Meeting Title: Weekly-Data-Review Date: 2024-01-26 Meeting participants: Daniel Schonfeld, Uttam Kumaran
WEBVTT
1 00:01:15.530 ⇒ 00:01:21.110 Uttam Kumaran: Hey, Dan! Hi, Buddy, how are you? Good! How are you? How was the week
2 00:01:21.410 ⇒ 00:01:28.849 Daniel Schonfeld: woof exhausting? We got back last night from it from A/C but it went really, really. Well, it was really cool.
3 00:01:29.460 ⇒ 00:01:35.759 Daniel Schonfeld: You’re definitely probably the head of the show with our big yeah, it was cool.
4 00:01:35.880 ⇒ 00:01:41.810 Daniel Schonfeld: It was it was definitely novel for the show. Everybody was talking about it. We had huge lines waiting to get in.
5 00:01:42.010 ⇒ 00:02:00.480 Daniel Schonfeld: It was pretty neat, nailed it. Let’s go. II remember you told me about last week, and I was like, II message, Ben, I was like, Yeah, I heard, you guys are going all out so excited to hear about it. Yeah, it was I was a little nervous cause we didn’t invite anyone. We didn’t tell anyone. We closed out the whole booth, but everybody wants to know what was inside.
6 00:02:00.490 ⇒ 00:02:01.680 Daniel Schonfeld: so went well.
7 00:02:02.150 ⇒ 00:02:03.040 Uttam Kumaran: Oh.
8 00:02:03.400 ⇒ 00:02:10.619 Daniel Schonfeld: awesome. Now we gotta follow up with that with all the people that that we let in. But
9 00:02:10.669 ⇒ 00:02:11.980 Daniel Schonfeld: and it was really nice
10 00:02:12.070 ⇒ 00:02:14.089 Daniel Schonfeld: worked awesome. Thank you.
11 00:02:14.380 ⇒ 00:02:22.449 Uttam Kumaran: Yeah, awesome. Yeah. I know I was a I was texting Chuck and Ben in between for some stuff for Eunice that I think we’ll talk about today.
12 00:02:22.520 ⇒ 00:02:34.899 Daniel Schonfeld: But I could tell they’re like, II think everybody is all over the place. Sounds like, okay, cool. I’m just gonna I’m gonna just probably best. And then I’ll catch up with everybody next week. Yeah, Chuck, they had their own booth, my father
13 00:02:36.000 ⇒ 00:02:45.920 Daniel Schonfeld: it was cool. We set up like a little like, speak easy, like lounge in the front, and then in the back was the whole the whole setup. I’ll send you a video. So you can see what it looks. Yeah, I would love to see it. Yeah.
14 00:02:46.040 ⇒ 00:02:50.190 Uttam Kumaran: it was definitely the first time the pool industry seen anything like this. So.
15 00:02:50.760 ⇒ 00:02:54.849 Daniel Schonfeld: oh, yeah, nice. That makes me happy. Thank you.
16 00:02:55.970 ⇒ 00:03:07.499 Uttam Kumaran: Cool stuff has been good on my end a lot of updates. and some interesting stuff to go through today.
17 00:03:07.650 ⇒ 00:03:11.719 Uttam Kumaran: So I’ll just jump right into it.
18 00:03:14.430 ⇒ 00:03:18.019 So the first thing that we
19 00:03:18.430 ⇒ 00:03:22.000 Uttam Kumaran: what I wanted to talk to you a bit about today is
20 00:03:22.150 ⇒ 00:03:27.960 Uttam Kumaran: estimated shipping costs. And I talked with Ben about this last week and the
21 00:03:28.110 ⇒ 00:03:55.309 Uttam Kumaran: conversation we had was that as orders come out yesterday, of course they have. Many of them have not been shipped yet, so what I was noticing, as I was looking at the dashboard. It was, I was like, it’s constantly low, and I was like duh. It’s cause things aren’t being shipped. So our discussion was, okay, let me see if I can make an estimated shipping cost. And so that’s actually what we did is we took the zone. The zone and a shipment provider
22 00:03:55.490 ⇒ 00:04:08.709 Uttam Kumaran: to actually calculate an estimated shipping cost. And what we were able to do is actually create this concept of estimate shipping costs. We have, like a tiering system, a little bit of a way of getting really accurate, but
23 00:04:08.780 ⇒ 00:04:12.319 Uttam Kumaran: just to share, kind of like. How accurate the estimation is.
24 00:04:12.370 ⇒ 00:04:29.909 Uttam Kumaran: We’re pretty much my goal was, I was like, let’s just get within 10%. And we’re actually pretty lower than that meaning the estimations you should see for previous day orders what we expect, and then the cost to ship is fairly accurate.
25 00:04:29.920 ⇒ 00:04:33.210 Uttam Kumaran: So this portion of the dashboard
26 00:04:33.430 ⇒ 00:04:36.169 Uttam Kumaran: where we previously had shipping costs
27 00:04:36.370 ⇒ 00:04:44.010 Uttam Kumaran: now have be very low now is like, this is pretty accurate. So what we should be doing.
28 00:04:44.410 ⇒ 00:04:55.069 Uttam Kumaran: which is great. That’s one part of dashboard that we were constantly having. I was like trying to think through what we do here, and so I’m actually glad we now have a metric that’s estimated shipping costs.
29 00:04:55.280 ⇒ 00:05:02.959 Daniel Schonfeld: And it’s fairly accurate. So did you do on a skew level? Did you like assign a skew within time the zone. We didn’t. So we did on weight
30 00:05:02.990 ⇒ 00:05:06.029 Uttam Kumaran: and zone, because we were just like
31 00:05:06.220 ⇒ 00:05:20.369 Uttam Kumaran: we initially like Kim. We can’t do on scoop. But I said, It’s it’s May. Their only factor is the weight and the factors, the zone. And we have all the historical data. And what we did is, we teared it up because the ideal thing is like, you know what the calculation is
32 00:05:20.670 ⇒ 00:05:42.759 Uttam Kumaran: weight to zone surprise. But what we did is we created different buckets. So like 5 to 10 pounds, we created a bunch of different buckets. And then we’re able to kind of create like it. Here’s the price per pound per shipping provider per bucket, and then we can scale that out, and we got pretty. If if it wasn’t accurate, I would have gone a little bit further. But it’s within 5% on like
33 00:05:43.010 ⇒ 00:05:57.149 Uttam Kumaran: that’s great. I thought it would take. I thought it would be a lot more difficult. So that’s great So that’s a really good estimation. I? So that’s that’s one thing on here. The second thing that we looked at is, I messaged.
34 00:05:57.250 ⇒ 00:06:04.549 Uttam Kumaran: I talked to Ben about this last week I messed Chuck about brushes. In particular. The
35 00:06:04.640 ⇒ 00:06:10.180 Uttam Kumaran: average price to ship a brush is about half of the sale amount.
36 00:06:10.230 ⇒ 00:06:14.539 Uttam Kumaran: So you can see in 2024. So far, about 50% of the
37 00:06:14.810 ⇒ 00:06:18.479 Uttam Kumaran: stale amount has gone to the shipping costs.
38 00:06:18.820 ⇒ 00:06:30.839 Uttam Kumaran: If you look at a week basis. For example, the week last week we sold about 112 brushes. 2,000 $300,000 shipping. So it’s our highest category of
39 00:06:30.900 ⇒ 00:06:46.029 Uttam Kumaran: shipping as a percent of sales. And so my message to, I emailed Chuck and Ben. And I said, Hey, like, what are our options here. Because we’re. This is like where we’re losing a lot on shipping on a per unit basis. And so there’s a discussion we’re having on
40 00:06:46.330 ⇒ 00:06:50.530 Uttam Kumaran: what we may have with Kelly about moving some stuff to sharepost
41 00:06:50.740 ⇒ 00:06:51.680 Uttam Kumaran: on
42 00:06:52.120 ⇒ 00:07:03.550 Uttam Kumaran: ups share posts versus usps. And that was again just one thing that I talked to Ben about was that was really glaring from our conversation last week.
