒ 00:23:21.429 Robert Tseng: Dude, that’s great, yeah.
204 00:23:22.040 ⇒ 00:23:22.610 Robert Tseng: Yeah.
205 00:23:22.610 ⇒ 00:23:27.190 Dan Buri: And we can hop on and talk about, kind of, the wholesale strategy, maybe as a separate thing, as… Yeah.
206 00:23:27.190 ⇒ 00:23:35.420 Dan Buri: We can get deeper into it. But the last thing I want to mention is we also have been looking at Shopify Collective as a way of kind of, you know, leveraging
207 00:23:35.420 ⇒ 00:23:52.449 Dan Buri: some partners to help. We got kind of a favorable reach out from a certain partner on Shopify Collective asking to work with us, so… Yeah. Kind of working out the kinks, if that’s gonna make sense for us, but, you know, if you guys have any insight on that angle as well, that’d be great, you know, to know more about, but yeah.
208 00:23:52.930 ⇒ 00:23:57.160 Robert Tseng: Yeah, yeah, no, I, yeah, I’m familiar with the Shopify method.
209 00:23:57.430 ⇒ 00:24:03.249 Robert Tseng: Yeah, I mean, I think just, like, kind of a couple, couple, like, quick, quick things there. Oh, I mean, I think
210 00:24:03.330 ⇒ 00:24:17.809 Robert Tseng: good analysis that would support, kind of, the wholesale strategy there is just to understand, like, I mean, just top-down, bottoms up. So top-down is, like, understanding your penetration rate, like, kind of, like, for your target, like, kind of wholesale partners, like.
211 00:24:17.870 ⇒ 00:24:31.360 Robert Tseng: yeah, if it’s Fleet Feeds, maybe there’s others, like, just kind of figuring out, you know, because I think wholesale strategy kind of gets to a point, which we’re running into in this other client now, where they’ve kind of, like, it grew really quickly, and then, now, like.
212 00:24:31.360 ⇒ 00:24:41.400 Robert Tseng: yeah, they’re not… they’re not getting as many new customers through the door, and they… then it feels like they risk, like, kind of cannibalizing on… like, as they’re expanding to other channels. If you can go and get the product in Target now.
213 00:24:41.400 ⇒ 00:24:53.020 Robert Tseng: then, like, and it’s cheaper than what you get at your, you know, at your gym or whatever, then, you know, that… that starts to… there starts to be some interesting trade-offs that you’re making there. But there, you know, I think
214 00:24:53.020 ⇒ 00:25:02.450 Robert Tseng: the… so we’re able to just basically really get really deep into, like, the margins that you’re actually making on each of these channels, be able to analyze, kind of, capitalization across each of them, and
215 00:25:02.450 ⇒ 00:25:17.320 Robert Tseng: try to… try to grow this in a way that’s incremental. So, that involved expanding into another category of wholesale partners that wasn’t really, kind of, gonna be that target and Walmart audience for this particular client. So, you know, stuff like that, I think is…
216 00:25:17.320 ⇒ 00:25:22.930 Byron Pittam: I think you kind of hit on, you know, I know that wasn’t your main goal, but, like, the gym crowd, right? Like, get the…
217 00:25:22.930 ⇒ 00:25:23.250 Robert Tseng: Yeah.
218 00:25:23.250 ⇒ 00:25:42.330 Byron Pittam: get those people in here so they can order for their gym, and it’s like, this is perfect, because, like… Yeah, I would pick a waffle at my gym. We’re in Lifetime, we’re in these other places, but, like, then you get to, you know, the Planet Fitnesses and stuff like that, the Hollywood whatever, like, if they’re still around, like, there’s, you know, there’s… there’s… they don’t want to maintain a lot of product.
219 00:25:42.350 ⇒ 00:25:56.400 Byron Pittam: So they might order through a distributor, and we’re totally blinded on their assortment and where it’s going, because, you know, McLean or whoever else is fulfilling that. And one thing that we’re working on with, you know, Walmart specifically, so we can go direct, is, like, making our master cases smaller.
220 00:25:56.740 ⇒ 00:26:08.619 Byron Pittam: We’re making it more consistent so that a store doesn’t have to have 274, you know, gels out of that one store. Like, you know, that’s a two-year supply for a fleet feet. Yeah.
221 00:26:09.200 ⇒ 00:26:11.590 Byron Pittam: But, yeah, I think there’s… there’s…
222 00:26:11.780 ⇒ 00:26:28.680 Byron Pittam: I would just want more people in here. I know there are more people that we can put in here, and, you know, gems and all those other pieces are a great start, because, yeah, once again, like, you get a $300 order, maybe it takes you 2 months to get through it, but, like, it’s easy, right? Your door, and you just unpack the cardboard box and put it on the shelf.
