Meeting Title: Weekly-Data-Review Date: 2024-01-12 Meeting participants: Daniel Schonfeld, Uttam Kumaran
WEBVTT
1 00:00:42.530 ⇒ 00:00:57.010 Daniel Schonfeld: and
2 00:01:03.500 ⇒ 00:01:17.989 Uttam Kumaran: play a little warm up music for you.
3 00:01:18.510 ⇒ 00:01:22.490 Uttam Kumaran: You said you play guitar, now for a while or you’ve always played guitar
4 00:01:22.500 ⇒ 00:01:28.689 Daniel Schonfeld: yeah, I’ve always played. I just this year. I started getting more serious. like lessons. And
5 00:01:29.270 ⇒ 00:01:31.820 Daniel Schonfeld: yeah.
6 00:01:32.590 ⇒ 00:01:35.649 Daniel Schonfeld: rhythm. But I like, I love blues.
7 00:01:36.180 ⇒ 00:01:44.239 Uttam Kumaran: Yeah, I played. I played guitar growing up, and I but I played brass, and in ensemble for like
8 00:01:44.730 ⇒ 00:01:51.110 Uttam Kumaran: 1213 years I played trumpet trombone. Based trombone. And I play it like, jazz
9 00:01:51.340 ⇒ 00:01:58.610 Uttam Kumaran: like, into college. It’s great. I miss it. It’s like feels like you’re part of it. It’s like a sports team.
10 00:01:58.710 ⇒ 00:02:11.439 Uttam Kumaran: Exactly. Yeah. I have a piano right behind me that I’ve been like slowly learning. But same thing. I just like I need to take lessons. And like, I, just, it’s such an investment.
11 00:02:11.560 ⇒ 00:02:22.310 Uttam Kumaran: And like, if you haven’t played music, I think it’s great cause you can butts around. And you’re like. okay, whatever. But I just know how much you have to practice. And knowing that almost is like.
12 00:02:22.560 ⇒ 00:02:27.700 Uttam Kumaran: I’ll have to get to it at some point. Yeah, I think you just like can’t think about
13 00:02:28.290 ⇒ 00:02:37.149 Daniel Schonfeld: You can’t think about like the end state you just have to like. There’s no end to playing music. It’s just you just commit to doing it. And just
14 00:02:37.510 ⇒ 00:02:43.039 Daniel Schonfeld: I say to myself, Lucky you’re just gonna you’re gonna suck. And then, like every once in a while, you’ll
15 00:02:43.090 ⇒ 00:02:51.549 Uttam Kumaran: you’ll get a lick or like and play a song, and then it makes me happy. It’s it’s also like I play like anywhere from one to 3 HA day now.
16 00:02:51.680 ⇒ 00:03:00.850 Uttam Kumaran: because everyone my house goes to sleep. At 90’clock my son goes sleep at 9. My wife goes to sleep at 9, and I sleep late. So between like 9 and one.
17 00:03:01.040 ⇒ 00:03:07.169 Daniel Schonfeld: I could go 9 to one, or I can go, you know, 10 to 12. It’s like my time.
18 00:03:07.350 ⇒ 00:03:09.539 Uttam Kumaran: Nice? Hell. Yeah.
19 00:03:09.580 ⇒ 00:03:11.480 Daniel Schonfeld: So I mean, I love it.
20 00:03:11.770 ⇒ 00:03:12.930 Uttam Kumaran: Cool.
21 00:03:13.250 ⇒ 00:03:20.239 Uttam Kumaran: Okay, I have a, I have a ton of stuff to share. Today. He’s just you and me traveling. So
22 00:03:20.370 ⇒ 00:03:24.899 Uttam Kumaran: yeah, that’s what he mentioned. Cool.
23 00:03:29.780 ⇒ 00:03:37.339 Uttam Kumaran: So a lot of this week was taking a lot of what you said last time, and really coming back and revisiting the stash from
24 00:03:37.350 ⇒ 00:04:07.189 Uttam Kumaran: Okay, if I logged in and just saw everything, what like W. What is the feeling? I get? What other questions. I have. So a couple of things, one on the data, a data accuracy piece. So every day we’ve been going through. And I have a person on my team that’s going through and pretty much checking all the things against the Ui. So there was some access issues. And then, now we’re going through and pretty much looking at all the high level numbers
25 00:04:07.260 ⇒ 00:04:20.789 Uttam Kumaran: from gross sales all the way to marketing, to shipping on if there’s matches. So for the last few days we’ve been pretty good. And then there’s just one
26 00:04:20.899 ⇒ 00:04:23.199 Uttam Kumaran: issue on the shipping side
27 00:04:23.360 ⇒ 00:04:43.890 Uttam Kumaran: from one order, actually, that like kind of rippled through a lot of different things and kind of caused some issues that I can actually show you today. And it’s kind of weird someone’s. This person is ordering a ton. Maybe you know him. But so this is kind of our auto process, and we’re going through and knocking this down. So again we have.
28 00:04:44.440 ⇒ 00:04:57.270 Uttam Kumaran: We now have a few days of matching everything except shipping, so I’m hoping going to the weekend, and then the next few days of early next week we shut some consistency every day. And then I’ll kind of give you
29 00:04:57.470 ⇒ 00:05:18.919 Uttam Kumaran: the notice then. So the I mean the only thing otherwise is, I’ll just show you some of the updates. And then I kind of also just wanna give you my feedback on it, cause I’ve now I’ve been looking at it in a kind of a different lens on the dashboard. So one thing we’ve done is, we have all the previous day orders, refunds and
30 00:05:18.920 ⇒ 00:05:32.590 Uttam Kumaran: discounts and warranties all here in a list directly from here, you can actually click on the link and go straight to the order and shopify
31 00:05:32.990 ⇒ 00:05:41.209 which has been like, so helpful for me, but hopefully also just like really great, to be able to go directly from
32 00:05:41.390 ⇒ 00:05:53.029 Uttam Kumaran: the record directly to the item similarly on the Amazon side, it’s the same thing. So this takes you to the Amazon seller.
33 00:05:53.770 ⇒ 00:05:59.540 Uttam Kumaran: Order. right? So that’s that’s
34 00:05:59.550 ⇒ 00:06:17.180 Uttam Kumaran: you know. I think a huge upgrade, so you could immediately go and dissect, and instead of having to copy those or find those issues, and then also we have. So we have each of these there. The rest of the dash is fairly similar. Again, we have all the top level, raw data, but
35 00:06:17.290 ⇒ 00:06:25.889 Uttam Kumaran: otherwise we have we have sorry. I think it just refreshed. We have all the most recent orders, the the only thing that
36 00:06:26.060 ⇒ 00:06:46.740 Uttam Kumaran: I otherwise, apart from that, what I’ve noticed in the dashboard is especially cause it’s January. Everything is like been low or negative, especially when compared to previous month and previous year. So what I’m trying to do is think about. If you came in, we’re like, why is everything red is kind of like, okay, what? How do I indicate?
37 00:06:46.910 ⇒ 00:07:12.210 Uttam Kumaran: How do I show that it’s not like? That’s a problem. It’s more like these are, this is just the trend. So, for example, if I’m looking at sales versus average day of the week. Okay, so I noticed that this is down 15%. So some of the questions are like, What is the time period. So right now, this is over the last year, so that something I kinda need to indicate this is like sales same month last year. But we’re only halfway.
38 00:07:12.260 ⇒ 00:07:16.250 Uttam Kumaran: We’re less than halfway through the month. And we’re, I would say we’re about.
39 00:07:16.820 ⇒ 00:07:29.940 Uttam Kumaran: you know, 45% through the month or 40%, and seem to be tracking so again, there’s a lot of red, but it is. These are all like mid month numbers. So I wonder whether there’s something to do to show.
40 00:07:30.020 ⇒ 00:07:38.030 Uttam Kumaran: not how we’re tracking versus the previous one. But are we tracking in terms of a trend like based on our velocity.
41 00:07:38.180 ⇒ 00:07:54.769 Uttam Kumaran: where we’re gonna kind of end up at. And that that seems like the next evolution versus just looking at, waiting for this month to close to do comparisons understanding based on our current velocity. And if we if every day looks like we’re doing 10% better or
42 00:07:54.780 ⇒ 00:08:02.120 Uttam Kumaran: 20% better than last same time last year. Where do we expect to get? And then having that as a figure here to say, like.
43 00:08:02.310 ⇒ 00:08:06.139 Uttam Kumaran: you know, what is the velocity we’re hitting? Because now it just looks like everything’s down.
44 00:08:06.190 ⇒ 00:08:15.390 Uttam Kumaran: But again, you’re to date net profit. Of course we’re only only January. So some of these are like kind of not like that valuable frankly at the moment. But
45 00:08:15.430 ⇒ 00:08:17.290 Uttam Kumaran: I don’t know if you kind of
46 00:08:17.660 ⇒ 00:08:20.620 Uttam Kumaran: concur with that. Or if yeah, that makes sense.
47 00:08:20.680 ⇒ 00:08:28.679 Daniel Schonfeld: Yeah, I think as long as it’s long as I understand, cause it’s just a lot of numbers. It’s a lot to remember. I think there’s like a little
48 00:08:28.700 ⇒ 00:08:30.050 Daniel Schonfeld: eventually
49 00:08:30.320 ⇒ 00:08:36.699 Daniel Schonfeld: We could do like a little i icon hover over it. It would just say
50 00:08:36.890 ⇒ 00:08:38.140 Daniel Schonfeld: full month
51 00:08:38.299 ⇒ 00:08:51.329 Daniel Schonfeld: like where it says sales for, say, month last year. What if I first open this? I would think, oh, shit! We’re down 65% from the first to the twelfth year this month. But we just said, This is full month.
52 00:08:51.710 ⇒ 00:08:54.630 Daniel Schonfeld: you know, something like that. So I think, okay, don’t worry about that
53 00:08:54.950 ⇒ 00:09:06.350 Daniel Schonfeld: Or maybe we just switch it to be. It just tallies up where you are, month to date from the previous year, so it would show of 2023
54 00:09:06.990 ⇒ 00:09:16.840 Uttam Kumaran: exactly, and that way, when you hit the end of the month. It’s the same thing, right? So at the end, but at least at any point during its
55 00:09:16.990 ⇒ 00:09:22.920 Uttam Kumaran: it’s apple to apples in terms of days. So that that’s the first thing that kind of noticed.