43 00:07:04.290 ⇒ 00:07:18.489 Uttam Kumaran: similarly, like the the next highest category is like cover pumps. and we don’t sell. There’s not much. Many units getting sold in this category. Everything else is covering around 10%. But I didn’t know if you even had like a
44 00:07:18.810 ⇒ 00:07:21.450 Uttam Kumaran: benchmark idea of
45 00:07:22.000 ⇒ 00:07:28.710 Uttam Kumaran: of the sale amount. What portion you think should go to shipping, and how you think about that?
46 00:07:28.770 ⇒ 00:07:32.480 Uttam Kumaran: Especially now, seeing some of these metrics and kind of seeing some of these ratios.
47 00:07:33.330 ⇒ 00:07:52.249 Daniel Schonfeld: Yeah, I think it’s on a case by case and you know, it depends on the profitability of that particular skew. So you have to just take some things to account. And also, is that how’s that broken out by zone? Is the shipment cost to sales. Stay consistent across zones, or is it maybe one zone that’s skewing the whole thing.
48 00:07:52.860 ⇒ 00:07:56.290 Uttam Kumaran: Yeah. So we that’s exactly what we talked about, is
49 00:07:56.570 ⇒ 00:08:02.149 Uttam Kumaran: there’s a lot of skew. There’s a lot of effects by zone 6.
50 00:08:02.460 ⇒ 00:08:03.480 Uttam Kumaran: Where
51 00:08:03.600 ⇒ 00:08:12.150 Uttam Kumaran: shipping, where there’s a ton of our costs are there and shipping as a percent of sales is higher
52 00:08:12.250 ⇒ 00:08:14.200 Uttam Kumaran: meaning. There’s like a
53 00:08:14.830 ⇒ 00:08:18.359 one thing that we’re emailing Kelly about is like in the summer months.
54 00:08:18.500 ⇒ 00:08:30.149 Uttam Kumaran: We’re expecting like that. That’s where a ton of our shipping expense again, if you look at Zone 6 for one month, Buff, it’s about 40 grand compared to right now. It’s
55 00:08:30.200 ⇒ 00:08:41.230 Uttam Kumaran: it’s about 20 grand in off season. And that’s this is where we’re. Gonna this is where we get eaten by the cost is 5, 6, and it’s pretty much own 5 and 6
56 00:08:41.309 ⇒ 00:08:44.819 Uttam Kumaran: and zone 8.
57 00:08:44.980 ⇒ 00:08:47.530 Daniel Schonfeld: What type you’re that’s actually a much
58 00:08:48.810 ⇒ 00:08:54.719 Uttam Kumaran: with this time period. These are the summer months. So this is this is this top row, here is
59 00:08:54.830 ⇒ 00:09:03.950 Uttam Kumaran: January. So January, February, January, December. November, October. So these are like this is gonna be April through June.
60 00:09:04.250 ⇒ 00:09:05.380 Daniel Schonfeld: Those are
61 00:09:06.470 ⇒ 00:09:10.690 Uttam Kumaran: month. Oh, if you go all the way to the left, maybe I just can’t see. Yeah.
62 00:09:12.520 ⇒ 00:09:20.850 Daniel Schonfeld: Oh, okay, maybe there’s also, I think I can just do that real quick.
63 00:09:21.750 ⇒ 00:09:22.680 Uttam Kumaran: See?
64 00:09:26.190 ⇒ 00:09:33.260 Uttam Kumaran: yeah, maybe I can check to see if I could freeze that. But yeah, it’s just it’s just 5 months. But
65 00:09:33.700 ⇒ 00:09:35.990 Uttam Kumaran: yeah, what what you could see is that
66 00:09:39.590 ⇒ 00:09:51.179 Uttam Kumaran: again, it’s like, it’s if you look at a percent of the row. Which is that what we talked about, I think a few weeks ago was like, Oh, we wanted to look at this not only just within its context, but percentage of the row. So of the month.
67 00:09:51.210 ⇒ 00:09:54.239 Uttam Kumaran: you can see, it’s 20%, 20%.
68 00:09:54.480 ⇒ 00:09:59.349 Uttam Kumaran: And then if you look at zone 8, it’s also around 18%.
69 00:09:59.590 ⇒ 00:10:01.220 Uttam Kumaran: So 60,
70 00:10:01.240 ⇒ 00:10:06.800 Uttam Kumaran: yeah, roughly, 60 is going to 3 zones out of 8 zones.
71 00:10:07.890 ⇒ 00:10:10.950 Uttam Kumaran: and if we break down those zones.
72 00:10:11.180 ⇒ 00:10:20.480 Uttam Kumaran: I think I saw before there were states there that were in those zones. Exactly. Yeah. So there’s a couple of egregious states.
73 00:10:20.700 ⇒ 00:10:24.989 Uttam Kumaran: and I will. I’ll kind of show you which ones those are.
74 00:10:25.440 ⇒ 00:10:33.020 Uttam Kumaran: in particular. These are the high price per pound.
75 00:10:33.260 ⇒ 00:10:42.560 Uttam Kumaran: So Utah, Colorado. And again, this is, you see, the months here. So Utah, Colorado, what Montana, Minnesota, South Dakota.
76 00:10:42.820 ⇒ 00:10:50.669 Uttam Kumaran: again. This may be an anomaly, but you could see the concentration here, and then, if we were to scroll
77 00:10:51.180 ⇒ 00:10:55.739 Uttam Kumaran: we also see high months in California, Hawaii.
78 00:10:56.050 ⇒ 00:11:02.399 Uttam Kumaran: Indiana, Nevada. So I’ll get all Zone 8 like preaching zone 8, pretty much.
79 00:11:03.370 ⇒ 00:11:08.560 Daniel Schonfeld: Okay. This could be interesting stuff also shared with Eunice and ask them cause they have so many Dcs.
80 00:11:08.600 ⇒ 00:11:09.950 Daniel Schonfeld: okay.
81 00:11:10.040 ⇒ 00:11:14.100 if maybe moving stuff to Houston, or something like that, or even California.
82 00:11:15.060 ⇒ 00:11:27.540 Uttam Kumaran: Yeah. So that’s also like, I think, a good transition. So I talked to chuck a bit about. There was an issue, I think, happening where orders were getting routed to both Eunice and to our ship station.
83 00:11:27.770 ⇒ 00:11:34.839 Uttam Kumaran: And one thing that we I message him about was trying to put together much. So we work, I think, on Wednesday or Tuesday.
84 00:11:35.040 ⇒ 00:11:38.240 Uttam Kumaran: with shift station and shopify and figure out some automations.
85 00:11:38.430 ⇒ 00:11:44.070 Uttam Kumaran: Basically, it’s like if it if it’s in. I think Unis provided us with a Zip code
86 00:11:44.500 ⇒ 00:11:49.330 Uttam Kumaran: where they’re gonna cover the shipments. So we wanna exclude that from ship station.
87 00:11:49.470 ⇒ 00:11:53.600 Uttam Kumaran: So we’re I’m working with him on setting up those automations
88 00:11:53.920 ⇒ 00:11:55.670 Uttam Kumaran: at ship station is like
89 00:11:55.900 ⇒ 00:12:07.069 Uttam Kumaran: really trash. And II kind of like I talked to so many people on their support team. And I pretty much is like you’re you need to change your product. This is a fairly easy
90 00:12:07.390 ⇒ 00:12:13.229 Uttam Kumaran: thing to build, actually, just add more like complexity to your shipping there.
91 00:12:13.480 ⇒ 00:12:20.849 Uttam Kumaran: Talk like the way they do automations, but there’s like some stuff to do there. So I’m hoping to work with him on that
92 00:12:21.960 ⇒ 00:12:27.109 Uttam Kumaran: The other thing that I wanted to talk about was
93 00:12:27.240 ⇒ 00:12:30.090 Uttam Kumaran: Amazon orders that come in.
94 00:12:30.180 ⇒ 00:12:40.589 Uttam Kumaran: So 2 things on Amazon one is, if you look at the dashboard here. So this is for previous, say, for yesterday you could see there’s an order that came in, but there’s nothing here.
