Meeting Title: Shipping-Meeting-Chuck-Jakob Date: 2024-07-03 Meeting participants: Chuck Gross, Nicolas Sucari, Jakob Kagel
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1 00:00:27.270 ⇒ 00:00:28.230 Chuck Gross: Even.
2 00:01:46.010 ⇒ 00:01:46.969 Chuck Gross: How are you?
3 00:01:47.660 ⇒ 00:01:49.609 Chuck Gross: Good, happy, you good!
4 00:01:53.190 ⇒ 00:01:55.359 Chuck Gross: I think this is one picket.
5 00:01:56.930 ⇒ 00:02:06.250 Chuck Gross: That’s it, right. I know it’s a pump. Oh, the pump. Yeah. Oh, twist lock. Okay, they have these pumps to the store, plus you have a 3 prong. But they said they didn’t.
6 00:02:08.600 ⇒ 00:02:10.320 Chuck Gross: Do. You need a 3 prong one.
7 00:02:11.430 ⇒ 00:02:12.850 Chuck Gross: Which which one do you need?
8 00:02:12.950 ⇒ 00:02:19.260 Chuck Gross: I need the 3 primary. Well, I was gonna change. So I’m gonna change the electric to twist
9 00:02:22.060 ⇒ 00:02:23.650 Chuck Gross: definitely to a sloppy.
10 00:02:26.380 ⇒ 00:02:30.762 Chuck Gross: It’s better to put the football there, anyway, though.
11 00:03:19.860 ⇒ 00:03:21.019 Chuck Gross: 5 through one.
12 00:04:14.390 ⇒ 00:04:16.459 Chuck Gross: I’m a personal assistant.
13 00:06:51.270 ⇒ 00:06:52.160 Nicolas Sucari: Hey! Chuck.
14 00:06:56.980 ⇒ 00:06:58.200 Chuck Gross: Hey! How are you?
15 00:07:00.350 ⇒ 00:07:02.830 Nicolas Sucari: I’m doing fine. Nice to meet you.
16 00:07:02.830 ⇒ 00:07:04.280 Chuck Gross: Nice to meet you, too.
17 00:07:22.600 ⇒ 00:07:27.229 Nicolas Sucari: Hey? Let me introduce myself while we wait for Jacob to join
18 00:07:27.935 ⇒ 00:07:29.930 Nicolas Sucari: I’m Nicholas. I’m
19 00:07:30.140 ⇒ 00:07:35.320 Nicolas Sucari: from Brain Forge. Obviously I joined like a month ago, brain Forge.
20 00:07:35.570 ⇒ 00:07:45.559 Nicolas Sucari: And I’m taking the role as project manager. So I’m helping the team on understand requirements set issues, tasks.
21 00:07:45.620 ⇒ 00:08:00.639 Nicolas Sucari: things to work for all of the engineers, and I’m trying to communicate better with with you, with Kim, with Ben and Dan, too. So yeah, that’s what I’ve been doing for the past month. It’s great to to
22 00:08:00.910 ⇒ 00:08:18.209 Nicolas Sucari: to to know you, because we will start like working on all of these shipment stuff and having your input and hope you, you helping us on how we want how to work and how to deliver to you the correct stuff will be super helpful.
23 00:08:18.490 ⇒ 00:08:19.390 Chuck Gross: Excellent.
24 00:08:20.110 ⇒ 00:08:24.779 Chuck Gross: I appreciate it. Hi, my name! I’m Chuck. I’ve been working with
25 00:08:25.350 ⇒ 00:08:28.520 Chuck Gross: one of many of the companies since 2,006,
26 00:08:28.930 ⇒ 00:08:32.920 Chuck Gross: and I’ve been working in distribution since 2,016. So
27 00:08:34.240 ⇒ 00:08:39.720 Chuck Gross: been around. Been around the block now, so I know I can usually help you whenever you need.
28 00:08:40.460 ⇒ 00:09:10.230 Nicolas Sucari: Great. Thank you. Thank you. Yeah. I’ve been working on software development companies for a couple of years ago. And now, yeah, introducing myself to this kind of data analytics world that is kind of new to me. And yeah, learning a lot. I already knew Ben and Dan and Kim, too, because different things that we were working on. So yeah, great to meet you. And I hope we can deliver to you stuff that you can really use for your day to in a day to day basis.