223 00:26:29.040 ⇒ 00:26:29.580 Robert Tseng: Yeah.
224 00:26:30.330 ⇒ 00:26:35.220 Robert Tseng: Okay, cool. Sorry, sorry to sidetrack, just… No, that’s good. You brought it up, sorry.
225 00:26:35.240 ⇒ 00:26:35.790 Byron Pittam: Oh, yeah.
226 00:26:35.790 ⇒ 00:26:37.150 Robert Tseng: Yeah.
227 00:26:39.170 ⇒ 00:26:44.800 Byron Pittam: But yeah, I mean, Dan, you can also look at the discounts, and we can together on the B2B portal.
228 00:26:45.430 ⇒ 00:26:47.710 Byron Pittam: And see how those orders spike over time.
229 00:26:48.010 ⇒ 00:26:53.019 Dan Buri: Yeah. Yeah, that’s part… I haven’t really dug too deep into that side of it yet, but yeah, absolutely.
230 00:26:57.080 ⇒ 00:27:05.589 Amber Lin: Alright, so the next part here, after we looked at, throughout the life cycle that there is a benefit.
231 00:27:05.720 ⇒ 00:27:18.200 Amber Lin: of having promotions used, during customer acquisition. We wanted to look at how does it influence the lifecycle? Does it, how does discount behavior
232 00:27:18.480 ⇒ 00:27:19.970 Amber Lin: influence…
233 00:27:20.270 ⇒ 00:27:32.130 Amber Lin: Or indicate about, like, a new customer or a returning customer. And what we found here is that people who are acquired on a discount, but later had
234 00:27:32.250 ⇒ 00:27:41.719 Amber Lin: at least one full price reorder. They have the most average number of orders. They have the…
235 00:27:41.880 ⇒ 00:27:46.460 Amber Lin: highest LTV, and also the highest net LTV.
236 00:27:46.580 ⇒ 00:27:53.780 Amber Lin: Which tells us that The promos, the benefit of,
237 00:27:55.000 ⇒ 00:28:01.050 Amber Lin: I would say that the best benefit is realized when we have A customer base that…
238 00:28:01.300 ⇒ 00:28:09.889 Amber Lin: We do acquire them using discounts, but they have the willingness to buy a full-priced order.
239 00:28:10.090 ⇒ 00:28:19.019 Amber Lin: And then, I think based on that, we can go further into what are these customers, what are the demographics like?
240 00:28:19.070 ⇒ 00:28:29.429 Amber Lin: What campaigns have they received? What discounts did they receive, or did they redeem? And see if we can pull more people towards
241 00:28:29.430 ⇒ 00:28:43.160 Amber Lin: that category, or find more people that is like that, so we can maximize our, our LTV and also net LTV, because discounts do, is a cost, and we want to
242 00:28:43.170 ⇒ 00:28:47.870 Amber Lin: Look at profit, or look at margin, instead of the overall
243 00:28:48.030 ⇒ 00:28:53.679 Amber Lin: sales. So, I want to pause here for questions and discussions.
244 00:28:54.090 ⇒ 00:29:11.669 Robert Tseng: Yeah, I’ll just say that, I mean, this is basically your… these are your champions. Like, these are… these are people who love Honey Stinger, they came in, maybe, and then they just… they’ll just buy, no matter what the price is. So, if you’re not talking to these customers already, like, they’re gonna give you such great feedback. Yeah, I mean, I think this type of audience was, like.
245 00:29:11.760 ⇒ 00:29:30.000 Robert Tseng: kind of the… what was, like, a core, like, audience that helped feed, like, the product, like, kind of helped feed our product loop when we were… when I was at Ruggable, like, you know, finding… finding this type of, customer. We created, like, the whole, like, program around them, and they really informed, like, the way that we, shipped, kind of developed new products.
246 00:29:30.000 ⇒ 00:29:37.029 Robert Tseng: But anyway, so I… I mean, you must be talking to them in some way already, but, you know, that’s my comment on what this cohort represents.
247 00:29:37.250 ⇒ 00:30:00.949 Byron Pittam: I mean, it’s something to be more consistent about, right? Because I think there’s something to be said for, like, we may have talked to them to get them to convert, and then we needed to talk to them consistently again. And I would say, potentially, Dan, like, these are the people that are going to get swept up the most just in campaigns, right? They’re not going to be in our system any other way. They might have come to our site and lurked around a little bit, but for the most part, they might… they’re just waiting for a campaign to come back.