95 00:12:40.960 ⇒ 00:13:00.240 Uttam Kumaran: and I, looking into this order, there are orders that come in where the payment has not been processed, or and that this sometimes I’ve looked even takes days where maybe there’s like there they haven’t swiped, or the cards expire or something. So I guess I was gonna ask you
96 00:13:00.690 ⇒ 00:13:02.440 Uttam Kumaran: what’s best. I think
97 00:13:02.460 ⇒ 00:13:08.190 Uttam Kumaran: we don’t like II we don’t get the order amount recorded here I can bring in
98 00:13:09.100 ⇒ 00:13:13.870 Uttam Kumaran: it. But again, I don’t know whether this may get cancelled like II was gonna ask you what you think is the best
99 00:13:14.960 ⇒ 00:13:17.819 Uttam Kumaran: thing to do in this in this situation for the dashboard.
100 00:13:20.050 ⇒ 00:13:24.839 Daniel Schonfeld: So you’re saying the order came in. We haven’t shipped it, nor has it been paid yet
101 00:13:24.950 ⇒ 00:13:34.770 Uttam Kumaran: correct. What when we if we look, I don’t know if you researched it. But what what typically happens to that, or does usually get paid within 2 days and usually gets paid.
102 00:13:34.840 ⇒ 00:13:35.860 Daniel Schonfeld: -
103 00:13:36.870 ⇒ 00:13:39.270 Daniel Schonfeld: okay? And in what timeframe
104 00:13:40.810 ⇒ 00:13:45.200 Uttam Kumaran: as I’ve been looking this week, it’s usually paid within a few days.
105 00:13:45.750 ⇒ 00:13:46.560 Daniel Schonfeld: Okay.
106 00:13:46.790 ⇒ 00:13:50.369 Uttam Kumaran: there’s not, there’s not. There’s not that many cancellations
107 00:13:50.510 ⇒ 00:13:58.839 Daniel Schonfeld: those get usually get. Those usually get accounted for in in refunds. If there’s an issue, and what percentage of the sales have come in like that on a daily basis.
108 00:13:59.550 ⇒ 00:14:04.630 Uttam Kumaran: That’s a good question. I don’t know. I’ve as I’ve been looking. There’s usually been 2 or 3
109 00:14:04.830 ⇒ 00:14:08.919 Daniel Schonfeld: that have come in with that payment issue that that don’t have a payment
110 00:14:09.640 ⇒ 00:14:14.190 Uttam Kumaran: after the the days close daily in average.
111 00:14:14.690 ⇒ 00:14:16.830 Uttam Kumaran: Daily, yes.
112 00:14:18.250 ⇒ 00:14:28.349 Uttam Kumaran: So as I open the dashboard I come. I’ve been seeing like probably 2 to 3 blanks, and I go in. And and again, it’s this is for yesterday. So this order. This thing still hasn’t been paid.
113 00:14:31.180 ⇒ 00:14:33.179 Daniel Schonfeld: We have to come back on that one and think about it.
114 00:14:33.440 ⇒ 00:14:34.210 Uttam Kumaran: Okay.
115 00:14:34.390 ⇒ 00:14:36.910 Daniel Schonfeld: not sure yet. I have to see how big the problem is.
116 00:14:37.420 ⇒ 00:14:38.160 Uttam Kumaran: Okay.
117 00:14:38.790 ⇒ 00:14:43.860 Daniel Schonfeld: I’ll put a little note here, cause I just don’t want it to. I was like, Oh, what’s happening here? But
118 00:14:44.660 ⇒ 00:14:48.770 Uttam Kumaran: again. It’s yeah. Let’s come back to that one. Okay.
119 00:14:49.800 ⇒ 00:14:52.520 Daniel Schonfeld: rain fog right now. So
120 00:14:52.630 ⇒ 00:14:58.340 Uttam Kumaran: okay, that’s all. I just wanted to flag that I’ll put a little note there.
121 00:14:58.740 ⇒ 00:15:07.010 Uttam Kumaran: The other thing was on refund. So we spent a lot of time this week on automating Amazon refunds. This is a thing that we were getting.
122 00:15:07.290 ⇒ 00:15:11.659 Uttam Kumaran: I was getting manually like we would go get Amazon refunds for the previous day every day
123 00:15:11.950 ⇒ 00:15:14.039 Uttam Kumaran: and fees
124 00:15:14.320 ⇒ 00:15:25.839 Uttam Kumaran: so I spent a lot of time with 5 trans team. And now that’s automated. So we’re getting in all the refunds. The thing I noticed about refunds. That’s that’s that’s like, really difficult. And I, this may be just like
125 00:15:25.910 ⇒ 00:15:37.490 Uttam Kumaran: you. You probably know as I’m just like learning a lot about how Amazon is like they. They not only have refunds for the item, but those can be partial refunds. And then there’s also sometimes where we pay the shipping.
126 00:15:38.200 ⇒ 00:15:41.070 Uttam Kumaran: And so it’s just like
127 00:15:41.400 ⇒ 00:15:47.389 Uttam Kumaran: more of a comment is just like, it’s, it’s like, really, it’s like, really crazy that they do things that way.
128 00:15:47.730 ⇒ 00:15:53.720 Uttam Kumaran: yeah. And and some in some situations we’re. We’re like not making money on the
129 00:15:53.860 ⇒ 00:15:58.040 Uttam Kumaran: on the order, because we have to pay the shipping back. And yeah, it’s
130 00:15:58.110 ⇒ 00:16:06.540 Daniel Schonfeld: yeah, I think, for those who have to come back, put them in a bucket, and look over a longer period of time. How how pervasive it is! On a skew level!
131 00:16:07.430 ⇒ 00:16:15.769 Uttam Kumaran: So that’s the one thing I wanted to do is I wanted to look at a skew level and find also the refund of reasons. So a lot of the times when I was looking at refunded orders.
132 00:16:15.980 ⇒ 00:16:23.690 Uttam Kumaran: I would go through and let me even show you an example of a refunded order that I was looking at.
133 00:16:24.010 ⇒ 00:16:27.620 Uttam Kumaran: this is a good example.
134 00:16:29.750 ⇒ 00:16:40.180 Uttam Kumaran: So this is an example of like what I see on just the ui for an order where they? They bought 2 brushes, and then
135 00:16:40.320 ⇒ 00:16:44.059 Uttam Kumaran: there was some sort of refund that happened. So if you actually see.
136 00:16:44.300 ⇒ 00:16:55.479 Uttam Kumaran: we sold 40 bucks and we refund at 30. What’s the problem with looking at it here is like you get no context of like, is this a partial refund? What was the refund reason?
137 00:16:55.640 ⇒ 00:17:11.780 Uttam Kumaran: So all of those things are things that I wanna try and dig up and really make obvious, not only that the refund happen and the amounts, but what are we refunding, and how does that split between shipping cause? In this K situation we did pay for shipping, and I have it on the back end.
138 00:17:12.050 ⇒ 00:17:17.210 Uttam Kumaran: How much we pay for shipping, and then how much do we pay to actually refund the item? Was it a partial refund? And then
139 00:17:17.640 ⇒ 00:17:19.040 Uttam Kumaran: how do we get the reason.
140 00:17:19.490 ⇒ 00:17:23.590 Uttam Kumaran: you know, attach the skew. So that’s
141 00:17:23.920 ⇒ 00:17:24.800 Daniel Schonfeld: okay.
142 00:17:26.420 ⇒ 00:17:37.709 Uttam Kumaran: There are quite a bit of refunds, though. So and again, as I’ve been, we can now see that here on the dashboard refunds and discounts and warranties so similarly on the discounts of warranties.
143 00:17:38.020 ⇒ 00:17:39.770 Uttam Kumaran: I want to be able to show
144 00:17:40.500 ⇒ 00:17:54.999 Uttam Kumaran: what we have here is like discount as a percentage of the total sale amount. But what I’m not showing is why, you know, and I think we talked about that as well. So how are you gonna get that from like Zendesk or something?
145 00:17:55.360 ⇒ 00:18:02.690 Uttam Kumaran: Yeah. So I’m either gonna get it for Zendesk. Sometimes. Also, there’s a couple of areas. Amazon sometimes has the refund reason
146 00:18:02.770 ⇒ 00:18:14.069 Uttam Kumaran: shopify sometimes as order tags. And then worst case, I want to get the Zendes data in to then Link, to see if that customer has any open tickets.