29 00:09:10.410 ⇒ 00:09:11.490 Chuck Gross: Absolutely.
30 00:10:08.770 ⇒ 00:10:10.380 Jakob Kagel: Hey? Good afternoon. Everyone.
31 00:10:10.700 ⇒ 00:10:11.120 Nicolas Sucari: And you have.
32 00:10:11.120 ⇒ 00:10:12.509 Chuck Gross: Hey! How are you?
33 00:10:12.990 ⇒ 00:10:14.470 Jakob Kagel: Doing well, doing well.
34 00:10:15.840 ⇒ 00:10:16.829 Jakob Kagel: Nice to meet you.
35 00:10:16.830 ⇒ 00:10:17.570 Nicolas Sucari: Come.
36 00:10:26.310 ⇒ 00:10:30.619 Nicolas Sucari: we were just doing some introductions here with Jack, too. So
37 00:10:31.370 ⇒ 00:10:32.320 Nicolas Sucari: yeah. Oh.
38 00:10:32.320 ⇒ 00:10:37.350 Jakob Kagel: Sure. Yeah, I know, Chuck, we haven’t been on a call yet, but
39 00:10:37.800 ⇒ 00:10:40.639 Jakob Kagel: My name is Jacob. I’ve been with Brand Forge for
40 00:10:41.050 ⇒ 00:10:43.457 Jakob Kagel: over 2 months now.
41 00:10:44.240 ⇒ 00:10:49.800 Jakob Kagel: sort of my background was as like a data scientist at expedia at verbo
42 00:10:50.151 ⇒ 00:10:54.969 Jakob Kagel: and now, like work on a lot of customer intelligence work for home depot.
43 00:10:55.680 ⇒ 00:10:56.979 Jakob Kagel: As well.
44 00:10:57.440 ⇒ 00:10:59.610 Jakob Kagel: And yeah.
45 00:10:59.620 ⇒ 00:11:04.099 Jakob Kagel: really interested in some of the shipment stuff. So excited to talk to you about it.
46 00:11:04.390 ⇒ 00:11:08.630 Chuck Gross: Awesome. Yeah. Sounds good. Let’s let’s dive in. What? What are we going over?
47 00:11:08.950 ⇒ 00:11:16.440 Jakob Kagel: For sure, for sure. I don’t know. Nicholas is Utah. I’m gonna join the call. Should we wait for him, or should we go ahead and get started. I’m cool either way.
48 00:11:17.241 ⇒ 00:11:20.259 Nicolas Sucari: I think we can get started. Yeah, I don’t know if he’s gonna join. So.
49 00:11:20.260 ⇒ 00:11:20.920 Jakob Kagel: Got it, and.
50 00:11:20.920 ⇒ 00:11:21.860 Nicolas Sucari: The right to it.
51 00:11:22.681 ⇒ 00:11:46.540 Jakob Kagel: So I know that you all are like planning on on wanting to open like a new shipping warehouse like most likely in Texas, right? One of the things I talked to Utam about. And sort of as I’m like going through the shipping data to that I just wanted to understand is like, is there already plans to open another one like in California? He told me that there might be just wanted to sort of like get an understanding on that real quick.
52 00:11:46.830 ⇒ 00:11:52.569 Chuck Gross: Yeah, we are looking to reduce most of the shipping into being like a
53 00:11:52.860 ⇒ 00:11:57.949 Chuck Gross: zone, 1, 2, or 3. In the worst case scenarios, for you know, 95% of our shipments.
54 00:11:57.950 ⇒ 00:11:59.310 Jakob Kagel: Okay. Yeah.
55 00:11:59.680 ⇒ 00:12:03.120 Chuck Gross: So depending on where we have the ability to open up
56 00:12:04.080 ⇒ 00:12:11.210 Chuck Gross: is where we’re really looking. So that’s why Texas, you know, we ship. It’s all pool equipment, and we’re right now shipping from New York.
57 00:12:11.490 ⇒ 00:12:12.590 Chuck Gross: So you know.
58 00:12:13.090 ⇒ 00:12:22.650 Chuck Gross: in July it’s great, because it’s everybody’s summer. But in January all of our shipments go to California, Texas, Florida. That’s the that’s the big, the biggest 3 areas.