56 00:09:23.170 ⇒ 00:09:47.190 Uttam Kumaran: other than that everything else. Again, we’re just really focused on data accuracy. And then being able to say, Okay, we did 21.9 5 yesterday. Going in here and just understanding like whether that matches here. What’s what are the main components with that. And then so the big thing I wanted to kind of understand, wait, it doesn’t match right there. It didn’t match.
57 00:09:47.720 ⇒ 00:10:03.879 Uttam Kumaran: Yeah. So this says so. I think the the one thing I want to confirm today is what we’re doing as a definition for gross sales, because there’s zoom zoom gross sales, and then, of course, removing out discounts, shipping tax
58 00:10:04.060 ⇒ 00:10:13.169 Uttam Kumaran: and refunds. So that’s something that I want to confirm today. And it’s kind of the diagram I sent before going in and making sure that this number
59 00:10:13.180 ⇒ 00:10:35.020 Uttam Kumaran: matches, because this is currently, I think likely gonna be removing some of those elements. So I have a little bit of like a diagram here, and that that way we can just align on this and then align on a naming. And I’ll put this diagram. So that’s available. And it’s like documented for us as well. So this is
60 00:10:35.160 ⇒ 00:10:38.559 Uttam Kumaran: kind of like how I have it outlined.
61 00:10:39.010 ⇒ 00:10:42.030 Uttam Kumaran: as all the way from the money that comes in the door.
62 00:10:42.290 ⇒ 00:10:56.149 Uttam Kumaran: all the things that get stripped away, and then some like intermediary calculations that we do. It’s not gonna always be viable to show all the components. So it’s helpful to have some of these, like.
63 00:10:56.240 ⇒ 00:11:00.280 Uttam Kumaran: you know, break points, at least. Of course, these 2.
64 00:11:00.630 ⇒ 00:11:08.659 Uttam Kumaran: But this is the one that’s, I think, important, because we can use this to say, we’re always gonna consider these 3 figures. So
65 00:11:08.670 ⇒ 00:11:10.100 Uttam Kumaran: taking a look at this.
66 00:11:10.620 ⇒ 00:11:13.799 Uttam Kumaran: does this make sense? Or what’s best?
67 00:11:14.000 ⇒ 00:11:16.569 Daniel Schonfeld: Yeah, I think that makes sense.
68 00:11:17.090 ⇒ 00:11:20.350 we just have to also define what are go down for a sec.
69 00:11:21.340 ⇒ 00:11:28.129 Daniel Schonfeld: Yeah. Scroll down. Oh, yeah. Marketing cost. Are you pulling that?
70 00:11:28.660 ⇒ 00:11:38.399 Uttam Kumaran: You? You know, Kim does some manual stuff. Yeah. So this. So it’s tough, because some of her
71 00:11:38.820 ⇒ 00:11:40.920 Uttam Kumaran: reporting manually
72 00:11:40.980 ⇒ 00:11:53.529 Uttam Kumaran: comes in weekly. I would say I would say the majority of the spend is going to to pay digital marketing and the stuff that we are tracking on an intraday
73 00:11:53.590 ⇒ 00:12:04.360 Uttam Kumaran: basis. But you’re right in that. There is stuff that I think what we’re do. What we’re focusing on is, how do you get things like direct mail?
74 00:12:04.440 ⇒ 00:12:10.959 Uttam Kumaran: any other sources closer to that daily. So that’s why it’s I don’t know how
75 00:12:11.200 ⇒ 00:12:13.039 Uttam Kumaran: helpful it is to look.
76 00:12:14.070 ⇒ 00:12:17.590 Uttam Kumaran: Yeah, I mean, you’re right like this is, gonna be a little bit off
77 00:12:17.620 ⇒ 00:12:28.600 Daniel Schonfeld: report which would be good.
78 00:12:28.610 ⇒ 00:12:33.479 Daniel Schonfeld: That just puts everything into a big excel table with also the
79 00:12:33.550 ⇒ 00:12:48.389 Daniel Schonfeld: the manual input everything. So it’s like a month end. P and L type report. That’s fine. This is just to give me an idea. So maybe we just say paid marketing costs or paid media costs. And then I can eyeball it throughout the week. When I do a weekly
80 00:12:48.420 ⇒ 00:13:02.800 Daniel Schonfeld: and eventually, one day, if we figure out how to automate it or to get it, scrape it. You know, we could just add that into there and change the nomenclature. But I think, for now that’s fine, give me a rough idea.
81 00:13:04.460 ⇒ 00:13:19.269 Uttam Kumaran: Okay, great. So I think those those were the big updates on this. So I I’m just gonna go ahead and make sure that the all these names kind of match. And then again, hopefully, by Monday or Tuesday
82 00:13:19.290 ⇒ 00:13:22.569 Uttam Kumaran: we should have all green checks on
83 00:13:22.610 ⇒ 00:13:32.170 Uttam Kumaran: the audit for a few days, and then again, I’m looking at this a lot with a lot of a different lens. And so the things that come out to me, even when we talked about it is like.
84 00:13:32.490 ⇒ 00:13:41.240 Uttam Kumaran: Okay, if everything’s negative, like now, we’ll just compare apples apple. So those things I’m glad like we’ll iterate on that, so that’ll be done by Tuesday or so. As well.
85 00:13:41.420 ⇒ 00:13:46.009 Daniel Schonfeld: Okay, great. And then I’ll spend once you give me the thumbs up that you feel confident
86 00:13:46.140 ⇒ 00:13:47.080 Daniel Schonfeld: I’ll
87 00:13:47.750 ⇒ 00:13:51.350 Uttam Kumaran: I’ll do like a week or 2 weeks of just yeah
88 00:13:51.430 ⇒ 00:13:52.850 Daniel Schonfeld: using this
89 00:13:53.240 ⇒ 00:14:08.350 Daniel Schonfeld: as my true north. But I’ll also just like every day, just double check things. And I’ll keep notes in an in a Google sheet or something. So I can say, okay, bug here. Error here, nomenclature, whatever the issue is, and everything for you.
90 00:14:08.520 ⇒ 00:14:26.200 Uttam Kumaran: I will even just send you where we’ve kind of done some of that diagramming, and the thing I just showed you in Figma. I’ll just send you that cause you could just take screenshots. Put a little sticky and be like this is. And and I’ll get updates. And it’s like a it’s like a whiteboard. I try to use that for a ton of stuff. So
91 00:14:26.330 ⇒ 00:14:33.230 Uttam Kumaran: that’s probably the easiest. And then so the other thing is, this is probably more closer to what you’re describing is like the weekly.
92 00:14:33.290 ⇒ 00:14:37.900 Uttam Kumaran: It’s almost like a weekly or monthly closeout. This will be the thing I kind of
93 00:14:38.220 ⇒ 00:14:39.890 Uttam Kumaran: revisit
94 00:14:39.960 ⇒ 00:14:44.359 Uttam Kumaran: after we close out and find out what’s kind of like.
95 00:14:44.370 ⇒ 00:14:52.160 Uttam Kumaran: what’s what we need to solidify at the end of a given week or month, and if it’s closed out, I don’t think there’s anything that can’t be closed out
96 00:14:52.470 ⇒ 00:15:15.710 Uttam Kumaran: on a weekly basis. That is gonna change within the month. So this is really great, because it has both week and month and pretty much the things I have here on on here now is what’s the change versus same month last year? So. But how about the same weeks versus last year each of the key kpis, which we can toggle these by month.
97 00:15:15.980 ⇒ 00:15:43.680 Uttam Kumaran: each of these by week, with some sort of, you know, like coloring or things to call out certain things and then everything. I wanna try to align towards something sort of driving towards a forecast again. Everything on a weekly or monthly basis will begin to understand velocity and then begin to actually project. We have some models that do that. They’re very rudimentary. Just put the skeleton up, and you can see that
98 00:15:43.800 ⇒ 00:15:57.180 Uttam Kumaran: I worked with Ben to say, like, Okay, let’s just assume like a flat increase, what was next year looking like. And so those things will get a little bit smarter on but the kind of skeleton for forecasting and measuring
99 00:15:57.390 ⇒ 00:16:10.209 Uttam Kumaran: progress to goals is all kind of ready, so you know, we can kind of get towards there. So this will be hopefully, the next kind of thing we can review on like weekend and month end.
100 00:16:10.320 ⇒ 00:16:11.210 Daniel Schonfeld: Okay.
101 00:16:11.470 ⇒ 00:16:19.549 Uttam Kumaran: the other thing is this order came this order came in.
102 00:16:19.820 ⇒ 00:16:21.490 Uttam Kumaran: by this guy, Tom.
103 00:16:22.090 ⇒ 00:16:26.990 Daniel Schonfeld: Yeah, we. We give him like wholesale orders. Cody.
104 00:16:27.700 ⇒ 00:16:34.420 Uttam Kumaran: Okay, cool. Because it. II mean, I think it was helpful, because it was like, kind of a unique order tonic quantity.
105 00:16:34.570 ⇒ 00:16:35.940 Uttam Kumaran: And
106 00:16:35.990 ⇒ 00:16:42.599 Uttam Kumaran: yeah. So so I just may not be talking. But I just wanted to flag that as like that was something that
107 00:16:42.750 ⇒ 00:16:48.099 Uttam Kumaran: I was using for a lot of like analysis to kind of make sure things flowed. But yeah, it was a kind of like a unique
108 00:16:48.460 ⇒ 00:16:52.880 Uttam Kumaran: order in like one shipment. Yeah.
109 00:16:53.160 ⇒ 00:16:58.789 Daniel Schonfeld: yeah, it’s an anomaly. But that’s the business I’m going after this year.
110 00:16:58.840 ⇒ 00:17:03.549 Daniel Schonfeld: are the pool service guys and wholesalers? People.
111 00:17:03.730 ⇒ 00:17:06.119 Uttam Kumaran: Yeah. And we’re doing a booth
112 00:17:06.210 ⇒ 00:17:10.380 Daniel Schonfeld: a black and decker booth in Atlantic City, and next week
113 00:17:10.460 ⇒ 00:17:17.150 Daniel Schonfeld: yeah, I’ll I’ll send you video from the show after I spent like 6 $60,000 on a
114 00:17:17.730 ⇒ 00:17:20.369 Uttam Kumaran: blessed because it’s
115 00:17:20.920 ⇒ 00:17:28.070 Daniel Schonfeld: every every booth in every trade show for the last 50 years at the pool and Spa shell is just an open booth showing product.