248 00:30:01.040 ⇒ 00:30:04.129 Byron Pittam: Or they have their subscription. And that’s, that’s where my kind of, like.
249 00:30:04.320 ⇒ 00:30:13.400 Byron Pittam: My thesis is here, is like, a lot of these are subscribe and save, and they just hit the repeat button, and they’re fine with it for whatever, you know, 5.7 orders.
250 00:30:13.960 ⇒ 00:30:14.730 Robert Tseng: Okay.
251 00:30:15.040 ⇒ 00:30:21.030 Robert Tseng: Yeah, so I mean, we should take out subscribe and save customers. I think that would change the story here.
252 00:30:21.030 ⇒ 00:30:25.119 Byron Pittam: Oh, maybe not, and hope, that’d be… that’d be rad if it doesn’t.
253 00:30:25.140 ⇒ 00:30:46.509 Byron Pittam: But, you know, I think that’s… that gives us at least a ledge to jump off of with regard to what we do with Subscribe and Save Next. Like, you know, Dan and I have already talked about packaging up four orders together for somebody, or, you know, things like that, where people will, you know, get somebody, like, really invested in their sports nutrition, or training regime or whatever else, you know, with the, you know, four-month plan.
254 00:30:46.870 ⇒ 00:30:47.460 Robert Tseng: Yeah.
255 00:30:48.300 ⇒ 00:31:08.070 Dan Buri: Yeah. Yeah, I think that that would be a great way to kind of, you know, add another dimension to these numbers, right? Just kind of in general, just looking at the subscribed numbers versus the non-subscribers, and just kind of seeing how that breaks down. And it might be, yeah, that that’s, you know… yeah, so it might just be that
256 00:31:08.580 ⇒ 00:31:18.119 Dan Buri: That, you know, we don’t really end up learning too much, but it could be that, you know, we gained some good insight from that. But yeah, I think, to Byron’s point about
257 00:31:18.420 ⇒ 00:31:26.049 Dan Buri: you know, how… I think we could be targeting these people more specifically, right? You know, like, it seems like they’re just kinda…
258 00:31:26.270 ⇒ 00:31:36.739 Dan Buri: I don’t know that… looking back at our Klaviyo account and kind of all of our targeted marketing, it doesn’t seem like that’s a metric that we really use, like that kind of first-time purchase, with discount.
259 00:31:36.740 ⇒ 00:31:47.079 Dan Buri: Yeah. So that’s a good thing to really kind of key into. I mean, we do target them, we just don’t target them specifically, you know, so, I think that would be a good angle, so great takeaway.
260 00:31:47.080 ⇒ 00:31:59.870 Robert Tseng: Yeah. I mean, this is kind of like, when we get to move back into the Play View side of analysis, we want to be able to give you these more, like, custom, like, complex, like, audiences, and how you should build them. You can see we’re kind of blending, like.
261 00:32:00.000 ⇒ 00:32:13.119 Robert Tseng: a few different things, like, you know, your order value, your repeat purchase rates, like, you know, what order number are you on? Like, you know, I think there’s all this dimensionality that you can start to pull into this.
262 00:32:13.370 ⇒ 00:32:30.939 Robert Tseng: That… and, you know, we don’t even have predicted LTV on here and stuff like that, where I think there are some… some other, like, metrics and data points that we could… we could build into a customer, like, data model that, would… would be able to feed into Klaviyo for more, you know, personalized targeting.
263 00:32:31.130 ⇒ 00:32:31.540 Dan Buri: Definitely.
264 00:32:31.540 ⇒ 00:32:49.949 Byron Pittam: I’m curious what either of you think about, like, you know, we have the average number of orders, right? But, like, what’s the average time to get to that number of orders, right? Is that 5.7 over 3 years, with, like, 2 sale periods at a time, right? Or is that 5.7 happen kind of quickly? I mean, that might, once again, figure itself out with the subscriptions, but…
265 00:32:49.950 ⇒ 00:32:54.140 Byron Pittam: I mean, I’m fine with it from my perspective, like, if they just…
266 00:32:54.140 ⇒ 00:33:01.720 Byron Pittam: stock up during our two major site-wide sales a year, and then kind of fill in at their local retailers, Walmart, wherever else to get their product.
267 00:33:01.790 ⇒ 00:33:02.560 Robert Tseng: Yeah.
268 00:33:03.040 ⇒ 00:33:08.080 Robert Tseng: Which we can’t… we can’t see that here, because I… we don’t really have, like, the Walmart local retailer data, but that would be good.