59 00:12:22.880 ⇒ 00:12:23.500 Jakob Kagel: Right.
60 00:12:23.500 ⇒ 00:12:25.340 Chuck Gross: Next to the northeast. So
61 00:12:25.610 ⇒ 00:12:32.630 Chuck Gross: like last year, we started in Jacksonville, and this year we really built Jacksonville up, and we’ve shipped a lot of orders out of there.
62 00:12:33.050 ⇒ 00:12:36.920 Chuck Gross: Aaron. It’s worked pretty well. It was bumpy start, of course, but it’s working well now.
63 00:12:37.510 ⇒ 00:12:41.630 Chuck Gross: So now that we were pretty much ready to go in California.
64 00:12:41.670 ⇒ 00:12:45.309 Chuck Gross: That’ll save a lot of the, you know, cross country shipping we’ve been paying.
65 00:12:46.040 ⇒ 00:12:55.819 Chuck Gross: So now we’re really looking to finesse from there, because at this point it’s all we have to do is really, you know, switch hit some switches, send them some merchandise, and that’s it.
66 00:12:55.820 ⇒ 00:13:00.430 Jakob Kagel: Right? So the one in California like is this
67 00:13:00.710 ⇒ 00:13:08.610 Jakob Kagel: like, so it is basically like already determined that you’re gonna open another one in California. It is like Northern or Southern California.
68 00:13:08.610 ⇒ 00:13:12.719 Chuck Gross: This one is in this in more so
69 00:13:13.080 ⇒ 00:13:19.509 Chuck Gross: Southern California. 1 1 in the north would be beneficial to to a degree
70 00:13:19.650 ⇒ 00:13:32.750 Chuck Gross: cause we we do ship some to Northern California. When we do ship orders to Oregon Washington State. You know parts of Montana up there, Idaho. It’s super
71 00:13:33.180 ⇒ 00:13:35.489 Chuck Gross: expensive because of the way the shipping lanes are.
72 00:13:35.490 ⇒ 00:13:36.410 Jakob Kagel: Right.
73 00:13:36.580 ⇒ 00:13:41.259 Chuck Gross: I mean, you know, it’s not a huge percentage of sales, but if we can have the
74 00:13:41.560 ⇒ 00:13:50.290 Chuck Gross: the other merchandise there, and we can save a couple 100 bucks. Each shipment, even if we sell 30 or 40, you know, units there. It adds up.
75 00:13:51.160 ⇒ 00:13:58.710 Jakob Kagel: Right? Right? Yeah. Was seeing like some of the sort of like the highest like price per pound was like more like in the Sacramento area, like for.
76 00:13:58.710 ⇒ 00:14:00.100 Chuck Gross: Yeah. Y’all wasn’t.
77 00:14:00.100 ⇒ 00:14:23.330 Jakob Kagel: Like a crazy amount of order volume. Like you said. So. That’s just like what I was curious about there. Okay, great. No, that’s good. Insight there. Another question that I have real quick sort of about like the shipments in general is like, so we always ship the brushes with like usps. Is that like? That’s what I’m seeing. Kind of like in the data. Is that right? Like.
78 00:14:23.330 ⇒ 00:14:23.750 Chuck Gross: And the.
79 00:14:23.750 ⇒ 00:14:28.040 Jakob Kagel: Brushes can only go with usps, or is it just like mainly with usps?
80 00:14:28.040 ⇒ 00:14:29.500 Chuck Gross: In the past.
81 00:14:29.600 ⇒ 00:14:38.019 Chuck Gross: We shipped all of the brushes with with usps. We used like 1st class mail or ground, whatever they call it. Now.
82 00:14:38.160 ⇒ 00:14:38.880 Jakob Kagel: Okay.
83 00:14:38.880 ⇒ 00:14:41.650 Chuck Gross: For the smallest model, because it’s under pounds.
84 00:14:42.710 ⇒ 00:14:45.969 Chuck Gross: and then we would use priority mail for the other models
85 00:14:46.590 ⇒ 00:14:51.380 Chuck Gross: starting in, maybe, like late April or May we switched
86 00:14:51.550 ⇒ 00:14:54.639 Chuck Gross: to all of the brushes that are over a pounds
87 00:14:54.760 ⇒ 00:15:02.859 Chuck Gross: to go with ups, either with their sure post program, or now they have a different program called Ground Advantage.