147 00:18:14.130 ⇒ 00:18:20.590 Uttam Kumaran: Yeah. But again, like, if you look at the previous day discounts, there’s about.
148 00:18:20.680 ⇒ 00:18:24.079 Uttam Kumaran: There’s about a thousand dollars in discounts or warranties.
149 00:18:24.140 ⇒ 00:18:28.019 Daniel Schonfeld: Yeah, definitely, it’s probably gonna be a warranty. But yeah.
150 00:18:28.610 ⇒ 00:18:30.380 Uttam Kumaran: yeah, so
151 00:18:31.500 ⇒ 00:18:44.779 Uttam Kumaran: okay, definitely, not as as impactful as some of the refunds. But like there, this is full. This has a lot every day where we’re we’re doing refunds or or discounts or warranties. So
152 00:18:44.950 ⇒ 00:18:52.289 Daniel Schonfeld: yeah, I would suspect to the lock, because there was some kind of sale or special. Yeah, and that that’s okay. I just want to flag where
153 00:18:52.640 ⇒ 00:19:06.250 Uttam Kumaran: even this may be like A, we just wanna make sure as a percentage. Or or we look at this again, and I think probably need another section for just refunds and discounts, and we can look at on a skew basis which skews are highly affected by refunds and discounts.
154 00:19:06.490 ⇒ 00:19:11.750 Uttam Kumaran: We’re looking at our discount percentage by month compared to last year.
155 00:19:12.160 ⇒ 00:19:13.490 Uttam Kumaran: so that’s
156 00:19:14.710 ⇒ 00:19:18.049 Uttam Kumaran: okay. Good. That’s what to do.
157 00:19:18.140 ⇒ 00:19:19.390 Uttam Kumaran: And then
158 00:19:21.070 ⇒ 00:19:39.459 Uttam Kumaran: the other thing is, yeah. I wanted to talk about Eunice for a second. I’m I’m working with Chuck, but I guess if there’s if maybe if there’s a point of contact there that I can start sending some data to and just learn how we’re going to leverage them. And also I want to get some data from their side.
159 00:19:39.550 ⇒ 00:19:46.459 Uttam Kumaran: what do you think is the best path, or what? What do you think? Some like things we could get from them that would be important to have here?
160 00:19:48.470 ⇒ 00:19:53.460 Daniel Schonfeld: Yeah, why don’t I introduce you to Michael? He’s really our rep there.
161 00:19:53.780 ⇒ 00:20:01.259 Daniel Schonfeld: but it might be more on their data team. But I can make an introduction or Chuck Ken asked Chuck to do it. That’s fine. Yeah.
162 00:20:02.150 ⇒ 00:20:10.389 Uttam Kumaran: again, because I want to show here that they’ll just become another shipping channel, and then we will begin to look at the price and comparison.
163 00:20:10.900 ⇒ 00:20:15.210 Uttam Kumaran: Because it’s actually working with Chuck. It seems really clear that we can actually divert
164 00:20:15.460 ⇒ 00:20:25.719 Uttam Kumaran: orders really quickly to other providers right? So this isn’t like on a lag. So if we notice that we’re getting better rates, I think we can quickly move volume towards there and
165 00:20:26.580 ⇒ 00:20:31.290 Uttam Kumaran: you know, I think we are. We are seeing on ups like really, really low
166 00:20:31.350 ⇒ 00:20:39.679 Uttam Kumaran: price for pounds like for the last week close. We’re seeing like 50 cents price per pound, and that keeps going down, cause we’re moving more volume. So
167 00:20:39.840 ⇒ 00:20:42.009 Uttam Kumaran: yep, that’s been really positive.
168 00:20:43.870 ⇒ 00:20:49.840 Daniel Schonfeld: Do you have be interesting to show like a spark of the volume sent at that price per pound, too.
169 00:20:50.050 ⇒ 00:20:51.749 Daniel Schonfeld: of the total shipments.
170 00:20:52.980 ⇒ 00:20:58.489 Uttam Kumaran: Yeah, I looked at this yesterday, and II wanted to do. I wanted to do almost this
171 00:20:58.600 ⇒ 00:20:59.990 Uttam Kumaran: by weight.
172 00:21:00.210 ⇒ 00:21:09.030 Daniel Schonfeld: Yeah, I could see just a line. It doesn’t be colored in, but like a kind of like a spark line going over this to show
173 00:21:09.250 ⇒ 00:21:11.150 Daniel Schonfeld: the volume in a line.
174 00:21:11.580 ⇒ 00:21:22.319 Uttam Kumaran: Or I mean, we’re it’s just gonna I have to probably take out. Ltl, but yeah, everything else. I think, yeah, I may do that. Yeah. Lt, I’ll even separate. But yeah, oh, so you’re saying, just have a line here of the total weight
175 00:21:22.560 ⇒ 00:21:24.899 Daniel Schonfeld: the on the bottom graph really.
176 00:21:25.360 ⇒ 00:21:26.970 Daniel Schonfeld: is that what that is?
177 00:21:27.340 ⇒ 00:21:46.049 Daniel Schonfeld: This is? No, this is price per pound. So this is a ratio. So in reality the highest curve on a spark line going across would be on the lowest, ideally on the lowest price per pound. Correct? Yeah, so that so. And that’s we want to be continuously moving volume towards
178 00:21:46.290 ⇒ 00:21:53.919 Uttam Kumaran: the lowest, and our contract with ups is structured in a way where we get discounts based on because volume discounts.
179 00:21:54.260 ⇒ 00:21:58.870 Daniel Schonfeld: Yeah, we’ll also eventually have to juxtapose the data of like.
180 00:21:59.520 ⇒ 00:22:04.350 Daniel Schonfeld: do we see any other kind of anomaly, not anomalies or trends like returns
181 00:22:04.600 ⇒ 00:22:06.650 Daniel Schonfeld: cetera
182 00:22:07.520 ⇒ 00:22:12.029 Daniel Schonfeld: based on volume sent through that channel, because maybe, like they’re not as careful
183 00:22:12.100 ⇒ 00:22:19.649 Daniel Schonfeld: with actually handling packages and the returns go up. You know, there’s other factors we have to make sure we’re looking at outside of cost. But yeah.
184 00:22:20.180 ⇒ 00:22:23.510 Uttam Kumaran: And then the only other thing, I guess just the random thought is like
185 00:22:23.550 ⇒ 00:22:27.919 Uttam Kumaran: where we don’t get a ton of information from. Ltl.
186 00:22:28.040 ⇒ 00:22:32.899 Uttam Kumaran: Is there anything on that product? And the way it gets shipped that you think there’s
187 00:22:33.470 ⇒ 00:22:38.010 Uttam Kumaran: things. I could go to them and say we need more data on, because
188 00:22:39.160 ⇒ 00:22:46.219 Uttam Kumaran: again, all we’re getting really is just cost. But I don’t know, because because it’s such a huge revenue driver
189 00:22:46.290 ⇒ 00:22:51.079 Uttam Kumaran: like the pumps we see. We seem not to have a ton of information about
190 00:22:52.090 ⇒ 00:23:00.700 Uttam Kumaran: delivery. And maybe again, when I look at the when I look at the returns for the pumps, maybe I can try to identify how much are related to shipping.
191 00:23:00.730 ⇒ 00:23:02.449 Daniel Schonfeld: Yeah. Warranties, too.
192 00:23:03.260 ⇒ 00:23:04.780 Uttam Kumaran: Yeah. And warranties.
193 00:23:06.400 ⇒ 00:23:11.950 Uttam Kumaran: How does what? What is like? Can you explain, though current like warranties on some of the items today?
194 00:23:13.920 ⇒ 00:23:16.030 Uttam Kumaran: And then how do they get exercise?
195 00:23:16.170 ⇒ 00:23:24.380 Daniel Schonfeld: I mean, we could send. We have a sheet. Actually. Cody has it of what we’re doing, warranties per skew or skew skew category.
196 00:23:25.180 ⇒ 00:23:27.090 Uttam Kumaran: Okay, so let me get that from him.