269 00:33:08.540 ⇒ 00:33:10.860 Robert Tseng: Like, I want to be able to make that report, yeah.
270 00:33:11.130 ⇒ 00:33:11.990 Byron Pittam: Yeah.
271 00:33:11.990 ⇒ 00:33:21.259 Dan Buri: getting an idea of cadence would be a great way to kind of, like, you know, so now we know who we’re targeting, and that would kind of supply, I think, a little bit more of that when piece, right?
272 00:33:22.000 ⇒ 00:33:39.649 Byron Pittam: And I think we can get some of that from our… you can get some of that from our AE at, at Amazon. Okay. Nonia is offering up some of that information. So there’s, like, I think we’re at, like, 36-ish days between order right now, over the last 18 months, I think is what they shared.
273 00:33:39.650 ⇒ 00:33:40.699 Robert Tseng: For Amazon, yeah.
274 00:33:40.700 ⇒ 00:33:51.489 Byron Pittam: For Amazon. Okay. So we’re kind of… we’ve been pushing our retargeting… our retargeting pools and our retargeting efforts to, like, 45, and now John, CEO, wants us to go to 60.
275 00:33:51.590 ⇒ 00:34:09.940 Byron Pittam: So we’re really letting them kind of lapse for a decent amount of time, and we’ll see how that pans out, right? If they just, like, switch… switch to a different brand, or if they, you know, are… if they stock up more, or if they had… do come back in the middle, then we can spend more money on prospecting efforts.
276 00:34:10.199 ⇒ 00:34:14.739 Robert Tseng: If the data’s telling you it’s 45, then why are you… why are you pushing it out to 60? I don’t really get that.
277 00:34:15.420 ⇒ 00:34:17.190 Byron Pittam: Because they’re still ordering.
278 00:34:18.360 ⇒ 00:34:21.989 Byron Pittam: They’re still ordering within, within, like, under 40 days.
279 00:34:22.830 ⇒ 00:34:23.750 Robert Tseng: Oh, okay.
280 00:34:24.520 ⇒ 00:34:25.320 Robert Tseng: I see.
281 00:34:25.320 ⇒ 00:34:35.339 Byron Pittam: ordering it under 40 days, we’re hoping that they just can… they… if they don’t order up to 40, that they’re… they still, in that last 20 days, we… yeah. Okay, okay. I see.
282 00:34:35.340 ⇒ 00:34:37.590 Robert Tseng: I don’t love it, but… Yeah.
283 00:34:37.600 ⇒ 00:34:46.039 Byron Pittam: I’d like to capture them more quickly after we know they, they, you know, have… will not purchase, which is the last 8 days or 9 days.
284 00:34:46.170 ⇒ 00:34:46.880 Robert Tseng: Yeah.
285 00:34:48.409 ⇒ 00:35:04.389 Amber Lin: Yeah, and we actually wondered about that as well. We looked into the timelines of when people repeat. This is still a work in progress. We didn’t have enough time to polish it, but overall, we know that, overall, people take about 3 months.
286 00:35:04.389 ⇒ 00:35:08.739 Amber Lin: But, within the different groups, especially…
287 00:35:09.229 ⇒ 00:35:18.629 Amber Lin: let’s say this group. They do come back in about 2 months. The people who are acquired on discount and then had full price reordered.
288 00:35:18.630 ⇒ 00:35:20.310 Dan Buri: This is great, yeah.
289 00:35:20.310 ⇒ 00:35:27.690 Amber Lin: this… this is the outlier that I wanted to point out of people who always use discounts. These are just…
290 00:35:27.690 ⇒ 00:35:36.490 Byron Pittam: These are the ones that are stocking up, yeah. That’s twice a year, yeah, they’re, you know, and they, they might have hit our new waffle launch. Proffle launch.
291 00:35:36.800 ⇒ 00:35:40.480 Dan Buri: I wonder how this lines up with, like, our ambassador promos.
292 00:35:40.700 ⇒ 00:35:41.250 Dan Buri: You know?
293 00:35:41.250 ⇒ 00:35:53.710 Byron Pittam: Yeah, those are… and those are quarterly. Like, we do that… we do… we give the 40% off code every… every 3 months, it’s supposed to be, but it’s kind of… we’ll push and pull on that time period based on product launches or other, you know, events that happen.
294 00:35:53.710 ⇒ 00:35:56.809 Robert Tseng: How many product launches do you have a year? I don’t know.
295 00:35:56.810 ⇒ 00:35:57.770 Byron Pittam: Call it 1.