88 00:15:03.130 ⇒ 00:15:03.970 Jakob Kagel: Okay.
89 00:15:04.260 ⇒ 00:15:10.339 Chuck Gross: So the last couple of weeks you probably would see a large difference in the amount we’ve been paying for shipping on those.
90 00:15:11.800 ⇒ 00:15:17.990 Jakob Kagel: Yeah, exactly. I mean, that makes sense. And that’s exactly sort of like what I was curious about was, yeah.
91 00:15:18.000 ⇒ 00:15:19.770 Jakob Kagel: because it was like
92 00:15:19.800 ⇒ 00:15:28.829 Jakob Kagel: all the brushes are like a a huge majority of them. Would be going through usps. So okay, that makes sense.
93 00:15:30.920 ⇒ 00:15:32.729 Jakob Kagel: So I guess like
94 00:15:33.430 ⇒ 00:15:40.359 Jakob Kagel: just wanted to sort of get like a little bit more to like. I feel like for Texas. Right? I know that. That is
95 00:15:40.450 ⇒ 00:15:46.093 Jakob Kagel: the location where you all are like sort of leaning towards putting the warehouse right?
96 00:15:47.120 ⇒ 00:16:07.910 Jakob Kagel: I guess. Like, what is there anything like sort of specific like to Texas that it like really like important as far as like that location or is it just more like, you know, general, like intuitively, it makes sense to have a warehouse more like in the middle of the country, where you can go like to either either coast, basically like in the same distance.
97 00:16:07.910 ⇒ 00:16:10.989 Chuck Gross: Right. We we wanted to to have Texas
98 00:16:11.360 ⇒ 00:16:12.939 Chuck Gross: because we could have.
99 00:16:13.330 ⇒ 00:16:18.530 Chuck Gross: Because if you look at the sales breakdown, I think Florida might be 40% of the sales.
100 00:16:18.530 ⇒ 00:16:21.170 Jakob Kagel: Right. It’s Florida, California, and Texas, exactly like.
101 00:16:21.170 ⇒ 00:16:33.039 Chuck Gross: Yeah. So that’s why you know, the northeast, I think, was like 15 to 20. So I’m just going from what I remember doing this a while ago. So I finally, in this year sales. But the breakdown of the sales having
102 00:16:33.480 ⇒ 00:16:37.500 Chuck Gross: being able to have Texas open and have extra stock there
103 00:16:37.936 ⇒ 00:16:43.789 Chuck Gross: we’d be able to ship to the State of Texas, which is a decent amount of sales, but also Arizona, New Mexico.
104 00:16:44.800 ⇒ 00:16:45.680 Jakob Kagel: Okay.
105 00:16:45.680 ⇒ 00:16:53.089 Chuck Gross: So those are there are hot areas, not, you know, not specifically very, very busy. But those those 2 areas.
106 00:16:53.280 ⇒ 00:16:54.520 Chuck Gross: No, that’s what they’re doing.
107 00:16:54.520 ⇒ 00:16:56.042 Jakob Kagel: Arizona, for sure.
108 00:16:56.550 ⇒ 00:16:57.050 Chuck Gross: Yeah.
109 00:16:57.050 ⇒ 00:16:59.530 Jakob Kagel: I was going through the data. I mean, obviously.
110 00:16:59.530 ⇒ 00:17:07.389 Chuck Gross: Very limited routes. That’s the part. The problem with shipping some of those areas is that the only way to get the Phoenix is down the same road that everyone else has to go to.
111 00:17:07.540 ⇒ 00:17:08.349 Jakob Kagel: Yeah, so.
112 00:17:08.359 ⇒ 00:17:13.099 Chuck Gross: On the map, on the map. It looks close to certain parts of Texas, but it’s not close
113 00:17:13.489 ⇒ 00:17:21.399 Chuck Gross: close to certain parts of Texas. The same problem we have in Oregon and Washington. Is that the only way to get to Oregon? Oregon? Washington is up through the north of California?
114 00:17:21.469 ⇒ 00:17:23.999 Chuck Gross: So that’s why those rates get a little crazy, too.