116 00:17:28.300 ⇒ 00:17:33.419 Daniel Schonfeld: I actually did the opposite. I fully enclosed it like a black box.
117 00:17:33.730 ⇒ 00:17:53.340 Daniel Schonfeld: Yeah, but even more discrete. It’s literally a black box over like I built a whole structure to hide everything. And the woman who runs it she’s like, I don’t understand. You’re spending all this money to block out what you’re what you’re trying to show. I’m like, Yeah.
118 00:17:53.780 ⇒ 00:17:55.309 Daniel Schonfeld: I’m like, it’s like.
119 00:17:55.930 ⇒ 00:18:05.320 Daniel Schonfeld: you know, you go to like a new nightclub, and it’s there’s no name to it. There’s just a bouncer outside. Actually, I’m putting red velvet ropes around the the front part
120 00:18:05.340 ⇒ 00:18:11.680 Uttam Kumaran: and like a like a supermodel standing in the front. Basically, they’re a telegram that they can’t come in.
121 00:18:11.700 ⇒ 00:18:22.150 Daniel Schonfeld: Yeah, And we’re doing the opposite where they, the industry treats pool service guys like trash. They look down on them.
122 00:18:22.330 ⇒ 00:18:34.919 Daniel Schonfeld: They only let big retailers and investors into their booths, and they, you know, they don’t give them the time of day. My model is to go after the full service guys and treat them like old, and not let anybody else in
123 00:18:35.200 ⇒ 00:18:37.510 Daniel Schonfeld: which is gonna be
124 00:18:38.530 ⇒ 00:18:45.890 Daniel Schonfeld: it’s never. It’s never been done before in this industry. And I look at the pool service guys almost like how Uber looked at drivers
125 00:18:46.170 ⇒ 00:18:59.480 Daniel Schonfeld: going out and building their own hiring their own team. You have an entire workforce that’s undervalued under appreciated that, I think could be considered almost employees right? And they’re selling your product.
126 00:18:59.480 ⇒ 00:19:27.040 Uttam Kumaran: They’re the advocates for your product, and they’re the ones with a customer interaction. I think a lot of. That’s why, when I first learned about your business is interesting because I didn’t, and I. Now I tell this, everyone like I didn’t know you could go do this for sort of stuff on your own. But it it is commonly gatekeep. Buy these Blood Pool Service guys right? But the same time, like they could be wearing your branding, they could be exclusively buying from you as their business grows.
127 00:19:27.110 ⇒ 00:19:33.940 Daniel Schonfeld: They’re they’re the ones going to the end customers home, the the customer trust full service guy.
128 00:19:33.980 ⇒ 00:19:42.720 Daniel Schonfeld: There’s no relationship with the with the with the top 3 largest pool manufacturers in the world. That’s 0 direct to consumer
129 00:19:42.800 ⇒ 00:19:49.659 Daniel Schonfeld: relationship. They sell distributors and retailers and then rely on the pull service guy to work with them to go install it.
130 00:19:49.770 ⇒ 00:19:53.040 Daniel Schonfeld: So they don’t have any first hand communication. We do.
131 00:19:53.340 ⇒ 00:20:04.969 Daniel Schonfeld: And we’re gonna mobilize that workforce over time to be our biggest ambassadors, our sellers, or, you know everything for us. So this is the kickoff, hopefully of that.
132 00:20:05.120 ⇒ 00:20:14.749 Daniel Schonfeld: That business model. And what you’re looking at here is hopefully what we see all day every day, because one service guy has 50 routes.
133 00:20:14.980 ⇒ 00:20:16.740 Daniel Schonfeld: Yeah, versus just it’s
134 00:20:16.890 ⇒ 00:20:22.909 Daniel Schonfeld: I’m I’m under the assumption. It’s gonna cost me around the same to get one service guys, it does one end customer.
135 00:20:23.090 ⇒ 00:20:24.300 Uttam Kumaran: Yeah.
136 00:20:24.400 ⇒ 00:20:34.480 Daniel Schonfeld: And so the model can really scale from there as long as we build the right program. So I’m really at the show, not even to sell. I’m really to to talk to service guys about how I can provide value.
137 00:20:34.650 ⇒ 00:20:50.590 Daniel Schonfeld: like things like life insurance and maybe equity in the company over time somewhere else. Starbucks did it with their employees, where health, insurance, and even options in the company. So I’m I’m feeding on that model plus the Uber model
138 00:20:50.700 ⇒ 00:20:56.229 Daniel Schonfeld: to figure out how we can create a real business that can scale quickly around the full service industry.
139 00:20:56.560 ⇒ 00:21:19.270 Uttam Kumaran: So it’s kind of maybe something we can do on the measurement side is, look for high quantity orders like this, right? Anything where I I’ll just I will. I don’t know. For the most part people are just ordering a couple of items, so I’ll I’ll go through and run some stuff and just find cause. Typically, what I was looking for before is
140 00:21:19.480 ⇒ 00:21:34.000 Uttam Kumaran: by just gross sales. And I pulled a report for Ben. I think on like folks in Florida. But maybe what I’ll do is on the monthly report, especially if that’s a new thing we could look at what are the highest quantity, single order quantities.
141 00:21:34.450 ⇒ 00:21:51.070 Daniel Schonfeld: So when ordering a pump is only an order once every 5 to 7 years pulling a heat pump once 7 years, if you see any one of those people come back in or order more than one at a time, it’s a retailer or or service person.
142 00:21:51.350 ⇒ 00:21:53.119 Uttam Kumaran: yeah, or builder.
143 00:21:53.220 ⇒ 00:21:59.290 Daniel Schonfeld: So we it would be. It would be great to tag those and actually database them separately, and then
144 00:22:00.170 ⇒ 00:22:06.150 Daniel Schonfeld: tell how many we have of those as they come in on a weekly quarterly. Whatever basis.
145 00:22:06.260 ⇒ 00:22:20.949 Daniel Schonfeld: as the I don’t know we call them. We can put them in a bucket. wholesalers, or whatever you want to call them because we are gonna start to target them differently, and send emails differently. So if we have a database, we can always pull it, throw it into playvl.
146 00:22:21.160 ⇒ 00:22:22.889 Daniel Schonfeld: Send whatever message we want to them.
147 00:22:23.370 ⇒ 00:22:27.810 Uttam Kumaran: Yeah, cause it looks like even, I mean, this is a huge one, but it looks like it’s
148 00:22:28.750 ⇒ 00:22:36.890 Uttam Kumaran: yeah. I mean, this is like, January, January, July, August. Today
149 00:22:37.180 ⇒ 00:22:44.800 Daniel Schonfeld: there is a huge order lasted until then. This is one. Yes, one guy dude. There’s probably about
150 00:22:44.970 ⇒ 00:22:53.399 Daniel Schonfeld: 20,000 plus service guys in this country. Yeah, yeah, so you can extrapolate those numbers out to get really big quickly.
151 00:22:53.910 ⇒ 00:23:03.540 Uttam Kumaran: And it’s one, and it’s one order, right? So the the risk. Like it’s great, because every order may have like return risk.
152 00:23:03.990 ⇒ 00:23:08.880 Uttam Kumaran: You know, again. And but I think the biggest thing you mentioned is like the customer acquisition cost on this.
153 00:23:08.940 ⇒ 00:23:10.339 Uttam Kumaran: It’s like.
154 00:23:10.520 ⇒ 00:23:24.530 Uttam Kumaran: because a consumer, if you go, if you personally, where do you live in Austin? Perfect lot of pools there? If you just walked up to
155 00:23:24.620 ⇒ 00:23:32.110 Daniel Schonfeld: 100 pool owners in Austin, maybe maybe 5 of them would know what equipment is in their pool, maybe.
156 00:23:32.420 ⇒ 00:23:34.390 Uttam Kumaran: Yeah, if you’re lucky.
157 00:23:34.670 ⇒ 00:23:48.189 Uttam Kumaran: No, I mean, I do. This is what I do now. Anyone that does a pool like running much. And like, I’m like, what are the pumps you’re using? Are you buying equipment? You should check it. Yeah, they have no idea, right? And so they don’t. They don’t fucking, know. But if you go into the service guys.
158 00:23:48.350 ⇒ 00:24:03.889 Daniel Schonfeld: there’s only 3 brands. There’s only a Pentare, Hayward and Jandy and Black and Decker is like, no. Oh, maybe I heard about it through Youtube or whatever. But Black and Decker doesn’t make bull pump. That’s what we want to hear. That’s actually music to my ears.
159 00:24:04.080 ⇒ 00:24:16.239 Daniel Schonfeld: Because when we first launch the pull service guys came out in their groups and we’re like Black and Decker cannot make a fucking pull pump. There’s no way like it. Look! And then, when they saw through like this, looks like a little baby toy
160 00:24:16.370 ⇒ 00:24:30.099 Daniel Schonfeld: those are! The people pulled up and said, We’re gonna send you a free pump. Just tell us honestly what you think, cause we knew it was good. And those, if you go to Youtube or anything. And you put in black and Decker, you’ll see the videos. Those are real
161 00:24:30.210 ⇒ 00:24:35.050 Daniel Schonfeld: unsolicited, real videos that people gave honest reviews on. We didn’t pay them.
162 00:24:35.190 ⇒ 00:24:41.179 Daniel Schonfeld: And so the more people that hate us and make noise and say, there’s only 3,
163 00:24:41.390 ⇒ 00:24:47.299 Daniel Schonfeld: those are. Those are going to be the people that we’re going to go after and once they become loyal, they’ll tell
164 00:24:47.350 ⇒ 00:24:52.540 Daniel Schonfeld: they have the loudest voices. And so those are the people I’m trying to get on board
165 00:24:52.950 ⇒ 00:24:56.899 Daniel Schonfeld: but we’re trying to change all that but a customer. If you said
166 00:24:57.320 ⇒ 00:25:04.189 Daniel Schonfeld: if you then went out to a thousand people in the country and polled them instead. Have you ever heard of Pentair? No, Hayward? No.
167 00:25:04.380 ⇒ 00:25:10.580 Daniel Schonfeld: Jandie, no black and Decker? Yeah, of course I would say 90 out of 100, which is.