296 00:35:57.770 ⇒ 00:35:58.730 Robert Tseng: One, okay.
297 00:35:58.730 ⇒ 00:36:05.390 Byron Pittam: We’ll have one and a half this year with two new waffles. It’s not a full, like, collection launch, it’s two new waffles of…
298 00:36:05.740 ⇒ 00:36:07.130 Byron Pittam: a flavor profile.
299 00:36:07.130 ⇒ 00:36:08.279 Robert Tseng: flavors. Okay.
300 00:36:08.280 ⇒ 00:36:23.799 Byron Pittam: Yeah, specifically flavors, and then kind of there’s an updated gel, with electrolytes, and then updated chews with caffeine. So, I mean, the caffeine profile on these chews is insane, so I don’t expect them to be, like, you know.
301 00:36:23.800 ⇒ 00:36:24.960 Robert Tseng: What is it, you know?
302 00:36:25.120 ⇒ 00:36:27.420 Byron Pittam: I think it’s 100 milligrams per pack.
303 00:36:27.420 ⇒ 00:36:29.950 Robert Tseng: Okay, so it’s like a full cup of coffee and a…
304 00:36:29.950 ⇒ 00:36:31.729 Byron Pittam: I think a cup of coffee’s, like, 40.
305 00:36:31.730 ⇒ 00:36:33.150 Dan Buri: It’s more, yeah.
306 00:36:33.760 ⇒ 00:36:34.149 Robert Tseng: Oh, really?
307 00:36:34.150 ⇒ 00:36:35.699 Amber Lin: own these days.
308 00:36:35.700 ⇒ 00:36:38.250 Dan Buri: One shot of espresso is, like, 40.
309 00:36:39.070 ⇒ 00:36:43.960 Robert Tseng: Okay, I was gonna be a double shot, because, like, 80 milligrams is what my… oh, I was kind of going off of, yeah, okay, sure.
310 00:36:43.960 ⇒ 00:36:55.669 Dan Buri: And Byron, correct me if I’m wrong, but basically the extra, you know, the high-caffeine shoes are basically the only new concept launch, that we have this year. Otherwise, it’s just new products and additions to existing lines.
311 00:36:55.670 ⇒ 00:36:58.880 Byron Pittam: Yeah, exactly, adding to the… the flavors.
312 00:36:59.170 ⇒ 00:36:59.750 Robert Tseng: Yeah.
313 00:37:00.340 ⇒ 00:37:01.100 Robert Tseng: Okay.
314 00:37:02.690 ⇒ 00:37:05.470 Robert Tseng: That was interesting. Yeah.
315 00:37:05.470 ⇒ 00:37:09.799 Amber Lin: Cool. So, to recap what we just said, I think,
316 00:37:10.400 ⇒ 00:37:14.830 Amber Lin: what we wanted to look at next is subscriptions.
317 00:37:15.090 ⇒ 00:37:25.199 Amber Lin: how that influences these numbers. We want to also look at how can we best talk to the champions? How do we communicate
318 00:37:25.200 ⇒ 00:37:35.839 Amber Lin: to them, how do we… what type of insights can they give us to dive a little bit deeper there? And then the third part is, the wholesale
319 00:37:35.970 ⇒ 00:37:38.270 Amber Lin: Strategy that we discussed.
320 00:37:38.530 ⇒ 00:37:52.410 Byron Pittam: Yeah, and I think, Robert, kind of what you dabbled in, right? Like, I’m fine with their, you know, recapping next steps here are great, but, like, throw us some, you know, information on what you’re seeing that’s working on other customers, like, similar or not, that we can implement, right? I mean…
321 00:37:52.590 ⇒ 00:38:15.529 Byron Pittam: Amazon might be 5%, you know, discount on Subscribe and Save, and we’re offering 15% just because the price… the base price is way lower on Amazon already. So, you know, I’m curious, like, what you’re… what else you’re seeing on Subscribe and Save costs, or other benefits that people really look for in that… in that Subscribe and Save vein. Like, we keep dabbling gifts or other things in your 8th order and perks and stuff like that, but…
322 00:38:15.820 ⇒ 00:38:27.470 Byron Pittam: sometimes I don’t think people give a shit. You know, they might want access to new products, and Dan and I have talked about that, like, sliding in a new waffle just to try in their subscription box.
323 00:38:27.490 ⇒ 00:38:44.649 Byron Pittam: You know, no commit. But it’s also, like, you know, how do we implement something? You know, it’s just, we haven’t had the single person at the wheel for this. Like, somebody around Black Friday, Cyber Monday, like, don’t cancel your subscription, like, use this code and get 25% off your next four.