115 00:17:24.000 ⇒ 00:17:39.979 Jakob Kagel: Right? Right? Is like part of this, like, like sort of what we’re working on, is it? Also that? Do you all want to understand, sort of like, more like your distribution of like inventory and like which products are getting like shipped out of where
116 00:17:40.340 ⇒ 00:17:47.509 Jakob Kagel: or is that something that you basically have to feel like you already have, like a good understanding of like in a good good feel for.
117 00:17:47.510 ⇒ 00:17:53.140 Chuck Gross: I mean having you guys do that, or probably get better numbers than when we did it last year.
118 00:17:53.940 ⇒ 00:18:02.230 Chuck Gross: We know we did. We did a lot of it kinda by hand, if you will. You know. We looked up sales of skews. We looked up zip codes of skews, and we tried to just make the best
119 00:18:02.300 ⇒ 00:18:05.230 Chuck Gross: mix that we could with the information that we had.
120 00:18:06.620 ⇒ 00:18:16.359 Chuck Gross: So you guys would be able to tell us a much. You know more precise amount of merchandise to order, and even when to get it to those distribution centers.
121 00:18:16.520 ⇒ 00:18:17.910 Jakob Kagel: Right that makes sense.
122 00:18:17.910 ⇒ 00:18:28.430 Chuck Gross: Right? Because it it’s it’s just once again. It’s a seasonal business. But having stuff in California, Texas, Florida, that’s you know, a 365 day season.
123 00:18:28.830 ⇒ 00:18:33.180 Chuck Gross: so the sales are slower in the wintertime there. But there’s
124 00:18:33.580 ⇒ 00:18:41.480 Chuck Gross: still sales, and there’s still a decent amount of sales we still need to get shipments there, where in New York, having a shipment in January, is crazy because it just sits here.
125 00:18:42.550 ⇒ 00:19:07.209 Jakob Kagel: Right? Right? Okay, that makes sense. That makes sense. Yeah, I mean, we are planning on doing like a center of gravity analysis. We would basically like factor in, like the geographical coordinates of, like the existing warehouses and the shipping costs. So that’s something that we’re working on right now. We just have to append sort of the lat long values like for the warehouses like into our table, into our shipping.
126 00:19:07.210 ⇒ 00:19:07.880 Chuck Gross: Stable.
127 00:19:07.880 ⇒ 00:19:09.146 Jakob Kagel: Do that?
128 00:19:10.290 ⇒ 00:19:29.660 Jakob Kagel: But yeah, I guess, like, you know, obviously, just want to be cognizant of like the things that you are like really keen on learning about, like as it pertains to shipments like what you kind of already have like visibility into, or have an idea of, and then like, what are the things that really matter like to you that you want to see.
129 00:19:30.680 ⇒ 00:19:33.150 Chuck Gross: Yeah. So I definitely, the one thing I want is.
130 00:19:33.650 ⇒ 00:19:42.049 Chuck Gross: see, the most is really the future ordering process. So we get the correct merchandise to those distribution centers.
131 00:19:42.050 ⇒ 00:19:42.890 Jakob Kagel: Utah.
132 00:19:43.110 ⇒ 00:19:53.359 Chuck Gross: That would be ideal, because we place most of our purchase orders like September, October, November, to start delivering to those places in February, March and
133 00:19:54.010 ⇒ 00:19:55.000 Chuck Gross: April.
134 00:19:56.070 ⇒ 00:19:56.890 Jakob Kagel: Okay.
135 00:19:57.780 ⇒ 00:19:59.170 Jakob Kagel: that makes sense.
136 00:19:59.985 ⇒ 00:20:03.789 Jakob Kagel: So like for the future ordering process. Like.
137 00:20:04.220 ⇒ 00:20:08.692 Jakob Kagel: I mean, obviously, the business is like somewhat seasonal right?
138 00:20:09.740 ⇒ 00:20:10.910 Jakob Kagel: so
139 00:20:11.500 ⇒ 00:20:23.600 Jakob Kagel: we would, I think, in that case, probably rely like on the previous year’s data. To sort of guide us in saying like, these are the products that would ship during this time from these warehouses.