168 00:25:10.870 ⇒ 00:25:24.179 Daniel Schonfeld: yeah. I’ve heard of that brand. whether they associate it with high quality equipment. No, but that’s why Black and Decker is a company which is 19 billion. We’re probably their smallest licensee.
169 00:25:24.380 ⇒ 00:25:32.649 Daniel Schonfeld: But we’re having the biggest impact on their 19 billion dollar business because we’ve raised the bar on what Black and Decker products could be
170 00:25:32.720 ⇒ 00:25:37.499 Daniel Schonfeld: by selling 3, $4,000 heat pumps. They’ve never sold a product over 200 bucks ever
171 00:25:37.710 ⇒ 00:25:39.540 Uttam Kumaran: in their 200 year history.
172 00:25:39.810 ⇒ 00:25:45.540 Daniel Schonfeld: So there’s just there’s there’s there’s a lot of upside to this.
173 00:25:45.630 ⇒ 00:25:59.009 Daniel Schonfeld: you know, long term in in making this stuff work. And we’re partnering with pool service guys who are the credibility in the pool service. The the reason I’m telling you all this is so you can start to. No, it makes. It’s a great context.
174 00:25:59.030 ⇒ 00:26:02.289 Daniel Schonfeld: And I mean again, it’s like, everything’s ramping up until
175 00:26:02.490 ⇒ 00:26:18.929 Uttam Kumaran: II mean, it’s it’s there’s gonna be a huge flight. It’s I don’t know if it’s gonna freeze here this weekend. But what? Probably march here, it’ll be back to pool season. So yeah, that’ll be exciting to kind of see all that kind of play out. And then hopefully, we have a lot of the reporting ready to kind of spot these guys.
176 00:26:19.440 ⇒ 00:26:20.300 Daniel Schonfeld: yeah.
177 00:26:20.570 ⇒ 00:26:26.250 Daniel Schonfeld: and your your territory is a big one for us, because when it does freeze suddenly everybody’s pumps break.
178 00:26:26.720 ⇒ 00:26:33.550 Uttam Kumaran: Okay, well, this is the weekend then supposed to be like 12 on Monday.
179 00:26:33.860 ⇒ 00:26:51.569 Uttam Kumaran: Yeah, like below is supposed to be 12 on Monday, and I know Midwest is getting a huge storm, but the people here like freak out, if, like, right now, it’s probably 50, and people will be like, I don’t wanna go outside. But again, yeah, people here at no preparation. So there’s a high chance.
180 00:26:51.830 ⇒ 00:26:53.499 Uttam Kumaran: Maybe something freezes
181 00:26:53.620 ⇒ 00:27:01.359 Daniel Schonfeld: when there was a big flood. It was like 3 years ago in Texas. There huge rains, floods.
182 00:27:01.580 ⇒ 00:27:02.970 Uttam Kumaran: Yeah, 3 years ago.
183 00:27:03.060 ⇒ 00:27:15.050 Daniel Schonfeld: like our booster pumps sold out in like 3 weeks because everybody’s booster pumps cracked and Bro. We need to replace it. It’s like 600 bucks. Ours was like 2 50, probably.
184 00:27:15.290 ⇒ 00:27:35.289 Daniel Schonfeld: But they’re just as good, if not better, than everyone’s. The booster pumps, you know. Control the the cleaner that’s in the pool it hooks inside. It goes around. If that booster pump breaks, you can’t clean your pool, so it’s a smaller pump, but we make one. We’re one of the only people who make the replacement and a lot cheaper. So
185 00:27:35.570 ⇒ 00:27:37.139 Daniel Schonfeld: those are the things.
186 00:27:37.510 ⇒ 00:27:51.410 Daniel Schonfeld: When I talk about weather and stuff like that you know, it’s hard to predict and say, Okay, it’s gonna be 12 degrees in Austin. But when we when we’re at scale and we’ve got booster pumps in New York at at our DC.
187 00:27:51.490 ⇒ 00:28:03.370 Daniel Schonfeld: And all of them are there, and let’s say we have a DC. In Texas, we might say shit the next 2 weeks. It’s forecast to be really cold in Texas. Let’s move some inventory to Texas this week.
188 00:28:03.560 ⇒ 00:28:09.669 Uttam Kumaran: Yeah. So then our shipping cost goes down. We have available inventory because people need it immediately.
189 00:28:09.730 ⇒ 00:28:18.580 Those are the things that will really get us to the next level. As we grow, and where I’d love for these analytics to go by use utilizing
190 00:28:18.620 ⇒ 00:28:21.080 Daniel Schonfeld: weather patterns and looking.
191 00:28:21.520 ⇒ 00:28:28.220 Daniel Schonfeld: you know, something in the system saying, Okay, there’s weird weather or very cold weather in Florida coming over the last 4 years.
192 00:28:28.650 ⇒ 00:28:35.230 Daniel Schonfeld: Every time there’s the temperature drops below 50. In Florida we get an influx of certain types of pumps.
193 00:28:35.320 ⇒ 00:28:45.439 Daniel Schonfeld: and some people would say, move pumps from move a hundred pumps from the DC. Up northeast to down down south to your DC. Down south to be prepared
194 00:28:45.530 ⇒ 00:28:46.990 Daniel Schonfeld: for the weather pattern.
195 00:28:47.000 ⇒ 00:28:51.590 Daniel Schonfeld: So like that type of intelligence to us will be a game changer.
196 00:28:51.990 ⇒ 00:29:00.069 Uttam Kumaran: Yeah, actually, I can come in. Yeah, that’s actually great. Cause. I we I actually last week just kicked off some of the weather stuff
197 00:29:00.110 ⇒ 00:29:17.530 Uttam Kumaran: with a friend of mine who formerly worked at Flexport. He was worked in logistics as like a operations analyst. And I just sent him some data on weather. And I’m I just owe him a couple of it’s it’s actually, surprisingly hard to get
198 00:29:17.840 ⇒ 00:29:20.370 Uttam Kumaran: daily forecast by Zip.
199 00:29:20.570 ⇒ 00:29:30.279 Uttam Kumaran: or like historical, really easily. So we’re working on getting that. But he just messaged me yesterday asking, what’s the update on that data set? So
200 00:29:30.360 ⇒ 00:29:41.480 Uttam Kumaran: again, I’m hoping that we kind of, he initially did like a brief analysis and mentioned that like, okay, it was tough to get a signal just from looking at total orders.
201 00:29:41.530 ⇒ 00:29:56.830 Uttam Kumaran: like zip code, zip code weather and sales. But I wanted to hit like weather events, which was, which are pretty much storms. But I said, Okay, let’s let’s go a little bit deeper instead of just weather events.
202 00:29:56.840 ⇒ 00:30:09.500 Uttam Kumaran: Let’s look at forecast variation. And so that’s the next thing. So that’s also I was kind of working on it in the background a little bit. So yeah, I think he’s got to look at if you went through our data and said.
203 00:30:10.590 ⇒ 00:30:17.720 Daniel Schonfeld: Show me spikes in sudden sales in a specific skew, and the biggest ones are going to be cover pumps.
204 00:30:18.250 ⇒ 00:30:25.500 Daniel Schonfeld: And if you overlaid and you said, Okay, there was a massive 200% increase in these types of cover pumps.
205 00:30:25.860 ⇒ 00:30:38.819 Daniel Schonfeld: And then you said, Okay, where? Where was the biggest increase? And it’s likely gonna be in a region southeast. And you say, Okay, what was going on? Weather wise during that time period, and you say, ship, there was 5 days of rain.
206 00:30:39.390 ⇒ 00:30:55.950 Daniel Schonfeld: and then you’ll say, Okay, great, that there’s a signal right there, and and kind of back date, and say, how much advance notice did we have of that weather event? Looked it up and said, Oh, shit! They actually knew that 10 days in advance there was gonna be.
207 00:30:56.120 ⇒ 00:30:59.909 Daniel Schonfeld: It was all over the news. They were talking about it. There were all these signals that it was coming.
208 00:31:00.180 ⇒ 00:31:01.780 Uttam Kumaran: Could we have
209 00:31:02.420 ⇒ 00:31:15.289 Daniel Schonfeld: sent a large amount of inventory there? And but also, if you looked at, then looked at the data for shipping zones that you now have created, and say, where do we ship these from? Well, we ship them from Yap Hank, Long Island. The average
210 00:31:15.450 ⇒ 00:31:29.789 Daniel Schonfeld: shipping costs was 35 bucks. Had we had them in Jacksonville. and it was a zone one send it would have been 12 bucks extrapolate. Now you have real data to say we ship 3,000 units save 30 bucks.
211 00:31:29.850 ⇒ 00:31:34.099 Daniel Schonfeld: That’s $90,000 we could have saved just on that intelligence right there.
212 00:31:34.600 ⇒ 00:31:41.909 Uttam Kumaran: Yeah. So that’s exactly what I’ll tell them is one is the look at cover pumps. Now that we have all the skews
213 00:31:41.990 ⇒ 00:31:48.650 Uttam Kumaran: categorized to, it’s a lot better to get to there and then also not only look at Temp, but I’m going to see whether I can find
214 00:31:48.790 ⇒ 00:31:52.039 Uttam Kumaran: precipitation.
215 00:31:53.540 ⇒ 00:31:57.880 Uttam Kumaran: and yeah, I mean, he only spent a few hours on it. But I think actually we have.
216 00:31:58.060 ⇒ 00:32:03.240 Uttam Kumaran: It’s nice that we have all the classification now, and it’s easy for me to run the query. The only thing is like finding.
217 00:32:03.380 ⇒ 00:32:11.520 Uttam Kumaran: though, you’d be surprised, like the government data website for weather. So I’m like having to find some like interesting paths to get to that. But
218 00:32:11.620 ⇒ 00:32:14.379 Uttam Kumaran: it’s there. I just need another couple of hours.
219 00:32:14.530 ⇒ 00:32:18.250 Daniel Schonfeld: Yeah. Other other data to plug in. That’s interesting.
220 00:32:19.060 ⇒ 00:32:20.950 Daniel Schonfeld: is, there’s public.