197 00:23:27.110 ⇒ 00:23:30.869 Daniel Schonfeld: Like variable fees, speeds, or 5 years parts and labor
198 00:23:31.070 ⇒ 00:23:38.379 Daniel Schonfeld: the above ground variable speeds are 3 so we should definitely, we should have like a table.
199 00:23:39.560 ⇒ 00:23:41.559 Uttam Kumaran: Yeah, I wanna get that in and and then
200 00:23:42.000 ⇒ 00:23:50.040 Uttam Kumaran: and then again, well, I think the next thing we wanna we’re attacking marketing. And then I wanna get we have all the data to look at refunds and discounts and warranties. So
201 00:23:50.370 ⇒ 00:23:52.599 Uttam Kumaran: I can ask him for that sheet. Okay?
202 00:23:54.780 ⇒ 00:24:01.099 Uttam Kumaran: The last thing to talk about was I went through with
203 00:24:01.140 ⇒ 00:24:07.600 Uttam Kumaran: this with Ben last week, but the only thing change here is you can now see on here.
204 00:24:07.910 ⇒ 00:24:11.779 Uttam Kumaran: how we’re tracking by day for last year this year.
205 00:24:12.190 ⇒ 00:24:27.960 Uttam Kumaran: So the blue is 2023 orange is this year, and this is gross sales. So you can actually see on here how many days we’re beating or losing and I think the nice thing is we could do this for any Kpi.
206 00:24:28.150 ⇒ 00:24:38.189 Uttam Kumaran: and on the main dashboard, we now have percent change versus the same time last year. So you can see that
207 00:24:38.440 ⇒ 00:24:44.020 Uttam Kumaran: we’re we’re lower on refunds. If you scroll up to the top here, instead of showing
208 00:24:44.040 ⇒ 00:24:49.680 Uttam Kumaran: year to date, you can actually see where we are on marketing costs versus the same time last year.
209 00:24:49.720 ⇒ 00:24:57.609 Uttam Kumaran: for what time? Period. So this is the 20 sixth to 26, first of 2620, 23 versus the first to 2620, 24
210 00:24:58.180 ⇒ 00:24:59.380 Daniel Schonfeld: year to date.
211 00:25:00.110 ⇒ 00:25:01.540 Uttam Kumaran: year to day. Correct.
212 00:25:01.570 ⇒ 00:25:04.670 Daniel Schonfeld: Okay, marketing change, 20%
213 00:25:08.200 ⇒ 00:25:10.320 percent change shipping costs.
214 00:25:11.080 ⇒ 00:25:17.429 Daniel Schonfeld: standard shipping cost. What about the revenue and profit during the same time is that on here?
215 00:25:17.720 ⇒ 00:25:23.249 Uttam Kumaran: Yeah. So the revenue is lower. And in particular, again, the comparison would be to look at
216 00:25:23.850 ⇒ 00:25:32.180 Uttam Kumaran: once. You notice, okay, our revenue is less than 12. For about 12 lower we can actually go and see which days
217 00:25:32.520 ⇒ 00:25:33.780 Uttam Kumaran: we got beat on
218 00:25:34.580 ⇒ 00:25:43.819 Uttam Kumaran: and you can see particularly this past the past 2 weeks. We have some days where we’re we’re getting beat on.
219 00:25:44.020 ⇒ 00:25:46.890 Uttam Kumaran: I don’t know whether you you think of it.
220 00:25:46.940 ⇒ 00:25:54.070 Uttam Kumaran: Even one step further is like again, the the thing I think about is these are all levers. Right? So you notice we’re spending less
221 00:25:54.380 ⇒ 00:25:57.180 Uttam Kumaran: on marketing. Maybe there is like
222 00:25:57.300 ⇒ 00:25:59.979 Uttam Kumaran: heavy correlation between that and the sales.
223 00:26:00.290 ⇒ 00:26:12.690 Uttam Kumaran: So these are the core. 4 or core 5. If we would include refunds. But like, do you think of these? All in conjunction. Is there anything else I can kind of help to like? Make this map a little bit easier?
224 00:26:13.970 ⇒ 00:26:30.380 Daniel Schonfeld: No, I think again, just I haven’t even had a chance to really look at all this without understanding if the data is accurate yet. So now that you feel it is just by looking at it. The profit percentage profit
225 00:26:30.750 ⇒ 00:26:32.000 Daniel Schonfeld: is that
226 00:26:33.220 ⇒ 00:26:38.689 Daniel Schonfeld: the amount of total profit at this time, or is that the profit margin percentage?
227 00:26:39.950 ⇒ 00:26:45.929 Uttam Kumaran: This is the total profit at this time. Dollars
228 00:26:46.470 ⇒ 00:26:52.220 Daniel Schonfeld: total profit verse last year, this time down 36%. But the revenue is only down 12%.
229 00:26:53.030 ⇒ 00:26:53.840 Uttam Kumaran: Yeah.
230 00:26:53.960 ⇒ 00:26:59.259 Uttam Kumaran: So if you were to take if you were to take out those people say, Okay, it’s not hopping the sales.
231 00:26:59.380 ⇒ 00:27:08.599 Uttam Kumaran: Then it’s got to either. This got. And it’s probably not gonna be a cost of goods. Then you could say, Okay, where’s it coming from? Well, it’s not. It’s not marketing.
232 00:27:09.180 ⇒ 00:27:13.470 Uttam Kumaran: because marketing is also down. So then it’s like, okay, let’s look at like refund discounts.
233 00:27:13.670 ⇒ 00:27:19.620 Uttam Kumaran: So. But but again, it’s Yeah, I don’t have it blown up except for just
234 00:27:19.760 ⇒ 00:27:28.259 Uttam Kumaran: the amount of refunds that are coming, but that’s what I will share is like a larger, deep dive into like how refunds is affecting
235 00:27:29.460 ⇒ 00:27:37.489 Uttam Kumaran: it says, what refunds, what’s getting refunded and refunds a week like same week last year? Something’s definitely not making sense. We could stop right there.
236 00:27:37.650 ⇒ 00:27:39.050 Daniel Schonfeld: Oh, go back
237 00:27:39.770 ⇒ 00:27:48.730 Daniel Schonfeld: I have it blown up. So I only see little portions where that go. No, it’s further the other way.
238 00:27:48.850 ⇒ 00:27:56.440 Daniel Schonfeld: You’re there. Okay, stop year-to-date refunds 13,000 down 95%.
239 00:27:57.070 ⇒ 00:28:02.520 Uttam Kumaran: So down 95% is versus last year, the total.
240 00:28:02.790 ⇒ 00:28:06.269 Uttam Kumaran: So this actually is not a great
241 00:28:06.740 ⇒ 00:28:07.970 Uttam Kumaran: number.
242 00:28:07.980 ⇒ 00:28:12.949 Uttam Kumaran: It’s actually should be. I think it’s down here.
243 00:28:13.470 ⇒ 00:28:17.620 Uttam Kumaran: Okay, let me just make this change. This is the actual
244 00:28:18.990 ⇒ 00:28:21.120 Uttam Kumaran: number which will show.
245 00:28:21.290 ⇒ 00:28:25.930 Uttam Kumaran: We are above where we were last year by by
246 00:28:26.320 ⇒ 00:28:27.899 Uttam Kumaran: 79%.
247 00:28:28.190 ⇒ 00:28:30.990 Uttam Kumaran: So let me, this is by discounts.
248 00:28:31.430 ⇒ 00:28:32.720 Uttam Kumaran: Let’s say this
249 00:28:33.810 ⇒ 00:28:40.210 Uttam Kumaran: know why this got flipped. So this is the percent change in discount versus the same time last year.
250 00:28:40.300 ⇒ 00:28:46.819 Uttam Kumaran: So we have 80% higher discounts than we did last year. Same time last year. That’s right.
251 00:28:47.550 ⇒ 00:28:48.990 Uttam Kumaran: Right here.
252 00:28:49.980 ⇒ 00:28:55.070 Daniel Schonfeld: percent change, discount amount alright. So we have 80% more discounts than last year.