140 00:20:24.090 ⇒ 00:20:36.590 Chuck Gross: Yeah, yeah, I was gonna say, something like that would be fine, like, wh, what we really like to see is the the breakdown of the skews. So like argument sake, if you take the variable speed pump program.
141 00:20:36.770 ⇒ 00:20:42.569 Chuck Gross: there’s 3 different pumps. So the 1,500, the the 150, the 200, the 300.
142 00:20:44.290 ⇒ 00:20:51.980 Chuck Gross: So we want to make sure we order the correct breakdown of pumps, you know. Maybe it’s 3 to 2 to one or 3 to 2 to 2
143 00:20:52.230 ⇒ 00:20:56.280 Chuck Gross: per distribution center. That’s where I got a little hairy last year.
144 00:20:56.480 ⇒ 00:20:57.280 Jakob Kagel: Right.
145 00:20:57.280 ⇒ 00:21:04.790 Chuck Gross: Cause we wanna make sure because it we, you know, we have a pretty good, pretty, solid understanding of which you know the which breakdowns are selling.
146 00:21:05.030 ⇒ 00:21:09.819 Chuck Gross: but which breakdowns are selling. Per geographical location is a bit challenging.
147 00:21:10.140 ⇒ 00:21:26.149 Jakob Kagel: Right? Right? Okay, no. I definitely think we can help with that. And that’s definitely very helpful like for us to know. So you want it all the way at the skew level, like the product class level, is not like of interest to you, basically like that’s that’s too aggregate.
148 00:21:26.150 ⇒ 00:21:29.860 Chuck Gross: Yeah, correct cause. Having it to the skew level
149 00:21:30.030 ⇒ 00:21:33.989 Chuck Gross: would be ideal. Because then we can say, you know, listen, we need.
150 00:21:34.610 ⇒ 00:21:38.569 Chuck Gross: even if it was just a percentage of the units that are selling in those areas.
151 00:21:39.470 ⇒ 00:21:44.039 Chuck Gross: Just so we can make a more educated purchase order, because obviously, you can
152 00:21:44.220 ⇒ 00:21:48.929 Chuck Gross: figure out what everyone’s gonna buy in the future. But but using all the past data
153 00:21:49.318 ⇒ 00:21:56.580 Chuck Gross: it’s usually a pretty good guide, and the you know, to the numbers that we need, the you know, the the breakdown. We need, the plays.
154 00:21:57.750 ⇒ 00:21:59.982 Jakob Kagel: Right? That makes sense.
155 00:22:01.520 ⇒ 00:22:02.530 Jakob Kagel: okay.
156 00:22:03.230 ⇒ 00:22:20.270 Jakob Kagel: cool. Yeah. I think I have a pretty good idea. I mean, I’ve done some digging like on the data already. Preliminarily. But you know, this is just helpful for me just to sort of get more of an idea of other things that like you’re really interested in. So
157 00:22:20.810 ⇒ 00:22:23.110 Jakob Kagel: yeah, if there’s anything else,
158 00:22:23.720 ⇒ 00:22:27.429 Jakob Kagel: I think definitely, just shoot in my way, like, obviously.
159 00:22:28.050 ⇒ 00:22:28.770 Chuck Gross: Right.
160 00:22:29.330 ⇒ 00:22:32.969 Jakob Kagel: So anything that’s top of mind be happy to look into
161 00:22:33.370 ⇒ 00:22:36.666 Jakob Kagel: But I think sort of like for the next time that we meet.
162 00:22:36.990 ⇒ 00:22:41.379 Jakob Kagel: that that will be. The thing that we can focus on is the skews
163 00:22:41.790 ⇒ 00:22:45.339 Jakob Kagel: by the top, 3 states by the warehouses.
164 00:22:45.826 ⇒ 00:22:53.749 Jakob Kagel: And obviously we include other metrics like you know how far like the shipping zones and the price per pound
165 00:22:54.128 ⇒ 00:23:00.029 Jakob Kagel: you know, splits by shipping provider. All those type things as well. We can include.
166 00:23:00.030 ⇒ 00:23:04.490 Chuck Gross: Okay, yeah, that that’s definitely a big, that’s a big help for sure.