221 00:32:21.180 ⇒ 00:32:34.759 Uttam Kumaran: yeah. So so you can get everything from like the national like I’m looking. I have it up right now. I was just looking at it before, and it’s like you can get it from the National Center for Environmental, which is like the Noaa they have, whether by zip
222 00:32:34.840 ⇒ 00:32:44.279 Uttam Kumaran: and a ton of weather data. You know, there’s there’s weather events, stuff like that. That’s more like, if it’s a weather event. This I was like, I’m just going to give you all the temperatures by Zip.
223 00:32:44.350 ⇒ 00:32:45.370 Uttam Kumaran: and then.
224 00:32:45.610 ⇒ 00:32:52.769 Uttam Kumaran: you know, I think we’ll I’ll need to do a little bit of research to find out what is pretty accurate for a forecast like if 2 weeks
225 00:32:52.960 ⇒ 00:33:11.510 Uttam Kumaran: are like pretty accurate. Then I’ll find out how to get at least 2 weeks out and see how much of forecast we can get. And the one thing I told is, I want you to see whether there’s a correlation between temp and sales. But actually, what I’m gonna have him now consider is skew. So maybe even like filtering out skews that have no
226 00:33:11.770 ⇒ 00:33:28.690 Uttam Kumaran: like real contribution. But excuse that don’t change based on the weather. And then, second, I wanted to not only like attempt but precipitation. and I think, like again, I think we’re gonna find it. We didn’t find in the first pass, because it’s just weather events.
227 00:33:28.880 ⇒ 00:33:31.580 Uttam Kumaran: So I want to see where he can go from here.
228 00:33:32.940 ⇒ 00:33:56.360 Daniel Schonfeld: Okay? And also again, if you, if you have anything anecdotal that’s really helpful, because we’ll we’ll look for that and then back kinda back up into the entire data set. So yeah, I think like the used case I gave you is actually one of the more likely used cases you’ll find, is rain, rain and cold, rain and freezing weather in territories that don’t typically experience. That
229 00:33:56.530 ⇒ 00:33:59.160 Daniel Schonfeld: is where we’ll see major increases. Whether it’s
230 00:33:59.200 ⇒ 00:34:13.450 Daniel Schonfeld: you have to remember someone buying this stuff. No one wakes up and says I’m gonna just go browse, pull pumps. It never happens. It’s always in an emergency. So we get ahead of those emergencies by understanding. And what causes that is weather
231 00:34:13.830 ⇒ 00:34:18.759 Daniel Schonfeld: or just general wear and tear their pump breaks. They got a pool party in the weekend. They’re like shit.
232 00:34:18.800 ⇒ 00:34:28.730 Daniel Schonfeld: I need one tomorrow, and the pull service guys telling me it’s 3 grand to buy a new one. Where can I find an alternative? That’s one use case. But for weather is what precipitates the next
233 00:34:28.770 ⇒ 00:34:30.429 Daniel Schonfeld: level of emergency.
234 00:34:30.600 ⇒ 00:34:31.600 Daniel Schonfeld: Yeah.
235 00:34:32.340 ⇒ 00:34:34.090 Daniel Schonfeld: But I think
236 00:34:34.280 ⇒ 00:34:44.060 Daniel Schonfeld: that’s one level of analytics I’d like to do, and also at the customer level is understanding which customers spend the most money over time and have the highest. Ltev.
237 00:34:44.090 ⇒ 00:34:47.510 Daniel Schonfeld: so that’ll help guide Kim.
238 00:34:48.000 ⇒ 00:34:53.849 Uttam Kumaran: I think there’s different levels of analytics. There’s market analytics where we can one day pull in
239 00:34:53.929 ⇒ 00:34:58.549 Daniel Schonfeld: Api data from new permits for pools.
240 00:34:59.130 ⇒ 00:35:07.420 Uttam Kumaran: Where is the where is the pool industry growing like a market standpoint? And is that for residential? Is that commercial is that, like both
241 00:35:08.160 ⇒ 00:35:20.230 Daniel Schonfeld: gonna be both most towns, it’s public data of like new permits for pool. But it will say residential, probably. And I can tell you. I got another consultant on my on the payroll that
242 00:35:20.580 ⇒ 00:35:29.789 Daniel Schonfeld: worked for penter fluid, or all the big companies. He’s an executive level that knows where to get a lot of this data. So I can just set you up with him.
243 00:35:29.810 ⇒ 00:35:41.780 Uttam Kumaran: That’d be great. Or, yeah, if you can. If you can send me one for here, locally, or something that I can at least have a a version of one. I can try to go find him. That’s great. I didn’t know you. I didn’t know you
244 00:35:41.900 ⇒ 00:35:47.759 Uttam Kumaran: have to register. I mean, I’m not surprised that they care about that. But
245 00:35:47.790 ⇒ 00:35:50.050 Uttam Kumaran: yeah, I didn’t know there’s actual permitting process.
246 00:35:51.520 ⇒ 00:35:52.360 Uttam Kumaran: Yep.
247 00:35:54.820 ⇒ 00:35:56.480 Uttam Kumaran: I mean, that’s great.
248 00:35:57.030 ⇒ 00:36:00.500 Daniel Schonfeld: not everywhere. But most most do it.
249 00:36:02.230 ⇒ 00:36:08.350 Daniel Schonfeld: The more intelligence we have, the better and the more valuable this company, because nobody’s doing this stuff.
250 00:36:08.410 ⇒ 00:36:10.999 Daniel Schonfeld: Yeah. And it just I mean it.
251 00:36:11.920 ⇒ 00:36:20.369 Uttam Kumaran: Yeah, it’s it’s great. Because, you know, even when you start a business here, shopify sends you a thing saying, Are you sign online? They’ll send it to your address
252 00:36:20.520 ⇒ 00:36:42.740 Uttam Kumaran: like, if you when you started those when you start a business here like when I started mile. See, I got a thing in the mail from shopify, saying, if you’re selling online to music, so they’re getting it from somebody somewhere, that there was a new registration. Right? So yep, you get a ton like I get new loan stuff all the time. Business loans I get inundated all the time
253 00:36:43.090 ⇒ 00:36:55.339 Daniel Schonfeld: we wanted to get inventory loans. I don’t know where they got it. I don’t know who sold it where it got, but somehow it’s been sold 400 times. I get calls all day every day. Email everything from lenders.
254 00:36:56.080 ⇒ 00:36:59.870 Daniel Schonfeld: like throwing money at us like we can get a better rate. It’s like crazy.
255 00:37:00.160 ⇒ 00:37:07.650 Daniel Schonfeld: but that we don’t want to do that. But there is this data available that we could be leveraging smarter decisions.
256 00:37:08.740 ⇒ 00:37:16.050 Uttam Kumaran: The last thing I wanted to show today is we did a lot of work on the shipping
257 00:37:16.450 ⇒ 00:37:29.119 Uttam Kumaran: like efficiency shipping measurement side. So this, I think, is a good start. And as we look at this hopefully on like a weekly or a monthly cadence.
258 00:37:29.170 ⇒ 00:37:35.200 Uttam Kumaran: we now have a ton of data categorized of how we’re spending on shipping across
259 00:37:35.470 ⇒ 00:37:40.389 Uttam Kumaran: the 4 major categories. So, Fedex. I’ll split. I ltl
260 00:37:40.400 ⇒ 00:37:59.589 Uttam Kumaran: ups and usps and so the biggest levers we have in this part is, we understand the time we understand the zone and we understand the the price per pound or the product. So really like, what I look for is.
261 00:37:59.780 ⇒ 00:38:03.869 Uttam Kumaran: I’m looking at volume, and I’m kind of looking at
262 00:38:04.790 ⇒ 00:38:17.049 Uttam Kumaran: price per pound. So basically, what we’re seeing here is, you can just see how the spend is shifting between all the 4 categories
263 00:38:17.340 ⇒ 00:38:26.890 Uttam Kumaran: and kind of more and more in a tabular format. You can kind of see, buy, product class how much we spent on shipping.
264 00:38:26.940 ⇒ 00:38:30.579 Uttam Kumaran: Scroll up, scroll down the other way. Sorry.
265 00:38:30.600 ⇒ 00:38:36.730 Uttam Kumaran: I’m only seeing half of it. There we go. Oh, okay, okay, yeah, you can see the
266 00:38:36.890 ⇒ 00:38:38.800 Uttam Kumaran: the product class.
267 00:38:38.860 ⇒ 00:38:46.330 Uttam Kumaran: The total shipping cost, the total sales, the quantity. And then the the thing that’s helpful for me is like, okay, the second question is like.
268 00:38:46.350 ⇒ 00:38:51.920 Uttam Kumaran: what percentage of the sale amount are we spending on shipping? And you can see the
269 00:38:51.940 ⇒ 00:38:58.769 Uttam Kumaran: categories in which shipping as a percent of sales is higher. And so that’s
270 00:38:59.100 ⇒ 00:39:01.960 Uttam Kumaran: kind of where I have conditional formatting to kind of
271 00:39:02.350 ⇒ 00:39:05.079 Uttam Kumaran: kind of indicate that this
272 00:39:05.220 ⇒ 00:39:07.729 Daniel Schonfeld: yeah, yeah, one. Sec,
273 00:39:09.580 ⇒ 00:39:10.740 Daniel Schonfeld: is this right?
274 00:39:12.350 ⇒ 00:39:17.879 Daniel Schonfeld: So inground pool pumps, it’s only 1% of the sale is the shipping cost
275 00:39:18.900 ⇒ 00:39:20.900 Uttam Kumaran: in ground pool palms.
276 00:39:21.050 ⇒ 00:39:26.880 Uttam Kumaran: So there haven’t been a ton of orders. But I can go in and look
277 00:39:27.080 ⇒ 00:39:33.589 Uttam Kumaran: that for each of these 84 what the shipping total is this week.
278 00:39:33.680 ⇒ 00:39:36.789 Daniel Schonfeld: Wait 84. Don’t, don’t move up that screen for a second.
279 00:39:38.800 ⇒ 00:39:49.350 Daniel Schonfeld: It looks to be in line with the past week. 57 divided by 84. No way, cause it. That means the average is 12 12 bucks to ship an in ground pool. Pub.
280 00:39:49.540 ⇒ 00:39:50.929 Daniel Schonfeld: that would be great.
281 00:39:51.700 ⇒ 00:39:53.359 Uttam Kumaran: Let’s look at.
282 00:39:54.720 ⇒ 00:39:58.719 Daniel Schonfeld: I’m talking about the 1,057 number number.