253 00:28:55.490 ⇒ 00:28:56.360 Uttam Kumaran: Yes.
254 00:28:56.810 ⇒ 00:28:57.620 Daniel Schonfeld: Okay.
255 00:29:03.110 ⇒ 00:29:09.099 Daniel Schonfeld: okay, there, I mean, there’s your number. Recent change. Okay, percent change refund amount for same team.
256 00:29:09.230 ⇒ 00:29:13.849 Daniel Schonfeld: So the refunds are down 63%. But the discounts are up 80%
257 00:29:14.060 ⇒ 00:29:17.010 Daniel Schonfeld: okay.
258 00:29:18.690 ⇒ 00:29:25.880 Uttam Kumaran: so these are quite drastic. So I want to look into this, both of these on like a separate thing, where we can look at.
259 00:29:26.330 ⇒ 00:29:31.149 Uttam Kumaran: buy skew by state, and then over time.
260 00:29:32.420 ⇒ 00:29:38.129 Daniel Schonfeld: Yeah. Also, what was the maybe you could even look at what the discount reason was or code.
261 00:29:38.370 ⇒ 00:29:44.540 Uttam Kumaran: Okay? And you can see where a lot of it came from something that makes sense. I don’t know why there would be an 80% discount
262 00:29:45.690 ⇒ 00:29:48.590 Daniel Schonfeld: ever actually of anything.
263 00:29:50.010 ⇒ 00:29:53.210 Uttam Kumaran: Yeah, it’s it’s a it’s a warranty bucket.
264 00:29:53.390 ⇒ 00:29:58.520 Daniel Schonfeld: and that maybe means that the for some reason there’s a spike in warranty somewhere.
265 00:29:59.690 ⇒ 00:30:13.029 Uttam Kumaran: Yeah, if I’m I want to bring in the reason here. But it looks like, yeah, there’s just some things that are getting just kind of like 1520, again. Maybe we can even just look at this right now, I just want to look at
266 00:30:13.370 ⇒ 00:30:16.250 Uttam Kumaran: for this month all the discounts.
267 00:30:20.180 ⇒ 00:30:21.530 Uttam Kumaran: And
268 00:30:23.200 ⇒ 00:30:24.920 Uttam Kumaran: just gonna say it’s
269 00:30:27.100 ⇒ 00:30:29.970 Uttam Kumaran: it’s in the current months.
270 00:30:33.810 ⇒ 00:30:36.969 Uttam Kumaran: We can even just go line by line and take a look.
271 00:30:38.140 ⇒ 00:30:42.210 Uttam Kumaran: So it’s around 84,000 in discounts.
272 00:30:42.570 ⇒ 00:30:43.960 Uttam Kumaran: And
273 00:30:44.190 ⇒ 00:30:48.829 Uttam Kumaran: I can even bring in the order. URL. Maybe we can even just spot, check a couple
274 00:30:49.540 ⇒ 00:31:02.199 Uttam Kumaran: just go by the so by the highest amount, like, let’s just look at some big ones, sort if you click the top of the table. Yeah. So here, this one’s might be better.
275 00:31:03.250 ⇒ 00:31:14.210 Uttam Kumaran: It should be sorted right now by highest discount amount. So this is the this is 100 discount on probably a pump. We can go open that. Those are all heat pumps. Yup.
276 00:31:15.700 ⇒ 00:31:17.290 Uttam Kumaran: Let’s see what we got.
277 00:31:21.950 ⇒ 00:31:23.640 Uttam Kumaran: Fedex. Oh.
278 00:31:36.120 ⇒ 00:31:38.960 Uttam Kumaran: yeah, there’s no tag. The tag is just Fedex
279 00:31:40.170 ⇒ 00:31:43.399 Uttam Kumaran: 100% discount
280 00:31:44.690 ⇒ 00:31:47.470 Daniel Schonfeld: the reason this will be in a ticket in Zendesk.
281 00:31:47.630 ⇒ 00:32:04.800 Uttam Kumaran: So let me take this, and I’ll ask. I’ll ask increase in discounts. A lot of them are coming back to
282 00:32:04.890 ⇒ 00:32:06.100 Daniel Schonfeld: heat pumps.
283 00:32:06.620 ⇒ 00:32:11.250 Uttam Kumaran: And just say, How do, how are you tracking this? Show me how. And is there
284 00:32:11.940 ⇒ 00:32:17.339 Daniel Schonfeld: of of a large reason? Let me just spot check this real quick, so that this is the same thing we’re looking at.
285 00:32:17.490 ⇒ 00:32:22.759 Daniel Schonfeld: where’s the discount amount. Yeah. So these are all 100%. Those are all.
286 00:32:24.090 ⇒ 00:32:26.699 Uttam Kumaran: So some of these are, some of these are canceled.
287 00:32:26.950 ⇒ 00:32:30.019 Uttam Kumaran: Well, okay, let’s take a look at this one. For example.
288 00:32:36.610 ⇒ 00:32:37.979 Uttam Kumaran: I wanna load.
289 00:32:43.110 ⇒ 00:32:45.040 Uttam Kumaran: Oh, this is okay. This is a test.
290 00:32:46.830 ⇒ 00:32:49.530 Daniel Schonfeld: Okay? So we get, yeah, we we gotta rip those out.
291 00:32:49.750 ⇒ 00:32:54.890 Uttam Kumaran: Okay, I gotta rip. Okay, yeah, I’ll rip anything that’s Chuck’s email.
292 00:32:55.920 ⇒ 00:32:59.570 Uttam Kumaran: Oh, yeah, okay. so that’s that.
293 00:32:59.700 ⇒ 00:33:10.040 Uttam Kumaran: And then let’s have a look at. Yes, that’s a large, pretty large number right there. That’s 1, 2, 8 grand right there.
294 00:33:10.200 ⇒ 00:33:11.480 Uttam Kumaran: Okay. Great
295 00:33:13.040 ⇒ 00:33:18.339 Uttam Kumaran: I’m going to get rid of anybody with the pull parts to go. Oh.
296 00:33:18.450 ⇒ 00:33:25.870 Uttam Kumaran: let me double check that. There’s not any people ordering on behalf. But I will look at that. Okay, let’s look at. Look at this one. For example.
297 00:33:34.650 ⇒ 00:33:35.840 Uttam Kumaran: warehouse.
298 00:33:46.570 ⇒ 00:33:50.729 Uttam Kumaran: Yeah, it’s another one. I don’t know to look in Zendesk, I guess.
299 00:33:52.850 ⇒ 00:33:53.710 Daniel Schonfeld: Yeah.
300 00:33:53.930 ⇒ 00:33:57.620 Daniel Schonfeld: that probably 7, 7, 4, 2, 2 p.
301 00:33:59.180 ⇒ 00:34:04.779 Daniel Schonfeld: I mean, I can go. Look this, this is gonna be another order. Id. So let’s go search for what this is.
302 00:34:07.010 ⇒ 00:34:08.420 Uttam Kumaran: Kurt Angle.
303 00:34:08.530 ⇒ 00:34:11.949 Daniel Schonfeld: It’s the same person a week earlier.
304 00:34:13.210 ⇒ 00:34:17.269 Daniel Schonfeld: So there’s probably something faulty, and we shipped them out of brand new unit. Looks like
305 00:34:17.690 ⇒ 00:34:20.239 Daniel Schonfeld: for free will. They put it through our system?
306 00:34:20.780 ⇒ 00:34:21.650 Uttam Kumaran: Yeah.
307 00:34:23.340 ⇒ 00:34:26.659 Uttam Kumaran: So let me look at the stick of this one to look at to.
308 00:34:29.460 ⇒ 00:34:32.210 Uttam Kumaran: and then maybe we spot. Check another one.
309 00:34:33.260 ⇒ 00:34:36.839 Uttam Kumaran: let’s take a look here.
310 00:34:47.230 ⇒ 00:34:49.590 Uttam Kumaran: Another one. Yeah. So if I look at
311 00:34:50.719 ⇒ 00:34:53.230 Uttam Kumaran: this, this is Chad.
312 00:34:55.469 ⇒ 00:34:59.310 Uttam Kumaran: yeah. same thing.