167 00:23:04.890 ⇒ 00:23:10.729 Chuck Gross: And then, if there was any other opportunities to open up any other warehouse distribution areas which
168 00:23:10.930 ⇒ 00:23:13.790 Chuck Gross: I mean from my standpoint. I
169 00:23:14.180 ⇒ 00:23:19.709 Chuck Gross: I think that once we had a Texas location open, I don’t think there’s any other real room to grow at that point.
170 00:23:19.980 ⇒ 00:23:26.179 Chuck Gross: I don’t. I don’t think there’s much, you know. There’s there’s not much business to the Northwest
171 00:23:26.320 ⇒ 00:23:29.019 Chuck Gross: where we’re sending that much stuff to. I mean, maybe
172 00:23:29.960 ⇒ 00:23:34.069 Chuck Gross: I have to look at the shipping zones like, I know Indianapolis, and that area
173 00:23:34.640 ⇒ 00:23:38.830 Chuck Gross: is is a fairly large market, but it’s still pretty close to New York.
174 00:23:40.340 ⇒ 00:23:46.423 Jakob Kagel: Right? Right? That makes sense. Yeah, I mean, I definitely think we wanna lean sort of on like the center gravity approach.
175 00:23:46.700 ⇒ 00:23:47.250 Chuck Gross: Yeah.
176 00:23:47.250 ⇒ 00:24:16.270 Jakob Kagel: It’s like for the location of the warehouse. But I definitely think, like all the data that we just talked about, that we’ll put together for you. I think that will also like help, inform our decision. I don’t think, you know. Obviously, we don’t want it like just lean on that we want to sort of use a combination of everything that we have. So I definitely think, like, yeah, when we can look at like which warehouses are having to ship the furthest, and they cost the most in those big 3 States. And I think, especially in Texas. Of course, since it’s already been
177 00:24:16.657 ⇒ 00:24:20.682 Jakob Kagel: you know. That’s the state or the area that you are already keen on.
178 00:24:21.620 ⇒ 00:24:24.880 Jakob Kagel: Help tell the story there a little bit more.
179 00:24:29.610 ⇒ 00:24:31.790 Chuck Gross: I’m sorry you broke up right there. What was that.
180 00:24:31.790 ⇒ 00:24:36.600 Jakob Kagel: Oh, sorry! I was just saying like, I think that data will like help. Inform that decision as well.
181 00:24:36.600 ⇒ 00:24:38.510 Chuck Gross: Oh, yeah, definitely for sure, for sure.
182 00:24:38.910 ⇒ 00:24:39.630 Jakob Kagel: Cool.
183 00:24:41.010 ⇒ 00:24:53.889 Jakob Kagel: awesome. Well, yeah, I don’t know if there is there any more questions that you have for me, or or anything else that you feel like, well, we need to discuss at this time. Otherwise I would say, we’ll probably just set up a meeting for like mid next week.
184 00:24:54.207 ⇒ 00:24:58.370 Jakob Kagel: And we can review all the data sort of that we talked about.
185 00:24:58.815 ⇒ 00:25:01.509 Chuck Gross: Yeah, let’s do that. Let’s we’ll. We’ll look at the data.
186 00:25:01.550 ⇒ 00:25:11.539 Chuck Gross: and then we’ll go from there, because I mean they’ll they’ll probably be ultimately more skews will end up shifting to all of the warehouses versus just the couple that we’re doing now.
187 00:25:12.510 ⇒ 00:25:13.970 Chuck Gross: you know you won’t, you won’t
188 00:25:14.360 ⇒ 00:25:19.259 Chuck Gross: ones? We did now, where the you know the heat pumps, because that was the biggest shipping costs, you know. That’s the biggest savings
189 00:25:19.600 ⇒ 00:25:29.869 Chuck Gross: and the variable line, because that’s once again one of the you know, the bigger sales. But we there’s a lot of other stuff we’re getting into like the like a replacement filters.
190 00:25:30.550 ⇒ 00:25:33.949 Chuck Gross: We just started carrying them this year. We’ve only sold a hand.
191 00:25:34.210 ⇒ 00:25:41.799 Chuck Gross: so it’s hard to tell where they’re going to go and where to ship them to. You know it’s hard still, but for me to ship a filter to Florida cost 200 bucks.
192 00:25:41.800 ⇒ 00:25:42.440 Jakob Kagel: Right.