283 00:40:00.190 ⇒ 00:40:01.160 Daniel Schonfeld: But yeah.
284 00:40:02.530 ⇒ 00:40:05.550 Uttam Kumaran: okay, so let me take that away. I’ll I’ll look at
285 00:40:06.030 ⇒ 00:40:12.129 Uttam Kumaran: what’s contributing to that. But yeah, you’re right. It’s $12 and 50 cents to ship a pool pump. That would be.
286 00:40:12.150 ⇒ 00:40:15.099 Uttam Kumaran: And then I wonder why it’s not the same.
287 00:40:15.130 ⇒ 00:40:26.660 Daniel Schonfeld: Yeah. The brushes seems very off this for sec. 1753, divided by 1950, that’s about right.
288 00:40:27.540 ⇒ 00:40:40.750 Daniel Schonfeld: but something else is spirit 3, 1, 3, 7, divided by 1, 95. Yeah, something’s not right there, because this. the brushes
289 00:40:41.350 ⇒ 00:40:46.089 Daniel Schonfeld: that would make the average sales 16 bucks, which I think is sounds low.
290 00:40:46.570 ⇒ 00:40:49.019 Uttam Kumaran: Yeah. I wonder if
291 00:40:49.230 ⇒ 00:40:57.150 Daniel Schonfeld: yeah, just double check it. You don’t have to figure out now, but just I would I would check those shipping glass. Something’s wrong with the in ground. And then he pumps.
292 00:40:57.660 ⇒ 00:41:07.949 Uttam Kumaran: Yeah? So so that the the issue, I think that’s happening. And this is kind of the issue we actually had with Tom’s order is that sometimes things get shipped together.
293 00:41:08.220 ⇒ 00:41:14.730 Uttam Kumaran: And so for me to understand, the attributed shipment costs to a specific item.
294 00:41:14.770 ⇒ 00:41:16.410 Uttam Kumaran: Yep, it’s like
295 00:41:16.860 ⇒ 00:41:28.790 Uttam Kumaran: difficult. So actually, this is something that II was actually thinking about asking you, but didn’t like pretty much put together what the phrasing was. But in that situation, if something is bundled orders.
296 00:41:29.160 ⇒ 00:41:33.689 Uttam Kumaran: And we’re looking at shipping costs by product class. What do you think is the best
297 00:41:34.390 ⇒ 00:41:39.400 Uttam Kumaran: option to divide that cost up by product? Should we do it
298 00:41:39.600 ⇒ 00:41:51.249 Uttam Kumaran: like as a proportion, or should we say we’re not looking at? I mean, I don’t. I would rather not do the second thing, which is, only look at single item.
299 00:41:52.020 ⇒ 00:42:00.630 Daniel Schonfeld: II think we could do. Yeah, I think you put the single items here. And then underneath, you can do say, just bundles.
300 00:42:01.220 ⇒ 00:42:08.339 Uttam Kumaran: I see. Okay, well, bundles could be one. Let me think about that or would just be like an other category.
301 00:42:08.430 ⇒ 00:42:14.109 Uttam Kumaran: And at least we can look at the total shipping to sales because
302 00:42:14.730 ⇒ 00:42:23.159 Daniel Schonfeld: and then we can drill into that if we need to and export it and say, Okay, what happened here. But I do think if we’re gonna look at.
303 00:42:24.120 ⇒ 00:42:37.709 Daniel Schonfeld: you know the shipping cost by class, you really have to bifurcated here and say, only show this, if a brush was shipped by itself or multiple brushes, II think we’re at the massage, because if they buy 3
304 00:42:38.870 ⇒ 00:42:45.019 Uttam Kumaran: it is a bundle technically. But but see, I can. I can do it by proportion based on weight.
305 00:42:45.620 ⇒ 00:42:47.460 Uttam Kumaran: Right? Because if you, if you, if you.
306 00:42:48.710 ⇒ 00:42:50.989 Uttam Kumaran: if you have a brush and a larger item.
307 00:42:51.040 ⇒ 00:42:57.240 Uttam Kumaran: and you’re getting priced by the pound. And there isn’t anything about the dimensionality
308 00:42:57.480 ⇒ 00:43:07.429 Uttam Kumaran: which we’re from for the most part getting a ton of discounts on. I might as well just do so on the on the discount side. So I do the similar thing for the discounts.
309 00:43:07.580 ⇒ 00:43:15.520 Uttam Kumaran: If we’re looking at discounts attributed to an individual item, I actually split the total order discount by proportion.
310 00:43:15.920 ⇒ 00:43:33.200 Uttam Kumaran: based on the contribution to the total sale amount. So if you have a brush and a pump. Most of the discount gets attributed to the pump. But again, that way it all adds up. So maybe it’s a similar thing, because this is it seems like it’s only weight is the big factor. So maybe I just do proportional
311 00:43:33.520 ⇒ 00:43:37.430 Uttam Kumaran: cost attribution like that. And that’s at least closer. And because I don’t.
312 00:43:37.440 ⇒ 00:43:50.510 Uttam Kumaran: I think we’re gonna have a lot of cases where their bundles and I don’t wanna lose like I just don’t wanna lose that fidelity and have. So that’s what’s exactly happening.
313 00:43:51.590 ⇒ 00:43:53.269 Uttam Kumaran: Yeah, that’s not that bad.
314 00:43:58.090 ⇒ 00:44:05.740 Uttam Kumaran: Okay, so I’ll make that change. The other thing that’s interesting. Coming to look at here is by zone and by month
315 00:44:06.110 ⇒ 00:44:09.410 Uttam Kumaran: what we’ve spent the sales.
316 00:44:09.620 ⇒ 00:44:19.930 Uttam Kumaran: And then looking at the shipping as a percent of sales. So you can see as we get to like. I think it’s typically Zone
317 00:44:20.410 ⇒ 00:44:21.600 Uttam Kumaran: 6,
318 00:44:23.210 ⇒ 00:44:25.909 Uttam Kumaran: where there’s a ton of shipping costs.
319 00:44:26.820 ⇒ 00:44:38.010 Uttam Kumaran: there’s also there’s a lot of sales. But as a total percentage of shipping costs are very high. Can you add that in at the total at the bottom?
320 00:44:38.210 ⇒ 00:44:41.050 Uttam Kumaran: Yes, it looks like
321 00:44:41.960 ⇒ 00:44:42.910 Uttam Kumaran: it’s
322 00:44:43.540 ⇒ 00:44:48.140 Uttam Kumaran: add it in right here. Yeah, this is a percentage
323 00:44:49.830 ⇒ 00:44:52.860 Uttam Kumaran: like, so we can quickly say.
324 00:44:53.050 ⇒ 00:44:57.619 Daniel Schonfeld: the total shipping cost of sales overall for that zone seems high.
325 00:44:58.700 ⇒ 00:45:03.679 Uttam Kumaran: I see? Yeah, I can. II I can do that. Yeah.
326 00:45:03.690 ⇒ 00:45:06.600 Uttam Kumaran: yeah, I can do that for that for that. Given
327 00:45:07.490 ⇒ 00:45:11.630 Daniel Schonfeld: for that given week or month or whatever. Yeah, yeah, total for that zone.
328 00:45:11.670 ⇒ 00:45:13.620 Uttam Kumaran: It’ll be like a percentage of the row.
329 00:45:14.640 ⇒ 00:45:27.160 Daniel Schonfeld: Yes, yeah. Cause I think that’s an important number, because that’s where we can figure out if all the zones are at 12, or whatever it is in the zone, 4. Is that 30%?
330 00:45:27.650 ⇒ 00:45:31.069 Uttam Kumaran: It’s a quick indicator saying, Okay, this is where we need to spend.
331 00:45:31.210 ⇒ 00:45:32.750 Daniel Schonfeld: Just like you just showed me
332 00:45:33.100 ⇒ 00:45:38.809 Daniel Schonfeld: spend a lot of our time. Because if we can fix this, that’s a big number
333 00:45:39.230 ⇒ 00:45:41.890 Daniel Schonfeld: and then we can break down the zone. 4
334 00:45:42.780 ⇒ 00:45:53.059 Daniel Schonfeld: smart. We can break out the zone 4 and say, Okay, where did these go? And if we opened up a DC. Where the majority are going, we can now bring that down to a zone, 2 or 3 or one.
335 00:45:53.290 ⇒ 00:45:57.719 Daniel Schonfeld: and save. you know, 1220, 12 to 15%.
336 00:45:58.210 ⇒ 00:46:07.129 Uttam Kumaran: Yeah. One thing I’m gonna do at the top of this is put like, try to do like a state by zone, or at least show some indication of
337 00:46:07.300 ⇒ 00:46:12.790 Uttam Kumaran: it’s quick for us to say, like, Okay, if it’s owned, for it’s like, usually the state these States.
338 00:46:13.050 ⇒ 00:46:22.370 Uttam Kumaran: And then I’ll have a I’ll have a little bit of a guide, for, like the it’s like something 50 miles. II forgot what what the logic was but
339 00:46:23.000 ⇒ 00:46:32.849 Daniel Schonfeld: where? Where was the? Because I’m looking at zone 4. Now, it looks okay. It’s like 10%. Where did I just see where it was like 300,000 or something.
340 00:46:33.230 ⇒ 00:46:36.159 Uttam Kumaran: In zone 6.
341 00:46:36.820 ⇒ 00:46:38.920 Uttam Kumaran: Zone 5 and zone 6.
342 00:46:42.470 ⇒ 00:46:45.249 Daniel Schonfeld: This is for what date range that I’m looking at right now, though.
343 00:46:45.420 ⇒ 00:46:49.109 Uttam Kumaran: This is so. These are just the most recent months.
344 00:46:49.750 ⇒ 00:46:53.519 Daniel Schonfeld: Oh, that’s each one is a month by month. Each one is a month.
345 00:46:54.750 ⇒ 00:46:58.880 Daniel Schonfeld: sure. Oh, okay, I see. I see. Got it.
346 00:47:06.670 ⇒ 00:47:12.599 Daniel Schonfeld: okay, so that’s what. What’s the the last one is December. What of 23?
347 00:47:13.320 ⇒ 00:47:14.270 Uttam Kumaran: Yes.