313 00:34:59.680 ⇒ 00:35:01.799 Daniel Schonfeld: we’ll put that one in if you go to the bottom.
314 00:35:03.690 ⇒ 00:35:04.950 Daniel Schonfeld: steven, okay.
315 00:35:07.390 ⇒ 00:35:09.029 Daniel Schonfeld: yeah, these are replacements.
316 00:35:09.270 ⇒ 00:35:12.100 Uttam Kumaran: Okay, there must be something defective about it.
317 00:35:16.080 ⇒ 00:35:18.689 Uttam Kumaran: So this is this one’s like a partial one.
318 00:35:20.230 ⇒ 00:35:23.170 Uttam Kumaran: Just take a look over one partial one.
319 00:35:29.530 ⇒ 00:35:36.500 Daniel Schonfeld: I’m gonna guess that there was like a ding in the in the unit, or it was something craft or something. So he just said.
320 00:35:36.940 ⇒ 00:35:44.399 Uttam Kumaran: What’s that? And the other 2. So I wonder if, when he creates this. you can just put in a little tag.
321 00:35:45.430 ⇒ 00:35:54.890 Uttam Kumaran: It’s definitely tagged in in Zendesk. Zendesk. Okay, cool whether we can get it from Zendesk tag into here or update it. I don’t know if there’s some kind of loop.
322 00:35:55.550 ⇒ 00:36:01.059 Uttam Kumaran: Okay, let me get. Let me. Let me get him to give me access to Zendesk. And I can bring that data in.
323 00:36:02.160 ⇒ 00:36:04.179 Uttam Kumaran: This one. Just looks like a
324 00:36:06.110 ⇒ 00:36:08.189 Uttam Kumaran: discount code.
325 00:36:08.580 ⇒ 00:36:25.250 Daniel Schonfeld: It. That’s I’m gonna guess that’s I’m gonna probably right. That’s what it was like. The the cage was dinged by a forklift or something. And they called it said, Hey, my units ding and the guy? And well, do you want a brand new one? Or do you want just a discount and keep that one? It’s gonna work perfectly fine.
326 00:36:25.680 ⇒ 00:36:31.339 Daniel Schonfeld: And they’ll say, Yeah, just 20% off or something. And I’ll keep this unit here.
327 00:36:31.770 ⇒ 00:36:36.739 Uttam Kumaran: That’s typically what will happen. But we do have to keep an eye on those numbers.
328 00:36:38.400 ⇒ 00:36:40.269 Uttam Kumaran: Yeah, it looks like
329 00:36:42.130 ⇒ 00:36:45.730 Uttam Kumaran: I mean, it’s quite it’s quite a lot with discounts.
330 00:36:46.620 ⇒ 00:36:49.190 Uttam Kumaran: Not all these are 100%.
331 00:36:49.600 ⇒ 00:36:53.900 Uttam Kumaran: But I can even I can even try to just like filter by
332 00:36:54.250 ⇒ 00:36:57.770 Uttam Kumaran: yeah, I could see most of them are heat pumps. And it’s that
333 00:36:57.780 ⇒ 00:37:12.279 Daniel Schonfeld: case I’m talking about where they were damaged on shipment. This 20% would mean that there were give if it was. If there was a complete loss on the order, it would be 100 will be 100%. 20% dinged up, or something was wrong wasn’t in perfect condition.
334 00:37:12.710 ⇒ 00:37:14.509 Uttam Kumaran: I see. Okay, okay.
335 00:37:15.610 ⇒ 00:37:30.179 Daniel Schonfeld: but those numbers are important. We’ll have to build a report. Well, Cody does a report, so we’ll want something in here that will tell us the trend of damage products or something like that, or discounts based on damage goods.
336 00:37:30.630 ⇒ 00:37:34.109 Daniel Schonfeld: Okay? Cause that’s another thing I look at, you know.
337 00:37:34.430 ⇒ 00:37:36.069 Daniel Schonfeld: like you asked me earlier.
338 00:37:36.280 ⇒ 00:37:41.989 Daniel Schonfeld: Something might be very cheap to ship, but they also might take less care, and it might cost us in the long run.
339 00:37:42.900 ⇒ 00:37:53.290 Uttam Kumaran: Yeah. And it’s also helpful for me to know that there 20 is the standard. So that so now I have, like, okay, segmented bytes, one or 100 anything in the middle.
340 00:37:53.840 ⇒ 00:37:56.570 Uttam Kumaran: we can kind of find out. So this is an example of like
341 00:37:57.170 ⇒ 00:38:05.319 Uttam Kumaran: a discount code. That’s non standard, right? This is about like 63. But it’s off. So maybe there was some promotion. Okay, so this is helpful.
342 00:38:05.820 ⇒ 00:38:06.610 Daniel Schonfeld: Yep.
343 00:38:09.980 ⇒ 00:38:12.310 Uttam Kumaran: Oh, return reason. Okay. Here
344 00:38:12.670 ⇒ 00:38:14.900 Daniel Schonfeld: the size was too small. There, you.
345 00:38:17.420 ⇒ 00:38:19.010 Uttam Kumaran: I see? Okay.
346 00:38:22.630 ⇒ 00:38:24.090 Uttam Kumaran: hmm, okay, great.
347 00:38:29.810 ⇒ 00:38:36.389 Uttam Kumaran: Oh, so oh, in this case, this was a dis initially discounted. And then we got a refund. So.
348 00:38:36.670 ⇒ 00:38:37.410 Daniel Schonfeld: yeah.
349 00:38:37.710 ⇒ 00:38:38.520 Uttam Kumaran: okay.
350 00:38:39.380 ⇒ 00:38:42.899 Uttam Kumaran: best best of both worlds.
351 00:38:42.930 ⇒ 00:38:50.349 Uttam Kumaran: Okay. okay, great. And then the last thing is, I’m meeting with Kim. I didn’t. I just got
352 00:38:50.560 ⇒ 00:38:59.999 Uttam Kumaran: kind of busy on some other stuff this week, but I didn’t hear back from her on the dashboard. I think she mentioned she wanted some help, just looking at some numbers and sponsoring.
353 00:39:00.280 ⇒ 00:39:10.299 Uttam Kumaran: Yeah, II she mentioned that she was there this week. And then she’s like, Yeah, I have some feedback. So I’m gonna try to meet with her to understand whether she has everything she needs. Okay, great
354 00:39:11.680 ⇒ 00:39:17.739 Uttam Kumaran: so I think the only thing is, yeah. If you don’t mind intro, or actually, I’ll ask. I’ll ask Chuck for the intro to units. And then I think that’s
355 00:39:18.370 ⇒ 00:39:25.489 Uttam Kumaran: really. And then, yeah, this dashboard is good to go also. Alright, I’ll start with, please start taking a look.
356 00:39:25.900 ⇒ 00:39:31.609 Daniel Schonfeld: Oh, well, I’m gonna I’m gonna dedicate an hour each day to to going through it, and I’ll come back. Stuff.
357 00:39:31.990 ⇒ 00:39:32.710 Uttam Kumaran: Okay.
358 00:39:33.090 ⇒ 00:39:34.920 Daniel Schonfeld: alright cool man. Thank you.
359 00:39:35.180 ⇒ 00:39:39.199 Uttam Kumaran: Yeah. And I’d love to see a picture of the of the booth or video, or.
360 00:39:39.250 ⇒ 00:39:41.470 Daniel Schonfeld: yeah, I’ll I’ll text it over to you.
361 00:39:41.670 ⇒ 00:39:42.510 Uttam Kumaran: Okay.
362 00:39:42.660 ⇒ 00:39:53.390 Uttam Kumaran: I’ll do that shortly. I’m gonna go back to bed, I think. So. We we got there Sunday. I got back yesterday. It was.
363 00:39:53.970 ⇒ 00:40:04.239 Daniel Schonfeld: And he’s not only just being at the show and talking to everyone. Then you go out for dinner, drinks everyone’s talk later. It was probably, like, you know, 15 HA day for 5 days straight.
364 00:40:04.270 ⇒ 00:40:06.750 Daniel Schonfeld: just exhausted. That’s the yeah.