193 00:25:42.440 ⇒ 00:25:47.509 Chuck Gross: Ship a flu, you know, from the ship. A filter from Florida to Florida will be like a hundred 9,
194 00:25:47.550 ⇒ 00:25:49.200 Chuck Gross: so it’s you know. It’s so.
195 00:25:49.200 ⇒ 00:25:50.290 Jakob Kagel: Oh, yeah, that’s that’s great.
196 00:25:50.290 ⇒ 00:25:50.660 Chuck Gross: Or.
197 00:25:50.660 ⇒ 00:25:56.590 Jakob Kagel: Information. That’s great information for us to know that that’s like a new product class or whatnot.
198 00:25:57.210 ⇒ 00:26:05.199 Jakob Kagel: And yeah, we definitely can kind of keep an eye on that as well, because it’s so new. We have to understand. Of course, like we don’t have the as much historical.
199 00:26:05.660 ⇒ 00:26:16.970 Chuck Gross: Right? Yeah, yeah, we’ve never done. We’ve never done anything in that class before until this year. So you know, if if we sold 30 filters. So far, you know, that will be a lot. So it it’s just starting.
200 00:26:17.270 ⇒ 00:26:22.189 Chuck Gross: But it’s definitely a big market because the Internet does sell a tremendous amount of pool filters.
201 00:26:22.190 ⇒ 00:26:23.730 Jakob Kagel: Yeah, no, I mean.
202 00:26:23.730 ⇒ 00:26:27.870 Chuck Gross: And they’re and some of them are very bulky. So that’s the problem
203 00:26:27.880 ⇒ 00:26:33.329 Chuck Gross: in the shipping area, you know. Then the ship for shipping them is, cuts into the the profit. There.
204 00:26:33.330 ⇒ 00:26:49.069 Jakob Kagel: Yeah, I mean pool filters. I mean that to me. I mean, obviously, I think you all are maybe a little bit more knowledgeable about the pool business than me. But to me that seems like, yeah, one of the things that you would have to replace, like the most often as like a potential to be sort of like the most repeat purchased. Item.
205 00:26:49.460 ⇒ 00:26:53.631 Chuck Gross: Right? So it it’s it’s a huge, it’s a huge area to grow in. So
206 00:26:54.550 ⇒ 00:27:01.869 Chuck Gross: the as we continue in selling those next year, it’s definitely a huge opportunity also to get them and the other warehouses.
207 00:27:03.320 ⇒ 00:27:15.939 Jakob Kagel: Okay, yeah, that makes sense for sure. Okay, sounds great. I’ll see. We might be able to pull some stuff on that as well, before our meeting mid next week. And just sort of see like what the orders are, and you know where they’re going.
208 00:27:16.080 ⇒ 00:27:17.480 Chuck Gross: Yup. Okay.
209 00:27:18.120 ⇒ 00:27:24.933 Jakob Kagel: For sure. Okay, sounds great. Yeah. Really appreciate your time. Enjoy the 4th of July and
210 00:27:25.690 ⇒ 00:27:26.929 Chuck Gross: You too.
211 00:27:27.266 ⇒ 00:27:31.634 Jakob Kagel: We’ll we’ll talk soon, hopefully. You’ll be by a pool or a lake.
212 00:27:31.970 ⇒ 00:27:34.119 Chuck Gross: I’ll be swimming tomorrow.
213 00:27:34.120 ⇒ 00:27:34.750 Jakob Kagel: There we go!
214 00:27:34.750 ⇒ 00:27:36.236 Chuck Gross: Oh, yeah.
215 00:27:37.180 ⇒ 00:27:37.880 Jakob Kagel: Sounds good.
216 00:27:37.880 ⇒ 00:27:38.760 Nicolas Sucari: Excellent.
217 00:27:39.190 ⇒ 00:27:39.940 Chuck Gross: Often.
218 00:27:39.940 ⇒ 00:27:40.870 Jakob Kagel: And chat. And yeah.
219 00:27:40.870 ⇒ 00:27:42.050 Nicolas Sucari: You very much. Chuck.
220 00:27:42.700 ⇒ 00:27:44.703 Chuck Gross: Thank you. Guys. Have a good day.
221 00:27:45.690 ⇒ 00:27:46.360 Nicolas Sucari: And mate.