324 00:38:44.910 ⇒ 00:38:45.640 Robert Tseng: Yeah.
325 00:38:45.640 ⇒ 00:38:59.069 Byron Pittam: So, you know, kind of give that value add in that way, versus, like, I assume, once again, like, there’s still noise in here, but, like, people who canceled their subscription got 40% off for, you know, for their next order, and then started their subscription up again, or didn’t.
326 00:38:59.070 ⇒ 00:38:59.610 Robert Tseng: Yeah.
327 00:39:00.690 ⇒ 00:39:17.750 Robert Tseng: Okay, yeah, no, we’ll be… we’ll be more tactical on the next… on the next round. We’ll just focus more on the recommendations. I think we’ve synthesized a lot now. We just have to filter out some of the noise, and then kind of speak to some of these very specific segments. Yeah, I feel like that’s probably a good… a good way to reorient for next week.
328 00:39:17.750 ⇒ 00:39:18.570 Dan Buri: Yeah.
329 00:39:18.570 ⇒ 00:39:23.080 Byron Pittam: And if you don’t have access to wholesale and want to kind of poke in there, too, I’m happy to make you… give you access.
330 00:39:23.080 ⇒ 00:39:27.119 Robert Tseng: Yeah, yeah, I can… I can look through it and just kind of tack that on, yeah.
331 00:39:27.490 ⇒ 00:39:34.349 Byron Pittam: Because I… what I was showing you real quick on that previous one, it was, yeah, it was just the month of… or the last 90 days, I think, but yeah, it’s like…
332 00:39:34.400 ⇒ 00:39:47.869 Byron Pittam: decent chunk of orders over the last, you know, the last year, with 1,100 now first-time orders of those, you know, 5,500 orders. So yeah, I mean, it was right or what I was saying, like, call it 43 and 55.
333 00:39:47.870 ⇒ 00:39:48.370 Robert Tseng: Yeah.
334 00:39:48.650 ⇒ 00:40:01.539 Byron Pittam: 43 returning. But then you look at how many… how often those people return, once again, and I’m like, it’s… it’s crazy. Like, you know, 300 bucks a pop, or now 410, like, that’s not… you know, that’s not a big order.
335 00:40:01.960 ⇒ 00:40:02.540 Robert Tseng: Yeah.
336 00:40:02.540 ⇒ 00:40:04.919 Byron Pittam: That’s, like, 12 caddies of product.
337 00:40:05.410 ⇒ 00:40:06.050 Robert Tseng: Yeah.
338 00:40:06.310 ⇒ 00:40:06.870 Dan Buri: Beautiful.
339 00:40:07.070 ⇒ 00:40:15.530 Dan Buri: And I think the only other thing that I would want to add real quick is just, if you go back to that, you know, breakdown by discount usage, slide, slide number 15, I think.
340 00:40:16.430 ⇒ 00:40:16.820 Amber Lin: This one?
341 00:40:17.060 ⇒ 00:40:36.119 Dan Buri: Yeah, I think my question then, and maybe this is just a matter of me kind of getting more, you know, used to the cadence and availability of discounts we have on our site, but looking at that top, you know, multi-purpose row there, or multi-purchase row, the always-use discount customers, my question is.
342 00:40:36.230 ⇒ 00:40:44.160 Dan Buri: to me, I see that, and I’m like, okay, that’s probably at least mostly ambassadors, right? Or, like, how does that kind of…
343 00:40:44.430 ⇒ 00:40:54.919 Dan Buri: Like, are those people that we should be targeting, you know, through our channels, or is that kind of more of that, that specific different channel that, that, you know, is speaking to those customers?
344 00:40:55.520 ⇒ 00:40:56.120 Robert Tseng: Yeah.
345 00:40:56.700 ⇒ 00:41:04.659 Robert Tseng: Okay, yeah, so Amber, we gotta just really, yeah, say who these, who these customers are, like, yeah, so I think that’s kind of, yeah, in each of these cohorts.
346 00:41:05.120 ⇒ 00:41:18.800 Byron Pittam: So, from a tactical perspective, Robert, this is, once again, what other brands do well, too, is I want to, you know, longer… or, you know, more frequently make these codes, you know, unique to each actual send.
347 00:41:18.800 ⇒ 00:41:19.150 Robert Tseng: Yep.
348 00:41:19.150 ⇒ 00:41:32.610 Byron Pittam: and consumer, so I’m curious if your other brands are doing that, like, Welcome 15 can be welcome 15. I don’t care. If you get 15% off your first order, that’s, like, anybody can share that. Throw it on a coupon site, throw it wherever, Honey, etc, like, that’s great.