365 00:40:06.870 ⇒ 00:40:08.340 Daniel Schonfeld: But it was great.
366 00:40:09.970 ⇒ 00:40:12.350 Daniel Schonfeld: Yeah, I’m excited to see it. Yeah.
367 00:40:12.380 ⇒ 00:40:14.330 Daniel Schonfeld: Here, I’ll send that over to you now
368 00:40:16.240 ⇒ 00:40:19.200 Daniel Schonfeld: find a good one. I know I took a video like a Walkthrough video.
369 00:40:20.270 ⇒ 00:40:23.320 Daniel Schonfeld: I don’t know if I got it when the bartender and everyone was in there but
370 00:40:24.430 ⇒ 00:40:29.750 Uttam Kumaran: I’m actually just like a pool, like, you know, I wanted to see like an aboveground pool in there.
371 00:40:30.290 ⇒ 00:40:34.559 Daniel Schonfeld: I have some pretty cool ideas for next year. Here, I’ll send this one to you.
372 00:40:36.260 ⇒ 00:40:40.920 Daniel Schonfeld: the people where we we monitor. What’s your cell phone? Actually, I don’t think I have it in here.
373 00:40:41.240 ⇒ 00:40:50.469 Uttam Kumaran: I think I text you earlier today is 925-78-6925, 7, 8, 6, 8, 2, 7, 3,
374 00:40:50.790 ⇒ 00:40:56.330 Uttam Kumaran: 8, 2, 7, 9, 2, 5, 7, 8, 6, 8, 2, 7, 3,
375 00:40:56.840 ⇒ 00:41:00.339 Daniel Schonfeld: right. I just sent it.
376 00:41:00.620 ⇒ 00:41:13.650 Daniel Schonfeld: So everyone in the groups and like the pool service groups saying, all these guys put walls up so we can’t see anything. So next year I was thinking about just having a maybe you know, someone who can help with this is having actually nothing there, nothing like literally just the carpet.
377 00:41:14.170 ⇒ 00:41:20.050 Daniel Schonfeld: And so last year we put a box on this year we put nothing there, and there’s nothing there. Maybe, like a seat in the middle.
378 00:41:20.770 ⇒ 00:41:21.470 Uttam Kumaran: Okay?
379 00:41:21.850 ⇒ 00:41:25.170 Daniel Schonfeld: And then what I was gonna do was, it’s at a certain time
380 00:41:25.320 ⇒ 00:41:28.999 Daniel Schonfeld: and say say to everyone, maybe like a us
381 00:41:29.130 ⇒ 00:41:31.869 Daniel Schonfeld: like a countdown timer in the middle on a TV screen.
382 00:41:32.550 ⇒ 00:41:41.009 Daniel Schonfeld: so like ever, like everyone’s wondering what that was gonna happen at that time, and then put out 2 VR headsets.
383 00:41:41.280 ⇒ 00:41:47.299 Daniel Schonfeld: and when they when they put on the VR headset they can see the booth inside of it in a virtual booth.
384 00:41:49.230 ⇒ 00:42:03.769 Uttam Kumaran: I do. I briefly was about to do some VR work with some people 2 years ago. And I know a lot of people in VR that our VR designers like they they build spaces in. VR,
385 00:42:04.040 ⇒ 00:42:07.449 Uttam Kumaran: yeah, that’s great, I mean, and hopefully the apple stuff.
386 00:42:08.180 ⇒ 00:42:13.089 Uttam Kumaran: I don’t know if you if you tried the octus or whatever, but they’re they’re probably to the point where you could try it out.
387 00:42:13.800 ⇒ 00:42:16.500 Daniel Schonfeld: yeah, my friend actually started. Oculus founded it.
388 00:42:17.210 ⇒ 00:42:34.039 Daniel Schonfeld: Oh, really awesome. Yeah, he he’s best friend of one of my best friends. I spent a lot of time with him in college, but he had sold the company for like a million. Brandon a rebase his name sold the company for like I don’t know. Maybe like a million bucks while he was in college, like some kind. I forget what it was like, maybe a casino site or something.
389 00:42:34.350 ⇒ 00:42:39.089 Daniel Schonfeld: Then he told all his buddies. I was in college with them. They told all his buddies. He’s like, Hey,
390 00:42:39.290 ⇒ 00:42:42.860 Daniel Schonfeld: This next thing I’m building is gonna be a hit. Just give put in like 5 grand.
391 00:42:43.880 ⇒ 00:42:55.970 Daniel Schonfeld: and he didn’t. He had he? He got No. 15 rounds. He got 5 of them to put it in. They like ask their parents for it. It’s like my buddy, just, you know, starting a company. I need 15 grand. Most parents like fuck. You
392 00:42:56.300 ⇒ 00:42:59.839 Daniel Schonfeld: and my best friend had the money to do it didn’t give it to him.
393 00:43:00.080 ⇒ 00:43:14.610 Uttam Kumaran: He ended up returning a check to them when they sold to Facebook. I think it was for like 1.8 million or something. Yeah, yeah, yeah, that’s crazy. I still fuck with them all the time. But he he takes care of alls, but he like bought one a house and like, but you know, he’s he’s a good deal.
394 00:43:15.080 ⇒ 00:43:24.960 Uttam Kumaran: But yeah, he sold documents. I think it was like a billion bucks or something. No, there was, I think it was like more than a billion bucks. I think him. And then this guy, Paul, more lucky, I think, was the other co-founder. Yeah.
395 00:43:25.010 ⇒ 00:43:28.339 Daniel Schonfeld: yeah. Brand is good guy, anyways. But
396 00:43:28.720 ⇒ 00:43:48.559 Daniel Schonfeld: yeah, something something to that effect I was thinking about doing and then creating like a virtual pool where the service guys have to like, fix it virtually like using like chemicals on a thing, and we’ll do like pool service wars and put that on the screen and just have, you know, like a bunch of guys going. It’ll be so novel like it’ll blow people’s minds there like no one does
397 00:43:49.090 ⇒ 00:43:59.200 Daniel Schonfeld: anything. It’s just like this is, this is awesome. By the way, yeah. yeah, it looks so much cooler than there’s like a bartender in there. And it was like, kind of set up. And but yeah, that’s kind of the idea.
398 00:44:00.500 ⇒ 00:44:14.220 Daniel Schonfeld: I mean, it looks like a. It just looks like an air like a delta lounge like the skylar. It’s exactly what I was going for. That’s what people said. It’s so loud on the show floor that when you go in there, for some reason it really like quiet it down. You felt like you were at like a like a high end lounge.
399 00:44:14.430 ⇒ 00:44:29.109 Uttam Kumaran: What? Where? Where is there? I assume there’s black and decker folks like company folks there, do they see? I mean, this isn’t. This is probably better than anything that company has done for themselves. I’m gonna send it to them, actually didn’t even tell them I’m really doing it. I was a little worried because it almost looks like we had Black and Decker
400 00:44:29.470 ⇒ 00:44:32.739 Uttam Kumaran: and everybody thought we were people like, what do you got in there? Tools?
401 00:44:35.090 ⇒ 00:44:38.280 Uttam Kumaran: This is great. And all the products actually like
402 00:44:38.580 ⇒ 00:44:41.330 Daniel Schonfeld: they can look really good.
403 00:44:41.340 ⇒ 00:44:43.790 Daniel Schonfeld: I heard the crazy stuff that was going on in America.
404 00:44:44.680 ⇒ 00:44:47.679 Daniel Schonfeld: and I’ll just put my second. I was like, if that’s by.
405 00:44:52.430 ⇒ 00:44:53.620 Daniel Schonfeld: But it was awesome.
406 00:44:54.290 ⇒ 00:44:55.340 Uttam Kumaran: Okay.
407 00:44:56.810 ⇒ 00:45:04.149 Uttam Kumaran: alright, get some rest. I really appreciate it. I will send some stuff over and then hopefully. Chat time next week.
408 00:45:05.070 ⇒ 00:45:08.610 Daniel Schonfeld: Alright, my man, thank you again. Appreciate it. Good work! Appreciate it.
409 00:45:08.680 ⇒ 00:45:10.889 Uttam Kumaran: I’ll talk to you so much like Justin.