348 00:47:14.450 ⇒ 00:47:16.009 Daniel Schonfeld: the bottom or the top
349 00:47:16.500 ⇒ 00:47:28.739 Uttam Kumaran: th these ones. So I wouldn’t look at these sums are going to be. I think it’s the last 12 months. So the bottoms aren’t gonna line up to December. It’s just a rolling number.
350 00:47:28.970 ⇒ 00:47:30.750 Uttam Kumaran: I’m actually
351 00:47:31.780 ⇒ 00:47:37.890 Uttam Kumaran: yeah, I need to decide. I make sure you remove these because it’s a little bit like, not if these aren’t exactly like the last.
352 00:47:37.970 ⇒ 00:47:40.170 Uttam Kumaran: I may just make it last 12 months. But
353 00:47:41.080 ⇒ 00:47:44.530 Uttam Kumaran: yeah, this, this is like
354 00:47:45.420 ⇒ 00:47:46.929 Uttam Kumaran: this month, currently
355 00:47:49.950 ⇒ 00:47:53.700 Daniel Schonfeld: shipping percentage of sales. Okay.
356 00:47:54.310 ⇒ 00:47:56.490 Uttam Kumaran: yeah. So mainly, it’s like.
357 00:47:56.980 ⇒ 00:47:58.800 where where’s this month? Again.
358 00:47:59.200 ⇒ 00:48:01.039 Uttam Kumaran: right here
359 00:48:02.500 ⇒ 00:48:10.839 Daniel Schonfeld: now, is there a total for this month? If I went across? Yes, okay, great. That’s what I wanna say right now.
360 00:48:15.460 ⇒ 00:48:22.089 Daniel Schonfeld: oh, on zoom, that’s why I can’t see the end of it. Okay, okay. So this was our most efficient month.
361 00:48:24.090 ⇒ 00:48:27.389 Uttam Kumaran: So far, so far, so far. Yeah.
362 00:48:30.030 ⇒ 00:48:33.989 Uttam Kumaran: But that’s not yeah. But you can see it. Kind of it. Kind of ramps up.
363 00:48:34.510 ⇒ 00:48:37.440 Daniel Schonfeld: Wait, go! Go back to there. Stay there for a second.
364 00:48:38.320 ⇒ 00:48:41.860 Uttam Kumaran: That’s saying we’ve only done 34,000 sales this month.
365 00:48:44.910 ⇒ 00:48:47.750 Daniel Schonfeld: Now I’m confused. Sorry! Oh, that’s zone. 8.
366 00:48:47.940 ⇒ 00:49:05.199 Daniel Schonfeld: Oh.
367 00:49:05.390 ⇒ 00:49:09.440 Daniel Schonfeld: for each of those going across just the total shipping, total sales.
368 00:49:09.560 ⇒ 00:49:13.850 Uttam Kumaran: shipping percentage. Actually just do that. It’s very quick for me to do that right now.
369 00:49:16.010 ⇒ 00:49:25.390 Uttam Kumaran: so I can do show row totals, and then what you’ll see on the right here
370 00:49:25.970 ⇒ 00:49:30.279 Uttam Kumaran: is you’ll see the totals. So if I, if I run this
371 00:49:30.290 ⇒ 00:49:32.109 Daniel Schonfeld: and are you able to add the percentage
372 00:49:32.930 ⇒ 00:49:34.929 Uttam Kumaran: I will.
373 00:49:35.210 ⇒ 00:49:44.200 Uttam Kumaran: I forgot what the function is to do the percentage of the row some that I’m not gonna do
374 00:49:44.600 ⇒ 00:49:50.179 Uttam Kumaran: alright, let’s see. But yeah, so yeah, here’s
375 00:49:50.920 ⇒ 00:49:53.539 Daniel Schonfeld: oh, again, I’m zoomed. Okay. So
376 00:49:58.680 ⇒ 00:50:03.029 Uttam Kumaran: it’s about 2022 K, and so again. I wanna
377 00:50:03.330 ⇒ 00:50:10.179 Uttam Kumaran: now that we have the definitions, I want to make sure it’s everywhere. It just says gross sales. And it’s just gross sales everywhere.
378 00:50:11.760 ⇒ 00:50:18.269 Daniel Schonfeld: 11% and 12%. And then starting in what month? What is the 805. Representative of December?
379 00:50:19.360 ⇒ 00:50:23.030 Uttam Kumaran: The 805. Yes, that’s December.
380 00:50:23.210 ⇒ 00:50:25.389 Daniel Schonfeld: and that’s total sales from where
381 00:50:27.210 ⇒ 00:50:28.840 Daniel Schonfeld: is that? Just shopify?
382 00:50:29.200 ⇒ 00:50:31.929 Uttam Kumaran: It’s shopify and Amazon, it’s everything.
383 00:50:32.700 ⇒ 00:50:37.850 Daniel Schonfeld: But do we have when it says shipping cost is the shipping cost? Just
384 00:50:39.240 ⇒ 00:50:46.260 Uttam Kumaran: oh, so you’re so you’re right. So if it’s Amazon fulfilled. there’s no
385 00:50:47.130 ⇒ 00:50:58.920 Uttam Kumaran: shipping costs associated, which is another thing I noticed this week all that was like kind of tripping stuff up. So I wonder if I should. Maybe we should get rid of the Amazon. Fulfilled orders from the sales.
386 00:51:00.030 ⇒ 00:51:10.779 Daniel Schonfeld: Yeah. Or just look at this as shopify separate from. But yeah, we could start that for now. And let’s see. for data accuracy. It may end up having to just be shopify.
387 00:51:11.060 ⇒ 00:51:12.140 Daniel Schonfeld: Okay.
388 00:51:12.260 ⇒ 00:51:17.780 Daniel Schonfeld: as as when we look at this cause, II honestly, I really don’t give a fuck about
389 00:51:18.100 ⇒ 00:51:22.379 Daniel Schonfeld: Amazon. Yes, it adds revenue, but a lot of cases. We lose money.
390 00:51:22.740 ⇒ 00:51:28.439 Daniel Schonfeld: shopify D to C business. It’s like ancillary, the other stuff.
391 00:51:28.910 ⇒ 00:51:35.679 Uttam Kumaran: There’s not much I can do to fine tune Amazon. There’s very few levers, but with this
392 00:51:35.770 ⇒ 00:51:40.820 Daniel Schonfeld: I could. There’s a lot I can do. So I almost like care much more about the shopify orders. But
393 00:51:41.120 ⇒ 00:51:45.710 Daniel Schonfeld: yeah, for this specific make sure we’re looking at.
394 00:51:46.680 ⇒ 00:51:48.659 Daniel Schonfeld: you know, factoring in, yeah, just
395 00:51:48.850 ⇒ 00:51:50.680 Uttam Kumaran: okay. Yeah. I mean.
396 00:51:51.160 ⇒ 00:51:56.240 Uttam Kumaran: again, we’re doing. It’s it’s like close to. It’s it’s somewhat
397 00:51:56.350 ⇒ 00:52:09.369 Uttam Kumaran: nearby in terms of what we’re doing per day. I mean not. It’s shopify is always more. But it’s not like insignificant. But you’re right, and that we do have Amazon fulfilled orders.
398 00:52:09.670 ⇒ 00:52:18.180 Uttam Kumaran: And that’s a complete brain, for I totally didn’t re, didn’t even like recall that.
399 00:52:18.510 ⇒ 00:52:20.370 Uttam Kumaran: yeah, we should.
400 00:52:21.250 ⇒ 00:52:26.309 Uttam Kumaran: yeah, I could just have a toggle to get rid of that. So let’s just do that.
401 00:52:26.740 ⇒ 00:52:27.420 Daniel Schonfeld: Okay.
402 00:52:32.000 ⇒ 00:52:36.640 Uttam Kumaran: lot of toggles. Is this what you and Ben do on your calls?
403 00:52:37.330 ⇒ 00:52:43.889 Uttam Kumaran: No, II mainly. I mainly find those issues, and I will call him and kind of ask like
404 00:52:43.970 ⇒ 00:52:51.609 Uttam Kumaran: like, why aren’t like what are like, what’s this issue, or like? I’ll call chuck and kinda ask about like a specific package
405 00:52:51.680 ⇒ 00:53:07.860 Uttam Kumaran: cause for me, it’s like these nuances or stuff. That’s like, okay, I’m like, I found an order with no shipping and no customer. Okay, it’s Amazon fulfilled. Okay, so where does the cost go? Okay, goes to fees. Okay, are we tracking fees? Yes, we’re now. I have the fees. So that’s like how these kind of things cascade.
406 00:53:08.360 ⇒ 00:53:13.600 Uttam Kumaran: But this is the first time we put this together, based on our last call. So I sent this to Ben
407 00:53:14.090 ⇒ 00:53:25.749 Uttam Kumaran: evening yesterday. But so I’ll kind of follow up with him on his thoughts on this. So yeah, I think there’s some, probably some adjustments to make, but basically wanted to show
408 00:53:25.880 ⇒ 00:53:31.830 Uttam Kumaran: by skew by year. The total shipping cost shipment volume by zone. Now this
409 00:53:32.090 ⇒ 00:53:38.310 Uttam Kumaran: except for the gross sales, should be pretty accurate in terms of what we spend on shipping.
410 00:53:38.340 ⇒ 00:53:42.960 Uttam Kumaran: So we have both the the week, the zone, and
411 00:53:43.670 ⇒ 00:53:48.050 Uttam Kumaran: all that. We spent same thing by week class.
412 00:53:48.090 ⇒ 00:53:51.599 Uttam Kumaran: and then also the most expensive shipment orders.
413 00:53:52.980 ⇒ 00:53:59.089 Uttam Kumaran: But I think this was helpful. So the one thing is looking at. Can we go to an individual zone?
414 00:53:59.420 ⇒ 00:54:02.530 Uttam Kumaran: Look at the zones, contribution to the total, spend
415 00:54:03.120 ⇒ 00:54:07.639 Uttam Kumaran: and then within those zones, look, try to do the
416 00:54:08.200 ⇒ 00:54:13.149 Uttam Kumaran: proportional shipment costs and then take out the Amazon fulfilled sales.
417 00:54:14.710 ⇒ 00:54:15.650 Daniel Schonfeld: Yeah.