349 00:41:32.610 ⇒ 00:41:43.499 Byron Pittam: But, like, when we start doing these other, you know, 25% repeat or 20% upsell from variety pack to, like, full-size purchase, like, we want to make sure those are unique and can’t be
350 00:41:43.500 ⇒ 00:41:46.569 Byron Pittam: can’t be shared to a wide degree.
351 00:41:46.860 ⇒ 00:42:00.189 Robert Tseng: Yeah, you’re right. There is, like, a… there’s a discount code that’s unique to every campaign that you run, so that you can obviously just, you can focus on… you can attribute back to that campaign
352 00:42:00.190 ⇒ 00:42:07.189 Robert Tseng: better, like, that’s… that’s… that’s the way to do it. I mean, I think one of the common… most common data things that we do in the first is just, like.
353 00:42:07.230 ⇒ 00:42:14.020 Robert Tseng: tag management and Shopify, honestly, like, just all the… the way that people are naming, like.
354 00:42:14.360 ⇒ 00:42:33.800 Robert Tseng: all these different discount codes or any other categories of customer… even the wholesale side. It’s like, before, you only had two wholesale categories, and now you have three or five, and, you know, things… people actually moved different… moved from one category to another, so there’s all these types of nuances that, like, you know, I don’t know how much of that is really plaguing you from
355 00:42:34.290 ⇒ 00:42:49.260 Robert Tseng: from pulling the data that you need when you’re doing, like, a planning exercise, but, like, you know, that’s all part of stuff that I would recommend kind of getting clear early, early on.
356 00:42:50.150 ⇒ 00:43:15.059 Byron Pittam: Yeah, Dan, we can chat more about Shopify tags specifically and how those look. We could definitely clean a bunch of them up. We have some that we migrated over as we, you know, pulled from NetSuite’s e-com option at one point, and all those tags came with it. And there was, like, a point, you know, we’re calling them ambassadors now, right? Like, that’s a pretty straightforward tag, that’s great. There are tags that are, like, Elite A, and Elite B, and Elite R,
357 00:43:15.060 ⇒ 00:43:26.770 Byron Pittam: and, like, Elite A retired, and, like, we have no use for any of these tags at all. Yeah. It just, it does create a little bit more of that noise, too.
358 00:43:26.770 ⇒ 00:43:41.489 Dan Buri: Yeah, and you know, I haven’t mentioned this to you yet, Byron, but the way that it’s set up in Klaviyo, to, you know, tag those customers, to tag ambassadors as such, it doesn’t actually sync to Shopify, so those two are probably not…
359 00:43:41.510 ⇒ 00:44:00.470 Dan Buri: you know, there’s some work to be done there, for sure, you know, just as an example. But yeah, I think in general, one of my kind of big things is to do a kind of a larger audit of the discount program, and wherever we can, you know, move to those more individual serialized discounts, you know, of course, with identifiable, you know, tags.
360 00:44:00.470 ⇒ 00:44:05.530 Dan Buri: In the discount, even, so that we can really, you know, track those on a campaign level, but yeah.
361 00:44:05.530 ⇒ 00:44:06.150 Robert Tseng: Yeah.
362 00:44:06.760 ⇒ 00:44:19.439 Robert Tseng: Okay, that… I mean, we have all that tag data coming through Shopify. I think we should double check, but if we do, we should just do a first pass at that, kind of just like a readout on what we’re seeing on tags.
363 00:44:19.470 ⇒ 00:44:32.560 Robert Tseng: I know we didn’t really have time to get to this too much, but just to kind of comment briefly, I’ve looked through the CASA data now. Yeah, I mean, we kind of have some open-ended questions and just things that we can send over in terms of
364 00:44:32.560 ⇒ 00:44:41.220 Robert Tseng: what I think you could pull out of this if you were trying to build a full-on, like, driver-based demand model. I don’t really know, Byron, in terms of, like.
365 00:44:41.370 ⇒ 00:44:58.979 Robert Tseng: you talked about, Q1 or planning previously, and that was an exercise you needed… you wanted to do. I don’t know if that’s still something that’s urgent for us to kind of talk about, and, but yeah, if not, we can kind of kick that until probably next time.
366 00:44:59.560 ⇒ 00:45:06.500 Byron Pittam: Yeah, let’s get into next time. I think, yeah, we can… we’ll… we’ll find the time with Megan, and talk with her about the, the.