418 00:54:15.970 ⇒ 00:54:27.300 Daniel Schonfeld: either take it out or just make a note of it or put a another line. But yeah, I think if we get that, the integrity of that specific data set will make more sense right now if we look at because the sales
419 00:54:27.380 ⇒ 00:54:46.640 Daniel Schonfeld: well, all of it right now, because we have to double accounting some shipping. Yeah. So once you fix that, then I’ll start going through it again. But we could keep doing these meetings, and I will keep you know. Question. I’ll keep questioning everything to make sure integrity is there just like we’re doing
420 00:54:46.950 ⇒ 00:54:51.619 Uttam Kumaran: and then we’ll get to a place where we feel good about
421 00:54:51.660 ⇒ 00:55:02.260 Daniel Schonfeld: the data. Look, we’re good enough, and as long as the we watch the trends and they’re the same data set and the integrity is there. As far as accuracy and timeliness
422 00:55:02.670 ⇒ 00:55:09.719 Daniel Schonfeld: we’ll be able to spot. you know, different anomalies within that, within those pattern, within those trends.
423 00:55:09.920 ⇒ 00:55:17.289 Uttam Kumaran: Okay, great. And then hopefully, this end of this month we could do a little bit of a we could do a month review and kind of poke at that as well.
424 00:55:17.440 ⇒ 00:55:28.479 Daniel Schonfeld: Yeah, that’s what I’d like to do with you is at the end of each month like actually run me the first couple of months. We’ll do it together as if I’m Pnl to look through this.
425 00:55:28.550 ⇒ 00:55:44.840 Daniel Schonfeld: and we’ll just double check everything and and make sure it’s accurate. And eventually I’d love to just be able to push a button, and it spits out all this data. I can then analyze it in, excel wherever I need to, and then start start to making changes or doing things differently based on the data.
426 00:55:45.100 ⇒ 00:55:45.920 Uttam Kumaran: Yeah.
427 00:55:46.140 ⇒ 00:55:48.250 Daniel Schonfeld: awesome dude, this is exciting.
428 00:55:48.850 ⇒ 00:55:57.739 Uttam Kumaran: We’re getting close. And again, the shipping stuff was really kind of interesting to work on. Because I think we’re starting like we’re starting. I’m starting to see ups really
429 00:55:58.030 ⇒ 00:56:02.510 Uttam Kumaran: come down in terms of like our price per pound. And then,
430 00:56:02.990 ⇒ 00:56:11.259 Uttam Kumaran: yeah, it’s been interesting to look at all the zone stuff. And then I’m really like, hopefully fingers crossed. We get something on the weather this week, so I’ll send that as soon as I get it
431 00:56:12.240 ⇒ 00:56:18.039 Daniel Schonfeld: awesome. And then we there’s so much more to do. Dude like the first conversation you and I had about all the
432 00:56:18.430 ⇒ 00:56:24.290 Daniel Schonfeld: you know, machine learning all that kind of stuff, especially with marketing. When we get to the marketing side of it.
433 00:56:24.300 ⇒ 00:56:31.840 Daniel Schonfeld: Looking at where the the the highest density of Ltv. Highest Lt.
434 00:56:32.190 ⇒ 00:56:39.000 Daniel Schonfeld: You know, overlaying different data sets on top of that to figure out where we should be spending our marketing dollars where
435 00:56:39.590 ⇒ 00:56:57.219 Daniel Schonfeld: especially with the service business I’m doing. I may be interested in opening up small retail locations. To service. to have have like a hub for service guys as well as end customers. So we’ll need a lot of good data to overlay to figure out what areas of the highest density. There’s already brand recognition
436 00:56:57.470 ⇒ 00:56:59.160 Daniel Schonfeld: and where they.
437 00:56:59.290 ⇒ 00:57:18.000 Uttam Kumaran: you know, buy certain types of products. I think you’re spot on, though on the Amazon thing is that the tough thing that I’m noticing, and the data is that if it goes through Amazon we lose so much information until pushing as many people to shop to the shopify flow
438 00:57:18.390 ⇒ 00:57:30.780 Uttam Kumaran: is like way better, because II can’t see. Repeat orders on Amazon for the first. I can’t even. I can’t see anything about the customer for the most part, and then half of them. We don’t even do the shipment for
439 00:57:30.910 ⇒ 00:57:36.709 Uttam Kumaran: so, or a proportion of them end up. There’s a ton of fees. So
440 00:57:37.010 ⇒ 00:57:47.839 Daniel Schonfeld: it’s like, it’s it’s definitely like, really apparent for for anybody. Unless you’re selling T-shirts, hats, commodities, little things.
441 00:57:47.940 ⇒ 00:57:59.280 Daniel Schonfeld: We just wanna get as make as much money as possible as quickly as you can. Amazon’s great. But for this type of business, where you really need to actually have close contact with the customer. Things break warranties.
442 00:57:59.320 ⇒ 00:58:13.329 Daniel Schonfeld: You also don’t want people haphazardly which they have been doing, sending back equipment cause they’re done with it at the end of the season, just cause it’s fun to send it back. And Amazon allows that when we can talk directly with them. And it interact. There’s
443 00:58:13.740 ⇒ 00:58:17.470 Daniel Schonfeld: less likelihood they do that. And also, you’re building a real relationship.
444 00:58:17.750 ⇒ 00:58:19.760 I know Amazon is built in relationship.
445 00:58:19.920 ⇒ 00:58:22.890 Uttam Kumaran: Yeah, I don’t care about Amazon. Yeah.
446 00:58:23.940 ⇒ 00:58:28.290 Daniel Schonfeld: So our focus is all on on the shopify side of this business.
447 00:58:31.060 ⇒ 00:58:41.250 Uttam Kumaran: Okay? Great. So ton of updates, I’ll share something by Tuesday, at least on the vital signs, date accuracy. And then this is all stuff to do for next week. So yeah.
448 00:58:41.590 ⇒ 00:58:45.590 Daniel Schonfeld: awesome, Tom dude you you really are the best you really, I really.
449 00:58:45.720 ⇒ 00:59:00.910 Daniel Schonfeld: this is awesome. I was gonna say, I think I’m the only one in the company really understands this. Even even Ben ben you know. I think he wanted to cut back on certain things, and I’m like, no, no, no.
450 00:59:00.960 ⇒ 00:59:06.110 Daniel Schonfeld: we need to go full court press with utam and all the things we’re doing and spend more and do more
451 00:59:06.240 ⇒ 00:59:17.990 Daniel Schonfeld: because we’re trying to cut costs. It’s been a little more difficult this year. But these are the things that are causing our our highest cost like shipping shipping definitely
452 00:59:18.150 ⇒ 00:59:22.380 Daniel Schonfeld: and not having the right price points at the right time.
453 00:59:22.440 ⇒ 00:59:34.079 Daniel Schonfeld: for our products cause it is dynamic. So I’m going to continue to invest heavily in data and analytics over the next bunch of years. And I think you’re you’re doing a great job at this.
454 00:59:34.320 ⇒ 00:59:50.349 Uttam Kumaran: I appreciate it. I think these these conversations. I’m happy. I’m thank thankful that we’re able to carve time during the week to do this, because, you know, I think there was a couple of weeks where, you know, we were just focused on getting infrastructure set up. And then we’re starting into reporting. And I think we’d be having these weeklies.
455 00:59:50.510 ⇒ 01:00:08.770 Uttam Kumaran: We’re moving like way quicker on iterations. And I’m able to share some updates during the week, get feedback over email and then have this where we just like did a ton of stuff. And then it’s nice, because now that I have everything clean, I like, and call a friend who who like, call my friend at Flexport, and be like
456 01:00:08.770 ⇒ 01:00:22.349 Uttam Kumaran: or doing stuff and shipping like, tell me where you guys found like, where your customers finding updates like I’m able to, and then get them to kind of help help out and do a couple run a couple of queries and find some stuff for us. So that’s been great is that
457 01:00:22.350 ⇒ 01:00:35.999 Uttam Kumaran: it’s easy for me to say if they just need all like a couple of order tables or something, and we’re able to move a lot quicker. And even the updates that you requested that we need to do on proportional discounts, like I know exactly where to go, make those changes. And so
458 01:00:36.460 ⇒ 01:00:43.139 Uttam Kumaran: like, it’s just like it compounds. It’s weird. Sometimes it’s but again, it just
459 01:00:43.180 ⇒ 01:00:46.339 Uttam Kumaran: it’s just like real data work. So I’m I’m glad it’s all come together.
460 01:00:46.550 ⇒ 01:00:57.910 Daniel Schonfeld: Yeah. And the more you learn about the business side of it, too, not just the data and doing that of trying to get into my head of what we’re trying to accomplish long term. I think the more ideas
461 01:00:57.940 ⇒ 01:01:04.759 Daniel Schonfeld: that will come to you and the smarter will be with how we display this data. The next once we get all this set, we’re gonna have to
462 01:01:05.050 ⇒ 01:01:11.760 Daniel Schonfeld: visually. I’d like to improve a lot of different things. I’m not concerned with that at the moment. But
463 01:01:12.010 ⇒ 01:01:15.999 Daniel Schonfeld: yeah, I’m excited. This is great. We’ll keep this weekly
464 01:01:16.240 ⇒ 01:01:22.419 Daniel Schonfeld: every Friday is a good cadence for you and II do like it. Once you and I, too. It’s like we can stay focused, too.
465 01:01:22.480 ⇒ 01:01:27.459 Daniel Schonfeld: And then maybe you and Ben do a separate one, or you guys to obviously talk more during the week.
466 01:01:27.510 ⇒ 01:01:36.059 Daniel Schonfeld: But I like this time where it’s just you and I, we get to go through quietly and just analyze the data because I need different data than Ben needs.
467 01:01:37.420 ⇒ 01:02:01.579 Uttam Kumaran: Yeah. And I again, I’ve spent a lot of time thinking about your business, and in my other, in my normal life, too, I’m spending a lot of time looking at other businesses and doing a lot of different stuff in data. So I am like, always like, I’m now that everything’s kinda set up to. I run a lot of explorations and things like that. So I’m hoping that, like a lot of interesting things that we find and the speed at which we can move on these things.
468 01:02:01.640 ⇒ 01:02:07.700 Uttam Kumaran: These conversations will continue to be like, we’re just running out of time for how much stuff we’re we’re trying to do so
469 01:02:07.730 ⇒ 01:02:12.880 Daniel Schonfeld: awesome, Jude. Alright, thank you so much.