367 00:45:06.500 ⇒ 00:45:06.910 Robert Tseng: Yeah.
368 00:45:06.910 ⇒ 00:45:25.530 Byron Pittam: tags, and I think if we are able to consolidate our data into, like, the Grains Looker dashboard, or whatever else that happens to be, if you have any suggestions on that, you know, I know that we talked about a lot of that middleware as well, that I don’t know if we’ll be jumping on, but once again, if Looker can kind of be a
369 00:45:25.630 ⇒ 00:45:29.849 Byron Pittam: get a decent amount of that data without there being a data warehouse or whatever else, like…
370 00:45:30.090 ⇒ 00:45:31.839 Byron Pittam: That could be good, but…
371 00:45:31.840 ⇒ 00:45:32.570 Robert Tseng: Okay.
372 00:45:32.570 ⇒ 00:45:34.789 Byron Pittam: I don’t… I don’t know. I mean, I just don’t…
373 00:45:35.110 ⇒ 00:45:42.269 Byron Pittam: with it being kind of, you know, call it 4-5% of our business, I don’t know how much… I mean, granted, it is,
374 00:45:42.290 ⇒ 00:45:52.799 Byron Pittam: great source of information, as you’ve seen, right, versus, like, the Amazons and the Walmarts. Yeah. But, you know, I just… I think there’s, once again, back to the whole, like.
375 00:45:52.800 ⇒ 00:46:07.579 Byron Pittam: redundancy, like, we just don’t have anybody, like, to look at the data a lot of times, too. And Dan and I can kind of also decide, once he gets more up to speed, also, what… how much he can dive into that stuff, too, to impart, you know, knowledge and kind of future wisdom.
376 00:46:07.900 ⇒ 00:46:09.700 Robert Tseng: Okay, yeah, that sounds good.
377 00:46:11.750 ⇒ 00:46:20.499 Amber Lin: Great. I have two things here. One is to follow up on last week of the tool budgets, so…
378 00:46:20.930 ⇒ 00:46:24.220 Robert Tseng: We just, we just covered that, so I think we’re good. Awesome, okay. Yeah, yeah.
379 00:46:24.220 ⇒ 00:46:42.479 Amber Lin: So, and then the meeting with Megan. Megan said she is free Monday, 2 to 3 p.m. Eastern Time, or Tuesday, 4 to 4.40 p.m. Eastern Time. Robert, you should be free both of those times, mostly Byron, what time works for you?
380 00:46:43.230 ⇒ 00:46:45.600 Byron Pittam: You said, you said 2 to 4 on Tuesday?
381 00:46:45.600 ⇒ 00:46:51.140 Amber Lin: Tuesday, 4 to 4.40. So, 4 to 5, essentially.
382 00:46:51.360 ⇒ 00:46:53.880 Byron Pittam: 4 to 440. That’s very specific.
383 00:46:53.880 ⇒ 00:46:55.320 Dan Buri: Eastern, you said? Yeah.
384 00:46:55.320 ⇒ 00:46:55.750 Amber Lin: Eastern time.
385 00:46:55.750 ⇒ 00:46:56.149 Robert Tseng: Yeah, yeah.
386 00:46:56.150 ⇒ 00:46:58.180 Dan Buri: I think we have the Digital Team Weekly at.
387 00:46:58.180 ⇒ 00:47:01.210 Byron Pittam: Let’s just move… let’s just move that. Yeah, we can do that.
388 00:47:01.790 ⇒ 00:47:09.460 Amber Lin: Okay, Tuesday it is. I’ll send… I’ll send the invite. Dan, you would also be there, right? I’ll include you in the invite.
389 00:47:09.600 ⇒ 00:47:10.470 Amber Lin: Awesome.
390 00:47:10.650 ⇒ 00:47:11.430 Amber Lin: Okay.
391 00:47:11.770 ⇒ 00:47:12.580 Amber Lin: Great.
392 00:47:13.110 ⇒ 00:47:14.270 Amber Lin: That’s all from me.
393 00:47:14.470 ⇒ 00:47:15.300 Robert Tseng: Alright?
394 00:47:15.590 ⇒ 00:47:16.610 Robert Tseng: Sounds good.
395 00:47:17.020 ⇒ 00:47:17.990 Robert Tseng: Thanks, guys.
396 00:47:18.320 ⇒ 00:47:19.190 Byron Pittam: Thanks, team!
397 00:47:19.190 ⇒ 00:47:21.280 Dan Buri: All right, team. Yeah, thanks for everything.
398 00:47:21.990 ⇒ 00:47:22.570 Amber Lin: Right?